Data governance creates the frame in which data management operates. Data governance rules are the first part of this frame.
CHECK OUT THE PREVIOUS ARTICLES OF THIS SERIES:
In our saga about the duality of data management and data governance, we will look at data governance. Data governance exercises a Yin force by following the requirements of data management.
The following two statements express the idea of the Yin-Yan duality between data management and data governance:
- To move on with the data management/data governance initiative, each company has to specify the definition, scope, and constituting components of data management (as defined in the first article of the series).
- The data governance framework has to conform to the specification of data management(we talked about this in our second article)
The second statement forms the basis for this article.
Before elaborating on it, I would like to re-cap the specification of data management that I have discussed in the third article of this series:
- Data management is a business capability.
One of its key goals is to control data and information resources.
- Data management can be seen from either a “narrow” or “broad” perspective, depending on its relation to the IT function.
The “narrow” perspective is the viewpoint on data management from the perspective of the tasks to be done by data management professionals. The “broad” perspective looks at data management from the enterprise’s viewpoint on the lifecycle of data and information assets.
- Key components of the data management capability depend on the chosen perspective.
The “narrow” perspective considers data management framework (data governance), data quality, data modeling and architecture, and partly application architecture as data management sub-capabilities. In the “broad” perspective, such sub-capabilities as business, application, technology architecture, information technology, and security are added.
- Data management delivers value propositions to different groups of stakeholders.
The data and information value chain delivers the value proposition. To do that, a set of data management sub-capabilities should enable the data and information value chain.
- The data management function delivers the data management capability.
The article gives practical advice to companies that are either starting their data governance initiatives or plan to (re)assess their current approach. It demonstrates decisions regarding data governance a company should make. The numbering of decisions shows the order of decision-making.
Decision 1. Data governance is a sub-capability of data management.
First, let us specify what a business capability is. According to the Open Group: ‘A business capability is a particular ability or capacity that a business may possess or exchange to achieve a specific purpose or outcome’1. In this respect, data governance is also a business capability. Data governance is one of the constituent components or a sub-capability of data management.
The purposes of data governance are:
- Establishment of a data management framework.
- Coordination of functioning and effective data management.
Decision 2. The data management framework has two key components: data governance rules and roles.
I understand a framework is a structure in which data management operates. Two key components of the structure are required to work correctly: data governance rules and roles. The set of rules and roles should match the specification of the data management capability as described above.
Decision 3. The set of data management rules depends on the business level.
Data governance rules specify the way things should be done. There are different types of regulations applicable to data management operations. They vary depending on the organizational level of business: strategic, tactical, and operational. An example of rules can be seen in Figure 1.
Figure 1. An example of rules per business level
STRATEGIC LEVEL
There are three data management rules at the strategic level.
Data (management) principles specify the key assumptions on which data (management) capability should be based. The principles specify:
- why a company needs data (management)
- what benefits will a company gain
- how a company will manage to get these benefits.
A data (management) strategy should:
- specify key business drivers in a long- and medium-term perspective to develop a data management capability
- define the scope and data management sub-capabilities
- align the data management goals with business goals
- outline the structure of each data management sub-capability and the mechanism to coordinate their activities
- specify a way to measure the maturity status.
A data management roadmap should specify the long- and medium-term plans and deliverables to implement the data (management) strategy. It should focus on the achievement of the desired maturity level.
The scope of the data (management) strategy covers the overall data management capability as well as its sub-capabilities.
Data governance should coordinate the integration of strategic plans of each sub-capability into the overall data management strategy.
TACTICAL LEVEL
On a tactical level, each data management sub-capability should specify its rules through policies and standards.
For example, a company would have separate policies, standards, and processes for data quality, enterprise architecture, and security.
A policy, according to Businessdictionary.com, is a ‘set of basic principles and associated guidelines, formulated and enforced by the governing body of an organization, to direct and limit its actions in pursuit of long-term goals’ (source). A policy usually defines rules on the operations of a specific data management sub-capability.
A standard specifies a level of quality or a norm. Think, for example, about data standards that define the rules to describe data.
A process documents actions to be performed to achieve results.
For example, the roadmap should be translated into medium-term plans with a 1-year time perspective.
The set of data management-related roles, accountabilities, and tasks should be specified.
Data governance on a tactical level should focus on the following:
- Development of a standard approach to documentation and a set of templates.
You should expect that, for example, policies and processes would be written in the same or similar format.
- Coordination of activities of different sub-capabilities.
Very often, activities and deliverables of one sub-capability have interdependencies with another. Data governance should assist in the alignment of documentation. For example, data architects’ deliverables would be required to perform data quality tasks.
- Alignment of documentation delivered by different data management sub-capabilities.
Each of the data management sub-capabilities will develop its processes. Data governance should take responsibility for checking these processes for consistency.
OPERATIONAL LEVEL
On the operational level, processes are detailed into procedures. Data management roles should be specified in the form of functional job descriptions.
Data governance should focus on auditing and controlling of results of the implementation of policies, standards, and processes.
I want to stress that each company should develop feasible rules for implementation and execution. They should correspond to the company size, needs, resources, and scope of data management.
Having discussed the rules, we can move on to the roles. We will do this in the following article as the topic is rather extensive.
For more insights, visit the Data Crossroads Academy site: //academy.datacrossroads.nl.
References
- The Open Group. Open Group Guide. Business Capabilities. Prepared by the Open Group Architecture Forum Business Architecture Work Stream. The Open Group, March 2016, p.3
