Data-Driven Decision Making for Terminal Operations

Technological change can overhaul companies and entire industries in the blink of an eye. Just look at the taxi industry: Ridesharing companies like Uber disrupted the traditional taxi system in just a few years by leveraging technology to make hailing a ride simpler and more convenient.

Similarly, big data has the potential to transform business decision making by allowing organizations to make more informed, predictive decisions. Some industries, such as advertising, have been leveraging big data for years, while others, such as terminal operations, are just starting to dip their toes in the big data pool. Data-driven decision making certainly has the power to disrupt the terminal operations industry, but what, exactly will the disruption look like? How will data be monetized? And what systems must be in place to leverage the power of data?

The Proliferation of Data

The first iPhone was introduced 10 years ago in 2007. Since then, a dizzying number of devices, programs, apps, sensors, gadgets and widgets have been developed, resulting in lots of data. Big data. Distributed data. Which then begs the question, how are we going to analyze all that data? How are we going to utilize it to improve business now and beyond?

At the 10,000-foot level, data analytics has morphed into two categories: human-focused, which help us (the humans) understand our data better, and machine-focused, which helps computer programs inform themselves about how they can act or react differently (a fundamental step towards artificial intelligence).

For humans: technology should provide access to information that allows our instincts to react to facts or “educated guesses” (as in the case of predictive analytics), rather than to opinions.

For machines: technology should be a driver, analyzing current information and past events to make better, real-time decisions about how to proceed.

Leveraging Data in Terminal Operations

Terminal operating systems are an ideal industry for leveraging data-driven decision making. It operates in the present (operational execution) and the future (yard, vessel and rail planning), and has a huge amount of data from the past that can be mined to inform decision making. The most natural area of focus for data analytics is efficiency gains and cost savings. 

Soon, we’ll see significant improvements leveraging data in two primary cost-saving capacities in terminal operations: detection and prediction.

In detection, we will see better utilities that help identify threshold violations, perhaps violations of service agreements, productivity, or even (if the data are robust enough) profitability. Systems will be used to detect regulatory or compliance delinquency and when terminal assets are underutilized or at actual capacity.

In prediction, we will see software that leverages predictive analytics to increase optimal planning capabilities. Perhaps, predictive analytics will help determine optimal labor or maintenance allocations more accurately. Or even predict profitability (given your planned services, schedules, and considering your history). What if you changed your labor allocation here, or your service offerings there, or your rates? How does that affect your prediction, your profitability, your efficiency?

So, we know that data will affect our bottom lines, but soon it will affect our top lines too.

For the past decade, a major trend in the software industry at large has been leveraging data as a source of revenue, not just as an indicator of past performance or successes. The advertising industry was the greatest pioneer of leveraging data in this fashion, but now modern companies across all industries are thinking about how they can generate more revenue by creating value for their customers using data.

In the terminal operations industry, the monetization of data could come in many forms. One such option could involve shifting terminal services to the airline model of demand-driven price modeling. Airline tickets go up in price as demand for the flight increases, allowing airlines to increase profitability from popular flights. Putting more adaptability into pricing models could allow terminal operators to leverage past data for creating dynamic rates. This model has been floated recently as a good idea in the shipping industry and could be a great revenue driver. 

But how will demand-driven pricing impact customers? Will the complexity of the model lead to the same frustrations as paying more for your flight by booking on a Saturday rather than a Wednesday? Alternatively, perhaps there’s an argument for pricing model simplification. What if you understood your margins so well, through data, that you could simplify or consolidate services into fixed or flat rate structures? Would you be more competitive in your region as a result of one or the other of these approaches?

Through data, you might discover new, necessary services highlighted by discrepancies between where you’re spending your money and where your revenue is being charted. Are there services you are offering for free when you could charge for them? Can you bundle services to reduce incremental rates and make the service more compelling? Can you leverage data to build new partnerships with your customers? Can you differentiate yourself through data access? Look carefully and consider all the ways you might use data to drive revenue for your organization.

Good Data In. Good Data Out.

For all of this – cost savings, predictive modeling, monetization and revenue driving – we need data. Good, clean, consolidated, often real-time, data.

It has become clear that to take full advantage of data, you need more than a business intelligence (BI) tool, or a series of nice dashboards and KPIs to enrich your data. You need a data platform, which serves as an enabler for data-driven decision making and a baseline for technology providers like Tideworks to perform more advanced analytics. Any BI or analytics tool should be able to access your data. Your primary goal should be to garner a wealth of data at your fingertips to slice and dice as you see fit, with as much or as little developer involvement as you’d like. Any software company that tells you otherwise has a different agenda for your data: to drive their wealth rather than your own.

Look for solutions that consolidate data from current and future products, leveraging proprietary technologies to extract, transform, and load (ETL) data into a primary data warehouse. When this method is successful, you incur no perceivable load on host databases. There’s no reason to incur unnecessary technical debt or financial obligations toward licensing fees to support ETL, or database technologies to store additional data. This is your data; used to support your objectives…the architecture to drive such business intelligence insight must work for you. 

Want to take it a step further? Look for tools designed using industry-standard data modeling techniques…meaning you can tap into the data directly, even expand the data to other business critical systems and easily extract it into an enterprise data warehouse. Or, you can navigate to it directly using your BI tool of choice and consolidate data there.

For those who manage enterprise offerings, work with your technology providers to leverage data platform tools as a baseline for enterprise data warehousing. A team of trusted, experienced data analysts and engineers can help you in any of these endeavors. You just need to be able to articulate where you and your data want to go. 

Seeing the Bigger Picture

It’s important to point out that KPIs and metrics, which are so often equated today with data analytics and presented as the “target,” are just a stepping stone to bigger things being done in technology today. Real transformational behavior starts to occur when data itself can be used to change business decisions and therefore real value should be placed on the data itself far more than the KPIs and analytics built on top of it. Data-driven decision making requires a shift in culture as much as it requires shift in technology. 

An industry or company must choose to no longer rely primarily on instinct and expertise as a basis of decision making. Intuition must be viewed as a valuable, non-quantifiable edge that companies can use to supplement data as the basis of long-term growth. Technology should be the enabler and driver.

Tom Van Buskirk is Vice President - Product Engineering at Tideworks Technology.