What is a data asset? How should data asset management be implemented?

2021 year 12 month , China Southern Power Grid released 《 China Southern Power Grid Data asset management system white paper 》, The white paper points out that , Data asset management , It is the only way to release the value of data assets .

The report says , Many enterprises do not have a unified understanding of how data can promote internal quality and efficiency improvement and the release of external value , Lack of institutional planning for the operation of data assets .

With the development of enterprise digitalization , Data asset management is no longer limited to data resources , Data products or data services after data processing should also be managed .

Why is data asset management so valued by central energy enterprises such as China Southern Power Grid , A special white paper , Data asset management is the only way to release data value ？ This article , Xiaoyi will lead you to systematically understand data assets , Data asset management value , And how data asset management should be implemented in enterprises .

—  01  —

What is data asset management ？

1. The concept of data assets

We need to manage data assets , First of all, understand what data assets are ？ For businesses , Data assets are owned or controlled by enterprises and organizations , Data resources that can bring future economic benefits to enterprises and organizations .

Internal sales data , User information , Financial information , Performance data , Organizations can have control , Data resources that can bring economic benefits to enterprises .

For example, e-commerce platforms can be based on user consumption and browsing records , Recommend more suitable products to users , The turnover rate is higher , Users' consumption and browsing data , It can bring economic benefits to the enterprise , It belongs to data assets .

2. The concept of data asset management

We know that data assets can give organizations , Enterprises bring economic benefits , So how to manage it ？

The big data technology standard Promotion Committee of Chinese Academy of communications has given the concept of data asset management , It means planning , A group of business functions that control and provide data and information assets , Including the development of , Implement and monitor the data plan , policy , programme , project , technological process , Methods and procedures , To control , Protect , Delivering and increasing the value of data assets .

For example, the sales data asset management center of an enterprise , The brand department can access , see , download , analysis , Through the empowerment of sales data , The brand department can better understand the market , More conducive to brand communication .

But manpower , financial , Library data assets , Cannot be seen by other department personnel , Protect the security of confidential data inside the enterprise .

3. Data asset management 3 Great value

（1） Promote internal quality improvement and efficiency improvement

Many enterprises have indeed started data governance , Such as manpower , financial , sales , Summary and arrangement of promotion data , Improved the efficiency of the Department , But many data cannot empower other departments , Not giving full play to the effect of data to improve the quality and efficiency within the enterprise .

Such as sales , product , Research and development , The brand data can be communicated with each other , Sales data feed back the brand , Brand promotion is more accurate and effective , The brand builds momentum for new products in advance, etc .

（2） Manage upstream and downstream industrial chains

Enterprises can share data with upstream and downstream companies in the industrial chain , Make the raw materials of the product , manufacturing , Propaganda , The product sales data are clear in the chest , Enable management to make more accurate decisions .

For example, the scale of Apple's suppliers is 200 A number of , Including Foxconn , samsung ,LG And other famous companies , Through powerful upstream and downstream data management , Apple can market , supplier , Production situation , Implement and adjust the business strategy , Whether to increase or decrease production , Timely feedback to the manufacturer , Adjust the production schedule , Ensure that products are delivered on time , Achieve lean management .

（3） Help the country modernize

For some industries related to people's livelihood , Like electricity , Medical care , traffic , Data asset management in banking and other industries , Can be the national industrial layout , Urban road planning , Smart city construction , Provide decision support for rural economic development .

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Data asset management is common 6 Big problem

There are many business organizations in the market , There is often this 6 A common data asset management problem ：

1. Lack of a unified data view

Data resources exist in multiple business systems of an enterprise , Distributed online and offline , Even distributed outside the enterprise , Data cannot be managed uniformly .

2. Weak data base

Most enterprises have no experience in data governance , The foundation is weak , There is a confusion of data standards , Uneven data quality , Data islanding , Hinder the effective use of data , Make data unable to become data assets .

3. Insufficient data application

Many organizations have weak data base and insufficient application capacity , As a result, data application has just started , Mainly in precision marketing 、 Some explorations have been made in limited scenes such as public opinion perception and risk control , The depth of data application is not enough , The application space should be expanded , For example, assist in company management .

