At Smartwyre, we talk about data health a lot. And if you are uncertain about the concept of “healthy” data, you are not alone.

In some ways, data health is similar to our typical idea of physical health: It’s not a switch you flip that will change everything overnight. There are things you need to do — and infrastructure you need access to— to maintain it.

To stay healthy, a person might need a regular exercise routine, access to nutritious food, and a robust, organized healthcare system to receive care for injury or illness.

Data health requires support infrastructure, too. This means more streamlined internal systems that reduce the chances of errors, the ability to make real-time updates, and security and privacy parameters that can protect sensitive data.

Strong data health will allow you to keep a pulse on how your company is doing, where there are opportunities within the market, where you might be losing out on potential revenue, and more.

So, you know data health is important. But to create it at your organization, your data needs to follow the 4 C’s of healthy data: it needs to be credible, current, complete, and connected.

Pillar #1: Data should come from a credible source

Do you know where the data came from? Does it come from a credible source? Can it be trusted?

For data to come from a credible source means that the information is obtained from a trustworthy and reliable origin. In the context of the Ag input supply chain, credible data is of paramount importance in decision-making.

Ag input supply organizations rely on data to make critical decisions regarding procurement, inventory management, production planning, and logistics. When the data comes from a reliable source, decision-makers can have confidence in the accuracy and relevance of the information, leading to more informed and effective decision-making.

But a lack of credibility can also happen when there’s no visibility over data’s migration up and down the supply chain — in other words, sending data into a “black box” where visibility to the originating organization is compromised. This is a particular frustration for retailers, who send through their transaction data, eventually get a rebate check in the mail, and don’t have full transparency in between.

Pillar #2: Data should be current

It’s tempting to take a “better late than never” approach regarding data. But outdated info can come with serious problems.

If data hasn’t been updated in days — or worse, weeks — that information is not useful for the Ag input supply chain and can also be detrimental to agribusiness profitability.

On the retailer side, this looks like salespeople attempting to sell with potentially outdated prices. In an environment where costs are increasing, this could mean a direct loss in margin if there is a delay in price adjustment. When costs are declining, your team could be over-pricing compared to the market.

For manufacturers, timely market data allows for identifying sales patterns and trends, enabling them to make more accurate demand forecasts to help optimize inventory management and product availability. Think about 2021 and 2022 – and how much efficiency was lost due to the lack of timely data being provided up and down the Ag input supply chain.

Pillar #3: Data should be complete and accurate

If data is incomplete or has errors, the data analysis will not be accurate. Decision-makers are not able to make sound data-driven decisions. This is fundamentally key to good data health and a healthy business. Think of all the negative implications of incomplete and inaccurate data:

• Incorrect pricing (leading to customer friction)

• Inaccurate incentive/rebate payments

• Misidentified products

• Poor purchasing decisions

• Damaged customer and supplier relationships

• And the list goes on…

Those agribusinesses that have strived to have the most complete and accurate data sets have the best-run operations and will continue to thrive.

Pillar #4: Data should be connected

Decision-making does not happen in a vacuum. Data must be available to the right decision makers – throughout an organization, but also with trading partners.

Within a single agribusiness, data elements will often be housed within multiple systems. Ensuring all these data elements are in sync with one another is critical to improve accuracy and to avoid manually rekeying information from one system to another. (Your team will thank you for saving those hours.)

But data also needs to connect between organizations. Trading partners must be digitally connected and (crucially) use the same data set to avoid friction. Operating from the same, connected data set improves transparency and gives all trade partners a better experience (given that the elements above are still met)

Other industries (consumer retail, pharmaceuticals, automotive, and the list goes on) already have digitally connected ecosystems to drive efficiencies and better decision-making. When will the Ag input supply chain be connected?

Why the Ag input supply chain needs healthy data

Regarding the future of the Ag input supply chain, good data health has to be part of the equation. At the agribusiness level, better data health will drive improved:

• Decision-making

• Operational efficiency

• Profitability

• Customers satisfaction

Zooming out beyond the individual businesses that stand to benefit from better data health, the entire Ag ecosystem needs these improvements too.

Other industries have already crossed this bridge. Online retail, the pharmaceutical world, and the automotive sector have all used the power of (healthy) data to streamline processes, glean better insights, improve customer experiences, and more.

That culture of data health is by no means an overnight fix. It’s something that must be consistently maintained with a systems infrastructure that will support it.

Ag can make that same transition.

These days, growers’ decision-making is more sophisticated than ever. They have access to troves of data and are using that to make better, profit-driving choices.

But the Ag input supply chain needs to match those efforts. For growers to improve their yields, profits, and efficiency, they need better data from the supply chain.