CHECK OUT THE PREVIOUS ARTICLES OF THIS SERIES:

In the previous articles of this series we have discussed challenges with existing models of data management and data governance, and talked about a possible approach to data management and governance that uses two models: business capability and data and information value chain.

Now, finally, it is time to transform the theoretical discussion into a practical approach to the implementation of data management/governance.

There are two key topics we will discuss in this last article:

  1. the roles of data management and data governance in their Yin-Yang duality
  2. main steps for optimization of the data management function in your company.
1. The roles of data management (Yang) and data governance (Yin) in their duality

Before making final conclusions about the roles of data management and data governance, we need to recall some of principles that we have already discussed.

Principle 1. Data management is a business capability.
The key purpose of this business capability is to be in control of company’s data and information resources.

Principle 2. Data management capability consists of the set of (sub) capabilities.
The list of these (sub) capabilities depends on the chosen perspective of specifying data management. This perspective depends on the relationship between data management and the IT function.

The “narrow” perspective is the viewpoint on data management from the tasks to be done by data management professionals. The “broad” perspective takes a look at data management from the enterprise viewpoint on the lifecycle of data and information resources.

The graphical representation of the data management model can be seen in Figure 1.

The green rectangles represent data management sub-capabilities that belong to the “narrow” viewpoint. The “broad” viewpoint on data management is extended by sub-capabilities marked in grey.

Figure 1. The universal metamodel of data management

Principle 3. Data governance is one of the data management sub-capabilities.
The purposes of data governance are:

  1. the establishment of a data management framework
  2. the coordination of effective functioning data management.

Principle 4. Data management and data governance perform their activities at different business levels.
Usually, each business considers strategic, tactical, and operational levels. Data management/governance deliverables vary, depending on the level.

Principle 5. Data management and data governance functions deliver data management and data governance capability correspondingly.
All said above allows us to make the following conclusion about the relationship between data management and data governance.

CONCLUSION

Data management is a business capability that safeguards company’s data and information resources and optimizes data and information value chains to ensure effective conduction of business.

Data governance is a sub-capability of data management that focuses on the establishment of the data governance framework and the coordination of activities of other sub-capabilities of data management.

It is the task of data governance to:

  • develop the data management framework
  • establish common practices and develop templates for framework deliverables
  • coordinate activities of other data management sub-capabilities
  • audit and control the correspondence between the designed and implemented framework

Other data management sub-capabilities should:

  • implement the data management framework
  • align activities with other data management sub-capabilities.

Now let us highlight the main steps in building a harmonized data management ~ data governance system.

2. Main steps in building a harmonized data management ~ data governance system.

The approach I have present in this article, is described in detail in my books published in the past two years:

The Data Management Cookbook

A summary of a practical approach to implementing data management function.

The Data Management Toolkit

A detailed step-by-step implementation guide of data management function for medium-sized companies.
The book includes templates and a case study.

The “Orange” Model of Data Management

A collection of techniques and templates for the practical establishment of the data management through the design
and implementation of the data and information value chain, enabled by a set of data management sub-capabilities.

There is one practical lesson that unites all these books:

The setup of the data management function within the company follows the logic of the development of the data and information value chain and data management sub-capabilities that enable this chain.

I define “data and information value chain” as the set of actions supported by the collection of data management sub-capabilities. This chain enables transformation of raw data into meaningful information to deliver the corresponding value propositions to the stakeholder groups.

Let’s see how it works in practice.

Step 1. Specify the definition, scope and key sub-capabilities of data management.
You should choose a set of data management sub-capabilities that fits your company goals, resources, size, and culture.

A common set of data management sub-capabilities includes data management framework (data governance), data quality, data architecture, and data modeling.

The rest of the capabilities such as application and technology architecture, security, different information technology capabilities belong to the information technology (IT) capability.

Step 2. Specify the value propositions that data management delivers to its key stakeholders.
The key stakeholders are, for example, internal management, governmental and regulatory bodies, external customers. Data management should deliver particular value propositions for each group. For example, for internal management enhanced decision making is one of the examples of the value proposition.

Step 3. Specify the key data and information value chains.
To deliver the specified value propositions, corresponding data and information value chains should exist.

Step 4. Assess the as-is and to-be levels of maturity of the data management (sub) capabilities.
Based on the assessment’s results, a gap analysis can be performed. The results of the gap analysis lead to the development of a data (management) strategy and/or the roadmap.

Step 5. Implement the data management function of the required scope.
For this step, I have developed a driver-based method for the implementation of data management function (which serves as the basis for my methodology, and is described in detail in The Data Management Toolkit). The graphical representation of the method called “The Data Management Star” can be seen in Figure 2.

Figure 2. The data management star by Data Crossroads.

The methodology includes the following sub-steps.

Preparation. Define the main business drivers for setting up data management.
The key success factor of each data management implementation is to make it feasible. The specification of key business drivers allows narrowing the scope of a data management initiative.

Step 5.1. Defining needs and requirements.
Specific to data management, each of the business drivers deals with a particular set of data. Major companies would start with customer data as the most critical one.

Step 5.2. Dividing tasks and responsibilities.
At this step, data governance comes to the scene. At this step, you set up data management framework by:

  • specifying the place of data management function in the organizational structure
  • designing policies, processes, tasks and roles, etc.

Step 5.3. Building the data management function
There are several questions that will be answered as a result of the implementation of data management function:

  • What information do data stakeholders really need and why do they need that information?
  • What is your data?
  • Where is your data located?
  • Which transformations does the data undergo on its way from the original source to the end user?
  • What is the required quality of your data?

The design of the function is highly dependant on the business drivers. During this step, you deliver all required data management sub-capabilities specified in Step 1.

Step 5.4. Intermediate assessment and gap analysis.
At a certain point in your implementation, you need to perform an intermediate audit. Its main purpose is to ensure that the work you have already completed complies with the original plan.

Step 5.5. Setting up new goals and planning new actions.
The data management star method is iterative by its nature. There are different iterations that you can perform interations:

  • within each step
  • between steps
  • of all steps when a new business driver comes along.

If you have reached this step, it means that you are ready to extend your data management function to meet the requirements of new business drivers.

At this point, we have reached the end of the series of articles about data management and data governance Yin-Yang duality.

Should you have some remarks or points to discuss, please share them in the comments section below, or reach out through LinkedIn or e-mail.