section 1 value generated by big data 1. expanding ... · (1)t advances drive the creation,...

1. Expanding applications for big data(1) ICT advances drive the creation, distribution, and

accumulation of big dataThe soaring volume of digital data in global distribu-

tion is predicted to jump from about 1.8 zettabytes (1.8 trillion gigabytes) in 2011 to about 40 zettabytes in 2020. This explosive growth is being driven by increasingly sophisticated networks and devices that have become entrenched worldwide as essential infrastructure for daily living and economic activities. The evolution of ser-

vices, especially the proliferation of social media and cloud services, is also powering the creation, distribu-tion, and accumulation of enormous amounts of digital data. Furthermore, devices long considered completely unrelated to communications are expected to become entwined with communications and generate massive amounts of data, due to the emergence of wearable de-vices and other new communication devices and the ad-vancement of M2M and IoT technology.

2. Ascertaining data distribution volumes(1) Framework for estimatesa. Surveyed entities

Big data, as it is called, includes data that individuals, corporations, governments, and all other economic enti-ties generate through a myriad of means and routes. Vast amounts of structured and unstructured data are generated, the use and application of which is thought to generate new social and economic value. Ideally, we would prefer to ascertain the distribution volumes of all data categorized as big data, but it is implausible to get an accurate measure of all data, especially data generat-ed by individuals. For this survey, we used a wider scope than last year’s survey to estimate the traffic volumes of targeted entities and targeted data.

As for the selection of entities to include in the esti-mates, we calculated the volume of data corporations receive electronically, the same criterion as last year, as corporations are regarded as the primary economic en-gines that generate social and economic value applying big data.

We selected nine of the 13 industries on the inter-in-dustry table—excluding the agriculture, fishing, and forestry industry, the mining industry, the government services industry, and the unclassified category—and tabulated the necessary data from these industries for the estimates.

b. Surveyed data typesThe types of data included in the data distribution vol-

umes estimates for this survey were selected on the ba-sis of whether the required data were obtainable and whether the data are used in economic activities at the corporate level, such as corporate marketing strategies and decision making. A total of 21 data types were in-cluded in the estimates: eight structured data types

(customer databases, accounting data, POS data, medi-cal receipt data, log data for e-commerce sales, GPS data, RFID data, and meteorological data) and 13 un-structured data types (business log data, CTI voice log data, fixed IP telephone voice data, mobile phone voice data, email, blogs, SNS, and other article data, access log data, electronic medical records data, image diagno-sis data, monitoring and surveillance camera data, sen-sor log data, traffic and traffic-congestion data, and movie and video viewing logs).

(2) Estimates of corporate data distribution volumesThe survey estimated the volume of data distribution

in 2013 for 21 types of data in nine industries (service industry, ICT industry, transport industry, real estate industry, finance and insurance industry, wholesale and retail industry, electricity, gas, and water industry, con-struction industry, and manufacturing industry). The total was estimated to be approximately 13.5 exabytes.

Looking at how data traffic volumes have transitioned finds that data distribution volume has climbed from ap-proximately 1.6 exabytes in 2005 to approximately 13.5 exabytes (estimated) in 2013. Thus, data distribution volume has increased by about 8.7 times (an average an-nual increase of 27.1 percent) over the eight-year period from 2005 to 2013 (Figure 3-1-2-1).

(3) Analysis of the relationship between data distribution volumes and economic growth

We attempted to verify, using the estimated data distri-bution volumes by industry, what kind of influence the distribution and use of data by corporations has on Ja-pan’s economic performance and whether data distribu-tion contributes to overall real GDP. The economic ef-fects of data distribution volume could not be confirmed

Section 1　Value Generated by Big Data

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Chapter 3The Future Society Shaped by Data

CW6_AX102_10_D13_C.indd 17 2014/11/05 14:38:18

using the total data distribution volume for all media channels. However, examining data distribution esti-mates for each media channel found that the channels were divided between those that have positive effects on real GDP and those that do not.

Positive real GDP effects were seen from data distri-bution volume in longstanding media channels, such as customer data, accounting data, and POS data, and from communication media, such as telephone voice data and

email data. On the other hand, media channels that have gained attention in recent years—sensor data and M2M data—have yet to show measureable real GDP effects.

Sensor, M2M, and other new media channels are ex-pected to have a major impact on Japan’s economy, as corporations begin to put these data types to use after trial-and-error iterations and combine them with tradi-tional types of media.

3. State of big data usage at corporations(1) Framework for estimatesa. Analysis with a survey of corporations

This survey questioned corporations in all industries in order to obtain more inclusive effect measurements. From the results, we ascertained the current state of corporate data usage by industry and by corporation at-tributes and analyzed the relationship between data us-age and business performance and effects. Corporations in the distribution industry, which is thought to lead other industries in data usage, were asked extra ques-tions, and the data usage effects were measured for each type of effect.

(2) Analysis with a survey of corporationsa. Analysis of surveyed corporations in all industriesA. Overview of data usage

Corporations were asked a multiple-answer question about the data types used in their business operations. “Information on customer / trading-partner attributes” was mentioned by over 50 percent of corporations, while “accounting information” and “documentation such as work operation communications or logs” were men-tioned by over 40 percent. “Transaction information” received over 30 percent support (Figure 3-1-3-1). Other data types were mentioned by less than 20 percent of the respondent corporations.

Next, respondents were asked about the depth of data

usage in data-driven operations at the respondent’s own departmental workplaces. The usage depth was divided into four levels: (1) visualization of operational statuses and the state of the company through data aggregation; (2) dynamic detection of abnormalities through data ag-gregation; (3) projections of future conditions based on aggregated data; and (4) automatic control of machinery or systems based on future projections. Visualization was overwhelmingly selected. The response rates for other levels were less than 30 percent (Figure 3-1-3-2).

Respondents were asked about issues with data us-age. The top replies were “difficult to assess the cost-benefit ratio of data use,” “data sets are scattered and difficult or impossible to analyze,” “no internal system to analyze and use data,” “don’t know how to use data,” and “costs to analyze and use data” (Figure 3-1-3-3).

