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