In a context of strong competition, economic uncertainty and technological acceleration, mid-sized companies (ETI) must be agile. However, this agility is based on a foundation that is often overlooked: data reliability. Behind each dashboard, each key indicator, each forecast, lies an essential question: can I trust the information I have?
Too many companies still make decisions based on approximate Excel files, partial exports or poorly maintained databases. The consequences, which are often underestimated, can be serious for the business.
An erroneous figure in a monthly report. A duplicate not detected in a client file. An inconsistent nomenclature between two internal systems. These are all weak signals that, combined, distort analyses and decision-making. This can lead to an investment being misdirected, missing a commercial opportunity or even generating tensions between services.
For an ETI, each reading or management error can directly impact profitability, commercial reactivity or the quality of the service provided. And yet, data quality management is often relegated to a technical subject, even though it is eminently strategic.
Certain situations should alert general managers as well as operational managers. Are your teams spending more time verifying data than exploiting it? Do your dashboards regularly contain “information to be taken with care”? Do you have clear governance over who validates, cleans, and updates data in the company?
These signals should not be taken lightly. They often reveal a data processing chain that is too traditional, not very scalable, and prone to errors.
Artificial intelligence now offers concrete, accessible and efficient answers to make data reliable on a large scale. It's not about replacing business experts, but about providing them with powerful tools to automate time-consuming tasks, detect inconsistencies, and enrich existing information.
Thanks to automatic natural language processing (NLP), pattern recognition, or even supervised learning, it is now possible to detect anomalies invisible to the human eye, to standardize formats, or to identify missing fields proactively. These technologies can be deployed quickly, without waiting for a long and complex transformation project.
Making data reliable means gaining clarity. It means offering management teams and operational staff alike readable, up-to-date and coherent indicators. It means allowing commercial departments to better understand their customers, financial services to better manage margins, and human resources to anticipate needs.
Even more, it means creating a common language between departments, where everyone can work on the same basis, with the same confidence in information. Decisions then become faster, better reasoned, and more in line with the realities on the ground.
Investing in data quality is no longer an option. For SMEs, it is a direct driver of performance and resilience. It is not necessarily a question of overhauling everything. It is often possible to start small: identify a strategic data flow, audit its quality, test an automation tool, set up an initial governance. It is these first concrete steps that trigger a virtuous dynamic.
Data reliability then becomes a competitive advantage. At a time when we are talking about generative AI, prediction, and real-time management, it is essential not to forget the obvious: no technology can produce value if the upstream data is not controlled.
If you are the manager or manager of a business department in an ETI, ask yourself a simple question: do I trust the data I use on a daily basis to make my decisions? If the answer is “not completely,” it's probably time to reassess your foundations.
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