My Vision No.39
2018.12

Towards Full-scale Utilization of Big Data
Big data is considered to be the core of the digital revolution, but we are still working towards
its full-scale practical utilization. This new paradigm of competitiveness is itself a site of fierce
competition between nations. Is Japan sufficiently prepared? In this issue of My Vision, we
consider the status of utilization of big data, based on discussions conducted at the joint DIJNIRA workshop “Big Data” (co-sponsored by the Japan Forum for Innovation and Technology at
UC San Diego and the Max-Planck Institute for Innovation and Competition), held on November
1, 2018.

About this Issue

On the threshold of full-scale utilization– Seeking to obtain an accurate grasp of the current status of big data
Noriyuki Yanagawa

NIRA Executive Vice President, Professor, Graduate School of Economics, The University of Tokyo
Keywords…How should we use big data, and for what?, how far can specific technological innovations takeus?

Expert Opinions

Towards Full-scale Utilization of Big Data
What are the possibilities for big data?
What should we do to promote its utilization?

Don’t discard analogue technology in theera of IoT

A safe and efficient data trading market
that promotes the utilization of big data

Jun Nakamura

Hiroshi Mano

Keywords…IoT solutions that connect everything, brandfree IoT modules, analogue technology

Keywords…Mediation of data provision to entities inneed of specific data, neutrality, fairness andtransparency

Persistent homology and big data inmanagement research

Which direction for corporate governancein a data-driven era?

Tristan W. Chong

Takuya Kitagawa

Deputy Director, Research Institute of Big Data
Analytics, Xi’an Jiaotong Liverpool University

Chief Data Officer and Executive Officer, Rakuten,
Inc.

Keywords…New business opportunities, data analysis
method for business administration, incorporation ofgeometry

Keywords…Discovering hidden value, influence on the
future of management, governance of big data
aggregation and utilization

Professor, Graduate School of Engineering
Management (MOT), Shibaura Institute ofTechnology

CEO, EverySense,Inc.

Preparing for digital transformation andbig data
Dietmar Harhoff

Director, Max-Planck Institute for Innovation andCompetition
Keywords…Further enhancement of competitiveness,
support for small and medium-sized enterprises, digital
infrastructure, digital education, open government,
establishment of future-oriented laws

This is a translation of a paper originally published in Japanese. NIRA bears full responsibility for the translation presented here.
Translated by Michael Faul
Copyright Ⓒ 2018 by Nippon Institute for Research Advancement

My Vision No.39
2018.12
About this Issue

On the Threshold of the Full-scale Utilization of
Big Data– Seeking to obtain an Accurate Grasp of the Current Status of Big Data
The Utilization of Big Data – Idea Preceding Actuality
The term “big data” now appears frequently in the media,
and its importance has come to be quite well recognized.
Broadly speaking, there are three reasons for this.
The first is that advances in computer performance havemade it possible to process large volumes of data very rapidly.
The second is the fact that technological innovation, as
exemplified by the Internet of Things (IoT), is making it possible
Noriyuki Yanagawa
to collect and transmit large volumes of data that would
NIRA Executive Vice President/
previously have been difficult to gather. Because these
Professor, Graduate School oftechnologies have not come into full-scale use, we tend not to be
Economics, The University ofTokyofully aware of this fact, but a new era is coming in which, unlike
today, the collection of “big data” will be a simple matter. The
third reason is the fact that the significance of analyzing large volumes of data has increased
considerably with the development of data analysis technologies and artificial intelligence (AI). The
evolution of machine learning in particular has made us strongly aware that large volumes of data
are an important element in AI learning.
At the same time, in a certain respect the term “big data” has perhaps gotten ahead of itself.
One sometimes encounters the argument that as long as a sufficiently large volume of data iscollected, it will be possible to appropriately analyze it using AI and to derive an appropriate
solution.
However, given that the basis of the functioning of AI is statistical processing, if it is notclear what type of data to collect and analyze in these large volumes, and for what purpose it is tobe collected and analyzed, meaningful results will not be obtained no matter how “big” the datacollected. It will therefore be essential for us to further discuss and examine precisely how and forwhat purpose we will use big data.
In order to conduct such an examination, we will be required to obtain a grasp of just what
type of technological innovation is occurring, and how far specific innovations can take us. It will
also be necessary to determine the extent to which systemic and legal infrastructure has been
established to allow adequate exploitation of these technologies, or whether it will be necessary to
put them in place. Unfortunately, however, in the case of big data the concept has conspicuously
come in advance of the reality, and we are not adequately engaging in these types of detailed
concrete discussions. Seeking to fill this gap, in this issue of My Vision, we discuss big data withJapanese and overseas experts from a variety of perspectives.

Diversifying Methods of Collecting Data
The first question to be asked is who will be collecting data, and what type of data will they
be collecting, using technological innovations such as the IoT. When we speak about the collectionof big data, attention focuses on the collection of data by global platform companies such as Google
and Amazon. In this issue, however, Professor Jun Nakamura of Shibaura Institute of Technology
This is a translation of a paper originally published in Japanese. NIRA bears full responsibility for the translation presented here.
Translated by Michael Faul
Copyright Ⓒ 2018 by Nippon Institute for Research Advancement

My Vision No.39
2018.12indicates the potential for aggregation of data by IoT modules, without reliance on platform
companies. And while discussing IoT modules, Professor Nakamura also admonishes Japan not tooverlook the importance of the analogue technologies that will form part of them.
Nevertheless, the data that can be utilized are not restricted to data that are directlycollected by a specific entity. Hiroshi Mano, CEO of EverySense, Inc., points to the importance oftrading data that has been collected. Given that it is rather difficult for individual companies tocollect big data, Mr. Mano projects a situation in which data is traded in the market. However, healso indicates that this is a field requiring the establishment of robust systems in order to ensure
fair and proper trading, for example to protect privacy and ensure that proprietary knowledge is
not leaked.

How will We utilize the Data?
How the collected data will be analyzed is also a significant issue. Taking into consideration
multifaceted problems such as management issues, which cannot be solved by simple data
analysis, Tristan W. Chong, the Deputy Director of Xi’an Jiaotong Liverpool University’s ResearchInstitute of Big Data Analytics, introduces us to the potential of a new analytic method thatutilizes topology*.
Takuya Kitagawa, Chief Data Officer and an Executive Officer of Rakuten, Inc., discusses
the forms in which it will be possible to make concrete use of big data, and investigates the effects
on business management in an era of full-scale utilization of big data. Dr. Kitagawa predicts thatmaximization of the value of big data will affect business management, and data-driven
management will continue to progress.

Establishing Regulations and Systems that accord with a New Era
How are these discussions regarding big data proceeding in other countries? Professor
Dietmar Harhoff, Director of the Max-Planck Institute for Innovation and Cooperation, provides us
with an overview of the situation in Germany. According to Professor Harhoff, Germany is lagging
behind in the utilization of big data, and small and medium-sized enterprises in particular are notadequately utilizing this resource. In addition, he claims that it is necessary for Germany toestablish regulations and systems that accord with this new era.
Without a detailed analysis of the situation, we cannot clearly say whether the utilization of
big data is further advanced or is lagging behind in Japan as compared to Germany. However, it
goes without saying that it is also essential to establish regulations and systems related to theutilization of big data in Japan. In particular, efforts to realize an appropriate balance between theuse of big data and privacy and the protection of personal information, and to reduce poorlydelineated “grey zones,” will be increasingly necessary in future. And in order to enable us to think
about these issues, it will be increasingly necessary to understand the real situation, to obtain an
accurate grasp of which ways different types of big data can actually be used.
*A method that incorporates geometry in data analysis, and conducts analyses based on thegeometric structure of data sets and the characteristics of their topological space.

