WHAT IS BIG DATA?
In recent years, big data has become prominent across a variety of economic sectors and is now being
increasingly applied to agriculture. Russo (2013)1
, states that big data refers to the “generation of enormous
amounts of data due to new technologies for measurement, collection and storage” that are being accumulated
in such vast quantities that they are impossible to assess using conventional analysis techniques. Within
agriculture, these technologies include sensors, geospatial datasets, as well as information from farm
management systems and smart-connected devices (e.g. machinery) linked to the Cloud via the Internet of
Things. Big data also encompasses datasets collected for other purposes (e.g. farm compliance data) which
would have remained in silos but whose potential can now be used in other contexts to deliver real-time
actionable insights for farmers and agricultural suppliers.
WHY IS IT CRUCIAL FOR SUPPLIERS IN AGRICULTURE?
According to SAS2
, it is not the amount of data that is important, it is what organisations do with the
data that matters. Big data can be analysed for insights that lead to better decisions to drive competitive
advantage. It therefore offers great opportunities in agriculture which include:
Vast potential to increase productivity and innovation: The McKinsey Global Institute3
states that big data has the potential to “transform economies, delivering a new wave of
productivity”. In the process, it will change the basis for competition and presents substantial
opportunities for those with the capabilities to exploit the potentially highly-valuable insights
available. Within agriculture, as global food demand is projected to double by 2050 due to
rising populations, farmers and agricultural suppliers will increasingly be expected to do more
with less by increasing productivity from limited resources and inputs. Due to these pressures,
innovative technologies such as Precision Farming will play a major role in the development of
agriculture and will present a multitude of opportunities to farmers to adapt their practices and
input applications to inter and intra-field variability in crops. As an example, for crop protection
suppliers, this means that products could be applied in a multitude of dose rates and tank mixes
within a single field. As a result the broad-brush approach of conventional analyses techniques (e.g.
surveys undertaken post-application) will become increasingly redundant and unreliable.
Real-time insights to help performance optimisation: advanced analytics can show how
farmers are utilising their inputs and what adaptations are required to take account of emerging
weather events or disease outbreaks. The key challenge will be to deliver such real-time insights
clearly and concisely to enable effective decision-making. To achieve this, advanced algorithms
are needed to swiftly unlock the highly valuable insights available from big data so that products
are performing to expectations on an ongoing basis despite changing conditions.
The development of highly specific customer segmentations: to tailor product offerings
to precisely meet customer needs as they evolve. For instance, if Black Grass becomes
problematic in a given region, suppliers can deploy big data techniques such as real-time
micro-segmentation of customers to target promotional and marketing activities, thus
facilitating better utilisation of marketing spend. Such analytics would also facilitate the
development of more sophisticated pricing strategies that better match price and value at the
Data becoming a major source of competitive advantage: therefore, big data is emerging as
a key way for companies to gain competitive advantage over their competitors by unearthing
valuable insights more quickly and by developing and adapting products that are better
matched to meeting specific customer needs on an on-going basis. Competitors that fail to
develop or gain access to sophisticated analytics expertise will be left behind.
To recognise these benefits, companies need to have access to expertise to help integrate, analyse,
visualise and leverage the growing torrent of big data. According to Harvard Business Review4
such insights requires a blending of mathematics, computer science, and business analytics techniques and
proving a major skills challenge to most companies. However, market intelligence platforms are a notable
example of how big data can assist arable agriculture, especially crop input suppliers. Whilst competitive
advantage is still available to agricultural suppliers seeking to defend or enhance their existing revenues,
competitive disadvantage is inevitable if suppliers fail to respond to the opportunity to adopt high-quality,
up-to-date market intelligence via the data collection, science and visualisation technologies developed
over the last 20 years. By utilising market intelligence, vast datasets can be cost-effectively deployed on a
real-time basis to form meaningful visualisations and insights for suppliers and farmers.
MAKING DATA IN AGRICULTURE A REALITY
There are developing market intelligence platforms for farming that have the potential to leverage the
power of big data to provide intuitive real-time, in-season analytics from the diversity of reliable datasets
available. These services will enable companies to achieve significant competitive advantage from big data
straightaway without incurring significant investment costs. By tailoring product offerings and pricing
strategies in-season, revenues can be increased significantly whilst permitting farmers to derive
maximum value from products in ways that optimise the use of scarce resources and improve
agricultural productivity to meet an ever-increasing global food demand.
1. Russo, J. (2013), “Big Data & Precision Agriculture”.
2. SAS (2015), “Big Data – What it is and why it matters”.
3. McKinsey Global Institute (2011), “Big data: The next frontier for innovation, competition, and productivity”.
4. Porter, M. and Heppelmann, J.E. (2015), How Smart Connected Products Are Transforming Companies, Harvard Business Review.