By Frank Dravis, Senior Consultant
I recently co-hosted a DM Review Radio show on data governance, and the topic of workflow came up. As you can imagine there were a variety of thoughts on the subject, but my position is this: workflow, specifically when focused on DG activities, is what binds the whole function together. Case in point: in a DG council one person suggests that a policy be created that says “customer privacy preferences will be made readily accessible and visible to customer call center attendants.” Imagine the workflow necessary to convince other stakeholders to agree to the policy, to refine it, to vote on it, amend it, and then forward it to the data stewards, line of business, the customer service department, and then to IT for design and implementation of the policy in the call center application.
Without a formalized workflow, the process—from formulating the policy to seeing it implemented in software--could fail fall flat on the floor, where it will languish until either a diligent user or steward realizes no progress is being made, or customers complain that their privacy has been violated.
It can be difficult to implement an automated workflow between disparate systems. But in the gaps between systems where there are no electronic interfaces, formalized manual hand-offs can be established and someone can be assigned the role of monitor. But many MDM solutions are beginning to incorporate a workflow layer where data stewards can log into the software, see what pending tasks are in their queues and post new activities to the committee, such as “Review draft policy on customer privacy. Be prepared to discuss at next committee meeting.” Such a workflow system will streamline DG activities, and will set the stage for them to be coupled with the business unit and/or data steward workflows so that when the policy is approved it can be automatically and electronically distributed to the steward’s task queue for implementation.
This accelerates accelerating not only the speed of decision making, but the speed of executing on the decision. Even better, if customer service discovers a problem with the policy that was invisible until implementation, the same automated workflow can be used to amend it. Now we’re talking about being adaptable. Data governance, just like any other business activity stands to gain much from automated workflows. Stay tuned, because they’re coming.
Frank Dravis is a senior consultant with 21 years of experience in enterprise information management (EIM) and data quality solutions design, implementation, and consulting. He specializes in data integration, data quality, and data governance solutions, advising key clients and industry vendors.
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