Analytics Strategy - Description .

1. by Narendra Mulani

2. 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.
Discovering real business opportunities and
achieving desired outcomes can be elusive.

3. Companies should pursue a
simpler path to uncovering the
insight in their data and making
insight-driven decisions that add
value.

4.  To Simplify their analytics strategy and
generate insight that leads to real outcomes:
1. Accelerate the data:
Fast data = fast insight = fast outcomes.

5.  A data service platform combined with
emerging big data technologies.
 Real-time delivery of analytics speeds up the
execution velocity and improves the service
quality of an organization.

6.  For example: a U.S. bank adopted such a
technology environment to more efficiently manage
increasing data volumes for its customer analytics
projects.

7. 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.

8.  BI does this by turning an organization’s data into
an asset by having the right data.
 When the data is presented to decision-makers in
such a visually appealing and useful way, they are
enabled to chase and explore data-driven
opportunities more confidently.

9.  FICO scores, compare lenders and loan types,
etc.

10.  Data discovery can take place alongside
outcome-specific data projects.
 Through the use of data discovery
techniques, companies can test and play
with their data to uncover data patterns that
aren’t clearly evident.

11.  Due to the insights gained, the firm was able to
prioritize where they should invest funds for
counter-failure measures and maintenance repairs.

12. 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.
For example: An advanced analytics app can
help a store manager optimize his inventory
and a CMO could use an app to optimize the
company’s global marketing spend.

13.  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.

14.  Recognize that each path to data insight is unique.
 Another main component of a company’s analytics
journey depends on the company’s culture itself: is
it more conservative or willing to take chances?

15.  No matter what combination of culture and technology
exists for a business, each path to analytics insight
should be individually paved with an outcome-driven
mindset.
1.For a known problem with a known solution.
2. For a known problem area, fraud.

16.  Of note, when determining which problem to
address, companies should first focus on the
one that can offer the highest value, then it
can choose a hypothesis-based or discovery-
based approach based on the degree of
institutional knowledge it has to solve that
kind of problem.

17. THANK YOU
By: Vaishali Pawar
Under The Guidance Of :
Prof. Sameer Mathur
IIM, Luknow.