Defining Data Governance
In this chapter, we will be:
Unpacking the definition of data governance Discovering the elements of a data governance program Understanding the role of data culture Determining data governance readiness.
Governance is not a word that most of us use on a regular basis. Sure, you create data. You use data. You store data. These concepts make sense. But governing data? That’s not something that comes up too often. It sounds abstract, a little exotic, and frankly, complicated.
Governance is the manner in which an entity chooses to oversee the control and direction of an area of interest. It typically takes the form of how decisions are made, regulated, and enforced. When entities grow and increase in complexity, formal governance becomes important. Left ungoverned, the possibility of devolving into chaos is very probable.
Governance is the system that formalizes control, processes, and accountabilities so that specific results such as meeting goals or sustaining standards can be attained.
Organizations want to reap the benefits of data abundance while managing their growing risks. In other words, organizations are now demanding data governance.
Today, when data is managed well, it can drive innovation and growth and can be an enterprise’s most abundant and important lever for success.
Well-managed data can be transformational, and it can support the desirable qualities of a data-driven culture. This is when decisions at all levels of the organization are made using data in an informed and structured manner such that they deliver better outcomes internally and to customers. Research confirms that most business leaders today want their organizations to be data-driven, but, according to a survey by NewVantage Partners, only around 32 percent are achieving that goal.
Successful data governance also means that data risks can be minimized, and data compliance and regulatory requirements can be met with ease. This can bring important comfort to business leaders who, in some jurisdictions, can now be personally liable for issues arising from poor data management.
Every organization manages data at some level. All businesses generate, process, use, and store data as a result of their daily operations. But there’s a huge difference between businesses that casually manage data and those that consider data to be a valuable asset and treat it accordingly. This difference is characterized by the degree to which there are formalities in managing data. organization acts in recognition of the value of its information assets (a fancy term for data with specific value to an organization, such as a customer or product record) is called #enterprise information management (EIM).
Data governance is focused on roles and responsibilities, policies, definitions, metrics, and the lifecycle of data. Data management is the technical implementation of data governance. For example, databases, data warehouses and lakes, application programming interfaces (APIs), analytics software, encryption, data crunching, and architectural design and implementation are all data management features and functions.
Data governance generally focuses on data, independent of its meaning. For example, you may want to govern the security of patient data and staff data from a policy and process perspective, despite their differences. The interest here is in the data, not as much in the business context. Information governance is entirely concerned with the meaning of the data and its relationship in terms of outcomes and value to the organization, customers, and other stakeholders.
Improved data quality
Expanded data value
Increased data compliance
Improved data-driven decision-making
Enhanced business performance
Greater sharing and use of data across the enterprise and externally
Increased data availability and accessibility
Improved data search
Reduced risks from data-related issues
Reduced data management costs
Established rules for handling data
The basic steps for creating a data governance program consist of the following. These steps also form the basic outline of this course.
Defining the vision, goals, and benefits.
Analyzing the current state of data governance and management.
Developing a proposal based on the first two steps, including a draft plan.
Achieving leadership approval.
Designing and developing the program.
Implementing the program.
Monitoring and measuring performance.
Maintaining the program.