Big-data asset assessment method

Abstract

The invention discloses a big-data asset assessment method. The big-data asset assessment method comprises the steps that firstly, data quality assessment is conducted, wherein data quality indexes comprise accuracy, integrality, consistency and timeliness; secondly, data size assessment is conducted, wherein data size indexes comprise the number of data attributes, the number of data tuples and the quantity of unit information; thirdly, data content assessment is conducted, wherein the data content comprises transaction data, personal information, commodity information, production management data, user evaluation data and social networking data; fourthly, industry value calculation is conducted; fifthly, data asset value calculation is conducted. The big-data asset assessment method provides a specific quantitative standard for assessment of big-data assets, the assessment process is simpler and clearer, and the subjective factor influence of assessors is eliminated, so that assessment results accord with actual conditions better.

Description

technical field [0001] The invention relates to the technical field of asset evaluation, in particular to an evaluation method for data assets. Background technique [0002] Considering that the value of data in different industries is different, and taxation can determine the size of the transaction amount of an enterprise, according to the tax yearbook and other materials, the data is divided into the following industries: [0003] (1) Agricultural data [0004] (2) Mining industry data [0005] (3) Manufacturing data [0006] (4) Data on the production and supply of electricity, heat, gas and water [0007] (5) Construction industry data [0008] (6) Wholesale and retail trade data [0009] (7) Data on transportation, warehousing and postal services [0010] (8) Data of accommodation and catering industry [0011] (9) Information transmission, software and information technology service industry [0012] (10) Financial industry data [0013] (11) Real estate data...

AI Technical Summary

Problems solved by technology

Sun Rongling and others first proposed to conduct research on the value of intangible assets and the quantification of value realization, but the traditional evaluation method is relatively rough; then Chen Changyun proposed the Black-Scholes option pricing model and EVA method, and introduced it to the evaluation of the overall value of the enterprise Among them, the model is more accurate, but it does not take into account the gap between different enterprises; then experts and scholars continue to study and discuss, forming a relatively complete intangible asset evaluation system, mainly including the income method, the market method, and the cost method, but the data There are still conflicts between the evaluation standards and elements of assets and these methods. Therefore, these methods cannot be fully applied to data assets; The lack of systematic data asset value evaluation dimensions and the lack of a specific quantitative standard for data asset evaluation have brought more difficulties to researchers

[0029] In addition, data evaluation has different importance for different categories, and if it is vaguely regarded as one category, the analysis results are somewhat unrealistic and contrary to the actual needs of society for data

In addition, the evaluation structure of data assets should consider more aspects, and the past research is also insufficient in structure

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