Tentative
Translation

The Conference toward AI Network Society
The Committee on AI Economy

Report 2020

July 20201

Introduction
Global trends toward the implementation of AI in society never stop. GAFA in the US and Alibaba and Tencent in
China are ranked high in the top 10 companies of the global market capitalization. These companies have developed AI on
their own or provided the basic tools for the development and utilization of AI, and take the initiative in the implementationof AI.
Japan should not be late for these trends. It’s important to show the expected future image of AI as well as organize
the possibilities, issues and concerns of AI and to show that various entities in Japan must work on the problems as soon
as possible. The government of Japan announced “AI Strategy 2019—AI for Everyone: People, Industries, Regions, andgovernment” (Decision of the Integrated Innovation Strategy Promotion Council on June 11, 2019), and outlined themeasures to be implemented immediately. Then, these measures were implemented based on the strategy, and someachievements have been already made for the advanced implementation of AI in society. These trends must be further
accelerated.
On the other hand, what is necessary for AI to exert its potential is data that is expected as “new capital”. The reportissued by The Committee on AI Economy in May 2019 clarified the thought that data is positioned as a production factor
and the effects of data on the improvement of productivity should be estimated is significant, and showed the necessity ofdeepening the discussion on fair remuneration in data processing.
Regarding how to utilize data, various measures have been developed based on the various strategies in thegovernment, and some Japanese companies have promoted advanced initiatives such as big data analysis and machinelearning, and understanding the role of data in production activities has been deepened. On the other hand, how data is
specifically connected with the improvement of productivity and how remuneration should be distributed depending on
the effects and value of data have not been established yet not only in Japan but also in the world.
To fully extract the value of data by AI, etc., we need to consider how the value of data should be estimated and how
the value should be distributed.
In addition, it can be said that the COVID-19 global pandemic brought a new perspective to the consideration of theideal implementation of AI in society. People are forced to avoid unnecessary outings and reduce opportunities to contact
other people by 80％, and review their economic activities and lifestyle substantially. On the other hand, the importanceof communication network and ICT tools such as teleworking and electronic application procedures is attracting attention
as a solution to the above situations. There is a possibility that the COVID-19 is getting the cause of acceleration of theimplementation of AI in society. In G20 Ministerial Meeting on Trade and Digital Economy in Tsukuba, Ibaraki held in
June 2019, the necessity of considering a new social model in the age of AI was shared under the concept of creating new
employment and industries by AI, and the necessity might be enhanced globally.
We hope the discussion in the Committee on AI Economy held in this timing will be a clue for future initiatives andstrategies for all types of players in Japan and overseas.

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Contents
Introduction ........................................................................................................................................................... 2
1.

2.

3.

Background (History from the establishment of “The Committee on AI Economy” to the present) ........................ 4
1.1

Establishment of “The Committee on AI Economy” ............................................................................................. 4

1.2“The Conference toward AI Network Society, the Committee on AI Economy Report Outline (May 2019) ....... 4

1.3

The scope of consideration in this report ............................................................................................................... 6

The ideal way to utilize data required for the implementation of AI in society...................................................... 8
2.1

The current status of Japan’s data utilization ......................................................................................................... 8

2.2

The ideal way to utilize data required for the implementation of AI in society ................................................... 18

Data economy policy in the AI era ................................................................................................................. 22
3.1

The functions/roles of data as “new capital” and estimation methods of the effects/value of data ...................... 22

3.2

The ideal fair remuneration depending on the effects/value of data..................................................................... 40

3.3

The current status and issues toward marketing and visualization of data transaction......................................... 46

4.

Future image (the image of “Inclusive AI Economy Society”) .......................................................................... 58

5.

Recommendation .......................................................................................................................................... 66

6.

5.1

The Ideal Policy toward the Promotion of the Utilization of AI/Data.................................................................. 66

5.2

The ideal data economy policy required for promoting the data-driven economy in the AI era .......................... 70

Addendum ................................................................................................................................................... 73
6.1

Questionnaire ....................................................................................................................................................... 73

6.2

Empirical analysis .............................................................................................................................................. 100

Attachment 1

The Conference toward AI Network Society, The Committee on AI Economy / Members of the SpecialInterest Group on Data

Attachment 2

Background of Holding the Conference

Attachment 3

Analysis on Prospect of Ecosystem Formed with Progress of AI Network3

1.

Background (History from the establishment of “The Committee on AI Economy” to the present)

1.1 Establishment of “The Committee on AI Economy”
In January 2019, the Committee on AI Economy (hereinafter referred to as ‘the Committee’) was established with theaim of considering what kind of social economy should be pursued and how the basic policy and medium-to-long term
strategies should be through the promotion of the implementation of AI in society among the items to be considered in The
Conference toward AI Network Society. The Committee did consideration, focusing on the following 5 key points and thereport was outlined in May 2019.
(1) Directivity to be pursued in AI economy (Realization of ”Inclusive AI Economy Society")(2) Ideal AI investment for sustainable economic growth and improvement of productivity(3) How the industrial infrastructure that supports AI economy (Labor/R&D) should be(4) Japanese enterprises’ global competitiveness over the utilization of AI(5) Ideal basic policies and strategies on AI economy
1.2 “The Conference toward AI Network Society, the Committee on AI Economy Report Outline (May 2019)(1)

Directivity pursued in AI Economy (Realization of “Inclusive AI Economy Society”)

The tools that make the development/utilization of AI possible (frameworks, learned models, etc.) are widely provided
by ICT companies as open source or via cloud/API, so various organizations can utilize them and develop/utilize AI forvarious purposes, depending on their capabilities. To work on the development of AI, it is also important to understandwhat kind of data should be collected depending on the purpose or how to combine the collected data and the specificknowledge on the on-site work (domains), etc.
It is important to aim for the economy society (“Inclusive AI Economy Society”) where various subjects (including
individuals and SMEs) can be actively involved in social and economic activities in an ingenious way based on their will
and judgment and get distribution, fulfillment, increased leisure time, etc. as well as contribute to the improvement inproductivity by making the open and inclusive operation of AI possible under the fair competitive environment.
(2) Ideal AI investment for sustainable economic growth and improvement of productivity
It can be said that ICT investment in Japan has not been enough quantitatively, and the effects in the improvement of
productivity was limited. In addition, ICT investment in which ICT companies are entrusted with development is the mainstream, so there is a possibility that complementary investment such as organizational reform was not enough. It isconsidered that the system centering on ICT companies being entrusted with development had economic rationality tosome extent, but there is a possibility that ICT companies that were excessively entrusted with development can’t doeffective and enough AI/ICT investment, so it’s also important for user companies to develop and utilize AI by themselves,
going forward.
The thought that data should be positioned as “new capital” and its effects on the improvement of productivity shouldbe estimated as one of the production factors is significant because data can be a source to create value by being analyzed
by AI. It is necessary to estimate effects/value after evaluating appropriate value, noting that there are issues in estimation4

in digital economy of national economic accounting such as GDP.
In addition, the platforms of some overseas companies are used in many countries in the world, and those companies
have accumulated a large amount of data on the Internet, and earned profits. Considering this situation, it is necessary to
deeply discuss how data ownership should be and the ideal distribution of remuneration to the providers of data that is a
production factor, etc. focusing on the thought that the distribution rate of data as intangible capital has increased whilelabor’s share rate has decreased.
(3) How the industrial infrastructure that supports AI economy (Labor/R&D) should be
Some people predict that the technological unemployment in ICT will occur mainly in “routine tasks” such as manualtasks and clerical work, and “non-routine manual work” such as services/physical work will be the source of employment
for unemployed people, but other people predict the function of non-routine manual work as the source of employment
will be lowered because the pressure of substitution for “non-routine manual work” will further increase due to thedevelopment of AI or robotics. In that case, it is considered that the necessity of personnel relocating and the reeducation
for that will increase. There is a possibility that new employment will be created due to the development of AI, but it should
be noted that the loss of employment due to the technological unemployment will be a big issue as a whole society until
the effects can be visible.
In Japan, there is a possibility that there has been the replacement by non-regular employment instead of thereplacement by ICT, so it should be noted that the impact of the development and spread of AI in Japan might be bigger
than that in other countries, because the technological substitution including substitution of the “routine tasks” will proceed.
In addition, both the quantity and quality of human resources in AI/ICT are insufficient in Japan. In the medium- tolong-term, fostering human resources for AI (promotion of appealing the attractions of AI and data and educational reformfrom elementary education to higher education (including the contents of education, ideal educational sites and securing
excellent AI instructors)) is important. And in the short-term, the perspective of how to promote the introduction of AI in
Japan (shifting human resources for ICT from ICT companies to user companies, improving the AI skills of these humanresources, and accepting high-level human resources who are familiar with AI from overseas and utilizing outsourcing
those human resources overseas) is important.
Regarding the research and development in Japan, the perspective of considering industrial issues by connecting themwith science and engineering is insufficient, so it is important to link basic research, applied research and developmentwith businesses. In particular, it is considered to be required that large enterprises enhance the diversity of the processing
and employment practices of high-level researchers, etc. and make further efforts to link businesses and research anddevelopment. In addition, it is also important for start-ups to have a perspective of focusing on research and development.
(4) Japanese enterprises’ global competitiveness over the utilization of AI
Japanese enterprises have correlating issues such as the shortage of Internet connection and data amount, inability toprocess data, lack of human resources for utilizing data. There are many cases where the status of data isn’t figured out ina company, so it is the most important for executives to build effective data strategies after figuring it out. To promote theutilization of data, it is important to build a system where newly collected data don’t need to be processed as much as
possible and promote fostering human resources who can utilize data.

5(5) The ideal basic policy and strategy on AI economy
As digital transformation is proceeding, it is important for the government to take this timing when the structure ofindustries is being rebuilt as a big chance and promote the policy to accelerate this trend, focusing on the following key
points.
(a) In-house development by user companies (utilize the base of Cloud, etc.)(b) Open innovation with start-ups that are conducting R&D (breaking away from “self-sufficiency”, cooperation
with enterprises that have specialty in specific technologies and entrepreneurs who have flexible ideas, etc.)(c) Agile-type development (putting services under development into the market, using them, and improving
them successively)(d) Securing real data through the promotion of IoT (collect data that can utilize the strengths of Japan, centeringon manufacturing sites and medical/healthcare fields, etc.)(e) Complementing data amount (initiatives for open data on public data, sharing various kinds of data beyond
enterprises and industries)(f)

Improvement of AI skills of human resources of user companies (internal human resources, external humanresources in Japan and overseas)(g) Reeducation of human resources to respond to the impact from the utilization of AI (planned personnel
relocating of human resources for routine tasks and reeducation for that)(h) Improvement of ICT companies’ capability to support user companies (strengthening the support for the
improvement of AI skills in user companies and the role to complement the shortage of ICT human resources
as well as promoting the development of AI after collecting knowledge on on-site work in industries andenterprises)
To promote the policy, the following points should be especially noted.
(a) How to utilize the strengths of Japan (plan specific measures that can be accepted in global markets, valuing
the perspective of adopting AI, and utilizing the superiority that Japan has accumulated)(b) Upgrading middle-scale enterprises and SMEs (The perspective of what kind of role is required in middlescale enterprises and SMEs and how effectively it can be incorporated in the whole Japanese economy)(c) Streamlining the government (The perspective that agile-type development is promoted through theprocurement by the government and the system and mechanism that hinder the utilization of AI are reviewed)(d) Environmental improvement to realize the Inclusive AI Economy Society (aiming to be the state of AI-readyas a whole society, improvement of social acceptance in the utilization of data, data free flow, securing thetrust on privacy and security (DFFT: Data Free Flow with Trust))(e) Promotion of global discussion on data(f)Monitoring

1.3 The scope of consideration in this report
To conduct further consideration based on the report in May 2019, the consideration was resumed in December, 2019.

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And the Special-Interest Group on Data was established under the Committee for the purpose of considering specialized
and technical items on the data economy policy among the items to be considered in the Committee.
Based on the contents of further consideration in the Committee that was resumed, the following 3 key points are thescope of the consideration in this report.
(1) Consideration on the ideal way to utilize data required for the implementation of AI in society(2) Consideration on the data economy policy in the

AI era(3) Consideration on the image of “Inclusive AI Economy Society”
Regarding the key point (1), various entities showed the importance of actively being involved in social and economic
activities utilizing AI and data in an ingenious way based on their will and judgment in the report published in May 2019,but further consideration will be conducted on the ideal utilization of data that can be the premise of the implementation of
AI in society. First of all, the status of the utilization of data in Japan will be considered. Then, the ideal way to utilize datarequired for the utilization of AI in society will be outlined. More specifically, the ideal way that all the entities, especiallythe socially vulnerable such as SMEs and local enterprises can conduct the utilization of data that can be the premise of theimplementation of AI in society will be considered. Based on the above, the ideal policy toward the promotion of theutilization of AI/data will be examined.
Regarding the key point (2), the thought that data 1 is positioned as one of production factors and the effects of data on
the improvement of the productivity should be estimated is significant, and the necessity of deeply discussing the ideal
distribution of remuneration to the data providers was shown in the report in May 2019. To cover these matters, further
consideration will be conducted. First, the methods to estimate the value of data and ideal remuneration depending on theeffects/value of data will be considered. More specifically, after checking the functions and roles of data, centering on theprior studies, factors to be considered in estimating the value of data will be outlined. And after breaking down theapproaches to estimate the value of data, each approach’s concept and issues will be checked. Then, the methods to estimate
the value of feasible data will be examined, noting securing the versatility and reproducibility applicable to various
industries and countries. In addition, we will consider the positioning of data and the degree of remuneration for individuals
who produce data and the process of paying remuneration, etc. as one of production factors as ideal remuneration dependingon the effects/value of data. Then, based on the above, the conditions for revitalization of data transaction will be outlined
and ideal data economy policy required for the promotion of data-driven type economy will be examined.
Regarding the key point (3), the image of “Inclusive AI Economy Society” that can be seen after overcoming the issues
shown in (1) and (2) will be viewed. More specifically, among the Analysis on Prospect of Ecosystem Formed with theProgress of AI Network that The Conference Toward AI Network Society has examined, the analysis on the perspective of
the utilization of AI will be referred, and the future image of expected “Inclusive AI Economy Society” will be viewed.
And some examples of utility scenes classified by individuals, enterprises, and administration will be taken. In addition,
human workstyles/how to spend leisure time expected when AI is fully utilized will be introduced.

1“Data” in this report is defined as currently digitized data or data that can be digitized easily.
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2.

The ideal way to utilize data required for the implementation of AI in society

2.1 The current status of Japan’s data utilization
First, we will consider the current status of Japan’s data utilization to pursue the society where various organizations canbe actively involved in social/economic activities, utilizing AI and data in an ingenious way based on their will andjudgment.
(1) Changes in global economy
When you see the global market capitalization Top 10 companies ranking, you will notice Japanese companies occupied
the top positions, and financial institutions and energy related companies were ranked high in 1989, and GAFA in the US
and Alibaba Group and Tencent Holdings that have developed their business based on collected/accumulated data of users
were ranked high in 2018 (Table 1). These changes in the global market capitaliztion Top 10 companies show that the
global economy has changed from a finance-led economy to the one led by digital companies that utilize ICT and data.
Table 1: Global Market Capitalization Top 10 (1989 and 2018)

Source: SuperMagazine(https://supership.jp/magazine/seminar-report/2849/)
In addition, in the World Digital Competitiveness Ranking in 2019 published by IMD World Competitiveness Center,
Japan was ranked the 23rd out of 63 countries, lowering its ranking by one from 2018 (Table 2). Regarding “businessagility”, one of the evaluation criteria in digital competitiveness, Japan was rated low and ranked the 41st. Among thecomponents of “business agility” factors, especially in the “use of big data and analysis”, Japan was rated the lowest andranked the 63rd, which shows that Japanese companies are lagging behind in the utilization of big data.

8

Table 2: World Digital Competitiveness Ranking in 2019 (Ranks 1-30)

Source: IMD World
Competitiveness Center(https://www.imd.org/wcc/world-competitiveness-center-rankings/world-digital-competitiveness-rankings-2019/)(2) The disparity of the utilization of data between enterprises
Regarding the utilization of data in enterprises, there are the disparity between large enterprises and small and mediumsized enterprises and the disparity between enterprises in urban areas and those in rural areas. In “the Questionnaire on theutilization of digital data” conducted by the Ministry of Internal Affairs and Communications, the percentage of the9enterprises that haven’t collected or accumulated any organizational data was 7.1% at large enterprises (with 1,000 or more
employees), but it was 45.5% at SMEs (with 300 or less than employees) (Table 3). The result shows that initiatives ofutilizing data at SMEs are lagging behind compared to those at large enterprises.
Table 3: The status of collection and accumulation of organizational data
Q: What kind of data among the following organizational data is collected or accumulated in your enterprise? (by numberof employees)

0%

20%

40%

38.8%

Data
of basic information to do business
業務を遂行するための基礎情報となるデータ

31.8%

Data on transactions
and actions
取引やアクションに関するデータ

16.1%

施設、工場、プラント、オフィス、店舗、 作
Data on facilities, factories, plants, offices, stores, work sites, etc.

その他

Others

いずれも収集、蓄積していない

Not collected or accumulated any data

12.6%
5.7%

79.1%
79.0%
63.7%
67.5%
57.5%
57.1%

27.8%
34.3%
less than 300
300人未満

15.0%
19.2%

300人～1000人未満
300 – less than 1000
1000人以上
1000 or more

4.0%
4.9%
6.7%
11.1%
7.1%

100%

31.0%
36.9%

業現場等に関するデータ

化学物質、 生物等に関するデータ
Data on chemical substances, living things, etc.

80%

42.0%

Basic data on an organization
組織に関する基本的なデータ

設備、機械、
機器、車両等に関するデータ
Data
on equipment, machines,
devices, vehicles, etc.

60%

45.5%

Source: MIC “The questionnaire on the utilization of digital data” (2,003 enterprises responded) 2
In addition, the same trend could be seen between enterprises in urban areas and those in rural areas. This resultshows that initiatives of utilizing data at enterprises in rural areas are lagging behind compared to those at enterprises inurban areas. (Table 4).

This web questionnaire was conducted on 2,003 companies in Japan to check the status ofproviding/receiving data at enterprises, issues of initiatives on data, etc. from March 19 (Thu.) to
March 25 (Wed.), 2020.
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Table 4: The status of collection and accumulation of organizational data (by location of headquarters)
Q: What kind of data among the following organizational data is collected or accumulated in your company? (by locationof headquarters)

0%

20%

40%

組織に関する基本的なデータ

Basic data on an organization

50.6%

Data
of basic information to execute business
業務を遂行するための基礎情報となるデータ

43.5%

取引やアクションに関するデータ
Data on transactions
and actions

37.0%

施設、工場、プラント、オフィス、店舗、 作業現場等に関するデータ

56.9%

47.8%

24.0%
17.2%

設備、機械、 機器、車両等に関するデータ

Data on equipment, machines, devices, vehicles, etc.

化学物質、 生物等に関するデータ

7.1%

Data on chemical substances, living things, etc.

その他

64.1%

27.3%
20.3%

Data on facilities, factories, plants, offices, stores, work sites, etc.

Others

80%

60%

14.4%

東京都/大阪府/愛知県
Tokyo/Osaka/Aichi

Others
その他

5.9%
3.7%
22.7%

いずれも収集、蓄積していない

Not collected or accumulated any data

37.6%

Source: MIC “The questionnaire on the utilization of digital data” (2,003 enterprises responded)(3) Recognition of lagging behind in initiatives on data
Since an AI system needs to learn from big data, data collection and data analysis are indispensable to the realizationof the utilization of AI. However, there are many Japanese enterprises that realize their initiatives on data including datacollection and analysis are lagging behind. 34.7% of the enterprises responded that their initiatives on data are much lagging
behind those of other enterprises in the same industry in foreign countries, and 23.5% of them responded that they are alittle behind other enterprises, so in total, 58.2% or almost 60% of them recognized they are lagging behind other enterprisesin the same industry in foreign countries. Compared to other enterprises in the same industry in Japan, 27.1% of theenterprises responded their initiatives on data are much lagging behind other enterprises, and 21.9% of them responded
they are a little lagging behind other enterprises, so in total, almost half (49%) of the enterprises recognized that theirinitiatives on data are lagging behind (Table 5).

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Table 5: Awareness on the status of initiatives on data
Q: Do you think your enterprise’s initiatives on data (data collection, accumulation, cooperation, analysis/utilization, etc.)
are lagging behind compared to other enterprises in the same industry?

■ Much more advanced than other enterprises

大きく他社よりも進んでいる

少し他社よりも進んでいる■ A little
lagging behind other enterprises
少し他社よりも遅れている
Compared to other enterprises

他国企業と比較してin foreign countries

9.7%■ Almost the same

同程度

■ Muchlagging behind other enterprises
大きく他社よりも遅れている

4.0% 10.7%

Compared to otherenterprises
日本企業と比較して■ A little more advanced than other enterprises

27.0%

11.0%

23.5%

30.4%

34.7%

21.9%

27.1%

in Japan

0%

20%

40%

60%

80%

100%

Source: MIC “The questionnaire on the utilization of digital data” (2,003 enterprises responded)
In the background of the status of initiatives on data lagging behind, there is a reality that they have issues such as theshortage of human resources for the utilization of data, unclear or low cost-effectiveness, insufficient datacollection/accumulation, even though they are eager to utilize data.
Regarding promoting initiatives on data, “the shortage of human resources who can utilize data” (33.6%) is the most,so the shortage of human resources is the biggest issue in the utilization of data.
In addition, there are quite a few enterprises that have the issue of costs such as “Cost-effectiveness is unclear or low”
(32.6%) and “There is no financial reserve for initiatives for the utilization of data” (17.8%). Furthermore, there are many
enterprises that are facing issues of the shortage of data such as “Sufficient data (amount) haven’t collected or accumulated”(28.4%) and “Sufficient data (types) haven’t collected or accumulated” (26.6%) (Table 6).

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Table 6: Issues for promoting initiatives on data
Q: What do you think is an issue when your enterprise promotes initiatives on data?

0%

10%

20%

30%

40%

データ利活用に関わる人材の不足
The shortage of humanresources for the utilization of data

33.6%

Cost-effectivenessis unclear or low
費用対効果が不明または低い

32.6%

Sufficient
data (volume) hasn’t collected or accumulated
十分なデータ（量）が収集、蓄積できていない

28.4%

十分なデータ（種類）が収集、蓄積できていない
Sufficient data (types) haven’t collected or accumulated

26.6%

There is no financial
reserve for initiatives for the utilization of data
データ利活用に取り組む資金的余裕がない

17.8%

The system that appropriately process data 適切にデータを取扱う
such as the preparation of data
データ取扱いガイドラインの整備等、16.2%体制が整備できていない
processing guidelines has not prepared
データのアクセス制限や情報セキュリティ等、 データインフ

14.8%

Data infrastructure such as data access restriction and information security has not preparedラが整備できていない

個人情報、営業秘密の流出リスクに対して十分な対策ができ

9.2%

Measures for the leakage of personal information and trade secrets haven’t taken enoughていない

炎上リスク等、
Measures 社会風土に対して十分な対策ができていないfor social climate such as flaming risks haven’t taken enough
その他
Others

6.8%
0.5%

There
is no issue
課題はない

25.6%

Source: MIC “The questionnaire on the utilization of digital data” (2,003 enterprises responded)(4) The effects of the utilization of data in enterprises are recognized
On the other hand, roughly 60% of enterprises feel that there have been some effects by utilizing data in theirenterprises. The number of enterprises who realized the effects of the utilization of data in the field of “production/manufacturing” was the largest, and 66.6% of the enterprises responded that “There were a lot of effects” or “There weresome effects” (Table 7).

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Table 7: Effects through the utilization of data
Q: How much effect did your enterprise have by utilizing data in each field?
非常に効果があった
多少効果があった■ Therewere a lot of effects■ Therewere some effects

どちらでもない
あまり効果がなかった■ Neitherof them■ Thereweren’t a lot of effects■ 全く効果がなかった
There was no effect

保守・メンテナンス・サポート
Maintenance / support

16.9%

47.2%

28.9%

5.3% 1.7%

Logistics /物流・在庫管理
inventory control

14.8%

50.5%

27.9%

5.4% 1.3%

生産・製造
Production / manufacturing

13.7%

52.9%

29.0%

3.8%0.5%

マーケティング
Marketing

12.9%

48.1%

31.0%

7.3% 0.8%

Planning /製品・サービスの企画、開発
development of products / services

12.2%

49.8%

32.3%

4.5% 1.2%

経営企画・組織改革
Management planning / organizational
reformation

12.1%
0%

44.8%
20%

40%

34.5%
60%

80%

6.8%

1.8%

100%

Source: MIC “The questionnaire on the utilization of digital data” (2,003 enterprises responded)(5) Careful attitude toward providing personal information 3 and behavior history for enterprises
Regarding the issues of consumers who provide data, consumers tend to be careful about providing their personal
information and behavior history for enterprises. The percentage of the consumers who don’t want to provide their data
in any scene was 52.4%, and it was higher than that of those who can provide their data such as their personal
information and behavior history even without any reward if their data are helpful in society (Table 8).
On the other hand, the percentage of consumers who responded that they would provide their data for the scene of“health/medical care (promotion of health, new drug development, improvement of medical technology, etc.)” even without
any reward was 33.7%, which was relatively higher than the utilization of data at the scenes of disaster prevention/crime
prevention, transportation, education, etc.

