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Tips for Building a Data Science Capability

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RAPIDLY PROTOTYPE IN LOW-FIDELITY WAYS<br />

Design thinking generates low-fidelity prototypes to<br />

demonstrate applicability and test ideas quickly and<br />

cheaply be<strong>for</strong>e making significant investments.<br />

These prototypes span mock-ups, illustrations,<br />

paper-based interfaces, narratives, and other<br />

techniques that are quick and low cost to produce<br />

and easy to test with customers and stakeholders in<br />

near-real time. As feedback is gathered, teams can<br />

build smarter implementation roadmaps. For<br />

example, one technique we practice is a “cupcake<br />

road mapping” approach in which solutions are<br />

planned in delightful portions with increasing<br />

complexity added only when a solution proves<br />

worthwhile. This allows organizations to dedicate<br />

resources to only the most viable solutions that can<br />

make a difference.<br />

CASE IN POINT<br />

A major supply chain company was struggling<br />

with manufacturing data and determining the right<br />

questions to ask of the data. The Booz Allen team<br />

introduced design thinking as a technique to help<br />

reframe the questions and hypothesis. Using design<br />

thinking trained data scientists, the team conducted<br />

a co-generative workshop to unpack the problem<br />

and iterated until they determined a hypothesis<br />

and variable to test. The workshop and subsequent<br />

analysis were so successful that the client<br />

launched two international centers to apply the<br />

same techniques on a larger scale.<br />

FINAL THOUGHTS<br />

The data science challenge is not going away, and it<br />

is often not as simple as creating an algorithm to<br />

generate organizational value. Rather, Booz Allen<br />

finds value by taking a holistic approach deriving<br />

meaningful insights by combining data science with<br />

innovative and creative methodologies such as<br />

design thinking. Booz Allen’s approach <strong>for</strong> realizing<br />

the real potential of analytics rests on this broader<br />

view of capabilities that moves beyond algorithms to<br />

creative and organizational approaches to institutionalize<br />

data science as more than a stove-piped<br />

function, but rather as part of organizational DNA.<br />

40 | HOW DESIGN THINKING CAN HELP YOU REALIZE ORGANIZATIONAL VALUE

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