CN111539770A - Intelligent data asset assessment method and system - Google Patents
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Abstract
The invention discloses a method and a system for intelligently evaluating data assets, wherein the method comprises the following steps: receiving and storing source data needing value evaluation; obtaining the constituent factors of different dimensions of the source data according to the evaluation indexes in preset evaluation dimensions, wherein the evaluation dimensions comprise data dimensions and data community dimensions; constructing and calculating influence correlation coefficients of the dimensions of the data community of the source data on the dimensions of the data; and obtaining the source data asset representation according to the different-dimension constituent factors and the influence correlation coefficient. By the method, the asset evaluation is carried out on the single or multiple source data from the data dimension and the data community dimension, the timeliness of the evaluation value of the data is high, and the evaluation index can be set by the data owner and the data maker according to the requirement and the use environment, so that the data asset evaluation value with high reliability is obtained, and the data asset evaluation value can be used for the trading measurement of both market suppliers and suppliers.
Description
Technical Field
The invention belongs to the field of computer application and financial innovation application, and particularly relates to a data asset intelligent evaluation method and system.
Background
Currently, the world has comprehensively entered the internet information era characterized by large data sharing and information explosion, and a large amount of data-standardized data assets play an increasingly greater role in national economic life, for example, some data assets can guide future research and development and sales directions of a company. However, since different enterprises in the same industry have different data capacities, information dimensions, information disabilities and effective information contents which can be mined by the data, how to measure the effective value of the enterprise data assets by using a value model becomes a hot spot of research in recent years.
Existing assessment data asset models, which are basically single variables, weigh the value of the data asset from the data structure itself. However, the evaluation method breaks away from the basis of real data, and for a data asset user, the effective value of the data asset needs to consider not only the effectiveness of the data itself, but also the potential value of the data asset; for example, GDP data of an economic area is obtained, and even if the GDP data information is comprehensive, the obtained value is still one-sided and has no referential property; if the carrier contrast is increased, such as the economic area, other economic areas with uniform grade, the economic ring ratio or the same ratio GDP data, and the like, the obtained data value is more accurate and applicable. The potential value is mainly reflected in the value of the data generation enterprise, such as net profit rate, asset liability, innovation capacity, industrial scale, industrial market occupation rate and the like. For example, data assets provided by a certain enterprise have the advantages of complete data, complete structure and large data volume, but the enterprise may have the situations of poor operation condition, no core competitiveness and insufficient innovation capability, so that the effectiveness of the data assets and the mining and analysis potential are not superior.
Disclosure of Invention
In view of the above, one of the objectives of the present invention is to provide an intelligent evaluation method for data asset value, which reduces the problem that the evaluation value obtained by the existing evaluation method is separated from the actual basis and has low reliability; and performing asset assessment on the single source data from the data dimension and the data community dimension to obtain the data asset assessment value with high reliability.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an intelligent evaluation method for data asset value comprises the following steps:
receiving and storing source data needing value evaluation;
obtaining the constituent factors of different dimensions of the source data according to the evaluation indexes in preset evaluation dimensions, wherein the evaluation dimensions comprise data dimensions and data community dimensions;
constructing and calculating influence correlation coefficients of the dimensions of the data community of the source data on the dimensions of the data;
and obtaining the source data asset representation according to the different-dimension constituent factors and the influence correlation coefficient.
Further, the source data is a data asset constructed from a plurality of data dimensions.
Further, evaluation indexes in a data dimension and a data community dimension are preset and comprise a labeling measurement index and a self-defined evaluation index of the source data; the annotation measurement index is formulated by international organization, domestic organization and enterprise, and the self-defined evaluation index is added by the source data owner and the user.
Further, the evaluation metrics for the data dimension and the data community dimension are stored separately in different storage computing devices; wherein the multiple evaluation indicators of the data dimension are sequentially stored in different storage computing devices, and the multiple evaluation indicators of the data community dimension are sequentially stored in different storage computing devices.
Further, the influence correlation coefficient comprises a complex function combined by simple functions; and/or, an international standard function; and/or custom protocol functions.
Further, the method comprises the following steps: and obtaining the evaluation value of the data assets combined by the source data according to the source data asset representation.
