The search for better data management practices has led to data stewardship and data governance efforts, but confusion over roles and disconnects between IT and the business have led to gaps in cooperation. The next wave will bring customer data integration and master data management initiatives that promise to relieve business experts from the drudgery of defining and maintaining customer data, while developers will dodge the chaos of point-to-point integration.
To say that companies thrive on information states the obvious. Skim the front page, turn on the Weather Channel, watch a press conference or crack open an annual report, and you'll witness the extent to which organizations depend on data. Regulations and increasingly savvy consumers are raising the ante, turning executives' heads, not only to today's data, but back to yesterday's and toward tomorrow's. From the FCC to the SEC to your marketing department, organizations need to know more in order to do more for customers. Requests to report what we know, make smart predictions, store historical records and track the lineage of our data continue apace.
So you'd think we would have mastered it by now.
But as demand for customer information grows, so does the data. The amount of data now captured and stored by businesses nearly doubles every 12 to 18 months, according to InformationWeek. Today's packaged applications are our new legacy systems, and external data is increasingly a de facto component of the information inventory.
Nevertheless, tactics and processes for managing customer data come and go. (Remember CASE tools?) Executives earnestly discuss customer data management in meeting rooms and boardrooms as the data continues to slip through our fingers. Thankfully, companies are beginning to confront the realities of their data management processes--or lack of them--and acknowledge failed attempts.
The search for a better way has led to data stewardship and data governance efforts, but confusion over roles and disconnects between IT and business have led to gaps in cooperation and frustration for participants. The next wave will bring customer data integration (CDI) and master data management (MDM) initiatives that promise a new "single version of the truth" to relieve business experts from the drudgery of defining and maintaining customer data while developers dodge the chaos of point-to-point integration.
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The Mythology of Data Governance
The phrase data governance is one of the most misused in business. IT organizations attempt to institute data governance to engage the business in bona fide data ownership discussions. Vendors throw the phrase around to convey any number of data management practices, from modeling to quality automation. As it's embraced, the term is watered down until--like knowledge management and CRM--data governance becomes a rubric for a hodgepodge of unrelated practices. Eventually, it's rendered a dirty word.
"We'll do it, but we can't use the word governance here," one IT executive told us. "Let's call it stewardship." And therein lies the problem: The vocabulary of data management is defining our very practices.
Unlike management or stewardship, data governance implies a level of organizational oversight that includes both business and IT. More important, it involves executives who are willing--indeed engaged--in defining their companies' data policies, with one eye on internal and external regulations and another on customer-focused strategies. We define data governance as the mechanisms and decision-making structures for treating data as an asset, instituting formal policies and overseeing the management of corporate data.
Data governance is ambitious. The process itself revolves around an executive committee that establishes policies, resolves conflicts and questions, remains mindful of customer commitments and measures success. It is often embraced prematurely by companies lacking the processes or skills to manage their data. Consequently, executives quickly sour on the monthly council meetings, rigorous measurement and accountability metrics required for effective governance. Until legitimate data management and stewardship take hold, data governance is just talk.
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