Data utilization in specific industrial categories, based on quantitative and qualitative effects found in specific cases. Using quantitative effects found in specific cases as estimation parameters, we conducted the extrapolation for operational and business method categories where Big Data may be utilized in similar ways.

## (2) Big Data used for various social areas

### a. Expanding horizons for Big Data utilization

Big Data has begun to attract public attention only recently. Even since earlier days, however, various data have been created, distributed, accumulated and utilized as far as possible.

In the distribution industry, point-of-sale systems for cash registers diffused in the 1980s, allowing retailers to growingly use sales or POS data for making decisions on product purchases. When enterprise-by-enterprise point cards were introduced around 2000, point card numbers were linked to POS data (to produce ID-POS data), enabling retailers to figure out customer-by-customer purchasing behaviors. These cards developed into common point cards or electronic money cards around 2010, prompting retailers to base their sales promotion on customers' purchase records covering multiple enterprises. Over the past several years, information handled by the distribution industry has substantially increased in variety and volume.

Next, the manufacturing industry began to share sales data with distributors in the latter half of the 1990s for the so-called supply chain management to avoid inventory surpluses or shortages. In the 1990s, machine builders began to mount sensors on their machinery products to figure out operation conditions for rationalizing maintenance operations. Since around 2000, remote surveillance data have been used for new services.

In the early 2000s, the utilization of data spread to cover transportation, infrastructure, agriculture and other various areas.

### b. Expanding ranges of industrial and operational categories utilizing Big Data

While a growing number of cases for the utilization of structured data emerged in the distribution and financial industries, the range of industrial categories or areas using Big Data has expanded, including the infrastructure area using data from sensors, as well as agricultural and medical areas where ICT utilization had failed to make progress. The range of operational categories using Big Data has also expanded, covering from product development, production and other earnings-oriented operations to business planning, as well as administrative operations and infrastructure maintenance beyond corporate business operations.

### c. Expanding range of sizes of enterprises using Big Data

A survey of enterprises on whether they are interested in Big Data utilization indicated that shares for enterprises considering utilizing Big Data are higher among enterprises with greater sales sizes. But those considering Big Data utilization accounted for a half of even smaller enterprises.

### d. Expanding range of Big Data utilization purposes

A survey of enterprises on operational categories where Big Data utilization was considered or implemented found that market and business planning were frequently cited and that they were considering or implementing Big Data utilization for various operational categories. Questioned about operational categories where future Big Data utilization is promising, enterprises cited various categories, indicating that they are considering utilizing Big Data for a wide range of operations.

## (3) Big Data utilization and its effects in major industrial categories

### a. Specifying Big Data utilization patterns and effect-producing mechanisms in specific categories

(a) Distribution industry

In the distribution industry, data originate mainly from sales and include sales records. The industry has long utilized POS data to optimize product purchases.

The recent utilization of Big Data has apparently pro\-duced the following five new effects:

First, retailers may add geographical and time data to sales data in order to figure out details of consumer needs and develop new products meeting consumption scenes.

Second, retailers have long depended on sales data for developing and procuring products. In order to reduce sales opportunity losses or dead stock to maximize profit, however, they have recently launched efforts to collect more demand data on a real-time basis for figuring out and forecasting demand.

Big data utilization has produced effects in sales planning and promotion operations as well. Retailers analyze customer-by-customer data (ID-POS data) on point cards and comments on SNS and other websites to improve the accuracy of their sales promotion. Mail-order retailers have attempted to improve catalogue production and distribution efficiency by producing and distributing catalogues meeting consumer needs. Utilizing common point cards that have emerged recently, retailers have launched cooperation with other industries to attract customers that they had failed to attract in the past.

## (b) Manufacturing industry

Big Data utilized frequently in the manufacturing industry represents operation conditions for products manufacturers have delivered. In many cases, they collect information from sensors mounted on products they delivered and manage and analyze operation data as Big Data to create new values.

In many cases, manufactures analyze machine operation data for improving the efficiency of their after-sale services. Through their Big Data analysis, they conduct preventive maintenance operations without any specific troubles in a systematic manner to improve their business efficiency. They can also improve customer satisfaction.