What is agri big data analytics and why it matters

 

For every industry, big data analysis is becoming an integral business strategy. In fact, The Global Big Data and Business Analytics Market is expected to grow from USD 192.24 billion in 2019 to USD 446.42 billion by the end of 2025. The rapidly increasing volume and complexity of data are due to growing mobile data traffic, cloud-computing traffic and burgeoning development and adoption of technologies including the Internet of Things (IoT) and artificial intelligence (AI), which is driving the growth of big data analytics market. Currently, there are more than 2.5 quintillion bytes of data generated every day by every click, swipe, share, search and stream, proliferating the demand for the big data analytics market on a global scale.

Big data analysis helps businesses to differentiate themselves from others and increase the revenue. Through predictive analytics, big data analytics provides businesses customized recommendations and suggestions. The agriculture industry is moving quickly toward digital adoption and increasing the use of data and analytics. The Global Agriculture Analytics Market accounted for $590.03 million in 2018 and is expected to reach $2,461.65 million by 2027.

Big Data Analytics in Agriculture

The agriculture industry is currently facing a challenge of enormous global scale – the need to increase agricultural production to feed a population that is expected to grow to 10 billion people by 2025. This incredible feat needs to be accomplished while maintaining sustainable agricultural systems and simultaneously facing additional barriers, such as increasingly unpredictable weather and the mass depletion of water resources.

Precision Agriculture emerged in the 1980s with the development of key technologies, including GPS and satellite imagery. Precision agriculture’s main objective is to ensure profitability, efficiency and sustainability using the big data gathered to guide both immediate and future decision-making. This covers a wide spectrum, such as identifying the best time to apply fertilizers or chemicals.

With the incorporation of big data analytics into Precision Agriculture, an important shift began within the agriculture industry. With big data gathering information from a huge number of sources and translating it into actionable information to improve business processes and solve problems at scale and speed, real-time performance optimizations have occurred across the industry. Now, big data analytics can show how farmers are utilizing their inputs and what adaptations are required to take account of emerging weather events or disease outbreaks.

Successful Use Cases

The recent boom in utilization of big data analytics within agriculture is driving a growing demand for specialized tools that can collect, analyze and integrate data to improve productivity and efficiency within agribusinesses. It has become clear that the ability to track physical items, collect real-time data and forecast scenarios can be a true competitive advantage. To date, there are multiple use cases that demonstrate the importance and future potential of big data in the ag industry. These include:

The ethical use of pesticides. The use of pesticides in agriculture has incredible benefits for increasing crop production and reducing the negative impact of insects and disease. However, as consumers gain more understanding of the potential negative side effects, including asthma and allergies and microbial contamination, the push for responsible use of pesticides is growing. Big data analytics allows farmers and growers to manage their use of pesticides, when they are applied, how much is applied and where they are applied. By using data to closely monitor all aspects of pesticide use, farmers can both adhere to government regulations and avoid the overuse of chemicals in the food production process. There is also the opportunity for increased profitability by reducing waste and preventing crop loss due to weeds or insects.

Keeping food production on pace with the growing world population. Farmers across the globe will need to increase crop production, either by increasing the amount of agricultural land to grow crops or by enhancing productivity on existing agricultural lands. Leveraging fertilizer, irrigation strategies and adopting new operational methods to maximize output will be critical in this effort. Big data analytics allows farmers to track information regarding data points like water cycles, rainfall patterns and fertilizer requirements in order to increase yield. By making more informed decisions about what to plant, where to plant it and when to plant, farmers gain the ability to increase both profitability and output.

Improving supply chain management. In every industry, supply chain processes are complex. With multiple functions interacting, potentially conflicting objectives and numerous dependencies between information and material flows, the complexity of the ag supply chain is increased by fragmented inbound and outbound networks. Recent surveys demonstrate that one third of food produced for human consumption is lost or wasted every year. To overcome this loss, food delivery cycles from producer to the market need to be reduced. The utilization of big data analytics can help achieve supply chain efficiencies by tracking and optimizing delivery truck routes and tracking the complete lifecycle of produce.

Optimizing farm equipment. With farm equipment manufacturers now placing sensors directly into the equipment, it is more important than ever for both farmers and manufacturers within the ag value chain to capture, track and analyze the data produced. In addition to providing information on the equipment itself, such as tractor availability, service due dates and monitoring fuel levels, this type of tracking allows farmers to optimize usage of the equipment and ensure the long-term health of their mechanical investment.

Getting Started

A successful strategy for managing big data analytics defines a comprehensive vision across the enterprise and sets a foundation for the organization to employ data-related or data-dependent capabilities. A well-defined and comprehensive big data strategy makes data actionable by establishing the steps that the organization should execute in order to become a “Data Driven Enterprise”.

In order to implement a successful, actionable big data analytics strategy, an agribusiness should:

  1. Define their business objectives and goals
  2. Execute a current state assessment of how data is collected and used throughout the organization
  3. Identify and prioritize use cases
  4. Formulate a Big Data Roadmap for how data will be collected, verified and utilized
  5. Establish and executive a change management plan to bring the entire organization on-board and make data a central part of the organizational strategy

 

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