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.
can get stuck
•Trying to analyze all that’s possible
•All that they could do through
•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.
8. Such an environment enables businesses to
move, manage, and mobilize the ever-
increasing amount of data across the
organization for consumption faster than
9. Real-time delivery of analytics
speeds up the execution velocity
and improves the service quality
of an organization.
10. Delegate the
work to your
11. • Here are ways to delegate the work to your
• Next-Gen Business Intelligence (BI) and data
• Data discovery
• Analytics applications.
• Machine learning and cognitive computing
12. Next-Gen Business Intelligence (BI) and data
• 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. 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. 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
14. •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. When
more insights and patterns are
discovered, more opportunities
to drive value for the business
can be found.
15. • 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.
• They can also be industry-specific, flexible, and
tailored to meet the needs of the individual users
across organizations — from marketing to finance,
and levels from C-suite to middle management.
16. • 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
17. Recognize that each path
to data insight is unique.
The path to insight doesn’t
come in one single form.
18. 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.
19. To do this, companies
can take two
on the nature of the
21. 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
22. 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,
23. •Managers should be able to
uncover the data patterns using
data discovery methods.
•Advanced analytics can help
managers their inventories.