Data quality in departmental councils: a lever for modernizing public services

Efficient public services start with reliable data

County councils are on the front line when it comes to sensitive issues such as social welfare, secondary schools, infrastructure, human resources and budgets. But their data is often scattered, incomplete, or even erroneous, which is detrimental to the quality of the services they provide.

To modernize their tools, secure their systems and meet regulatory obligations, départements need to regain control of their data.

1. Data governance in departmental councils: a lever for public performance

In the face of technical, organizational and regulatory challenges, rigorous management of data quality is becoming a strategic focus for :

• effectively manage social policies,
• make budget forecasts more reliable
• and guarantee RGPD compliance.

👉 For an overview of data-related issues in the public sector, see our dedicated page.

2. Concrete examples of improving data quality in Departments

Use case 1: fraud detection through data reconciliation

Departmental councils manage a large number of assistance schemes, often divided between services with no direct link between databases. This organization makes it difficult to detect duplicate beneficiaries, especially when they are registered under slightly different identities.

Relying on a data quality management platform, one department has implemented inter-base reconciliation rules, enabling information from different systems to be cross-referenced. This made it possible to identify unauthorized accumulations, detect inconsistencies and prevent cases of fraud or payment errors.

This approach has strengthened the integrity of social assistance systems, limited budgetary risks and enabled better traceability in the event of external control or audit.

Use case 2: making budget forecasts more reliable via the DVF database

The open data DVF (Demandes de Valeur Foncière) database is a reference source for estimating revenues from property transfer tax (DMTO). But when it contains duplicates or anomalies, départements can overestimate their tax resources and distort the construction of their budgets.

By deploying an automated cleaning solution, one département council was able to detect and correct duplicates in its DVF flows. It also improved the consistency of amounts, harmonized wording and secured the entire calculation chain.

As a result, budget forecasts are now based on realistic data, and discrepancies in budget execution can be avoided. This also supports more reliable planning, particularly in the context of internal budget dialogues.

Use Case 3: Cleaning and standardization of RSA databases

RSA recipient databases are often heterogeneous, fed by several sources, with risks of duplication, data entry errors or inconsistencies between fields. These shortcomings hamper the ability of the départements to effectively manage their social services.

An innovative platform was deployed to automate file cleansing: standardizing names, checking the format of INSEE numbers, standardizing addresses, detecting and merging duplicates. These operations were carried out without coding, directly by the business teams.

Thanks to this initiative, the department now has a consolidated database for each recipient, which is more reliable, better structured, and directly usable for monitoring non-use, assessing social needs and forward-looking aid management.

White paper: 10 use cases for structuring a data quality approach

The white paper brings together 10 concrete examples from departmental projects, covering cases such as :

• fraud detection by cross-referencing social assistance files,
• automated RGPD mapping of personal data,
• enrichment of databases with Sirene and DVF,
• address standardization for roads and schools,
• or the implementation of real-time alerts on public procurement contracts.

📘 This guide is designed for business teams, DGSs, DSIs and quality managers who want to structure their data governance.

👉 Download the white paper - Data quality in departmental councils

3. No-code AI platform: equipping departmental teams without technical complexity

Tale of Data offers a no-code platform accessible to business departments. It enables teams to :

• launch audits in complete autonomy,
• define their own quality control rules,
  automatically correct identified errors,
• share data repositories between departments.

It's a pragmatic approach to equipping departments without complicating their organization.

Making data reliable: a prerequisite for departments' digital transformation

Data quality determines the efficiency of each department. By structuring clear governance, departments can secure their public policies and prepare for the future.

The white paper presents 10 real-life cases for taking action now.

📘 Download the white paper - Départements

📎 To find out more, check out our article dedicated to the Regions and the associated use cases.