“Personal information” in this report was used as a general term and it’s not used as a legal termin “Act on the Protection of Personal Information (Act No. 57 of May 30, 2003)”, etc.
14

3

Table 8: Intention to provide data (by useful scene)
Q: For what scene in society would you provide your information even without any reward (including but not limited tomonetary benefits, provision of information based on provided personal information and behavior history, etc.) if your dataon your personal information and behavior history are helpful?

0%

20%

Health/medical care (promotion of health, new drug development,

健康・医療（健康促進、新薬開発、医療技術の向上等）

60%

40%
33.7%

improvement of medical technology, etc.)

Crime prevention/disaster prevention (improvement of monitoring,

防犯・防災（見守りの高度化、復旧の迅速化等）

30.0%

expediting of restoration, etc.)
Transportation (relieving traffic congestion,
optimum allocation of cars, etc.
交通（渋滞緩和、最適配車等）

16.6%

Environment/infrastructure (improvement of environment,

環境・インフラ（環境改善、省エネ等）

14.3%

教育（教育内容の高度化、カスタマイズ化等）
Education(improvement of education contents, customization, etc.)

13.7%energy saving, etc.)

Industries (automationof authentication when purchasing, etc.)
産業（購買時などにおける認証の自動化等）
Others
その他

10.0%
0.1%

I don’t wantto provide my information at any scene
いずれの場面でも提供したくない

52.4%

Source: MIC “The questionnaire on the utilization of digital data” (2,155 people responded) 4
Also, regarding the information for which they would like to provide their data such as personal information andbehavior history if they receive monetary consideration or points, more than 50% of consumers responded that they
don’t want to provide their information at any scene, which was relatively high (Table 9).

The web questionnaire about the awareness on personal information, conditions for providing
personal information, etc. carried out for men and women older than 20 years old in Japan during
the period of March 24 (Tue.) – March 26 (Thu.), 2020.
15

4

Table 9: Intention to provide data (by content of data)
Q: Is there any information that you would provide for a company (except for your company if you work for a company)
if you get a reward out of the following data on your personal information and behavior history, etc.?

0%Sex, age, educational
background
性別、年代、学歴
Birthplace
出身地
Purchase informationon EC sites
ECサイトの購買情報
Family information, housing
information
家族構成、住居情報

E-mail address
メールアドレス
Address information
住所情報
Annual income
年収Purchase information
at retailer shops and restaurants
実際の小売店や飲食店での購買情報
Telephone電話番号
numberBrowsing
history of websites
Webページの閲覧履歴
Workplace information
勤務先情報
Bio-information
生体情報
Electric money,
usage history of IC cards, etc.
電子マネー、ICカード等の利用履歴
加入保険の情報
Insurance
information
移動情報
Mobile information
SNS、ブログでの書き込み情報
Information
posted on SNS and blogs
銀行などの口座情報
Bank accountinformation, etc.
クレジットカード番号
Credit card number
顔写真
Face photograph
パスポート番号
Passport
numberFingerprints
指紋
その他
OthersI don’t want toいずれも提供したくない
provide any information

20%

40%

60%

80%

35.6%
34.0%
25.5%
23.6%
24.6%
28.1%
26.8%
18.6%
17.3%
18.4%
18.6%
17.7%
12.6%
11.1%
11.1%
20.6%
17.4%
11.3%
11.2%
10.5%
16.1%
15.0%
12.8%
12.5%
13.1%
13.8%
12.2%
12.8%
11.3%
10.7%
16.3%
14.9%
9.0%
8.3%
8.5%
14.5%
14.0%
10.0%
8.4%
8.4%
12.5%
11.6%
11.0%
10.2%
9.8%
12.3%
11.0%
6.5%
5.4%
6.3%
8.2%
7.7%
4.8%
5.0%
5.2%
8.7%
7.9%
4.6%
4.3%
4.6%
8.2%
7.7%
3.6%
3.9%
4.0%
7.8%
6.8%
3.7%
3.3%
3.5%
6.8%
6.3%
3.4%
3.6%
3.9%
5.4%
5.2%
3.8%
3.3%
3.5%
2.8%
2.0%
1.0%
0.9%
1.3%
金銭での対価Monetary consideration
1.3%
0.8%
1.0%
1.1%
1.1%
ポイント付与Receiving points
1.3%
1.0%
0.7%
0.6%
0.6%
商品サンプル等がもらえる
Getting product samples
0.5%
0.5%
0.4%
0.6%
一定期間サービスを無料で利用できる
0.3%
Free use of services for a certain period
0.3%
0.6%
0.4%
0.4%
特別な機能・サービスを利用できる
0.5%
Use of special functions/services
0.0%
0.0%
0.0%
0.0%
0.0%
49.8%
50.1%
58.9%
62.3%
61.9%

Source: MIC “The questionnaire on awareness on data” (2,155 people responded)

16

The main reason why consumers take careful attitude toward providing data is that they are concerned about theirpersonal information or behavior history being collected. The percentage of consumers who chose “I’m concerned thatmy personal information might be leaked unintentionally” about personal information or behavior history being collected
and used by a company was 67.4%, which was the highest, and the percentage of consumers who chose “I don’t know
how my information is being used” was 61%, which was the second highest, and the percentage of consumers who chose“I’m concerned about my information being used against my will” was the third highest at 57.8%. These results showthat there are a lot of concerns about the risk of the leakage of personal information and how their information is used. In
addition, the percentage of consumers who feel “I’m concerned that various information (data) is going to be collected
than today” was 34.9% (Table 10).
Table 10: Concerns about personal data being used
Q: What kind of concern do you have about your personal information or behavior history, etc. being collected and usedby a company (*except for your company if you work for a company)?

0%

20%

40%

60%

I’m concerned that my個人情報が自分の意図せずに流出しないか不安
personal information might be leaked unintentionally

67.4%

自分の情報がどのように使われているのかがわからない
I don’t know how my information is being used

61.0%

I’m concerned about
my information being used against my will
自分の意図しない利用のされ方をしないか不安

57.8%

I’m concerned that various information (data) is

今以上にいろいろな情報（データ）が収集されようとし

34.9%

going to be collected than todayていることに不安

I’m concerned that I might get some disadvantage
情報（データ）を提供しないことによって何らかの不利

18.9%

を受けるのではないかという不安
by not providing my information (data)
その他
Others

I have no particularconcerns
不安は特にない

0.3%
6.8%

Source: MIC “The questionnaire on awareness on data” (2,155 people responded)

17

80%

2.2 The ideal way to utilize data required for the implementation of AI in society
Based on the situation mentioned in 2.1, the ideal way to utilize data required for the implementation of AI in society
can be outlined as follows.
(1) Positioning data/AI in management strategies and business architectures in enterprises
Data is one of management resources, and AI can be tools. You should not consider only data or AI, but consider themin the entire management resources such as humans/goods/money/data (AI) (e. g. putting how humans/goods/money/data(AI) can contribute to corporate P/S and B/S into strategies).
In addition, designing architectures is also required in the implementation of AI in business. It is important to designbusiness related architectures as well as the ICT system. (e. g. value chain (find strengths that can be the core of enterprises),
physical architectures (the ideal way of allocating resources), digital architectures (building data platforms), legacy systems(the ideal way of rebuilding)).
(2) Building the collection system of physical data for the utilization of data, the organization system of the utilization ofdata and organizational data infrastructure in enterprises
Building the collection system of physical data is required for the utilization of data. Quite a few enterprises don’t
have a digital data collection system or have issues for collecting physical data effectively, though they have accumulated
an enormous amount of data resources. It’s important to put data of resources into digital servers and build a data collection
system.
For example, designing the system of data collection that can detect several billion pieces of baggage in real time isrequired to allocate human resources such as drivers and sales bases.
In addition, designing an organization and allocation of human resources are essential in the utilization of data.
Authorizations and budgets should be granted to an organization that take a lead role in the utilization of data under theunderstanding and commitment of the enterprises, and it is required to consider that the organization should be independentor located directly under the executive team in designing the organization. At the same time, it is also required to consider
designing the organization with a good balance of concentration and decentralization, by scrutinizing the functions broken
up into companies/business units.
Furthermore, it is also important to build the data distribution and shared infrastructure in an organization. Morespecifically, it is required to meet the requirements to respond to various data such as the visualization of data through databrochures in the organization, complicated procedures in rights/intellectual properties/contracts/securities, andstructuralizing/non-structuralizing.
(3) Relieving/reducing the concerns of consumers who provide data as well as responding to the existing laws of data ofcustomers, etc. in enterprises
The response to the existing laws of data of customers, etc. is also required. When enterprises conduct structuralreform for data-driven management, complete integration of customer data toward the new management system isimportant. However, there are some enterprises that haven’t caught up with the existing law system for their contract.
For example, in the field of logistics, personal information listed on slips of parcel delivery services conforms to old18agreements that haven’t expected the accumulation/utilization of data, so it is necessary to review the agreements
corresponding to related law systems.
In addition, as mentioned in 2.1 (5), consumers who provide information tend to be careful about providing theirpersonal information and behavior history, etc. for enterprises. So, to promote the distribution of data, initiatives forrelieving and reducing consumers’ concerns about providing data are necessary. For example, in collecting/analyzing
information or providing information to a third party, initiatives for confirming the intention of the providers of information,acquiring the agreement from users, clarifying the purpose of utilizing the information are necessary.
(4) Accelerating initiatives of the digital government in administration
Necessary initiatives must be taken in administration so that various organizations can receive benefits of theutilization of data.
The government of Japan pointed out the promotion of collaboration of administration data such as dissemination ofstandardization of data, open strategies of data held by administration, promotion of solving regional issues by promoting
open data in local governments in the “Digital Government Implementation Plan” 5. The private utilization of public datasuch as public transportation information, meteorological information and disaster prevention information is expected tocontribute not only to the implementation of AI in society but also to the solution to elaboration of the BusinessContinuation Plan (BCP) of enterprises and social issues Japan and rural areas in Japan have. It is important to acceleratethese initiatives, secure the interoperability of data (standardization of symbolic convention of data, universal vocabulary
and used characters/letters), promote opportunities to utilize public data such as DATA.GO.JP where API for acquiring
data is implemented and e-Stat (statistic GIS function is implemented), and promote using open data that include the design
and maintenance of the information system based on the concept of open data by design in local governments.
(5) The issues we are facing through the outbreak of COVID-19
The outbreak of COVID-19 in Japan and the world showed us how important for enterprises to utilize communication
networks and ICT tools in order to continue their business. In Japan, after the first person infected with COVID-19 was
confirmed on January 15, 2020, the number of people infected exceeded 17,500 as of June 17, 2020 6. In particular, manycases of uncertain infection routes were found, and many clusters of infection were also found in medical institutions andnursing care institutions. On April 7, 2020, the government declared a state of emergency over the COVID-19 outbreak
to prevent the COVID-19 pandemic and recommended the people should refrain from non-essential and non-urgent goingout, and avoid “3Cs” ((1) Closed spaces, (2) Crowded places, (3) Close-contact settings), and the government asked
enterprises to promote working at home through teleworking. Because the awareness of enterprises and individuals is
expected to change from the current status of the utilization of data in Japan mentioned in 2.1 regarding the data in the
behaviors and health conditions of individuals, etc., it is important to consider the ideal ways to manage these data. On
the other hand, there are many enterprises, especially SMEs that have issues such as the shortage of ICT human resources5

Cabinet Office, Strategic Headquarters for the Promotion of an Advanced Information and Telecommunications
Network Society (IT Strategic Headquarters) “Outline of Digital Government Strategy Plan (Cabinet Decision on
December 20, 2019)” (https://www.kantei.go.jp/jp/singi/it2/kettei/pdf/20191220/gaiyou.pdf)
6 The official website of Ministry of Health, Labour and Welfare(https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000164708_00001.html#kokunaihassei)

19and funds for capital investment, so the government, private groups, and business operators proposed several support
measures as follows (Table 11). To fully exert the potentials of communication networking including teleworking and
ICT tools, it’s important to realize the ideal way to utilize data required for the implementation of AI in society mentionedin (1) - (4).
Table 11: Major teleworking related support programs
Responsible organizations/title ofOutline of teleworking related support programs

measures, etc.
Preparation of information communication networking that supports
studying at home/working at home/online medical examination, etc.

Promotion of introduction of teleworking by enterprises and local
governments

Ministry of Internal Affairs and①

Expansion of consultation system by telework managers, etc.

②

Regarding the capital investment of SMEs for teleworking, etc.,

Communications

expand the taxation scheme to strengthen SMEs management, and“MIC’s major economic measures”

include them for the targets for exceptional measures for corporate
tax/income tax 7.
③

Launch of teleworking support network business collaborated withorganizations that support SMEs④

Building measures for special tax allocation regarding the costs ofintroducing teleworking for local government workers

In the “Project to strengthen the productivity revolution of SMEs”,
capital investment and the introduction of IT of entrepreneurs working
on the improvement of teleworking environment will be supported on apriority basis, and “special quotas” with raised subsidy rates will be

Ministry of Economy, Trade and Industry

established. For example, raising the subsidy rate from 1/2 to 2/3 in“Project to strengthen the productivity

Project to support the introduction of IT to improve productivity inrevolution of SMEs” “Project to support

services and other areas (IT introduction subsidy) (to support SMEs, etc.

digitalization of SMEs, etc.”

for the introduction of IT tools to get over the impact of COVID-19) 8
In the “Project to support digitalization of SMEs, etc.”, if IT experts
provide support services such as building EC sites and consultation
services for the introduction of teleworking for SMEs, fixed amounts
will be subsidized depending on the unit prices designated per routine

7

Ministry of Internal Affairs and Communications “MIC’s Major Economic Measures” (April 20, 2020)(https://www.soumu.go.jp/main_content/000683969.pdf)
8 Cabinet Office “Emergency Economic Measures to Cope with the Novel Coronavirus (COVID-19)～Thoroughly
secure people’s lives and move toward economic revitalization～” (May, 2020)(https://www5.cao.go.jp/keizai1/keizaitaisaku/2020/20200420_economic_measures_all.pdf)

20

work and support achievement, etc. 9
Subsidies (up to 2 million yen) for the costs of introduction/operation of

Ministry of Health, Labour and Welfare

communication devices will be granted for SMEs that newly introduced or“Work Style Reform Promotion Support

continuously utilized teleworking, depending on the status of achievement of

Subsidy (Teleworking course)”

targets (the number of employees who did teleworking and times ofteleworking) 10

Japan Telework Association



Introduction of support programs for urgent introduction of teleworking“COVID-19 Measures: Introduction of thesuch as Cisco Webex Meetings (Web-meeting system) 90 days free

support program for urgent introduction of

support program by member enterprises/organization for enterprises thatare to urgently introduce teleworking 11

teleworking”Microsoft Japan Co., Ltd.

Support educational institutions closed due to COVID-19 throughinitiatives such as free issuance of Office 365 account, free provision of
Microsoft Teams that can distribute live events, and free rental of
Surface terminals 12

Source: Prepared from a published document

Ministry of Economy, Trade and Industry “Productivity improvement through supporting SMEs for the
implementation of digitalization (promotion of comprehensive digitalization)” (April 2020)(https://www.kantei.go.jp/jp/singi/keizaisaisei/miraitoshikaigi/suishinkaigo2018/chusho/dai9/siryou3.pdf)
10 The official website of Ministry of Health, Labour and Welfare(https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/koyou_roudou/roudoukijun/jikan/telework_10026.html)
11 The official website of Japan Telework Association(https://japan-telework.or.jp/anticorona_telework_support/)
12 Future Classroom －Learning Innovation－ Official website(https://www.learning-innovation.go.jp/covid_19/microsoft/)
9

21

3.

Data economy policy in the AI era

3.1 The functions/roles of data as “new capital” and estimation methods of the effects/value of data
In the AI era when the implementation of AI in society becomes full-fledged, it is considered that data will be thesource to create values and play an important role in contribution to productivity improvement. However, the actual
situations such as the degree of value brought by the utilization of data and the process of creating values are extremely
hard to understand. Therefore, it’s difficult to clarify the impact of the utilization of data to society/economy and the sizeof effect brought by the system/technology in the said impact, etc. Based on the above, we clarified data for estimation andthe effects of data, and outlined the factors to be considered when estimating the value of data, after organizing thefunctions/roles of data, centering on prior studies. In addition, we divided approaches to estimate the value of data into
three types, and clarified each type’s concepts and issues, then considered estimation methods of the value of data infeasible methods currently available, taking care of securing versatility applicable to various industries and countries andreproducibility.
(1) Targets for estimation
To estimate effects/value of data, data for estimation and the effects of data are needed to be clarified. In doing so, weorganized the items to be considered below (Table 12). Since there are various units and values in the estimation of data,targets for estimation are needed to be considered, depending on the estimation method and scope of estimation/purposesof estimation, etc.
Table 12: Targets for estimation
Items to be considered

Target for estimation
Data

・Data in this report is defined as “currently digitized data or data that can be digitized easily”.
・Targets must be considered, taking the estimation method of value into consideration, notingthat data can be classified from various aspects,
・Since data has different units depending on types (byte, people, cases, minutes, etc.), it’snecessary to take care of units when processing several different data.

Effects of data

・To add value to data, processing and analysis are needed. As a result, improvements of ideas,
innovation, and estimation accuracy are expected. In addition, through these improvements,
effects on the entire society (CO2 reduction, etc.), enterprises (sales, profits, etc.), and individuals(benefits, etc.) are expected.
・Various effects can be considered, so it’s necessary to clarify which effect can be the target for
estimation.
・The scope of targets and understandability are conflicting (If the target is only individual effects,
the results will be easily understood but the entire picture won’t be grasped. On the other hand,
if the targets are entire things, it will be hard to understand the results.)

Source: Prepared from a published document22

(2) Functions/roles of data
First, when we checked prior studies on the functions/roles of data and intangible capital that includes data (Table 13- 16), we noticed that data have economic characteristics such as nonrivalness, externality, and partial exclusivity. In
addition, it is said that the value of data is determined due to various elements/factors such as the contents of data andmarket environment.
Table 13: Prior study on functions/roles of data①
Prior study

Functions/roles of data

thesis, etc.
Avi Goldfarb and・Saving, estimation, transportation costs of data will be reduced due to digital technologies. As a result, theCatherine Tucker

costs shown below will be also reduced. *The value is more like the value of digital technology than that of(2017) “Digital

data.

Economics,”(1) Search costs: Costs for searching information

NBER Working(2) Replication costs: Anyone can replicate with almost zero costs without deteriorating the quality.

Paper, No.

(3) Transportation costs: Transportation costs via the Internet. Distribution costs of data are almost zero.

23684.

(4) Tracking costs: Costs for tracking individuals. Activities on the Internet can be recorded easily andautomatically, so it will be easy to individualize data based on past activities.
(5) Verification costs: Costs related to the verification of identities and reputations.

Yan Carriere-

・Data has 3 economic characteristics important to public policies.

Swallow and(1) Nonrivalness

Vikram Haksar

Many people can use the same data. If data such as new ideas are widely shared, more users can use data to(2019) “Theenhance the efficiency and make a innovation, and the society will most benefit from data. However, whether

Economics andit is realized or not depends on the policy and the decision-making of individuals.

Implications of

There is a high possibility that private enterprises don’t have any incentive to allow their competitors to

Data: An

access their collected data in the existing policy. So, there is a possibility that social interests gained from

Integrated

data and competitivity in market are restricted.

Perspective,”
IMF(2) Externality

Departmental

Collecting, sharing and processing individual data by an agent can impose costs on others by having an

Paper, No.19/16.

impact on the privacy of others. In other words, there is a possibility that collecting, sharing and processing
individual data might cause excessive data collection and lack of privacy in the data market where datacollectors can process data in the way they like, which might cause inefficiency.

(3) Partial exclusivity
Since data can be excluded just partially, saving data on the system where many people are connectedmutually means that continuous investment is needed to prevent the loss of data by cyber attacks.

Source: Prepared from a published document

23

Table 14: Prior study on functions/roles of data ②
Prior study thesis,

Functions/roles of dataetc.
Yan Carriere-・Economic value of data from the perspective of demand (2 roles of data):

Swallow and(1) Data are input in the production of goods and services and contributes to innovation andVikram Haksar

efficiency(2019) “The

Data has a function as the input of production in properties and services. Data make the creation of

Economics andknowledge possible, and data will be used for continuous production of properties or the development

Implications ofof new products and services.

Data: AnTo extract the value from data as an input, costly processing and analysis are needed, and they will be

Integrated

complementary provided by skilled workers.

Perspective,” IMF(2) Creating information and shifting to information in the entire economy will have an impact on

Departmental

strategic interaction

Paper, No.19/16.

Since information on the entire economic subjects including consumers and enterprises is included indata, asymmetry of information in the market which these subjects participate in. When the access todata is helpful in reducing the asymmetry of information between buyers and sellers, it will contributeto more efficient economic transaction.
For example, sellers who can access data on characteristics on potential consumers’ interests andpurchase habits, etc. can provide products and serivces for more individuals. In a similar manner,customers who have data on characteristics of potential products can make a decision of purchase
based on more information.

Heterogeneity of data:
Data are heterogenious in terms of subjects, timing, formats, quality, etc., which will affect economic
characteristics.

Collection costs of data:
As the collection amount of data increases, the avarage costs for data collection will be reduced (Thereis a possibility that economy of scale will be created).

Source: Prepared from a published document

24

Table 15: Prior study on functions/roles of data ③
Prior study thesis,

Functions/roles of dataetc.
Yan Carriere-・Factors to determine the prices of data:

Swallow and Vikram(1) Contents of data

Haksar (2019) “ThePrices of data are not homogeneous, and they are determined based on many attiributes such as

Economics andwhen, where, and by whom they were collected, how the structure of data is, if they can be

Implications of Data:

integrated with other data. And they greatly change as time goes by.

An Integrated(2) Market environment

Perspective,” IMF

Prices are determined by many factors such as how much information asymmetrically different

Departmental Paper,from customers’ and competitors’ exists, the degree of influence on market and size of market.

No.19/16.

Evaluating the value of personal data is pretty difficult even if the data has a direct incentive. For
example, advertisers have invested much funds on personal data about online users, etc. on thepremise that sales will increase by displaying advertisements, focusing on targets. However,though the profits gained from targeted advertisements seem significant, the causal effects on sales
are small, which are smaller than the expenditure for targeted advertisements. (Marotta, Veronica,
Vibhanshu Abhishek, and Alessandro Acquisti. 2019. “Online Tracking and Publishers’ Revenues:
An Empirical Analysis”.).

Charles I. Jones,・Same data can be used by several companies due to nonrivalness of data, which will bring great social

Christopher Tonetti

profits. However, companies might hesitate to sell data due to the concern of creative destruction.

(2019) “Nonrivalry

・Data are byproducts of consumption and new data will be produced every time products are consumed,

and the Economic ofwhich will contribute to the improvement of productivity. Data can be considered as the quality of ideas.

Data,” NBER

While data is a production factor, a new idea is a new function for production.

Working Paper,・Ideas and data are kind of information. An idea is a series of orders to make economic profits and a

No.26260.

part of information, and it may include other ideas. Data shows other forms of information. Data itself is
not an order to create good products, but data might be helpful in the production process including the
production of new ideas. Therefore, an idea is a production function, and data is a production factor.

Source: Prepared from a published document

25

Table 16: Prior study on functions/roles of intangible capital
Prior study thesis, etc.

Functions/roles of intangible capital

Corrado, C., C. Hulten, and D.

・Classification of intangible capital (an example):

Sichel (2006) “Intangible(1) IT capital

Capital and Economic
Growth,” NBER Working

Softwares and databases(2) Revolutional capital

Paper, no.11948.

R&D in science and engineering fields, resource search rights, copy rights/licenses, etc.,

Cambridge, MA; National

development and design of other products, etc.

Bureau of Economic Research.

(3) Economic competitivenessCorrado, C., C. Hulten, and D.

Brand capital, corporate special human resources, organizational change

Sichel (2009) “Intangible
Capital and U.S. Economic
Growth,” Review of Income
and Wealth, 55, pp.658-660.