In view of the above, the second objective of the present invention is to provide an intelligent evaluation system for data asset value, which constructs a data dimension and a data community dimension, so as to evaluate and obtain a data asset evaluation value with high reliability.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an intelligent assessment system for data asset value, comprising:
the source data transceiver module is used for receiving and acquiring source data needing value evaluation;
the source data storage module is connected with the source data transceiving module and used for storing the source data received by the source data transceiving module;
the data asset module is connected with the source data storage module and used for processing the source data according to the evaluation index of the preset data dimension to calculate and obtain the data asset value;
the data community module is connected with the source data storage module and used for processing the source data according to the evaluation index of the dimension of the preset data community and calculating to obtain the actual data asset value;
and the evaluation module is connected with the data asset module and the data community module and used for constructing an influence association coefficient according to the data asset value and the actual data asset value to obtain the data asset representation of the source data.
Further, evaluation indexes in a data dimension and a data community dimension are preset and comprise a labeling measurement index and a self-defined evaluation index of the source data; the annotation measurement index is formulated by international organization, domestic organization and enterprise, and the self-defined evaluation index is added by the source data owner and the user.
Further, the evaluation metrics for the data dimension and the data community dimension are stored separately in different storage computing devices; wherein the plurality of evaluation metrics in the data dimension are stored in different storage computing devices in order, and the plurality of evaluation metrics in the data community dimension are stored in different storage computing devices in order.
Further, the influence correlation coefficient comprises a complex function combined by simple functions; and/or, an international standard function; and/or custom protocol functions.
Further, the assessment module can also obtain the value of the data asset combined by the source data according to the obtained asset representation.
The invention has the beneficial effects
The invention provides an intelligent evaluation method for data assets, which is used for carrying out asset evaluation on single source data from a data dimension and a data community dimension, and has strong timeliness on the evaluation value of the data, and evaluation indexes can be set by a data owner and a data maker according to requirements and use environments, so that the evaluation value of the data assets with high reliability can be obtained; the method breaks through the evaluation of the traditional data assets only on the data, increases data asset carriers, and calculates the influence of virtual source data from all dimensions of the data asset community, such as the perspective of physical assets, so that the data asset value evaluation is more objective and fair; on the basis, when the method is used by each data provider and user, each data asset is measured on the evaluation index, so that the unified measurement of the data asset value is realized, and the method can be used as a unified measurement unit of the transaction on the financial market to standardize the data asset value. Meanwhile, the invention also provides an intelligent evaluation system for the data assets.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive exercise.
FIG. 1 is a functional block diagram of one embodiment of an intelligent evaluation system for data assets in the present invention;
FIG. 2 is a flow diagram illustrating an embodiment of a method for intelligent assessment of data assets in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The examples are given for the purpose of better illustration of the invention, but the invention is not limited to the examples. Therefore, those skilled in the art should make insubstantial modifications and adaptations to the embodiments of the present invention in light of the above teachings and remain within the scope of the invention.
Example 1
Fig. 1 is a functional module diagram of an embodiment of an intelligent evaluation system for data assets according to the invention. Specifically, a data asset intelligent management system includes:
the source data transceiver module 1 is used for receiving and acquiring source data needing value evaluation;
in this embodiment, the source data is a data asset constructed from multiple data dimensions.
The source data storage module 2 is connected with the source data transceiving module 1 and is used for storing the source data received by the source data transceiving module 1;
in this embodiment, the source data storage module 2 stores source data, and a plurality of source data from the same source may be combined into a total data asset, where the total data asset may be all data assets belonging to one enterprise or organization, may also be a part of a certain enterprise or organization, or may be data assets belonging to a certain group.
The data asset module 3 is connected with the source data storage module 2 and used for processing source data according to the evaluation index of the preset data dimension to calculate and obtain the data asset value;
in this embodiment, the evaluation index in the data dimension is preset, or may be standard, similar to a unified international standard, for example, when evaluating the value of a data asset including GDP, the evaluation dimension using an industry standard is multiple, and includes a labeling measurement index and a custom evaluation index for the source data itself, specifically, the labeling measurement index is formulated by a group including but not limited to international organization, domestic organization, enterprise, or organization, and the custom evaluation index is automatically increased by a data asset provider or evaluator, and is used as a data additional value or a data asset data reliability verification or anti-counterfeiting verification index.