B. Benefits gained from data useRespondents were asked to give the specific size of

benefits gained from data use, as a percentage, in four areas: (1) cost cutting; (2) increased sales; (3) increased added value; and (4) increased customer satisfaction. 61.2 percent said they received benefits in the form of cost cutting, followed in order by increased customer satisfaction, increased sales, and increased added value, all with response rates over 50 percent. The average size of the cost-cutting benefits corporations received was a

(Source) “Study Report on Data Distribution Volume Measurements in the Big Data Era,” MIC

Figure 3-1-2-1 Transitions in data Distribution volumes (total of all surveyed industries)

1,556,589

2,004,7302,614,878

3,477,4804,076,772

4,913,0646,050,339

8,020,140

13,516,492

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

2005 2006 2007 2008 2009 2010 2011 2012 2013

（TB）

About an 8.7-fold increase in eight years

（estimate）

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15.0 percent decrease in costs. The following benefit sizes, in decreasing order, were increased customer sat-isfaction, increased sales, and increased added value (Figure 3-1-3-4).

C. Estimates of sales increase benefits from data useBased on this survey’s results, the sales increase ben-

efits from data use in industries other than the distribu-tion industry (wholesaling and retail industry) were es-timated to be 32.8 trillion yen according to the formula below.

b. Analysis of surveyed corporations in the distribution industryThis survey included more detailed questions about

the effects derived from data usage in the distribution industry. The questions asked about the state of data us-age in the distribution industry and the resulting quanti-tative benefits in eight operational areas: (1) develop-

ment of private brand products; (2) product procurement / inventory management; (3) sales promotion; (4) opti-mization of advertising and marketing; (5) reciprocal customer transfers; (6) optimization of sales floor traffic patterns; (7) analysis of store locations; and (8) other.

A. Overview of data usageRespondents were asked whether data are used in

their workplaces. When asked whether data such as POS data, customer purchase histories, social media postings are used in the eight operational areas given above, product procurement / inventory management, sales promotion, and advertising and marketing opera-tions received the largest affirmative responses rates (Figure 3-1-3-5).

Figure 3-1-3-6 shows the results from the question about issues with data usage given to corporations in the distribution industry. The five most common responses

(Source) “Study Report on the Ripple Effects of Operational and Service Innovation through Advanced Data Usage onJapan’s Economy and Society,” MIC (2014)

Figure 3-1-3-1 Types of data in use

50.242.8

41.733.8

19.014.7

14.313.5

13.212.312.311.4

7.46.6

5.33.22.9

1.41.8

0.0

0

N=1000

20 40 60(%)

Information on customer / trading-partner attributes (customer databases, trading partner databases, etc.)

Website visitation logs (own site)

Data from sensors (sensors attached to machinery, weather sensors, motion sensors, etc.)Location information from GPS or other systems

Member attributes on Facebook, Twitter, or other social media

Accounting informationDocumentation such as work operation communications or logsTransaction records (POS, sales data, ordering information, etc.)

Statistical informationImage information obtained in work operations (security cameras, product images, images for product inspections, x-ray images, etc.)

Credit informationVarious system logs

Website content (own site)Questionnaire data

Experiment recordsWebsite visitation logs (visits to other company sites)

Website content (other company sites)

Comments or likes on Facebook, Twitter, or other social media

OtherDo not use any of the above

Figure 3-1-3-2 Depth of data usage in operations at the respondent’s department

(Source) “Study Report on the Ripple Effects of Operational and Service Innovation throughAdvanced Data Usage on Japan’s Economy and Society,” MIC (2014)

78.6

71.8

72.8

72.8

72.6

72.7

17.3

20.6

23.3

22.1

21.8

23.1

28.0

29.3

21.0

26.1

23.1

18.9

2.9

4.2

6.6

5.1

3.0

5.7

0.0 20.0 40.0 60.0 80.0 100.0

Visualization of operational statuses and the state of the company through data aggregation Dynamic detection of abnormalities through data aggregation Projections of future conditions based on aggregated data

General management operations (N=346)

Product and service planning and development (N=287)

Product and service production and distribution (N=257)

Sales planning and sales promotion (N=272)

Sales and service provision (N=303)

After-sales service (N=264)

Automatic control of machinery or systems based on future projections

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CW6_AX102_10_D13_C.indd 19 2014/11/05 14:38:20

were “don’t know how to use data,” “no internal system to analyze and use data,” “difficult to assess the cost-benefit ratio of data use,” “costs to analyze and use data,” and “data sets are scattered and difficult or impossible to analyze.”

B. Benefits gained from data useRespondents were next asked to give the specific size

of benefits gained from data use, as a percentage, for

each operational area where data sets are used. To as-certain the benefits in more detail than in the questions asked to all industries, the benefit indicators were changed depending on the operational area (Figure 3-1-3-7).

C. Estimates of sales increase benefits from data useBased on the results to these questions, the sales in-

crease benefits from data use in the distribution indus-


Figure 3-1-3-3 Data usage issues

26.9

29.2

24.3

21.8

22.5

16.5

10.1

10.6

6.8

5.9

6.0

15.8

15.7

14.7

13.6

13.5

6.5

4.8

4.7

3.2

2.5

2.4

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0(%)

Data sets are scattered and difficult or impossible to analyze

No internal system to analyze and use data

Don’t know how to use data

Costs to analyze and use data

Don’t know how to analyze data

Legal limitations on the use of data

Commercial practices make it difficult to use data

Cannot get understanding of supervisors or management

Not trusted by sales or sales locations

Cannot get customers’ understandingFeel it is a problem (N=1000)

Difficult to assess the cost-benefit ratio of data use

Feel it is the biggest problem (N=1000)

Figure 3-1-3-4 Benefits gained from data usage (total of all industries)


0 10 20 30 40 50 60 70 80 90 100

1-10 11-20 21-30 31-40 41-50 51-

(%)

23.823.8

26.126.1

28.728.7

26.926.9

15.415.4

12.112.1

10.910.9

13.913.9

9.49.4

7.47.4

8.38.3

7.07.0

2.62.6

1.61.6

1.01.0

2.12.1

6.16.1

4.94.9

3.43.4

5.35.3

3.83.8

1.81.8

1.51.5

3.13.1

38.838.8

46.146.1

46.246.2

41.741.7

No benefit

Cost cutting (N=998)

Increased sales (N=1000)

Increased added value (N=1000)

Increased customer satisfaction (N=1000)

Obtained benefits: 61.2%Average size of Decrease of 15.0%benefits:

Obtained benefits: 53.9%Average size of Increase of 11.1%benefits:

Obtained benefits: 53.8% Average size of Increase of 10.0%benefits:

Benefits gained from big data use and application (as a percentage of the original measurement indicators)

Obtained benefits: 58.3%Average size of Increase of 13.1%benefits:

Figure 3-1-3-5 Data usage in the distribution industry (by operational area)


100806040200

65.0

58.9

51.0

56.0

37.6

36.9

31.4

55.6

18.9

24.3

27.0

26.3

29.1

26.7

24.3

17.6

16.0

16.8

22.1

17.8

33.3

36.4

44.2

26.8

(%)

2. Product procurement / inventory managemen (N=452)

8. Other operational areas (N=206)

6. Optimization of sales floor traffic patterns (N=408)

5. Reciprocal customer transfers (N=411)

4. Advertising and marketing (N=433)

3. Sales promotion (N=442)

1. Development of private brand products (N=437)

7. Analysis of store locations (N=399)

Use data No plan to use data in the future Do not use data currenty but want to use data in the future

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CW6_AX102_10_D13_C.indd 20 2014/11/05 14:38:21

try (wholesaling and retail industry) were estimated to be 28.1 trillion yen — 13.0 trillion in the wholesaling in-dustry and 15.1 trillion in the retail industry. The esti-mated total sales increase benefits in industries outside

the distribution industry were 60.9 trillion yen. This fig-ure is equivalent to 4.6 percent of the total sales in Ja-pan’s entire industry (1,335.5 trillion yen).