Jonathan Haskel, Stian・Economic characteristics of intangible capital:

Westlake (2018) “Productivity(1) Marginal costs are low

and secular stagnation in the

The business size in intangible capital can be more easily expanded than that in tangible capital.

intangible economy”.

(Example) While Uber can provide services for more customers, using intangible capital(softwares), taxi companies need to buy tangible capital (cars) to expand the size of their business.
(2) Spillover effects are highIntangible capital can be widely shared. Other people’s tangble capital can’t be used, but otherpeople’s intangible capital can be potenatially used.
(3) Sunk costs are highIt’s difficult to resell intangible capital in most cases.
(4) Synergy effects are highIt’s possible to create big value by combining intangible capital with another intangble capital.
(Example) iPhone became a profitable product by combining R&D and its design, and theorganizational design and brand in the supply chain.

Source: Prepared from a published document
In Table 17, we organized the functions/roles of data to be considered when considering the estimation of data including
the factors referred in the prior studies.

26

Table 17: Items to be considered on the functions/roles of data
Functions/roles of data

Items to be consideredComplementary factors

・Various subjects utilize data, so even if each entity uses the same data, the added value might

required for adding value tonot be necessarily the same.

data

・It is necessary to consider complementary factors (human resources, investment,
organizations, business models, market environment, etc.) required for adding value to data.

Changes in the value of data・In single data, the effects of data utilization on business are different depending on the point ofover time

time (today’s data, data 1 month ago, data 10 years ago, etc.). On the other hand, if data areutilized by combining data and old data are included, the accuracy of estimation might be
improved.
・There is a possibility that the values of data are different depending on the purpose of utilizationand method of analysis, so how to process data needs to be considered.

Negative economic value

・By holding data, negative risks such as management costs for security measures and impression
drops due to information leakage will arise (negative economic value).
・How much the negative economic value should be considered in measuring the value of dataneeds to be considered.

Relationship between data and・ICT (hardware and software) is required for data analysis, so the value of data can be considered

ICT

by replacing it with the value of ICT in a broad sense.
・It is necessary to consider how clearly the differences between the value of data and the valueof ICT should be shown.

Relationship between data and・(Part of) data can be considered to be included in intangible capital, and there are some common

intangible capitalcharacteristics between data and intangible capital, so there is a possibility that referring toanalysis methods on intangible capital is useful, considering the similarities and differences
regarding functions/roles, etc.
・There are prior studies on the impact of patent on the performance of companies as theapplication of analogy, so it is necessary to consider if referring to them is useful for measuring
the value of data.

Source: Prepared from a published document
Regarding the functions/roles of data, characteristics of data are that data can be used many times without losing its
value, data are replicable with almost zero marginal costs (additional costs) 13, and so on (Table 18).

As the case where additional costs arise, a case where purchase costs of hardware/software, etc.
associated with adding ICT infrastructure and strengthening the processing capacity arise is
expected.
27

13

Table 18: Characteristics of data
Characteristics of data

OutlineMarginal costs (replication

・Data can be replicated with almost no costs. That is, it is easy for several enterprises to utilize

costs) are almost zero

the same data technically.

Value is created only when・ Collecting data itself isn’t always worthwhile, and value is created by utilizing various

data are utilized or

meanings and knowledge acquired from data.

accumulated

The value and how to

・ The classification of contents of data are for example, personal information, industrial

process data are different

information, open data, etc. Strict management is required for processing personal data, but value

depending on the contents ofthrough usage is considered to be significant.

data
When data amount (the・When data amount is small, the notice acquired from that is also small, so the effect throughnumber of cases) is notanalysis is limited.

much, the effect of analysis

・However, it’s hard to consider that effects of data can be infinitely big in proportion to data(value of data) is small

amount.

There is a risk of data・In case personal information is leaked, enterprises will produce negative value.

leakage
Even if the same data is

The effects of utilizing data on actual business are different depending on the time when data wasused, the value (effect) ofcollected (today’s data, data collected 1 month ago, data collected 10 years ago).

data is different dependingon the region and usage

Source: Prepared from a published document
The number of observations and the value of data are considered to be different depending on the methods of analysis.
In a standard statistical analysis, if data are estimated with a certain number of observations, the marginal value of data
will suddenly diminish. On the other hand, in machine learning, there are several tasks to do, so the more complexity oftarget is required for solving a problem, the more value will be added to data. However, even in machine learning, if data
are estimated within a certain kind of tasks, the marginal value of data will diminish as in a standard statistical analysis,
when the number of observations reaches a certain point (Table 19).

28

Table 19: The number of observations and value of data ①

標準的な統計分析におけるデータの価値
Value of data in standard statistical analysis

機械学習におけるデータの価値
Value of data in machine learning
UnderstandValue of data

Value of data

Recognize humans

behaviors

Label subjects

Number of observations

Number of observations

Source: Eric A. Posner and E. Glen Weyl (2018) “Radical Markets”
It is shown that in machine learning, as learning data amount increases, the progress rates gradually decrease, though
the expected average accuracy increases. In image recognition competition in recent years (the learning data amount isfixed in the period), error rates have declined significantly, and there is an opinion insisting that other factors such as the
improvement of algorithm, hardware, and expertise are much more important than learning data amount (Table 20).
Table 20: The number of observations and value of data ②
学習データ量と平均精度
Learning data
volume and average accuracy

Changes画像認識エラー率の推移
in image recognition error rates
ImageNet Image
Recognition
ImageNet
画像認識

Average accuracy

平
精
度

エ
ラ

ー

Error rates

均率
Human level
人間のレベル

トレーニング画像の数
Numberof training images

Source: Hal Varian (2018) “Artificial Intelligence, Economics, and Industrial Organization,”
NBER Working Paper, No.24839.
With regard to the roles of data when producing value, there is a concept of “data pyramid” consisting of data,
information, knowledge, and action in information science (Table 21). The concept is based on the idea that data areconverted into information by collecting data and organizing/analyzing data, and the insight from the information isembodied to turn into knowledge, which is linked to action. In addition, while there are fully developed markets andregulatory environment with regard to information (publications, articles, websites, music, video) and knowledge (labormarket, consultants), the data market (unprocessed collection of bits) has not developed. This is because that data greatly29depend on contexts and are not helpful very much until data are converted into information.
Table 21: The concept of the “data pyramid” in information science

Source: Hal Varian (2018) “Artificial Intelligence, Economics, and Industrial Organization,”
NBER Working Paper, No.24839.
(3) Estimation methods of effects/value of data
Data has various functions, roles and characteristics, so it’s difficult to define or express the value of data uniquely.
Therefore, the attempt to estimate the value of data from several approaches is underway, and methods to estimate the valueof data (or data-related things) can be roughly divided into three based on prior studies, etc. (Table 22). Each approach has
issues.
Table 22: Estimation Methods of the Value of Data
Approaches

Methods

Tools

Limitations

IssuesCost-based

・Estimation method

・Calculating personnel• This approach can’t take・Which aspect of cost canApproachwhich pays attention

expenses

into account quality andbe appropriately ascertained

to costs related to the・Calculating time costs

productivity.

in order to define the value

creation, management,

・Calculating security• Since prices or qualityof data?

and utilization of datameasure costs

of ICT equipment are(personnel, and time,

diverse, the estimatedetc.). It is based on thevalue of data may be

assumption that theaffected by other factors.

value of data is worth• It is difficult tothe cost.

intuitively envision a
relationship between costand value.

30

Market-・ Estimation method

・ Market prices (data・Lack of data for・ How is data in general

basedwhich is based on thetransactions, etc.)

estimates

estimated

Approachmarket prices of similar/Representation

・In the case of stateddata)?

products

preference method

preference method, the・ Which point of view iswillingness of users to・Case studies of M&A,

result can be affected bymore appropriate for data

pay for those products

negative effects

the user's bias.

estimation, the business side・Experimental works

・The value could beor the consumer side?

or

of

(not

individual

perceived differently
between the business sideand the consumer side.
Impact-・Estimation method

・Econometric・Statistical estimates are

・ It is necessary to clarify

basedwhere the effects

analysis (productionneeded.

subjects for which the value

Approach(productivity, andfunction analysis, and・It is difficult toof data is to be estimated (therevenue, etc.) ofregression analysis,

differentiate between thedefinition and value of data)utilizing data is

etc.)

effects of one type of・How can a good analysis

estimated

・Experimental works

data and the effects ofmodel be established based

other types of data.

on consideration ofcomplementary elements
which are necessary for datato generate value?

Source: Prepared based on a published document

Information and knowledge acquired from data usually can’t be targets in marketing, so various estimation methods
are proposed in International Monetary Fund (IMF) (Table 23). Currently, there is no fixed method, because there arevarious issues depending on the approach and there is a possibility that a different value is indicated depending on theapproach.
Table 23: Estimation of digital economy in the macroeconomic statistics
Approaches

Concept

IssuesMarket-・Valued based on

・Products that can be genuinely compared in market don’t exist, excepting thebasedmarket prices of products

products sold in markets.

Approachthat can be compared in

・The value of “unprocessed data sold in a market” and that of “processed data

markets

suitable for adjusting, cleaning and using” are different, and the prices on the stock
market are different from the intristic value.

31

Cost-based・ Valued based on costs

・Setting of the range of costs (the entire data assets need to be expanded, considering

Approachthat produce information

the costs of data scientists).

and know-how acqured・If the costs of platforms are considered as costs for acquiring data, there is afrom data

possibility that profits other than the profits that platforms get from users might beignored.

Incomet-・Valued by estimating

・The discounted present value to be aquired from data (that shows how much value

based

future cash flow acquired

the value to be acquired in the future has currently) might be different from the value

from datain cost-based approach.

Approach・In 2008SNA, causion is needed when income is used in the valuation of assets(It’s difficult to decide appropriate assumption on durable years of assets, future
cash flow, and discount rates).
・In most cases, a platform’s income is attributable to other tangible and intangible
capital such as technical and business capability, relationship with customers,
network externality, and innovation.
Source: Marshall Reinsdorf, Jennifer Ribarsky (2020) “Measuring the Digital Economy in Macroeconomic Statistics: The Role of Data.”

Some specific initiatives related to the estimation of the value of data based on prior studies will be introduced below.
The first is a study that estimated the value of personal information, targeting American platformers. It adopted theapproach that estimates the differences between the amount companies pay for targeted advertisements based on personal
information and the amount companies pay for untargeted advertisements as the value of personal information, focusing
on the advertising revenue that platformers acquire. More specifically, it analyzed the data from AdChoices, a selfregulatory program in the Internet industry, in 2018, and disclosed the advertisement costs that weren’t based on personal
information were 52% lower than those based on personal information. Based on this, 52% of advertising revenue wasregarded as the value of personal information, and estimated approximately 57.7 billion dollars as the value of personal
information in advertising platforms in 2018, by multiplying advertising revenue of platformers who utilized personal
information by 0.52 (Table 24).
Table 24: Values in personal information in US digital advertisements (Unit: Million dollars)

Source: Robert Shapiro and Siddhartha Aneja (2019)“Who Owns Americans’ Personal Information and What Is It Worth?”.

32

The second is a study that investigated the impacts of the leakage of personal information on stock prices. 28 companies
that were listed in New York Stock Exchange (NYSE) and had more than 1 million cases of the leakage of personal
information were investigated. To get rid of various impacts from political economy, changes in stock prices comparedwith those in NASDAQ (If the stock price in NYSE increases by 1% and that in NASDAQ decreases by 1%, that would
be regarded as an increase by 2%) were observed. When you see them from several categories, you will find the impacts
from confidentiality of information were bigger than those from the number of the leakages and the impacts in old days
were bigger than those in recent years (There is a possibility that investors have been used to the leakages of personal
information that frequently occur and they don’t respond strongly) (Table 25).
Table 25: Changes in stock prices due to the leakage of personal information
Category

Classification

Time when theleakage occurred

Industry

Number of leakage
cases

6 months before the leakage

6 months after the leakage

Before 2011-15.71%-3.73%

2012 - 2015

＋9.99%＋0.99%

After 2016-9.26%＋4.11%

Finance-6.42%-4.71%

Technology＋6.79%-4.48%

e-Commerce, SNS-6.1%＋9.87%

Retailing-4.26%-0.47%

Healthcare＋4.76%＋2.97%

More than 100 million cases-2.15%＋11.74%10 - 99.99 million cases-1.34%-1.12%

1-9.99 million cases-0.36%-5.90%-1.74%-3.52%-8.86%＋11.02%＋3.57%-0.11%

Highly confidential information
Leakedinformation(credit card numbers, etc.)
passwords, etc.
Address, phone number,e-mail address, etc.

Source: Prepared based on Comparitech “How data breaches affect stock market share prices”
The third is a case study on the amount of damage compensation regarding the leakages of personal information in
Japan. When personal information leaked out, companies tend to voluntarily pay compensation (including cash vouchers,etc.), so the amount of compensation can be regarded as the value of personal information. When you check the past cases,
you will find there are many cases of compensations of 500 yen - 1000 yen per person (Table 26). Also, there are somecases where a lawsuit is filed in addition to voluntary compensation. Sometimes there are some different leading caseseven in the same leakage cases, so currently there are no established business practices on the cause of a compensation (ifa leakage itself is the cause of a compensation) and how to determine the amount of compensation.
Table 26: Examples of leakages of personal information and compensation in Japan

33

Year

Organizations that leaked

Scale of leakage

Average amount

Total compensation

information(people)(per person)

amount (million yen)

5,000 yen

7

1998

University A

1,400

1999

City B

220,000

10,000 yen＋attorney's fee
5,000 yen

3,300

30,000 yen / 17,000 yen
2002

Company C

37,000(depending on the

1,110

secondary damage)
2002

Company D

560,000

500 yen

280

2002

Company E

183,000

1,000 yen

183

2002

Company F

132,000

5,000 yen

660

2003

Company G

1,150,000

5,000 yen

5,750

2003

Company H

180,000

1,000 yen

180

500 yen
2004

Company I

4,517,000

compensation 5,000 yen＋ attorney's fee 1,000 yen

2,259

in the lawsuit
2004

Company J

478,000

500 yen239

2005

Company K

122,000

500 yen

61

2007

Company L

8,640,000

500 yen

4,320

2008

Company M

123,000

1,000 yen

123

2009

Company N

49,00010,000 yen

490

10,000 yen / 3,000 yen
2009

Company O

18,000(depending on the leakage of180

credit card information)
2009

Company P

149,000

500 yen75

2014

Company Q

28,950,000

500 yen

14,475

Source: Prepared based on Mizuho Chuo Law Firm “Cases, standards and market prices of civil legal responsibility (the amount ofcompensation) of the leakages of personal information”,
Tomoaki Sato, “The leakages of personal information and liability for damages” and afterwards”, etc.

The fourth is the referring that how the importance of data should be considered when we consider the merger andacquisition (M&A). Currently, specific methods of estimating data are not clearly indicated, though the necessity of
estimating the value of data and data related risks and the perspective of estimation are referred (Table 27).

34

Table 27: The value of data and data related risks to be considered in M&A, etc.
Value of data
・Value of data as capital

Risks of legal aspects
・Existence of legal

Risks of technical aspects
・Cyber security risks

・Value of data through usage

regulations that restrict the・Forms of data storage

・Expected future value of datatransmission or use of data

・Management methods of・Existence of contractual use
restriction

data
・Quality of data

・Existence of data-related

Risks of other aspects
・Risks of hollowing-out ofhuman resources
・Risks of losing intellectual
property
・Data protection/privacy
policies

great responsibility

Source: Prepared from a published document
The fifth is the estimation method of value of consumer data that are provided for business operators in regulations ofCalifornia Consumer Privacy Act in the US. Though a specific estimation formula isn’t mentioned, enterprises are required
to consider at least one of the following items 14.
1.

Marginal prices for the businesses of sales, collection and deletion of consumer data

2.

Average prices values for the businesses of sales, collection and deletion of consumer data

3.

The total prices of businesses of selling, collecting, and deleting consumer data divided by the total numberof consumers

4.

Profits produced from businesses through selling, collecting and holding personal information of
consumers

5.

Costs related to selling, collecting, and holding personal information of consumers

6.

Costs related to presenting, providing and requesting the differences between monetary incentive/prices
and services

7.

Profits produced from businesses through selling, collecting, and holding personal information of
consumers

8.

Other practical, reasonable and reliable estimation methods

TEXT OF MODIFIED REGULATIONS [CLEAN VERSION] TITLE 11. LAW
DIVISION 1. ATTORNEY GENERAL CHAPTER 20. CALIFORNIA CONSUMER PRIVACY ACTREGULATIONS PROPOSED TEXT OF REGULATIONS(https://oag.ca.gov/sites/all/files/agweb/pdfs/privacy/ccpa-text-of-second-set-clean-031120.pdf)
The final drafts of these regulations are being examined with other regulations as a package by
the California Office of Administrative Law (OAL).
35

14

(4) Empirical analysis on the estimation of the value of data
After figuring out holding data/use of data through questionnaires for enterprises and analyzing the relationshipbetween financial information and holding data/ use of data, we positioned “Capital”, “Labor”, “Data”, “Others” as thefactors to produce output of enterprises, and analyzed the relationship between holding data/use of data and output throughempirical analysis. When doing that, we estimated a production function that is the impact-based approach as a feasible
method currently available to estimate the value of data, noting securing versatility/reproducibility applicable toorganization of the functions/roles of data and various industries/countries, etc.
Since the actual status of utilization of data in enterprises needs to be grasped in detail in consideration of the method,
we conducted the following questionnaire survey for enterprises.
<Questionnaire survey on the utilization of data>① Target of the survey: All the listed enterprises (3,819 enterprises)＋Non-listed companies (467 enterprises): In
total: 4,286 enterprises 15② Period of the survey: February 7, 2020 (Fri.) – March 27, 2020 (Fri.)③ Content of the survey: Data (amount, diversity, etc.) enterprises utilize and the status of analysis (organizations,
methods, frequency, etc.) etc. 16First, when you see the relationship between the “total amount of data” accumulated by enterprises and “added value 17”(Table 28/29) by matching the results of the questionnaire survey and financial information of enterprises, you will findthat the enterprises that have bigger total amount of data have bigger added value, which suggests that data have a positive
impact on production activities.
Table 28: The relationship between total amount of data (logarithmic value) and added valuelog（付加価値）(added value)y = -0.0011x3 - 0.0042x2 + 0.3337x + 2.9333
R² = 0.0969

12
10
8
6
4
2

-10

-5

0
-2

0

5

10

15

-4

log（データ総容量）(total volume of data)
Source: Prepared based on MIC, “Survey on the utilization of data”

Enterprises that were confirmed to have disclosed financial information were targeted.
Regarding the detail of questionnaire, please refer to 6. Addendum.
17 It was defined as “Net sales”－“Sales costs”－“SG & A”＋“Depreciation costs”.
36
15
16

Table 29: Relationship between the total amount of data and added value
Number of responded

回答数
enterprises
（社）

回答企業数

Number of enterprises responded

180
160
140
120
100
80
60
40
20
0

平均付加価値（右軸）Average added value (right axis)

160
3154
54
116

44
527

10TB以下10TB
or less（0TBは除く）(excluding 0TB)

44

1046

10TB～
More
than 10TB

100TB～
Morethan 100TB

100TB以下
100TB or less

1,000TB以下
1,000TB or less

Added value
付加価値
（億円）
(100 million yen)

4000
3500
3000
2500
2000
1500
1000
500
0

1,000TB～
Morethan 1,000TB

Source: Prepared based on MIC, “Survey on the utilization of data”
Likewise, we analyzed the relationship between the “total number of cases of data 18” accumulated by enterprises and“added value” (Table 30/31). In this survey, the enterprises that have larger total number of cases of data have bigger added
value, as the same as the enterprises that have bigger total amount of data.
Table 30: The relationship between the total number of cases of data (logarithmic value) and added valuelog（付加価値）(added value)y = -0.0059x3 + 0.2016x2 - 1.7957x + 6.6061
R² = 0.23

10
8
6
4
2
0

0

5

10

15

20

25

-2
-4

log（データ総件数）(total number of cases of data)

The total of the number of the targets of data (based on units such as number of people,
companies, customers, etc.)
3718

Source: Prepared based on MIC, “Survey on the utilization of data”
Table 31: The relationship between the total number of cases of data and added value
Number of回答数
responded

回答企業数

（社）

Number of enterprises responded

enterprises

平均付加価値（右軸）Average added value (right axis)

Added value
付加価値
（億円）
(100 million yen)

68

70

547

60
50

20

92

10

400

30

30

600
500

391

36

40

700

25

300
200

55

100

0

0
10,000 cases or less

1万件以下

More than
10,000 cases
1万件～(excluding 0 case)and 100,000
cases or less
（0件は除く）10万件以下More10万件～
than 100,000 cases

100万件～
Morethan 1 million cases

100万件以下

and 1 million cases orSource: Prepared based on MIC, “Survey on the utilization of data”
Since it is assumed that data affects production activities of enterprises, the study positioned data as a production factor, aswith other production factors (capital and labor), and conducted empirical analysis with the Cobb-Douglas Production
Function modified to include data as a factor (linear homogeneity was not assumed).

𝑉𝑉 = 𝐴𝐴𝑜𝑜 𝐾𝐾 𝛼𝛼 𝐿𝐿𝛽𝛽 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝛾𝛾 ,log(𝑉𝑉) = log 𝐴𝐴𝑜𝑜 + 𝛼𝛼 log(𝐾𝐾) + 𝛽𝛽 log(𝐿𝐿) + 𝛾𝛾 log(𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷) + 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷**Classified into manufacturing and non-Here, V stands for added value, K stands for capital (tangible fixed assets ＋ intangible fixed assets), L stands forlabor (the total number of employees 19), and Data stands for data variables. As estimated analysis results are shown in
Table 32, the amount/number of cases of utilized data have a positive impact on added value as well as other production
factors (capital, labor), and in the analysis conducted using the amount/number of cases of utilized data in 3 years ago, theamount/number of cases of utilized data had a positive impact on added value. Due to the fact, it can be presumed that theutilization of data has an impact on the increase in added value, even if you consider the timeline. In addition, an increasein the amount/number of cases of utilized data by 1% resulted in an increase in added value by 0.05%. This result is areflection of the current status of the utilization of data, and it should be noted that the increase in the amount/number ofcases of utilized data is not necessarily connected with the increase in added value and the average results of the enterprisesthat have enhanced effects and the enterprises that have not enhanced effects are shown in this study.

19

The total number of employees was set as ‘the number of employees at the end of the term ＋ the number of
temporary employees,’ and when the number of temporary employees was deficient, it was treated as zero.

38

Table 32: Empirical analysis results 20

Data variable

Sample

Adj R

2

K

L

Data

(Capital)(Labor)(Data)

0.44 ◎

0.50 ◎

0.05 ◎

0.34 ◎

0.55 ◎

0.07 ◎

size
258

0.8343135

0.8157

Utilized data amount (FY2015)258

0.83320.44 ◎

0.51 ◎

0.05 ◎

Utilized data number (FY2015)

135

0.81360.34 ◎

0.56 ◎

0.06 ○

Externally acquired data amount (= total data amount267

0.84010.47 ◎

0.47 ◎

0.05 ◎

140

0.81930.35 ◎

0.51 ◎

0.07 ◎

267

0.83790.47 ◎

0.48 ◎

0.05 ○

140

0.81340.35 ◎

0.55 ◎

0.06 ○

261

0.83340.44 ◎

0.49 ◎

0.05 ◎

Total number of data × data utilization rate

137

0.81500.32 ◎

0.58 ◎

0.05 ○

Total data amount × data diversity (diversity of data

173

0.85370.53 ◎

0.42 ◎

0.04 ○

85

0.85760.41 ◎

0.49 ◎

0.06 ◎

Utilized data amount (= total data amount × ratio ofdata used for analysis)
Utilized data number (= total number of data × ratioof data used for analysis)× ratio of data acquired from outside)
Externally acquired data number (= total number ofdata × ratio of data acquired from outside)
Internally owned data amount (= total data amount externally acquired data amount)
Internally owned data number (= total number of data externally acquired data number)
Total data amount × data utilization rate (usearea/type/processing method)acquisition and provision)
Total number of data × data diversity(Note) ◎: Significance level 1%, ○: Significance level 5%, △: Significance level 10%

When what the above empirical analysis means applies to actual business scenes, the following situation can beassumed. Having said that, however, it’s just an “assumption” and it should be noted that the assumption isn’t based on thegrasp of initiatives of enterprises via hearing, etc.
① Manufacturing industry: The enterprises that grasp the operational status of facilities and detect abnormities offacilities through the visualization of data of sensors, etc. tend to have realized stable production.
② Retailing industry: The enterprises that have finely classified the types of customers through customer attributes
and past purchase data, etc. and have taken optimum marketing measures based on data tend to have increased theirsales.