In a specific embodiment, the evaluation indexes in the preset data dimension include data integrity, data size, error ratio, data timeliness, data sensitivity, desensitization, valid data dimension, data authorization period, data industry association degree, data normalization, and the like.
And the data community module 4 is connected with the source data storage module 2 and used for processing the source data according to the evaluation index of the preset data community dimension to calculate the actual data asset value.
In this embodiment, the evaluation indexes in the dimension of the data community are preset or standard, similar to a unified international standard, for example, when evaluating the value of a data asset including GDP, a plurality of evaluation dimensions are provided, including a labeling measurement index and a self-defined evaluation index for the source data itself, specifically, the labeling measurement index is set by an international organization, a domestic organization, an enterprise or an organization, and the self-defined evaluation index is automatically increased by a data asset provider or an evaluator, and is used as a data added value or a data asset data reliability verification or anti-counterfeiting verification index.
In one embodiment, the predetermined evaluation indexes in the dimension of the data community include profit margin, fixed asset size, liability rate, creativity (liquidity of stock market), government policy support, patent number, high-tech talent proportion, automation equipment level, data storage size, tax amount, etc. in this embodiment, a plurality of evaluation indexes in the dimension of the data community sequentially exist in different storage computing devices of the data community module 4.
And the evaluation module 5 is connected with the data asset module and the data community module and used for constructing an influence association coefficient according to the data asset value and the actual data asset value to obtain the data asset representation of the source data.
In this embodiment, the influence correlation coefficient includes a complex function combined by simple functions; and/or, an international standard function; and/or custom protocol functions.
Further, when the source data transceiver module 1 receives a plurality of single data assets, which are all data assets belonging to one enterprise or one organization, or a part of one enterprise or one organization, or a group of data assets belonging to one group, in this embodiment, the evaluation module 5 may also obtain data asset representations of a plurality of source data through corresponding construction influence association coefficients according to a plurality of one-to-one corresponding data asset values and actual data asset values obtained by the plurality of single data assets.
Example 2
Referring to fig. 2, a schematic flow chart of an embodiment of an intelligent data asset assessment method according to the present invention is shown, and specifically, based on the system of embodiment 1, the present embodiment provides an intelligent data asset assessment method, which includes the following steps:
s10: receiving and storing source data needing value evaluation; then, step S20 is executed;
in a specific embodiment, the source data is data asset data assets constructed by a plurality of data dimensions, a plurality of source data of the same source can be combined into a data total asset, and the data total asset can be all data assets belonging to one enterprise or organization, can also be a part of a certain enterprise or organization, or data assets belonging to a certain group.
S20: obtaining the constituent factors of different dimensions of the source data according to the evaluation indexes in the preset evaluation dimensions; then, step S30 is executed;
in this embodiment, the evaluation dimension includes a data dimension and a data community dimension, and a plurality of evaluation indexes in the data dimension and the data community dimension are preset and include a labeling measurement index and a self-defined evaluation index for the source data itself, specifically, the labeling measurement index is formulated by an organization including but not limited to an international organization, a domestic organization, an enterprise or an organization, and the self-defined evaluation index is automatically increased by a data asset provider or an evaluator and is used as a data added value or a data asset data reliability verification or anti-counterfeiting verification index; the source data contributing factors obtained according to the data dimensions are the data asset value in embodiment 1, and the source data contributing factors obtained according to the data community dimensions are the actual data asset value in embodiment 1.