Professor Yanagawa is NIRA’s Executive Vice President, and a Professor in the Graduate School of Economics of
The University of Tokyo. He took his Ph.D. in Economics from The University of Tokyo. His research specializations
are contract theory and financial contracts.

This is a translation of a paper originally published in Japanese. NIRA bears full responsibility for the translation presented here.
Translated by Michael Faul
Copyright Ⓒ 2018 by Nippon Institute for Research Advancement

My Vision No.39
2018.12Expert Opinions

Don’t discard Analogue Technology in the Era of IoT
The spread of smart devices, in particular smart phones, hasallowed us to make use of an enormous number of applications andservices in any way that we desire. The mutual linkage of theseapplications and services will allow the creation of a diverse rangeof IoT services. As an example, we might consider a shopping
service for the elderly. The placement of robots fitted with cameras
in shopping centers that users at home could control via their
televisions would allow the elderly a highly realistic experience of
shopping that also offers a high degree of freedom, all without
Jun Nakamuraleaving their own homes.
Professor, Graduate School
In order to realize services of this type, it will be necessaryof Engineering Management
to connect devices and aggregate data in the cloud. The(MOT), Shibaura Instituteof Technology
combination of IoT modules and service platforms will be thesolution to realizing these necessary conditions. In the case of a
shopping service like the one described above, an IoT module would encompass devices andfunctions including a Wi-Fi transceiver, an HDMI terminal, voice recognition, and a video
conversion function, and would play the role of connecting a variety of devices, such as a camera, atelevision, and a robot. At the same time, data transmitted from these devices would be aggregatedin the cloud, allowing the provision of necessary services through the combination of various
applications. Users would be able to receive services such as remote window shopping, operating acontroller or using voice control while watching images transmitted from the robot on the televisionin their home.
Because it would be possible to fit IoT modules in devices produced by any manufacturer,
there would be no concern that they might be tailored to a specific brand’s edge devices. It would
become possible to aggregate data from all devices, and the analysis of this data would enable theprovision of interrelated services in an integrated fashion through the cloud. It would be important,while making use of platforms such as Google and Microsoft, to also be able to extend theseplatforms in order to create unique service platforms. The use of open source software would enable
a wide variety of functions (increased speed, bandwidth, interfaces, etc.) to be developed and added.
But while software will be important to the realization of IoT solutions that are able to connect
everything, Japan should also not discard the analogue technologies that will be fitted in IoT
modules.
In addition, looking back at our previous example, while the importance of image and voice
conversion and recognition processing will no doubt increase in future, because the informationthat humans are able to see, touch and feel is exclusively analogue, technologies that connect
analogue data and service platforms will certainly also be essential. I believe that Japan will beable to make use of its strengths by creating devices for the IoT era that utilize analogue electricaldesign technologies.
In addition to his academic position, Professor Nakamura also serves as a board member of Persol-AVCTechnology Co., Ltd., a company that is mainly involved in the design of audio, video and communication
products and those technological developments, chiefly for Panasonic. Persol-AVC-Technology is also working
together with Taiwan’s Dynalab Inc. in the IoT field, creating service platforms that utilize big data. Professor
Nakamura’s field of specialization is cognitive science, and his particular interest is the visualization of humanthought process. He holds a Ph.D in Engineering from The University of Tokyo’s Graduate School of Engineering,and completed coursework in the same institution’s Department of Technology Management for Innovation,
graduating with the highest honors. He worked for a general trading company and was involved in management
consulting before taking up his present position. Professor Nakamura also serves as a Managing Director of theInternational Academy of Strategic Management.
This is a translation of a paper originally published in Japanese. NIRA bears full responsibility for the translation presented here.
Translated by Michael Faul
Copyright Ⓒ 2018 by Nippon Institute for Research Advancement

