Measuring what matters, Baltic Transport Journal

By May 24, 2024November 14th, 2024Articles
Data team analyzing terminal information

May 2024

Terminals are continually pressured to increase the efficiency of how goods are moved. As such, operators must be able to obtain and leverage insight from data quickly to make decisions that impact their business and customers. But how can this be done when terminals often lack the infrastructure and resources necessary to sort through terabytes of data, the majority of which could change several times within a day? The secret lies in creating a well-formed data strategy that can inform decision making and ensure smooth operations across ports and the broader supply chain.

The big buzzword that comes across most frequently when I speak with customers is “optimization.” “We want to optimize.” “Your software should optimize our operations for us.” “We want to be fully optimized within the next five years.”

But what does this actually mean? What stands behind “to optimize your terminal’s operations?” Because each facility operates differently, and because they are unique snowflakes per se, their definition of optimization and data strategies need to be unique.

A data strategy is a framework that a terminal develops to effectively manage, use, and leverage its data assets to achieve business objectives. Some of these are tactical, like keeping gate turn times below a certain threshold, while others are strategic, such as minimizing unproductive moves to maximize margin. A good data strategy outlines business goals and offers a comprehensive roadmap for how a terminal should use and share its data across the organization and with customers and vendors.

As I write this, a copy of John Doerr’s Measure What Matters is staring at me
from my bookcase. In this seminal tome, he introduces the concept of Objectives and Key Results (OKRs) for setting clear and measurable objectives across the organization, providing alignment, transparency, accountability, adaptability, and perhaps most importantly, focus. A good data strategy starts here, identifying what matters, why it matters, and with what frequency decisions on that data need to be made.

Read the full article on pages 60-61.