This is a presentation made under data analytics internship under Prof. Sameer Mathur, IIM Lucknow

1. Making Advanced Analytics
Work For You
By Dominic Barton and
David Court

2. Big Data possesses the capability to
transform the way companies do
business, delivering performance
gains. Big Data is attracting serious
investments from technology leaders.
the time has come to define a
pragmatic approach to big data
and advanced analytics—one
tightly focused on how to use the
data to make better decisions.

3. Key Findings in the Study

4. According to research by Andrew
McAfee and Erik Brynjolfsso
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

5. The time has come to define a pragmatic
approach to big data and advanced
analytics—one tightly focused on how to
use the data to make better decisions. Fully
exploiting data and analytics requires three
mutually supportive capabilities.

6. 1.
Choosing
the right
data

7. The volume of information is
expanding every instance. The ability
to expand insights by combining data
is also accelerating. This ability is
giving companies the insights which
were previously invisible.
The companies should source the
data creatively. The companies
should be specific with the problems
they want to tackle and hence, use
data in that particular manner.

8. 2.
Build
models
that predict
and
optimize
business
outcomes.

9. The most effective approach to
building a model originates with
identifying the business opportunity
and determine how the model can
improve performance. The
companies should repeatedly ask,
“What is the least complex model
that would improve our
performance?”

10. 3.
Transforming
the
company’s
capabilities

11. Many initial implementations of big
data and analytics fail because they
are not in sync with the company’s day
to day processes and decision making
norms.
Managers need transparent methods
for using new models and algorithms
on daily basis. Managers must come to
view analytics as central to solving
problems and identifying
opportunities—to make it part of the
fabric of daily operations.

12. Managerial Relevance

13. Big data could transform the way
companies do their business, delivering
huge performance gains. But most
companies are unsure how to proceed
with using big data analytics. Leaders
are convinced that their organizations
are not ready to invest in Big data
technologies. Companies may not fully
understand the data they already have
and may have lost loads of money in
investing in technologies without getting
any gains from it.

14. Companies often possess the data
that is needed for solving problems
but the managers simply don’t know
how to apply that data in solving
problems. The managers can impel a
more comprehensive look on the
problem by being specific on the
business problems that need to be
solved or the opportunities that they
hope to exploit.

15. Managers also need to get creative about
the potential of external and new sources of
data. Data are essential, but performance
improvements and competitive advantage
arise from analytics models that allow
managers to predict and optimize
outcomes.
The leading concern is that most managers
don’t trust data-based models. The don’t
believe the model’s results and are not
familiar with the working of the model.

16. The data analytics tools that are being
designed are being designed for experts in
modelling rather than for the managers who
need to use them. The managers need to
change the organizational structure in order
to be able to make use of big data analytics.
1. Business relevant analytics need to be
developed that are in sync with the
company’s decision making norms.

17. 2. Managers must come to view analytics
as central to solving problems and
identifying opportunities—to make it part of
the fabric of daily operations.
3. Executives should concentrate on
targeted efforts to source data, build
models, and transform the organizational
culture. Such efforts will play a part in
maintaining flexibility

18. By-
Jyotsana Manglani
UCER, Allahabad