The data variables without the description of FY2015 are values in FY2018. In addition, theproportion of the data obtained from external was corrected based on the acquisition status of datafrom the external.
39

20

③ EC sites: The EC sites that are providing services for recommending optimum products for customers based on
customers’ favor and past behavior pattern data tend to have increased sales.
3.2 The ideal fair remuneration depending on the effects/value of data
To enjoy the benefits of effects/value of data in the entire society, fair remuneration should be distributed toappropriate subjects. When you see the distribution to capital/labor in recent years, you will see the distribution has
shifted to intangible capital including part of data (Table 33). If there is a possibility that most of the wealth produced
from data is distributed to enterprises that collect and utilize data, it should be considered what the ideal fair remuneration
is depending on the effects/value of data that is expected to occupy an important place in economic activities.
Table 33: Distribution ratio of intangible capital, tangible capital, and labor (cumulative variation width)（％）

データの一部を含む無形資産
Intangible capital including part of data

有形資産
Tangible assets

Labor

労 働
28 EU countries and 15 other major countries in the world

Source: Chen, Los & Timmer (2018) “Factor Incomes in Global Value Chains: The Role of Intangibles,” NBER Working
Paper 25242.
Based on the above, we will consider the issues on the approach of “paying remuneration to users who produced data”
after reviewing the concept of approaches that take data as capital or labor as a production factor. Though data is generally
treated as capital, there is an opinion that data should be treated as labor and monetary rewards should be given to
individuals who produce data, considering the characteristics of AI that depends on the active involvement of individuals
who produce related data. “Data as Capital” is a concept that values the utilizers of data (AI companies/platformers/data
scientists, etc.) as the contributor to the value created from data. On the other hand, “Data as labor” is a concept thatvalues the producers of data as the contributors to the value created from data from the perspective that recognize thecontribution of individuals who produce data as general labor.

40

Table 34: Major characteristics of “Data as Capital” and “Data as Labor”
Data as Capital

Data as Labor

Value utilizers of data

Value the producers of data

Entrepreneurialism

Usual contribution (equal to labor)

Contributors tocreating thevalue of data
Incentive

Future of Labor Universal Basic Income

Data Labor

・AI’s contribution to improving productivity
might be thwarted because AI depends on active
Expected
concerns

involvement of individuals who generate related
data.
・Market dynamism might become stagnant if
the data generated by individuals are・Tragedy of the anticommons (possibility that
assets to be shared are subdivided and privatized,then utilization of resources useful in society canbe hindered) might arise.
・The original motivation for the activity might belowered due to the monetary consideration.

concentrated in certain companies.

Source: Prepared by referring to Imanol Arrieta-Ibarra, Leonard Goff, Diego Jiménez-Hernández, Jaron Lanier and E. Glen
Weyl. (2018) “Should We Treat Data as Labor? Moving Beyond ’Free’,” American Economic Association Papers &Proceedings, Vol.108, pp.38-42, other reference information, and discussion in the Special-Interest Group on Data
With regard to the approach that values data producers as contributors to the value created from data, EU proposed
data producer’s right as an item of “A future EU framework for data access” presented by EU in 2017, about non-personal
data collected via IoT devices/connected cars, etc. that are exempt from the protection based on General Data Protection
Regulation: GDPR 21(Table 35). After that, concerns about establishing a new proprietary right on data were expressed bymany stakeholders in public comments, and the necessity of securing the access to data shared by enterprises was rather
claimed, then the proposal was excluded from policy options 22.

21

In May 2018, it was fully enforced, and unified rules on the protection of personal data in EU were enacted. The
right to data portability (it’s the right to retrieve his/her personal data that they have provided for a controller in amachine-readable format, and it allows for data subjects to directly transmit their personal data to another data
controller.) is also included in this regulation.
22 The perspective of sharing data among enterprises referred to the said framework is inherited (such as support
for dealing with problems concerning the commonly generated data (in industrial IoT, etc.) in the plan concerning
the Data Act (scheduled to be enacted in 2021) in the European strategy for data (February 2020) and clarification of
the legal responsibility for data sharing).

41

Table 35: Outline of A future EU framework for data access

将来におけるデータアクセスのためのフレームワーク（Afuture EU framework for data access）
A future EU framework for data access
・企業のデータ共有の誘引付けに関するガイダンス（Guidanceon incentivising businesses to share data）
・Guidance on incentivizing businesses to share data
・信頼できるデータの識別と交換のための技術ソリューション開発促進（API等）（Fostering the development
・Fosteringdevelopmentof technicalsolutions for reliable
identificationand exchange of data (API, etc.)
oftechnicalthesolutionsfor reliable
identificationand exchangeof data）
・デフォルト契約ルール（不公正契約条項指令の見直しを含む）（Default
contract rules）
・Default contract rules (including reviewing Unfair Contract Terms Order)・ 公 益 及 び 科 学 的 目 的 の た め の ア ク セ ス （ B2G 含 む ） （ Access for public interest and scientific・Access for public interest and scientific purposes (including B2G)
purposes）
・データ製作者権（Data
・Data producer’s right producer‘s right）
・ ア ク セス へ の 対 価（ FRAND （Fair, Reasonable And Non-Discriminatory ） 等 ） （Access against
・Access against remuneration (FRAND (Fair, Reasonable, And Non-Discriminatory), etc.)
remuneration）
Source: Prepared based on COMMISSION STAFF WORKING DOCUMENT on the free flow of data and emerging
issues of the European data economy Accompanying the document Communication Building a European data economy{COM(2017) 9 final}(https: //ec.europa.eu/digital-single-market/en/news/staff-working-document-free-flow-data-andemerging-issues-european-data-economy)
In addition, regarding personal data, “MyData Global 23 ” was established on 11 October 2018, and it promotes
initiatives to aim for the world where individuals fully understand their own data and utilize their personal data forthemselves with independence and self-direction. This organization upholds reforms and principles through MyData,aiming to promote individual-centered visions on personal data, by recovering from unbalanced situations under therecognition of the current situation that the power balance on personal data is extremely biased on organizations who havethe authority to collect and distribute personal data and make a decision on them, and individuals can’t control what will
happen to their personal data (Table 36).

It established many bases around the world, centering on Europe. It is organized by approximately 100
organization members and 400 individual members in over 40 countries.

23

42

Table 36: MyData Global’s Reforms and Principles
Reforms by MyData Global
1. From formal to actionable・We intend access and redress, portability, and the right to be forgotten, to become “one-clickrightsrights”: rights that are as simple and efficient to use.

2. From data protection to・We intend to change common practices towards a situation where individuals are both protected

data utilizationand empowered to use the data that organizations hold about them.
・Today’s data economy creates network effects favoring a few platforms able to collect and3. From closed to open
ecosystems

process the largest masses of personal data.
・By letting individuals control what happens to their data, we intend to create a truly free flow ofdata – freely decided by individuals, free from global choke points – and to create balance,
fairness, diversity and competition in the digital economy.

MyData Global's Principles
・Individuals should be provided with the practical means to understand and effectively control who
1. Human-centric control of

has access to data about them and how it is used and shared.

personal data

・Ultimately, we want the terms and conditions for using personal data to become negotiable in afair way between individuals and organizations.
・The value of personal data grows exponentially with their diversity; however, so does the threat to2. Individual as the point ofprivacy.

integration

・This contradiction can be solved if individuals become the “hubs” where, or through which crossreferencing of personal data happens.
・We want individuals to be able to securely manage their personal data in their own preferred way.

3. Individual empowerment

We intend to help individuals have the tools, skills and assistance to transform their personal data
into useful information, knowledge and autonomous decision-making.
・The portability of personal data, that allows individuals to obtain and reuse their personal data for

4. Portability: access and re-their own purposes and across different services, is the key to make the shift from data in closedusesilos to data which become reusable resources. Data portability should not be merely a legal right,
but combined with practical means.
・Organizations that use a person’s data should say what they do with them and why, and should do

5. Transparency andaccountability

what they say.
・They should take responsibility for intended, as well as unintended, consequences of holding andusing personal data, including, but not limited to, security incidents, and allow individuals to callthem out on this responsibility.
・In order to maximize the positive effects of open ecosystems, we will continuously work towards

6. Interoperability

interoperability of data, open APIs, protocols, applications and infrastructure, so that all personal
data are portable and reusable, without losing user control.

Source: Prepared based on MyData Global (https: //mydata.org/declaration/)
As for paying remuneration to users who produced data, while people in the standpoint of “Data as labor” take it

43

positively, people in the standpoint of “Data as capital” have pros and cons. In addition, there are expected issues in bothstandpoints (Table 37).
Table 37: Issues on the approach of “payment of remuneration to users who produced data”
Data as Capital

Data as Labor

Payment of
Positive

remuneration to users

Negative

Positive

who produced data
・Free data sets and free machine・The importance of data willlearning algorithm are available

be enhanced along with the・The value of data can beadvance of AI, so incentive tocreated only when data is

Reasons for paying/not

provide data will be needed

analyzed, so data analysts

paying remuneration

・Existence of enterprises thatshould take remuneration.

have acquired most of the・There is a possibility that thevalue produced from data as

original motivation of activities

private profit

might be lowered due to giving

・Data as Labor will give an
important opportunities tocomplement the lack of income,and bring citizens who are
suffering from the expansion of
disparity the awareness ofcontribution to society.

monetary consideration
・Market mechanism (micro

Process of payment of
remuneration

payment/information trust・(Limitations) Estimate thefunction (Information trust

contribution and build anbanks))―

appropriate technology system to・Regulations (competition

retroact and detect the value

policy/tax system/social

individual users produced

security, etc.)
・(Limitations) Estimate theExpected issues

・Market mechanism:

There is a possibility that thecontribution and feasibility ofEstablishment of business

uneven distribution of wealthtechnology

modelsbetween enterprises, data

・There is a possibility that the・Regulations：objective

analysists and other individuals

estimation of contribution willvalidity of introduction(further lowering of laborbring surveillance society and(quantitative index, etc.)

distribution rates) will arise.

promotion of disparity anddiscrimination

Source: Prepared from a published document
The conclusion of whether data as a production factor should be positioned as capital or as labor cannot be reached at
this point. On the other hand, it is considered that considering the system where individuals who produce data are given
some consideration (other than using the service for free) is needed so that the entire society can enjoy the benefits of active

44

utilization of data and effects/value of the utilization, data.
On this point, we need to consider what kind of system will function and how much consideration should be given.
For example, if the estimation method of the value of data mentioned in 3.1 is established, utilizing the method will bepossible. To distribute the estimated value of data to individuals in the most precise form, the degree of contribution ofindividuals to individual data should be estimated. However, regarding the contribution of individuals to data production,some people may contribute a little in various fields, and other people may contribute to data production greatly to one
field but may not contribute to other fields very much. Therefore, grasping these situations in a precise, safe and securemanner has a technical limitation at this point. It is pointed out that if we try to estimate the degree of contribution precisely,
there is a risk that it will bring surveillance society and promotion of disparity and discrimination. On the other hand, if thedegrees of contribution to the value of data between data utilizers such as platformers and data scientists and individuals
who are data producers in the entire value of data can be estimated, the total value of data to be distributed to individuals
will be able to be estimated, so based on that, the “average” remuneration will be able to be paid to individuals 24. However,there are some technical difficulties at this point as mentioned above.
As another approach to consider the consideration from the perspective of individuals who produce data, there is amethod to check the minimum “monetary consideration” requested when they are providing their information forenterprises. When “monetary consideration” requested by individuals were checked in a questionnaire survey for
individuals, the results of central values were 500 yen - 1,000 yen in many data (Table 38).
In highly confidential “Fingerprints”, “Credit card numbers” and “Face photograph”, the average values of data wereover 10,000 yen, which shows that individuals highly hesitate to provide their data to enterprises. It is required to consider
the system where monetary consideration is given, keeping in mind that average values and median values are quite
different in any data, and there are a certain number of people who request high monetary consideration (don’t want toprovide their data without high monetary consideration).

Eric A. Posner and E. Glen Weyl (2018) “Radical Markets” stated the “average value” should be
paid on condition of satisfying the standard of overall quality.
45

24

Table 38: Minimum “monetary consideration” required for providing information for enterprises
Average value (yen)Central value (yen)Fingerprints

10,8795,000

Credit card number

10,641

1,000

Face photograph

10,1143,000

Passport number

7,905

1,000

Bank account information, etc.

7,710

1,000

Mobile information

5,705

1,000

Telephone number

4,654

1,000

Information posted on SNS and blogs

4,648

500

Bio-information

4,565

1,000

Address information

4,378

1,000

Purchase information at retailer shops and restaurants

3,364

500

Workplace information

3,163

500

Electric money, usage history of IC cards, etc.

3,028

1,000

E-mail address

3,004

500

Browsing history of websites

2,915

500

Purchase information on EC sites

2,866

500

Family information, housing information

2,445

500

Sex, age, educational background

2,409

500

Insurance information

2,177

500

Annual income

1,627

100

Birthplace

1,575

100

Source: MIC “The questionnaire on awareness on data” (2,155 people responded)
3.3 The current status and issues toward marketing and visualization of data transaction
While the roles of data are enhanced in production activities of enterprises, the utilization of data has not been
sufficient yet in Japan, which was mentioned in 2.1 and 3.1. Mega ICT companies have developed their business based on
the data collected/accumulated from users, and the utilization of data is concentrated on large enterprises and companies
in big cities in Japan. Therefore, while these enterprises enjoy benefits brought from data, other enterprises don’t because
the process of utilizing data from collecting data to creating added value hasn’t prepared well in them. One of the means
to lower the barrier of utilizing data at enterprises and enable many enterprises to enjoy the benefits brought from data ismarketing and visualization of data transaction. Currently, data transaction is partly done, however, creating theenvironment where various enterprises can freely do data transaction under the condition that the value of data and contract
terms are secured will enable enterprises that wish to expand their business by utilizing data to lower the cost of acquiringdata. As a result, that might ease the concentration of benefits brought from data on some large enterprises.
In the following, issues to be addressed toward marketing/visualization of data transaction, after checking the current
situation in Japan.

46

(1) The current status of data transaction in Japan① Global trends
Here, regarding global trends on marketing/visualization of data transaction, the following 4 cases will be introduced.
The UK has a program called ‘midata’ run by the government 25. This program allows individuals to receive
personal information owned by private enterprises in an easy-to-use form in real time, and the objective of
this program is to allow individuals to receive better services through the provided data. In this program,the targeted fields for personal information are the 4 fields of energy, banks, mobile phones, and credit card 26. Currently,
the program is operated in the 2 fields of energy and banks. At the beginning, it was an initiative with no legal ground,but in 2013, Enterprise and Regulatory Reform Act was revised, which enabled the government to enforce providing databy establishing a regulation. (Having said that, however, there is no establishment of the regulation at this point).
As an international agreement, New Zealand, Singapore, and Chile concluded Digital Economy Partnership
Agreement (DEPA) 27 . This is a rule actively responding to the expansion of business transaction on the Internet bydigitalizing information. More specifically, the objectives of this agreement are: ①to build a trustworthy digital transaction
system, and through this system, ②to make trustworthy data distribution possible ③to promote end-to-end seamless
transaction. The 3 countries aim to attract global enterprises for investment on their digital economy.
In the US, Data Transfer Project (DTP) was launched by 5 private enterprises; Apple, Facebook, Google, Microsoft
and Twitter in 2018. This project has been promoted in the form of Open Source Initiative to encourage more and more
providers in addition to the 5 enterprises to participate in the project. DTP has expanded data portability and provides
functions to directly send data to the enterprises participating in DTP in order to save time and labor for downloading thereplication of data from data owners. It is considered that providing these functions in DTP will enable both data owners
and users to reduce the burden of investing in equipment, which will accelerate the utilization of data and make therationalization of work possible as well as increase the supply of services based on the premise of mutual utilization.
One of the initiatives for utilizing data without owing it is Federated Learning 28 system whose concept Google
announced. It’s the construction system of learning data necessary for machine learning, etc. While the existing AI system
individually collect an enormous amount of learning data and use them, this system tries to build learning data on asmartphone, etc., by decentralizing data. Through this system, the current model can be downloaded to a smartphone froma cloud service first. And the model will be partially improved on the device by learning from the data accumulated on thesmartphone, and the changes focusing on the improvement will be sent to the cloud service as the update through encrypted
communication, then the model will be improved in the end. Eventually, through this system, the shared model will be
25

In April 2011, this program was set up by Department for Business, Innovation & Skills.
These 4 fields were selected as the targets for regulation that match the concept of midata because of these: ①making
a contract (use) for a long period is required ②doing transactions frequently is required, ③the payment system iscomplicated and it’s hard to compare the payment system with that of a competitor.
27
The websites on DEPA of the governments are as follows.
New Zealand：https://www.mfat.govt.nz/en/trade/free-trade-agreements/free-trade-agreements-concluded-but-not-inforce/digital-economy-partnership-agreement/
Singapore：https://www.mti.gov.sg/Improving-Trade/Free-Trade-Agreements/Digital-Economy-Agreements/TheDigital-Economy-Partnership-Agreement
Chile：https://www.gob.cl/noticias/chile-nueva-zelandia-y-singapur-cierran-las-negociaciones-del-primer-acuerdosobre-economia-digital/
28
Federated Learning: Collaborative Machine Learning without Centralized Training Data
(https://ai.googleblog.com/2017/04/federated-learning-collaborative.html)
26

47

used, and the convenience for many users will be improved.
As mentioned above, initiatives for the marketing and visualization of data transaction have been implemented in theworld, so these global initiatives should be noted in formulating policies, business strategies, etc. in Japan.
② Data transaction with other enterprises is inactive
In the questionnaire on the utilization of digital data for enterprises, more than 70% of the enterprises were notproviding/receiving data on individuals 29 for/from other enterprises, which shows data transaction with other enterprises
is inactive (Table 39). When seen by the combination of providing data or receiving data, and the same industry or different
industries, the percentage of receiving data from different industries was the lowest (less than 10%), and even the highest
percentage of providing data for different industries was just 14.3%. It is important to specify the factors that impede
providing/receiving data in designing a system toward marketing/visualization of data transaction.
Table 39: The status of providing/receiving data on organizations and/or individuals for/from other enterprises(the same industry or different industries)
0%
Providing data for other enterprises

他社（同業種）にデータを提供している(the same industry)

20%

Providing data for other enterprises(the same industry)

Receiving data from other enterprises

他社（異業種）からデータを受領している(different industries)

Not providing/receiving data for/from

他社とデータの受け渡しは行っていない

other enterprises

80%

14.3%

(different industries)

Receiving data from other enterprises

60%

11.4%

他社（異業種）にデータを提供している他社（同業種）からデータを受領している

40%

12.3%

7.5%

71.9%

Source: MIC “The questionnaire on the utilization of digital data” (2,003 enterprises responded)③

Organizational data have different statuses of providing/receiving data, depending on the type of data
When you see the differences in the statuses of providing/receiving data on organizations for/from other enterprises(Table 40), you will find “Basic data on organizations” and “Data to become basic information in order to implement tasks”
have higher percentages in providing data, and “Data to become basic information in order to implement tasks” and “Data29

Please note that “data on individuals” in this report is used as a general term, and it’s not used asa legal term such as “personal data” in Act on the Protection of Personal Information (Act No. 57
of May 30, 2003).
48

on transactions and actions” have higher percentages in receiving data. On the other hand, “Data on equipment, machines,
devices, vehicles, etc.” and “Data on chemical substances, creatures, etc.” have lower percentages in providing/receiving
data. It is important to consider the statuses of providing/receiving data are different depending on the type of data in orderto smooth data transaction.
Table 40: The statuses of providing/receiving data on organizations for/from other enterprises
0%

20%

40%

80%

57.4%

組織に関する基本的なデータ
Basic data on organizations

31.6%
53.3%
51.1%

業務を遂行するための基礎情報となるデータ
Data to become basic
information in order to implement tasks

41.7%
44.3%

Data
on transactions and actions
取引やアクションに関するデータ
Data
on facilities, factories, plants, offices,stores,
施設、工場、プラント、オフィス、店舗、作業現場等に関するデータ

60%

34.3%
37.8%

work sites, etc.

28.3%
31.6%

設備、機械、
機器、車両等に関するデータ
Data on equipment,machines,
devices, vehicles, etc.

17.9%
23.2%

Data on chemical
substances,
creatures, etc.
化学物質、生物等に関するデータ
その他
Others5.5%
5.9%

Notいずれも提供（受領）していない
providing/receiving any data

1.9%
4.3%

Providing data
データを提供している
データを受領している
Receiving data

Source: MIC “The questionnaire on the utilization of digital data” (2,003 enterprises responded)④

The use of data on individuals is asymmetric between the sides of providers and receivers
Regarding data on individuals, “Basic data on individuals” and “Data on life, habits, life events, etc.” have higher

percentages in providing data, and “Data on codes allocated to individuals” and “Data on purchase history” have higher
percentages in receiving data, but “Data on actions on the Internet” and “Data on behaviors” have lower percentages inboth providing data and receiving data. Regarding providing data and receiving data, the differences between the highest
and the lowest in the percentages of providing data tend to be larger than those in the percentages of receiving data (Table
41). It is important to consider that the tendency of providing data and that of receiving data are asymmetric in order tosmooth data transaction.

49

Table 41: The status of providing/receiving data on individuals for/from other enterprises
0%

40%

20%

個人に関する基本的なデータ
Basic data on individuals

60%
50.7%

30.3%
34.3%
28.5%

Data onphysical characteristics
身体的特徴に関するデータ
生活、習慣、ライフイベント等に関するデータ
Data on life, habits, life events, etc.

30.7%

39.0%

29.8%
31.9%

個人に割り当てられた符号に関するデータ
Data
on codes allocated to individuals

26.2%
29.4%

Data corresponding to personal information要配慮個人情報に該当するデータ
required to be treated with care

24.8%
31.6%

購入履歴データ
Data on purchasehistory

23.8%
26.3%

インターネット上のアクションに関するデータ
Data on actions on the Internet

16.7%
23.2%

行動に関するデータ
Data
on behaviors

3.3%
2.2%

その他
OthersProviding data
データを提供している
9.0%

いずれも提供（受領）していない
Not
providing/receiving any data

14.6%

データを受領している
Receiving data

Source: MIC “The questionnaire on the utilization of digital data” (2,003 enterprises responded)⑤ Individuals are concerned about their data being collected or used
Regarding the concerns about individuals’ data being collected and used, “I’m concerned about my personal
information being leaked against my will”, “No idea about how my information is being used”, and “I’m concerned about
my personal information being used against my will” have higher percentages (more than 50%) in the responses (Table
42). It is important to consider relieving concerns of individuals in order to smooth data transaction.
⑥

Enterprises tend to be afraid of being criticized for doing data transaction by societyWith regard to ⑤, it can be said that concerns about personal data being collected and used include the possibility

that the enterprises that collect/use the data might be criticized by society. In particular, in the society where there is a highpossibility that enterprises are criticized, the risks of collecting or using data will be excessively increased, and corporate
activities on collecting/using data or data transaction might shrink. It should be noted that relieving concerns of individuals
about personal data being collected or used has an impact on corporate activities on data transaction.

50

Table 42: Concerns about personal data being collected or utilized

0%

20%

40%

個人情報が自分の意図せずに流出しないか不安
I’m concerned about
my personal information being leaked against my will

67.4%

I have no idea about how my information is being used
自分の情報がどのように使われているのかがわからない

61.0%

I’m concerned about
my personal information being used against my will
自分の意図しない利用のされ方をしないか不安

57.8%

I’m concerned about various information (data) being collected
今以上にいろいろな情報（データ）が収集されようとしていることに不安

80%

60%

34.9%

more than everI’m concerned that I may get some disadvantage
情報（データ）を提供しないことによって何らかの不利

18.9%

を受けるのではないかという不安
for not providing information (data)
Othersその他

0.3%

No particularconcern
不安は特にない

6.8%

Source: MIC “The questionnaire on awareness on data” (2,155 people responded)⑦

Individuals are expecting enterprises to ensure the purpose of using data and data security
When you see the results of “Conditions required for providing information for enterprises (Table 43), you will find“The purpose of usage is clear and convincing” and “There is no concern about the leakage of data” havehigher percentages, and “The company for providing data is trustworthy” and “It is expected that the services
being used will be more convenient” have lower percentages. These generally apply to the data confirmed this time.
On the other hand, regarding the items of data, especially in “E-mail address”, “Address information”, “Telephone number”,“Annual income”, “Workplace information”, “Insurance information”, “Electric money, usage history of IC cards, etc.”,
the percentage of “There is no concern about the leakage of data” was highest in the responses. It is importantto minimize the risks of leakage of data in order to smooth data transaction.

