abril 11, 2024
Organizations up and down the agricultural value chain, including processors, have more data at their fingertips than ever before. That’s good news. But without the right strategy — and strong data foundations — an organization won’t be able to effectively use this expanse of data and extract the business value it contains.
Facing an endless stream of information, it’s harder than ever for processors to access the right insights and identify the right opportunities. Not all segments of the value chain capture data in a standardized way. This leads to siloed data in multiple formats, using different units of measurement, which can cause a lot of confusion.
In this scenario, people have to spend more time cleansing and aligning their data to prepare it for their use cases. Often, teams can spend too much time wrangling and prepping their data and not enough time leveraging it to inform business decisions.
Beyond this surface-level messiness and short-term frustration, there is a deeper problem. When data isn’t organized well, it can’t be used well. Making business decisions based on data that is incomplete, inaccurate and ungoverned can be a costly mistake.
For example, take sustainability data. Your organization is trying to answer what seems like a basic question: On average, how much carbon is emitted to get one bushel of wheat to our grain processing facility?
But, after many acquisitions and little data governance, every region of your business has its own way of collecting, storing and organizing data. So, the answer is not a simple average. Rather, it’s an effort looking across 100 different enterprise resource planning (ERP) systems, each with its own distinct setup.
To get real, actionable insights out of a poorly organized data, time and energy must be invested into unpeeling layers, organizing data and getting everything in the same format.
Only by building robust data foundations can leaders start to make better, data-driven decisions.
Why is access to well-organized and high-quality agriculture data important?
There’s obviously no shortage of data in the agriculture value chain. But that higher quantity doesn’t automatically translate to higher quality.
Here are some of the questions data processors must ask themselves:
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Is there an enterprise data platform to support data produced from many different source systems?
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Is available data well-documented and registered in a data catalog?
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Is data well-governed and monitored for data quality issues?
The answers to these questions can have a huge downstream impact.
With clean, organized, accurate data, your organization can better understand the big picture behind the numbers you collect. And with that story in place, you can offer better insights to customers.
Better quality data also sets you up to take advantage of future opportunities to monetize that data.
Let’s look at monetization through the lens of corn prices. If processors can maintain clean data for average corn prices they’ve paid across markets (centralized and standardized, instead of being pulled from disconnected silos), they can look further out into the future. They can start predicting where the price of corn might be in six months, or what kind of demand there could be for corn grown using regenerative methods (and the prices that demand could drive).
Creating these forecasts based on real-world data isn’t just a nice-to-have. Customers will also pay for those additional insights because it gives them a window into the future they wouldn’t otherwise have. That value-add is a potential business opportunity for processors — but only if they have a strong data foundation from which to build it.
Strategies to build a solid agriculture data foundation
Building a lasting data foundation could involve major structural changes and significant investment of time and money. This can be tough to justify in a low-margin environment.
Luckily, improving your data approach doesn’t have to be all-or-nothing. There are smaller steps to take now to start building a strong foundation for the future.
1. Identify one or two high-value data domains
Cleaning up all of your data at once to unlock helpful insights can feel overwhelming. How do you choose where to start?
Take a look at which data domains (e.g., customer data, sales data, supplier data) are involved in your most critical business functions. Work backward, starting at the outcome you want.
For example:
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Do you have critical business outcomes related to your customers? In this case, your customer data domain may be a good starting point.
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Do you have a goal to increase sales revenue via pricing optimization? Then you may need to tackle both the sales and customer data domains.
With that end in mind, decide what data domains are of highest value in achieving key business goals. Making these data domains trustworthy and consistent should be the goal of your data foundation. From there, you can replicate success in your remaining data domains, in order of their value to the business.
For example, your customers might want to know the amount of carbon it took to bring a particular food product to market. Your first data congruity project could be condensing all the data sources you have for that product regarding carbon use. Meanwhile, something like corn pricing data could be reviewed as part of a less-urgent, longer-term project.
2. Involve all stakeholders
Data projects should be a collaboration across multiple segments of the business, not just IT or tech departments. When everyone gets involved, you can ensure the projects you take on will drive impact and ROI.
Ask your whole team where they see the most data problems. It’s likely you’ll find a common thread that can bring multiple departments on board for cleaning up data.
3. Choose the right vehicle
It’s important to ensure your data is driving the insights your customers need. But you’ll also want to understand how your customers want those insights delivered.
For instance, do they want a monthly or weekly report outlining the most recent data? Do they want an API that will pull the data itself? Are they after a specific label for an end product (like a “low-carbon” banner for a nutrition facts panel)?
This is another spot where working backward is helpful. Once you understand the insights you need to provide and the vehicle through which you’ll communicate them, you can build better internal data processes and platforms to facilitate all of the above.
Get ready for a data-driven future
The bottom line: Lack of access to high-quality, consistent data isn’t just a day-to-day business operations problem. Fixing it can be a business development opportunity.
There’s immense value in processors being able to do something with all the data pouring into their business. Whether that’s offering pricing forecasts for customers, an end-product label or something else, understanding the story behind your data can help drive future offerings.
And that all starts with a commitment to putting data at the heart of your business. The processors who build a strong data foundation now will be poised to take advantage of valuable opportunities in the future.
