The Power and Pitfalls of Big Data

There’s been a lot of buzz about “big data” recently in both urban areas and out in the countryside. Big data has always surrounded us in varied shapes and forms, but many times we don’t realize it.

How many times do we turn to Google or ask our smartphones for answers or even directions to a desired location? How often do we connect with family and friends through social media platforms such as Facebook or Twitter? How much of your Christmas shopping did you do online last year?

Every time we connect digitally, data is generated, collected, and on an ever-increasing basis, analyzed to help achieve our desired outcomes. Even though most of us think that typing a question on an Internet search engine is free, the reality is that these simple questions provide valuable data to a company or a provider that uses the information to make products or services better and more efficient. Aggregating data can help analysts, scientists and economists tailor products, refine research objectives, answer key research questions and further innovation beyond the societal and technological norms of today.

The agriculture industry has been front and center on the topic of big data for years.

Technology has improved to the point that during the 2012 and 2013 crop years, several seed, fertilizer and equipment companies worked with farmers to monitor and collect farm data on variables such as number of plants in a given area, seed hybrids, soil topography, fertilizer usage and crop yield data on a site-specific basis. In some cases these sites were only a few square yards. These variables generate data and are just a pile of numbers comprised of 0 through 9, but when analyzed and interpreted correctly, the information can provide a great benefit to the farmer and the company providing the service.

However, the situation is more complex than interpreting a pile of numbers. Consider…what value does farm-level data have? What value do we place on privacy? When does my data stop and your analysis begin?

Assessing the payback value of each farm’s data generated from precision technologies will vary from farm to farm. But the company analyzing the data will almost always charge the farmer a fee for that analysis. The company usually counts on a positive return as the analysis of the data will strengthen its predictive analytic capabilities, strategic benchmarking and help solidify advantages in marketing.

The key word for the farmer, though, is “may.”

Farmers already using precision technologies may see a productivity gain by providing data and then gaining access to a company’s analytical supports. This support may help maximize output while reducing inputs such as seeds and fertilizer on specific pieces of crop ground. But the output generated from the services provided by the company does not necessarily guarantee the farmer will see an increase in overall farm output from year to year. From the company’s end, the guarantee is solidified by the farmer giving the company the data and paying a service cost. Should this degree of guarantee be considered when determining the fair value of big data, or is this just an associated risk carried by the farmer?

The use of data through precision technologies has the potential to revolutionize the agricultural industry by making farmers better at producing food.

Data derived from the use of technologies will offer a new competitive advantage and could provide economic benefits for farms that implement and interpret data correctly. For instance, the economic impact on agriculture from drones is expected to be $2 billion by 2015. However, even with that big of an expected payoff from big data, farmers need to be cognizant of the potential pitfalls, especially the possibility that their private farm-level data might not remain so private.

The question should not be only whether or not farmers are ready to give up the privacy of their individual farm data. Those days are long gone. The real question for farmers to consider is: Do the short- and long-term benefits of giving farm-level data to another entity outweigh the costs associated with the possibility that the farm-level data would be released or misused by others?

Courtesy: