Data Governance & Data Quality: Trusted Data as an Operating Asset
Every analytics and AI ambition dies or lives on the same substrate — data someone actually owns, measures and answers for.
Format
Classroom · Virtual
Upcoming sessions
Pick a session to applyADMISSIONS OPENThe programme
Data governance has a reputation problem: committees, glossaries and policies that change nothing. This programme builds the version that works — ownership assigned to people who can actually fix the data they own; quality measured as fitness for the decisions that consume it, with rules, thresholds and remediation queues; lineage documented where it matters — the regulatory report, the model input, the board KPI; metadata and cataloguing kept proportionate; and the programme design that survives year two: incremental scope, visible wins, and governance wired into projects rather than policed after them. Principles align with BCBS 239 where regulated, but the discipline is universal.
What you will do
Who attends
Data governance and quality teams; chief data officers and their staff; risk and finance data owners; analytics and AI teams who inherit the substrate; auditors of data controls.
Programme agenda
Built for the decisions no textbook prepares you for
I.Ownership that works
- Owners, stewards, custodians: roles cut by ability to act
- The governance operating model: light enough to run, firm enough to bind
- Policies that fit on pages people read
II.Quality and lineage
- Quality dimensions turned into rules, thresholds and remediation queues
- Lineage where it pays: regulatory reports, model inputs, board KPIs
- BCBS 239 alignment where regulated — the same discipline everywhere else
III.The programme that lasts
- Incremental scope: the critical data first, the rest by appetite
- Wired into projects: governance at design time, not audit time
- Measuring the programme itself: adoption, issues closed, decisions trusted
Frequently asked
How is this different from the data governance that changes nothing?
It builds the version that works: ownership assigned to people who can actually fix the data they own, quality measured as fitness for the decisions that consume it — with rules, thresholds and remediation queues — and lineage documented where it matters, not everywhere. Governance is wired into projects at design time rather than policed after them.
Does the course cover BCBS 239?
Yes — the principles align with BCBS 239 where your institution is regulated, and the same discipline is applied everywhere else: lineage for regulatory reports, model inputs and board KPIs. The course is professional training on the discipline, not legal or regulatory advice.
Who should attend?
Data governance and quality teams, chief data officers and their staff, risk and finance data owners, analytics and AI teams who inherit the substrate, and auditors of data controls. Every analytics and AI ambition lives or dies on data someone actually owns, measures and answers for — this course is for those people.
Is an in-house edition available, and how are dates and fees set?
Yes — like every BIZENIUS programme, it is available in-house, tailored to your data landscape, critical data sets and governance maturity, and delivered in English and French. Sessions run on a rolling calendar, with dates confirmed on request, and fees and quotations are provided on enquiry.
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In their words
Knowledge transfer, emphasised throughout
“We worked with BIZENIUS for our Fresh Graduates Programme — they are simply amazing. Knowledge transfer and practical learning were emphasised throughout.”
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Take the brochure with you.
One request — the full agenda, the faculty and the next cohort dates, sent personally by the admissions team.