Knowing your company’s Data IQ is the key to Logistics success

We interview Nate Endicott of RateLinx about getting data gathered, then making sure it makes sense. From there, decisions & insights, and predictions, too. The technology perspective.
29 July 2021 | 4251 Shares

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There are many businesses today that put a significant emphasis on data. As the primary way people and organizations do business, organizing, processing, and managing data is an essential part of business today.

It’s a fundamental resource on which stakeholders base decisions, but in specific verticals, real-time (or near-real-time) data makes a big difference in day-to-day operations. Logistics and supply chain is a prime example of an industry that’s increasingly driven by data. Previously, companies would look at last month’s data and ask themselves, “what did we do right, and what did we do wrong?” Now, successful companies are using data to drive their day-to-day decisions.

In an exclusive interview with Tech HQ, we spoke with Nate Endicott, Senior VP of Global Sales, Marketing and Partnerships at RateLinx, about the data revolution in logistics and supply chain. (If you’re not acquainted with RateLinx, read up on the company’s offerings here and here on the pages of Tech HQ).

We asked first about the concept of “Data IQ”, where IQ stands for Integration and Quality—the primary strands on which successful business is built in this connected age.

Integration, of course, simply means tech that talks to other tech, and in logistics and supply chain environments, it can mean two things. Firstly, it means connecting a company’s systems together (like the TMS, OMS, ERP, and WMS, for example). Secondly, it means integrating data of disparate systems to map and maintain the relevant data attributes and context between the two systems. An important distinction to make is the differences between aggregating and integrating. Virtually any provider can aggregate (collect) by connecting systems. It’s what happens afterwards that’s vitally important—as we’ll see.

When companies deploy RateLinx’s solutions, Nate says it’s a matter of integrating the data across multiple systems in ways that are as adaptable as possible, but always with a view to practical, positive use cases specific to this industry. “Most guys, I think, on the integration side, they either have an [external] integrator or consultant. We do it ourselves. So, we can control the speed of integration; we can control the cost of it. And we only have an upfront fee, so there’s no extra charge if we take longer. So, we’re responsible for making a faster integration, to allow [RateLinx users] to get their arms around their data faster. They may have attempted to internally build data lakes to complement their control tower strategy over three to five years. We do it in 30-45 days.”

Ensuring data is both clean and relevant forms the “Q” of “IQ”, where data quality is the issue. Mass pulling together data silos across the business and from third parties is valuable, of course, but as any data scientist will tell you, that’s just the easy part. Sorting through and parsing the data to find relevant, actionable information is tedious and challenging, if not impossible. And without quality data, actionable insights will be limited and untrustworthy.

It’s like a puzzle. Data aggregation and connectivity is having all the pieces in the same box. Data quality and integration is putting the puzzle together and making it look like the picture on the box.

Rather than a data scientist or technologist, the process needs the industry expertise of a supply chain/logistics specialist. They bring an ability to discover the relevance of the information for daily operations and how it can be used to bring out predictions and trends.

RateLinx might have a great technology product, but the integration and implementation teams are primarily industry guys, not geeks in white lab coats. It’s the industry expertise that sets RateLinx apart from other solution providers. We asked Nate first about the use of data in everyday work via the RateLinx platform, rather than hardcore statistical analysis and deep-level number crunching.

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“Visibility itself is not enough,” he said. “If the trucks are already late, you can’t speed them up. So, tell me the problem. Is it customer service? Is it customer-side OTIF charges or penalties or upstream in the supply chain? Is it sales that needs data, or is it a finance issue on the invoice side? We try to solve the true underlying problem. […] Instead of a world in which a consultant takes six weeks just to diagnose the problem, we diagnose, develop, and deploy the right solution in the same time frame to give the customer a quick time to value.”

The RateLinx solution also keeps tabs on what’s often the biggest problem in integrated systems—that of updates or patches elsewhere “breaking” the link between companies’ tech. In this case, Nate told us that “our system is constantly looking to deliver accurate, complete, and timely data. […] We have AI and machine learning prescriptive insights that are running on that server-side, actively monitoring master data, and if data anomalies appear, our data quality team will pick up the phone and call the source, and we could cleanse that problem.”

Accurate, complete, and timely, quality data means that operational issues can be fixed quickly, acting on a reliable source of real-time information, not a month afterward when reports get pulled—the “traditional” way companies evaluate their logistics network and processes. We’re talking the next day, or even the same day. Nate knows that the people in the industry he talks to have “their bonuses sitting over their head, and even their job is on the line to deliver on certain initiatives.”

The RateLinx platform gives people under pressure the insights they need to address issues head-on as they happen. That means there are immediate savings on overheads and fuel costs, for example, but in the longer term, companies can hit their KPIs more often and even stand up KPIs of their own for partners and suppliers to hit too.

In the next article, we’ll discuss more business benefits that companies get when they’re better aware of their Data IQ. Integrations that yield data, then data quality that feeds actionable insights—it sounds like a simple enough equation and one that relies heavily on technology, but more importantly, from industry experience.

To start your own data IQ journey, reach out to a RateLinx representative to talk through your options today.


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