Simplify Your Analytics Strategy

1. This presentation is made as
part of Data Analytics Internship
by Prof Sameer Mathur
IIM LUCKNOW
Simplify Your
Analytics Strategy

2. •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,
•for their customers, stakeholders, and employees companies should be
taking that next step of recognizing what’s important and what they
should be doing
•companies should pursue a simpler path to uncovering the insight in
their data and making insight-driven decisions that add value.

4. Fast data
=
fast
insight =
fast
outcomes

5. Here are ways
to delegate the
work to
Accelerate
analytics
technologies:

6. 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.

7. Example:
a financial services company applied BI and data
visualization to see the different buckets of risk across its entire
loan portfolio. After analyzing its key data and displaying the
results via visualizations

8.  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

9. 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.

10. 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.

11. 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.

12. Example
a retailer combined data from multiple sales channels
(mobile, store, online, and more) in near real-time and used
machine learning to improve its ability to make more personalized
recommendations to customers.

14. Each path to data insight is unique. Each and every manager should follow
all the previous data and should not neglect a single one

15. Another main component of a company’s analytics journey depends on
the company’s culture itself: is it more conservative or willing to take
chances? Does it have a plethora of existing data and analytics
technologies to work with, or is it just starting out with its first analytics
project? 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. Companies can take two approaches depending on the nature of the
business problem.
 First, for a known problem with a known solution
 Second, for a known problem area .The company could take a
discovery-based approach to look for patterns in the data to find
interesting correlations that may be predictive

18. Name:
Soumyadeep Sengupta
College:
University of Engineering and
Management , Kolkata
Email:
Soumyadeepsen97@gmail.com