The term “data governance”, which has been around for more than a decade, has become very popular in recent years. I do not know when, the river’s lake flow out: ** “digital transformation, governance first” **.

As a result, we see: Is not only a traditional provide, BI, master data management, data warehouse metadata management, data integration and data service software vendor in the said data governance, “BATJ” and other Internet companies, large state-owned enterprises, state-owned enterprises are also talking about data management, many enterprises are the data governance as a necessary measure of digital strategy, on the enterprise’s strategic plan of action.

In many enterprises and individuals who talk about data governance, I find that there is a general consensus on data governance: “Data governance is easier said than done”!

Have you really thought through why data governance is necessary?

In the process of data governance consulting, we often encounter the following dialogue scenarios:

Q: Why do you do data governance?

A: We need to establish data standards, improve data quality and achieve unified management of data assets.

Q: Why establish data standards and improve data quality, and what if you don’t?

Answer: There are many data quality problems, and accurate data reports cannot be provided, which affects business efficiency and cannot support the digital transformation of enterprises.

Q: What data reports and businesses are affected?

A: XX report is inaccurate, statistical caliber is inconsistent, data islands between systems, and data integration is difficult…… Blah blah blah…

Q: Why does it cause inaccurate data reports, inconsistent caliber, and difficult system integration?

A: Because the data standard is consistent, the data quality of the data source is poor.

At this point, the 5Why analysis method commonly used in consulting seems to have gotten the status and goals of data governance. We summarize it as follows: Through data governance, enterprise data can be standardized, data quality can be improved, business processing efficiency can be improved, accurate data support can be provided for data analysis, business can be empowered and enterprises can realize digital transformation.

However, we carefully analyze such research results are floating on the surface, around the problem of data in situ, did not really think through why to do data management.

In order for data to generate value, there needs to be a reasonable “business goal”, and all activities of data governance should be carried out around real business goals. Establishing data standards and improving data quality is a means, not an end. so

The first step in data governance is not analyzing data problems, but analyzing business problems, identifying the core business needs of the enterprise, and defining the goals and scope of data governance.

Data governance is not a lofty thing, is basically dirty work, back-breaking work!

Data governance is fire, the DAMA data management body of knowledge in the guide, data governance is located in the “wheel” is the central data management, data structure, data modeling, data storage, data security, data quality and metadata management, master data management and so on ten big sum in the field of data management, data management activities provide overall guidance strategy.

Dama-dmbok 2.0 data management wheel diagram

When it comes to data governance, we often talk about it as a combination of enterprise strategy, organizational structure, data standards, management practices, data culture, and technology tools. Those who have no experience in data governance are bound to think: Wow, data governance is “superior”! It’s strategic, it’s standard, it’s cultural.

However, only if you have really done data governance people know: data governance is not only hard work, tiring work, but also a thankless, often carry the blame, the leadership can not see the value of the work.

They say data is an asset and data governance is important. Although everyone says that data governance is very important and leaders attach great importance to it, in the actual implementation process of many enterprises, there are always problems such as insufficient support from senior leaders, insufficient cooperation from business personnel, and data governance always giving way to business.

The reason: do leaders really value data when they say they do, or are they just saying they do? Has it been incorporated into your strategic action plan?

Data governance requires strategy, system and organization, which is a top-level strategy. Each of these strategies is related to the whole body and requires strong support and promotion from senior leaders, as well as close coordination between business departments and technical departments.

Data governance needs to establish standards, manage processes and clear data, and it needs to sort out and standardize each data domain, data entity, data entry and data item. Sometimes, it even needs to manually define data standards and verify data quality item by item and field by field. Data management personnel should not only have good data thinking, but also have enough care, patience and physical strength to realize the continuous improvement of enterprise data quality and polish the data standards suitable for enterprises.

Data governance is sometimes not understood. Data governance is a foundation project. What people see is always a “high-rise building” of data application. Data governance teams are busy every day, and the leaders do not know what the “group of people” are doing. However, whenever there is a problem with data, the data governance team is the first to be held accountable.

Data governance is not a “project”. Want instant results? Difficult!

A project is a series of unique, complex, and interrelated activities that have a clear goal or purpose and must be completed within a specified time, budget, and resource constraints according to specifications.

So, is data governance a project?

Yes, of course it is.

Whether it’s overall asset management or domain-specific data governance, it involves building a project team, defining project objectives and scope, developing a project plan, advancing the project implementation, and finally summarizing and closing the project.

Data governance is of course a project by definition, with clear goals and specific scope, quality, cost, time, and resource requirements.

However, through the implementation of a data governance project, even if the project budget is large, long cycle, can solve the various problems in enterprise data management and use? Can we cultivate the data culture of the enterprise and change people’s digital thinking? Whether can realize enterprise management and business model innovation?

It can’t be!

The ultimate goal of data governance is to empower the business and increase the value of data. This is a long and continuous operation process that needs to be improved step by step and iterated step by step. It is unrealistic to expect to complete data governance in one step.

Project-based data governance is not comprehensive and has no continuity. It can solve temporary data problems, but it is difficult to obtain sustainable data value.

Therefore, data governance is not a “project”, but rather an ongoing process. We can also think of this process as a continuous, spiraling model consisting of data governance “microprojects.” The end of a project is not the end of enterprise data governance, but the real starting point of enterprise data governance!

What is data governance and how do you do it?

Some time ago, I saw a summary article on the key elements of data governance on the Internet. I thought it was well written and quoted here for your reference:

Data governance needs system construction: to give full play to the value of data, it needs to meet three elements: reasonable platform architecture, perfect governance services, and systematic operation means.

Choose the appropriate platform architecture according to the enterprise scale, industry, data volume, etc. Governance services need to run through the whole life cycle of data to ensure the integrity, accuracy, consistency and effectiveness of data in the whole process of collection, processing, sharing, storage and application. The means of operation should include the optimization of norms, organizational optimization, platform optimization and process optimization.

1. Data governance needs to lay a solid foundation:

Data governance needs to be gradual, but at the beginning of construction, at least three aspects need to be focused on: data specification, data quality, and data security.

Standardized model management is the prerequisite for ensuring that data can be governed, high-quality data is the prerequisite for data availability, and data security control is the prerequisite for data sharing and exchange.

2. Data governance requires IT empowerment:

Data governance is not a pile of normative documents, but needs to implement the norms, processes and standards generated in the process of governance on the IT platform. In the process of data production, data governance should be carried out in a forward way of “beginning from the end”, so as to avoid various passivity and increase of operation and maintenance costs caused by post-audit.

3. Data governance needs to focus on data:

The essence of data governance is to manage data. Therefore, it is necessary to strengthen metadata management and master data management, manage data from the source, complement data related attributes and information, such as metadata, quality, security, business logic, and blood relationship, and manage data production, processing, and use in a metadata-driven way.

4. Data governance needs integration of construction and management:

The consistency of data model blood and task scheduling is the key to the integration of construction and management, which helps to solve the problem of inconsistency between data management and data production caliber and avoid the inefficient management mode of two skins.

The last word

Data governance is not a quick fix. It is a long and ongoing process, with no magic bullet or quick fix. Only by turning data governance into a regular mechanism, just like when we eat and sleep every day, can we form a habit and a culture, persevere, stay true to our original aspiration, and make unremitting efforts, can we achieve the desired goals.