In the face of COVID-19, industries of all kinds have been forced to rapidly change how they work, and agriculture is no different. Agriculture is affected by climate change, changes in supply and demand, the workforce in lockdown, supply chain disruptions, and more. 

Though the industry is accustomed to responding to unexpected events like floods, draughts, and various kinds of disruption, everybody agrees that it needs a more resilient supply chain system and farmers need to leverage the technological advances to mitigate the risks. 

Big Data analytics can help to solve these problems as it can help improve forecasting and operational efficiency. What are the key benefits of big data in agriculture? What are the key challenges of its adoption and how to overcome them? 

Key benefits of big data and data science in agriculture:

  • Improved forecasting of yields and production.

  • Faster delivery of goods to distribution centers and consumers.

  • Real-time decisions and alerts based on data from fields and equipment.

  • Preventive and predictive maintenance of equipment

  • More efficient energy usage and greater operational efficiency. Big data helps to save water and electricity thanks to smart metrics and data analytics capabilities.

  • Improved decision making based on production and business performance data

  • Identifying correlations between farm field, weather, and commodity data for more efficient irrigation, fertilization, and harvesting of crops. 

  • Predicting the demand for seeds, fertilizers, and animal feed and enabling agribusiness suppliers to take appropriate steps to meet demand.

  • New pricing programs to help match demand with available supply. For instance, demand for some products is often strongly related to commodity pricing.

  • Reducing food waste. 20% to 30% of food is wasted today at various stages of the supply chain. Big data in agriculture can help save as much as $155–405 billion a year by 2030.

  • Cost savings and business opportunities. According to Tufts University research, smarter farming practices could generate $2.3 trillion in cost savings and business opportunities annually. $ 250 billion of those yearly savings could be generated by AI and data analytics. 

  • Better supply chain management and faster delivery of goods to distribution centers and consumers. Farmers can trace their products throughout the supply chain more easily, while retailers, distributors, and other key stakeholders can offer the products and services that meet the needs of the agricultural market.

big data development

Big Data in agriculture: How to make it work? 

 Agritech providers need to consider several critical success factors:

Enable continuous monitoring:

Farmers need to be able to constantly monitor key parameters that directly impact yield and profits. Technologies such as wireless sensors and variable rate technologies will help farmers track key agricultural parameters including temperature, weather, nitrate content, soil quality, plant health, weed, pest detection, etc.

Here are some of the key data sources that can help you drive value from big data in agriculture:

  • Traditional enterprise data from operational systems

  • Farm  field  sensor data  (e.g.  temperature, humidity, rainfall, sunlight)

  • Farm equipment sensor data (from tractors, plows, and harvesters)

  •  Harvested goods and livestock delivery vehicles (from farms to processing facilities) sensor data

  • Commodities trade data

  • Financial forecast data,

  • Weather data

  • Animal and plant genomics research data

  • Data collected by drones

  • Geospatial data and satellite images ( for instance to predict crop yields)

Ensure the data is accurate

Farmers need to be able to accurately collect and store data for continuous decision-making. Thus, technologies that ensure the accuracy of the data collected (aerial mapping, field harvesting, weather conditions, chemical detection, and so on) are necessary to make agritech solutions more reliable.

Increase automation of crop cultivation and livestock production systems

 Automated tools and equipment for precision agriculture such as robotics and drones enable continuous data collection analytics.

Big Data development: How to make it work for your agritech business case

1.Define clear business KPIs and estimate ROI

First of all, it is important to come up with clear KPIs and estimate Return On Investment. If you need to validate whether your big data solution in agriculture is going to be feasible and profitable, you need to undertake a Business Strategy Discovery Phase and based on rigorous calculations for different scenarios, choose the big data analytics system that is the best fit for your specific case. The Product Discovery phase will provide you with all the deliverables needed to efficiently kick off the implementation phase while helping you to mitigate potential risks and optimize costs. 

