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As our Conversations on Big Data is a series of discussions about using analytics in creative and interesting ways, Lara Shane of the Partnership for Public Service and I speak to people who are already doing just that. Not surprisingly, these people all share a common vision of an end state where the power that quantitative analytics brings to decision making is leveraged daily through processes of discovering and interpreting complex patterns of everyday life and then capturing that knowledge. If you have ever wondered what that vision, an Analytics Utopia, is like, Carter Hewgley, Director of Enterprise Analytics at FEMA described it for us recently.

Carter says “in an ideal world, everybody is capable of doing analysis. Quantitative analysis is a professional thing that you should not treat as another duty.” All citizens would understand the concepts behind analytics, which fosters an environment where “data-driven decision making” is the norm, rather than a capability starting to proliferate in innovative organizations. Even those who do not enjoy doing analytics would at least have a comfort level that helps built trust, which Carter says “is absolutely the thing that you have to establish with the people that you’re working with.”

Data would be readily available through a totally invisible “behind the scenes connectivity to really powerful information” that is generated and collected through standard processes that are well defined, auditable, and timely. That data is integrated in a secure environment that leverages common standards and definitions. Unstructured data is mined for patterns that can be analyzed and ultimately mapped to those standards, which includes the veracity of each data source. Supplemental data that provides pertinent dimensions and history is robust with sufficient levels of aggregation to enable initial analyses by everyday users. Identity management tools would actively monitor, protect and enable access to data by defining common user groups and roles that apply privacy or security standards.

Professional analysts use standard tools to build analytical models, acquire the data needed to fully test their models and the hypotheses they represent, run the models, and then interpret the outcomes. They use versatile and simple tools to present their findings at various levels of detail, using media suited to differing audiences. They engage with business SMEs and process owners across wide areas of function in joint “stat sessions” that are conducted in complete confidence and trust that their purpose is continuous improvement. Outcomes are transformed into knowledge that is retained and actively managed. There are no wrong answers; the outcomes of analysis contribute to learning—even if they are unexpected.

Leaders invest a great deal of their own time in analytics and are expected to provide a common vision across their organization. Executives are expected to frequently review their strategic initiatives and needs against the current knowledge base and analytic programs to ensure that relevant data exists to make any possible foreseeable data-driven decision. They are also expected to lead more strategic efforts that identify knowledge gaps so that the data needed is acquired and integrated well in time to answer the questions of the medium to long term future.

Business managers include support for analytics as standard budget items. They leverage past successes using analytics to justify further programs to widen or build on current knowledge in logical, incremental steps. They know when to “let go of solving the whole problem in favor of solving one particular part of the problem”, and when digging deeper is justified. Process owners insists on simplification, standardization and re-use of shared process so that process performance can be readily measured, evaluated, and the process tweaked if needed.

Does this sound like your idea of Analytics Utopia? Then you may be interested in knowing Carter Hewgley’s top three pieces of advice for building it from scratch:

1.       Find a Champion: Carter says the first step is to find someone “at a leadership level in your organization that does care about data. Find them and make sure that they are championing your cause and that you can leverage their belief in this to further it.”

2.       Assess the Culture: You have to know what your starting point is. According to Carter “none of us has the luxury of pretending … if they’re not into it, then you need to have a different strategy than if they are on board.”

3.       Aim for a Quick Win: “Pick a problem that they care about that you have data … and show them really quickly that, hey, if you did this differently you could” get a better outcome. The value of that outcome can then be associated with the analytics and justify the next step.

Check out past Conversations on Big Data for more tips on how to build your own Analytics Utopia.


This article represents the views of the author only, and the information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. No one should act on such information without appropriate professional advice after a thorough examination of the particular situation.

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