In an embodiment, evaluation metrics for a data dimension and a data community dimension are stored separately in different storage computing devices; in a specific embodiment, the evaluation indexes in the preset data dimension comprise data integrity, data size, data timeliness, data sensitivity, desensitization, effective data dimension, data authorization deadline, data industry relevance, data normalization and the like; the evaluation indexes in the preset data community dimension comprise profit margin, fixed asset scale, liability rate, innovativeness (liquidity of stock market), government policy support, patent quantity, high-tech talent proportion, automation equipment level, data storage scale, tax payment amount and the like, and specifically, the data dimension is marked as M, and the data community dimension is marked as N; wherein, the data dimension M is provided with i indexes (i is more than or equal to 0), and MiThe ith evaluation index is the data dimension of the source data; j indexes are set in the dimension N of the data community, NjThen the j-th evaluation index (j is more than or equal to 0) of the dimension of the data community of the source data is obtained, and the constituent factor of the finally obtained data dimension is a matrix [ M ]1,M2,M3,…,Mi，Mi+1]The data community dimension is formed by a matrix [ N ]1,N2,N3,…,Nj，Nj+1]。
In this embodiment, the datamation of the evaluation indexes in the data dimension and the data community dimension is to set all the evaluation indexes to be a single evaluation function, which may be a complex function formed by combining different simple functions, including but not limited to a discrete function, a continuous function, a function(s) of one degree, a power function, a trigonometric function, and the like; can be a national (international) standard function defined for a specific index, and can also be a person, an enterprise or an organization, including an industry custom protocol function; or an empirical model; for example, the data dimension evaluation index M1For evaluating the index of the data integrity, a field data quantity base number is set as a, the missing quantity is set as b, and the integrity is set as:
the evaluation base is A as MiThe basic values of the index are as follows:
the values of a, and b in the formula can be set by itself, for example, in a specific embodiment, a is 10, a is 10000, and b is 50, then M is1Of course, this formula is merely an example, and may be a function of any other model.
S30: constructing and calculating an influence correlation coefficient of a data community dimension of the source data on the data dimension; then, step S40 is executed;
in this embodiment, let the correlation coefficient of the influence of the data community dimension on the data dimension be Pk，N1Indexes are evaluated for data carrier dimensions (physical assets, i.e. data community dimensions): the profit margin. Through the historical data of the last 40 months, the monthly profit rate can be synthesized by analyzing:
wherein A is the evaluation base number of the data dimension, A is 10, B is the standard interest rate of the bank in the month of 4%, and y is the profit rate of the month of 6.5%. By calculating N1The influence correlation coefficient in the present embodiment includes: complex functions combined by simple functions; and/or, an international standard function; and/or, custom protocol functions; and/or empirical models, which of course can be calculated with reference to a single evaluation function set by the evaluation index, e.g. M1Is shown in the formula (1); specifically, in one embodiment, when the data dimension M is the net profit margin of the enterprise, N is calculated according to a set single evaluation function1Coefficient of data correlation P1The correlation is taken as 100%; and enterprise size N2Correlation is P2The correlation is taken as 50%; and so on.
S40, obtaining source data asset representation according to the constituent factors and the influence association coefficients of different dimensions; then, step S50 is executed;
in one embodiment, a value measure for an asset of source data is set to VkV is obtained from the constituent factors and influence correlation coefficients obtained in step S20 and step S30kIn one embodiment:
a is the evaluation base of the data dimension, M1、M2、M3…MiIs a constituent factor of a data dimension, C is an evaluation cardinality of a data community dimension, M1、M2、M3…MiI evaluation indexes of data dimensionality are respectively, and subscripts are serial numbers of the evaluation indexes; c is an evaluation cardinality of the dimension of the data community; p1、P2、P3…PiThe numerical values corresponding to the i correlation coefficients are represented by subscripts which are serial numbers of the i correlation coefficients; n is a radical of1、N2、N3…NiI evaluation indexes of the dimension of the data community are respectively shown, and the subscript is the serial number of the evaluation indexes.
In one embodiment, A is 10 and C is 1, and evaluation indexes and correlations of the dimensions are calculatedCoefficient of coefficient to obtain a specific VkThe value:
v according to the source datakThe characterization of the set source data assets is as follows:
S50: and obtaining the evaluation value of the data total assets combined by the source data according to the source data asset representation.
In one embodiment, the value of the data assets is evaluated not by a source data (single data asset), but by all the data assets belonging to an enterprise or an organization, or by a part of an enterprise or an organization, or by a group of data assets belonging to a group, in this embodiment, the method of steps S10-S40 is used to obtain a plurality of source data asset characterizations, and finally, the evaluation value of the data assets is obtained.