2. Ensure effective Big Data engineering

If you want your Big Data analytics project to be successful, you need to:

  • choose the right data sources;

  • develop an orchestrated ecosystem of platforms that collect siloed data from hundreds of sources;

  • focus on cleaning, aggregating, and preprocessing the data to make it fit for a specific business case;

  • in some cases, apply Data Science or Machine Learning models to enable predictive capabilities;

Challenges of big data development in agriculture

Big data development challenge # 1. Poor quality of data  

Clean, valid, complete data is the key to successful big data in agriculture as any discrepancies in the dataset may cause misleading results and affect your operational decisions.


To ensure good quality of data, our specialists recommend following these best practices of big data development: 

  1. Choose the right data sources and have the data continuously checked.

  2. Focus on data preparation and cleaning. 

  3. Ensure automated checks for all incremental pipelines. 

  4. Build data governance and master data management solutions to improve the quality of the data.

Big data in agriculture challenge # 2 Lack of big data skills

 To get accurate results in your big data in agriculture project, you need to have the data appropriately collected,  structured and cleaned up. Only then, you can extract insights from it. That’s why the demand for Big Data developers is going up. For instance, Germany has over 30,000 professional data scientists and big data engineers, but the number of tech companies fighting for experts is enormous, and enterprises often allure the best talent.


That's why many tech companies look for Big Data analytics partner in Eastern Europe. And they do it for a good reason. The number of Data Scientists and Big Data engineers here amounts to more than 150, 000, according to Linkedin, with Poland and Ukraine taking the lead.

How to choose Big Data experts:

How to choose the right experts for outsourcing big data development:

  1. Choose the location with a vast talent pool

  2. Settle on a Big Data development vendor with solid expertise in Big Data engineering, Data Science, ML, BI, Cloud, DevOps, and Security.

  3. Hire Big Data developers with expertise in:

  • Hadoop ecosystem and Apache Spark. They allow large data storing and processing by distributing the computation on several nodes.

  • Such cloud-based tools as Snowflake, EMR, Dataprots, Cloud Composer, BigQuery, Synapse Analytics, DataFactory, DataBricks

  • SQL/NoSQL databases.

  • Such big data tools as RedShift, Hive, Athena for querying data.

  • Maintaining old MapReduce Java code and rewriting it using a more recent Spark technology.

  • Scala, Python, and Java.

  • Kubernetes constructs that are used to build Big Data CI/CD pipelines.

  • Kafka, AWS Kinesis or Apache Pulsar for real-time big data streaming. 

Why choose N-iX for big data in agriculture:

  • We have over 18 years of experience in remote work and management of distributed teams and have built long-term partnerships (5+ years) with companies such as Gogo, Lebara, Anoto, Currencycloud, and others.

  • N-iX is compliant with PCI DSS, ISO 9001, ISO 27001, and GDPR standards;

  • N-iX is trusted in the global tech market: the company has been listed among the top software development providers by Clutch, in the Global Outsourcing 100 by IAOP for 4 consecutive years, recognized by GSA UK 2019 Awards, included in top software development companies by, and others.

  • Our expertise in cloud computing includes cloud-native services, on-premise-to-cloud migration, cloud-to-cloud migration, as well as multicloud and hybrid cloud management;

  • We offer professional DevOps services, including Cloud adoption (infrastructure set up, migration, optimization), building and streamlining CI/CD processes, security issues detection/prevention (DDOS & intrusion), firewall-as-a-service, and more;

  • N-iX has broad data expertise to design different kinds of data solutions: Big Data / Data Warehouse / Data lake development, Business Intelligence, Data Science, Artificial Intelligence & Machine Learning, etc.

  • N-iX is a Select AWS Consulting Partner, a Microsoft Gold Certified Partner, a Google Cloud Partner, and an OpenText Reseller Silver Partner;


Have a question?

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N-iX Staff
Igor Tymchuk
Head of Data Unit, AVP Delivery

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