How will data analytics change the way we provide services and products for our customers now and in the future?
Blog by: INTENT
In 2016, INTENT’s founders saw a need in the industry to accelerate and streamline product adoption on the farm. The objective was to connect agribusinesses with farmers, help introduce emerging ag products and solutions, and deliver valuable insights to everyone they worked with. Understanding that working directly with farmers is foundational to our business, we’ve also discovered what a significant role technology plays in delivering more value and transparency in the innovation process. As we work to build an efficient innovation pipeline, we leverage data science, artificial intelligence, machine learning, and more, bringing value to everyone we work with–from farmers to ag businesses.
Data science, artificial intelligence, and machine learning will help to build a more efficient innovation pipeline.
Where We’ve Been
When INTENT began providing field trial services in 2017, we gathered information on 25 fields with 25 different farmers in the Midwest. In a few short years, the business had grown to encompass the most comprehensive field insights in the industry, with a growing client base. It became necessary to develop an efficient way to store and analyze all the data that was being collected, and in 2020, a foundational database was built. The objective was to build a robust database and structure to service our clients, with the ability to ingest a wide range of field-level data. “Building the database was an interesting project because we started with a sizable chunk of data, then analyzed and extracted insights, and built a machine learning model to make predictions, states Kamal Dhungana, Director of Data Science at INTENT. “In terms of statistical analysis, at that time our focus was on creating product performance reports for our clients.”
With the database built, in 2021 we turned our focus to building a streamlined platform, with the ability to visualize field-level data, generating reports and analysis through the season. The INvision platform became a one-stop shop for all data that was relevant to a field trial. This allowed our clients to see how their product was performing at more than one point during the season. Instead of waiting for yield data at harvest, businesses could access in-season data to make decisions in near-real-time.
Present data analytics
Recognizing that it is not just planting and harvest data that impacts a trial, we continued to build the INvision offering, further broadening data sets to include soil profiles, weather, imagery, lab data (of tissue sampling, for example), and expanded the ability to visualize precise product- and application-specific data layers within field boundaries.
INTENT reached a critical juncture in 2022, when it became evident, we needed a platform that was more robust and versatile to continue to ingest and manage the millions of data points collected through a growing season. The team designed and built INvision Trials, backed by the Apogee technology infrastructure. The new software quickly and efficiently organizes data from diverse sources, allowing INTENT to further expand our analytical capabilities. “Until recently, we were very focused on real-time reporting.” Dhungana shared, “Now, our depth of historical data has enabled us to move to the next level of data analytics, while our data collection process has become very robust.” We can extract trends using historical and real-time analysis, leading to the development of machine learning models and predictive analysis for our clients, helping them to model the market for their new innovations before they even get to the field.
Director of Data Science at INTENT, Kamal Dhungana
What does the future hold?
INTENT’s latest advancements offer more than end of the year yield predictions and employ Apogee and INvision Trials to monitor crops from the beginning of planting to harvest. Utilizing historical data on soil characteristics, weather patterns, such as temperature and precipitation variations, and other variables, INTENT can now predict appropriate timing of in-season applications, along with building models to guide the decision-making process. Depending on the type of product being analyzed, various sections from INvision Trials can be plugged in to provide and analyze data to build a projection.
Predictive models and advancements in data analytics will provide clarity and transparency to our business clients building go-to-market strategies for new innovations. “All crops go through different environmental scenarios that can be favorable or unfavorable to the crop. Different stress variables will create diverse scenarios for the crop, and based on these scenarios, we can make predictions early in the season, allowing our clients to make timely decisions about their product long before harvest,” says Dhungana.
With predictive modeling, businesses will be able to proactively choose a region where a product should perform best or avoid regions where the data shows a product would not do well, providing key advantages to on-farm trial placement. Considering all the circumstances that affect a product’s efficacy, real-time monitoring affords clients an opportunity to pivot or regroup and consider all their options in moving a new product to through the innovation pipeline before it’s too late to change course. By harnessing the power of data models, INTENT is finding new ways to accelerate the innovation process.
From building a database, to designing a robust platform for farm data management, to developing predictive models that provide innovators and farmers with a look at what could be, INTENT has forged new paths in helping our clients go from ideation to commercialization. Our growth in technology and data science has allowed us to serve our clients in unique ways, while always keeping the farmer’s perspective top of mind. As we look to the future, INTENT is committed to tapping into the hidden potential of data and shining a light on the value of connecting businesses with farmers.