Big Data in Logistics

There are many buzzwords in the supply chain and logistics industry today, and perhaps there’s none bigger than “Big Data.” Everybody talks about it, but what does Big Data really mean? When it comes to leveraging Big Data effectively in your organization, where do you begin? How do you apply it and what are the challenges and benefits?

Those are some of the questions I discussed with Bob Daymon, Senior VP of Operations at Transplace, in a recent Talking Logistics episode.

What is Big Data?

Like many buzzwords, Big Data means different things to different people. So I began our conversation by asking Bob for his definition and I really liked his simple, to-the-point answer:

“Big Data is pulling information from multiple sources into a single view to see what story it will tell you. It can tell you what your problem is and often how to solve it.”

With supply chain being a very siloed endeavor, getting all the information in one place can be difficult, but it also can provide major benefits. The objective, Bob says, is to make better decisions faster.

Big Data is not a Technology

One mistake I have seen many companies make is to assume that some shiny new technology is going to solve their problems (see the hype around blockchain). It won’t — technology is just an enabler. This is true with Big Data too. Companies will buy business intelligence (BI) or visibility applications and think they will provide all of the answers, but the benefits they actually realize often fall short of expectations.

Bob likes to approach Big Data from a different perspective. He says, “It’s all about deciding what you want to measure and what data you have, and then taking some form of action.” He recommends starting with a manual approach, perhaps augmented with spreadsheets, to validate that the data collected is going to provide the insights you’re looking for, such as trends that denote a problem or an opportunity. He says the worst thing to do is to spend a lot of time and expense implementing new technology only to realize it doesn’t help solve the problem you need to solve.

Bob points out that the data itself isn’t the answer either. He recommends you begin by creating a roadmap of what you want to do with the data you are collecting and how you’re going to make it actionable.

Big Data vs. Tribal Knowledge

The flipside of Big Data is that not all information you want to gather is necessarily measurable or available. Bob says this is where tribal knowledge and organizational experience is helpful. Often it takes interpretation and may involve reaching across organizational silos, such as going to manufacturing or suppliers. He says, “That is what makes Big Data so powerful. By combining quantifiable data and non-quantifiable data you can make better decisions moving forward.”

However, “remember that data is only an indicator,” Bob cautions. “It’s what you do with the data that turns it into useful information. That’s why starting with a roadmap is so important. We like to do hypothesis testing to challenge the data and make sure we’re getting the information we’re looking for.”

Challenges and Opportunities

Bob recommends that all companies begin their journey with Big Data now because of the tremendous opportunities it presents. But he also understands there are many challenges. First, as with all applications, data quality is an issue. He says not to get hung-up there. Begin with simple actions and improve as you go.

For companies looking to begin this journey, Bob provided a number of examples, from very simple to more complex, of what other companies are doing to leverage Big Data in their organizations, as well as how to get started. I encourage you to watch the full episode for more insights and advice on this topic. Then post a question or comment and keep the conversation going!