What Is Data Governance?
Data governance refers to a system and approach to assigning rights, duties, and authorities for an enterprise, its regulators, or its shareholders to receive reliable, authentic, and accurate information.
Companies with data governance programs are better at extracting value from data. These organizations are more data-literate, more data-driven, and more capable of adapting to changing business environments. Many organizations have difficulty getting started with Data Governance Consulting and how to ensure success and deliver defined value. Our experience has shown that organizations are more successful when they have a small, focused, and right-sized data governance initiative. Focusing on one critical business use case, which requires high-quality data, is the best way to start. Too large a data scope can lead to failure.
These steps give organizations a framework to create a data governance plan and begin thinking strategically about data assets and managing them.
- Establish “Why” – Do You Need A Data Governance Program?
The first and most important step in establishing a data governance program is to identify the top reasons your company needs to improve data governance.
This is essential because you will need to align stakeholders with your drivers to implement and operationalize the program and justify formalizing data governance. It is important to identify clear use cases that your organization will use to help you align resources and priorities.
Three best-practice scenarios are recommended when you start a program
Compliance And Risk
Compliance with data protection regulations, and risk management, is often the primary driver of data governance policies and controls. This is a board- and executive-level problem. Identify the key compliance requirements of your business, assess your compliance risk and develop a core program to manage it.
Every business has data that flows through its information systems. Data is used in every business process. Data drives strategic and tactical decisions via analytics and reporting. The more careful management of data assets can bring a company greater value. This is a multifaceted value that can be achieved through increased customer satisfaction and retention, higher top-line revenues, lower costs, new market expansion, and competitive advantage. You need to identify a strategic initiative or project that is ongoing or upcoming and which relies on accurate, reliable data to make sure it succeeds.
- Define Data Domains
When people are identified as being responsible for making decisions about data priorities, issues and priorities, organizations can begin to define their data domains, the critical elements within those domains, and who owns those elements. The best way to decide which data domain(s) or data object(s), is to start small and consider the following scenarios.
- Identify regulatory or compliance risks that must be addressed
- Identify and identify an operational improvement or strategic initiative that requires precise master data.
- Identify the most critical report (or group of reports) that you rely on to make decisions.
Once you have selected one of the scenarios above, identify the most crucial data elements for that scenario. Once you have chosen a scenario, start building your data catalog by documenting the key elements and collecting metadata about these elements. Your data dictionary and data catalog can be built quickly and easily using metadata and data catalog tools. Some organizations find that implementing a data-catalog tool is a great way to start. Others may be able to use their existing technology and people skills. This activity will align people around data definitions, and business use, and help to establish priorities and identify the most important data. To establish the concept of data management, it is important to identify and assign data owners to each element.
- Establish Data Governance Policies & Standards
The data governance bureaucracy must remain agile and efficient by having a modern, foundational and effective data governance program. But, every program must have policies and standards that are followed by people and functions.
You will then create policies to drive compliance and adherence with your data controls. It is important to follow these principles when you are defining your policies or standards.
- Clear and understandable policies are needed to ensure compliance with data protection regulations as well as internal information requirements.
- Define and approve data specifications for critical elements, defining what an acceptable format, value, and structure are.
- You should ensure that overhead costs for setting and enforcing policies don’t hinder your program’s adoption, growth, or scalability.
- These governance policies and standards can be supplemented with defined stewardship.
Your data community should be able to find, understand, and consume policy and standards easily. They should be managed from a central repository. Regular communication about new standards, policies, and changes to existing policies must also be established.