Data governance,
made executable
and provable.

No. A Data Catalog mainly describes data and its metadata. Data Recycling® links business objects to their data, uses, rules and lifecycle — so you can understand your application data estate through reverse-documentation, then qualify, correct, protect, archive, anonymise, move or delete it, and produce evidence of the actions taken.

The ODOM is the operational twin of a business object (an automated, meaningful data model): contract, invoice, customer, claim or case. It links that object to its applications, data, flows, rules, owners, lifecycle actions and evidence.

M4R® is a proprietary Systnaps methodology that applies the principles of the circular economy to data:

• Map: understand and map;
• Regulate: apply the rules;
• Reduce: reduce volumes, defects, costs and risks;
• Reuse: reuse data with confidence;
• Recycle: manage its lifecycle.

An organisation can start with its highest-priority challenge and progress scope by scope.

Start with a priority scope: an application, a database, a flow, a document repository or a business object. Data Recycling® reverse-documents the existing system to rebuild structures, metadata, dependencies, flows and observable rules. These results are then matched to business uses, validated and enriched by the teams. You get a usable first map without waiting for perfect documentation or launching an exhaustive inventory of the whole organisation.

Yes. Data Recycling® doesn’t produce a one-off document destined to become obsolete. Models are versioned and observation campaigns can be run regularly. The platform detects changes in structures, flows, rules or dependencies and keeps knowledge of the estate current over time.

Quality is assessed in the context of the business object and its use. Data Recycling® lets you define and run completeness, consistency, accuracy, uniqueness or timeliness checks, then track anomalies and the corrections made. Quality is part of the REDUCE approach: reduce errors, duplicates, inconsistencies and non-compliance risks before reusing the data.

Data Recycling® helps identify available data, its origin, quality, traceability, usage restrictions and business context. Data and AI projects can then rely on better-qualified, governed data, without restarting their search and preparation for every new project.

Data Recycling® links requirements to the relevant assets, then to the controls and actions to carry out. The platform helps identify sensitive data, over-retention, uncontrolled copies, quality defects or insufficient protection. Correction, protection, archiving or deletion actions can then be tracked and documented.

Yes. Data Recycling® keeps the full chain: applicable rule, scope concerned, decision, action executed, result and validation. These are gathered in an Evidence Pack usable during an audit, an inspection or an internal review.

Data Recycling® lets you define complex lifecycle rules and identify every representation concerned: applications, databases, files, document repositories or test environments. The platform can orchestrate the appropriate actions — archiving, anonymisation, masking, moving or deletion — then check the result and manage any exceptions.

The solution maps dependencies, distinguishes useful data from data that can be archived or deleted, prepares the mappings and checks the results after transformation. This avoids carrying useless volumes, errors and historical debt over to the new system.

No. Data Recycling® favours processing directly at the source, as close as possible to the systems where the data lives. The platform collects the metadata and results needed for governance without creating useless copies of business data. This reduces transfers, storage needs, data exposure and resource consumption.

Acting at the source improves performance and avoids moving large volumes to an intermediate platform. This approach helps to:

• reduce duplication;
• limit transfers;
• better control access;
• lower storage and compute needs;
• produce evidence closer to actual execution.

Yes. Data Recycling® is built on a hybrid architecture that keeps sensitive processing within the client’s controlled environment: on-premise, private Cloud or edge depending on the project. Data stays in authorised systems and operations can run locally. Data Recycling® provides governance, observability, orchestration and evidence without forcing centralisation of the estate. Data Recycling® is a proprietary, sovereign solution developed by Systnaps.

No. Data Recycling® connects to the systems already in place: business applications, databases, files, data platforms, storage, Cloud environments and governance tools. It adds a common layer of context, rules, execution, observability and evidence across these environments.

No. A first scope can be launched with a small team: a business owner, a Data or IT representative and, depending on the topic, the DPO, the CISO or Compliance. In smaller organisations, several roles can be held by the same people.

The timeframe depends on the scope, access to sources and the depth expected. The recommended approach is to start with a priority application or business object to quickly obtain a first reverse-documentation, a usable map, measured gaps, an action plan and the first evidence.

The Data & AI Diagnostic assesses your organisation’s ability to master, improve and leverage its data, especially for its artificial-intelligence projects. From interviews, an analysis of your practices and a representative scope, Systnaps identifies:

• priority data, applications and uses;
• quality, traceability or accessibility issues;
• security, compliance and over-retention risks;
• barriers to industrialising Data and AI projects;
• the highest-value use cases;
• priority actions and the associated roadmap.

The deliverable includes a status assessment, a maturity evaluation, reasoned recommendations and a first experimentation scope. The diagnostic isn’t about finding an AI use case at any cost: it first checks that the problem is properly framed and that the required data can be used under reliable, secure and controlled conditions.

The Data & AI Diagnostic represents an investment of €10,000 excl. VAT. It can be financed up to 40% by Bpifrance, subject to eligibility. Remaining cost for an eligible company: €6,000 excl. VAT.

You can start with the M4R® maturity diagnostic or by scoping a first perimeter: application, business object, migration, sensitive data, quality, storage costs or AI readiness.