4. Promoting combined geospatial information and ICT usage(1) Awareness of applications for geospatial information among

local governmentsa. State of integrated GIS usage (taken from “Outline of Local Govern-

ment Information Management,” MIC)Local governments share integrated GIS among taxa-

tion bureaus, urban planning bureaus, disaster manage-ment bureaus, and other internal sections. Local govern-ments are moving ahead with preparations of the shared spatial data (digital map data that can be shared inter-

nally), including basic map information for more effi-cient execution of administrative operations, ahead of GIS deployment. The national government provides technical assistance and supplementary financial mea-sures.

According to MIC’s Outline of Local Government In-formation Management (2014) report, as of April 2013, 40.4 percent of prefectural governments, along with 44.8 percent of municipal governments, had introduced inte-

Figure 3-1-3-6 Data usage issues (distribution industry)


29.230.6

25.424.624.6

21.4

9.65.25.8

4.63.8

19.416.8

15.212.8

119.2

4.82.6

2.21.8

1

0 10 20 30 40(%)

No internal system to analyze and use dataDifficult to assess the cost-benefit ratio of data use

Costs to analyze and use dataData sets are scattered and difficult or impossible to analyze

Don’t know how to analyze dataCommercial practices make it difficult to use data

Cannot get understanding of supervisors or managementLegal limitations on the use of data

Cannot get customers’ understanding

Feel it is a problem (N=500)

Don’t know how to use data

Not trusted by sales or sales locations Feel it is the biggest problem (N=500)

Figure 3-1-3-7 Benefits gained from data usage (by operational area)


0 20 40 60 80 100

31-401-1041-50

11-2051-

21-30

(%)

32.532.5

29.929.9

35.935.9

9.49.4

13.713.7

10.310.3

3.43.4

6.86.8

2.62.6

0.00.0

0.90.9

3.43.4

9.49.4

6.06.0

5.15.1

17.117.1

18.818.8

20.520.5

28.228.2

23.923.9

22.222.2

Development of private brand products


Increased customer numbers N=(117)

Increased sales N=(117)

Increased profits N=(117)

No benefit

Obtained benefits: 71.8%Average size of Increase ofbenefits: 28.2%



0 20 40 60 80 100(%)

Reduced defect losses (N=194)

Reduced disposal losses (N=195)


Product procurement / inventory management

Increased customer numbers (N=200)

Increased sales (N=200)

Increased profits (N=200)

Reduced clearance losses (N=196)

30.530.5

35.535.5

35.035.0

33.033.0

29.729.7

30.130.1

9.09.0

10.010.0

11.011.0

11.311.3

9.29.2

7.17.1

1.51.5

3.03.0

1.51.5

4.64.6

3.63.6

3.63.6

1.01.0

1.51.5

1.51.5

0.00.0

1.01.0

0.00.0

6.06.03.03.0

3.53.5

2.12.1

2.12.1

3.13.1

8.5 8.5

9.5 9.5

9.0 9.0

4.1 4.1

5.1 5.1

5.6 5.6

43.543.5

37.537.5

38.538.5

44.844.8

49.249.2

50.550.5

31-401-1041-50

11-2051-

21-30No benefit

Obtained benefits: 56.5%Average size of Increase ofbenefits: 16.5% Obtained benefits: 62.5%Average size of Increase ofbenefits: 17.4% Obtained benefits: 61.5%Average size of Increase ofbenefits: 16.5% Obtained benefits: 55.2%Average size of Increase ofbenefits: 13.7% Obtained benefits: 50.8%Average size of Increase ofbenefits: 13.4% Obtained benefits: 49.5%Average size of Increase ofbenefits: 13.0%

0 20 40 60 80 100(%)

31-401-1041-50

11-2051-

21-30

41.041.0

43.543.5

39.839.8

42.542.5

30.230.2

9.39.3

8.78.7

8.78.7

10.010.0

6.96.9

4.34.3

3.73.71.21.2

3.83.8

4.44.4

0.60.6

2.52.5

0.60.6

1.91.9

1.31.3

3.73.7

5.05.0

3.13.13.13.1

2.52.5

8.78.7

8.78.7

9.39.3

8.88.8

6.96.9

32.332.3

28.028.0

37.337.3

30.030.0

47.847.8

Sales promotion

Increased customer numbers N=(161)

Increased sales N=(161)

Obtained benefits: 67.7%Average size of Increase ofbenefits: 17.1% Obtained benefits: 72.0%Average size of Increase ofbenefits: 17.9% Obtained benefits: 62.7%Average size of Increase ofbenefits: 15.1% Obtained benefits: 70.0%Average size of Increase ofbenefits: 15.1%

Increased unit sales price (N=161)

Increased profits (N=160)

Reduced sales promotion costs (N=159)

Obtained benefits: 52.2%Average size of Decrease ofbenefits: 14.8%


No benefit

0 20 40 60 80 100(%)

31-401-1041-50

11-2051-

21-30

32.632.6 17.717.7

2.12.1 1.41.4

5.05.0 7.17.1 34.034.0

Advertising and marketing

Reduced costsN=(141)


No benefit

Obtained benefits: 66.0%Average size of Dencrease ofbenefits: 16.0%

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CW6_AX102_10_D13_C.indd 21 2014/11/05 14:38:22

grated GIS. When asked about the application fields for integrated GIS, the top-three responses from prefectural governments were “agriculture and forestry administra-tio” (94.7 percent), “environment” (89.5 percent), and “education” (84.2 percent), while the top-three respons-es from municipal governments were “roads” (64.4 per-cent), “property taxes” (63.5 percent), and “fire depart-ments and disaster management” (59.7 percent).

