This is peng Wenhua’s 158th original article \
Yesterday, CAI chunjiu, director of DAMA international data management association China branch, shared the in-depth interpretation of DMBOK2.0 internally, and the content was very rich in 2 hours. I can’t match teacher CAI’s height and breadth. As a member of DAMA, I will not make a fool of myself. Today, I would like to choose a point from Teacher CAI’s sharing and share my own understanding and project experience with you.
In addition, I challenge the Spring Festival not closed, every day to share original articles, welcome to add my personal wechat: Shirenpengwh, join to urge more group, small whip urges me to write quickly
Data governance project success rate
When I took the advanced examination, the teacher shared a data with us. According to the survey result of American Project Management Association, the scope, time and cost of the project completely met the requirements, only a pitiful 26%. Others are either scope creep, or time and cost overruns.
Again, all the projects are mixed together. This kind of data management does not love the mother, most of them are led by THE IT department, and IT is difficult to do, and there is no immediate benefit of the project, the success rate of course is lower.
To be honest, such projects in the government industry, state-owned enterprises such mandatory control of the local probability of success is relatively greater. Internet enterprises are mostly business oriented, really very difficult to do. Don’t believe me, look at this:
The following was seconded by many:
So, not say big factory data management to do very good. On the contrary, it is quite normal for them to have these problems precisely because they develop so fast and often neglect their work.
Correct open posture for data governance
Teacher CAI shared a very classic film, in fact, can also explain the data management project construction difficult, low success rate. The original film had a line break, and I redrew it:
Let’s look at this graph. What are we talking about when we talk about data governance? Leadership is more about improving data quality, and the underlying message is “don’t let data quality bother me”!
What about the things you do? Basically focus on “07 data quality management” on. However, data governance is not pure data quality management at all, and data quality management is not a single point of problem, but a systemic problem.
As far as I am concerned, a data display actually needs to go through a series of lengthy chains such as product design, program design, database design, business use, data index standard, index data calculation, business data display and so on. Any link in the chain that goes wrong is likely to go wrong. Only the latter three fall under the responsibility of the data department.
In other words, the data quality problem is the result of the whole company’s operation, but the data department is always blamed. Similarly, governance data, of course, also need to mobilize the power of the entire company to do a good job. This point is made very clear in the figure above.
In DAMA’s DABOK, the first three chapters are the most important, respectively:
- 01 Data strategy and planning;
- 02 Data organization and responsibility;
- 03 Data policy and system.
These three parts are the cornerstone of the entire data management, supporting the entire data governance system! Enterprise data governance must start from the top-level design of data governance, interpret how to plan data strategy, how to build data governance organizational structure, ensure that the data governance system clarifies clear goals and directions, and implements responsibilities to ensure effective progress of work. \
In other words, if these three points are not done well, data governance is out of the question and data quality is blank SLATE.
What about strategy, planning, organization and system?
Some students will complain. What’s the point of just saying a system? How exactly do you land? Come, take a data to manage whole case today, make clear to you!
Here’s what I like about the govt/SOE, at least at the strategic level they really get their hands dirty! I don’t think I’m wearing anything. Of course, that’s where the discomfort comes in, and a lot of the work is backwards. The chart above is very clear, business development needs (there is a policy need not cut out), so it is necessary to develop a strategy, under the guidance of a department, a general manager responsible for starting the integrated data management project. If nothing else, at least the responsibility to the people, put in place. \
Look at the target, it doesn’t look like our Internet’S OKR, not even quantified. But I can reliably tell you that this goal is worth more than going from 60 percent to 80 percent. Because that’s the underlying thing, the way to solve the problem from the root. In other words, what we want is not a one-time improvement, but a long-term, continuous improvement in data quality. This is where we Internet companies and all kinds of private enterprises should learn.
This plan seems to be different from the task dismantling mentioned in our project management, which feels very unprofessional.
However, if you look closely, this schedule is not the implementation of the ground, but the strategic implementation of the schedule. Several points: perfect system, clear assessment mechanism, organization construction, system construction. All of these points perfectly support the above objective. What’s more, if we recall the map of DAMA just now, is it a perfect fit between strategy and planning, organization and responsibility, policy and system? There is even a system construction, taking into account the data application effect level.
This is “the data management of some company manages method”, it is directory only, of course, and the directory after still desensitization. To tell the truth, why the government, state-owned similar projects success rate is greater? Because they’re all tightly controlled scenarios. That said, the following must be done.
We through the directory to see all of the core, goals, principles, organization need cure to team for support, the concrete working method should be how to carry out, on the system how to support, how to track and to supervise, through the system of performance appraisal to further strengthen traction, mobilize the power of the entire organization together to do a good job of data governance.
To be honest, this sort of thing is a bit like “army making beds” — useless but very important “housekeeping” work that doesn’t quickly make everyone understand its true value. The best thing you can do is just call the shots and everyone else does the same. All that talk is useless.
conclusion
DAMA’s “DABOK2.0” was released, and CAI shared it very well. As a member of DAMA, I would like to share my knowledge and experience in strategy, planning, organization and system.
In the whole data management system, data strategy must take the lead and lead the development of the whole thing. And the most fundamental, is in the planning, organization, system and other levels. But the vast majority of companies think of data management/data governance as improving data quality, even if the leaders just get bored with data fights and don’t want to be in this situation anymore. This kind of recognition is most undesirable and will directly lead to the failure of data governance.
The correct way to open the data strategy first, then clear the target, and then make the implementation plan, from the system, organization, mechanism, system several levels, do a good job of support; A series of policy documents such as “management methods”, “assessment standards” and “data governance principles” must be issued; The most important thing is that the leader takes the lead and all levels work together.
Extended reading: 4 data governance theory +11 data governance cases, public account “big Data Architect” background reply “complete governance” can be downloaded. \
And that’s it for today’s share. Welcome to add my wechat account: ShirenpengWH to discuss big data and data analysis. Share an original content to everyone every day, we learn together and make progress together. \
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