There is an inextricable relationship between enterprise digital transformation and data asset management. The key to unraveling this relationship is to clarify the logic of the following two:
Enterprise digitalization VS data center
Is it necessary for enterprises to build a data center for digital transformation?
You can understand that the digitalization of an enterprise does not require a data center, but the digital transformation of an enterprise cannot be separated from the middle stage. Why do you say that?
Just imagine, turning back only needs to twist the neck, but turning around requires the rotation of the whole body. The digitalization of an enterprise is like the movement of a person turning his head, only a partial change is required, while the digital transformation of an enterprise is a systematic overall change, just like turning around is an overall movement. The data center is the data base for the digital transformation of enterprises. When an enterprise starts to consider digital transformation from an overall perspective, systematically, and systematically, it must consider the construction of the enterprise data center.
Data Management VS Data Governance
The two words data management and data governance are easily confused in literal meaning, and they are also easily confused in daily work communication.
The data management system that everyone is most familiar with is DAMA. In DAMA, the data management functional framework is divided into 10 functional areas, one of which is data governance. From this perspective, it seems that data governance should be part of data management, but looking at other 3 core activities (data architecture, data development, data operations management), 3 data characteristics (data quality, metadata, data security), 3 comprehensive solutions (data warehouse and business intelligence, documents and content, reference data and master Data) it is not difficult to find that data governance seems to cover these contents.
So is there any difference between the two, and where is the difference?
Data management and data governance seem to talk about the same thing, but the perspectives of the two are different, just like two sides of a coin.
In order to understand the difference between data management and data governance concepts more clearly, take a car as an example. A car consists of four parts: engine, chassis, body, and electrical equipment. These four parts can be independently designed and manufactured. But whether these four parts can be assembled together to make the car drive is a systemic question.
The perspective of data management is to disassemble activities into independent parts, which is easy to understand and accept for the construction of each activity itself. However, as users, they are more concerned about how to efficiently collaborate among various activities to achieve business goals on this basis. How to coordinate and collaborate with each other is the scope of data governance.
To sum up, data management focuses more on the ability of independent data activities, while data governance pays more attention to how the collaboration between various data activities can form an efficient whole that serves business goals.
Data Management VS Data Asset Management
As everyone recognizes the value of data, enterprises have mentioned the concept of data asset management more in the past two years. The core of distinguishing the difference between data and data assets is to deepen the understanding of the concept of "assets".
Assets refer to the resources formed by the past transactions or events of the enterprise, owned or controlled by the enterprise, and expected to bring economic benefits to the enterprise. The following is a popular understanding of assets from three perspectives:
value perspective
As an asset, it means that it can bring economic benefits to the enterprise, which means that the asset can bring value. The understanding of this concept is very important in data management and data asset management, because enterprises will generate a large amount of data on a daily basis. Management is unrealistic in terms of cost and energy. The concept of data assets guides everyone to focus on management. This point is also relatively easy to understand, just like a chair is only used as a tool for people to put forward various opinions on it: quality issues, comfort issues, convenience issues, etc. Talking about quality out of context, performance is useless. This is also an important reason why many companies have proposed data governance activities based on business applications in the past two years.
cost perspective
Cost means considering the benefit or return on investment on the basis of considering the value, which is rarely involved in data management in the past.
Belonging perspective
Assets mean ownership and use rights, and it also means that data ownership, data privacy, and data security are in a very important position. The above is also a very significant difference between data management and data asset management.
Enterprise Digitization VS Data Asset Management
From the two different dimensions of starting point and focus, enterprise digitalization is aimed at the application of data value, and data asset management is more concerned with the orderly management of data. It can be understood that enterprise data asset management is the channel to realize the digital transformation of enterprises , the goals set by enterprise digital transformation for enterprise data asset management.
Enterprise digitalization involves a larger scope, and data asset management is only the core part of it. When it comes to enterprise digital transformation, in addition to enterprise data asset management, more consideration needs to be given to the innovation and change of enterprise business models brought about by digitalization. Digitalization promotes Production equipment, product upgrading and the impact on the market, etc.
These contents are not within the scope of enterprise data asset management. Enterprise data asset management only enables enterprises to have a data foundation for digital transformation. From this perspective, it can be understood that enterprise data asset management is only a necessary condition for the success of enterprise digital transformation. is not a sufficient condition.
Enterprise data asset management is like a fitness process. The enterprise has a corresponding strong body, but how the enterprise should develop depends on the wisdom and actions of the enterprise management team.
The series is to be continued, so stay tuned...