The document provides recommendations for simplifying an analytics strategy in 3 key steps: 1) Accelerate data processing to enable fast insights and outcomes. 2) Delegate analytical work to technologies like business intelligence, data discovery, analytics applications, and machine learning to uncover patterns and insights. 3) Companies can take either a hypothesis-based or discovery-based approach depending on whether the business problem is known or unknown, with the goal of deriving insights to inform decision-making.

1. Simplify Your
Analytics Strategy

2. Overview
•
Some businesses are challenged by the complexity and
confusion that analytics can generate.
• Companies can get stuck trying to analyze all that’s
• To overcome this, companies should pursue a simpler path
to uncovering the insight in their data and making insight-
driven decisions that add value.

3. Insights

4. To simplify their analytics strategy and generate insight
that leads to real outcomes:
Accelerate the data:
Fast data = fast insight = fast outcomes.

5. Ways to delegate the work to
analytics technologies:

6. ONE
Next-Gen Business
Intelligence (BI) and
data visualization.

7. • Next-gen business intelligence is bringing
data and analytics to life to help companies
improve and optimize their decision-
making and organizational performance.
• 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 confidently.

8. TWO
Data discovery.

9. • 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

10. THREE
Analytics applications

11. • 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

12. FOUR
Machine learning and
cognitive computing.

13. • 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.

14. Deriving analytical insights:
Companies can take two approaches depending on the nature of the
business problem.
• First, for a known problem with a known solution company could
take a hypothesis-based approach by starting with the outcome,
pilot and test the solution with a control group and then scale
broadly across the customer base.
• 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.

15. Managerial
Relevance

16. • The manager should recognize that each path to data insight
is unique and path to insight doesn’t come in one single
form.
• 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.
• 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.

17. Thank You!