51

Table 43: Conditions required for providing information for enterprises

0%

10%

20%

性別、年代、学歴
Sex, age, educational
backgroundE-mail address
メールアドレス
Address information
住所情報
Purchase informationon EC sites
ECサイトの購買情報

Annual income
年収Telephonenumber
電話番号
Purchase実際の小売店や飲食店での購買情報
information at retailer shops and restaurants

Browsing
history of websites
Webページの閲覧履歴
Bio-information
生体情報
Workplace 勤務先情報
information
Electric電子マネー、ICカード等の利用履歴
money, usage history of IC cards, etc.
移動情報
Mobile information
加入保険の情報
Insurance
information
SNS、ブログでの書き込み情報
Information
posted on SNS and blogs
銀行などの口座情報
Bank accountinformation, etc.
クレジットカード番号
Credit card number

Face photograph
顔写真
パスポート番号
Passport number
指紋
Fingerprints
その他
OthersI don’t want いずれも提供したくない
to provide any information

40%

33.1%
28.9%
29.3%
29.3%
25.4%
21.6%
21.5%
21.8%

出身地
Birthplace
家族構成、住居情報
Purchase informationon EC sites

30%

50%

60%

37.4%
37.1%

17.4%
14.8%
20.6%
21.9%
20.0%
17.9%
18.7%
20.9%
17.3%
14.7%
17.8%
17.4%
15.2%
15.3%
16.8%
17.5%
14.7%
11.8%
16.4%
18.8%
15.5%
12.2%
14.2%
13.9%
12.0%
11.7%
11.1%
11.7%
9.1%
9.3%
9.8%
10.0%
7.5%
6.5%
9.7%
12.1%
9.6%
7.9%
7.5%
8.2%
6.3%
6.4%
7.1%
7.4%
5.6%
6.4%
6.9%
7.7%
6.1%
5.2%
5.4%
5.5%
4.5%
4.1%
3.0%
3.9%
2.5%
利用目的が明確になっており、納得できる
The purpose of usage is clear and convincing
2.0%
1.2%
2.4%
1.9%
There is no concern about the leakage of data
1.4%
データ流出の心配がない
1.0%
1.3%
0.9%
1.0%
提供先企業が安心できる企業である
The company for providing data is trustworthy
0.6%
0.7%
0.7%
0.8%
0.3%
利用しているサービス等がより便利になるこ
It is expected that the services being used will
0.6%
0.4%
とが期待できる
0.3%
be more convenient
0.0%
0.0%
0.0%
0.1%
48.4%
49.7%
53.9%
55.2%

Source: MIC “The questionnaire on awareness on data” (2,155 people responded)52

(2) Issues to be addressed toward the marketing/visualization of data transaction① Standardization of contract formats concerning data transaction for the utilization of data
The characteristics of data utilized for AI are different from those of the existing data, so different factors are required
for the contracts of data transaction. It is considered that the standardization of contract formats that incorporated necessary
factors after considering the characteristics of data to be utilized for AI is required in order do appropriate transactions. The
3 important factors to be considered for the standardization will be explained below.
(a) Contractual coverage according to sales channels/price negotiation/manufacturers’ responsibility
The important thing about the differences between the characteristics of existing data and those of data to be utilized
for AI is that learning data to be utilized for AI might create value beyond the expected scope and keep creating value
permanently by changing its form while existing data mostly end playing its role with the visualization and analysis oftendencies and characteristics. This characteristic is very close to that of proprietary technology, so the contract contentsof data to be utilized for AI should be determined based on distribution channels (whether it will be distributed to a third
party, etc.) as well as those of proprietary technology.
The price negotiation of data to be utilized for AI should be also determined based on distribution channels. Here,
distribution channels have various factors; whether the licensee is the end user or a component user will resell the data to
another manufacturer. In addition, one of the characteristics peculiar to the data to be utilized for AI is whether the qualityof data is good enough for providing for business cannot be found until the data is used for trial. This characteristic shouldbe also considered in price negotiation.
Regarding the responsibility of manufactures of products 30 , it is generally considered that the contributor to thedamage is liable for the damage in the Civil Code or other regulations, so the liability for damages may accrue if there isan accident in a product that incorporated the software utilizing AI (such as a learned model). However, in case of thesoftware that utilized AI, there will be cases where it is difficult to grasp where the responsibility lies because of the
distribution of responsibility (whether there is delinquency on a debt, attribution of responsibility/cause and effectrelationship, etc.) and difficulties learned models have (risks of abortion of development, problems of quality andperformance of learned models, integrated problems of external systems). For example, if the data to be utilized for AI
have malicious data or data with large deviation, the quality of products may have a problem in quality as a result. In thiscase, it is considered that there might be cases where grasping the liability for damages in a learned model becomes difficult
due to the following factors 31.


It is technically difficult to assure the performance of learned models with unknown input data in advance.



It is technically difficult to verify cause and effect relationship, etc. after an accident.

The performance of learned models, etc. depends on the quality of learning data sets.



The property of AI products depends on the quality of input data at the stage of utilization.

Therefore, how much responsibility manufacturers of products should take depending on distribution channels needs tobe stipulated in contract forms.

Please refer to METI “Contract Guideline on the Utilization of AI/Data－AI Edition－” (June2018)
31 Please refer to METI, the above mentioned, p33-p34.
53
30

(b) Necessity of grasping the distribution/quality/traceability/freshness, etc. of data
The distribution of data is important in the data to be utilized for AI. For example, when the quality of English voicerecognition and that of Japanese voice recognition are compared, the quality of English voice recognition is higher thanthat of Japanese voice recognition. Because there are more English voice data as learning data. On the other hand, since
there are little Japanese voice data of seniors and specific dialects of local people, the purchase needs for these data areconsidered to be larger to enhance the quality of these voice recognition. It is necessary to grasp the distribution of theselanguages/regions/ages, etc. in data transaction.
In addition, low-quality data (for example, data with wrong labeling such as cars labeled as persons, knives labeled
as pens, railroads labeled as crosswalks, etc.) is utilized as learning data without knowing the wrong labeling, that willaffect the processing of AI. Therefore, grasping the quality of data is necessary in data transaction.
To grasp the distribution and quality of data, checking where the data was produced and who gave the informationsuch as labeling data, etc. is necessary, so grasping the traceability is necessary in data transaction.
Furthermore, when utilizing data for AI, the value of data is subject to change as time goes by depending on the field.
Therefore, the freshness of data is also important. For example, if a conversation model is prepared by utilizing theconversation data of teenagers collected five years ago and the model is applied to the current teenagers, the quality will
be deteriorated. However, if the model is applied to the conversation data of the youth who were teenagers 5 years ago, thedeterioration of the quality will be relieved to some extent. This is because the way of speaking of the youth and terms usedby the youth can greatly change in 5 years. That’s why the freshness of data needs to be grasped in data transaction.
Having said that, however, it is not always necessary to grasp all the above mentioned. There are some cases where
using the data won’t affect and damages are indemnified or restored if some problems occur. It is desirable to determine if
grasping the said distribution, etc. is necessary, considering the balance with efficiency in each data transaction.
(c) Identification and fidelity guarantee of persons in charge of data labeling/annotation
In Japan, there are a few AI vendors who collect data and conduct data labeling and annotation on their own.
Therefore, there may be cases where problems occur if malicious annotation (for example, data with wrong labelingsuch as cars labeled as persons, knives labeled as pens, etc.) is done. In data transaction, the identification and fidelity
guarantee of persons in charge of data labeling/annotation need to be stipulated when signing a contract.
②

The necessity of management of data owned by enterprises by a third party
The data owned by enterprises are always exposed to the danger of being exploited in society through the enterprisesor the products/services. When an enormous impact of exploited data on society is considered, it is required to prepare
the system that manage data owned by enterprises if necessary, in order to smooth corporate activities in the future data
economy.
The transaction of data to be utilized for AI is quite different from the existing market transaction, so many
complicated factors such as contract contents following distribution channels are involved in data transaction. Therefore,
if enterprises do all the procedures necessary for purchasing and utilizing data such as price negotiations, confirmation ofrights, contract procedures, and security/authentication individually, they would spend much time and labor in negotiations

54

and procedures and have many risks of making mistakes, which will bring about inefficiency to the providers and users ofdata, or the entire society.
In case of large enterprises, the legal department can take on the role and handle confirmation of rights, contract
procedures, etc. However, when small and medium-sized enterprises including start-ups don’t have the system to manage
data such as the legal department, they will make a mistake in various procedures to process data and data transaction itself,
and they might be forced to give up doing data transaction. Therefore, to disseminate the provision/use of data especiallyamong small and medium-sized enterprises smoothly, data management by a third party is indispensable.
③

The necessity of management of personal data by a third party (Personal Data Trust Bank’s potential)
For the purpose of enhancing effective controllability and promoting the distribution and utilization of personal data,

the existence of Personal Data Trust Bank who can entrust trustworthy entities to provide personal information within thescope of individuals’ consent is required for the marketing of individual data transaction.
Table 44: System of Personal Data Trust Bank
Consent

on

the

terms

Securing individuals’ofcontrollability

providing data for a third party

Business operator A

Personal Data Trust Bank

Individuals
EntrustProvide it for a third party

Personal information

Business operator B

BANK

Benefits

Information Providing Destination

within the scope of consent

Manage for individuals

Personal information

Use of information

Advisory System (Data Ethics Committee)
Consultation system for individuals

Business
operator CpBenefits

Source: “Guideline of Certification Schemes Concerning Functions of Information Trust ver2.0”(https://www.soumu.go.jp/main_content/000649152.pdf)
Regarding the contents of services Personal Data Trust Bank can provide for individuals (types of data Personal Data
Trust Bank can handle, conditions for choosing information providing destinations, terms of use in the information
providing destinations), when the Personal Data Trust Bank appropriately makes a proposal and secure the responsibilityof the Personal Data Trust Bank regarding the services by concluding contracts, etc. with the consent of individuals,
screwing up important life events such as marrying and getting a job through identity theft by using personal information
due to illegal acquisition of personal information or spreading/misusing of inaccurate personal information can beprevented.
There is a possibility that providing personal information may not be proceeded if this kind of environment for theutilization of data is not prepared. For example, according to the questionnaire survey conducted by NTT Data Institute ofManagement Consulting, Inc., consumers tend to refuse providing information that can easily identify individuals such as“location information” and “address/telephone numbers”, and there are a lot of opinions that request“authentication/certification by a third party” about the information providing destination. On the other hand, regarding“hobbies/tastes”, “the history of viewing TV programs”, “electricity, gas, and water usage charge”, “height, weight,

55

pedometer measurement, etc.”, there are many opinions that they can provide those data on condition that they get paid 32.
Regarding the transaction of individual data, consumers’ attitude is stern in terms of security and economy. And theconsumers’ attitude brings about withering of enterprises, so the system which can endure that is required.
The existence of the Personal Data Trust Bank is important in the transaction of individual data. The Personal Data
Trust Bank business has already started, but the dissemination of this business is important in marketing and visualizationof data transaction. In addition, the system of the Personal Data Trust Bank deserves to be evaluated as the market-based
system that gives some consideration to individuals who produce data.
On the other hand, there is a movement that credit scores on the social evaluation of individuals are calculated andutilized for services. According to the document of the Review Meeting for the Ideal Certification Schemes Concerning
Functions of Information Trust 33, the Personal Data Trust Bank and credit scores are described as follows.


If the system of the Personal Data Trust Bank spreads, the collection of various data on individuals will also spread,and the production and distribution of “credit scores” might be promoted.

The “credit scores” don’t have a clear definition, but as for scores given to individuals, the evaluation of credit
capabilities and the scores of English tests can be called scores. These scores in the broad sense are currently usedwidely and generally, and the convenience is expected to be improved through the distribution via the Personal
Data Trust Bank.



On the other hand, regarding the credit scores on the social evaluation of individuals beyond partial capabilities ofindividuals, there was an opinion that points out the risk that the utilization of credit scores might cause thediscrimination or screening by impairing a diversified society due to the increase in the number of people whovalue scores too much or by using scores for marriage and employment.

Considering the above, regarding how to treat credit scores that might cause discrimination through the utilizationin the Personal Data Trust Bank, the treatment policy of credit scores will be shown below.
Thus, if the Personal Data Trust Bank also has the function of calculating credit scores, there might be the risks thatcredit scores have, but that will make the marketing/visualization of data transaction smooth and there will be potential tosecure the credibility of the market transaction.
Personal Data Trust Bank business and credit scores can be provided individually. However, by utilizing both ofthem together, preparing credit scores based on the personal information treated at the Personal Data Trust Bank, etc. andentrusting the Personal Data Trust Bank to manage the task will be possible.
For example, while individuals can acquire credit scores in the Personal Data Trust Bank by utilizing their personal
information, enterprises can do marketing, etc. based on the credit scores and scored personal information. On the other
hand, individuals will have more services available, such as renting money at low rates by raising their credit scores, and32

NTT Data Institute of Management Consulting, Inc. ““The survey of general consumers’ awareness on theuse of Personal Data Trust Bank”－Check the traceability of personal data / Interest in safety andthe issues on the current consent process of providing personal data” (https://www.nttdatastrategy.com/newsrelease/200407.html)。
33 MIC “The outline of the review meeting on the ideal authorization scheme of Information Trust functions”(https://www.soumu.go.jp/main_content/000648745.pdf)
56

will enhance their life style. For enterprises, this might contribute to the expansion of good/sound customers. In addition,
due to the merits of credit scores, individuals might actively provide their data for enterprises and the data enterprises canutilize will increase, which will make their marketing and product development possible, then enterprises might be able toacquire royal customers. Furthermore, it is expected that the sensitivity of providing individual data and the IT literacy will
be enhanced.
On the other hand, regarding the operation, building the system where personal data is not used by unknown
enterprises without the consent of individuals and the system that prevents enterprises from screening job applicants byusing credit scores is required.
Table 45: Patterns in case Personal Data Trust Bank handles “credit scores”(Pattern 1)

Information Providing

ProvidingCredit scores

Consumer Individual(Pattern 2)

Information

Personal Data Trust BankEnterprise

Information Providing

Credit scores

Information Providing

Personal Data Trust Bank

Data for credit scores

Data for credit scoresEnterprise
Consumer Individual(Pattern 3)

Destination

Use data for scoring

Information Providing

Information Providing

Personal Data Trust BankEnterprise
Consumer Individual

Destination

Data for credit scores

Destination*Including credit scores preparedUse data for scoring

Credit scoresby other enterprises

Source: MIC “The outline of the Review Meeting for the Ideal Certification Schemes Concerning Functions of Information
Trust”(https://www.soumu.go.jp/main_content/000648745.pdf)④

Finding “hidden needs” in data transaction
The data transaction has been already proceeded on a bilateral basis, and the initiatives mentioned in ② and ③
have been under way. However, the data transaction is not fully active, which was explained in (1) ② (Data transaction
with other enterprises is inactive).
(1) As mentioned in ⑤ (Individuals’ concerns about their data being collected and used) and ⑦ (Individuals are

expecting enterprises to ensure the purpose of using data and data security), concerns about individuals’ data being collected
and used and the needs for enterprises ensuring the purpose of using data and data security are main factors for inactive
data transaction. Resolving these problems can remove the factors for impeding data transaction. However, it is consideredthat just doing that can’t lead to the revitalization of data transaction.
It is important for enterprises that do data transaction to find out what the enterprises that utilize data and individuals
who receive specific services based on the utilization potentially want about the activities and services through datatransaction (“hidden needs”). Also, they need to clarify where the merits of utilizing data lie; enterprises that utilize data
or individuals who use the service.

57

4.

Future image (the image of “Inclusive AI Economy Society”)
Here, the image of “Inclusive AI Economy Society” that can be seen after the issues shown in Chapter 2 and 3 areresolved will be viewed.
In “Inclusive AI Economy Society”, the inclusive operation of AI is required. That is, it is desirable that all entities
including individuals, small and medium-sized enterprises and local enterprises are actively involved in social/economic
activities based on their will and decision, utilizing AI and data in an ingenious way, and contribute to the improvement of
productivity, etc., acquire the distribution, fulfillment, expansion of leisure time, etc. and share the richness in the entire
society rather than AI and data are utilized only by some advanced enterprises and they are used for discrimination or
screening, and the richness brought about by the utilization of data is exclusively enjoyed by the enterprises.
Regarding this perspective, out of the analysis on prospect of ecosystems formed in accordance with the advancementof AI network the Conference Toward AI Network Society has considered, the part of AI utilization scenes will be referred.
■ AI utilization scenes
In the analysis on the perspective of utilization of AI, firstly the AI utilization scenes in individuals, enterprises, andgovernments were classified into 9 scenes (Use in urban areas, Use in rural areas, Use at home, Use in research anddevelopment, Use in manufacturing/construction, Use in distribution and sales, Common use (people, goods, and money),
National government/local government use, and How to work and spend leisure time), then we picked out the 4 major AI
utilization scenes in the classification as shown below and comprehensively examined the utilization of AI.
Table 46: Classification of AI utilization scenes
Outdoors

Use in urban areas

Use in rural areas

Personal useHow toUse at home

Indoors

End-users

work andUse in research

Use in manufacturing/

Use in distribution

and development

construction

and sales

spendleisure time

Corporate use
Common use (people, goods, and money)

Government use

National government/local government use

Analysis as a world view ofwhere AI has been fully
utilized

Source: “Sorting out of fields of AI utilization scenes from the perspective of end users”
Also, the realization timing of the AI utilization is divided into 2 stages of “already practically applied and to be58

realized in the near future (until around 2025)” and “to be realized in the medium-term (until around 2035)” 34. Then, theAI utilization scenes regarding these utilization scenes in use scenes were viewed. The details of the perspective of AI
utilization scenes are described in Attachment 3.
For example, as shown in the example of AI utilization scenes: individuals (urban areas), by utilizing AI insightseeing/traveling, tickets will be automatically arranged as soon as an optimum travel is planned, and people can
comfortably enjoy sightseeing through face authentication and translation without worrying about baggage and languages.
More specifically, the following practical application use cases are expected to be realized.


Proposals of perfect travel plans in accordance with the past travel history, hobbies/tastes, budgets, etc. andimplementation of the procedure

Smoothing of communication with foreigners, using multi-lingual translation



Simulation experiences of the history connected with VR/AR according to hobbies/tastes

All the itinerary from going out to going home will be concluded with empty-handed (all concluded with faceauthentication, etc.)

In addition, thanks to the utilization of AI in education/fostering human resources, providing optimum learning
contents through collaborating with schools, cram schools and homes and providing optimum contents for acquiring
necessary skills will be possible. More specifically, the realization of practical application use cases can be expected.
Providing optimum learning contents depending on the capability and aptitude, collaborating with learning at
schools, cram schools and homes (regardless of the place and grade, necessary learning will be provided)

Providing optimum contents for acquiring skills (including passing down the know-how and expertise of skilled
persons)Support for reemployment after retirement, support for lifelong learning

Effective remote education will be implemented through the AI data diet while communication zones are limited

The realization timing of AI utilization scenes is supposed to be the timing when the service isplaced on the market without considering the service penetration rate and functional fulfillment.
59

34

Table 47: AI Utilization Scene (Example): Individuals (Urban Areas)

Source: “Sorting out of fields of AI utilization scenes from the perspective of end users”
Also, as shown in the example of AI Utilization Scenes: Enterprises (Distribution/Sales), by utilizing AI, unattended
warehouses and crewless transportation will be realized for enterprises in logistics. Management of entire distribution will
be implemented by AI, so delivery services that are not possible manually will be realized. More specifically, the realizationof the following practical application use cases can be expected.
Unattended warehouses by utilizing AI robots and AGV 35, etc.

Crewless transportation due to the realization of fully autonomous driving and delivery services that are notpossible manually will be realized.

Management of the entire distribution by AI (demand prediction, inventory adjustment, optimization of delivery
roots, arrangement of transportation resources, etc.)

Furthermore, by utilizing AI, advertisement enterprises will be able to allocate advertisement budget, draw up plans,customize delivery contents that fit customers, and enhance the estimation of advertisement effects. More specifically, therealization of the following practical application use cases can be expected.


Automatic production of digital advertisement suitable for the hobbies/tastes of customers (change the contents
and characters, etc. depending on the delivery destination)

Estimate/present the engagement (effects) of advertisements that integrated adoption rates on SNSs and blogs
in addition to the viewing rates/click rates



Allocation of advertisement budget and drawing up plans (how much money should be used for which

AGV stands for Automated Guided Vehicle which means unmanned transport vehicle. AutomatedGuided Vehicles are driverless, mobile vehicles programmed to transport materials other thanhumans in certain areas. The vehicles cannot be used on the roads stipulated in the Road
Transportation Act.
(“JISD6801 Terms on Automated Guided Vehicle System”(http://www.jiva.or.jp/pdf/Kind%20of%20AGVS.pdf))
60

35

advertisement media, when the advertisement will be done, etc.)
Table 48: AI Utilization Scenes (Example): Enterprises (Distribution/Sales)

Source: “Sorting out of fields of AI utilization scenes from the perspective of end users”
Also, as shown in the example of AI utilization scenes: Administration (National Government/Local Government), in
the national government’s policy making, by AI’s analyzing various information and the opinions of stakeholders, andmaking policies, streamlining and total optimization will be done and agile type administration will be realized. Morespecifically, the realization of the following practical application use cases can be expected.
Analyze policy trends of many foreign countries and make policies in an optimized form in Japan

Do macro/micro statistics and simulate the effects of financial policies/monetary policies, using real time data,
and make optimized policies



Analyze the regulations and administrative documents related to the bills to be formulated, and analyze theconsistency of the bills to be formulated and related regulations, etc. then formulate the drafts of regulations

After making and executing policies, take the opinions of stakeholders involved in policies and optimize theadjustment instantly

In addition, in the administrative affairs/execution in basic municipality, by AI’s streamlining administrative affairs,
promoting collaboration, optimizing ideal ways to run the administration in towns in real time, the satisfaction level ofresidents will increase. More specifically, the realization of the following practical application use cases can be expected.


AI Chatbot will respond to inquiries from citizens and streamlining counter services



Analyze and predict traffic volume of vehicles and volume of pedestrians, and adjust the fares of public

61

transportation and approach zones of private passenger vehicles in real time

AI speakers, etc. will be introduced to the households of isolated people, and their needs and situations will begrasped in detail. Cooperation with local residents and organizations will be adjusted if needed, and sensitive
services will be provided even in a situation of downsized civil services.
Table 49: AI utilization scenes (Example): Administration (National Government/Local Government)

Source: “Sorting out of fields of AI utilization scenes from the perspective of end users”
In addition, if AI is fully utilized in these AI utilization scenes, it is expected that the workstyle/how to spend leisurewill change. For example, regarding workstyles 36, working beyond spaces will be realized due to the spread of remote
working, supply and demand matching will be possible beyond the borders of companies and national borders, participating
in society, providing values and experiencing that have been impossible due to temporal, spatial, regional and physicalrestriction become possible because of the expansion of human capabilities thanks to AI and robots, etc. As a result, it is
The change in workstyles mentioned here is the assumed one in a society where AI is fully utilized in mid- to longterm future. There is a possibility that the COVID-19 pandemic that is ongoing in Japan and the world might
accelerate this change. On May 4, 2020, in a government panel on the new coronavirus (chair: Takaji Wakita, head of the
National Institute of Infectious Diseases), practical examples of “the new life style” to prevent the spread of COVID-19 wereshown, and regarding workstyles, teleworking, continuing staggered commuting hours and online business meetingand online exchange of business cards were introduced as specific examples. Also, on May 14, 2020, “Guidelines forPreventing the Spread of Novel Coronavirus Disease (COVID-19) in Offices” were posted by Japan Business
Federation (https://www.keidanren.or.jp/policy/2020/040.html), and as specific measures to be addressed, through avariety of forms of work programs such as remote work (work from home or from satellite offices), staggered start and finish
times, rotation work (dividing workdays and working hours into multiple shifts), flexible working hour system, and four-day work
weeks, to reduce the frequency of commutes and relieve congestion on public transport are proposed.
3662considered that “labor for living” will be minimum, and the participation in society and degree of freedom in living will
expand due to the expansion of capabilities and requests for cooperation or reconciling the differences in views thanks toAI, and people will work and participate in society with a sense of mission or within the scope of hobbies. On the other
hand, regarding how to spend leisure time, some companies have already adopted the four-day week system, and it isconsidered that human work will be streamlined by AI and our free time (leisure time) will increase in the future. Therefore,
how to spend the increased leisure time will be enriched by AI, and opportunities to restudy will be provided. In addition,
it is also considered that the increased time will be used for creating art works and people will pursue more creative value.
As a result, it is considered that people will live in a place where income and living costs are well balanced, adjust theirliving environment, and spend time and costs, considering “what is really important to humans (themselves)”. On the other
hand, the possibility of the expansion of the gap between workers due to the technical capabilities such as the use of AI
should be noted in this social change. So far, in developed nations such as Japan and the US, the number of “medium/routinetask” workers has been decreasing, and the numbers of highly skilled workers and lowly skilled workers have been
increasing. There is a possibility that this trend will be spurred on due to the thorough utilization of AI and the gap in wages
will be bigger (Table 52).

