The latest trends of data asset management and data security at home and abroad

Data asset management should conduct full lifecycle data governance through a unified data model management and control solution , Build an assessment that includes an understanding of the current situation 、 Establish and improve the sustainable data governance system 90 Sky data management project .

In terms of data security compliance and data governance , Balance data security compliance with business value , To understand data 、 Governance data 、 Open data and improve data , And through the establishment of a new generation of data security solutions RUAC（Real User Access Control）, structure “ people - Count - Security ” 3D frame of .” 

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Trend one ： be based on DataOps Realize full lifecycle digital asset management

2022 year 6 month 21 Number , Internationally renowned research institutions Forrester Just released Enterprise Data Catalogs For DataOps Enterprise data asset catalog report . in the light of DataOps The data asset Catalogue under the agile data development and operation scenario gives an in-depth research report .

 1. background ： 

• Metadata is an old concept , But in the modern data platform, it is the fastest changing and most critical component .
• Modern architecture emphasizes loose coupling 、 Microservices 、 Distributed , Need deeper context 、 blood kinship 、 Automated metadata to support
• It is feasible to understand data intelligently , Scenarios supporting consumption metadata ： analysis 、 government 、 The business process 、 compliance .
• Data engineers can obtain business logic and data exploration through data directories , Trade off data architecture 、 Data applications , Locate data flow and performance issues

2. Diversity of data assets （ surface 、 Field 、 indicators 、 Reports, etc. ）、 Multi granularity （ You can run down and roll up ）、 Dynamic update ：

• Support agile data development , Management of the whole life cycle of data products （ Data assets 、 The rules 、 Components ）
• Data product requirements and backlog management
• Data Engineer 、 Data scientist 、 Development process synchronization between application development

3.DataOps Emphasize continuous integration （CI)、 Continuous delivery (DI), Therefore, data kinship becomes more and more critical ：

• It can reflect the current and future data source integration
• Impact change analysis , Data compliance
• SQL/ Code parsing 、 Check , infer

4. Provide modern DataOps And data engineering best practices

• Data engineering is gradually extended to data warehouse 、 Beyond the data Lake ,  Participate in the development of data application
• CI/CD Practice and software engineering skills require the integration of database platform and development platform , Two way communication between data and development  

The above features can be seen in the figure below Datablau DDM、DAM、DDC Integrate with third-party data development platform , Form a unity DataOps The linkage effect of .

 

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Trend two ：90 God complete Data governance startup project

This content comes from the famous netizens in the data circle , Jianghu people “ Data whistleblower ” Of Scott Taylor.  The old man is very active , He has published many popular articles and speeches .

Here is a set 30-60-90 Days of data governance start-up plan , Special landing , It's very instructive . 

1. 30 Sky goal ：“ Understand and evaluate ”

Understand the current data status of the enterprise 、 Interview key stakeholders 、 Inventory IT Environment and high-order information chain 、 data source These preliminary works are common in China .

“1-3 Data exploration of key data sets ”, It is often missing in China , It's very instructive . The preliminary work cannot be done too falsely , The actual data needs a simple exploration , Get a quick look at the data quality . Only in this way can we do solid work . 

Positioning pain points refer to business pain points , It's also very important , Large and extensive data governance is often criticized as having no bright spot and no explicit business value . We need to make detailed roles for business pain points 、 Process research and sorting .

2. 60 Sky goal ：“ To set up ”

Establish a data governance system that supports the long-term sustainability of the organization , Identify short-term and long-term improvement directions .

Here's the data owner It's a difficult point , Data governance is a top-level project , If there is high-level support , Data accountability can be pushed down .  Another option is to recruit subject area experts , To lead the construction of data governance in different business domains .  Identify a key business domain or data governance dimension , Set short, medium and long-term goals .

Of course , These systems need a tool platform to land , Corresponding resources are also needed for construction and long-term operation and maintenance , At this stage, the evaluation can be started .

3. 90 God The goal is ：“ improvement ”

Make a significant improvement , Prove value . 

first 30 We did business interviews and data exploration the other day , the second 30 Days to locate some business pain points . Now we can start to make improvements . Improvement must be implemented , Reflect the effect of repairing the problem . Then collect feedback from stakeholders , Publicity and Implementation . Be recognized , Secure long-term plans for the future .

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Trend three ： Data governance is Data security compliance The basis of

Data security compliance and data governance are inseparable .  Data governance is the foundation of data security compliance .  This trend has matured internationally .

Data is a double-edged sword , On the one hand, it can release the value of data , On the other hand, we need compliance data 、 control risk .  The bottom layer is data governance . 

Therefore need Go ahead and classify data assets 、 Apply data security policies to data assets .  Controlling from the source side is more than just the falling of data standards , Data assets are also collected at the business system development and design stage .  At the same time, the back-end data services are controlled .  Finally, a closed loop is formed through feedback .

Internationally mature data security platform , Secure data 、 Risk control and compliance 、 Unified governance .

 

 

The picture below is Datablau Solutions implemented in many financial industries . The bottom layer is data assets , adopt people - Count - Security Manage data security in a three-dimensional integrated way . To achieve RBAC/ABAC Granularity for data security management .

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By understanding the latest trends of data asset management and data security at home and abroad , It's not hard to see The data model management and control solution is effective in the management of the whole life cycle of data assets . As a pioneer in data asset management ,Datablau Innovate data governance mode , Independent research and development DAM Data asset management platform and data security management platform .

DAM The metadata management module of the platform can collect and summarize all data in the enterprise , Through the data asset management module , With certain technical management means, a large number of data assets can be easily classified , And realize the redefinition of attributes of classified data assets , It belongs to the data authority department .

Data security management platform , Intelligent classification and grading mechanism 、 Data is designed according to the master partition rules , Support the maintenance and management of various data classification and classification attributes , Design the system and process of C landing data access throughout the county , Realize the process control of data access . Based on data capitalization 、 From the perspective of asset business , Ensure that data assets are known 、 Manageable 、 controllable 、 You can use .