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  • Todd Harbour

North to Alaska

While more and more CDOs are talking about data governance, they should also realize that governance is technically complex, organizationally challenging and politically sensitive. Besides, securing executive-level sponsorship for governance programs is challenging because, in general, business leaders do not recognize the need for governance. Lack of data appreciation among business leaders is starting to change, however, as executives realize that many business problems — such as regulatory compliance, reduced litigation costs and decision-making transparency — are grounded in good data management practices data governance.


What often happens, though, is data governance often lands, by default, in hands of IT. Unfortunately, when organizations define data as a byproduct of IT, the landscape changes entirely: no longer is data a business asset; it is simply the objective of computing and treated like other IT assets. Technology is a marvelous thing, and we support it wholeheartedly. However, CDOs must separate business problems and technology problems. So, if data is only as valuable as the business processes, decisions and interactions it enables and improves, it MUST remain a business asset, one for which business takes the decisions concerning its acquisition, use and final disposition. The ultimate objective of data governance is to generate the highest possible return on data assets If a business wants to be sure it captures critical opportunities to leverage data to support operations, strategy and customer experience, it needs to govern data assets as it does other enterprise assets — such as financial securities, cash and HR.


In this way, data governance must fill the same sort of role as finance and HR: a coordinated enterprise effort that protects and optimizes the business value of the organization’s assets — including its data assets. As with other business functions, people, policies and processes are required to measure success, compliance and organizational effectiveness clearly. Data governance and the supporting data stewardship processes rely on technologies such as data integration, data quality, master data management, metadata management, data masking, data security, data archiving and BI software. But business needs should drive the process, be supported by robust technology solutions.


More organizations are hiring CDOs and forming data governance bodies to establish data policies that promote data quality and usability when they recognize that data governance is a priority. However, implementing data governance can be complicated, confusing and frustrating. Programs that focus only on organizational structure and operating models or attempt to survey data structure and semantics from the bottom up may need additional support to develop the socialization, championship and resources required to ensure sustainability.


Because today’s organizational data environments are involved, corporate teams must manage numerous systems and platforms for transaction processing, operational processing, BI, reporting and analytics. Some organizations cope with the growing complexity by implementing practices to organize, manage, govern and facilitate use of data assets across business functions and technological boundaries.


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