Simplify our analytics strategy

1. Simplify Your Analytics Strategy

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

3. Companies are facingchallenges….
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 Simplerpath
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. Waysto delegate the work to your
 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.

9. Next-Gen BIand datavisualization
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.
by turning anBI does this
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.

10. 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 theirneeds.

11. 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.

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

13. AnalyticsApplications:
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.

14. 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.

15. Machine Learning & CognitiveComputing

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

17. Each path to Insight isunique….
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.

18. TwoApproaches:
 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

19. Name – Saurabh
Sethia
Mail id-
saurabhsethia12@g
mail.com