The document provides tips for simplifying an analytics strategy. It recommends accelerating data processing to enable fast insights and outcomes. Companies should delegate analytics work to technologies like business intelligence, data discovery, analytics applications, and machine learning. Each company's path to insights is unique, so they can take either an outcome-driven approach for known problems or a discovery-based approach to find patterns for unknown solutions. The ultimate goal is to uncover insights from data and make data-driven decisions.

1. Simplify Your Analytics
Strategy
by Narendra Mulani
Sachin
REC Sonbhadra

2. 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.

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

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

5. 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:

6. 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

7.  Delegate the work to your
analytics technologies.
Uncovering data insights doesn’t
have to be difficult. Here are ways
to delegate the work to your
analytics technologies:

8. 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.

9. Data discovery. Data
discovery can take place
alongside outcome-specific
data projects

10.  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.

11.  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

12. Recognize that each path
to data insight is unique.
The path to insight doesn’t
come in one single form.

13. 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.

14. To do this, companies can
take two approaches
depending on the nature of the
business problem. First, for a
known problem with a known
solution

15.  Second, for a known problem
area, fraud for example, 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

16.  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.