63

Table 50: AI Utilization Scenes (Example): Individuals/Enterprises (Workstyle/How to spend leisure time)

Source: “Sorting out of fields of AI utilization scenes from the “end user perspective”
Table 51: Effects/Goals of the Utilization of AI (example): Individuals/Enterprises (Workstyle/how to spend leisure time)

Source: “Sorting out of fields of AI utilization scenes from the “end user perspective”

64

Lowly skilled

Medium routine

Medium non-routine

Highly skilled

Lowly skilled

Medium routine

Medium non-routine

Highly skilled

Lowly skilled

Medium routine

Medium non-routine

Highly skilled

Table 52: Percentage-point change in employment shares by occupation category (2002 - 2014)

Source: OECD “Automation and Independent Work in a Digital Economy” POLICY BRIEF ON THE FUTURE OF
WORK (2016).

65

5.

Recommendation

5.1 The Ideal Policy toward the Promotion of the Utilization of AI/Data(1) Ideal directivity
To aim for a society where various entities are actively engaged in social/economic activities that utilize AI anddata in an ingenious way based on their will or decision, the situation where Japan is positioned is described as follows.
①The global economy has changed from a finance-led economy to the one led by digital companies that utilize ICT
and data, ②In the utilization of data in enterprises, there are disparities in sizes between large enterprises and small
and medium-sized enterprises and disparities between enterprises in urban areas and those in rural areas, ③There aremany Japanese enterprises that realize their data-related initiatives including collecting and analyzing data are lagging
behind, ④Many Japanese enterprises feel the utilization of data in the office was effective, ⑤While consumers arecareful about providing their personal information and behavior history and have concerns about their data being
collected and used, they don’t have much resistance about their data on health/medical scenes being utilized comparedto those on disaster/crime prevention, transportation and education related scenes.
Based on the above, to find the ideal way to utilize data required for the implementation of AI in society, the issues
to be addressed in enterprises and administration were confirmed. The issues to be addressed in enterprises are ①
Positioning data and AI in management strategies and business architecture, ② Building the system to collect physicaldata for the utilization of data, organizational system for the utilization of data and organizational data infrastructure,③ Relieving or reducing consumers’ concerns about their data being used as well as responding to the current
regulations of data for customers. The issues to be addressed in administration are ④ Accelerating initiatives of thedigital government in administration, ⑤ Issues administration is facing through the outbreak of COVID-19.
The ideal directivity of AI/data will be explained in the following.
The COVID-19 brought about the economic shock that is beyond Lehman’s collapse to the world when monetaryeasing policies were proceeding in the world. Various restrictions brought about by the COVID-19 enabled enterprises torealize the importance of communication networks and ICT tools in continuing their business, and what’s more, there isa possibility that it will greatly change individuals’ work styles and life styles, and the ideal way for enterprises to exist
as an organization, which will create a “new normal” way to survive. For example, it is considered that many social
activities will be done in virtual spaces, and the necessities of “opportunities” to create real spaces will relatively decreaseby constantly continuing the utilization of teleworking and VR/AR.
There is a high possibility that the transition to the economy society based on the utilization of AI will be accelerated
in the “post-coronavirus” society. WHO has built the platform to commonly utilize data and intellectual properties(COVID-19 Technology Access Pool) 37. Voluntary sharing of COVID-19 related intellectual properties is considered to be
desirable in WIPO 38 . Also, in the government of Japan, “Emergency Economic Measures to Cope with COVID-19”
disclosed the initiatives of remote working promoted as the “acceleration of digital transformation due to remoteization,

37

WHO “COVID-19 technology access pool”(https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019ncov/covid-19-technology-access-pool)
38 WIPO “Some Considerations on Intellectual Property, Innovation, Access and COVID-19”(https://www.wipo.int/about-wipo/en/dgo/news/2020/news_0025.html)

66

etc.” due to the spread of COVID-19 and the increased needs for remote services that utilize ICT such as remote educationthat can handle the emergency situation like this. Stating “we have to turn this crisis to a chance and advance digital new
deal policies under the concept of wise spending and immediately accelerate the social change in order to accelerate therealization of Society 5.0” 39, the government has enforced the initiatives 40. Furthermore, in the spread of COVID-19, thegovernment has promoted initiatives to exert social intelligence 41, the capacity to support people, take care of people, andprovide emotional support or promote people’s cooperation and reconcile differences in views, using AI that is considered
to lack social intelligence. Initiatives by Civic Tech can be introduced as an example. Civic Tech is a grass-roots movementin which citizens resolve social issues, etc. with other stakeholders such as administration by utilizing technology. As a
specific example of Civic Tech, Taiwan’s initiatives in the spread of COVID-19 can be cited. This is an example offunctioning of digital technology, activities of activists and citizens, and it is attracting people’s attention as a model of
economy society based on the utilization of AI 42. This kind of activities are also done in Japan, and organizations such as
Code for Japan and Civic Tech Japan were established 43 . These organizations have connected activists, citizens andadministration by using digital technology to tackle the spread of COVID-19, and stakeholders have voluntarily cooperated
with each other and contributed to preventing the spread of COVID-19, through providing API and information disclosure
utilizing API. The “social intelligence” humans should exert in the society where various entities are actively engaged insocial/economic activities that utilize AI and data in an ingenious way based on their will and decision has been alreadyrequired in the coronavirus crisis.
On the other hand, the struggle with the lingering COVID-19 pandemic in the world has revealed the fragility ofmodern economy society. For example, in the society that pursues the productivity and efficiency, the redundancy as a
response to emergency was removed from the society and economy. This can be applied to the world of ICT. The
utilization of ICT at home increased due to home teleworking, which caused the excessive burden of networks and servers
and excessive processing capacity, and triggered the deterioration of the quality of services. It is obvious that there is alimit in responses of individual enterprises in this unprecedented crisis, and that can be true to the whole economic sectors
as well as ICT, and the public roles the national government should play became clear.
To promote the utilization of AI/data, the digital transformation of the entire stakeholders in Japan will be necessary,and the changes in behaviors responding to new situation in the future will be also necessary. Also, it is necessary to fully
realize that these changes are actually occurring and the utilization of ICT including AI can make these changes possible.
The ideal directivity that enterprises, individuals, and government should pursue will be explained in the following.
Specific initiatives toward the establishment of social economy based on AI has already taken in enterprises in the39

The 6th Council on Economic and Financial Policy in 2020 “Document 1, Emergency Economic Measures to Cope with COVID-19(Cabinet Decision on April 20, 2020)」(https://www5.cao.go.jp/keizai-shimon/kaigi/minutes/2020/0427/shiryo_01.pdf)
40 This was featured in the joint session of the 77 th IT Strategic Headquarters and the 8th Strategic Conference for the Advancement of
Public and Private Sector Data Utilization. “Initiatives to fully mobilize IT and data are necessary” as a medium-to-long terminitiative, “Changes in a social structure through digital transformation”. In addition, “after the COVID-19 pandemic subsides, we willturn this pinch to a chance and strongly promote digital transformation that takes digitalization as the driving force of a social reformin order to reboot the economy of Japan”. (https://www.kantei.go.jp/jp/singi/it2/dai77/siryou1.pdf)
41 Richard Baldwin, The Globotics Upheaval: Globalisation, Robotics and the Future of Work, Hachette UK, 2019.
42
Jaron Lanier and E. Glen Weyl, “How Civic Technology Can Help Stop a Pandemic : Taiwan’s Initial Success Is a Model for theRest of the World”, Foreign Affairs, Volume 99, Number 3, 2020.
(https://www.foreignaffairs.com/articles/asia/2020-03-20/how-civic-technology-can-help-stop-pandemic)
43 Civic Tech Japan was established in May 2019 (https://www.civictech.jp/).
Code for Japan was established in 2013 (https://www.code4japan.org/).

67

world. To promote the utilization of AI/data, boosting SMEs and enterprises in rural areas that haven’t started the initiatives
yet to launch them is necessary.
For example, regarding digital transformation (DX), the promotion movement of boosting initiatives of enterprisesby the government can be found 44, but it is important to create the system whereSMEs and enterprises in rural areas cantake initiatives rather than to promote initiatives by some advanced enterprises or large enterprises. Regarding this, Digital
Transformation business companies established by Fujitsu, Toshiba, and Sumitomo Corporation (Ridgelinez 45, Toshiba
Digital & Consulting Corporation 46 , Insight Edge 47 ) and Digital Transformation promotion business of ITOCHU
Corporation (business partnership with AKQA Inc. in the US) 48 are attracting attentions as advanced initiatives topromote the spread of not only AI but also the entire ICT to enterprises. It is important to realize the merits of the utilizationof data to appeal for small and medium-sized enterprises that haven’t started the utilization of AI/data and the basic of theutilization of data through these initiatives in the public and private sectors, and encourage SMEs to utilize data as the
fourth investment factor ranking next to humans, things and money.
If individuals deepen understanding new technologies such as AI, they will recognize AI as “something to utilize”
as well as computers and smartphones, not as “something brought about”, and the tendency to utilize AI usefully will bepromoted in the economy society. Dissemination and public relation are required for enterprises and administration to
deepen the understanding. In addition, to share the image of “Inclusive AI Economy Society” in society, building thesystem to disseminate the ideal new AI economy society and the attitude to embody the needs of the dissemination are
required for enterprises and administration.
What’s more, it is necessary for administration to work on further organizing systems where people can utilize data
in a way trusts of organizations and individuals are secured. Also, it is important to further accelerate initiatives of thedigital government that can increase the opportunities for private sectors to use public data.
(2) Specific measures
Based on (1), specific measures to be placed as particularly important toward the promotion of the utilization of
AI/data will be explained in the following.
①

Support for the promotion of the utilization of AI/data in SMEs and enterprises in rural areas by the public and private
sectors
For SMEs and enterprises in rural areas, the lack of management know-how necessary for fully exerting the effectsof the utilization of AI/data such as positioning the utilization of data in management strategies and business architecture,

44

In “(4) the acceleration of Data Transformation / Promotion of the utilization of AI/data, etc.” of Intellectual Property Strategy
Headquarters “Promotion Program for Intellectual Property 2020－Intellectual Property Strategy toward the “New Normal” after the
COVID-19” (May 27, 2020), for example, “Big change caused by Data Transformation” and the necessity of boosting by the governmentin “COVID-19 and Data Transformation” were pointed out, and sharing of “analysis of cases of DX in enterprise”, the necessity of“preparation of rules for promotion of the utilization of real data”, pointing out “various cross-cutting issues on AI/data”, the necessityof “cooperation between fields”, presentation of ”issues in each field”, “fostering human resources responding to the age of AI” or ”theideal patent system suitable for the age of AI/IoT technology” were considered.
45 Ridgelinez Limited’s website(https://www.ridgelinez.com/)
46 Toshiba Digital & Consulting Corporation’s website (https://toshiba-dx.com/toshiba-dx/)
47 Insight Edge, Inc.’s website (https://insightedge.jp/)
48 Through the business partnership with AKQA Inc. they review the issues of customer companies in Japan from the perspective ofcustomers, utilizing ITOCHU Corporation’s network in Japan and the world and AKQA’s consulting ability in the promotion of
Digital Transformation which is the strength of AKQA Inc.
(ITOCHU Corporation’s website (https://www.itochu.co.jp/ja/news/press/2020/200305.html))

68preparing the system of collecting physical data, organizational system of the utilization of data and building of data
infrastructure in organizations, responding to the current regulations for data of customers, etc. and investment capitaland human resources to realize the management know-how is considered to be an impediment. Therefore, it is required
to clarify the issues to be addressed and prepare comprehensive consulting to realize the said management know-how byrecommending necessary initiatives beyond the economic support concerning the introduction of AI, etc. Also, topenetrate the initiative, fostering human resources who can undertake the initiative is necessary.
②

Enlightenment activities about understanding new technologies such as AI by public and private sectors
New technologies such as AI are just tools as well as the existing ICT devices/services and means to achieve goals. It

is important to understand that using them is connected with easily achieving goals, and the system of the technologies andwhat can be done and can’t be done with the technologies. At this point, there are quite a few people who have a negative
image about AI. In the questionnaire survey conducted by Consumer Affairs Agency, more than half of the people have anegative image about AI such as “I’m concerned” or “I’m somehow scared” (Table 53). These concerns about AI can be afactor of impeding utilizing AI. Therefore, it is necessary to take initiatives to reduce the negative image about AI, and bothpublic and private sectors need to work together for enlightenment activities to continuously encourage people tounderstand new technologies such as AI rightly and use AI well.
Table 53: Images of AI
Q1.

Please choose an applicable one about the image of AI (Artificial Intelligence).

[Must select one]

(n=1,236)■ applicable■ A little applicable■ Neither of them

■ Not applicable very much

■ Not applicable

Images of AI
It enriches my daily lives
It gives a good impact on my daily lives
I’m concerned
I’m somehow scared
I have no connection with it
I feel familiar with it

Source: Consumer Affairs Agency “The 1st Consumer Awareness Survey” (1,226 persons responded) 49③

Initiatives toward sharing the image of “Inclusive AI Economy Society”
To realize the “Inclusive AI Economy Society”, it is desirable that individuals disseminate the ideal new economy

society through their dissemination tools such as SNS, etc., and they are connected with making policies through big data
analysis.

49

On February 12 - February 18, this web questionnaire survey about general consumers image of AI and thestatus of use AI services in daily life was conducted, targeting males and females from 10s to 60s (1,226 persons) who
live in Japan.
(https://www.caa.go.jp/policies/policy/consumer_policy/meeting_materials/assets/consumer_policy_cms101_20316_03.
pdf)

69

④

Review of the system that promotes the utilization of AI/data
To promote the utilization of AI/data, it is important that systems are well-organized so that people can utilize data ina way trusts of organizations and individuals are secured. In addition, it is also important to prepare a system that is beyond
the existing vertical administrative division, the nation and local districts, and public and private sectors in order to increase
the opportunities to use public data in private sectors, so it is important to further accelerate initiatives of the digital
government.
⑤

Promotion of the utilization of AI/data, keeping in mind the “post-COVID-19” society
Through the COVID-19 crisis, it became clear that how to settle the contradiction between the efficiency to realize

inexpensive services and the redundancy to respond to this kind of pandemic is important. It is required to promote themeasures to utilize AI/data, considering the balance between the efficiency and redundancy of communication networks
including teleworking and the entire ICT tools.
5.2 The ideal data economy policy required for promoting the data-driven economy in the AI era(1) Ideal directivity
It is considered that the data-driven economy will be more developed in the future. On the other hand, it can’t be said
that the market mechanism concerning data transaction is fully functioning now. Therefore, it is desirable that the current
imperfect data transaction will be supplemented and the resource allocation based on the market mechanism will beachieved as soon as possible as an ideal form for the future.
As the mechanism of creating added value on data, the processes of producing, collection, accumulation, processing,transaction, and utilization are complicatedly interacted. And the stakeholders in the mechanism are roughly divided into
4 entities; the entity who produces resource data (net service users), the entity who analyzes data (data scientists), the entity
who owns data (enterprises/platforms, etc.), data transaction mediators (Personal Data Trust Bank, etc.). It is desirable that
we consider the reasonable grounds for paying remuneration to each stakeholder (visualization of digital activities) andmake policies to promote the development of data economy. By visualizing digital activities by stakeholder like this, it will
be possible to grasp the following;Reasons for paying/not paying remuneration

How to pay remuneration (process)

The standard of remunerationand make policies where the market mechanism functions.
Table 54: Concept of paying remuneration by stakeholder

Reasons forpaying/not paying
remuneration

Resource data
producers (netservice users)
Approval of thecontribution by
resource data
producers

Data analysts (datascientists)

Data owners(enterprises/
platforms, etc.)

Data transaction
mediators(processing, rating)(Personal Data Trust
Bank, etc.)

Contribution to
analysis

Revenue from data

Commission fee
from data transaction

70

Payment methodsof remuneration(process)
Standard
remunerationofFree services,
information
provision, points,
cash, etc.
Intention of data
owners/datatransaction mediators

Evaluation foranalysis results

Business revenue

Business revenue

Labor market

Advertising market,
etc.

Cross trading

Source: Prepared from a published document
Also, it is important to foster individuals’ awareness of managing data on their own initiative as well as to promoteindividuals complying rules on personal information from the perspective of the revitalization of data transaction. However,it should be noted that various costs will be incurred in connection with complying with the rules.
In addition, to execute appropriate data economy policies, we have to consider the economic characteristics of data.
As the economic characteristics of data, almost zero marginal costs, profits brought about from data accumulation, and thequality (contents) of information as well as nonrivalness, externality and partial exclusivity are given as examples.
(2) Specific measures
Specific measures to extract economic benefits utilizing the market mechanism as much as possible based on (1) will
be explained in the following.
①

Building data infrastructure
To contribute to the economic development of ICT capital centering on AI, building data infrastructure to processdata at high speed is positioned as an important infrastructure. Not only speeding up of access lines including 5G/Beyond
5G/FTTH but also securing redundancy and speeding up of CPU and GPU, etc. are required from the perspective of
information processing, and taking the balance between distributed processing and centralized processing should be noted.
②

Realization of data cooperation
It is considered that the versatility of utilizing data will be expanded by promoting data cooperation.
In the “Digital Society Construction TF” published by the government, “by taking inventory of initiatives of datacooperation promoted by the public and private sectors in various fields such as “agriculture, disaster prevention/disastermitigation, infrastructure, logistics, and autonomous driving, we will consider data formats, functions, and communicationof rules concerning the treatment of data required for collaboration that makes the collaboration beyond fields” 50. It is
important that these initiatives will be further promoted.
③

Promotion of corporate data management by a third party/initiatives of Information Bank
In the transaction of data owned by enterprises, the relationship in transaction and the relationship between

stakeholders are complicated, so it is necessary to clarify where the responsibility lies by case. It is important to clarify
where the responsibility lies by case through the standardization of contract formats concerning data transaction by a third
party. In addition, supporting untangling the rights of the provision/usage of data and doing the procedures of contracts

50 Council for Integrated Innovation Strategy (The 6th) “About Establishment of Digital Society Construction TF”(https://www.kantei.go.jp/jp/singi/tougou-innovation/dai6/siryo4.pdf)

71

by a third party is required to promote data transaction in small and medium-sized enterprises where it is hard to do datatransaction systematically.
To promote distribution of data on individuals, the role of Personal Data Trust Bank as an intermediary of datatransaction will be more important in the future. It is important that the

Personal Data Trust Bank secures thetrustworthiness of personal information, and it is promoted as a system that gives consideration to individuals who produce
resource data, as well as plays an important role in enhancing individuals’ awareness to manage their personal informationon their own initiative.
④

Review of the legal system to promote the utilization of data
To promote ① - ③, there is a possibility that the existing legal system can be the impediment in the future. In sucha case, it is required to constantly review the legal system.
(3) Issues in the estimation method of the value of data
To realize the data transaction market, the actual estimation and analysis of the reality surrounding data are important.
The empirical analysis on the value of data in 3.1 is a clue to that. From the way of thinking in the production function,
data is added to capital equipment to expand the capacities of workers and positioned as a provider of new capacities.
Therefore, it is necessary that data is positioned as a factor of production (stock) and is examined as a clue of theoreticalexamination. The directivity to sort the issues of this method and further polish the method will be explained in thefollowing.
①

Building capital stock based on the economic characteristics of data
As the economic characteristics of data, the changes in value of data with the lapse of time, expansion of value due tothe accumulation of data and so on are pointed out. After taking these characteristics into consideration, how to build capitalstock in the year for analysis from the status of owing/utilizing data needs to be examined.
②

Further analysis of the relationship between data and the value created by data
It is necessary to analyze the relationship between data quantity/quality and the value created by data by industry andproduction process. In addition, multidimensional analyses using many survey data are required, but to do so, it is necessary
to consider building a system to collect information on a large scale and continuously.
③

Social enlightenment toward data economy
To further polish the estimation method of data value, it is necessary to improve the social recognition of the data

value and expand initiatives of analysis. To do so, it is important prepare and publish data that can be used for analysis,
and it is considered public statistics or reflecting public data on corporate financial statements 51 is one of the ways.

51

For example, in the consideration of government’s intellectual property strategy, “Discussion on the ideal way ofutilizing data that can contribute to corporate value (from the perspective of creating a P&L statement, enhancing
corporate value, creating a market, etc.)” are introduced as issues. Intellectual Property Strategy Headquarters Planning
Committee (The 5th) “Document 4 Digital Intellectual Property” Please see(https://www.kantei.go.jp/jp/singi/titeki2/tyousakai/kousou/2020/dai5/gijisidai.html)

72

6.

Addendum

6.1 Questionnaire
In 3.1, the questionnaire survey, “Survey on the Utilization of Data” conducted to estimate the value of data forenterprises is as follows.
Survey on the Utilization of Data
[Notes when responding to the survey]〇About the holding company system
・Regarding the companies that adopted the holding company system, please answer about the status of the entire
companies in the group, not on a non-consolidated basis (the status of main business companies in the group, if it’s difficult).
Furthermore, regarding listed parent and subsidiary companies, we want subsidiary companies to answer the questionnaire
regardless of the answer of parent companies.
〇About the timing of the status in the response
・As for the response to this survey, please answer about the status as of the end of FY2018 if the timing is not clarified.
First, please read the following explanation about the definition of terms in this survey.
・The “data” in this survey means the one currently digitalized and processable by computers, and it is limited to the onethat can be utilized for your own company for your product development, expansion of services, and streamlining of business(excluding the one that is provided for others for business or projects).
*For example, map data, meteorological data, and data sets prepared only for providing for others are excluded here.
・The data “analysis” in this survey means the act that extracts information by processing data such as characters, values,
images and video. This includes the one in case you use data analysis tools/services provided by external companies, etc.,
but doesn’t include the one in case of browsing, automatically collecting data by computers, judging and transmitting alert(for example, transmitting alert if the temperature becomes over a certain degree).
・The data “utilization” in this survey includes browsing data, automatically collecting data using a computer, judging andtransmitting alert in addition to data analysis.

73

1．About the utilization of data
Here, we ask you about the status of the utilization of data in your company.
<A question for everyone>
Q1: What kind of data and how processed data do you use in each field? Please choose all the corresponding ones.
[For 1 – 7, please answer in multiple responses, and if there is no corresponding one in 1 - 7, please choose 8 or 9.](Regarding data in A -G, please circle all the corresponding ones.)(If there is no business area, please choose “No area”. You don’t have to answer in A-G regarding the area.)(Regarding processing, please answer the following 4 questions.)
Browsing: Browsing data itself
Aggregation:

Aggregating by period, and processing of aggregation, etc. by enterprise size

Analysis: Statistical analysis (analysis of correlation, variation, etc.)
AI:

Prediction utilizing Artificial Intelligence such as machine learning/deep learning (including not only the cases
where AI is utilized to analyze the relationship of things and optimum behaviors but also the cases where AI isutilized for the automation, judging stockout/cheating, and monitoring based on data.)(Regarding analysis/AI, data analysis tools/services provided by external companies are included.)
6

We own data, but we haven’t
utilized it in any field.

We don’t own any data.

No area9

No areaa. browsingb.
aggregationc. analysis
d. AI

8

9

risk

No area8

research,

No area7(BasicMaintenance/Support

No areamanagement, etc.)

Logistics/Inventory control

C. Data of communication withcustomers, etc. (daily sales
reports, purchase history,
inquiry history, results ofmarket research, etc.)

5

Production/Manufacturing*Including municipalities/
organizations, etc.

4Marketing(name, address, capital, etc.)

