Research On Data Assetization And Its Supporting System

As the technologies of Internet,big data and artificial intelligence develop,many industries are faced with challenges of transformation and upgrade.Different from past technological changes,data has become an important form of asset,and how to manage and utilize data asset is a key factor to a successful transformation.But there has not been a clear definition of data assetization till now.There is also a lack of systematic theory and methodology.There are six categories of problems concerning data assetization.The first category includes problems about definitions,namely definitions of data and data assets as well as different types of data assets and their position in accounting statements.The second category is about legal issues,including data ownership,data rights,data copyrights,data rights transfer and so on.The third category includes issues about institutional issues,namely whether data should belong to the state,the public or private corporations and issues about open government data.The fourth category is about market activities,including data product pricing,data market function structure,data market supervision,data taxation and some other issues.The fifth category is about international flow of data,including transborder data flow,data sovereignty,data localization,data autonomy,et cetera.The sixth category is about technical issues,including data asset measurement,data product form,monitoring data flow,data circulation system as well as technical support for the other five categories of problems listed above.This paper focuses on data assetization and its supporting system based on technical realization and technical attributes of data.In discussing these topics,this paper addresses general issues such as data asset definition,data assetization framework,data production and data product circulation.And it also addresses issues about circulation system of data products,as well as issues about data pricing and measuring,data product form,data publishing and data self-governing openness.A definition of data asset was proposed alongside with a data assetization framework,a formal framework model of data asset measurement and pricing based on measure spaces,as well as a“two-step authorization pattern”for data product supporting platform.A product form of big data products based on Data Box is designed,and the supporting system of big data products based on data self-governing openness is constructed.The main innovations of this paper are as follows:(1)Based on the attributes of data,a definition of data asset was proposed.In order to address the problem of vagueness in the definition of data asset,the related concepts of data asset as well as their historical development were reviewed.In addition,the physical attributes,existence attributes and information attributes of data and data asset were discussed.Based on these attributes,information assets,digital assets and data assets were merged into data assets.Data asset was defined as valuable,measurable,and accessible data resources in cyberspace owned by an accounting subject.According to the definition and attributes of data asset,data assets possess the characteristics of both tangible assets and intangible assets,as well as those of current assets and long-term assets.Therefore,data asset should be considered as a new category of assets and required new accounting titles in accounting statements.(2)A basic framework of data assetization was proposed.The criteria of data assets and the transformation from datasets to data assets are critical questions for data industry and the actors involved in data economy to solve.The four necessary and three additional requirements for data to become assets were discussed.A basic framework for data assetization based on features of data assets was proposed,including five phases of segments,namely rights concerning data resources,data value confirmation and quality control,data box building and storage,asset pricing and evaluation,as well as data asset depreciation and appreciation management.(3)A formal framework model of data asset measurement and pricing based on measure spaces was proposed.To address technical problems of data asset measurement and pricing,we propose a framework model of measurement and pricing based on measure spaces,which includes measure spaces for discrete,continuous and product data.By introducing measure spaces,we could measure the volume of dataset A?B reasonably,which solves the problem of pricing for joint datasets.In case of repeatable data services such as subscription services and API services,the integral?1)(9indicates the price,in which1)describes the service counts.Moreover,the Lebesgue integral is utilized for modeling usage-based pricing products.Finally,we use product measures to formulate more complicated pricing scenarios.The case studies proved the effectiveness and generality of our proposed framework.(4)A two-step authorization pattern of data product circulation was proposed.To address the problem concerning the production and circulation of data products,we propose that the nature of data product circulation is a form of authorization that usually does not require actual ownership transfer.We propose a two-step authorization pattern of data product distribution.In the first step of authorization(namely platform authorization),the platform obtains the authorization of data product source.In the second step(namely end-user authorization),end-users are authorized by the platform to acquire standardized data products.In addition,we designed a corresponding structure of data product supporting platform system,which includes a data platform,reader,pricing rules,copyright protection management mechanisms,as well as data infrastructure.The two-step authorization pattern may cover most of the current supporting systems of data product.(5)A form of big data product was designed.In order to address the issue about the form of big data product,we propose a design based on Data Box.The designed form includes scale requirements,content integrity,a certain measuring unit and machine readability.The scale requirements refer to the possibility to include a large number of single-type data products in a big data product.Content integrity refers to the completeness of every single-type data product packed in the big data product besides the requirement that all the content,individually or bundled together,can be interpreted independently.The measuring unit chosen in the designed form is the Data Box.The requirement of machine readability means that data included in the big data product should be machine-readable format.This designed form of big data product may be applied to big data products supporting system based on data self-governing openness.