The real data struggle is making sense of unstructured, inconsistent information and relying on off-the-shelf AI tools that aren’t built for BevAlc.
Most ERPs capture some, but not all, of the wine-specific facets that matter – like region, subregion, grape blend, appellation, container size, and sales channel. Without that structure, extracting deep insights is impossible, even before you try AI.
ChatGPT and other off-the-shelf LLMs are powerful tools. Still, they aren’t built to reliably clean messy BevAlc data or extract structured data from sales descriptions or brand item names at the level of accuracy, consistency, or scale that teams need for actionable reporting or powerful AI insights.
At TippleTech, we’re solving the upstream issue: creating faceted, structured data at ingestion. That’s what lets AI and BI tools actually work.
Great article via Wine Industry Advisor on the critical role data plays in winery resilience. But the first step is this: structure your source data, or you’ll be stuck telling the wrong story – or no story at all.
Originally published on LinkedIn →