Rohit Sharma13PGDM29IIPMB

NATIONAL AGRIBUSINESS CONFERENCE-2015

AGRIBUSINESS-2.0(THE NEXT LEVEL)

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.

To study the feasibility of data analytics into agribusiness.

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.

“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.

VELOCITY

VARIETY

VOLUME

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

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.

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.

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.

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.

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.

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.

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.

Questions???