What is Data Governance at element61?

Data Governance at element61 defines how an organization manages, organizes and controls its data assets to ensure trust, compliance and scalability. It provides the rules, roles and practices that ensure data is used responsibly across people, processes and systems. La Data Governance chez element61 définit la manière dont une organisation gère, organise et contrôle ses actifs de données afin de garantir la confiance, la conformité et la scalabilité. Elle fournit les règles, les rôles et les pratiques qui assurent une utilisation responsable des données à travers les personnes, les processus et les systèmes.

Why is Data Governance important?

Data Governance ensures that data is trustworthy, consistent and usable for decision-making. It reduces risks related to poor data quality, inconsistent definitions and regulatory non-compliance, enabling organizations to become truly data-driven. La Data Governance garantit que les données sont fiables, cohérentes et exploitables pour la prise de décision. Elle réduit les risques liés à une mauvaise qualité des données, à des définitions incohérentes et au non-respect des réglementations, permettant ainsi aux organisations de devenir réellement pilotées par la donnée.

How does element61 practice Data Governance?

element61 embeds Data Governance in all data services, but also offers a specialized governance program covering key pillars such as policies & standards, data quality, metadata and master & reference data. This ensures robust data management and improved analytics value. element61 intègre la Data Governance dans l’ensemble de ses services data, tout en proposant également un programme spécialisé couvrant des piliers clés comme les politiques et standards, la qualité des données, les métadonnées et les données de référence et de base. Cela garantit une gestion robuste des données et une meilleure valorisation analytique.

What are the core components of Data Governance?

The core components of Data Governance at element61 include Data Standards & Policies, Data Profiling & Data Quality, Metadata & Business Glossary, and Reference & Master Data Management. These components help align operational and analytical use of data. Les composantes clés de la Data Governance chez element61 comprennent les standards et politiques de données, le profiling et la qualité des données, les métadonnées et le glossaire métier, ainsi que la gestion des données de référence et des master data. Ces composantes permettent d’aligner l’usage opérationnel et analytique des données.

What business problems does Data Governance solve?

Data Governance helps organizations address inconsistent master data across systems, low trust in analytics insights, unclear business definitions of key metrics, difficulty complying with regulations like GDPR, and slow or error-prone decision processes. Improving governance results in faster adoption, better decisions and stronger regulatory compliance. La Data Governance aide les organisations à résoudre les problèmes de données de base incohérentes entre les systèmes, de faible confiance dans les insights analytiques, de définitions métier floues pour les indicateurs clés, de difficultés à se conformer à des réglementations comme le RGPD, ainsi que de processus décisionnels lents ou sujets aux erreurs. L’amélioration de la gouvernance conduit à une adoption plus rapide, de meilleures décisions et une conformité réglementaire renforcée.

Who benefits from Data Governance services?

Data Governance benefits organizations that need reliable, high-quality data for analytics and AI, consistent business definitions and terminology, and control over data usage and compliance. It is relevant for leadership teams, analytics professionals, data engineers, data stewards and IT stakeholders. La Data Governance bénéficie aux organisations qui ont besoin de données fiables et de haute qualité pour l’analytics et l’IA, de définitions et d’une terminologie métier cohérentes, ainsi que d’un contrôle sur l’utilisation des données et la conformité. Elle est pertinente pour les équipes de direction, les professionnels de l’analytics, les data engineers, les data stewards et les parties prenantes IT.

How does Data Governance support analytics and AI initiatives?

Good Data Governance provides the foundation for analytics and AI by ensuring data is clean and profile-checked, clearly defined and contextualized, and consistently integrated across systems. This allows analytics and AI models to be trusted and operationalized with confidence. Une bonne Data Governance fournit les fondations de l’analytics et de l’IA en garantissant que les données sont propres et contrôlées, clairement définies et contextualisées, et intégrées de manière cohérente entre les systèmes. Cela permet de faire confiance aux modèles analytiques et d’IA et de les opérationnaliser avec confiance.

Can element61 assess an organization’s Data Governance maturity?

Yes. element61 offers assessments to determine a company’s current Data Governance maturity level and recommend pragmatic steps to improve governance practices based on real organizational needs and lessons learned. Oui. element61 propose des assessments pour déterminer le niveau actuel de maturité en Data Governance d’une entreprise et recommander des étapes pragmatiques afin d’améliorer les pratiques de gouvernance sur la base des besoins réels de l’organisation et des enseignements tirés du terrain.

What standards or frameworks does element61 use in Data Governance?

element61 aligns governance programs with recognized frameworks such as the Data Governance Institute standards, and can integrate industry best practices into policies, roles, decision rights and process definitions. element61 aligne ses programmes de gouvernance sur des référentiels reconnus, tels que les standards du Data Governance Institute, et peut intégrer les meilleures pratiques du marché dans les politiques, les rôles, les droits de décision et les définitions de processus.

How does element61 help start a Data Governance program?

element61 helps organizations by defining clear data standards, roles and responsibilities, establishing data quality processes, building metadata & glossary frameworks, and implementing reference & master data structures. This creates a roadmap from governance basics to mature data-driven operations. element61 aide les organisations en définissant des standards de données clairs, des rôles et responsabilités, en établissant des processus de qualité des données, en construisant des frameworks de métadonnées et de glossaire, et en mettant en place des structures de données de référence et de master data. Cela crée une feuille de route allant des bases de la gouvernance à des opérations matures pilotées par la donnée.

Data Governance

Competence · Data Governance

Data governance that works

Every organization wants to be data-driven, and increasingly, AI-driven. But analytics and AI are only as good as the data behind them, and regulators keep raising the bar with GDPR, DORA, NIS2 and the EU AI Act. Data governance is the foundation that turns data chaos into a strategic advantage: trusted analytics, AI you can deploy with confidence, and an organization that stays audit-ready.

At element61, we make data governance practical. No paper tigers, no endless policy documents, but an operating model, tooling and habits that fit your organization and deliver visible results within months.

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Why now

Three forces are making more governance relevant than ever

AI raises the bar

Copilot and generative AI put new demands on your data landscape: clear definitions, reliable quality, and sensitive data under control. Well-governed data is what makes AI adoption safe and successful.

Regulation is accelerating

GDPR was only the start. DORA, NIS2 and the EU AI Act expect you to know what data you have, where it lives, who can access it, and how it feeds your models.

Trust decides adoption

A data platform in production is worth little if users don't trust the numbers or understand the definitions. Governance turns a technical platform into a decision-making engine and empowers teams to find answers independently.

What we do

How our data governance team can help you

Each topic delivers tangible outcomes on its own, and together they form a complete governance capability, tailored to your organization's data maturity.

Governance Strategy & Data Organization

Governance succeeds or fails on organization, not technology. We help you clarify who owns, manages and is accountable for your data, and design a data governance organization that fits your culture and maturity: data domains and ownership, roles and responsibilities, and the decision-making bodies that keep governance alive. We tailor industry-standard frameworks to your reality instead of imposing a textbook model.

Typical deliverables: governance framework and policies, governance operating model, role descriptions and RASCI, domain model, governance roadmap.

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Data Cataloging & Business Glossary

Just like your data gives you insight into your business, metadata gives you insight into your data. By cataloging your data estate and enriching it with business context in a glossary, you make data discoverable and understandable, and everyone speaks the same data language. That is also what unlocks self-service analytics: teams that find, understand and use data independently. For organizations further in their maturity, we take this a step further with semantic layers and ontologies: turning agreed definitions into computable models that power consistent reporting and give AI agents the business context they need to answer reliably.

Typical deliverables: catalog implementation, business glossary, automated lineage, stewardship workflows, catalog adoption program, semantic layer and ontology design.

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Data Security & Classification

Knowing your data is one thing; protecting it is another. We help you discover and classify sensitive data across your estate, protect it with the right controls such as information protection and data loss prevention, manage insider risk, and prepare your data landscape for AI with data security posture management. Aligned with GDPR, DORA, NIS2 and ISO 27001, so security controls and compliance evidence come from the same foundation. Not sure where you stand? Our fixed-price Purview assessments (Data Risk Visibility and AI Risk) give you a concrete picture of your exposure within weeks.

Typical deliverables: sensitive data discovery and classification, information protection and DLP rollout, DSPM for AI, insider risk management, compliance reporting.

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Data Quality & Profiling

Trust in data starts with knowing its actual state. Data profiling gives you insight and statistics on your data to uncover and quantify quality issues, or to confirm that your perceived quality is real. From there we build structural data quality management: rules, monitoring, remediation workflows and ownership, so your data is accurate, complete and trustworthy, and stays that way.

Typical deliverables: data quality assessment, DQ rules and monitoring setup, remediation process, quality dashboards.

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Master & Reference Data Management

Some data is so important it deserves special attention. Customers, products, suppliers, materials: master and reference data are the building blocks of both your operations and your analytics. We help you create a single source of truth for your critical data, define golden records across departments, functions and systems, select and implement the right MDM platform, and embed the governance processes around it.

Typical deliverables: MDM strategy and roadmap, platform selection and implementation, match-and-merge design, data stewardship model.

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AI Governance

AI adoption raises the stakes for every topic above, and adds questions of its own: which models are in use, what data feeds them, and who is accountable for their outcomes? We help you deploy AI responsibly, transparently and compliantly, with an AI governance framework covering model inventory, risk classification and accountability, aligned with the EU AI Act, with guardrails on sensitive data before Copilot and generative AI roll out across the organization, and with a well-defined semantic foundation (glossary, semantic layer, ontology) so AI answers with your business definitions rather than guesses.

Typical deliverables: AI governance framework, model inventory and risk classification, AI Act readiness assessment, Copilot data readiness.

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Our partners

Best-of-breed platforms, chosen for your architecture

We work with the leading data governance, quality, MDM and security platforms, with deep roots in the Microsoft ecosystem: Microsoft Purview for cataloging, lineage and data security, and Microsoft Fabric as the data platform foundation. Technology choices always follow your architecture and ambitions, not the other way around.

