Data

Artificial intelligence fascinates, promises and is gradually becoming an essential performance driver. But behind the technological revolution, a simple truth stands out: no AI can be smarter than the data that feeds it. Before investing in models, co-pilots or augmented dashboards, companies must face a challenge that is often underestimated: the quality of their data. Because projects rarely fail because of algorithms — they fail because of a clean, coherent and governed data base. This article brings together the key figures that remind us why data control is the real starting point of any AI strategy.

Where the majority of companies limit themselves to their internal data, the most agile combine their information with external sources — open data, weather, transport, market. This simple reflex turns numbers into understanding, accelerates decision-making and redefines the role of business departments.

Many businesses want to “analyze their data.” But analyze what, if the data is incomplete or inconsistent? In the majority of data projects, errors do not come from analysis tools, but from the quality of the data beforehand. That's why, at Strat37, our golden rule always remains the same: Build Reliability → Enrich → Analyze. Each stage consolidates the next, and skipping one of them is building on sand.

Do your databases contain duplicates or inconsistencies? Learn how artificial intelligence is transforming data preparation and reliability into an automated, fast, and cost-effective process. An essential item for businesses that want to regain control over their data.

Are your AI projects waiting “waiting for perfect data”? And if it were the other way around: start to become more reliable more quickly. With the AI Act, data becomes an asset to be qualified, traced, explained — without immobilizing your teams. In this article, we show how to place AI in the right place in the flow, measure the gains (F1, completeness...), document each step and achieve concrete results in less than 60 days. Real cases, figures, operational method. The transition from diagnosis to impact is yours.

Is your data really reliable? Each error, duplicate, or missing piece of information seems trivial, but added up, they cost businesses millions. What if your organization's biggest hidden expense was... in your data?