Tips for Building a Data Science Capability
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CASE IN POINT<br />
<strong>Data</strong> scientists who are centralized in one government<br />
agency’s organization are empowered to<br />
prioritize their own projects. Often the data scientists<br />
use financial return as the primary criterion to<br />
prioritize their ef<strong>for</strong>ts. This essentially means that<br />
projects focused on other strategic objectives, such<br />
as improving customer experiences or decreasing<br />
propensity <strong>for</strong> operational errors, are deprioritized.<br />
Those responsible <strong>for</strong> such outcomes must often<br />
design and plan solutions without the benefit of the<br />
analytical insight that data science can provide.<br />
The risk here is that data scientists may expend<br />
their tremendous talent on questions that only<br />
serve pockets of the enterprise, rather than<br />
delivering on the promise of data science that<br />
can drive the collective enterprise to the next level<br />
of per<strong>for</strong>mance.<br />
DESIGN THINKING RISES TO THE DATA SCIENCE<br />
CHALLENGE: GROUNDING AND AMPLIFYING THE<br />
ANALYTIC METHOD<br />
Solving the data science challenge is about an<br />
organization’s ability to focus, embrace, and use<br />
analytics to generate meaning and impact that can<br />
result in the next level of organizational per<strong>for</strong>mance.<br />
One way to do this is to inject the art of design<br />
thinking into an organization’s analytics approach.<br />
Design thinking is a problem-solving and innovation<br />
methodology—a tool box of techniques born from the<br />
designer’s mindset. It emphasizes solving problems<br />
by starting with people (e.g., customers, employees,<br />
patients) rather than starting with technology or<br />
business positioning.<br />
In particular, design thinking can be a powerful<br />
complement to data science, given its natural ability<br />
to support the seamless shift between deductive<br />
and inductive reasoning. Design thinking follows a<br />
BOOZ ALLEN’S DESIGN THINKING METHODOLOGY<br />
Immerse<br />
Observe and document<br />
human experiences to<br />
gain qualitative knowledge<br />
Synthesize<br />
Use discovered knowledge<br />
to reframe the problem and<br />
shape understanding<br />
Ideate<br />
Combine and contrast<br />
dissimilar in<strong>for</strong>mation to<br />
provoke unexpected ideas<br />
Prototype<br />
Build, test, and and iterate iterate<br />
light and lean examples<br />
The <strong>Data</strong> <strong>Science</strong> Challenge | 37