Microsoft
Profisee
OvalEdge
Ataccama
Soda
DataHub
Informatica

How we deliver

Assess, design, embed

One thing to know upfront: data governance projects are business-heavy, not merely technological. The tooling is the easy part. Expect workshops with your data owners, decisions about definitions, ownership and priorities, and change management to make new habits stick. That is where the value is created, and it is exactly what we facilitate.

Assess

A Data Governance Maturity Scan benchmarks where you stand today across strategy, organization, processes and technology, and identifies the gaps that matter most.

Design

A pragmatic roadmap that sequences quick wins and structural improvements, with a governance operating model tailored to your organization.

Implement & embed

Hands-on implementation of tooling and processes, side by side with your teams, so governance becomes a habit rather than a project.

Get started

Know where you stand in a few weeks

Whether you have a specific use case in mind or simply want to know where to begin, the fastest first step is a Data Governance Maturity Scan: an objective view of your current maturity, a benchmark against peers, and a prioritized roadmap.

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FAQ

For element61, data governance is the practical discipline of making data trusted and usable: clear ownership and accountability, shared business definitions, reliable quality, protected sensitive data, and well-managed master data. It is not a paper exercise but an operating model with tooling and habits that fit your organization. Some form of governance is embedded in all our data and analytics work, and our specialized offering covers six topics: governance strategy and data organization, data cataloging and business glossary, data security and classification, data quality and profiling, master and reference data management, and AI governance.

Because trust decides whether your data investments pay off. Without governance, users doubt the numbers, definitions differ between departments, and adoption of data platforms stalls. On top of that, regulation keeps raising the bar: GDPR, DORA, NIS2 and the EU AI Act all expect you to know what data you have, where it lives, who can access it and how it is used. And as organizations adopt Copilot and generative AI, well-governed data becomes the prerequisite for doing so safely and successfully.

Pragmatically, in three steps:

Assess: a Data Governance Maturity Scan to see where you stand today.
Design: a roadmap and a governance operating model tailored to your culture and maturity.
Embed: hands-on implementation, side by side with your teams, so governance becomes a habit rather than a project.

On the technology side we work with the leading platforms, with deep roots in the Microsoft ecosystem (Microsoft Purview and Microsoft Fabric) and partners such as Profisee, OvalEdge, Ataccama, Soda, DataHub and Informatica.

We structure data governance around six components:

Governance strategy & data organization: who owns, manages and is accountable for data.
Data cataloging & business glossary: making data discoverable, understandable and consistently defined.
Data security & classification: discovering, classifying and protecting sensitive data.
Data quality & profiling: making data accurate, complete and trustworthy.
Master & reference data management: a single source of truth for your critical data.
AI governance: deploying AI responsibly, transparently and compliantly.

Very recognizable ones:

Master data that is not aligned between source systems, with duplicate or incomplete records.
Reports that contradict each other because definitions differ between departments.
A data platform that is live but not adopted, because users don't trust its content.
No complete overview of personal or sensitive data for GDPR or audit purposes.
Hesitation to roll out Copilot because sensitive documents are not under control.

Solving these translates into better adoption, less rework, faster and more accurate decisions, and avoided regulatory risk.

The whole organization, at different levels. Business users get data they can find, understand and trust, enabling self-service analytics. Data and IT teams get clear ownership, less firefighting and rework. Executives and risk functions get compliance evidence and confidence that analytics and AI are built on solid ground. We serve organizations across industries, from manufacturing and construction to financial services, healthcare and logistics: any organization scaling its analytics or AI ambitions, or facing regulatory pressure, benefits.

Analytics and AI are only as good as the data behind them. Governance provides the shared definitions and data quality that make reports and models reliable, the catalog that makes data findable for analysts and data scientists, and the security guardrails that keep sensitive data out of places it shouldn't be before Copilot and generative AI roll out. For AI specifically, we add an AI governance layer: model inventory, risk classification and accountability, aligned with the EU AI Act.

Yes. Our Data Governance Maturity Scan benchmarks where you stand today across strategy, organization, processes and technology. In a few weeks you get an objective view of your current maturity, a comparison against peers, and a prioritized roadmap that identifies the gaps that matter most and the quick wins to start with. It is the fastest, lowest-risk first step into governance.

We build on industry-standard frameworks such as DAMA DMBOK as a reference, but we never impose a textbook model: we tailor the framework to your organization's culture, maturity and ambitions. On the compliance side, our work is aligned with GDPR, DORA, NIS2, ISO 27001 and the EU AI Act, so governance and compliance evidence come from the same foundation. On the technology side we apply platform best practices from Microsoft Purview and our partner ecosystem.

We recommend starting small and concrete rather than big and theoretical. Typically that means a maturity scan first, followed by a roadmap that pairs structural improvements with a tangible first use case: for example cataloging one data domain, cleaning up one master data object, or securing your estate ahead of a Copilot rollout. From there we help you build out the governance organization and embed the processes step by step. Contact us to discuss your use case or to request a maturity scan.