My Vision No.39
2018.12Expert Opinions

A Safe and Efficient Data Trading Market
that promotes the Utilization of Big Data
While companies have been urged to make use of big data forquite some time, it is difficult for individual companies to collect big
data. The key to solving this problem is the market.
The data trading market is made up of data providers, data
users, and data trading market-forming companies that mediate
between the two and provide data trading platforms. My company is
one of these market-forming companies.
Data providers collect and organize data from their own
Hiroshi Manobusiness or other businesses, and provide it to companies, research
institutions and other entities that need this data. The data provider
CEO, EverySense,Inc.
is able to freely set the extent to which the data can be made public,
and the exchange price of the data is determined between the data provider and the data user thatreceives the data. When a data user makes a request regarding the type of data that it requires,
my company conducts a search for data providers that match the specific conditions, andautomatically matches the data user with data providers. While we are a private enterprise, we donot buy, sell or retain data, and we do not have the right to set prices. It is essential for a service
that acts as an intermediary to ensure neutrality, fairness, and transparency in transactions.
Data providers in this market may experience anxiety over the possibility of specific aspectsof their professional expertise being made public via the market. For example, farmers may notwish aspects of their professional expertise such as the timing for watering of their crops or their
methods of cultivation to be made public. But what the data trading market deals in is data andnothing more. For example, if the data user’s focus is on temperatures, then only temperatures areincluded in the data – as this indicates, only primitive data is made public.
Naturally, the market is not entirely perfect. While market-forming companies mediate datatransactions, they do not survey individual data from the standpoint of the protection of privacy,
and it can therefore be the case that low-quality data is sold, or that undesirable companies collectdata. However, in the case of the service provided by EverySense, all transactions are tracked, anddata is only initially released to the data user when both the data provider and the data user haveapproved the transaction. If we are so instructed by either party, data disclosure and datatransmission and reception can be stopped instantly.
The Data Trading Alliance, which was launched in November 2017, has formulated andpublished standards for the certification of data trading market-forming companies. I believe that
this mechanism will make it possible to ensure the reliability of these companies.
In addition to conducting research and development in the field of IoT, EverySense Japan Co., Ltd. offers the IoT
data distribution platform “EverySense.” The company provides a service that matches IoT data and entities
including companies and research institutions that will make use of that data for purposes such as businessdevelopment and academic research, acting as an intermediary in the sale and purchase of data. In 2014,
EverySense established EverySense Inc. in Silicon Valley. Mr. Mano also serves as the President and Secretary
General of the Data Trading Alliance, a general incorporated association. He works tirelessly for the realization ofsound growth and the establishment of an appropriate technological and institutional environment in the data
distribution market.

This is a translation of a paper originally published in Japanese. NIRA bears full responsibility for the translation presented here.
Translated by Michael Faul
Copyright Ⓒ 2018 by Nippon Institute for Research Advancement

