Does All Your Data Really Have Value?

Businesses have never stored so much data. And yet, a large portion of it is never consulted, never analysed, never used. Between duplicated files, logs retained by default, databases that are never cleaned and automatically generated streams, volumes keep growing — but value does not always follow.

This silent accumulation has a cost: oversized infrastructures, slower applications, soaring cloud bills. Rethinking data management means taking back control of one of the most underestimated factors in IT performance.

The Myth of Useful Data

There is a persistent belief in organisations: keeping data costs nothing, and it might always come in useful one day. This logic, amplified by falling storage costs, has led to a generalised accumulation reflex.

The reality is more nuanced. Storing data has a direct cost — infrastructure, energy, licences — but also an indirect cost that is often underestimated: the degradation of application performance. A database bloated with obsolete or redundant data slows down queries, weighs down backups and complicates scaling operations.

The Main Sources of Data Inflation

Uncontrolled duplication. The same file saved in three different places, the same customer record present in two systems… Duplication is often the result of poorly designed integrations or a lack of governance. It inflates volumes without creating additional value.

Cold data that is never archived. Not all data is accessed at the same frequency. Rarely consulted data — old histories, project archives, application logs — should be moved to cheaper storage tiers, or even deleted. Without a lifecycle policy, it often remains on premium infrastructure.

Unfiltered automated streams. Connected objects, business applications, monitoring tools… Modern systems generate continuous data flows. Without filtering or aggregation rules, everything is stored — including data with no analytical value whatsoever.

Rethinking Data as an Asset to Be Managed

One quality piece of data is worth more than ten useless ones. This obvious truth, simple to state, implies a real transformation of IT practices.

Implement a data lifecycle policy. Define clear rules: which data to keep, for how long, at which storage tier. Automatic archiving and scheduled deletion are immediate levers for volume reduction.

Map and deduplicate. Before optimising, you need to understand what you have. An audit of data flows and stocks makes it possible to identify redundancies, orphaned data and volumes that are disproportionate to actual usage.

Align data governance with business needs. The most valuable data is the data that informs decisions. Working with business teams to identify truly strategic data makes it possible to prioritise quality efforts and reduce noise.

To go further on the environmental impact of this accumulation, our article Digital Footprint: Why Is It Getting Bigger and Bigger? provides additional insights. And if you want to integrate these challenges from the design phase of your projects, discover our digital eco-design approach.

Conclusion

The value of data is not measured by its volume, but by its usage. Organisations that take the time to rationalise their data assets reap a double benefit: better-performing applications and more appropriately sized infrastructures.

Data management is not a topic reserved for data scientists. It is a concrete operational challenge that starts with simple decisions: archive, deduplicate, delete.