In the urgent need to “make data speak”, many companies are launching directly into analysis or automated reporting. But without a solid foundation, no artificial intelligence can draw reliable conclusions. High-performance data projects follow a simple logic: Make the data reliable before enriching it, and only then analyze it. It's the only way to get consistent, actionable, and sustainable insights.
Data reliability consists in guaranteeing their quality, consistency and completeness before being used. This step is often underestimated, although it determines the success of any analysis or artificial intelligence project. Reliability means ensuring that the information on which strategic decisions are based is accurate, up-to-date and standardized.
Concretely, this involves the cleaning duplicates, the correction of anomalies, the format check, and theharmonization of sources from different systems (ERP, CRM, e-commerce, etc.). Reliable data is data on which reliable models, reports, and predictions can be built. Without it, even the best algorithm will produce biased results.
Once the data is reliable, the next step is to enrich. The aim is to provide context, depth and relevance. Enrichment transforms raw data into intelligent data, capable of informing decisions.
It can be a question of integrating external data (open data, market data, weather, geolocation, sectoral trends) or cross-referencing internal sources to reveal invisible links between logistics, sales or marketing. For example, linking sales to weather or seasonality makes it possible to identify much more accurate predictive models.
Enrichment is what makes an organization move from descriptive logic (“what happened”) to explanatory and prospective logic (“why it happened and what will happen”).
It is only after having made the data reliable and enriched that the time comes for analysis. This is the most visible stage, the one that brings all the preparatory work to fruition. Analysis makes it possible to transform data into indicators, trends and recommendations exploitable.
With smart dashboards, predictive models, or AI assistants, businesses can identify performance drivers, detect anomalies, and simulate different scenarios. But the analysis is only valid if the first two steps have been respected: without reliable and rich data, the interpretation becomes random.
The challenge is therefore not only to “make the data speak”, but to Give him a reliable, clear and useful voice for decision making.
Data is now the fuel for any digital transformation. However, most companies forget that before wanting to analyze, you must first make them reliable and rich. Artificial intelligence is only effective when it is based on a solid and structured basis.
Chez Strat37, we support companies at every stage of this journey: make their business data reliable, enrich it with relevant sources, then analyze it using AI dashboards and automated insights. This methodical approach makes it possible to go from a submitted report to a intelligent control, where each data becomes a strategic driver of performance.
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