1.
Making Advanced
Analytics Work for
You
This presentation is made as
part of Data Analytics Internship
by Prof Sameer Mathur
IIM LUCKNOW

2.
Overview
Big data and analytics have rocketed to the top of the
corporate agenda. That most companies are unsure
how to proceed. Leaders are understandably leery of
making substantial investments in big data and
advanced analytics. As data-driven strategies take hold,
they will become an increasingly important point of
competitive differentiation.

3.
HOW IMPORTANT IS ADVANCED ANALYTICS FOR
PROFESSIONALS
• Best-in-class companies and
organizations in North and South America
lead the way when it comes to predictive
and advanced analytics.
• On the other hand, the trend is much less
important in telecommunications
companies and the German-speaking
region of Central Europe.

4.
WHY ADVANCED AND PREDICTIVE ANALYTICS IS
BECOMING SO IMPORTANT
New big data technologies enable cost-
effective storage, processing and analysis of
large amounts of data
Modern and intuitive user interfaces allow
more user groups to draw insights and make
informed decisions
Advanced analytics software enables better
analysis, and analysis of relationships and
future events.

5.
BIG DATA, THE REVOLUTION
 According to research by Andrew McAfee and
Erik Brynjolfsson, of MIT, companies that inject
big data and analytics into their operations
show productivity rates and profitability that
are 5% to 6% higher than those of their peers.
 Experience reveals that most companies are
unsure how to proceed

6.
Theory that certain knowledge is
impossible
• Leaders are understandably leery of making substantial investments in
big data and advanced analytics.
• They’re convinced that their organizations simply aren’t ready. After all,
companies may not fully understand the data they already have, or
perhaps they’ve lost piles of money on data-warehousing programs that
never meshed with business processes, or maybe their current analytics
programs are too complicated or don’t yield insights that can be put to use.
• Or all of the above. No wonder skepticism abounds.

7.
Traditional reports only show data. If the data is correct, then reports are highly
likely to be reliable as well, as most modern environments are now quite mature
and their reporting methods and concepts have reached a high level of
sophistication.
Today, a large number of standard algorithms and methods are available for specific
use cases (e.g., customer classification), and new ones are constantly being
developed. Finding the most appropriate one for a dataset depends largely on the
abilities of the user and the software used.
Furthermore, algorithms can also fail due to lack of data (e.g., the customer
classification model). If an advanced analysis project shows that no results can be
found, it should be aborted and the next project started.

8.
Rather than undertaking massive overhauls of
their companies, executives should
concentrate on targeted efforts to source data,
build models, and transform the organizational
culture

9.
That nimbleness is essential, given that the
information itself—along with the technology
for managing and analyzing it—will continue to
grow and change, yielding a constant stream of
opportunities.

10.
Name:
Soumyadeep Sengupta
College:
University of Engineering and
Management , Kolkata
Email:
Soumyadeepsen97@gmail.com