AUG 20, 2014 5:00am ET

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Data Governance: The Silent Hero to Achieving MDM Triumph


Companies today have deployed multiple systems that continually aggregate, consolidate, store and maintain a tremendous amount of operational information. Yet in most organizations there are few clear-cut roles, business processes and responsibilities for protecting or enhancing that information as it moves across the enterprise from design to engineering, to procurement, to distribution, to marketing, to eCommerce, and eventually to service and support. As a result, information often becomes replicated and fragmented, which leads to duplicate, conflicting, incomplete and erroneous information that hinders business responsiveness and decision-making.

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Comments (8)
I would argue that the ultimate goal of data governance is to achieve and experience the highest level of data quality, which I define as "the best usable valued data". Organizations want to do this to fulfill one ultimate purpose: to create and keep a customer.

To do this they have pursue data governance initiatives. One of those data governance initiatives is MDM. In other words, MDM is intended to achieve data quality, not the other way around.

Discussions about MDM, data governance and even data integration need to begin with data quality. When all is said and done, MDM, data governance and data integration only exist to pursue the best usable valued data.

Some argue MDM, data governance and even data integration have a greater purpose beyond data quality. This is usually because their understanding of data quality is too narrow and incomplete. In the end, all the other purported purposes for MDM, data governance and data integration turn out to be about data quality.

MDM, data governance and data integration beget data quality, and data quality begets operational, interactional and analytical excellence and better organizational performance...solely for the reason to create and keep customers.

Right now, we have this sequence jumbled. Data quality isn't something needed to attain MDM, data integration and data governance, which then result in some higher organizational and business goal. Instead, MDM, data integration and data governance is needed to experience better data quality which then gets us customers.

It's data quality that gives data governance, MDM and data integration purpose and usefulness. Not the other way around.

It's ultimately data quality that delivers on the promises typically associated with MDM, data integration and data governance. Data quality - good or bad - exists with or without MDM, data governance and data integration.

Anyone and everyone that uses and values data experiences data quality, regardless of the existence of MDM, data governance or data integration.

The latter were only born out of our desire for better data (or information) quality. They are not a pre-requisite for data quality. Nor is data quality a pre-requisite for MDM, data governance and data integration. Improved data quality is, after all, their purpose. Peter Perera @

Posted by Peter P | Wednesday, August 20 2014 at 10:48AM ET
In my experience, Data Governance is Orwellian speak for Data Anarchy. The author quite accurately highlights the consequences about the lack of governance, but ought to have paid more attention to the real problem: Lack of Governance. Imagine the scenario where a society had all the laws it needed, and even kept them up to date, but failed to provide police to monitor violations, courts to adjudicate violations, and prisons to punish transgressors. What would "governance" actually look like? Most organizations think they can put data governance in place, wring their hands, and move on to the next task.
Posted by Edan P | Wednesday, August 20 2014 at 11:57AM ET
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