Companies often struggle with the complexities of analytics and need to streamline their approaches to focus on key insights for customers and stakeholders. Utilizing next-generation business intelligence, advanced analytics applications, and machine learning can help organizations turn data into valuable assets, leading to data-driven decisions and improved performance. Each organization’s path to insight is unique, requiring a combination of proper technology, mindset, and strategies to simplify the process and enhance decision-making.

1. by Narendra Mulani

3. 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 — for
their customers,
stakeholders, and
employees.

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

5. Steps to simplify analytics
strategy

6. Accelerate the data
Fast data = fast insight = fast outcomes

7. Ways to delegate the work to your
analytics technologies:

9. 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. 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 form (heat map,
charts, etc) for each
individual decision-
maker, so they can use it
to reach their desired
outcome..

12. 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.

14. 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.
With an influx of big data, and
advances in processing power,
data science and cognitive
technology, software intelligence
is helping machines make even
better-informed decisions.

15. 5.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.
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.

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.

17. Managerial
Relevance

18. A Manager should employ steps
discussed in ii insight in his/ her
organisation to make the analysis
simpler: A proper supply chain should be
built to accelerate the data.
Also a manager should try to have
advanced technology for the organization
because n organization equipped with
advanced technology have an ease with
analytics.

19. 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 a known
problem with a
known solution
the company
could take a
hypothesis-
based
approach by
starting with
the outcome .

20. PRESENTED BY:
POOJA
KESERWANI