I’ve been presenting research on big data and data governance for the past several months where I show a slide of a businesswoman doing a backbend to access data in her laptop.

The point I make is that data management has to be hyper-flexible to meet a wider range of analytic and consumption demands than ever before. Translated, you need to cross-train for data management to have cross-fit data.

The challenge is that traditional data management takes a one-size fits-all approach. Data systems are purpose built. If organizations want to reuse a finance warehouse for marketing and sales purposes, it isn’t often a matter of a simple match and then a new warehouse is built. If you want to get out of this cycle and go from data couch potato to data athlete, a cross-fit data training program should focus on:

Context First: Understanding how data is used and where it will provide value drives platform design. Context indicates more than where data is sourced from and where it will be delivered. Context answers: operations or analytics, structured or unstructured, persistent or disposable? These guide decisions around performance, scale, sourcing, cost and governance.

Data Governance Zones: Command and control data governance creates a culture of “no” that stifles innovation and can cause the business to go around IT for data needs. The solution is to create policies and processes that give permission as well as mitigate risk. Loosen quality and security standards in projects and scenarios that are in contained environments. Tighten rules and create gates when called for by regulation, where there are ethical conflicts, or when data quality or access exposes the business to significant financial risk.

Speed and Democracy: Intelligence and insight limited to a few in the organization also limits business potential. Consider how to move insight gained in analysis and business intelligence into automated processes and real-time events. The ability to amplify insight into operations speeds up the rate of insight return and casts a wider net into the market. 

Data Performance Management: As data becomes more crucial to doing business, managing its performance is critical. Organizations that create a tangible connection between data investment and business outcomes out perform their peers. 

Hub and Spoke: In a context first data world, how you centralize or federate data management and capabilities is aligned to information needs and subject matter expertise. Seven data patterns have emerged that take a hub and spoke concept to manage data and demonstrate what can be gained by this flexible reference architecture approach.

You don’t have to go it alone. Research and toolkits are available to help you discover, plan, act and optimize your data management program. Research provides the frameworks and client examples to make you successful. Toolkits provide assessments, models, templates, and checklists to get started.  This is living research, so, check back regularly. To get started, take a look at our executive overview, which walks you through the different modules of the playbook. And let me know in the comments below your thoughts on data system flexibility and what you think Forrester should address in its Data Management Playbook.

This blog originally appeared at Forrester Research.

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