My Vision No.39
2018.12Expert Opinions

Persistent Homology and Big Data inManagement Research
An increasing number of Asian countries have adopted Internet
economic conductive to rapid growth and a presence in different
business industries. In the age of the Internet of Things, thedevelopment of big data technology has brought significant changes inrecent years. For instance, in China, consumers are exposed to an
expanding, fragmented array of management touch points acrossdigital media and sales channels, little is known the challenges facedby businesses in this regard. Powered by the integration of social
media, e-commerce, cloud computing, and mobile computing, big data
Tristan W. Chongtechnologies provides fundamentally new insights into business’s
Deputy Director, ResearchInstitute of Big Dataeffect on revenue. Therefore, big data analytics is increasingly
Analytics, Xi’an Jiaotong
recognised as an emerging approach for integration between
Liverpool University
public/private sector organisations and as a way to identify new
business opportunities in the region.
However, it is unclear in current research what approaches are available and optimal fordata analytics in management, which needs to be clarified and comparatively evaluated. To seek abetter analytical method in order to have a better understanding in management studies,
theoretical development is in a need to provide general guidance for researchers to implement big
data methods. Dealing with vast amount of data is difficult, and it is even more challenge if thedata structure with noise, high-dimensional, and incomplete. Therefore, to include other elements
such as computational geometry and topology in “traditional” technique will enhance the visibilityof the complicated structure of data.
In the past decades, scholars have found that persistent homology (PH) is an effective
computational tool for characterisation with topological features for large data sets. In comparison
with other traditional techniques, PH has unique ability to compute data at different topologicalspace and spatial resolutions. Over a wide range of geometric data analysis, more topologicalfeatures are detected and are deemed more likely to reflect true features of the underlying space,rather than artifacts of noise, sampling or particular selection of parameters. Academics canuncover a new and more insight characteristics that can deepen the understanding of big data. It
would be easy for scholars to experiment and study on various business issues because it offers
topological insight for geometric structures and/or intrinsic features against noise. In other words,
PH can provide excellent results for the identification, characterisation and classification of various
business behaviours. Consequently, PH is a better tool for topological characterisation from vast
amount of complex data which is a realistic alternative and can contribute to innovative theory
development.
(Submitted manuscript)
Dr. Tristan W. Chong is Deputy Director of the Research Institute of Big Data Analytics and Associate Professor ofMarketing Analytics at the Xi’an Jiaotong – Liverpool University, Suzhou, China. His research interests are in theareas of B2B e-marketing, topological data analysis and applications, and marketing analytics. His research has
been continuously funded by both internal and external funding bodies including the Natural Science Foundation ofChina. He is also a Visiting Scholar at the National University of Singapore, Singapore; Deakin University,
Australia; Heriot Watt University, UK; and South University of Science and Technology of China.
This is a translation of a paper originally published in Japanese. NIRA bears full responsibility for the translation presented here.
Translated by Michael Faul
Copyright Ⓒ 2018 by Nippon Institute for Research Advancement

My Vision No.39
2018.12Expert Opinions

Which Direction for Corporate Governancein a Data-driven Era?
What does “the utilization of big data” refer to in concreteterms? For me, it is the discovery of hidden value. One example is theestimation of corporate value. Investors today want to obtaincorporate data quickly, without waiting for quarterly results. A US
company that provides a service analyzing companies’ production
status and their level of appeal to customers based on satellite images
became a major talking point in 2018 when it identified the status ofproduction of electric vehicles by Tesla. Credit assessment provides
Takuya Kitagawa
another suggestive example. No-one trusts a friend based on the
Chief Data Officer andamount of money that they have. Trust is earned by keeping promises
Executive Officer, Rakuten,– arriving at appointments on time, returning loans of money orInc.
property, etc. In other words, we could obtain a more accurate credit
assessment by analyzing data including the history of repayment. And while this is not given muchattention, in the future, analyses of where user interest is tending will generate considerable value.
Web businesses optimize their sites by analyzing the direction of user interest from user behavior,and it can be predicted that the use of such methods will expand in future.
This maximization of the value of big data will affect the future of management, and a datadriven management orientation is continuing to advance in companies. It is already the case thatthe role of top management is no longer to issue the company’s quarterly results. The role of top
management is to predict what the situation will be three years in the future, and to make decisions
as to what type of data to utilize and retain. Nevertheless, aggregating data from multiple services
and group companies is not an easy matter. Rakuten itself offers more than 70 services, and there
are more than 100 million Rakuten members using these services. From 2017, therefore, Rakuten
has been testing a hub and spoke model in relation to its Chief Data Officer (CDO) positions. This isa model in which the CDO for the entire Group (a position that I fill at present) cooperates with the
CDOs of each Group company. Our approaches include data collection and data governance. In
addition, every quarter we hold conferences attended by the CEOs and executives of each Group
company, at which we spend an entire day discussing data strategy. All data utilization must be
something that provides value to the customer. Leadership by top management to create acustomer-oriented corporate culture will be essential to business into the future.

