The document provides steps for companies to simplify their analytics strategy. It recommends companies accelerate data, use next-gen BI and data visualization, perform data discovery, leverage analytics applications, and adopt machine learning. Companies can take either a hypothesis-based approach for known problems or discovery-based for unknown solutions. The key is to focus on high-value problems and place action behind uncovered insights.

1. Simplify Your Analytics
Strategy
Narendra Mulani

3. 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.

4. Companies can get stuck
trying to analyze all
that’s possible and all
that they could do
through analytics, when
they should be taking
that next step of
recognizing what’s
important and what they
should be doing

5. To overcome this, companies should
pursue a simpler path to uncovering the
insight in their data and making insight-
driven decisions that add value.

6. Following are steps that we have seen
work in a number of companies to
simplify their analytics strategy and
generate insight that leads to real
outcomes:

7. Accelerate the data
• Fast data = fast insight = fast outcomes.
• Liberate and accelerate data by creating a
data supply chain built on a hybrid
technology environment — a data service
platform combined with emerging big data
technologies.

9. Next-Gen Business Intelligence (BI)
and data visualization.
•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.

10. Data discovery
•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.

12. 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.

14. Machine 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.

15. •Recognize that each path to data
insight is unique. 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.

16. • Another main component of a company’s
analytics journey depends on the company’s
culture itself.
• 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.
• To do this, companies can take two approaches
depending on the nature of the business
problem.

17. • First, 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 (e.g. cross-sell/up-sell to
existing customers), pilot and test the solution
with a control group and then scale broadly
across the customer base.

18. • Second, for a known problem area, but 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.

19. •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.

20. • Once insights are uncovered, the next step
is for the business, of course, to make the
data-driven decisions that place action
behind the data. It is possible to uncover
the business opportunities in your data and
increase data equity, simply.

21. RECAP
• Companies get stuck trying to analyze all that’s
possible.
• Following are steps that we have worked in a number
of companies to simplify their analytics strategy:
1. Accelerate the data
2. Next-Gen Business Intelligence (BI) and data
visualization.
3. Data discovery
4. Analytics applications
5. Machine learning and cognitive computing
• Companies can take two approaches
1. For a known problem with a known solution
2. For a known problem area, but with an unknown

22. THANK YOU