This presentation is about the requirements for completely exploiting data. Also,it deals with the actions needed for transforming the company's capabilities in big data models.

1. Data Analytics Internship
under
Prof. Sameer Mathur, Associate
Professor of Marketing,
IIM Lucknow
Submitted by,
Shivaprakash.B,
NIT Trichy

2. WEEK 4 DAY-4- Task 3-b
Q1) List the two most (important /
interesting / informative) insights
from this article?
Q2) Why and how are these insights
relevant to a manager in India?

3. Q1) List the two most (important /
interesting / informative) insights from this
article?

4. Insight 1- 3 important requirements
for completely exploiting data
• 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.

6. • For completely exploiting data and
analytics requires 3 mutually supportive
capabilities. They are:
(a) Choosing the right data:
 Bigger and better data give companies
both more-panoramic and more-granular
views of their business environment.

7.  There are 2 steps to be followed while
choosing the right data. They are:
1) Source data creatively
2) Get the necessary IT support.
(b) Build Models That Predict and
Optimize Business Outcomes:
 The most effective approach to build a
model, originates with identifying the
business opportunity and determining how
the model can improve performance.

8.  Hypothesis-led modeling generates faster
outcomes and also roots models in practical
data relationships that are more broadly
understood by managers.
(c) Transform your company’s
capabilities:
 The main problem is that most managers
don’t believe in big data models.

10.  There might be a mismatch between the
organization’s existing culture and
capabilities and the emerging tactics to
exploit analytics successfully.
 So, these problems are solved by developing
relevant business data and by embedding
analytics into simple tools.

11. Insight 2- The 3 actions for
transforming company’s capabilities
(a) Develop business-relevant analytics
that can be put to use:
• Many initial implementations of big data
and analytics fail simply because they
aren’t in sync with the company’s day-to-
day processes and decision-making norms.

12. • Model designers must inquire about the
types of business judgments that managers
make to align their actions with broader
company goals.
(b) Embed analytics into simple tools for
the front lines:
• Managers need transparent methods for
new models and algorithms.

13. • The statistic experts and software
developers need to be separated from the
managers who use the insights from the
data.
• Thus, the approach of using a simple tool is
to produce complex analytics with an
improved workforce planning and reduced
need for new hires.

14. (c) Develop capabilities to exploit big
data:
• Using simple models, most organizations
need to upgrade their analytical skills and
literacy. Managers must view analytics as
central to solving problems and identifying
opportunities.
• The efforts will vary depending on a
company’s goals and desired time line.

15. • 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.

16. Q2) Why and how are these insights
relevant to a manager in India?

17. MANAGERIAL RELEVANCE
• Managers should know, how to use the
data and information effectively, to make
key decisions in the organization.
• Managers need to think creatively about
the potential of external and new sources
of data.

18. • Managers should be able to predict and
optimize outcomes from the existing data-
models.
• While dealing with huge datasets,
managers should appropriately find the
correlations within them, for making wise
moves in the organization.
• Managers are easily able to understand the
hypothesis-led modeling.

19. • Managers should start believing big-data
models for transforming the companies’
capabilities.
• They need to align their actions with
broader company goals.
• They need transparent methods for using
the new models and algorithms.
• Managers must come to view analytics as
central to solving problems and identifying
opportunities.

20. THANK YOU !!