Dr. Kitagawa is involved in the establishment of a data science organization within Rakuten. He serves as both an
Executive Officer and the Chief Data Officer for the Rakuten Group as a whole, and is responsible for the Group’s
data strategy. Dr. Kitagawa also serves as a Director of Rakuten Data Marketing Co., Ltd. He is currently engagedin efforts including the creation of data infrastructure, the expansion of the customer experience based on scientificanalysis, the creation of an advertising business using data, and the stimulation of business innovation. Dr.
Kitagawa was previously a theoretical physicist, and took his doctorate through coursework in Harvard
University’s Graduate School of Physics. He has had more than 20 papers published in scientific journals including
Science, Nature Physics, and Physical Review Letters.

This is a translation of a paper originally published in Japanese. NIRA bears full responsibility for the translation presented here.
Translated by Michael Faul
Copyright Ⓒ 2018 by Nippon Institute for Research Advancement

My Vision No.39
2018.12Expert Opinions

Preparing for Digital Transformation and Big
Data
Digital change is an extremely rapid process. Artificial
intelligence, big data, cloud computing and the disruptive businessmodels associated with them challenge Germany‘s specialization
advantages which were attained over decades. In the next few years,
many countries, and Germany in particular, must develop new technicaland economic strengths. This will require consistent and prompt policy
measures.
Big data and related innovative services represent the kind ofinnovations that supersede existing technologies, products or services.
Dietmar Harhoff
Frequently, they lead to the emergence of entirely new markets. At thisDirector, Max-Planck
point, Germany lags behind in the use of big data. Furthermore,
Institute for Innovation
and Competition
considerable differences between large companies and SMEs in the useof big data approaches pose the risk of a ‚digital divide’ in the corporate
sector. Many SMEs do not seem to be fully aware of the importance of the imminent changes or alack of financing makes it difficult to tackle them. SMEs may need special support with digitalchange.
Other important areas for policy-making will be the development of a future-proof digital
infrastructure suitable for high performance and upgradability. Moreover, digital education should
be strengthened for all age groups and levels of training, but most notably in elementary andsecondary schools. Computer science should be understood as a new key discipline andincorporated more into the curricula of other training courses.
Administrations can also foster the use of big data by providing public sector data as open
government data. In Germany these are not yet automatically made available via well-structured
access systems. When it comes to digital governmental and administrative processes, Germany
still has a lot of catching up to do. Recently, promising legislative framework conditions have beencreated for the establishment and operation of efficient public data and e-government portals.
It is tantamount to create a future-oriented legal framework for the digital economy - e.g., in
the fields of copyright, data protection and consumer protection. In the long term, grandfatheringand perks for established business models will jeopardize a country’s competitiveness in the digitalage. Thus, legislation must not be geared towards building protective fences around establishedsectors. Rather, the framework should enable the fast introduction of models of the digital economy.
(Submitted manuscript)
Professor Harhoff is Professor for Entrepreneurship and Innovation at the Ludwig-Maximilians-Universität (LMU)
Munich. His research focuses on innovation, entrepreneurship, intellectual property, and industrial economics. He
holds a Diploma degree in mechanical engineering from the Technical University of Dortmund, a Master’s degree in
Public Administration from Harvard University, and a doctoral degree from the MIT Sloan School of Management.
Since 2004, he has been a member of the Scientific Advisory Boards of the Federal Ministry of Economics. Professor
Harhoff has also been a member of the Economic Advisory Group on Competition Policy (EAGCP), the Chair of the
Economic and Scientific Advisory Board (ESAB) at the European Patent Office, and the Chairman of theCommission of Experts for Research and Innovation (EFI) of the German Federal Government. He has also
published numerous books and papers.

This is a translation of a paper originally published in Japanese. NIRA bears full responsibility for the translation presented here.
Translated by Michael Faul
Copyright Ⓒ 2018 by Nippon Institute for Research Advancement