Data professions have become a field in their own right, yet their job titles remain unclear: CDO, data steward, data owner, DPO, data engineer… The same term can refer to different roles from one organization to another. TNP Digital Advisory has identified 37 of these professions, grouped them into six categories and analyzed their responsibilities, maturity levels and key competencies.
Key takeaways
- 37 data and AI professions, divided into 6 categories.
- A maturity level for each profession: 14 established, 14 consolidating and 9 emerging.
- Seven key competency areas identified across these professions, from business and technical expertise to ethics, security and compliance.
- One key finding: data governance is becoming a prerequisite for deploying artificial intelligence.
CDO, data steward, data owner: why job titles create confusion
Ask three organizations what a data steward does, and you may receive three different answers. In one organization, the role involves monitoring data quality within a specific scope. In another, it refers to a business representative who makes decisions about access rules. Elsewhere, the position does not yet exist.
Behind identical job titles, practices differ, responsibilities overlap and new roles emerge every year. This ambiguity comes at a cost: job descriptions become difficult to compare, and responsibilities are not clearly assigned. Yet deploying artificial intelligence requires a high level of maturity in data management.
What is the difference between a data owner and a data steward?
The data owner decides how data may be used and approves access rules within their business area: they are accountable for the data. The data steward carries out the controls that ensure data quality and integrity within a defined scope. The former makes decisions; the latter implements and verifies them.
At the strategic level, the CDO (Chief Data Officer) defines the data strategy and organizes governance. The CDO is responsible for optimizing data usage. The data governance officer defines and arbitrates governance policy at the organizational level, working alongside data owners, while the data governance manager oversees its implementation within a defined scope.
These three roles — CDO, data owner and data steward — form the backbone of data governance, helping organizations establish clear responsibilities and maintain consistent data management practices.
How did we classify the 37 data professions?
Rather than creating an alphabetical list, we grouped the professions into six complementary categories, based on the contribution each makes to the organization:
- Strategy and leadership: CDO, CIO, AI Factory Director (3 professions);
- Governance: data governance manager and officer, data steward, data owner, knowledge manager (5 professions);
- Security, compliance and risk: DPO, CISO, cybersecurity analyst, data risk manager (8 professions);
- Document and information asset management: data ethicist, data librarian, records manager, digital archivist (8 professions);
- Architecture and engineering: data architect, data engineer, DataOps engineer (6 professions);
- Data value creation and innovation: data scientist, data analyst, data product manager, data broker (7 professions).
For each profession, we added a brief description, a maturity level (emerging, consolidating or established) and the meta-competencies it requires: business, technical, interpersonal, ethical, design, security and compliance skills.
How mature are data professions?
Maturity levels vary considerably. We classified each profession into one of three levels: 14 are established, 14 are consolidating and 9 are emerging. Established professions, such as DPO or database administrator, have clearly defined responsibilities and are widespread across organizations. Emerging professions, such as AI Factory Director, do not yet have a universally agreed scope. This classification reflects TNP Digital Advisory’s assessment and is not a statistical measurement.
The underlying trend is clear: data management is becoming a professional field in its own right, building on the IT and digital sector, just as human resources and finance previously developed into distinct professional functions. Maturity levels differ across industries, organizations and skill sets, but the field as a whole is becoming increasingly professionalized.
Why data professions matter for AI
Data is a pharmakon: both a remedy and a poison, valuable when properly governed and potentially harmful when neglected. Organizations that invested early in data expertise (CDOs, data stewards, data engineers) now have a head start in adopting artificial intelligence.
The challenge is no longer purely technical; it is also cultural. Building a recognized data profession framework, capable of engaging with strategic business functions, begins with clearly defining roles and responsibilities. Well-defined job titles and responsibilities help organizations structure their data teams, improve collaboration and support the successful adoption of AI.
Discover our memo, Data Professions: Structuring the Unpredictable, featuring a comprehensive overview of all 37 data professions, their responsibilities, maturity levels, key competencies and the complete terminology reference table.
Sources and references
- Cigref, Nomenclature des profils métiers du SI, 2025 edition (October 2025): 9 families, 52 profiles.
- ANSSI, Observatoire des métiers, enquête 2020: Panorama des métiers de la cybersécurité.
- CNIL, “Délégué à la protection des données (DPO)”.
- Choisir le service public, job profile “Directrice / Directeur des données”.
- France Compétences, RNCP37843: “Directeur de la donnée (chief data officer)”.
- Regulation (EU) 2016/679 (GDPR); Regulation (EU) 2024/1689 (AI Act); Regulation (EU) 2023/2854 (Data Act); Directive (EU) 2022/2555 (NIS 2); Regulation (EU) 2022/2554 (DORA).
How to cite this article
Bessot, D., TNP Digital Advisory, “Data Steward, Data Owner, CDO: Understanding the 37 Data Professions”, TNP Consultants, November 2026.
With contributions from Bilal Ben Haroual, Amaryllis Blouin, Carryce Diebkilé, Ramzy Khizioua and Ghita Mechkour. Artificial intelligence was used for proofreading, formatting and documentary research.
