Several years ago, during the course of an engagement with a large retailer, I was struck by how the snowballing of data quality issues over time can cripple an organization. Over the years, the company’s head office had actually lost track of the address and phone number of some of its stores. Locations had moved, cities had renumbered or renamed streets, area codes had changed, fingers on keyboards had juxtaposed numbers and letters. With dozens of systems supporting the organization, it was clear that the processes that were used to keep everything in sync were not living up to expectations, to put it gently.
To a retailer, store locations are about as fundamental as it gets. Every phone call or Google hunt to confirm the correct address of a store was costing employees valuable time and made the financial and legal risks inherent in poor data quality very tangible. HQ employees began to distrust the company’s master data, leading them to call and confirm basic information almost by default. Ultimately, location management had become a game of telephone – literally and figuratively.
As a data management professional, I’ve come to loath the words “dual maintenance”. Too often, “dual” really means “multiple”, and there are rarely controls on these processes. Data maintenance efforts tend to cluster where the effects can be immediately felt. More complex or less directly pertinent systems tend to naturally fall to the wayside, forgotten on lists of “Things to Catch Up On Tomorrow”. The divergence of data between systems becomes inevitable.
The answer lies in information governance, and is one that can take multiple forms. A passive data governance strategy is an important and attainable first step. Implementing a robust set of cross-system reporting can provide tactile confirmation of data quality to stakeholders and quickly identify gaps in data and business processes. Combine this with a robust governance organization to enforce and manage this strategy, and most organizations are well on their way to restoring trust in their systems of record.
With a robust passive strategy in place, one can begin laying the groundwork for a systemic approach to active data governance. This can be accomplished either through an MDM solution or in middleware solutions to synchronize data; once and for all removing the human element from the equation and permanently banishing the words “dual maintenance” from the vocabulary.
About the Author
Nate LaFerle brings extensive enterprise data management experience through large-scale global data strategy and migration engagements in retail, manufacturing, finance, pharmaceuticals and the public sector. Based in Chicago, he currently leads a multi-year BackOffice engagement at a Fortune 500 life sciences organization.Follow on Twitter More Content by Nate LaFerle