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How Big Data Analytics Is Transforming the Transportation Industry

The term big data was coined in the 1990s to describe large data sets. At the time, “large” meant perhaps a few hundred gigabytes. Today, you can easily find a service that will let you set up a data lake for big data analytics that hold multiple petabytes. One petabyte is more than a million gigabytes.

But the biggest difference between big data now and 30 years ago isn’t the sheer amount of data we’re generating—it’s how pervasive it is.

• Search engines log your queries and browsing habits to offer you targeted ads.
• Credit card companies track purchase histories to flag potential fraud.
• Universities analyze data for patterns that predict a student’s risk of dropping out.
• Researchers mine anonymized healthcare data for links between lifestyle and disease.

And the list goes on . . .

As big data revolutionizes the social and economic landscape, businesses must adapt to reap the benefits. The transportation industry is no exception. Big data analytics has the potential to streamline any area of your operations, boosting efficiency, saving money, and enhancing customer service.

Big Data in the Transportation Industry

Academic types like to talk about three V words that characterize big data:

• Volume: Huge quantities of data are involved.
• Velocity: Data are produced quickly and continuously.
• Variety: Data points are diverse—numbers, text, media, etc.

Let’s look at these aspects of big data in the context of transportation operations.

Data Capture in the Transportation Industry

If you operate a private truck fleet or run a third-party logistics (3PL) company, you’re already collecting data—a lot of data.

For example:

• Planned routes in route planning software
• Actual routes, idling time, and driving behaviors in onboard systems
• Driver logs from electronic logging devices (ELDs)
• Dashcam video footage
• Data from Internet of Things (IoT) sensors (e.g., a wireless thermometer in a reefer)
• Driver scorecards
• Fleet maintenance records
• Customer information and orders
• Warehouse records and load plans
• Driver hours of service and payroll

Not only is there a large volume of data coming in, but it’s coming in daily at high velocity. Sending just one driver out may generate data in all the categories above.

In addition, there’s a huge variety of data. Some data are in traditional databases or tables. Other data are less structured and more eclectic—GPS coordinates, regulatory reports, text or XML files, and even videos.

So, we’ve seen how the transportation industry captures data. But simply having all that information isn’t a game-changer. Applying the principles of big data analytics to it is.

Big Data Analytics for the Transportation Industry

Amassing data is relatively easy. But turning those data into actionable insights may seem like a monumental task.

Big data analytics tools are up to the job:

• First, they process large data sets to prepare them for analysis—for example, sorting, removing duplicates, and resolving conflicts.
• Next, they employ analytic technologies (e.g., data mining, artificial intelligence, and machine learning) to spot trends and patterns in the sea of information.
• Last, they provide reports and visualizations—like charts, graphs, timelines, and heat maps—that give you insight into how to optimize your operations.

How Are You Analyzing the Data You Collect?

Many trucking companies and 3PLs collect tremendous amounts of data—but the data live in separate systems.

For example, a company might have route data in route planning software, orders and customer data in customer relationship management software, and driver records in yet another system. Perhaps they even have physical records for some categories, such as accident reports.

Most transportation operations have the information they need to:

• Shave time and miles off routes
• Reduce idling time
• Reduce fuel consumption
• Reduce over, short, and damaged (OS&D) claims
• Improve driver safety
• Improve fleet maintenance

But with scattered data and inadequate analytics, they can’t leverage that information to gain a competitive edge.

More and more transportation companies are using big data analytics to streamline their operations. Older, established companies are moving their legacy systems into new transportation management system (TMS) software that gives them analytics capabilities. Newer companies, recognizing the advantages of big data analytics, are building digital systems from the ground up.

It’s a brave new world.

Big Data Meets Trucking: Syntelic Transportation Analytics

Syntelic’s Transportation Analytics software provides a single repository for your company’s disparate sources of information. It pulls in data from our Route Planning and Load Planning applications, and we can connect it to external systems, such as ELD and on-board diagnostic (OBD) logs.

Our software is adapted to the transportation industry—in fact, it’s built for it. Transportation Analytics puts the power of big data business intelligence at your fingertips.

You can:

• Easily access key reports on customized dashboards
• Manipulate the data with sorting, filters, and calculations
• Create charts and other visualizations to understand the data
• Use drill-downs to zoom in from the big picture to isolated records

Every company is unique, so our software isn’t a cookie-cutter solution. We get to know our clients and tailor each implementation to meet their specific needs.

Contact us today to learn more about what Syntelic Transportation Analytics can do for your transportation operation.