No area3a. browsing
b. aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a.
browsingb.
aggregatio
nc. analysis
d. AI

a. browsing
b. aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a.
browsingb.
aggregatio
nc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

8

9

a. browsing
b. aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a.
browsingb.
aggregatio
nc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

8

9

B. Customers’(corporations’) basic data

Planning of products/services/

No areadevelopment

A. Customers’ (individuals’)
basic data (name, address,
sex, etc.)

Management planning/

Organizational reformIf there is no business area:

2

Others174

a. browsing
b. aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

E. Data on human activities(biological information, location
information, camera image, etc.)

a. browsing
b. aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

F. Data on the operation ofmachines, equipment, vehicles,etc.
(operationalsituation,
location information, speed, etc.)

a. browsing
b. aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

G. Data on nature/public (mapinformation,
meteorologicalinformation, etc.)

a. browsing
b. aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

a. browsingb.
aggregationc. analysisd. AI

D. Action data on the Internet(websites, SNS, mobile app,etc.) (search/access log, word of
mouth/posted data, etc.)a.
browsingb.
aggregatio
nc. analysisd. AIa.
browsingb.
aggregatio
nc. analysisd. AIa.
browsingb.
aggregatio
nc. analysisd. AIa.
browsingb.
aggregatio
nc. analysis
d. AI

a. browsingb.
aggregationc. analysis
d. AI

8

9

a. browsingb.
aggregationc. analysis
d. AI

8

9

a. browsingb.
aggregationc. analysis
d. AI

8

9

a. browsingb.
aggregationc. analysis
d. AI

8

9

<Please answer about the data you chose 1 or more “aggregation” or “analysis” or “AI” in A-G in Q1>
Q2: When you analyze data, how old research data do you usually use?
(Please circle the corresponding one in the data of A – G.)(If you analyze data for several purposes, please answer the status for the most frequently analyzed purpose.)
3

4

5

6

7

8

Ones until around 1 week ago

Ones until around 1 month ago

Ones until around 3 month ago

Ones until around half a year ago

Ones until around one year ago

Including ones older than one year ago

No ideaaddress, sex, etc.)2

Almost only the data of the day
A. Customers’ (individuals’) basic data (name,

1

1

2

3

4

5

6

7

8

1

2

3

4

5

6

7

8

1

2

3

4

5

6

7

8

B. Customers’ (corporations’) basic data (name,address, capital, etc.)*Including municipalities/ organizations, etc.
C. Data of communication with customers, etc.
(daily sales reports, purchase history, inquiry
history, results of market research, etc.)75

D. Action data on the Internet (websites, SNS,
mobile app, etc.) (search/access log, word of
mouth/posted data, etc.)

1

2

3

4

5

6

7

8

1

2

3

4

5

6

7

8

1

2

3

4

5

6

7

8

1

2

3

4

5

6

7

8

E. Data on human activities (biologicalinformation,

location

information,

cameraimage, etc.)
F. Data on the operation of machines,
equipment, vehicles, etc. (operational situation,
location information, speed, etc.)
G. Data on nature/public (map information,
meteorological information, etc.)<Please answer about the fields where you chose “aggregation” or “analysis” or “AI” one time or more in the fields of AF in Q1.>
Q3: How frequently do you do data analysis?
(Please circle a number in each field of A – F.)
1

2

3

4

5

6

7

Almost every day

Once a week

Once a month

Once in 3 months

Once half a year

Once a year

Once in more than a year

A. Management planning/Organizational reform

1

2

3

4

5

6

7

B. Planning of products/services/development

1

2

3

4

5

6

7

C. Marketing

1

2

3

4

5

6

7

D. Production/Manufacturing

1

2

3

4

5

6

7

E. Logistics/Inventory control

1

2

3

4

5

6

7

F. Maintenance/Support

1

2

3

4

5

6

7

<For those who chose 1. - 7. one or more times in Q1>
Q4: What kind of initiative related to the quality of data does your company take when utilizing data? [multiple responses]
1．Check of data source
2．Check of the accuracy of data (error check, etc.)
3．Acquisition of a privacy mark4．Acquisition of ISMS (ISO27001)
5．Other []76

<A question for everyone>
Q5: When your company does data analysis, what organization (departments/persons in charge) do you use for data analysis(including the use of data analysis tools/services provided by external companies, etc.)? [multiple responses]
If you have a specialized department for data analysis, please answer how many years ago the department was launched.
(The specialized department for data analysis is the one that specializes in data analysis in a business unit, excluding thedepartment that specializes in the development of hardware and software related to data analysis for external sales. Pleaseanswer about the department that is involved in data analysis.)
1．Persons in charge in a specialized department that handles data analysis --- The specialized department was launched approx. [years ago]

2．Persons in charge in each business unit who are specialized in data analysis
3．Persons in each business unit who are not specialized in data analysis
4．Entrusting an external company to do that
5．Other []6．We haven’t done any data analysis (no entrusting)<A question for everyone>
Q6: How many employees who work mainly for data analysis (including not only permanent employees but also parttimers/part-time workers/contract employees/dispatched employees) do you have? And what’s the percentage of employees
who graduated from graduated school in the employees who work mainly for data analysis?
(Please circle just one number corresponding to each one.)
Employees who work mainly for data analysis (as of the end of FY2018): [approx.

persons]

Employees who work mainly for data analysis (as of the end of FY2015): [approx.

persons]

5

6

7

8

9

10

3% or more and less than 5%

5% or more and less than 10%

10% or more and less than 20%

20% or more and less than 30%

30% or more and less than 40%

40% or more and less than 50%

50% or more

school (as of the end of FY2015)4

1% or more and less than 3%

Employees who graduated from graduate

3

Less than 1%

school (as of the end of FY2018)

2

0%
Employees who graduated from graduate

1

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

<For those who chose “AI” one or more times in Q1>
Q7: What kind of AI technology does your company use in the utilization of data? [multiple responses]

77

1．Supervised learning (regression analysis, discriminant analysis, etc.)
2．Unsupervised learning (clustering, principal component analysis, etc.)
3．Reinforcement learning (Monte Carlo method, Q learning, etc.)
4．Deep learning
5．Other []6．Utilizing AI but, but have no idea about the specific technology<A question for those who chose 1. - 5. in Q5. >Q8: Please answer about the effects (contribution to corporate activities) of utilizing data. How much does each process
regarding the utilization of data contribute to the effects in your company?
(Please circle a corresponding number in A – E.)
1

2

3

4

5

Contribute very much

Contribute to some extent

Neither of themNot contribute very much

Not contribute at all

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

A. Data collection(Consideration of data collection, building the system to collectdata, etc.)
B. Data processing(Errorcheck,

annotation/labeling,

formatting

estimating

analysis/processing, etc.)
C. Data browsing/aggregation(Browsing and aggregation of data)
D. Data analysis/Utilization of AI(Statistical analysis, prediction utilizing AI, etc.)
E. Interpretation of analysis results(Discussion and decision-making based on analysis results)

78

2．About the accumulation (owning) of data
We ask you about the accumulation (owning) status of data in your company.
<Please answer about the data in A – G in Q1 where you chose 1. – 8. >Q9: Regarding the data your company has accumulated (owned), where is the source of data? Also, do you provide or sharethe data you have owned for/with other companies, etc. after processing it if necessary? [multiple responses](Please circle all the corresponding numbers about each data in A-G.)(It includes not only your own data but also the data purchased or acquired from outside, but please answer about the data
limited to the data utilized for your product development/service expansion, and the efficiency in work, etc. The data
temporarily kept from other companies for the delivery of analysis results should not be included here.)
1

3

4

5

(including

have not grasped thatcompanies

Provided the owned data for

othercommon use by alliance, etc.)

data

by

Acquired public data, etc. foruse

companies

free (statistical data, open data,

othercommonAPI utilization, etc.)

byPurchased/acquired theowned(including

alliance, etc.

address, sex, etc.)

Acquired by collecting on ourown or from the inside

A. Customers’ (individuals’) basic data (name,

2

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

B. Customers’ (corporations’) basic data (name,address, capital, etc.)*Including municipalities/ organizations, etc.
C. Data of communication with customers, etc.
(daily sales reports, purchase history, inquiry
history, results of market research, etc.)
D. Action data on the Internet (websites, SNS,
mobile app, etc.) (search/access log, word of
mouth/posted data, etc.)
E. Data on human activities (biologicalinformation,

location

information,

cameraimage, etc.)
F. Data on the operation of machines,
equipment, vehicles, etc. (operational situation,
location information, speed, etc.)
G. Data on nature/public (map information,
meteorological information, etc.)79

<A question for those who chose 1 - 8 one time or more in Q1.>
Q10: What percentage is the data purchased from outside (excluding the data published for free of charge and the data for
free common use due to the alliance, etc.) in your accumulated (owned) data? Please answer on the number of cases
basis, not on data amount basis.
(If you don’t grasp that exactly, please answer within the scope of the data you have grasped.)[Approx.

％]< Please answer about the data in A – G in Q1 where you chose 1. – 8.>
Q11: Regarding the data your company has accumulated (owned), please answer the rough makeup percentages (on dataamount basis) as of the end of FY2018, making the total of A-G 100%. In addition, please answer the number of subjects(persons/companies/customers/units) of accumulated (owned) data in round numbers with 2 significant figures(examples: 12,000, 450,000). You don’t have to answer about the items whose numbers are unclear. Besides, please answer
the specific type of accumulated (owned) data within the scope of your knowledge.
(Regarding the number of subjects, please answer assuming the number of customers or units of installed equipment(sensors), etc.)(Regarding the data in C, if both individuals and corporations are included in the subject, please answer the total numberof individuals and companies/organizations.)(It includes not only your company’s data but also the data purchased or acquired from outside, but please answer about
the data that can be utilized for product development/ service expansion and efficiency in work, etc. in your company. The
data temporarily kept from other companies for the delivery of analysis results should not be included here.)80Amount ratio in the

Number of subjects in[Arbitrary] Specific type of data you

accumulated (owned) data (％)

accumulated (owned) data

have accumulated (owned)

A. Customers’ (individuals’) basic％

data (name, address, sex, etc.)

persons

B. Customers’ (corporations’) basic
data (name, address, capital, etc.)％*Including municipalities/

companies/organizationsorganizations, etc.
C.

Data

of

communicationwithcustomers, etc. (daily sales reports,％

purchase history, inquiry history, results

customers (persons＋companies/organizations)of market research, etc.)
D. Action data on the Internet (websites,
SNS, mobile app, etc.) (search/access log,
word of mouth/posted data, etc.)％

persons

E. Data on human activities (biologicalinformation, location information, camera％

persons

image, etc.)
F. Data on the operation of machines,
equipment, vehicles, etc. (operational％situation, location information, speed,

units

etc.)
G.

Data

on

nature/public(map

information, meteorological information,

％

etc.)

Total

100％

About the data you answered above, please tell us about the total amount of data you owned as of the end of FY2018. The
total amount should be rounded to 2 significant figures (examples: 12,000, 450,000). If you don’t figure out the precise
value, please answer in the intermediate value (example: If you think it’s between 1,000 and 2,000TB, please input
1,500TB).
,,

TB

Notes) TB (terabyte)= about 1000GB. 1PB (petabyte)= about 1000TB. 1EB (exabyte)= about 1000PB.

81

< Please answer about the data in A – G in Q1 where you chose 1. – 8.>
Q12: Regarding the data your company has accumulated (owned), what percentage is the data purchased/acquired fromoutside as of the end of FY2018 (including the data published for free of charge and the data for free common use due toalliance, etc.) in your accumulated (owned) data? Please answer in the number of cases basis, not on data amount basis.
(Please circle a number in each data of A – G.)(Please answer, assuming the number of customers and units of equipment (sensors), etc.)(Regarding the data in C, if both individuals and corporations are included in the subject, please answer the total numberof individuals and companies/organizations.)(Please answer about only the data that can be utilized for product development/service expansion and efficiency in work,
etc. in your company. The data temporarily kept from other companies for the delivery of analysis results should not be
included here.)
3

4

5

6

7

8

9

10

11

12

Less than 5%

5% or more and less than 10%

10% or more and less than 20%

20% or more and less than 30%

30% or more and less than 40%

40% or more and less than 50%

50% or more and less than 60%

60% or more and less than 70%

70% or more and less than 80%

80% or more and less than 90%

90% or more

address, sex, etc.)

20%
A. Customers’ (individuals’) basic data (name,

1

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

B. Customers’ (corporations’) basic data (name,address, capital, etc.)*Including municipalities/ organizations, etc.
C. Data of communication with customers, etc. (daily
sales reports, purchase history, inquiry history, resultsof market research, etc.)
D. Action data on the Internet (websites, SNS, mobile
app, etc.) (search/access log, word of mouth/posted
data, etc.)
E. Data on human activities (biological information,
location information, camera image, etc.)
F. Data on the operation of machines, equipment,
vehicles,etc.

(operationalsituation,

location

information, speed, etc.)
G.

Data

on

nature/publicmeteorological information, etc.)(map

information,82

< Please answer about the data in A – G in Q1 where you chose 1. – 8.>
Q13: Regarding the data your company has accumulated (owned), how much the data as of the end of FY2018 has changed
compared with the one as of the end of FY2015? Please answer on the number of cases basis, not on data amount basis.
(Please circle a number in each data of A – G.)(Regarding the data in C, if both individuals and corporations are included in the subject, please answer the total numberof individuals and companies/organizations.)(It includes not only your own data but also the data purchased or acquired from outside, but please answer about the data
limited to the data utilized for your product development/service expansion, and the efficiency in work, etc. The data
temporarily kept from other companies for the delivery of analysis results should not be included here.)
3

4

5

6

7

8

9

10

Decreased by more than 50%

Decreased by 10 – 40%

Almost no change

Increased by around 10%

Increased by 20 – 30%

Increased by 40 – 60%

Increased by 70 – 90%

Increased more than 2 times

No ideaaddress, sex, etc.)2

Not owned as of the end of FY2015
A. Customers’ (individuals’) basic data (name,

1

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

B. Customers’ (corporations’) basic data (name,address, capital, etc.)*Including municipalities/ organizations, etc.
C. Data of communication with customers, etc. (daily
sales reports, purchase history, inquiry history, resultsof market research, etc.)
D. Action data on the Internet (Websites, SNS, mobile
app, etc.) (search/access log, word of mouth/posted
data, etc.)
E. Data on human activities (biological information,
location information, camera image, etc.)
F. Data on the operation of machines, equipment,
vehicles,etc.

(operationalsituation,

location

information, speed, etc.)
G.

Data

on

nature/publicmeteorological information, etc.)(map

information,83

< Please answer about the data in A – G in Q1 where you chose 1. – 8.>
Q14: Regarding the data your company has accumulated (owned), what percentage is the non-structured data (data inwhich the writing of business diaries and SNS and data in which rules (structures) such as voice/images/video are notclearly defined) in the accumulated (owned) data? Please answer on the number of cases basis, not on data amount basis.
(For each data in A – G, please circle only one corresponding number.)(Regarding the data in C, if both individuals and corporations are included in the subject, please answer the total numberof individuals and companies/organizations.)(Please answer about the data limited to the data utilized for your product development/service expansion, and theefficiency in work, etc. The data temporarily kept from other companies for the delivery of analysis results should not be
included here.)
3

4

5

6

7

8

9

10

11

12

Less than 5%

5% or more and less than 10%

10% or more and less than 20%

20% or more and less than 30%

30% or more and less than 40%

40% or more and less than 50%

50% or more and less than 60%

60% or more and less than 70%

70% or more and less than 80%

80% or more and less than 90%

90% or more

address, sex, etc.)

20%
A. Customers’ (individuals’) basic data (name,

1

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

B. Customers’ (corporations’) basic data (name,address, capital, etc.)*Including municipalities/ organizations, etc.
C. Data of communication with customers, etc. (daily
sales reports, purchase history, inquiry history, resultsof market research, etc.)
D. Action data on the Internet (Websites, SNS, mobile
app, etc.) (search/access log, word of mouth/posted
data, etc.)
E. Data on human activities (biological information,
location information, camera image, etc.)
F. Data on the operation of machines, equipment,
vehicles,etc.

(operationalsituation,

location

information, speed, etc.)
G.

Data

on

nature/publicmeteorological information, etc.)(map

information,84

< A question for those who chose 1 - 7 one time or more in Q1.>
Q15: What percentage was the data actually utilized in the data accumulated (owned) in your company in a year (FY2018)?
Please answer on the number of cases basis, not on data amount basis. In addition, answer about the status as of FY2015(3 years earlier than that).
(Please circle a number for each fiscal year.)(If you don’t grasp that exactly, please answer within the scope of the data you have grasped.)
1

2

3

4

5

6

7

8

9

10

11

Less than 5%

5% or more and less than 10%

10% or more and less than 20%

20% or more and less than 30%

30% or more and less than 40%

40% or more and less than 50%

50% or more and less than 60%

60% or more and less than 70%

70% or more and less than 80%

80% or more and less than 90%

90% or moreA.

FY2018

1

2

3

4

5

6

7

8

9

10

11

B.

FY2015

1

2

3

4

5

6

7

8

9

10

11

85

3．About market structure
We ask you about the competitive environment in the market where your company put your representative
products/services.
<A question for everyone>
Q16: How much new products/new services did your company put in the market in FY2018?
(New products/new services include not only the ones that renewed functions/performances/designs/materials/
components/purposes but also the ones that are combined with existing technologies and technically improved existingproducts/services. However, they don’t include that ones that have the same functions of products/services and usage
purposes, and only the design change or the ones just for the sales/providing for other companies’ products/services,)
1．0 case (No products/services were brought to the market)
2．1 - 2 cases
3．3 - 5 cases
4．6 - 10 cases
5．11 - 20 cases
6．21 - 30 cases
7．31cases or more
<A question for everyone>
Q17: In the market where your company put your main products/services, how many competitive companies did you haveat the end of FY2018 in Japan and the world in total? In addition, please answer about the changes from the end of
FY2015 to the end of FY2018.
(Please circle a number for each.)(Please answer assuming the market of the same products/services as of the end of FY2015 and the end of FY2018.)

Number of competitors at the end of
FY2018

1

2

3

4

5

6

0

1-2

3-5

6 - 10

11 - 2021 companies

company

companies

companies

companies

companies

or more

1

2

3

4

5

6

1
Decreasedby 6 ormore
Change from the end of FY2015

1

2

3

4

5

6

Decreased

DecreasedNo

Increased

Increasedby 3 - 5

by 1 - 2changeby 1 - 2

by 3 - 5

2

3

4

5

6

86

7
Increasedby 6 ormore
7The number of responses to the questionnaire survey (by industry and by total capital) and aggregation results are as
follows.
Table 55: The number of responses to the questionnaire survey by industry
Other, 98,
その他,
17%

Communication,
通信, 26, 4%

Manufacturing,
製造業,

174,
31%
174,
31%
Retailing,
小売業, 27, 5%
Construction,
建設, 29, 5%Service,

サービス,
130, 23%
130, 23%

銀行,35,
35,6%
6%
Banking,
商社, 50,9%

Trading, 50,
9%Table 56: The number of responses to the questionnaire survey by total capital#N/A, 11,
1兆円以上, 2%More than 1 trillion yen,95, 16%

Less than 1 billion yen,

10億円未満,16, 3%

More than 1 billion and less than 10 billion yen,
10～100億円未満,
119, 21%

More than 100 billion and less than 1 trillion yen,

1000億円～1兆円未満,
141, 25%
More than 10 billion and less than 100 billion yen,

100～1000億円未満,
187, 33%

87

Table 57: Questionnaire results (Q1-1)(n=524)
Management planning/

経営企画・
Organizational
組織改革reform■ Browsing
閲覧■ Aggregation ■ Analysis
集計
解析
ＡＩ
0%

A.顧客(個人)の基本データ
A.Customers’ (individuals’) basic dataB.顧客(法人)の基本データ

B.Customers’ (corporations’) basic data

10%

0.2%

C.顧客等とのやり取りデータ
D.インターネット上のアクションデータ
D.Action data on the Internet

E.人の行動に関するデータ

E.Data onF.機械、機器、車両等の動作に関するデータ
human activities

G.自然・公共由来のデータ
F.Data on the operation of machines,
equipment, vehicles, etc.
G.Data on nature/public

マーケティング
Marketing■ Browsing
閲覧A.Customers’ (individuals’) basic dataA.顧客(個人)の基本データ

B.顧客(法人)の基本データ

B.Customers’ (corporations’) basic dataC.顧客等とのやり取りデータ
C.Data of communication with customers, etc.

D.インターネット上のアクションデータ
D.Action data on the Internet

E.人の行動に関するデータ

E.Data on F.機械、機器、車両等の動作に関するデータ
human activities

G.自然・公共由来のデータ
F.Data on the operation of machines,
equipment, vehicles, etc.

30%

製品・サービス
services / Developmentの企画・開発

40%

18%
17%

8%

30%
27%

5%

E.Data on human activities

F.機械、機器、車両等の動作に関するデータ

G.自然・公共由来のデータ
F.Data on the operation of machines,
equipment, vehicles, etc.

20%
3.1%
2.3%
4.6%

4.2%
5%
4%
4%
1.3%
5%
4%
3%
0.8%
12%
7%
6%
1.0%

40%

21%

60%

28%
31%
32%
26%

1%
0.2%
2%
0.4%

C.顧客等とのやり取りデータ
C.Data of communication with customers, etc.

D.インターネット上のアクションデータ

D.Action data on the Internet

E.人の行動に関するデータ

F.機械、機器、車両等の動作に関するデータ

E.Data on human activities

G.自然・公共由来のデータ

F.Data on the operation of machines, equipment, vehicles, etc.

G.Data on nature/public

G.Data on nature/public

88

32%
32%

19%
22%
19%

0.8%
2%
2%
0.2%
1.1%

15%

10%

5%

B.顧客(法人)の基本データ
B.Customers’ (corporations’) basic data

40%
44%

5%

A.顧客(個人)の基本データ

A.Customers’ (individuals’) basic data

40%
39%

31%
29%

■ Aggregation ■ Analysis ■
集計
解析
ＡＩ
0%

Manufacturing

30%
32%

20%

■ Browsing
閲覧Production/

生産・製造

22%

14%

14%
3.2%
6%
5%
5%
3.2%
8%
9%
8%
3.6%
11%
6%
6%
2.5%

E.人の行動に関するデータ

G.Data on nature/public

18%

2.3%

D.インターネット上のアクションデータ
D.Action data on the Internet

14%

2.3%

C.顧客等とのやり取りデータ■

10% 15% 20% 25% 30% 35%

1.7%

C.Data of communication with customers, etc.

■ Aggregation ■ Analysis ■
集計
解析
ＡＩ
0%

0%

B.顧客(法人)の基本データ

18%

12%

8%
0.8%
4%
3%
2%
0.4%
4%
4%
3%
0.4%
8%
3%
3%
0.2%

■ Aggregation ■ Analysis
集計
解析
ＡＩ

B.Customers’ (corporations’) basic data

24%
22%

13%

1.0%

■ Browsing
閲覧A.Customers’ (individuals’) basic dataA.顧客(個人)の基本データ

13%

0.6%

C.Data of communication with customers, etc.

20%

Planning of products/

■

16%

9%
5%
6%

4%
4%

20%

6%

10%

13%

6%

3.1%
5%
2%
2%
0.6%

11%

13%
13%

Table 58: Questionnaire results (Q1-2)(n=524)
Logistics/

物流・
Inventory control
在庫管理■ Browsing
閲覧0%

A.Customers’ (individuals’) basic dataA.顧客(個人)の基本データ

10%
6%

2%
0.2%

B.顧客(法人)の基本データ

B.Customers’ (corporations’) basic dataC.顧客等とのやり取りデータ
D.インターネット上のアクションデータ
E.人の行動に関するデータ

1.0%
2%
2%
0.4%

G.自然・公共由来のデータ
F.Data on the operation of machines,
equipment, vehicles, etc.
G.Data on nature/public

その他(基礎研究、
Others (Basic research, risk management, etc.)
リスク管理等)A.顧客(個人)の基本データ
A.Customers’ (individuals’) basic dataB.顧客(法人)の基本データ

D.インターネット上のアクションデータ
C.Data of communication
with customers, etc.
D.Action data on the Internet

E.人の行動に関するデータ

F.機械、機器、車両等の動作に関するデータ
E.Data on human activities

G.自然・公共由来のデータ
F.Data on the operation of machines, equipment, vehicles, etc.

21%

13%

0%

B.顧客(法人)の基本データ

B.Customers’ (corporations’) basic data

14%
15%

0.6%

D.インターネット上のアクションデータ
E.人の行動に関するデータ

E.Data onF.機械、機器、車両等の動作に関するデータ
human activities

5%7%

G.自然・公共由来のデータ

F.Data on the operation of machines, equipment, vehicles, etc.

5%

■ Analysis
ＡＩ
10%

1.9%

20%

13%
12%

7%

4.6%
6%
6%
2.9%

15%
16%

8%
8%
8%

18%

12%

8%

2.7%
4%
4%
4.4%

11%
9%

11%
11%

7%

1.9%

23%

11%
14%

G.Data on nature/public

■

15%

2.7%
4%
4%
1.5%

9%

28%

17%

7%

6%
0.8%
4%
3%
2%
0.6%

30%

19%

13%

2.5%

■

20%

5%

0.6%

C.顧客等とのやり取りデータ

8%
8%

2.3%

10%

C.Data of communication with customers, etc.

