TDWI: Reach business goals with a unified data management strategy

Organizations should implement a unified data management approach to better support business goals, according to TDWI's Philip Russom.

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A business intelligence competency center (BICC) is a great way to make better use and reuse of your various BI and data warehousing assets. So why not apply the BICC concept to your entire data management infrastructure?

That's the advice of Philip Russom, research director for The Data Warehousing Institute (TDWI). In his latest research report, Russom urges companies to embrace a concept he calls unified data management (UDM).

The crux of UDM, Russom explained in an interview, is to coordinate data management's various disciplines and associated assets -- business intelligence, data warehousing, database management systems, data integration and data quality -- in order to manage them more efficiently and to better align them with strategic business goals.

Much more than a technical exercise, UDM is aimed primarily at making companies understand that corporate data is a critical business asset and helping them treat data as such.

"Unified data management isn't purely an exercise in technology," Russom writes in the report. "Once it paves the way to managing data as an organizational asset, the ultimate goal of UDM becomes to achieve strategic, data-driven business objectives, such as fully informed operational excellence and business intelligence, plus related goals in governance, compliance, business transformation, and business integration."

Practically speaking, UDM means establishing a data management competency center that consolidates various, previously siloed IT teams into a coherent group that can share and reuse technology and create enterprise-wide best practices, Russom said.

In large organizations, for example, different divisions might have their own data integration teams that use different data integration tools. With UDM architecture in place, those teams would be combined and tasked with streamlining data integration techniques and tools throughout the organization.

In addition, Russom said, business stakeholders must also get involved in order to determine how best to align data management practices with the organization's strategic business goals.

"If you really want to be successful at this, you've got to satisfy the business requirements," he said. In other words, companies shouldn't take the UDM approach just for the sake of it but should instead identify key business drivers for doing so.

Facilitating better business intelligence capabilities to improve decision making around sales channels, for instance, could be a driver for creating a data management competency center. Another example is improving customer service by giving call center reps quicker access to accurate customer data.

By reusing data management assets, UDM can not only help companies achieve specific business goals but can also help them learn from their experiences to extend the successes to other parts of the business, Russom said. "UDM really forces business people to think hard [about how they leverage their data management assets]."

As part of his report, Russom polled 179 organizations about the current state of their data management practices. Only 9% reported having a high level of coordination among data management disciplines and teams. But 40% said they plan to achieve a high level of coordination via UDM or a similar concept within three years.

The most frequently cited reason for wanting to implement UDM was to improve BI and data warehousing capabilities, according to the report. Better data quality was also a significant factor.

Of course, any time a company tries to overhaul its data management practices, there are risks. There are bound to be turf battles among previously independent data management groups, Russom said. And making decisions on which vendor tools to consolidate around can be a time-consuming challenge.

Still, Russom encourages organizations to implement UDM for the benefit of the business as a whole.

"Unifying data management practices tends to also unify data," he wrote, "which is good for decision making and strategy development that's based on data."

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