Local governments that indicated they had no plans to introduce integrated GIS were asked about the factors impeding integrated GIS adoption. The most frequently quoted reasons, by both prefectural and municipal gov-ernments, were “public financial conditions” and “lack-ing personnel.”

b. State of GIS usage (taken from a survey of local governments)Local governments were asked about what fields they

hope to expand GIS applications into. As in last year’s survey, disaster management was the No. 1 response by a wide margin, mentioned by over 80 percent of the re-

spondents. This was followed in order by city infrastruc-ture, tourism, healthcare / nursing / social welfare, and crime prevention (Figure 3-1-4-1).

When asked about problems facing expanded GIS use, “tough public financial conditions” was the most common response, given by almost 60 percent of the re-spondents. As in last year’s survey, this was followed in order by “have not established systems that can be used and shared across divisions,” “lacking promotion sys-tems within government offices,” and “lacking employ-ee skills (operational abilities, analytic and application abilities, etc.) or software / tools to use data” (Figure 3-1-4-2).

(2) MIC initiatives to promote geospatial information–ICT inte-gration

As ICT dramatically changes and evolves, both quan-titatively and qualitatively, MIC is looking to bring new innovation to the daily lives of citizens by converging spatial information with ICT. To this end, it launched the

Figure 3-1-4-2 Problems facing expanded GIS use

Figure 3-1-4-1 Promising fields for GIS application expansion

82.582.5

57.657.655.555.5

53.953.949.449.4

40.940.937.837.8

33.833.8

32.232.230.230.2

20.320.39.19.1

78.178.149.749.7

52.652.652.652.6

48.648.635.635.6

34.034.029.429.4

28.928.926.826.8

18.118.1

7.77.7

0 10 20 30 40 50 60 70 80 90(%)

City infrastructureTourism

Healthcare, nursing, and welfare

Security

Local communitiesEmployment

FY 2014 survey (N=733) FY 2013 survey (N=895)

Disaster management

TrafficEducation

Various kinds of information inenergy and the environmental

IndustryGovernment services

(Source) “Study Report on the Current State of Regional ICT Application and Use,” MIC (2014)

59.959.947.647.6

43.543.5

41.141.1

40.440.4

34.434.4

32.732.7

32.132.131.231.2

29.229.228.028.0

26.626.6

22.822.8

10.610.6

8.78.7

7.17.1

59.059.047.647.6

42.142.1

39.039.0

35.435.4

27.427.433.633.6

32.332.30.00.0

31.731.70.00.0

27.527.5

26.326.3

19.719.77.07.07.07.0

7.27.2

0 10 20 30 40 50 60 70（％）

36.836.8

Lacking handling of sensitive information and personal information and guarantees of thecorrecthess of information in the safety and security field and other fels

FY 2014 survey (N=733)

Tough public financial conditionsHave not established systems that can be used and shared across divisions

Lacking promotion systems within government officesLacking employee skills (operational abilities, analytic and application,

abilities, etc. ) or software / tools to uce dataLacking personnel

Have not established systems that can provide geospatial information to the publicDo not have a specific usage image

Concerned about data updating work processesSystems have not been astablished that can be uced together with related organizations

Have not fully established a geospatial information system (GIS)Lacking information (necessary data sets, the coding systems for the data sets,

etc.) needed in addition to geospatial informationBenefits and merits are not clear

Lacking the establishment of cost sharing and beneficiary sharingLacking private-sector and other external organization applications

Difficult to find appropriate ICT vendors and servicesLack of understanding within the organization and among residents

FY 2013 survey(N=895)

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G-Space x ICT promotion council in March 2013, which conducted a study in this area and compiled a report in June 2013. In its report, the council set out three visions (targets): revitalizing the economy by creating new in-dustries and services, establishing the world’s most ad-

vanced disaster management systems, and revitalizing regional communities through advanced and leading models.

Based on the council’s report, MIC is now taking on the G-Space x ICT Project.

1. Open data initiatives by the Japanese government(1) IT Strategic Headquarters initiatives

The IT Strategic Headquarters released the Open Government Data Strategy in July 2012 as an open data strategy for the Japanese government. To study specific policies and measures based on the Open Government Data Strategy, the IT Strategic Headquarters set up the e-Government Open Data Executive Meeting. This meeting is moving ahead with examinations of basic matters including (1) the establishment of the neces-sary rules and regulations to make use of public data, (2) the preparation of data catalogs, and (3) the creation of standards for data formats and structures.

On a related note, the Declaration to be the World’s Most Advanced IT Nation states three measures to ad-vance private sector access to public data (open data): (1) prepare and release a roadmap based on the Open Government Data Strategy; (2) review usage rules that permit the unrestricted secondary use of public data starting in FY 2013 and widen the release of public data in international standard machine-readable data for-mats; and (3) launch a trial version during FY 2013 of a data catalog site that provides guidance to and enables cross-sectional searching of public data released by min-istries and agencies and begin full-scale operation of the site in FY 2014.

The G8 Summit in June 2013 adopted the G8 Open Data Charter and Technical Annex. In response to this policy paper, the Ministry CIO Liaison Committee, at its October 2013 meeting, agreed on Japan’s Open Data Charter Action Plan. On December 20, 2013, the Cabi-net Secretariat launched a trial version of the data cata-log site, DATA.GO.JP.

(2) Open Data Promotion Consortium initiativesThe Open Data Promotion Consortium was formed

on July 27, 2012 with the objective of industry, govern-ment, and academia coming together to lay the ground-work for the realization of an environment conducive to open data distribution. The consortium is currently dis-seminating information on the significance and potential of open data along with examining technical specifica-tions and secondary use rules for open data. It is also looking at approaches to licensing needed to implement open data schemes.

In addition, the consortium has created a common collaborative platform API for information distribution with the aim, by specifying construction methods, of simplifying the construction of applications and servers to register and use open data. The results of a pilot pro-

gram are now being scrutinized ahead of a future revi-sion to the API. It is also preparing to release the Open Data Guide (Version 1) that will summarize the neces-sary technical matters and knowledge pertaining to us-age rules when converting public data held by organiza-tions to open data.

The consortium also carries out various activities to develop and proliferate open data.

(3) MIC initiativesMIC is working to build an environment conducive to

open data circulation by (1) formulating a common col-laborative platform API for information distribution and translating it into an international standard, (2) enacting rules on secondary data use, and (3) carrying out start-ing in FY 2014 demonstration tests to make visible the merits of open data. MIC has made a public request for the development of applications that use public data con-verted to open data during FY 2013 demonstration tests in order to encourage the private sector to make use of public data. And it joined with the Ministry of Economy, Trade and Industry to hold an Open Data Use Case Con-test and to develop ideas and applications that utilize public data to resolve various social issues.

