Data is everywhere — often invisible, but constantly at work behind the scenes. As we move through our day, it quietly powers much of what we experience. A simple card payment in a shop sets off a chain reaction: your bank processes the transaction, the store updates its stock levels, capturing vehicle location and driving behaviour location data by telematics box, and the company’s central system records the sale.
It’s data that informs the display board on a train platform, letting you know your train is just two minutes away. From our morning routines to our evening commutes, data is woven into how we live in 2025.
And the scale of it is immense.
Today, it’s estimated that there are around 181 zettabytes of data globally. That’s equivalent to one trillion gigabytes or one billion terabytes. In just a few years, this figure is expected to soar to 394 zettabytes — a rapid expansion that highlights just how central data has become to everyday life.
We may not always see it, but at every digital touchpoint, data is shaping the world around us.
Data in logistics
The logistics industry has long recognised the value of data and has been quick to adopt technologies that help improve performance and efficiency. As new tools and systems have emerged, the sector has consistently found ways to use them to its advantage.
It started with the basics. Early telemetry services, such as GPS tracking, gave operators a clear view of their vehicles’ location on a map – a simple yet powerful tool. From there, the industry moved into deeper insights, analysing fuel consumption patterns and driving behaviours to improve overall fuel efficiency and road safety.
Since then, the capabilities have expanded significantly.
Today, vehicles can generate ten times more data than they did just ten years ago. Thanks to advances in both hardware and software, operators now have access to a wealth of information that can transform decision-making and drive smarter logistics operations.
But this volume of data doesn’t come without challenges. More data doesn’t always mean better outcomes or deeper insights. Businesses are beginning to recognise that without the right systems; high-quality and relevant data; and effective analysis, they can become overwhelmed rather than empowered.
The real opportunity lies not just in capturing data, but in turning it into meaningful, manageable and actionable insight. It can drive operational efficiency, informed decision-making and measurable business outcome.
The appliance of data science
It’s easy to assume that simply collecting data is enough to transform logistics and haulage operations. But in reality, raw data alone won’t deliver results. To drive real value, that data needs to be refined, analysed in context of strategic business objectives. This is where the real analytical challenge begins.
There’s a well-known saying in data science: garbage in, garbage out. And it’s more relevant than ever in an era where artificial intelligence tools – like ChatGPT – are increasingly part of the conversation where the quality of data directly determines the accuracy and effectiveness of the AI model’s output.
Anyone with deep subject matter expertise will quickly spot the flaws when these models are asked about highly specific topics. They may generate convincing answers based on flawed or outdated sources, and while experts can see through the inaccuracies, others may accept them at face value. When that misinformation is reused and reinforced, the cycle continues, leading to skewed conclusions and poor decisions.
The bottom line? Better data leads to better outcomes.
This principle becomes even more important in real-world applications, such as complying with the government’s updated requirement to inspect trailer braking systems at least four times a year instead of once. With accurate, well-managed data, operators can confidently predict when inspections should take place, helping to reduce downtime, avoid unnecessary checks and keep fleets moving efficiently.
Turn around, go back
Geofencing is another area where accurate data is critical to the success of logistics operations. When systems misreport how long a delivery takes after entering a geofence (delivery site), the ripple effects can disrupt far more than just one delivery.
Inaccuracies here can throw off turnaround times, leading to incorrect arrival and departure times, delayed subsequent jobs, inaccurate performance metrics and ultimately frustrated customers. What begins as a small data issue can quickly escalate, leading to missed expectations, strained relationships and inefficiencies across the board. Moreover, if this inaccurate turnaround time is fed into a machine learning model to improve future logistics planning, it can lead to a systematic degradation in the model’s reliability and usefulness, and consequently, in the effectiveness of the plan itself.
High-quality data helps avoid these pitfalls entirely. When the source information is precise, the systems built around it work as intended. And importantly, solving data issues upstream before they feed into larger workflows is far simpler than trying to fix the consequences later on.
In logistics, precision isn’t a luxury. It’s essential.
Open source informs much more
Modern technology plays a key role in identifying the behaviours that impact operational efficiency. Actions like harsh braking, rapid acceleration or excessive cornering speed all contribute to increased fuel consumption. And today’s systems don’t just monitor them, they help correct them. Moreover, onboard sensors and telematics devices track and monitor vehicle health in real time, flagging issues before they become costly problems. Whether it’s the driver, the transport manager or fleet manager, having this information early enables proactive maintenance rather than reactive fixes.
The story doesn’t stop at the vehicle.
Open-source and crowd-sourced data brings another layer of intelligence, offering a broader context that goes beyond what’s happening inside the cab. By combining internal data with external sources, hauliers can gain insight into accident-prone areas, localised weather patterns or planned road closures; all of which influence route planning and delivery performance.
This level of enrichment adds real value. Rather than simply receiving updates every mile or minute, operators benefit from a fuller picture of the journey, making location data smarter, not just more frequent.
Reporting for duty
Accurate data – whether it’s tracking punctuality, fuel consumption or driver performance – underpins a wide range of operational reports. These insights can be tailored to suit each customer’s needs, helping them streamline operations, drive efficiencies and stay competitive in a fast-moving industry.
As we move toward an expected 394 zettabytes of global data by 2028, the value of this information lies not just in volume, but in context and quality. Future data won’t simply indicate what happened, it will increasingly help explain why it happened, too.
Take driver behaviour as an example. Instead of just recording that a driver braked harshly, new systems will identify the circumstances behind the action. This shift means drivers will be recognised for making safe, responsive decisions rather than penalised by isolated statistics.
It’s a powerful step forward. But unlocking the full potential of this data-driven future depends on how well the information is used. Data must be processed, applied and interpreted thoughtfully.
When done right, it not only enhances internal operations, but it also delivers measurable value to customers as well.
- AI in Supply Chain
- Digital Supply Chain