4. The value of data is hard to estimate

It is difficult for enterprises to evaluate the contribution of data to the business , Thus, it is difficult to operate data as tangible assets . There are two reasons for this problem ： First, there is no reasonable data value evaluation model ; Second, data value is closely related to the business model of the enterprise , In different application scenarios , The value of the same data asset may be quite different .

5. Lack of secure data environment

The value of data is increasingly recognized by the whole society , But then comes the increasingly rampant crime against data , The data reveal that 、 Personal privacy is infringed and other phenomena emerge one after another . Many data crimes are caused by the imperfect security management system 、 Lack of corresponding data security control measures .

6. Data management floats on the surface

There is no set of data-driven organization management system and process , No advanced data management platform tools have been built , This makes it difficult to implement data management .

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Data asset implementation 4 Key points

In the process of implementing data asset management in many organizations , Yes 4 There are four common key points worth noting ：

1. Data asset scoping

The first is the definition of data scope , What data will be included in the data assets ？

For example, what system data needs to be managed by internal data , How to access and manage external data , Whether cloud data is included in the management scope , Need to have a clear boundary .

2. Data specification

The second key point is the formulation of data specifications , What criteria should be followed to accurately define data assets , What kind of management processes and specifications should be formulated , What are the requirements of the asset service interface specification , All need to make .

3. Data value identification

The third is the identification of data value , How to judge which data is valuable within the organization , For example, some backups , redundancy , Tests and other worthless data need to be screened , Delete and process in the data asset database .

4. Data application design

The last one is the design of data application , The organization should base on the counted data assets , Think about what data sharing services can be provided , What application scenarios can be met .

Like sales data , There are specific sales categories , Sales amount , This data can be shared with the brand department , Let the brand know more about the marketing information , Make publicity and promotion more in line with market demand .

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Data asset management implementation 7 Big steps

Data asset management is so important , It is the only way for enterprise digital transformation , So how should the organization implement the implementation ？ There are mainly the following 7 A step ：

Data asset management implementation 7 Big steps

1. Set goals and scope

First step , The organization should set goals for data asset management , What goals does the organization need to achieve , To what extent , Need a standard .

next , We need to define the scope of data assets , What important data is classified into our data assets , What data doesn't need too much focus , Not included in the scope of data assets .

2. System status survey

The second step is to investigate the current situation of the system , It is the basis of data asset inventory , Through research, we can understand the current situation of the enterprise , And it can collect system access information and documents , Prepare for the next step of data search .

At the beginning, I investigated the enterprise system construction , We can use the form of research , Collect system information , Documentation and test environment information .

next , We can check the collected data , By querying the database , Access to the system, etc enrich, Check whether the database connection is normal ; Test whether the system can be accessed ; Whether the data dictionary has table structure description information, etc .

Finally, deal with the verified problems , By discussing solutions . Such as incomplete documentation , The development department or software supplier can be asked to provide ; If the test environment is missing , It is determined that the newly built test environment also provides access to the production environment .

3. Data asset model development

The third step , We need to design an asset model , The main purpose is to manage information , Business information , Technical information, etc , Form a standardized template .

Generally, the data asset model will design a template that can be used by all categories , Public information for primary management data assets , This attribute template can be created by EXCLE management , Metadata information can also be used for management .

With the data asset model , New data , The model can automatically determine whether it is included in the scope of data asset management .

4. Data asset classification , catalogue , Coding and services

Step four , We need to classify all data assets , catalogue , Coding and providing daily services .

We can use the system theme , Business direction , Industry classification , Organizational structure, etc , Classify data assets .

For example, the data assets of business topics , There are interested parties , Contract account , financial , event , Resources, etc .      

Cataloging data assets , It means that the data owner sorts, edits and sorts the asset data under management , In this way, there will be no omission in the catalogue , And the rights and responsibilities are clear .

Data asset code , It means using a fixed string , Serial number , Time date , How fields work , Imprint data assets , Convenient for subsequent query .

Data Asset Services , It means that the platform provides users with data asset downloads , In exchange for , Inquire about , Agile analysis and other services .

5. Data asset system and process construction

The fifth step , It is necessary to establish corresponding systems and management processes for data asset management within the organization , Including clarifying the role of data asset management , Design data asset management process , Develop data asset management system .

Data asset management role , Usually there are 4 individual ：

Data asset manager , The role is to ensure that data assets are effectively understood , classification , management , Share and use , Ensure that data assets have value , It's usually IT The person in charge of the department acts as .

