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Demand for Data Grows in Agriculture
Demand for Data Grows in Agriculture

Demand for Data Grows in Agriculture

The modern data stack becomes critical as agribusiness looks to improve operations, stay competitive, and meet global demand.

By Ciara Rafferty, July 8, 2020

Agriculture is the oldest and largest industrial vertical in the world, and its importance continues to grow as it becomes more challenging for people to access healthy and fresh food. A recent Agriculture Analytics Market report, released by Markets and Markets, estimates that by 2023, the global agriculture analytics market size will grow from $585 million to $1.2 billion dollars as demands for real-time data analysis and improved operations increase. In this way, agriculture is no different from other industries: To improve decision-making, businesses need to analyze data from many aspects of their operations, including weather, soil, fertilizer, seeds, equipment, finances and stock. In the case of agriculture analytics, having access to all aspects of farming operations means growers can improve yields in the most cost-effective way.

Big data analytics are transforming agriculture by boosting productivity and innovation, helping to manage environmental challenges, reducing costs, and improving supply chain management. With both competition and the global demand for food increasing, agriculture can’t ignore analytics any longer. What remains a challenge though, are data resources — both tools and people — that can help in this space.

Bringing Tech to Ag

Dan Maycock is a prime example of an individual bringing data to the agriculture space. The VP of IT/Data at Loftus Ranches, Dan always had a passion for tech. Growing up in a small farming town in Washington, one of his first jobs in the early nineties was selling computers to farmers to dial in for weather data. He left eastern Washington and held a number of jobs in tech, including heading up Alexa analytics at Amazon, but ultimately returned to settle down in the area, bringing with him 15 years of analytics experience.


His first job when he returned was to help build the end-to-end infrastructure for HR Spinner, a produce packaging distributor in central Washington state that has been operating for more than 100 years. He found himself in a position where he didn’t have a qualified team of engineers to perform ETL like he was accustomed to in the tech world. It was here that he discovered Fivetran. Currently, he is the VP of IT/Data at Loftus Ranches, one of the largest hop farms in the world, where he also brought on Fivetran and is currently building out the data architecture. 


As Dan has witnessed, bringing data to these traditional companies offers massive returns. At a previous job, providing KPIs via PowerBI built on top of Azure SQL Server cost a fraction in staffing over several months compared to what the company grossed in additional throughput, attributed to increased visibility over staffing and labor metrics for the fruit packing line. He notes that growing economic and environmental challenges only heighten need for data in agriculture:

Several trends are hitting growers today, including changing weather patterns from climate change, trade disruptions worldwide, strain on supply chains from covid, and several labor-related issues hitting agriculture at every stage of the supply chain. This creates several risks to our food systems, with farmers being placed in the worst spot of all. The industry as an entire vertical needs to be brought up to speed with data analysis, KPI access and BI solutions, let alone broad scale ML/AI. Estimates from state that globally, we will need to grow 50% more food on 30% less farmland in the coming years, to avoid massive food shortages — getting there will require better tools to analyse farming, which means really strong data analysis.

Dan sees the lack of resources across agriculture as a whole, compared to verticals such as retail, as a huge problem, and data integration as a critical step to scaling analytics:

Fivetran stands out as the best data integration solution on the market because it gets the ETL done without a team of data engineers, at a cost any ag company can stomach. Loftus has thousands of acres and hundreds of employees, but has had to rely up until now on older database tools built several years ago for operational decision making. With Fivetran, Azure SQL Server, and Tableau we are rapidly migrating off that solution into something much more powerful — putting those several years of data to work in a more effective way.

Below are case studies from two agriculture businesses that Dan has worked for in central Washington: HR Spinner and Loftus Ranches.

Case Study: HR Spinner

Around 2010, HR Spinner decided to bring on a new ERP to replace its legacy, home-built system, and went through about four ERP transitions until it settled on NetSuite in 2017. “It was a brutal time because of the sheer amount of money and resources involved. We were pulling people in all different directions, through different systems trying to find one that could harness the quantity and complexity of our transactions,” explains Sean Kinney, CFO at HR Spinner. While NetSuite itself was an improvement, the in-app reporting had limitations. For example, NetSuite didn’t allow for multi-level joins last year. 

Dan joined in 2018 and started building out the data stack with Fivetran and Tableau. “We have reports on daily cash flow and average AR which we can graphically illustrate in Tableau," explains Sean. "We use the email connector to upload a twice-daily report from our main manufacturer so we know what orders we can fulfill with what we have on hand. This accurate, real-time information is critical for our sales people."

