Companies should simplify their analytics strategies by focusing on discovering real business opportunities and outcomes for customers, stakeholders, and employees. They can do this by creating a hybrid data environment that enables fast data movement and using techniques like next-gen business intelligence, data discovery, analytics applications, and machine learning to delegate work to analytics technologies. The optimal path depends on a company's goals, culture, and existing technologies, but generally involves either testing known solutions or taking a discovery-based approach to find patterns for known problem areas. The highest value problems should be addressed first using the most appropriate approach.

1. Simplify your analytics strategy
Data Analytics Internship under Prof Sameer Mathur,
IIM Lucknow
Submitted by:N.Girish
IIT Madras.

2. 1st Insight
•Companies should implement simpler paths
and strategies to uncover the insight in their
data and making insight-driven decisions that
add value.
•They must focus over what they should be
doing — for their customers, stakeholders,
and employees. Discovering real business
opportunities and achieving desired outcomes
can be elusive.

3. •Accelerate the data.
•What we actually need to do is to create a data
supply chain built on a hybrid technology
environment in combination with emerging
big data tecnologies.
•Such an environment readily enables the fast
movement of data and its execution.

5. Ways to delegate the work to your analytics
technologies:
• Next-Gen Business Intelligence (BI) and data
visualization:This is done by turning an
organization’s data into an asset by having the
right data, at the right time and place, and
displayed in the right visual form.

7. • Data discovery:Through 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.There
may be benefits due to the insights gained from
such techniques and we can prioritize for our
products by analyzing the risks.

8. •Analytics
applications:These
can simplify
advanced analytics
as they can be dealt
with and also allows
to make data-driven
business decisions
easily.

9. •Machine Learning and cognitive
computing:Machine learning removes much of
the human element from the data modeling
process to produce predictions of customer
behavior and enterprise performance.
•Advances in processing power, data science and
cognitive technology, software intelligence is
helping machines make even better-informed
decisions.

10. Insight 2
•Each path to data insight is unique.There may
be many insights and goals for a company
and those may change accordingly.
•Also a companies' analytics journey depends
upon its culture and many more questions
like:is it more conservative or willing to take
chances? Does it have existing data and
analytics technologies to work with?

11. • First, for a known problem
with a known solution —
such as customer
segmentation and propensity
modeling for targeted
marketing campaigns.First
we need to test the solution
with a control group and then
scale broadly across the
customer base.

12. • 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.

13. • 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.
• Then next we make the data-driven decisions that place
action behind the data. It is possible to uncover the
business opportunities in our data and increase data
equity, simply.

14. Managerial Relevance
•As a manager,his first and foremost task
would be to improve the strategy and
approach to analytics. He must try for simpler
and easier methods to deal with data.Also
encouraging the applications and technology-
driven techniques would be a lot more helpful
in improving the business model.
•Next he must know what is actually
important for his organization at that point of

15. Thank You