Data asset Applicants , It is based on business scenario requirements , The person who reviews or applies for the required assets , Include people at all levels , From asset maintenance personnel to enterprise senior personnel .

Data asset approver , It is the manager or supervisor of the management department , Expert team , Generally, it is business / The technical director acts as , Be responsible for approving the addition and deletion of data assets , Application for review and approval .

Users of data assets , It refers to the internal and external users of data , Data users provide data asset usage requirements , Generally speaking , People in various roles can be users of data assets .

The data asset management process needs to be developed , Including the generation process of data assets , Maintenance process , Use process, etc .

Data asset management system , Including data asset flow list , Mainly responsible for data asset review , List of asset flows ; Data asset catalog , It contains various asset classification methods and catalog templates , Convenient for users .

6. Data asset platform services

Step six , Is the use and management of the data asset platform , Input various data asset data , The data asset management platform provides corresponding services , To empower people inside and outside the organization .

Common services are ：

Asset search , The user can find the target resource by criteria

View the data , Managers can view asset data records and clear them , View asset library table structure

Apply for an exchange , To meet the applicant's acquisition needs for assets

Download data , Enables users to download data files

Asset analysis , The data asset management system can analyze data assets

7. Data asset sustainability management

The last step , Is the data asset sustainability management , Need management , System and technical guarantee . Management guarantee , Organized guarantee , Specific data asset management organization ; Institutional safeguards , There are relevant systems that clearly stipulate the data asset management methods ; Technical support , Relevant data asset management system support .

Data asset management capability , It refers to the orderly daily management of data assets , Including asset count , Changes in assets , Asset review , Have a complete set of systems and processes .

Data asset enabling capability , It refers to how data assets enable organizations , Such as asset sharing , Use of assets , Asset analysis , How it is used within the organization , Provide decision support .

—  05  —

Ruizhi data asset management platform

Data asset management is the only way for enterprises to bring digital data value into play , Therefore, organizations need a powerful data asset management platform to manage important data assets .

Ruizhi is a data governance platform for the full life cycle of data independently developed by Yixin Huachen , It integrates data integration , Data exchange , Real time computing storage , Metadata management , Data standards management , Data quality management , Master data management , Data asset management , Data security management , Ten product modules of data lifecycle management , Get through all aspects of data governance , It can satisfy the government quickly , Different data governance scenarios for enterprise users .

Ruizhi data asset management platform , Provide users with a complete view of assets , Managers can have an overview of enterprise assets from the platform , Manage internal data and provide external services in a reasonable way .

1. Cataloguing data assets from different perspectives

Different roles have different requirements for the asset view they view , Therefore, the platform provides data asset cataloguing from different perspectives , Such as technical perspective 、 Business perspective 、 Management perspective, etc .

Cataloging assets by choreographing metadata , The cataloged assets can only be viewed by the corresponding users after publishing the authorization , So as to ensure clear management of role perspective and authority .

meanwhile , Once new assets are added , The asset directory supports editing and modifying again , Ensure the timeliness of assets 、 integrity .

Cataloging management of data assets , To better support the application of various data . Ruizhi platform supports rich service interface expansion , So as to realize the multi-channel application of the assets under management , Maximize the value of data assets .

     

Cataloging data assets

2. Powerful data asset retrieval

The platform provides powerful retrieval functions , Various types of assets in the platform can be retrieved according to various dimension indicators .

The search conditions include : keyword 、 Search type 、 View times 、 amount of downloads 、 Number of exchanges 、 Rich search conditions such as creation time , So as to realize the rapid search and positioning of data assets .       

Data asset retrieval   

3. Rich data asset applications

Ruizhi data assets provides rich applications , We can learn about asset types 、 size 、 Creation time , And view the asset metadata information 、 data 、 Apply for exchange 、 Download data 、 Archive data and other operations , To achieve rapid and convenient management of data assets .

Data asset application

4. Efficient and comprehensive data asset analysis

The platform provides multi-dimensional asset monitoring , And show it in an intuitive chart , It makes it easy to control data assets at a glance . The system is based on the asset type 、 The number of assets in the asset directory 、 Statistics of asset data volume , It also provides the basis for Different databases count the amount of asset data , Statistical dimensions such as monthly statistics of asset change .

Visual asset monitoring