Analysis on the Harvard Business Review article Simplify your analytics strategy by by Narendra Mulani.

1. Simplify Your
Analytics Strategy
-by Narendra Mulani

2. Introduction To
Analytics
 While the interests
in analytics and
resulting benefits
are increasing by
the day, some
businesses are
challenged by the
complexity and
confusion that
analytics can
generate.
To overcome this,
companies should
pursue a simpler
path to uncovering
the insight in their
data and making
insight-driven
decisions that add
value.

3. How to Simplify
Analytics Strategy
1. Accelerate the data.
2. Recognize that each path to data insight is
unique.

4. Accelerate the data
1

5. ◎ Fast data = fast insight = fast
outcomes.
◎ Real-time delivery of analytics speeds
up the execution velocity and
improves the service quality of an
organization.

6. ◎ At its core, next-gen
business intelligence
is bringing data and
analytics to life to
help companies
improve and
optimize their
decision-making and
organizational
performance.
1.1 Next-Gen Business Intelligence
(BI) and data visualization
◎ BI does this by
turning an
organization’s data
into an asset by
having the right data,
at the right time and
place (mobile, laptop,
etc.), and displayed in
the right visual.

7. 1.2 Data discovery
◎ Through the use of
data discovery
techniques,
companies can test
and play with their
data to uncover data
patterns that aren’t
clearly evident.
◎ When more insights
and patterns are
discovered, more
opportunities to drive
value for the business
can be found.

8. 1.3 Analytics applications
 Applications can simplify advanced analytics as they put
the power of analytics easily and elegantly into the hands of
the business user to make data-driven business decisions.
 They can also be industry-specific, flexible, and tailored to
meet the needs of the individual users across organizations
— from marketing to finance, and levels from C-suite to
middle management.

9. 1.3Machine learning and
cognitive computing
 Machine learning is an evolution of analytics that removes
much of the human element from the data modeling process
to produce predictions of customer behavior and enterprise
performance.

10. Recognize that each
path to data insight is
unique.
2

11.  The path to insight doesn’t come in one
single form. There are many different
elements in play, and they are always
changing — business goals, technologies,
data types, data sources, and then some
are in a state of flux.
 Another main component of a company’s
analytics journey depends on the company’s
culture itself.

12. 1. For a known problem with a known solution
2. For a known problem area, fraud
The two approaches depending on the
nature of the business problem

13. 1. For a known problem with a
known solution
 Such as customer segmentation and propensity
modeling for targeted marketing campaigns —
the company could take a hypothesis-based
approach by starting with the outcome ,pilot
and test the solution with a control group and
then scale broadly across the customer base.

14. 2. For a known problem area,
fraud
With an unknown solution, the company could
take a discovery-based approach to look for
patterns in the data to find interesting
correlations that may be predictive—for instance,
a bank found that the speed at which fields were
filled out on its online forms was highly correlated
with fraudulent behavior.

15. • Insight 1
There are 2 ways to simplify analytics Strategy
1. Accelerate the data.
2. Recognize that each path to data insight is unique.

16. • Insight 2
There are 2 approaches depending upon the
nature of the business problem:
1. For a known problem with a known solution
2. For a known problem area, fraud

17. Managerial Relevance
• Managers should always simplify analytics strategy
• Managers should follow the 2 approaches depending
upon the nature of business problem.
• Managers should to make the data-driven decisions
that place action behind the data.

18. Thanks!
-Rahul Chawla