Furthermore, as a test case of converting information held by administration bodies to open data, MIC re-leased the Information and Communications in Japan White Paper / Information and Communications Statis-tics databases as open data on April 19, 2013. And MIC’s Statistics Bureau, a core government statistics organiza-tion, in partnership with the National Statistics Center, is updating its provision methods of enormous and diverse statistical data records for the next generation, making efforts to enable sophisticated data usage, and spear-heading government initiatives to be a top runner in pro-moting open data.

Section 2　Promoting the Use of Open DataPart 1

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2. Awareness of open data among local governments and private corporations

(1) Awareness among local governmentsa. State of open data initiatives

Questions about the state of open data initiatives by local governments, divided by prefectures, cities and special zones, and municipalities, show that a high per-centage of prefectures are moving ahead with initiatives while initiatives by cities, special zones, and municipali-ties are still forthcoming. This state of affairs is un-changed from last year’s survey, but more cities, special zones, and municipalities answered they are “already moving ahead with initiatives” or “examining specific ways to move ahead with initiatives” this year than last year. Noteworthy is that 27.4 percent of cities and spe-cial zones and 50.0 percent of municipalities answered, “not interested and not undertaking any initiatives,” a decline from the percentages in last year’s survey (Fig-ure 3-2-2-1).

b. Provided data typesNext, the survey asked local governments already un-

dertaking open data initiatives what data types they pro-vide. The No. 1 response was survey and statistical data

sets and the No. 2 response was information on govern-ment services and resident services, the same as last year’s survey. However, disaster-management and relat-ed information moved up to third place with a 42.3 per-cent response rate from fifth place (29.5 percent) last year. Response rates also rose for provision of informa-tion on education and public transportation (Figure 3-2-2-2).

Local governments undertaking open data initiatives and local governments considering moving toward tak-ing initiatives were asked what types of public data they were considering for provision. The top response was survey and statistical data sets, followed in order by di-saster-management and related information, tourism and related information, map, topography, and geologi-cal feature information, and information on public facili-ties (Figure 3-2-2-3).

c. Issues in promoting open dataFinally, local governments were asked about high-

priority issues in the promotion of open data initiatives. No significant differences were seen in the response

Figure 3-2-2-1 State of open data initiatives by local governments


2014 survey 2013 survey 0 20 40 60 80 100 0 20 40 60 80 100

Total (N=733)

Prefectures (N=32)Cities andspecial zones (N=427)Municipalities(N=274)

Total (N=895)

Prefectures (N=34)Cities andspecial zones(N=455)Municipalities(N=406)

Already moving ahead with initiatives Examining specific ways to move ahead with initiatives Interested, and now gathering information Interested, but not undertaking any special initiatives Not interested and not undertaking any initiatives No answer

(%) (%)

Figure 3-2-2-2 Public data currently provided

FY 2014 survey（N=71） FY 2013 survey（N=61）

70.450.7

42.338.0

32.422.522.5

21.116.916.9

14.112.7

11.39.9

7.04.2

72.162.3

29.527.9

31.131.1

16.424.6

11.519.7

18.013.111.513.113.1

9.8

0 10 20 30 40 50 60 70Survey and statistical data sets

Information on government services and resident servicesInformation on disaster management

Information on public facilities (location, usage instructions, etc.)Sightseeing information

Healthcare, nursing, and welfare informationInformation on education

Map, topography, and geological feature informationInformation on public transportation

Information on energy and the environmentInformation on land uses and infrastructure

Information on crime preventionInformation on the location and changes to private-sector facilities

Local community informationInformation on employment

Information on industry

(%)80

24

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CW6_AX102_10_D13_C.indd 24 2014/11/05 14:38:23

tendencies between local governments that are already taking open data initiatives or considering open data ini-tiatives and local governments interested in open data (Figure 3-2-2-4).

(2) Open data needs among private corporationsPrivate corporations were asked what types of public

data they want to use. The top response, given by 59 cor-porations, was maps and underground data, followed in order by public transportation (43 responses), disaster management, security, and safety (38 responses), and city planning and construction (35 responses) (Figure 3-2-2-5).

Next, private corporations were asked which organi-zations held the public data they want to use. The top response, given by 122 corporations, was local govern-ments, followed in order by the Ministry of Land, Infra-structure, Transport and Tourism (116 responses), MIC (54 responses), independent government agencies (35 responses), and the Ministry of Health, Labour and Wel-fare (34 responses) (Figure 3-2-2-6).

Figure 3-2-2-7 indicates the responses of private cor-porations when asked what types of public data held by

local governments they want to use. Maps and under-ground data was the top response, at 16 percent, fol-lowed by public transportation, individual / resident in-formation, and city planning and construction, all at 13 percent.

Figure 3-2-2-3 Public Data currently considered for provision


23.023.022.022.0

18.018.017.017.017.017.0

16.016.015.015.0

14.014.014.014.014.014.0

11.011.011.011.0

10.010.09.09.0

8.08.07.07.0

0 10 20 30 (%)Survey and statistical data sets

Information on disaster managementSightseeing information

Map, topography, and geological feature informationInformation on public facilities (location, usage instructions, etc.)

Information on crime preventionHealthcare, nursing, and welfare informationInformation on land uses and infrastructure

Information on government services and resident servicesInformation on public transportation

Information on educationInformation on energy and the environment

Information on industry

Local community informationInformation on employment

N=100

Information on the opening/closing of businesses, construction work, etc. through notifications and licenses

Figure 3-2-2-4 High-priority issues in promoting open data initiatives


69.069.0

54.054.0

49.049.0

48.048.0

46.046.0

37.037.0

35.035.0

34.034.0

34.034.0

28.028.0

27.027.0

24.024.0

22.022.0

18.018.0

17.017.0

63.263.2

51.951.9

34.134.1

49.549.5

33.533.5

38.138.1

29.729.7

34.634.6

29.229.2

38.638.6

30.830.8

19.719.7

17.317.3

13.813.8

13.513.5

Clarification of specific usage images and needs

Realization of benefits and advantages to the provider

Reduction of costs and labor associated with provision

Systematic arrangements for handling personal information and other sensitive information

Realization of advantages for local communities, such as spillover effects in local economies

Arrangement of areas of responsibility between users and providers

Establishment of specific, comprehensive government policies on open data

Arrangement of provided information details and cost sharing

Systematic establishment of rights processing for intellectual property, etc.