Having the data in Tableau eliminates the need for manual reporting and frees up resources. “We don’t have a lot of resources to allocate to building out a data architecture, so having these solutions is critical," says Kyle Eaton, IT Director. "Fivetran is user-friendly, I can increase sync frequencies and add connections on my own, and I have no background in data."

HR Spinner has only hit the tip of the iceberg when it comes to the data it wants to explore. “We’re looking into creating forecasting models and doing predictive analytics," says Kyle. "We want to identify trends prior to market shift."

“There’s a lot of room for growth in this area, but it takes time,” adds Sean. “Understanding how to use data is a process but for those in agriculture who invest the time and resources in understanding their data will see a return on their investment.”

Case Study: Loftus Ranches

Loftus Ranches previously used a legacy operational metrics system in addition to a number of new systems leveraging IoT to help manage different parts of the ranch: weather, irrigation, etc. Pulling data from these systems, however, proved very difficult. Financial systems (payroll, accounting, etc.) were siloed. Consequently, there was no way to combine and analyze the data and a lot of effort was put into manually extracting data for reporting. As Dan explains, this isn’t an unusual case in the industry:

Outside of US-based row crop companies, most companies are just starting to get tools installed that leverage IoT across their growing and packing operations, which means they’re seeing reporting for the first time. The big opportunity now is to build a centralized data collection to put all of the data to good use.

It was critical to migrate to the cloud and get the data into a more dynamic state so more of the company could rely on reporting and analysis:

We had to move into a reliable cloud environment using a modern data stack to build the foundation for the company’s data footprint. Having solutions on premise or using an older data stack creates more administrative challenges, thereby pulling time away from insight creation. In the cloud, we have greater reliability and accessibility with less chance for unplanned outages, and we can leverage remote workers to help us further build out our modelling capabilities.  

For Loftus Ranches, some of the pros of Fivetran are ease of use, automatic detection of schema changes, and the ability to get new connectors up and running quickly either out-of-the-box or through custom connections for unique APIs. As Dan explains:

Other solutions provide similar capabilities as Fivetran, but some are up to five times the price. With the consumption-based pricing, smaller operations with less data can scale the cost as the business scales. Companies with smaller data teams can quickly build pipelines without having to contract expensive consultants or take on expensive headcount before the value of data can be realized. Fivetran is a no brainer for ag companies.

Loftus Ranches uses the Fivetran MySQL connector out of the box, and built connectors for Arable using Azure functions and Picktrace using SFTP. It integrates these data sources into an Azure SQL Server and analyses the data using Tableau. Currently, the business is tracking irrigation scheduling, chemical use and treatment, annual yield, weather, and several other core metrics related to growing and harvesting hops, apples, and several other types of crops across the company. It is currently working on bringing together operational metrics with data from its irrigation system and weather stations to better manage water usage across the entire operation.


Once fully operational from a reporting standpoint, Dan expects several dozen Tableau users in a matter of months to begin consuming this data. Reporting previously relied 100% on manual data entry and siloed reports with tools like MS Access for operational metrics and Excel for several other kinds of data. Here's how Dan describes the impact of Fivetran from a data integration and analytics perspective:

With Fivetran, we are able to automatically extract data and generate metrics and combine data from multiple sources, which will really help supercharge our analytical capabilities. There is considerable excitement in place today around the potential impact of having reliable data at our fingertips. We have several years of use cases that folks across the company have lined up, and Fivetran will be critical in helping us get those solutions delivered in a timely and cost-effective manner. Having a modern data stack will put Loftus in a prime competitive position in a time where there’s a lot of uncertainty in the world.

The Road to AgTech

The impact of being data-driven in the agriculture industry can’t be overstated. To become data-driven, agriculture companies, like any other industry, need to adopt modern architecture to begin analysing their data effectively. Given how far behind the industry is as a whole outside of the largest players, incorporating BI alone can dramatically impact every piece of the agriculture supply chain. 


But it isn’t just up to the businesses themselves to figure it out. Large data companies need to consider agriculture a serious business. “There are no large data companies focusing on agriculture today, but companies such as Microsoft and Google are starting to build agtech teams to help tackle some of these problems,” says Dan. “I’ve been able to join several groups nationally to keep my finger on the pulse of agtech. Every day I’m surprised by just how much opportunity exists.”

If you’d like to learn more about agriculture and join the agtech discussion, follow Dan Maycock’s agtech blog or connect with him via LinkedIn or email. If you’d like to see Fivetran in action, request a personalized demo today.


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