Why Logistics Needs a Data Strategy for the AI Era
Logistics is entering a phase in which the limiting factor may no longer be access to information. Transport platforms, warehouse systems, sensors, automated equipment and customer-facing tools are generating data at a rapidly increasing rate. The industrial question is becoming more fundamental: can companies convert that information into reliable action?
Robert Jordan, CEO of iFactory, explored the issue with Paul Hamblin during a recording of Logistics Business Conversations at Parcel & Post Expo in London. Jordan’s description of a “data junkyard” captures the risk facing connected operations: information can accumulate faster than organisations can classify, secure or use it.
Digitalisation needs direction
More data can support better planning, service visibility and process control. It can also produce duplication, inconsistent records and disconnected sources. When information is not linked to a defined operational decision, its maintenance consumes resources without necessarily improving performance.
This matters for Industry 5.0 because resilient operations require more than automation. They require technology to remain understandable, controllable and useful to the people responsible for the process. Human judgement is still needed to decide which indicators matter and what response should follow.
Governance becomes an operational capability
AI adds urgency to the issue. Automated systems rely on dependable inputs, while logistics companies need clear answers about data ownership, access, security and retention. Information that crosses several platforms can become difficult to trace, especially when businesses are unsure which records remain valuable and which can be removed.
A practical data strategy should therefore begin with the decisions a company needs to improve. It should identify critical sources, establish responsibility for them and connect data quality with measurable operational outcomes. This approach can help businesses use automation without simply increasing digital complexity.
The distinction is important for profitability and resilience. A company that is merely data rich may still react slowly. A company that is decision rich can use trusted information to adjust processes, focus human attention and deploy AI more effectively. The next stage of logistics digitalisation will depend less on collecting everything than on making purposeful use of what is collected.
