Welcome — you’re looking at the details of training a team by Mastering Data Academy.

I’m Ronald Baan, co-author of the DMBOK3 (expected next year) and trainer of data professionals in business, IT, data management and leadership. This page explains one thing: how you train a whole team in data management — self-paced, guided by my trainers, or guided by your own senior people — for less than it costs to send a lucky few to a classroom.

Everything you need to decide is on this page, including the prices.

Train the whole team, not only the lucky few.

You scanned the brochure, came from LinkedIn or a search — good. Here’are the details: how team learning access actually works, what’s in the curriculum, and what it costs, all of it, no “contact us for pricing”.

The problem you already know

You’ve seen this fail before. Someone goes on a two-day data governance course, comes back full of ideas, and within a month the organisation has sanded them back down to how things were. Not because the course was bad — because one person can’t hold a language the rest of the organisation doesn’t speak.

Data capability is a team sport. Governance only works when the business people who create data, the IT people who move it, and the data people who manage it share the same concepts and the same vocabulary. The same is true for data quality, and it is brutally true for AI: models are now cheap, but the disciplined data practice underneath them is not — and that practice lives in teams, not individuals.

Classroom training can’t fix this economically. At classroom prices, training a whole team means picking the lucky few. So the knowledge stays thin, the language stays fragmented, and the data ambitions stay on slides.

Team access exists to break that trade-off.

How team access works

You buy seats for a group — your data team, a department, a cross-functional mix of business, IT and data people. Every seat gives one person twelve months of access to the full curriculum, self-paced, available whenever they are.

On top of that foundation, you choose how much structure you want:

1. Self-paced

Everyone learns on their own schedule. Best for organisations with self-directed learners, or as the always-on layer underneath everything data management.

2. Trainer-guided cohorts

A group moves through the programme together on a fixed rhythm, guided by one of our trainers: a kick-off, regular live sessions to discuss the material and apply it to your own organisation, and structured preparation for certification. The cohort creates the deadline; the platform carries the content. This is how most teams reach the CDMP exam.

3. Cohorts guided by your own people

The option you won’t find elsewhere. Your senior practitioners lead the cohort — we provide the full structure: the curriculum, session guides, discussion materials, exercises and the certification path. Your seniors do what they’re already good at (context, judgment, knowing your organisation); we do what we’re good at (curriculum, structure, keeping it current). No external trainer fee, and the expertise that develops stays visibly attached to your own people.

On-site workshop days with one of our trainers can be added to any of these — a day in the room to launch a programme, work through your own cases, or push a group over the certification line.

What’s inside the curriculum

Everything is built by practitioners around one question: what does each role actually need to know to make data work?

• Data management fundamentals — the shared language layer. What data management is, why it matters, and how the disciplines fit together. Built for business and IT people as much as data specialists; this is the module that gets a whole team speaking the same language.
• The CDMP certification path — structured preparation for the DAMA Certified Data Management Professional exam: the full body of knowledge, practice questions, exam strategy and support up to exam day. CDMP is the international, vendor-independent benchmark for data management professionals.
• Data governance that fits — governance calibrated to what your organisation actually needs, rather than the maximal frameworks that collapse under their own weight. How to decide what’s enough — and defend that decision.
• Data quality in practice — from measuring what matters to embedding quality in the processes that create data, instead of cleaning it downstream forever.
• AI-ready data practice — what AI genuinely demands from your data foundations, separating the real requirements from the vendor noise.

New material is added continuously — a team access year includes everything published during it.