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

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

using the total data distribution volume for all media channels. However, examining data distribution estimates 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 distribution 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 measurable real GDP effects.

Sensor, M2M, and other new media channels are expected 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 traditional types of media.

### 3. State of big data usage at corporations

## (1) Framework for estimates

### a. 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 attributes and analyzed the relationship between data usage and business performance and effects. Corporations in the distribution industry, which is thought to lead other industries in data usage, were asked extra questions, and the data usage effects were measured for each type of effect.

## (2) Analysis with a survey of corporations

### a. Analysis of surveyed corporations in all industries

### A. 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 mentioned 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 aggregation; (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 usage. 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 use

Respondents 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