Tips for Building a Data Science Capability

to use a fraction of their power. By “over-purchasing”
on analytical tools, an organization can unnecessarily
increase its analytical investment and make
it harder to prove a return on that investment.
Furthermore, the learning curve associated with the
high-end tools may make it harder for organizations
to realize the most value from their data. In the
end, information consumers must understand the
analytical insights from these tools with at least
enough certainty that they are confident in its output.
Embrace simplicity where simplicity will do, and save
complexity for the hairy problems. If the information
consumers understand the analytical results 95
percent of the time, perhaps they will “just trust us”
the other 5 percent.
WHEN DONE RIGHT, THE PAYOFF IS WORTH IT
Like most things in life, if it is hard, it is worth it.
Building an analytics-driven culture doesn’t happen
overnight and it is not something that you can
force. As many can attest, organizational inertia is
powerful. However, by following these foundational
steps and taking care to avoid common myths and
pitfalls, organizations can begin a journey to set the
cultural conditions needed to deliver on the promise
of analytics. It is up to organizational leaders and
employees alike to build upon these elements and
design and grow their own version or interpretation
of an analytics-driven culture. Only then can the
promise of game-changing performance be
truly realized.
12 | WHY HASNT YOUR DATA SCIENCE INVESTMENT DELIVERED ON ITS PROMISE?