The Role of Data Analysis in Agribusiness


Data analysis is a critical part of improving business operations across all sectors and industries. Organizations can utilize data analytics to improve decision making, analyse customer trends, track customer satisfaction and identify opportunities for new products and services to meet growing market needs. Integrating information systems to gather data across the business, organizations can gain real-time insights into marketing, product demand, sales, and finance.

Agriculture is an economic sector that employs the largest number of people around the world, thus providing a main source of food and income for many people living in or below the poverty line. Agriculture has evolved over the years from its traditional roots, now farmers are able to able to make use of historical data to come to a conclusive analysis on crops and planting methods to be used. Agribusiness refers to economic activity originating from or related to farm products. To put it simply, crop cultivation, as well as crop processing, transportation, and distribution, all fall under the purview of agribusiness.

The Food and Agriculture Organization of the United Nations (FAO) states that over 70% of the world’s food needs are met by small holder farmers and with world population expected to exceed 9 billion by the year 2050 (Source: This has led to a greatly increased interest in and a utilization of data analytics in agribusiness.

Benefits of Data Analytics and Agribusiness

  1. Improved Crop Management:

With crop data, analysis and useable insights, farmers are able to make informed decisions on the type of crops to grow, the type of variant that would be best suited for the prevailing atmospheric conditions, and the soil conditions. It has been proven that data analysis has been helpful in recommending varieties and breeds that are most resistant to disease and spoilage.

  1. Better Risk Assessments

The ability to predict and effectively manage risks at every stage of the farming lifecycle makes the farmer better equipped to take tactical decisions. Big data and Cloud Computing utilize data from google earth, global weather conditions as well as information supplied by the farmers to plan right from crop selection, to harvesting and to distribution. It also takes note of local market prices, natural calamities, pest infestations and other things that may affect the value of commodities and possible supply chain shifts. This data aids the farmers in making decisions that reduce potential high-risk scenarios.

  • Efficiencies in supply chain

With data analytics, farmers are enlightened with useful insights that can aid in predicting the market conditions, consumer behaviour towards the finished goods, factor in inflation, and other dependent variables that aid in planning the entire process even before seeds are planted. This enables farmers manage conditions that enable them maximize returns on investments and avoid unnecessary losses.

How it works?

Data analysis is very essential in

How Data Analysis is used in Agribusiness

  1. Data Collection: The collection of historical data of previous planting seasons enables the data analyst aggregate data from trusted and selected sources, to create a data bank. Storing the data in centralized and safe location helps simplify the process.
  2. Data Standardization and Cleaning: Collating the multiple data sets together in a single structure, gives the data analyst opportunity to run comparisons, track real-time trends and changes. A cleaned data set uncover patterns in the data to identify new opportunities and timelines and aids effective decision making.
  • Data Enrichment: Some useful information may exist outside the information give, like weather data, soil analytics, insect tracking and wind movements which could be retrieved from secondary sources.
  1. Collected Data Analysis: The analysed data is very essential to gaining value and insights. Utilizing the right tools is also important in getting the desired and accurate results of timed planting and harvest and resultant profitable yield.

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