B.Customers’ (corporations’) basic dataC.顧客等とのやり取りデータ

30%■ Aggregation ■ Analysis
集計
解析
ＡＩ

A.顧客(個人)の基本データ
A.Customers’ (individuals’) basic data

■ Browsing ■ Aggregation
集計
閲覧
解析
0%

■ Browsing
閲覧保守・メンテナ
Supportンス・サポート

D.Action data on the Internet

E.Data on human activities

F.機械、機器、車両等の動作に関するデータ

20%

6%
5%

0.2%
2%
1%
0.0%
1%
1%
1%
0.2%

Maintenance/

■

9%

4%

0.4%

C.Data of communication with customers, etc.

D.Action data on the Internet■ Aggregation ■ Analysis
集計
解析
ＡＩ

11%

8%
8%
8%
10%

G.Data on nature/public

Table 59: Questionnaire results (Q2)
■ Almost only the data of the day

ほぼその日のデータのみ■ Ones until around 3 month ago
3か月程度前のものまで■ Includingones older than one year ago
1年以上前のものも含む■ Ones until around 1 week ago■ Ones until around 1 month ago

1週間程度前のものまで■ Ones until around half a year ago
約半年前のものまで■わからないNo idea

1か月程度前のものまで
約1年前のものまで■ Ones until around one year ago

0%

20%

40%

60%

A.顧客(個人)の基本データ(n=280)3%(n=280)

11%

68%

11%

72%

13%

69%

A.Customers’ (individuals’) basic data

B.Customers’ (corporations’) basic dataB.顧客(法人)の基本データ
2%(n=328)(n=328)

C.Data of communication with customers, etc.

C.顧客等とのやり取りデータ
2%(n=365) (n=365)

D.Action data on the Internet
D.インターネット上のアクションデータ
3%(n=295)(n=295)

E.人の行動に関するデータ(n=126)

E.Data on human activities

19%

6%

11%

41%
30%

(n=126)

F.機械、機器、車両等の動作に関するデータ
F.Data on the operation of machines, equipment, vehicles, etc.
3%(n=218)14%

51%(n=218)

G.自然・公共由来のデータ
G.Data on nature/public(n=148)(n=148)

7%
89

8%

49%

80%

100%

25%

Table 60: Questionnaire results (Q3)
■ Almost every day■ Once a week

■ Once a month■ Once half a year

■ Once a year

■ Once more than a year

ほぼ毎日

半年に1回程度

毎月1回程度

1年に1回程度

1年以上の間隔

0%A.

A.経営企画・組織改革
Management planning/organizational
reform(n=227)B.

(n=227)B.製品・サービスの企画・開発
Planningof products/services/development(n=287)(n=287)C.

MarketingD.

Production/Manufacturing

C.マーケティング(n=345)(n=345)

20%

40%

60%

80%

9%

30%

19%

15%

18%

14%

20%

32%

19%

12%
37%

100%

11%

15% 7%
8% 9% 6%33%

E.物流・在庫管理(n=149)

31%

8%

38%

7% 9% 5%

F.保守・メンテナンス・サポート(n=221)(n=221)

29%

12%

31%

8% 10% 6%

13%

33%

6% 7% 5%

Logistics/Inventory control(n=149)F.

14%

3か月に1回程度

D.生産・製造(n=150)(n=150)E.

■ Once in 3 months

毎週1回程度

Maintenance/Support

Table 61: Questionnaire results (Q4) (n=468)

0%

20%

データの入手元の確認
Check of data sources
Check of the accuracy of data

80%

75%

チェック等)(error check, etc.)

プライバシーマークの取得
Acquisition of a privacy mark18%

Acquisition
ISMS (ISO27001)
ISMSof(ISO27001)の取得

16%

Others60%70%

データの正しさの確認(エラー

その他

40%

4%

90

Table 62: Questionnaire results(Q5-1)(n=529)

0%

10% 20% 30% 40% 50% 60% 70%

Persons
in charge in a special department that handles data
データ分析を行う専門部署の担当者

23%

Persons in charge 各事業部門のデータ分析専門の担当者
in each business unit who are specialized in data analysis

28%

Persons
in each business unit who are not specialized in data analysis
各事業部門のデータ分析が専門ではない人

60%

Entrusting 外部に委託している
an external company to do that
Othersその他

19%1%

We haven’t done any data analysis (no entrusting)
データ分析は行っていない(委託もしていない)

16%

Table 63: Questionnaire results(Q5-2) (n=529) The timing when the specialized department was launched

Average value

9.8 years

Median value

5.0 years

Minimum value

1.0 yearMaximum value

60.0 years

Mode

10.0 years

Table 64: Questionnaire results (Q6-1)(Unit: persons)
FY2018FY2015

Average48

40

Average excluding 080

76

Minimum excluding 0

1

1

9,999

9,999

Maximum

91

Table 65: Questionnaire results (Q6-2) Percentage of employees who graduated from graduate school■ 0%■ Less than 1%■ 1% or more and less than 3%■ 3% or more and less than 5%

0%
1%未満
1%以上3%未満■ 5% 5%以上10%未満
or more and less than 10%■ 10% or more
and less than 20%■ 20% or more20%以上30%未満
and less than 30%
10%以上20%未満
50%以上■ 40%40%以上50%未満
or more and less than 50%■ 50% or more
0%
20%
40%
60%

62%

2018年度(n=485)
FY2018 (n=485)

80%

100%

11%

68%

FY2015 (n=483)
2015年度(n=483)

3%以上5%未満
30%以上40%未満■ 30% or more and less than 40%

8%

10%

7%

Table 66: Questionnaire results (Q7) (n=104)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Supervised learning (regression analysis, discriminant analysis, etc.)

教師あり学習(回帰分析、判別分析等)

78%

Unsupervised
learning (clustering, principal component analysis, etc.)
教師なし学習(クラスタリング、主成分分析等)

63%

強化学習(モンテカルロ法、Qラーニング等)
Reinforcement learning(Monte Carlo method, Q learning, etc.)

Deep learning36%

深層学習(ディープラーニング)
その他

Other

69%
3%

ＡＩを活用しているが、具体的な技術はわからない

13%

Utilizing AI but, but have no idea about the specific technology

92

Table 67: Questionnaire results (Q8)
■ Contribute very much

とても貢献している■
Not contribute very much
あまり貢献していない■ Contribute to some extent

多少貢献している■ Not contribute at all
まったく貢献していない

0%

A.A.データの収集(収集データの検討、データを収集する仕Data collection (Consideration of data collection,
組みの構築等)(n=444)
building the system to collect data, etc.) (n=444)

26%

Data
browsing/aggregation (Browsing and aggregation of
C. データの閲覧・集計(データそのものの閲覧、集
data)

40%

34%

B.B.データの加工(エラーチェック、アノテーション・ラベ
Data processing (Error check, annotation/labeling,
ル付け、分析を想定した整形・加工等)(n=442)
formatting estimating analysis/processing, etc.) (n=442)
C.

20%■ Neither of them

どちらでもない(n=444)
D. データの解析・ＡＩ活用(統計的な分析、AIを活用した
D. Data analysis/Utilizationof AI (Statistical analysis,
予測等)(n=435)

19%estimation utilizing AI, etc.) (n=435)
E. 分析結果の解釈(分析結果を元にした議論や意思決E. Interpretation of analysis results (Discussion and
定)(n=441)
decision making based on analysis results) (n=441)

26%

93

80%

42%
27%

24%
40%

7%
12% 2%

49%
33%

100%

20% 3%

36%

36%計)(n=444)

60%

10%
23%

6%

Table 68: Questionnaire results (Q9) (n=508)■ Acquired by collecting on our own or from the inside

自社で直接収集または自社の内部から入手
他社等が保有するデータを購入・入手(アライアンス等による共同利用を含む)■ Acquired publicdata, etc. for free (statistical data, open data, API utilization, etc.)
公開データ等を無料で入手(統計データ、オープンデータ、API活用等)■ Provided the owned data for other companies (including common use by alliance, etc.)
自社で保有するデータを他社等に提供(アライアンス等による共同利用を含む)■ Have not grasped
that
把握していない■ Purchased/acquired the data owned by other companies (including common use by alliance, etc.

0%

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

A.Customers’ (individuals’) basic dataA.顧客(個人)の基本データ

5%
3%
5%

B.Customers’ (corporations’) basic dataB.顧客(法人)の基本データ

C.顧客等とのやり取りデータ

2%

9%E.人の行動に関するデータ

6%
3%
1%

F.機械、機器、車両等の動作に関するデータ

6%
2%
3%

E.Data on human activities

F.Data on the operation of machines, equipment, vehicles, etc.

G.自然・公共由来のデータ

1%

G.Data on nature/public

87%

11%

4%
2%
4%

D.インターネット上のアクションデータ
D.Action data on the Internet

80%

27%

16%

2%
4%

C.Data of communication with customers, etc.

73%

10%

57%

17%
17%

27%

13%

46%

10%
13%
19%
20%
11%

Table 69: Questionnaire results (Q10) (n=461)■ 10% or more and less than 20%■ 20% or more and less than 30%
0%以上10%未満
10%以上20%未満
20%以上30%未満■ 30% or more and less than 40%■ 40% or more and less than 50%■ 50% or more and less than 60%
40%以上50%未満
50%以上60%未満
30%以上40%未満■ 60% or more 60%以上70%未満
and less than 70%■ 70% or more and less than70%以上80%未満
80%■ 80% or more and less than 90%80%以上90%未満■ 90% or more 90%以上100%未満
and less than 100%
■ 100%
100%■ 0% or more and less than 10%

0%

Ratio
割合

20%

40%

60%

80%

13%

69%

94

100%

6% 5%

Table 70: Questionnaire results (Q11-1) (n=426)■ A.Customers’ (individuals’) basic data
■ B.Customers’ (corporations’) basic dataA.顧客(個人)の基本データ■ C.Data of communication with customers,etc.

B.顧客(法人)の基本データ
C.顧客等とのやり取りデータ■ E.Data on human activities D.インターネット上のアクションデータ
E.人の行動に関するデータ■ F.Data on the operation of machines, equipment, vehicles, etc.
F.機械、機器、車両等の動作に関するデータ■ G.Data on nature/public
G.自然・公共由来のデータ■ D.Action data on the Internet

0%

容量割合
Volume
ratio

20%

22%

40%

60%

19%

80%

31%

100%

11% 2% 12% 2%

Table 71: Questionnaire results (Q11-2) Number of subjects of data

A. Customers’(individuals’)
basic data (name,address, sex, etc.)[persons](n=141)

B. Customers’(corporations’) basic
data (name, address,
capital, etc.)[companies/
organizations](n=169)

C. Data ofcommunication
with customers, etc.
(daily sales reports,
purchase history,
inquiry history,
results of market
research, etc.)
customers [persons+ companies/
organizations](n=143)

Averagevalue

9,299,791

100,287

12,905,002

1,302,429

246,115

32,212

Median value

60,0002,000

10,000

1,000

300

200

Averagevalue
excluding 09,959,351

103,254

13,356,226

1,526,985

365,443

36,451

Median value
excluding 0

20

5

2

1

10

4

Maximum
value

1,000,000,000

4,500,000

1,000,000,000

35,000,000

10,000,000

700,000

D. Action data on
the Internet(Websites, SNS,
mobile app, etc.)(search/access log,
word ofmouth/posted data,etc.)[persons](n=87)

E. Data on
human activities(biologicalinformation,
location
information,
camera image,etc.)[persons](n=33)

F. Data on theoperation ofmachines,
equipment,
vehicles, etc.
(operationalsituation, location
information,
speed, etc.)[units](n=76)

Table 72: Questionnaire results(Q11-3) (n=380) Total of data amount
Average value

6,373 TBMedian value

10 TB

Minimum value

0 TB

Maximum value

1,000,000 TB

Mode

1 TB

95

Table 73: Questionnaire results (Q12)■ 0%■ less than 5%■ 5% or more and less than 10%■ 10% or more and less than 20%

5%未満■ 30% or more and lessthan 40% ■ 40% or more and less than5%以上10%未満
50%■ 50% or more and less than 10%以上20%未満
60%
20%以上30%未満
30%以上40%未満
40%以上50%未満
50%以上60%未満■ 60% or more and60%以上70%未満
less than 70%■ 70% or more and lessthan 8%■ 80% or more and less than80%以上90%未満
90%■ 90% or more
70%以上80%未満
90%以上■ 20% or more and0%
less than 30%

0%

20%

40%

60%

80%

100%

17% 4%

5%

6%

6%

A. Customers’ (individuals’) basic dataA.顧客(個人)の基本データ(n=369)(n=369)63%

B. Customers’ (corporations’) basic dataB.顧客(法人)の基本データ(n=436)(n=436)42%

C.顧客等とのやり取りデータ(n=424)(n=424)

C. Data of communication with customers, etc.

24%
60%

D.インターネット上のアクションデータ
D.Action data on the Internet(n=349)17% 6%

46%

22%

3%
6%

7%

(n=349)E.人の行動に関するデータ(n=193)71%

E.Data on human activities(n=193)
F.機械、機器、車両等の動作に関するデータ
F. Data on the operation(n=266)
of machines, equipment, vehicles, etc.

15% 5%

65%

G.自然・公共由来のデータ(n=266)(n=211)34%

G. Data on nature/public(n=211)

96

18% 4%
20%

4%

27%

3%
2%

Table 74: Questionnaire results (Q13)■ Didn’t own any at the end of FY2015■ Decreased by more than 50%

2015年度末時点では保有していなかった■ Almost no change5割以上減少した
ほぼ変化なし■ Increased1～4割程度減少した
around 10%■ Increased by around20-30%
1割程度増加した
2～3割程度増加した■ Increased by around 40-60%■ Increased by around 70-90%
4～6割程度増加した
7～9割程度増加した
わからない■ Increased2倍以上に増加した
by more than 2 times■ No idea
0%
20%
40%
60%
■ Decreased by around 10-40%

80%

100%

A. Customers’ (individuals’) basic dataA.顧客(個人)の基本データ
4%(n=378)(n=378)

31%

18%

15%

B.顧客(法人)の基本データ
3%(n=446)(n=446)35%

19%

13% 4% 21%

B. Customers’ (corporations’) basic dataC.顧客等とのやり取りデータ
4%(n=435)(n=435)24%

C. Data of communication with customers, etc.

D.インターネット上のアクションデータ
D.Action data on the Internet(n=355)

11%

17%

20%

19%

11% 15%

6%

7%

20%

22%

11%

25%

10%5% 11%27%

(n=355)

E.人の行動に関するデータ(n=192)

E. Data on human activities(n=192)
F.機械、機器、車両等の動作に関するデータ
F. Data on the operation(n=268)
of machines, equipment, vehicles, etc.
(n=268)
G.自然・公共由来のデータ
G. Data on nature/public(n=212)

20%
11%
15%

(n=212)

97

25%
24%
33%

11% 9%

15%

25%

8% 8% 6%27%

Table 75: Questionnaire results(Q14)■ 0%■ less than 5%■ 20% or0%
more and less than 30%■ 30% or more5%未満
and less than 40%

20%以上30%未満■ 60% or60%以上70%未満
more and less than 70%■ 5% or more and less than 10%■ 10% or more and less than 20%

5%以上10%未満■ 40% or more and less than50%
■ 50% or more and less than10%以上20%未満
60%
30%以上40%未満
40%以上50%未満
50%以上60%未満■ 70% or more70%以上80%未満
and less than 80%■ 80% or more and less than90%
■ 90% or more
80%以上90%未満
90%以上

0%

20%

40%

60%

80%

100%

A. Customers’ (individuals’) basic dataA.顧客(個人)の基本データ(n=363)(n=363)

52%

26%

6% 5%

4%

50%

28%

4% 7%

3%

B. Customers’ (corporations’) basic dataB.顧客(法人)の基本データ(n=428)(n=428)

C.顧客等とのやり取りデータ(n=417)(n=417)

C. Data of communication with customers, etc.

29%

21%

D.インターネット上のアクションデータ
D. Action data on the Internet(n=337)(n=337)38%

E.人の行動に関するデータ(n=181)43%

F.機械、機器、車両等の動作に関するデータ
F. Data on the operation of machines, equipment, vehicles, etc.
(n=253)45%

E.Data on human activities

7% 7%

18%

10%

7% 9%

19% 4% 4%10%19%(n=181)(n=253)
G.自然・公共由来のデータ
G.Data on nature/public (n=199)

8% 8%

4%

18% 7% 5%

5%

23%

52%

(n=199)

Table 76: Questionnaire results (Q15)■ less than 5%■ 5% or more and less than 10%■ 10% or more and less than 20%■ 20% or more and less than 30%

5%未満
5%以上10%未満
10%以上20%未満
20%以上30%未満■ 30% or more and less than 40%■ 40% or more and less than 50% ■ 50% or more and less than 60%■ 60% or more and less than 70%
30%以上40%未満
40%以上50%未満
50%以上60%未満
60%以上70%未満■ 70% or more70%以上80%未満
and less than 80%■ 80% or more 80%以上90%未満
and less than 90% ■ 90% or more
90%以上

0%
FY20182018年度(n=425)(n=425)FY20152015年度(n=424)(n=424)

20%

9%

14%

7%

15%

13%

40%

13%

60%

10%

17%

14%

98

9%

9%

80%

11%

100%

7% 4% 4% 9%

3%
7% 7% 4% 3% 8%

Table 77: Questionnaire results (Q16) (n=492)

0%

5%

10%

15%

20%

25%

30%

0 case
0件

28%

1-2
cases
1～2件

22%

3-5
cases
3～5件

16%
12%

6-10
cases
6～10件

6%

11-20
cases
11～20件

1%

21-30
cases
21～30件15%

31 casesor more
31件以上

Table 78: Questionnaire results (Q17-1) (n=484)

0%

10%

0 company
0社
1-2 companies
1～2社

20%

30%

40%

50%

6%
3%

3～5社
3-5 companies

18%

6～10社
6-10 companies

18%
11%

11-20 11～20社
companies
21 companies21社以上
or more43%

Table 79: Questionnaire results (Q17-2) (n=480)0%decreased by 6 companies
or more6社以上減少decreased by 3-5
companies
3～5社減少decreased by 1-2
companies
1～2社減少

10%

20%

30%

50%

60%

5%
2%
4%

No変化なしchange56%

increased by 1-21～2社増加
companies

5%

increased by 3-53～5社増加
companies

6%

6社以上増加increased by 6 companies
or more

40%

22%
99

6.2

Empirical analysis
The detail of empirical analysis to conduct the trial estimation of the value of data in 3.1 is as follows. First of all, theestimated formula is as mentioned in 3.1, and it’s the Cobb-Douglas Production Function modified to include data as afactor (linear homogeneity is not assumed). In estimation, it was estimated by putting logarithms in both sides and adding
Industry Dummy (1=manufacturing、0=non-manufacturing).

𝑉𝑉 = 𝐴𝐴𝑜𝑜 𝐾𝐾 𝛼𝛼 𝐿𝐿𝛽𝛽 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝛾𝛾log(𝑉𝑉) = log 𝐴𝐴𝑜𝑜 + 𝛼𝛼 log(𝐾𝐾) + 𝛽𝛽 log(𝐿𝐿) + 𝛾𝛾 log(𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷) + 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷**Classified into manufacturing and non-manufacturing

In addition, fundamental statistics of variables are as shown in Table 80. Because logarithmic transformation is donein the estimation, the enterprises who have the value zero in either variable are excluded from the targets of estimation.
Added value, tangible fixed assets＋intangible fixed assets, and total number of employees were acquired from Nikkei
NEEDS-FinancialQUEST, and the results of the questionnaire survey in 6.1 was utilized for variables related to data.
Table 80: Fundamental statistics
Variable

Unit

N-NumberAverage valueStandard deviation

Added value

100 million yen

509

428

5,781

Tangible fixed assets ＋ Intangible fixed assets

100 million yen

478

2,112

7,734

Total number of employees

persons

493

12,04937,157

Utilized data amount

TB

349

3,622

49,968

Number of utilized data cases

cases

189

8,271,450

55,300,000

Utilized data amount (FY2015)

TB

349

3,053

48,340

Number of utilized data cases (FY2015)

cases

189

5,312,146

31,000,000

External data amount

TB

373

2,100

37,360

Number of external data cases

cases

196

7,076,037

68,300,000

Internally owned data amount

TB

373

4,385

53,316

Number of internally owned data cases

cases

196

10,600,000

57,300,000

Total data amount x data utilization degree

TB

352

2,475

33,960Total number of data cases x data utilization degree

cases

192

2,388,490

13,900,000

Total data amount x data diversity

TB

380

2,223

34,627

Total number of data cases x data diversity

cases

201

2,691,044

18,400,000

Total data amount

TB

380

6,373

72,761

Total number of data cases

cases

201

17,300,000

104,000,000

Data utilization degree

-

473

0.10

0.09

Data diversity

-

508

0.08

0.11

Industry dummy (manufacturing = 1)

-

569

0.31

0.46

The details of estimated results are shown in Table 81 - 83. The variables related to data are significant in any model,
and coefficients are around 0.05. Also,

the Industry Dummy are not significant in any model, so it is considered that thedifferences between industries are relatively small when they are seen in added values and the entire production factors.
Table 81: Estimated results①100

Unexplanatory variable
Explanatory variable
log (Tangible fixed assets＋Intangible fixed assets)log (added value)

0.44 ( 10.44 ) ***log (Total number of employees)

0.50 (

8.76 ) ***log (Utilized data amount)0.05 (

2.96 ) ***log (added value)

0.338 ( 5.69 )***0.55 ( 7.78 ) ***log (added value)log (added value)

0.436 ( 10.01 ) ***0.337( 5.64 )***0.51 (

8.79 ) ***0.56( 7.86 )***0.05 (

2.64 ) ***0.06 ( 2.38 )**0.07 ( 2.69 ) ***log (Number of utilized data cases)log (Utilized data amount, FY2015)log (Number of utilized data cases, FY2015)
Industry Dummy (Manufacturing)-0.05 (

-0.40 )

0.10 (

0.54 )-0.05 (

-0.44 )

0.09

( 0.46 )Constant-2.11 (

-7.78 ) ***-2.71 (

-7.55 ) ***-2.11 (

-7.74 ) ***-2.67( -7.39 ) ***Number of samples

258

135

258

135

Adj R-squared

0.83430.81570.83320.8136log (added value)

From left, coefficient, t value, p value(Note) *Significant level 10%, ** Significant level 5%, *** Significant level 1%

Table 82: Estimated results②

Unexplanatory variable

log (added value)log (added value)log (added value)

0.47 ( 11.24 ) ***0.35 ( 6.43 )***0.47 ( 11.14 ) ***0.34( 6.16 )***log (Total number of employees)

0.47 (

8.48 ) ***0.51 ( 7.67 )***0.48 (

8.60 ) ***0.55( 8.25 )***log (External data amount0.04 (

3.11 ) ***0.06 ( 3.28 )***0.05 (

2.44 )

0.06( 2.48 )**Explanatory variable
log (Tangible fixed assets＋Intangible fixed assets)log (Number of external data cases)log (Internally owned data amount)**log (Number of internally owned data cases)
Industry Dummy (Manufacturing)-0.02 (

-0.16 )

0.09 (

0.49)-0.04 (

-0.31 )

0.07

( 0.41 )Constant-1.96 (

-7.25 ) ***-2.34 (

-7.71 ) ***-2.17 (

-8.24 ) ***-2.71( -7.89 ) ***Number of samples

267

140

267

140

Adj R-squared

0.84010.81930.83790.8134

From left, coefficient, t value, p value(Note) *Significant level 10%, ** Significant level 5%, *** Significant level 1%

101

Table 83: Estimated results③

Unexplanatory variable
Explanatory variable
log (Tangible fixed assets＋Intangible fixed assets)log (added value)

0.44 ( 10.18 ) ***log (Total number of employees)

0.49 (

8.68 ) ***log (Total data amount x data utilization degree)

0.05 (

2.77 ) ***log (added value)

0.32 ( 5.42 )0.58 ( 8.11 ) ***0.06 ( 2.38 )log (Total number of data cases x data utilization degree)***log (added value)log (added value)

0.53 ( 9.36 ) ***0.41( 5.17 )***0.42 ( 5.61 ) ***0.49( 4.96 )

***

0.06

( 2.67 )*****0.04 (log (Total data amount x data diversity)

2.06 ) **log (Total number of data cases x data diversity)
Industry Dummy (Manufacturing)-0.07 (

-0.60 )

0.07 (

Constant-1.96 (

-7.15 ) ***-2.64 (

0.38 )-7.93 ) ***-0.04 (

-0.29 )

-1.92 (

-5.51 ) ***0.08( 0.40 )-2.52( -5.47 ) ***Number of samples

261

137

173

85

Adj R-squared

0.83340.815

0.85370.8576

From left, coefficient, t value, p value(Note) *Significant level 10%, ** Significant level 5%, *** Significant level 1%102