Data Governance

According to DAMA International, "Data Governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.”

Data Governance Process

The Process Stages of Data Governance
To truly manage data as a valued enterprise asset, data governance must be managed as a business function like finance or human resources. The primary business processes (illustrated in the figure below) enable data governance and stewardship, which include processes that cleanse, repair, mask, secure, reconcile, escalate, and approve data discrepancies, policies and standards. There are over twenty distinct processes segmented into four core process stages – all of which are iterative and encompass many parallel activities depending on the stage of maturity:

source: Informatica

• Discover processes capture the current state of an organization’s data lifecycle, dependent business processes, supporting organizational and technical capabilities, as well as the state of the data itself. Leverage insights derived from these steps to define the data governance strategy, priorities, business case, policies, standards, architecture and the ultimate future state vision. This process runs parallel and is iterative to the Define process stage as Discovery drives Definition, and Definition drives more targeted focus for Discovery.
• Define processes document data definitions and business context associated with business terminology, taxonomies, relationships, as well as the policies, rules, standards, processes, and measurement strategy that must be defined to operationalize data governance efforts. This process runs parallel and is iterative to the Discover process stage as mentioned above.
• Apply processes aim to operationalize and ensure compliance with all the data governance policies, business rules, stewardship processes, workflows, and cross-functional roles and responsibilities captured through the Discover and Define process stages.
• Measure and Monitor processes i) capture and measure the effectiveness and value generated from data governance and stewardship efforts, ii) monitors compliance and exceptions to defined policies and rules, and iii) enables transparency and auditability into data assets and their life cycle.

A data governance initiative must build competencies, assign roles and responsibilities and invest in technologies to enable these core processes no matter the scope and scale of your business objectives. A pilot data governance project focusing on improving the quality or security of a single data item, phone number as an example, should follow the same approach as a holistic data governance function that’s managing all business critical data assets.

Benefits of Data Governance

Benefits of Data Governance Include:

• Decrease the costs associated with other areas of Data Management.
• Ensure accurate procedures around regulation and compliance activities.
• Increase transparency within any data-related activities.
• Help with instituting better training and educational practices around the management of data assets.
• Increase the value of an organization’s data.
• Provide standardized data systems, data policies, data procedures, and data standards.
• Aid in the resolution of past and current data issues.
• Facilitate improved monitoring and tracking mechanisms for Data Quality and other data-related activities.
• Increase overall enterprise revenue.

Data Governance Drivers and Regulations

The Drivers and Regulatory Requirements of Data Governance
While data governance initiatives can be driven by a desire to improve data quality, they are more often driven by C-Level leaders responding to external regulations. In a recent report conducted by CIO WaterCooler community, 54% stated the key driver was efficiencies in processes; 39% - regulatory requirements; and only 7% customer service. Examples of these regulations include Sarbanes–Oxley Act, Basel I, Basel II, HIPAA, GDPR, cGMP, and a number of data privacy regulations. To achieve compliance with these regulations, business processes and controls require formal management processes to govern the data subject to these regulations. Successful programs identify drivers meaningful to both supervisory and executive leadership.

Common themes among the external regulations center on the need to manage risk. The risks can be financial misstatement, inadvertent release of sensitive data, or poor data quality for key decisions. Methods to manage these risks vary from industry to industry. Examples of commonly referenced best practices and guidelines include COBIT, ISO/IEC 38500, and others. The proliferation of regulations and standards creates challenges for data governance professionals, particularly when multiple regulations overlap the data being managed. Organizations often launch data governance initiatives to address these challenges.