Information and knowledge gathering using early-adoption examples and advisors

Development and commercialization of techniques, tools, and services

Promotion leadership within regions and administration

Undertaking + Studying (N=100)Interested (N=370)

(%)0 10 20 30 40 50 60 70

System aspects needed for standardized use, such as standardization of data formats and structures and promotion of standard application programming interfaces (APIs)

Reviews of internal administrative work operation procedures, methods, and responsibilities and establishment of work operation manuals

Realization of effects that encourage resident autonomy, such as expanded opportunities for resident participation

Part 1

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CW6_AX102_10_D13_C.indd 25 2014/11/05 14:38:23

1. Trends in the usage and distribution of personal data(1) Treatment of personal data in the big data era

Today, entities both in the public and private sector routinely create and distribute big data over networks. Some of these distributed data sets contain personal data as well. In the past, our country’s rules on the use and application of personal data have been somewhat vague, causing some businesses to hesitate to use big data. Over time, however, new industries and new ser-vices around the world have become more active with the use of big data, and a succession of companies are embarking on testing and launching services that use personal data as companies have become more familiar with using and applying data.

(2) Government movements pertaining to the domestic use and distribution of personal data

In the wake of the Declaration to be the World’s Most

Advanced IT Nation, decided by the Cabinet in June 2013, the IT Strategic Headquarters, in June 2013, set up the Study Committee on Personal Data, which examined (1) defining basic frameworks for the use and applica-tion of personal data, (2) approaches to rules on the use and application of personal data, (3) approaches to mechanisms to ensure the effective functioning of per-sonal data protection, and (4) organizing ideas on the establishment of independent third-party organizations. On December 20, 2013, the IT Strategic Headquarters released the Policy on Revisions to the Use and Applica-tion of Personal Data.

The IT Strategic Headquarters continued to examine a detailed system plan based on this policy and on June 24, 2014, released the Policy Outline of the Institutional Revision for Utilization of Personal Data. The IT Strate-gic Headquarters has started work on drafting a related

Section 3　Harmonious Use and Distribution of Personal Data

(Source) “Study Report on Industry Use of Public Data,” Keidanren (March 19, 2013)

Figure 3-2-2-5 Types of public data most needed

Maps and underground dataPublic transportation

City planning and constructionHealthcare and nursing

Statistics and surveysIndividual / resident information

Disclosure methodsMeteorological informationBids, procurement, and subsidies

Environment and energyPatents

Company informationPermits and licenses

Radio spectrumLaws and ordinances

Other

5943

3835

3030

1918

1613

1211

88

44

42

Disaster management, security and safety

[Quantity of responses]

Figure 3-2-2-6 Institutions holding public data most needed

0

20

40

60

80

100

120

140122

116

5435 34

27 21 19 16 16 13 10 9 8 7 6 6 6 5 4

33

94

Loca

l gov

ernm

ents

Min

istry

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Wel

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istry

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Met

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Nat

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istry

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inist

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logy

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istry

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Figure 3-2-2-7 Breakdown of desired data types (held by local governments)

(Source) “Study Report on Industry Use of Public Data,” Keidanren (March 19, 2013)

Maps and underground data

Maps and underground data

16%

Publictransportation

Publictransportation

13%

Individual / resident information

Individual / resident information

13%

City planning andconstruction

City planning andconstruction

13%

Disaster management,safety, and security

Disaster management,safety, and security

9%

OrdinancesOrdinances7%

OtherOther5%

Statistics and surveysStatistics and surveys4%

Permits and licensesPermits and licenses4%

Healthcare and nursingHealthcare and nursing4%

Bids, procurement,and subsidies

Bids, procurement,and subsidies

3%

Meteorological informationMeteorological information3%

Energy and the environmentEnergy and the environment3%

Disclosure methodsDisclosure methods2%Radio spectrum1%

26

Part 1

CW6_AX102_10_D13_C.indd 26 2014/11/05 14:38:24

bill through a public comment process and plans to sub-mit the related bill to the National Diet as soon as possi-

ble after January 2015.

2. User attitudes on the use of their personal data(1) Attitudes on the privacy of personal data

The current Act on the Protection of Personal Infor-mation defines personal information that should be pro-tected as any personally identifiable information. As this definition ranges from information that is normally pub-lic, such as names, to information that people do not want known publicly, different degrees of privacy are thought to apply. Furthermore, the degree of privacy is thought to vary depending on the circumstances and situations where the personal data are used.

Users were asked to rate how private 37 different types of personal information are to them. The results are collected in Figure 3-3-2-1.

From the figures above we know many users tend to feel a strong sense of privacy toward data that allow di-rect access to the individual, such as name, address, email address, and telephone number. Many users also tend to feel a particularly strong sense of privacy toward financial and credit information, such as bank account information and credit card numbers, and authentica-tion information, such as personal ID numbers and bio-

logical information.

(2) User attitudes on important aspects to them when providing data

Users were asked what aspects they view as impor-tant when they, as service users, provide their personal data to a service provider (public institutions or private companies). The top aspect viewed as most important was “obtain proper consent,” given by 35.5 percent of the respondents. This was followed in order by “proper information handling methods” (17.5 percent) and “type of information provided” (17.4 percent) (Figure 3-3-2-2).

Users were also asked what information given by a service provider they view as important when the ser-vice provider seeks their consent, as a service user, to use information related to them. The top response was “purpose of using information” (57.0 percent), followed in order by “whether information is sent externally / provided to third parties” (44.6 percent) and “details of collected information” (41.7 percent) (Figure 3-3-2-3).

Figure 3-3-2-1 Attitudes on the privacy of personal data

3.2 3.2 10.5 10.5

10.0 10.0

12.1 12.1

13.3 13.3

39.0 39.0

37.9 37.9

38.2 38.2

21.8 21.8

22.9 22.9

24.0 24.0

23.2 23.2

49.0 49.0

16.0 16.0

16.5 16.5

14.9 14.9

12.7 12.7

11.6 11.6

11.6 11.6

11.6 11.6

0 20 40 60 80 100

3.9 3.9

4.0 4.0 4.7 4.7

4.8 4.8

12.8 12.8

14.2 14.2

18.0 18.0

16.5 16.5

26.0 26.0

26.8 26.8

32.5 32.5

32.8 32.8

46.0 46.0

43.7 43.7

33.1 33.1

35.3 35.3

11.3 11.3

11.3 11.3

11.7 11.7

10.6 10.6

0 20 40 60 80 100

3.2 3.2

5.0 5.0

13.3 13.3

17.7 17.7

17.3 17.3

19.6 19.6

28.5 28.5

31.4 31.4

30.6 30.6

29.2 29.2

44.0 44.0

36.2 36.2

36.2 36.2

31.6 31.6

11.0 11.0

11.4 11.4

12.6 12.6

14.6 14.6

0 20 40 60 80 100

1.6 1.6

1.9 1.9

3.5 3.5

4.9 4.9

4.7 4.7

11.9 11.9

11.9 11.9

11.8 11.8

10.9 10.9

24.7 24.7

21.3 21.3

71.7 71.7

70.6 70.6

48.3 48.3

45.2 45.2

10.0 10.0

11.9 11.9

11.6 11.6

17.7 17.7

0 20 40 60 80 100

(%)

