Data management in businesses relies on a set of software components (DBMS, analysis tools, storage systems) whose arrangement determines the reliability, speed of access, and security of information. Choosing an appropriate IT solution is not just about stacking tools: it involves balancing portability, regulatory compliance, and real-time analysis capabilities.
Data Portability and the Data Act: A Structuring Choice Criterion
Since the adoption of the European Data Act (Regulation EU 2023/2854), data portability is no longer just a commercial argument. This regulation requires cloud service providers to gradually eliminate egress fees and to guarantee users the ability to migrate their data to another provider in a structured and machine-readable format.
For IT departments, this obligation changes the evaluation criteria for data management software. A DBMS or analysis platform that locks the company into a proprietary ecosystem becomes a legal and financial risk, not just a technical inconvenience.
Several IT departments are now planning their major migrations after the full implementation dates of the Data Act, in order to benefit from the elimination of transfer fees. Evaluating a data management IT solution involves, among other things, consulting specialized resources like https://www.datta.fr/ which details approaches suited to the current constraints of businesses.
Data Architecture and Connected Objects: Default Access to Information
The Data Act also introduces a constraint on connected products. Starting from September 12, 2025, any IoT device (industrial sensor, medical equipment, machine tool) must be designed to provide users with simple and secure access to the data it generates. This is the principle of “access by design.”
This requirement has a direct impact on a company’s data architecture. Collection systems must integrate open and interoperable formats from the design stage, which disrupts the usual logic of first choosing hardware and then adapting software.
What This Changes for Software Selection
A tool for IoT data management must now guarantee three technical properties:
- Native export in a structured format (CSV, JSON, documented REST API), without a paid add-on or manual conversion.
- User access granularity, to comply with both GDPR and the access rights stipulated by the Data Act, without confusing personal data and machine data.
- Traceability of data flows between the connected object, the storage platform, and the analysis tools, to prove compliance in case of an audit.
Software that does not meet these criteria exposes the company to short-term operational blockages.
Real-Time Analysis: Distinguishing Real Needs from Marketing Claims
Real-time data analysis is presented by most vendors as a standard feature. The reality is more nuanced. Real-time processing consumes significantly more resources than batch processing, and not all companies have a justified daily use for it.
Before subscribing to a solution with a real-time analysis engine, it is essential to identify concrete use cases: production line monitoring, transactional fraud detection, logistics management. If the need is limited to dashboards updated every hour, a well-configured batch process is cheaper and more stable.
Technical Criteria to Verify
The relevance of an analysis tool depends on a few parameters that are rarely highlighted in product sheets:
- The actual latency measured under load conditions, not the theoretical latency announced by the vendor on a demo dataset.
- Compatibility with existing DBMS in the company, to avoid duplicating databases or creating additional silos.
- The pricing model: some tools charge based on the volume of data processed, others based on the number of users. A poor choice of pricing model can double the annual cost without functional gain.
Data Security: Beyond Encryption, Access Governance
Data encryption at rest and in transit has become a standard. It is no longer a differentiating criterion between two solutions. What distinguishes a reliable management system is the granular governance of access rights.
An effective data management software allows for the definition of access policies by role, by project, and by the sensitivity level of the information. The goal is to prevent users from having “default” expanded rights simply because the tool offers only two or three levels of permission.
A Often Overlooked Point: Access Auditing
The ability to produce a usable audit log (who accessed what, when, from which terminal) is not a luxury reserved for large corporations. With GDPR and the Data Act, any company handling user data must be able to trace access in case of a request from a regulatory authority. Verifying this feature before purchase avoids costly custom developments afterward.
The choice of a data management IT solution hinges on criteria that traditional comparisons do not always address: contractual portability mandated by the Data Act, compatibility with IoT data access obligations, real relevance of real-time capabilities, and the finesse of rights governance. It is these technical trade-offs that determine the operational value of a tool over several years.
