How can you determine if the data you're getting about your ocean cargo is accurate? Sometimes it's not easy to figure out, especially if the shipping line tells you one thing, and the terminal tells you something different. Who's right?

That's one of the problems we set out to solve with our container tracking platform. It's difficult to plan for your container arrivals if one source says the vessel is arriving this week, and another source says it's arriving next week. This kind of thing happens more often than you may realize. In fact, we've found that 30% of ocean cargo data is either incomplete, missing, or flat-out wrong.

If you're relying on data like this to make key decisions for managing your supply chain, it's a problem if the data is wrong 30% of the time. We knew we could do better.

To deal with this, we first aggregate and normalize data from multiple sources, including all major terminals in North America and shipping lines from APL to ZIM (24 at last count). 

Our algorithms do a bunch of work behind the scenes to determine if there's conflicting data, and automatically reduce the error rate to just 7%. But that still isn't good enough, so we have an operations team in place to handle the remaining exceptions.

With redundant sources of data in place, we're able to provide the most complete and accurate data about North American ocean cargo available today. 

That also enables us to detect when there are problems with a data source, sometimes even before their system operators are aware there's something wrong.

When a terminal recently upgraded their operating system, for example, we were able to confirm that the new data feed was accurate based on our correlation with other data sources. But a few days later, when our system detected discrepancies between the data we received from the terminal and the data we received from the shipping lines, we were able to quickly take action.

To start, we notified all of our customers who were tracking cargo through the terminal that information about their container arrivals could be affected. For some, like those shipping reefer containers with perishable goods, it was critical for them to know about the issue so they could assess the impact right away.

But even for those who didn't have time-sensitive cargo, using inaccurate data to coordinate their shipments could still cause significant disruptions to their business.

While the terminal was working to address the issue, we were still able to provide information to our customers because we had other sources of data. When one source goes down, we make sure our customers aren't left in the dark.