Is enterprise data governance a blind follow-up?

In the wave of digital transformation, enterprise data governance has become a hot topic. However, not all enterprises can truly understand the value and significance of data governance, and sometimes they even blindly follow the trend. The author emphasizes that enterprises should formulate reasonable data governance strategies based on their actual conditions, avoid formalism, and ensure that data governance work can truly bring value to the enterprise.

Last week, we published an article titled "Why is enterprise data governance so painful?", which sparked a heated discussion in a data community. During the discussion, a friend gave us a blow: "Don't manage if it's painful. You're in pain, and you're making others suffer!" I was always tongue-tied and didn't know how to respond for a while. Fortunately, other friends helped me out and gave examples of the application of data governance in business value from their own work practices, which eased the embarrassment!

In fact, this brother's feelings are very understandable. As we mentioned in the article "Why is enterprise data governance so painful?", many colleagues in business departments believe that data governance not only does not really improve their business status and help them solve specific problems in the business process, but also increases their daily workload and even assesses the data governance work they participate in. No one would be happy if they had a troublemaker for no reason!

Summarizing the discussion in the group, there are three schools of thought regarding enterprise data governance:

①Thinking that data governance is just looking for trouble and adding to everyone’s pain;

② Believe that the ultimate solution to data governance may not be to govern data;

③ Believe that data governance is necessary and should be carried out from a business perspective to help the business reduce costs and increase efficiency.

Nietzsche once said, "There is no truth in this world, only perspective." Obviously, the above are all different perspectives based on everyone's own position, but there is no lack of reflection of a phenomenon from everyone's discussion: for some companies, data governance has not been truly understood and accepted, but is suspected of blindly following the trend.

If enterprises cannot truly realize the significance and value of data governance and take the initiative to learn and practice it, but only passively follow suit and follow the trend in terms of "slogans", then enterprise data governance will be a waste of money and time, and full of complaints. "Others are doing it, so we have to do it too" has never been the reason for us to start something.

We once said in the article "Enterprise data is rotten like shit, is there any hope?" Enterprise data governance is the governance of enterprise data processes and data rules, so that data has rules to follow, measures to measure, and people to manage in the production and circulation process, ensuring the health, stability, smoothness, and accuracy of data. On the one hand, it prevents data from getting sick during the development of the enterprise, and on the other hand, it provides rehabilitation treatment for sick enterprise data.

Data governance is a task that runs through the entire life cycle of enterprise digitalization from beginning to end. The importance of data governance is self-evident. So what exactly are enterprises blindly following?

1. Blindly following trends in goals and methods

Perhaps every leader has a proud lion in his heart. When setting data governance goals, it is necessary to benchmark against industry leaders, and the methodology should not be worse than that of industry leaders. Therefore, no matter what the actual situation of the enterprise is, a high-sounding data governance goal and plan that can make the group's senior management applaud is a must.

Of course, there are also some "merchants" who add fuel to the fire, resulting in the same theoretical solutions being everywhere in the market, and some companies with no governance experience are very likely to regard them as "treasure books" and copy and apply them.

When goals and methods are out of touch with reality, pain and failure follow. "Fuzzy governance goals, full of theories, unclear needs, eager for quick success, and the more you do, the more tired you become" has almost become a common data governance phenomenon.

At the same time, closely following the data governance goals is the pace and plan of data governance. Once the goals are not set reasonably, the execution pace and plan will also go astray.

The lack of a holistic understanding of enterprise data issues, the lack of data governance goals and methods that are in line with the actual situation of the enterprise, and the lack of progressive governance plans and strategies are the primary problems caused by blindly following the trend of data governance.

2. Blindly following the implementation plan

Blindly following the trend of implementing plans is actually a desire for quick success and instant benefits. "If others can do it, we can do it too." This kind of "confidence" is not uncommon in data governance. In order to cope with reports, assessments, performance, etc., everyone expects to see significant results in the short term. From the beginning, they blindly follow the trend to formulate unrealistic implementation plans, and have no patience to move forward step by step. This leads to the actual governance work being unsolid and out of touch with objective facts.

Moreover, with the frequent downsizing and layoffs in enterprises and the frequent job changes of employees, no one has the patience and energy to cultivate a sapling that will not bear fruit for the time being. People are more concerned about how much the sapling has grown during the reporting period, rather than how much fruit the sapling will bear in the future.

No one cares whether the implementation plan is reasonable or not. Everyone only cares about whether there will be any output in tomorrow's report.

3. Blindly following the trend of team configuration

In fact, the personnel, scale, collaboration mechanism, and personnel capabilities of each company are not exactly the same. If we just blindly refer to the data governance methods of other companies to configure personnel instead of adapting to our own actual situation, it is very likely that there will be difficulties in personnel coordination and poor project progress.

For example, in some companies, the business department has its own data owner, and these people are often appointed as the data owner of the business data; in other business departments, there is no data owner, so the responsibility of the data owner may need to be assumed by other colleagues in the business department or product personnel in the product department who are responsible for the corresponding business system. The two situations cannot be copied in the same way, such as the data owner must be the business personnel, and in this case, it is necessary to make adjustments based on the actual situation.

4. Blindly following the trend of assessment mechanism

In enterprise data governance solutions, data assessment is almost a standard feature, as if no one will execute without assessment. In reality, this may be true. Without a unified governance assessment standard, the quality of our data cannot be guaranteed, and the subsequent troubles may be more and more painful. This is the significance of governance assessment.

But is it necessary to conduct an assessment? I think not. First of all, the assessment mechanism can be lenient or strict. It is necessary to adopt different assessment mechanisms at different governance stages. For example, some e-commerce platforms have almost zero threshold for registration in the early stage of investment promotion. In the later stage, when there are more merchants, the registration threshold is raised and the registration review is strengthened. Secondly, the introduction of the assessment mechanism must be timed correctly. Many companies have not yet started to implement data governance projects, but they have introduced the assessment mechanism. Which cow or horse will be happy to cooperate if the performance is deducted and the bonus is deducted?

In fact, our team has recently had some discussions and exchanges on the issue of "assessment of data governance", and one consensus is: no one likes to be assessed! Therefore, we are thinking about the purpose and significance of the assessment mechanism, and under what circumstances and in what form it is more reasonable and effective to launch it. We will also write a special article to analyze this issue in the future, so stay tuned!

How can enterprise data governance avoid blindly following trends?

Regarding this issue, some friends in the data community have given some very good suggestions. Combined with the above analysis, the summary is as follows:

① Fully understand the actual situation of enterprise data, formulate reasonable and feasible phased goals, strategies and plans based on the current situation and business goals of the enterprise, and avoid false and empty talk;

② Focus on business pain points and find data focus points from business pain points;

③ Pay attention to employees’ emotions and adjust strategies and methods in a timely manner;

④ Rationally utilize existing resources and tools within the enterprise to help advance governance work. For example, a friend suggested that the "data asset list" is a good tool for data governance!

⑤ Be patient and down-to-earth. Data governance is a long-term task that requires continuous practice and improvement. Therefore, your mindset is very important!

In short, there are thousands of methods, and the best one is the one that suits you!