Figure 1-3-2-1 Data distribution volume estimation scope

(Source) MIC "Survey Research on Consideration of Data Distribution/Accumulation Volume Measurement Approaches" (2013)

ation/distribution/accumulation process, it is very difficult to estimate data volumes after the first data distribution process while taking all data distribution cycles into account. In estimating the data distribution volume, therefore, we limited estimation targets to data volumes in the first data distribution process (Figure 1-3-2-1).

### d. Big Data distribution/accumulation volume estimation approach (a) Big Data distribution volume estimation approach

In estimating data distribution volumes, we selected nine industries (services, information and communications, transportation, real estate, finance and insurance, commerce, electricity/gas/water, construction and manufacturing) and 17 categories of data as estimation targets. We used the following model to estimate a data distribution volume for each industry and aggregate industry-wise volumes into a macro data distribution volume. In the distribution volume estimation, we retroactively estimated past distribution volumes as far as possible, building time-series data distribution volume data (for 2005, 2008, 2011 and 2012).

## (b) Big Data accumulation volume estimation approach

In estimating data accumulation volumes, we focused on data stored on servers installed inside or outside enterprises. Therefore, data for the estimation were not limited, while targets for the data distribution volume estimation were limited. All data stored on servers were subjected to the estimation. Attention should be paid to the difference in estimation scopes.

## (2) Big Data distribution and accumulation volume estimates

As a result of the Big Data distribution volume estimation, we estimated the total Big Data distribution volume for the nine industries in 2012 at about 2.2 exabytes. The time-series data indicate that the Big Data distribution volume increased about 5.2-fold in seven years from about 0.4 exabytes in 2005 to about 2.2 exabytes in 2012 (Figure 1-3-2-2).

The Big Data accumulation volume (for the nine industries) in 2012 was estimated at about 9.7 exabytes for 2012. Among the industries, the commerce and services industries posted greater data accumulation volumes than the others. The finance/insurance and services industries logged higher outside server accumulation shares than the others (Figure 1-3-2-3).

## (3) Analyzing relations between data distribution volumes and macroeconomic indicators

In order to assess impacts of an increase in data that enterprises receive electronically on economic performances, we analyzed relations between growth rates for the data distribution volume per employee and for labor productivity (real gross domestic product per employee) between 2005 and 2011.

Figure 1-3-2-4 shows average annual growth rates (from 2005 to 2011) for the data distribution volume per employee on the horizontal axis and average annual growth rates (from 2005 to 2011) for labor productivity on the vertical axis, indicating their correlations. As far as other factors influencing the correlations are deemed constant, the data distribution volume and labor productivity have positive correlations.

Figure 1-3-2-2 Trend of Big Data distribution volume (total for 9 industries)

(Source) MIC "Survey Research on Consideration of Approach for Measuring Information Distribution/Accumulation Volumes" (2013)