In one embodiment, the total data assets with the same attribute comprise k single data assets (source data), the source data constructs and calculates influence association coefficients of data community dimensions of the k source data on the data dimensions, and V is setkFor the value evaluation of the kth source data, according to step S20, the ith data dimension index M of the kth source data is constructediPiIn one embodiment:
then all evaluation metrics for the data dimensions of the k source data can be set as:
wherein, a single MiPiData is stored in the ith independent computer device, MiPiConnected by key fields and independent of each other. Further, may be based on MiPiFunction mining is carried out through analysis and learning so as to determine MiIncluding but not limited to automatic or manual analysis by means of auxiliary tools; learning includes, but is not limited to, learning means such as AI, machine learning, and the like. In this embodiment, the set characterization of M represents the composite data value of from 1 to i source data in the data dimension. For example, calculate the value of GDP data in multiple sectors, sector 1 government data value M1Indicating GDP 1; district 2 government data value M2Representing its GDP2 … … district i government data value MiRepresents the GDPi thereof; through the calculation of the steps, M can be obtained1P1+…+MiPiThe integrated data value of the dimensions from region 1 to i GDPs is characterized.
Similarly, all the evaluation index components of the data community dimensions of the k source data are obtained by the same method:
in another embodiment, a source data asset representation is obtained in step S40, and according to the asset representation, the asset value of the total data asset of k source data is set as V, where V is:
while the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. An intelligent evaluation method for data asset value is characterized by comprising the following steps:
receiving and storing source data needing value evaluation;
obtaining the constituent factors of different dimensions of the source data according to the evaluation indexes in preset evaluation dimensions, wherein the evaluation dimensions comprise data dimensions and data community dimensions;
constructing and calculating influence correlation coefficients of the dimensions of the data community of the source data on the dimensions of the data;
and obtaining the source data asset representation according to the different-dimension constituent factors and the influence correlation coefficient.
2. The method of claim 1, wherein the source data is a data asset constructed from a plurality of data dimensions.
3. The method of claim 1, wherein the evaluation metrics in the data dimension and the data community dimension are preset, including a tagging metric index and a custom evaluation index for the source data itself; the annotation measurement index is formulated by international organization, domestic organization and enterprise, and the self-defined evaluation index is added by the source data owner and the user.
4. The method of claim 2, wherein the evaluation metrics for the data dimension and the data community dimension are stored separately in different storage computing devices; wherein the multiple evaluation indexes of the data dimension are sequentially stored in different storage computing devices, and the multiple evaluation indexes of the data community dimension are sequentially stored in different storage computing device storage computing devices.
5. The method of claim 1, wherein the impact correlation coefficient comprises: complex functions combined by simple functions; and/or, an international standard function; and/or, custom protocol functions; and/or, empirical models.
6. The evaluation method according to any one of claims 1 to 5, further comprising the steps of:
and obtaining the evaluation value of the data total assets combined by the source data according to the source data asset representation.
7. An intelligent assessment system for data asset value, comprising:
the source data transceiver module is used for receiving and acquiring source data needing value evaluation;
the source data storage module is connected with the source data transceiving module and used for storing the source data received by the source data transceiving module;
the data asset module is connected with the source data storage module and used for processing the source data according to the evaluation index of the preset data dimension to calculate and obtain the data asset value;
the data community module is connected with the source data storage module and used for processing the source data according to the evaluation index of the dimension of the preset data community and calculating to obtain the actual data asset value;
and the evaluation module is connected with the data asset module and the data community module and used for constructing an influence association coefficient according to the data asset value and the actual data asset value to obtain the data asset representation of the source data.
8. The system of claim 7, wherein the evaluation indexes in the data dimension and the data community dimension are preset, and comprise a labeling measurement index and a custom evaluation index of the source data; the annotation measurement index is formulated by international organization, domestic organization and enterprise, and the self-defined evaluation index is added by the source data owner and the user.
9. The system of claim 8, wherein the evaluation index for the data dimension and the evaluation index for the data community dimension are stored separately on a storage computing device; wherein the plurality of evaluation indicators in the data dimension sequentially exist on different storage operation device storage computing devices, and the plurality of evaluation indicators in the data community dimension sequentially exist on different storage operation device storage computing devices.
10. The system of claim 7, wherein the impact correlation coefficient comprises a complex function composed of simple functions; and/or, an international standard function; and/or custom protocol functions.
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