(%)

(%)

(%)

3.2 3.2 5.0 5.0

6.8 6.8

3.2 3.2

3.2 3.2 8.3 8.3

6.9 6.9

12.9 12.9

2.5 2.5

8.2 8.2

21.3 21.3

24.3 24.3

11.1 11.1

7.9 7.9

31.4 31.4

24.8 24.8

34.5 34.5

8.6 8.6

21.6 21.6

31.5 31.5

34.0 34.0

28.1 28.1

22.8 22.8

26.2 26.2

32.4 32.4

25.2 25.2

18.7 18.7

58.5 58.5

33.4 33.4

24.8 24.8

49.2 49.2

57.7 57.7

16.8 16.8

23.4 23.4

15.0 15.0

55.2 55.2

8.5 8.5

8.8 8.8

10.1 10.1

8.4 8.4

8.4 8.4

17.3 17.3

12.5 12.5

12.4 12.4

15.0 15.0

0 20 40 60 80 100(%)

NoneRelatively low Relatively high Very high (importance of privacy) Can’t decide / not applicable

3.9 3.9

3.3 3.3

3.3 3.3

0 20 40 60 80 100 (%)

5.8 5.8 20.1 20.1 29.1 29.1

10.1 10.1 28.9 28.9 26.3 26.3 21.3 21.3 13.4 13.4

29.4 29.4 15.6 15.6

4.7 4.7

19.1 19.1

16.7 16.7

3.2 3.2

3.7 3.7

5.9 5.9

17.0 17.0

36.9 36.9

35.9 35.9

12.5 12.5

15.5 15.5

22.5 22.5

24.0 24.0

17.5 17.5

19.6 19.6

30.4 30.4

32.1 32.1

29.7 29.7

45.3 45.3

17.0 17.0

18.1 18.1

35.6 35.6

32.3 32.3

22.5 22.5

9.0 9.0

9.5 9.5

9.7 9.7

18.3 18.3

16.4 16.4

19.4 19.4

8.6 8.6

9.3 9.3

6.4 6.4

28.0 28.0

27.7 27.7

22.5 22.5

25.0 25.0

23.9 23.9

27.3 27.3

20.4 20.4

18.2 18.2

25.3 25.3

18.0 18.0

20.9 20.9

18.5 18.5

6.2 6.2 23.1 23.1 23.1 23.1 26.8 26.8 20.8 20.8

Basic information Information about personal history / actions

Name

Address

Date of birth

Gender

Nationality

Company name

Occupation

Job history

Email address

Telephone number

Qualifications

School name

Education history

Interests and hobbies

Personal ID number

Biological information(face, iris patterns, retina,

fingerprints, veins)

Location information

Behavior history

Purchasing history

Browsing history

Information about assets

Information about personal relationships

Biological and physical information

Other information

Bank account information

Credit card number

Annual income / earnings

Loans

Height

Weight

Blood type

Health status

Medical history / medical condition

Thoughts and beliefs

Religion

Propensity

Union membership

Family relationships

Friend relationships

PersonalrelationshipsAlma materinformation

(Source) “Study Report on the Social Impacts of ICT Advancement,” MIC (2014)

Part 1

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(3) User attitudes on purposes for using personal dataa. Organizations to whom it is acceptable to provide data

Users were first asked whether they would provide personal data about themselves to a number of different organizations, assuming proper consent were obtained. The envisioned purposes of use were broadly divided into public purposes and business purposes (Figure 3-3-2-4).

When the envisioned use was for a public purpose, “national government,” selected by 29.8 percent of re-spondents, was the most frequently mentioned organiza-tion that it was acceptable to provide personal data to. This was followed in order by “local governments” (24.2 percent) and “hospitals” (19.0 percent).

When the envisioned use was for a business purpose, “large corporations with a public nature (lifeline-type corporations),” selected by 12.4 percent of respondents, was the most frequently mentioned organization that it was acceptable to provide personal data to. This was fol-lowed by “large corporations with a public nature (trans-port-related corporations)” with 11.8 percent support. On the other hand, more than half of the respondents answered “will not provide personal data under any cir-cumstances” to “other ordinary corporations (that you

have not heard of),” which indicates the willingness to provide personal data varies substantially depending on familiarity with the organization.

b. Situations where it is acceptable to provide personal data (by purpose of use)Next, users were asked whether they would provide

personal data about themselves for a number of differ-ent purposes, assuming proper consent were obtained. When the envisioned use was for a public purpose, “in large natural disasters or other emergencies,” selected by 45.5 percent of respondents, was the most frequently mentioned situation where it is acceptable to provide personal data. This was followed in order by “for disas-ter-management purposes” (33.6 percent), “for the health and welfare of citizens” (26.4 percent), and “for the protection of national security or safety of citizens” (25.3 percent).

On the other hand, when the envisioned use was for a business purpose, 14.0 percent of respondents agreed it was acceptable to provide personal information “to im-prove services for the user” and 13.8 percent agreed with “to receive personal economic benefits” (Figure 3-3-2-5).


Figure 3-3-2-2 Important aspects when providing data

First priorityFourth priority

Second priorityFifth priority

Third priority sixth priority

35.5 35.5

17.5 17.5

17.4 17.4

11.7 11.7

11.4 11.4

6.5 6.5

13.6 13.6

21.6 21.6

33.1 33.1

15.9 15.9

4.9 4.9

10.9 10.9

15.3 15.3

34.1 34.1

18.8 18.8

15.3 15.3

6.7 6.7

9.8 9.8

13.2 13.2

15.3 15.3

15.4 15.4

33.0 33.0

8.0 8.0

15.1 15.1

13.4 13.4

7.3 7.3

10.2 10.2

14.2 14.2

11.6 11.6

43.3 43.3

9.0 9.0

4.2 4.2

5.1 5.1

9.9 9.9

57.4 57.4

14.4 14.4

0 10 20 30 40 50 60 70 80 90 100(%)

Obtain proper consent

Proper information handlingmethods (deletion of name, etc.)

