TentativeTranslation

Data Value Measurement-Findings through Estimates of Data Contribution
to Added Value of Japanese Enterprises-

Institute for Information and Communications Policy (IICP),
Ministry of Internal Affairs and Communications (MIC), Japan

Issues Considered at the Committee on AI Economy● The MICʼs Institute for Information and Communications Policy (IICP) establishedʻthe Committee on AI Economyʼ in January 2019 to examine what kind of socioeconomics should be pursued in promoting the social implementation of AI.
● The Committee has been examining specialized and technical issues on data
economic policies in the AI era. Data was regarded as a factor of production andits effect on productivity improvement was estimated.
*The Committee on AI Economy 2020 Report was published in July 2020.
https://www.soumu.go.jp/main_sosiki/joho_tsusin/eng/pressrelease/2020/7/21_2.html

Conference toward AI Network Society〔Chaired by SUDOH Osamu, Professor at the Faculty of Global
Informatics, Chuo University and Project Professor at the Graduate
School of Interdisciplinary Information Studies, University of Tokyo〕

Committee on AI Governance〔Chaired by HIRANO Susumu, Dean at the Faculty of Global
Informatics, Chuo University〕

Committee on AI Economy〔Chaired by IWATA Kazumasa, President, Japan Center for
Economic Research〕

Special-Interest Group on Data
〔Chaired by OHASHI Hiroshi, Dean at the Graduate School of
Public Policy, University of Tokyo〕

1Data Value Measurement Methods
 The roles and characteristics of data are diverse. It is difficult to uniquely identify
the value of data.
 Approaches for measuring the value of data are classified in the following threeways:
 Cost-based Approach - Calculation method which pays attention to costs related to thecreation, management, and utilization of data.

 Market-based Approach - Calculation method which is based on the market prices of similar
products or of usersʼ willingness to pay for those products.

 Impact-based Approach - Calculation method where the effects (productivity, and revenue,etc.) of utilizing data is estimated.

 From the point of view of versatility and reproducibility of method application todifferent industries and countries, the impact-based approach was adopted
and the effect of data on the added value of companies was estimatedaccording to production function.
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Analysis Model The study positioned data as a production factor, as with other production factors(capital and labor), and conducted empirical analysis with the Cobb-Douglas
Production Function modified to include data as a factor.

※Estimate is based on the results of a survey with Japanese enterprises.
（Survey target）4,286 Japanese enterprises（569 responses）（Survey period）From February 2020 to March 2020
（Details of Survey）Amount/number of data utilized by enterprises, and method used for data
analysis, etc.

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Analysis Results
 Results from an empirical analysis highlighted the following: The utilized data amount/number has a positive relationship with the added
value as well as other production factors (capital and labor).
 The added value increased by 0.05% when the utilized data amount/number
increased by 1%.

 However, it should be noted:
 That this reflects the current status of (not the change in) data utilization efforts,
and that increasing the amount/number of data used does not necessarily leadto an increase in added value, and That the results show the average between enterprises with more gains by datautilization and otherwise.

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Remaining Challenges● Necessity of further study on the relationship between data and value
generated by data
 Studies to refine the model for analysis
 Precise analysis that considers the difference by industry sector and production
process Continuous research to collect more data for diversified analysis⇒ These issues are to be discussed at the Committee on AI Economy from
December 2020. The outcome will be published as a report next summer.

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(Reference) Method of Data Value Measurement

Approaches

Methods

Tools

Limitations

IssuesCost-based
Approach• Calculation method whichpays attention to costs
related to the creation,management, andutilization of data
(personnel, and time, etc.)• It is based on theassumption that the valueof data is worth the cost.

• Calculating
personnel expenses• Calculating timecosts• Calculating security
measure costs• This approach canʼt take into
account quality andproductivity.
• Since prices or quality of ICT
equipment are diverse, theestimated value of data may
be affected by other factors.
• It is difficult to intuitively
envision a relationshipbetween cost and value.

• Which aspect of cost can beappropriately ascertained in order todefine the value of data?

Marketbased
Approach• Calculation method whichis based on the market
prices of similar products
or of willingness of users
to pay for those products• Market prices (datatransactions, etc.)• Representationpreference method• Case studies of M&A,
negative effects• Experimental works• Lack of data for estimates• In the case of statedpreference method, the result
can be affected by the userʼsbias.
• The value could be perceived
differently between thebusiness side and theconsumer side.

• How is data in general measured (notindividual data)?
• Which point of view is more
appropriate for data measurement,
the business side or the consumerside?

Impactbased
Approach• Calculation method where
the effects (productivity,and revenue, etc.) of
utilizing data is estimated• Econometric analysis(production functionanalysis,andregression analysis,etc.)• Experimental works• Statistical estimates areneeded.
• It is difficult to differentiate
between the effects of one
type of data and the effects
of other types of data.

• It is necessary to clarify objects forwhich data is to be measured (thedefinition and the value of data)• How can a good analysis model beestablished based on consideration ofcomplementary elements which arenecessary for data to generate value?

6(Reference) Analysis Results
𝑉𝐴 𝐾 𝐿 𝐷𝑎𝑡𝑎 , log 𝑉log 𝐴

𝛼 log 𝐾𝛽 log 𝐿𝛾 log 𝐷𝑎𝑡𝑎

𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐷𝑢𝑚𝑚𝑦※Manufacturing/Non-manufacturing

Data variable

Samplesize

Adj R2

CapitalLabor

Data

Utilized data amount (= total data amount x ratio of data used for analysis)

258

0.83430.44 ◎0.50 ◎0.05 ◎Utilized data number (= total number of data x ratio of data used for analysis)

135

0.81570.34 ◎

0.55 ◎0.07 ◎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 amount x ratio of data acquired

267

0.84010.47 ◎

0.47 ◎

0.05 ◎

Externally acquired data number (= total number of data x ratio of data

140

0.81930.35 ◎

0.51 ◎0.07 ◎Internally owned data amount (= total data amount - externally acquired data267

0.83790.47 ◎

0.48 ◎

0.05 ○

Internally owned data number (= total number of data - externally acquired

140

0.81340.35 ◎

0.55 ◎

0.06 ○

Total data amount x data utilization rate (use area/type/processing method)261

0.83340.44 ◎

0.49 ◎

0.05 ◎

Total number of data x data utilization rate

137

0.81500.32 ◎

0.58 ◎

0.05 ○

Total data amount x data diversity (diversity of data acquisition and provision)

173

0.85370.53 ◎

0.42 ◎

0.04 ○

Total number of data x data diversity

850.8576

0.41 ◎

0.49 ◎0.06 ◎from outside)

acquired from outside)amount)data number)※Variables not described as FY2015 are values for FY2018.
The ratio of externally obtained data was corrected based on the situation of external data acquisition.

（Note） Significance level ◎：1％、○：5％

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