how data analytics can be incorporated into agribusiness. talks more on the feasibility aspect.

1. Rohit Sharma
13PGDM29
IIPMB
NATIONAL AGRIBUSINESS CONFERENCE-2015
AGRIBUSINESS-2.0(THE NEXT LEVEL)

3. It seems that one can’t go through a work day
without seeing some mention of Big Data, its
application and its potential to have
unprecedented impact. The potential for Big
Data application in the agricultural sector is
examined. Integration of data and analysis
across business and government entities will be
needed for successful implementation. The
eventual impact of Big Data within the
agricultural sector likely will require both
organizational and technological innovation.

4. To study the feasibility of data analytics into
agribusiness.

5. When you consider the number of variables in
farming, you realize the field is ripe for big data
and predictive analytics.
Since the days of Poor Richard’s Almanac,
farmers have been eager to gain knowledge of
factors such as the weather that affect their
crops.
Recent years have seen an explosion in the use
of huge data sets to boost the farm industry.

6. “Big data” refers to datasets whose size is
beyond the ability of typical database software
tools to capture, store, manage, and analyse.
This definition is intentionally subjective and
incorporates a moving definition of how big a
dataset needs to be in order to be considered
big data—i.e., we don’t define big data in terms
of being larger than a certain number
of terabytes (thousands of gigabytes).
We assume that, as technology advances over
time, the size of datasets that qualify as big data
will also increase.

7. VELOCITY
VARIETY
VOLUME

8. The volume dimension of Big Data is not
delineated in quantitative terms. Rather, Big
Data refers to datasets whose size is beyond the
ability of typical database software tools to
capture, store, manage, and analyze. This
definition is intentionally subjective and
incorporates a moving definition of how big a
dataset needs to be in order to be considered
big

9. The velocity dimension refers to the capability
to acquire, understand, and interpret events AS
they occur. For analysts interested in retailing,
anticipating the level of sales is critically
important.

10. Variety, as a dimension of Big Data, may be the
most novel and intriguing of these three
characteristics. For many senior managers, the
personal computer freed us from the tyranny of
the IT department’s chokehold on data.
Usually these numbers summarized operating
and financial performance.

11. Big Data applications are being employed
throughout the economy and society. The
technologies employed are exciting, involve
analysis of mind-numbing amounts of data and
require fundamental rethinking as to what
constitutes data.
And the potential for gain through use of these
technologies seems to far exceed the benefits
achieved so far.

12. Consumer and societal forces also can
materially affect technology adoption. In
agribusiness, two such important forces relate
to environmental and food safety concerns.
The path by which Big Data could affect
agriculture is not determined at this point.

13. Today, low-cost sensors can measure soil
conditions, seeding rates, crop yields and many
other factors.
When you subject those data sources to
analysis, the results can provide valuable
guidance to farmers who are always seeking
new ways to become more efficient.
That’s important not only for farmers, but for
the billions of people they feed around the
world.

15. Wal-Mart transformed retailing through an
aggressive focus on price facilitated through
path-breaking use of IT and by using those
capabilities to alter relationships with suppliers.
Amazon enhanced, in some dimensions, the
customer’s shopping experience and employed
ICT to learn how to improve each customer’s
next experience.

16. Cutting across these and other examples, common
features of changes in the basis of competition
include:
Dramatic cost reductions.
Quality enhancements desired by customers.
Redefined relationships across stages of the
value chain.

18. Questions???