Type of information provided

What organization / corporationhas access to information

Benefits retumed to user (provisionof points or discounts, etc, )

Publicity of the purpose of use

Figure 3-3-2-3 Important information when consenting to providing data

57.0 57.0

44.6 44.6

41.7 41.7

32.2 32.2

30.0 30.0

28.4 28.4

26.4 26.4

26.3 26.3

24.8 24.8

24.7 24.7

23.2 23.2

23.0 23.0

0 20 40 60 80 100(%)

Purpose of using information

Whether information is sent externally / provided to third parties

Details of collected information

Information about the information collector

Proper information disposal mechanisms

What third parties receive information if provided to third parties

Deletion of information after a certain time and provisions to that effect

Method of information collectionWhether there is a mechanism for

disabling at any time the collection and use of information (opt out)

Procedure for changing privacy policies and means of communicating changes

Contact for inquiries

None apply


Figure 3-3-2-4 Organizations to whom it is acceptable to provide personal data

29.8

24.2

19.0

12.7

6.4

51.0

54.8

58.6

62.5

56.7

19.2

21.0

22.4

24.8

36.9

0 10 20 30 40 50 60 70 80 90 100

11.8

12.4

7.9

5.6

4.7

3.5

64.7

63.8

57.9

59.3

56.9

45.5

23.5

23.8

34.2

35.1

38.4

51.0

0 10 20 30 40 50 60 70 80 90 100(%) (%)

Will provide Will provide with conditions Will not provide under any circumstances

National government

Local governments (prefectural,municipal)

Hospitals

Education / research institutions

Tourism associations and other non-profitorganizations with a public nature

Large corporations with a public nature (railways, roadways, airlines, and other

transport-related corporations)Large corporations with a public nature

(electricity, gas, water, communications, and other lifeline-related corporations)

Banks, security companies, insurers, and other financial nstitutions

Large ordinary corporations (listed corporations, etc.)

Other ordinary corporations (that you have heard of)

Other ordinary corporations (that you have not heard of)

Public purposes Business purposes

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1. Signs of changes in data usageA succession of data usage examples are appearing in

many disparate fields due to the improved performance and lower prices of various devices and sensor technolo-gies, which are being driven by advances in ICT. Con-current with this, data usage in many industries and fields is being pushed by the construction of environ-ments that link all sort of things around us together, which is being driven by M2M and IoT technologies.

Conversely, most analysis using data to date has been to “visualize” operational statuses or the state of the company by collecting and aggregating various data sets distributed among various internal sections of a compa-ny. Nevertheless, some corporations answered that they obtaining benefits by further deepening their data us-age.

In addition, corporations have mainly performed data analyses using internal corporate or internal organiza-tion data sets only. But there is every reason to believe there will be increased efforts to obtain new insight by

using external data sets and contrasting them with inter-nal data sets. And the external data sets getting the most attention are public data sets held by government bod-ies. As much as corporations have high hopes for open data, open data initiatives are important for govern-ments too, from the viewpoints of improving transpar-ency and credibility, encouraging public-private cooper-ation, and making administration more efficient. For these reasons, the pace of open data initiatives is expect-ed to accelerate further.

Data that users communicate via social media and elsewhere are also likely to be used as important data resources in conceiving management strategies and de-veloping product and service plans. These trends, taken together, will probably accentuate the infrastructure-like nature of data, as data sets transcend the limitations of being management resources belonging to a single cor-poration or organization and are shared throughout so-ciety.

2. Issues complicating data usage advancementThe advancement of data usage, however, is not free

of problems. The most commonly acknowledged across-the-board issues are the handling of personal data and securing specialized data personnel.

For the corporations and other organizations engaged in data usage, the higher the value of data they obtain and analyze, the greater the received benefits. From this standpoint, then, if data directly tied to users can be used, it will be possible to directly ascertain the user’s attitudes and tendencies, which is valuable to corpora-tions and other organizations. On the flip side, for con-sumers, this creates concerns about violations of their

privacy and misuse and abuse of their personal informa-tion by third parties. A key issue is how to strike the right balance between the usage and protection of per-sonal data.

It is important, in order to obtain more value from data usage, for corporations and other organizations to secure personnel who can derive beneficial insights from processed data and who can apply these insights to resolve issues at the corporation / organization. It is said, particularly in Japan, that there are insufficient numbers of these specialized personnel. To be fair, dif-ferent corporations require different levels of data usage

Section 4　Advent of a Real Data-Driven Society


Figure 3-3-2-5 Situations where it is acceptable to provide personal data (by purpose of use)

45.5 45.5

33.6 33.6

26.4 26.4

25.3 25.3

16.2 16.2

16.6 16.6

14.0 14.0

13.2 13.2

44.9 44.9

54.4 54.4

58.4 58.4

59.5 59.5

63.3 63.3

62.1 62.1

64.0 64.0

63.3 63.3

9.6 9.6

12.0 12.0

15.2 15.2

15.2 15.2

20.5 20.5

21.3 21.3

22.0 22.0

23.5 23.5

0 10 20 30 40 50 60 70 80 90 100

14.0 14.0

13.8 13.8

8.1 8.1

7.5 7.5

6.6 6.6

5.3 5.3

66.5 66.5

66.3 66.3

64.7 64.7

62.9 62.9

61.7 61.7

60.7 60.7

19.5 19.5

19.9 19.9

27.2 27.2

29.6 29.6

31.7 31.7

34.0 34.0

0 10 20 30 40 50 60 70 80 90 100(%)(%)

Will provide Will provide with conditions Will not provide under any circumstances

In large natural disasters or other emergencies

For disaster-management purposes

For the health and welfare of citizens (developmentof medical treatments and new drugs, etc.)

For the protection of national security or safety ofcitizens (anti-terror, crime prevention, criminal

investigations, etc.)To improve the quality and convenience of public

services (accelerate administrative procedures,expansion of public services, etc.)

To resolve social problems, such as trafficcongestion or aging of roads and bridges

To aid academic developments in education,research, etc.

For regional development, tourism, and other localeconomic stimulation

To improve services for the user (able to use services for free or able to access

additional services or functions)

To receive economic benefits (receive discounts, points, coupons, etc.)

Improve product functionality or service quality

To develop new products or services

To analyze corporate activities or to provide more accurate marketing

To establish or judge corporate management policies

Public purposes Business purposes

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sophistication, and it is possible in some cases to obtain sufficient insights needed for operations without special-ized personnel. Still, a critical issue is how to secure, ei-

ther internally or through outsourcing, personnel who can shoulder such operations.

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section 1 value generated by big data 1. expanding ... · (1)t advances drive the creation,...

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