ON-DEMAND VIDEO
Retail sales data is one of the most valuable inputs in CPG commercial planning — and one of the hardest to use reliably. In this session, UpClear Director of Product Andrew Dentinger breaks down what data readiness actually means in practice: the labeling, cleansing, harmonization, and validation work that has to happen before retail sales data can support meaningful analysis. He also addresses where AI fits into that process — and where it doesn't.
Key Insights:
- Data readiness is never a complete state — the goal is defining what "good enough" looks like for your specific use case, and building toward it incrementally
- Advanced modeling isn't the bottleneck — the biggest near-term value of AI in CPG data is accelerating the cleansing and preparation work that most teams are still doing manually
- Anomalies in your data are either noise to be filtered out or your most important business signals — telling the difference still requires human context that no model can supply
- Common self-inflicted mapping problems create downstream data integrity issues that compound over time if left unaddressed
- Off-the-shelf AI tools aren't trained on CPG-specific data structures; implementing them without accounting for category nuances will produce confident results on flawed inputs
"The real application of AI in this space as it relates to CPG data isn't so much using it to do advanced modeling and forecasting — it's more to prepare data to be able to do those activities."
Andrew Dentinger, UpClear
