Data Analyst

Main objectives

Simplify administrative procedures through better control, quality and reuse of data, in order to reduce redundancies, limit re-entries and facilitate interactions with the administration.

Strengthen the management and evaluation of public policies by making greater use of available data to inform decision-making, monitor results and objectify public action.

Improve the efficiency of the internal functioning of the ours by promoting better knowledge, circulation, documentation and reuse of data assets within the organization.

To ensure transparency in public action by improving access, sharing and utilization of data, in compliance with the applicable legal framework.

Tasks and activities

The analyst will be responsible for analyzing the various data sources of the ours (Walloon Public Service) in order to compile this inventory; they will also be responsible for leading working groups to develop a common conceptual framework around a shared dictionary of key reference concepts. They will also facilitate working groups to resolve issues of divergent data semantics.

Objective: To analyze business data sources in order to create a data inventory.

Coordination with data managers

The Data Analyst ensures the identification and mobilization of the relevant stakeholders for each data source.

He organizes and leads analysis meetings with data managers, business experts, technical teams, and project stakeholders. He formalizes the results of the work in the form of minutes, analysis reports, and recommendations for the project and governance bodies.

Functional and business data analysis

For each source analyzed, the Data Analyst:

Identifies the data manager and person responsible for the data;

Documents the data managed by the source;

Describes their job definition;

Describes the processes of creation, collection and updating;

Identifies the legal basis governing their processing;

Characterizes data according to its level of criticality, sensitivity, personal, strategic or reusable nature;

Describes the update frequency and associated uses;

List the quality control mechanisms already in place.

The results of these analyses contribute to enriching knowledge of the ours's information assets and support data governance work.

Analysis of data exchange capabilities

The Data Analyst documents the current methods of data exchange of the systems studied.

It specifically identifies:

Existing exposure mechanisms;

The available interfaces;

The constraints of exchange;

Application and organizational dependencies that may influence integration.

This analysis helps to prepare future integration work and to guide technical choices.

Creation and maintenance of the inventory of company data

Objective: To build and maintain a raw inventory of company-related data present in the ours systems.

Inventory model definition

The Data Analyst designs and develops the inventory structure to enable :

The progressive documentation of data assets;

Identifying the manager;

The preparation of future Metawal descriptive sheets;

Monitoring data governance;

The future reuse of the collected information.

Supplying and updating the inventory

Each source analysis results in the integration of new information into the inventory.

The Data Analyst ensures the consistency, quality and updating of the information collected, in order to have a consolidated view of the business data used within us

This analysis is carried out with close involvement of the manager of the source concerned.

Identification of the designated manager of a reference source

Where applicable, an analysis of the various services involved in the collection and storage of the same data and identification of the designated manager. This analysis will take into account the governance framework currently being developed within the ours.

Data characterization and qualification

The Data Analyst enriches the inventory with the characteristics necessary for data governance and sharing:

Sensitivity level;

GDPR status;

Strategic nature;

Potential Open Data ;

Broadcast restrictions;

Conditions of reuse;

Expected quality level.

This inventory forms the basis for identifying redundant data and avenues for simplification; Each source analyzed, once its maturity has been validated (absence of duplicates, manager identified for each of its data, documented quality, collection and update processes), is then moved up as a reference source, via a descriptive sheet, in the Metawal data catalogue.

Identify the opportunity to create a single common definition

To propose a common, harmonized definition based on the realities and constraints of different professions.

Identifying opportunities for simplification and harmonization

Objective: To use the inventory compiled in order to improve the consistency of information assets relating to companies and to support administrative simplification efforts.

Redundancy analysis and data reconciliation

The Data Analyst identifies potentially redundant data present in several ours data sources: collected and/or stored multiple times, similar or close in terms of definition.

For each group of similar data, it:

Evaluates the relevance of the diagnosis: of truly similar data or data having a similar label but dissimilar definitions and/or calculations.

Evaluates the degree of semantic proximity;

Compare the job definitions;

Analyze the calculation or collection rules;

Identify any differences;

Evaluate the scenarios and impacts of simplification and consistency: harmonization around a semantic base, definition of a supra-data element allowing the calculation of derived data and thus avoiding its collection…

Propose, based on working groups with all the managers involved (all actors collecting the data concerned) and the actors concerned (DDT, DORU, …), avenues for simplification and their impacts (legislative, process…):

Harmonization of concepts: feasibility, consequences on processes, budgets, professions…

Data above used to calculate derived data

He facilitates the necessary workshops with data managers and relevant stakeholders in order to build a common vision of the concepts analyzed.

Skills

Business data analysis and modeling (design of conceptual models, data dictionaries and structuring of concepts)

Semantic analysis and harmonization of business concepts

Facilitating and leading professional workshops

Knowledge of data reuse principles, Only Once, reference data

Knowledge of the Only Once, Open Data and reference data principles.

Excellent oral and written communication skills, ability to synthesize and formalize information, assertiveness, and a strong sense of direction.

Data governance and clarification of responsibilities

Languages

French - Level Proficiency (C2)