Why Does AI Need So Much Data – and Is My Serialization Data Enough?

Companies in pharma traceability sit on enormous datasets – billions of serialized events, years of aggregation hierarchies, complete audit trails. So the question comes naturally: “We have all this data, surely we can do AI?” The honest answer is: it depends on what your data actually contains – and, above all, on what question you are asking.

AI needs data for one reason: models learn by example. To learn what a “suspicious shipment pattern” looks like, a model needs to see many shipments – normal and anomalous – with enough context to tell them apart. Three properties matter far more than raw volume:

Relevance. Data must contain the signal you want to learn. Serialization data tells you what moved where and when – excellent for detecting diversion patterns, forecasting flows, or spotting anomalous aggregation behavior. It does not tell you why a line stopped: for that you need machine and sensor data.

Labels. For most industrial AI (supervised learning), you need examples annotated with the correct answer: this image is a defect, this event sequence was a genuine diversion. Labels are often the true bottleneck. Millions of rows with no reliable ground truth can be worth less than ten thousand carefully labeled ones.

Quality and consistency. Missing timestamps, inconsistent master data, mixed granularities (unit vs. batch vs. shipment), and undocumented process changes silently poison a training set. In my experience, in a serious industrial AI project 60–80% of the effort goes into understanding, cleaning, and structuring data – not into modeling.

So, is your serialization data enough? For supply-chain-level questions – anomaly detection, flow prediction, network intelligence – it is a genuinely privileged asset: structured, standardized, complete by regulatory design. Few industries have anything comparable. For questions about physical products and machines, it must be combined with inspection images, sensor streams, and maintenance records.

The practical takeaway: don’t start from the data you have and ask “what AI can we do?” Start from a business question, then verify – honestly – whether your data can answer it.