The document summarizes an analysis of the shortage of data scientists. It notes that while large organizations try to hire expensive experts, more prudent enterprises are taking alternate approaches like empowering small cross-functional data teams. These teams should be charged with delivering measurable benefits quickly by building data capabilities rather than infrastructure. The document advises managers to consider metrics that prevent organizational downfalls and to help employees understand data science principles to create a context for future hiring, rather than trying to find a perfect data scientist.

1. Analysis of Stop Searching for
That Elusive Data Scientist by
Michael Schrage
By Darpanraj Deoghare

2. What do we read in the paper
Surveys say there simply aren’t
enough people with the unusual
blend of software skills and
statistical savvy to go around.
 For many organizations, a
mediocre data scientist may be
worse than none at all.

3. Big organizations can afford — or
think they can afford — to throw
money at the problem by hiring
laid- off Wall Street quants or hiring
big-budget analytics boutiques.
 More frugal and prudent enterprises
seem to be taking alternate
approaches.

4. Insights

5. Insight – No.1
The concurrent rise in Big
Data and analytic
opportunities means that
smart organizations would
be foolish to outsource this
away from the very people
who need to be more data-
driven.

6. Insight – No 2
 Stop hunting for that data
science unicorn and/or
silver bullet. Chances are
slim that your organization
would even be able take full
advantage of their talent.

7. Insight – No 3
Empowering small cross-
functional data- oriented teams
• The teams must be explicitly
charged with delivering tangible
and measurable data- driven
benefits in relatively short periods
of time.
• The emphasis is on building
greater data capability than better
digital infrastructures.

8. Relevance

9. Relevance for Managers
Better Collection Understanding of
the Employees
o Better the opportunities to the
team.
o Better Relations between the staff.
o Better learned the workforce will be

10. COST-SAVING FOR A COMPANY
• Big savings deal for the
company by investing in small teams,
rather than hiring the savvy quants.
• Limited ambition could do a
better job attracting credibility and
support than BHAGs.(Big Hairy
Audacious Goal)

11. Relevance for Managers
Managers need to consider
all possible metrics to lead a
successful organisation.
Managers need to critically
think all metrics without
ignoring the stubborn facts
of fewer metrics which
efficiently help in preventing
the downfall of the
organisation.

12. Conclusion
People don’t need to become data
scientists, but they do need to
understand and appreciate key
principles and practices of data
science.
 The temporary fix of data science
teaming doesn’t solve the problem,
but it creates the cultural and
organizational context for the
necessary hires to follow.

13. Submitted for Internship
"Data Analytics " under
Professor Sameer Mathur ,
IIML
By, Darpanraj Deoghare

14. Summary – Contd.
 This is only the first step,
but a critical one if we are
to derive sustainable
advantage from data, big
and small.

15. Thank You