Behind the grand pronouncements about data-driven transformation lies a more pragmatic reality: Without data quality, there is no value.
Companies have never invested so much in data. Catalogues, cloud platforms, governance, AI: everything seems to be in place to exploit this strategic asset.
However, one finding persists: nearly 80 % of Data Products produced are never used. The reason? Data that is too often incomplete, inconsistent, or obsolete.
Data quality is not just a technical subject. It directly affects reliability of decisions, the Operational performance and the Trust between business and IT teams.
Some tangible impacts:
The data managers know it: Measuring quality is no longer an option, but a performance lever in its own right.
Despite the advances in modern architectures, data quality remains a major challenge for most organisations.
Manual checks, often inherited from older processes, consume considerable time and rely on hard-to-maintain scripts or ad-hoc checks. As volumes increase and environments diversify, these approaches become not only time-consuming but also unreliable in the long run.
The multiplication of sources and formats (SQL Server, Snowflake, BigQuery, business APIs, flat files) multiplies the Risks of inconsistency, duplication, or outdated data. Dans ce contexte, chaque équipe met en place ses propres règles et outils de contrôle, ce qui engendre une wide variety of practices. Without a consolidated vision, it becomes impossible to obtain a reliable and continuous assessment of the overall quality of the data heritage.
Ultimately, teams spend more time correcting errors, identifying the causes of discrepancies, or fixing faulty pipelines than on truly leveraging data.
This situation erodes confidence in the indicators produced, slows down analytical projects, and fuels a vicious cycle where data, which should be informing decisions, becomes a source of complexity and uncertainty.
It is becoming essential today to move from a logic of occasional control to a real ongoing data quality surveillance. Organisations can no longer afford to check the reliability of their data at irregular intervals: data evolves too quickly, circulates through too many systems, and now feeds critical uses, from daily decision-making to generative AI.
That is the whole issue with Data Quality as a Service (DQaaS). This approach involves integrating data quality right into the core of the pipelines, in a way that automated, measurable and scalable. Controls are no longer isolated tasks entrusted to technical teams, but integrated and permanent mechanisms that guarantee the reliability of the data asset as a whole.
Specifically, DQaaS relies on The automation of checks to eliminate repetitive tasks and limit human errors, on a centralised visualisation via a clear and intuitive dashboard for tracking quality indicators in real-time, and on a continuous integration within existing data workflows to ensure constant and consistent monitoring over time.
By placing quality on the same level as performance or security, DQaaS transforms data management into a living, self-controlled and sustainable process.
At JEMS, we have designed a DQaaS solution based on technologies robust open source, deployable within days on 5 to 7 datasets.
The benefits are immediate:
Implementing a DQaaS setup transforms a costly and disjointed process into a measurable performance driver.
| Objective | Avant DQaaS | With DQaaS by JEMS |
|---|---|---|
| Quality visibility | Scattered data, lack of centralised tracking | Single dashboard and consolidated indicators |
| Time spent on testing | Time-consuming manual checks | Full automation of checks and alerts |
| Reliability of decisions | Risk of errors, inconsistencies and duplicates | Clean, traceable and contextualised data |
| Job adaptation | Generic tests of little relevance | Custom controls according to business rules |
| Team productivity | Repeated maintenance, high operational load | Focus on business value and uses |
Quality management is not an end in itself. It is the first maturity indicator from a truly data-driven organisation. Before industrialising governance or deploying advanced AI uses, it is necessary to be able to to trust the data who powers these systems.
Quality therefore becomes a foundation: what allows the data strategy to deliver on its promises.
Would you like to improve the structuring of your data governance or automate your quality controls?
