Analysis of HBR article "Simplify your analytics strategy" authored by Narendra Mulani.

1. “Simplify Your Analytics Strategy”
By: Narendra Mulani

2. Outline
1- Challenges
2- Accelerate The Data
3- Next-gen Bi And DATA
Visualization
4- Data Discovery
5- Analytics Applications
6- Machine Learning and
Cognitive Computing

3. Companies are facing challenges….
 While 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.
 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

4. Pursue a Simpler path
 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. Accelerate the DATA….
Fast Data  Fast Insights
Fast
Outcomes

6. Accelerate the DATA….
 Liberate and accelerate data by creating a data supply
chain built on a hybrid technology environment — a data
service platform combined with emerging big data
technologies.
 Real-time delivery of analytics speeds up the execution
velocity and improves the service quality of an
organization.

7. An Example:
 A U.S. bank adopted such a technology environment to
more efficiently manage increasing data volumes for its
customer analytics projects. As a result, the firm
experienced improved processing time by several hours,
generating quicker insights and a faster reaction time.

8. Ways to delegate the work to your
analytics technologies:
 Delegate the work to your analytics technologies.
Uncovering data insights doesn’t have to be
difficult.
 Next-Gen Business Intelligence (BI) and data
visualization is extensively useful in delegating work
to your analytics technologies.

10. Next-Gen 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.
 BI does this by turning an
organization’s data into an
asset by and displaying in the
right visual form (heat map,
charts, etc) for each individual
decision-maker, so they can
use it to reach their desired
outcome.

11. An Example:
 A financial services company applied BI and
data visualization to see the different buckets of
risk across its entire loan portfolio.
 The firm identified the areas in the U.S. where
there were high delinquency rates, explored
tranches based on lenders, loan purposes, and
loan channels, and viewed bank loan portfolios.
Users were also able to interact with the results
and query the data based on their needs.

12. Data discovery
 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.

13. An Example:
 A resources company was able to
predict which pipelines are most risky
from both physical and atypical
threats through data discovery
techniques.
 Due to the insights gained, the firm
was able to prioritize where they
should invest funds for counter-
failure measures and maintenance
repairs.

14. 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.
 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.

15. An Example:
 An advanced analytics app
can help a store manager
optimize his inventory and a
CMO could use an app to
optimize the company’s
global marketing spend.

16. Machine Learning & Cognitive Computing

17. Machine Learning & Cognitive Computing
 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.

18. Each path to Insight is unique….
 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.

19. Two Approaches:
First-
 For a known problem with a
known solution — such as
customer segmentation and
propensity modeling for
targeted marketing campaigns
— the company could take a
hypothesis-based approach by
starting with the outcome
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