Charlie’s Produce is an employee-owned produce distribution company that started in Seattle more than 45 years ago. We have slowly but surely expanded to Spokane, Boise, Portland, Alaska, Salt Lake City, Los Angeles, and just recently Phoenix. 

Still, what does produce distribution have to do with data? A lot, as it turns out.

About five years ago, Charlie’s realized that using data analytics in the food industry was something we needed to be a part of. Whether you sell engines or eggplants, every industry produces tons of data. And business data should be used as a business asset. 

From tool to transformation

For years, data helped our employees complete daily tasks but was never looked at as something that could bring added value to us. A lot of users were still at what I call the “admiration phase” of data, presenting their sales from the day or the week before in neat rows and leaving it at that. What people weren’t doing was asking how their numbers compared to the same time the previous month or year, or how those numbers might be trending into the future. We had plenty of data, but what was it really telling us? 

We just weren’t looking at our data as a way to do business better. 

Like most distributors, Charlie’s is a supply chain-driven company and generates a lot of procurement and sales data. Where the dial started to move for us was when we started drilling into data feeds and food analytics information from outside sources on current pricing and comparing it to the prices we were paying. So if we uncovered that we were paying 10 percent more for kiwis than our competitors in 2022, that was data we could use in 2023. Or if we determined that we had just landed a great deal on apples, we could buy more and move them. Data tools that alert us about price fluctuations are a big step forward from just hoping the intuition of the procurement teams was accurate.

From data to real-time knowledge

Charlie’s has a massive warehouse operations, with more than 50 semi-trailer  trucks with IoT devices on them being loaded or unloaded on our property at any given time, 24 hours a day. New data technology allows us to track mileage, route efficiencies, idle time, maintenance status (like data, trucks are an asset, too), and many other metrics. We also generate an insane amount of data about movement in the warehouse, which includes the efficiency of warehouse workers, stock levels, and information about in-route shipments and deliveries. The more we see all this data as a business asset, the more we manage it, and the closer we get to real-time knowledge of our operations.  

Show don’t tell

When people ask me for advice on how to get a modern data function off the ground, I always say that there’s not much use sitting in meetings all day and telling people how great it is that we have this data and what we can do with it. The most important thing is to show your colleagues what data can do with the right analytics.  

For example, you can build a dashboard that fills a need your colleagues have talked about. In essence you can come to them and say, “I know you were wishing you could measure this or do this. Well, here’s a way to do it.” That success quickly starts turning into more requests – and as a consequence, fewer boring business reports that don’t allow you to see trends at a glance. People start  bringing forward requests about topics you weren’t aware of, like a product they suspect the company is losing money on. With the right data and great data tools, you can confirm their suspicions but also point to why.

Hub-and-spoke insights

Another approach I like to promote is a hub-and-spoke data organization. With the hub being our specific data analytics team, we are largely responsible for the general care and feeding of our reporting and analytics. But also we work closely with the data engineering team to make sure that we have good data. And then I’m always on the lookout for somebody on a business team who has an interest in what we do. They might not want to switch jobs, but they typically have an interest in learning more about how we work. They may need some help pushing them along, whether it’s giving them a creator license of a data tool or a crash course on how to start digging into data. In some cases this is a great solution, because it allows the person to transfer a lot of complicated tribal knowledge from a department where we otherwise wouldn’t be able to gather the information.  

In the relatively short amount of time I’ve been here, I’ve witnessed our data culture begin to grow and flourish. Data is something we all use to do our jobs, but data with great analytics makes our jobs better, makes us more money, and helps us save money. We’re also our helping end users – and by extension, our customers – understand the benefits of analytics.