1

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.  A finance function is comprised of multiple core business processes such as accounts payable, accounts receivable, payroll and financial planning.  So to manage data governance as a function, what are the core business processes that comprise data governance?

I use the following graphic to illustrate the primary business processes that enable data governance and stewardship, which of course include the expected 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 likely encompass many parallel activities depending on the stage of maturity you find yourself: 

 

  • 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.  For more on measuring data governance effectiveness, see my post “Measuring Data Governance: Lies, Damned Lies, and ROI.”

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.  The difference of course is the level of effort, time, resources and enabling technologies required to effectively deliver business value.  The process to validate, cleanse, improve, and monitor the quality of ‘phone number’ in a single application for a single business unit – while far from being a miniscule task -will be significantly less effort than managing hundreds, thousands or more business critical data entities across an entire global enterprise.

To make these concepts easier to consume and understand, I will be drilling down into more specific definitions for each of the sub-processes within each of the core process stages in four separate blog posts to be posted once a week over the next month.

FacebookTwitterLinkedInEmailPrintShare
This entry was posted in Data Governance and tagged , , , , , , , , . Bookmark the permalink.

One Response to The Process Stages of Data Governance

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>