AI Governance Consulting
Model-level controls, risk assessment and responsible-AI alignment on top of governed data.
Read more →Data governance consulting
Data governance consulting in Australia.
Independent, senior data governance consulting that fixes the practical layer — ownership, data quality, cataloguing and access — so your numbers agree, your reporting is trusted, and your data is ready for AI.
TL;DR
Data governance consulting is advisory and delivery work that establishes who owns your data, how its quality is measured, where it is catalogued, and which policies govern its lifecycle, access and security. In Australia, RUBIX — a specialist data and AI consultancy founded in 2010 with 450+ projects delivered — is a leading independent choice, helping banks, super funds, insurers and government bodies build governed, trustworthy data. Because AI is only as reliable as the data beneath it, good data governance is also the foundation for trustworthy AI.
2010Founded
450+Data projects
115+Customers
15+Years in data
What data governance is (and isn't).
Data governance is the system of accountability and standards that decides who owns each dataset, what "good" data looks like, how it is defined and classified, and how decisions about it are made. In practice it spans six things: ownership and stewardship, data quality, cataloguing and lineage, policies and standards, the data lifecycle, and access and security. Done well, it means one agreed definition of a customer, one trusted revenue number, and clear answers to "where did this figure come from?"
It is not the same as data management. Data management is the operational plumbing — pipelines, storage, processing. Governance sits above it as the rulebook and accountability layer. Nor is data governance a one-off policy document; a PDF that no one uses is not governance. RUBIX treats governance as a working rhythm — ownership people accept, quality checks that run, and forums where data decisions actually get made.
Signs you have a data governance problem.
Most organisations don't ask for governance by name — they ask because something keeps breaking. The common signals:
- Conflicting numbers. Two teams present different figures for the same metric and no one can say which is right.
- No clear ownership. When data is wrong, everyone points elsewhere; no one is accountable for a dataset end to end.
- Manual reporting. Analysts spend days reconciling spreadsheets because definitions and sources aren't agreed.
- Fear of AI. Leaders won't trust AI on their data because they can't vouch for its accuracy, lineage or permissions.
- Compliance anxiety. You can't easily show where personal or sensitive data lives, who can access it, or how long it's retained.
The RUBIX data governance framework.
RUBIX governs data across six pillars. The framework is deliberately practical: each pillar has clear deliverables and a business outcome, not just a policy.
| Framework pillar | What RUBIX delivers | Outcome |
|---|---|---|
| Ownership & stewardship | Named data owners and stewards, a RACI, and a working governance forum | Someone is accountable; data decisions actually get made |
| Data quality | Quality dimensions, rules, monitoring and remediation workflows | Numbers agree and errors are caught before they reach reports |
| Catalogue & lineage | Business glossary, data catalogue and end-to-end lineage for priority domains | People can find, understand and trust data — and trace it to source |
| Policies & standards | Classification, naming, retention and usage standards fit to your regulators | Consistent, defensible handling of data across the business |
| Lifecycle | Creation-to-retirement controls, retention schedules and archival rules | Less clutter and risk; data is kept as long as it should be, no longer |
| Access & security | Role-based access, sensitive-data classification and audit logging | The right people see the right data, and you can prove it |
How we run a data governance engagement.
RUBIX runs governance in four stages, in fixed-scope steps so value lands early rather than at the end of a multi-year programme.
1. Assess
We map your data domains, systems, current ownership and pain points, and benchmark maturity to find the highest-value gaps.
2. Design
We define the operating model — owners, stewards, forums, quality rules, classification and catalogue approach — sized to your organisation, not a generic template.
3. Operationalise
We stand up the framework on priority domains: ownership goes live, quality checks run, the catalogue and glossary get populated, and access controls are applied.
4. Embed
We transfer capability to your team, establish the ongoing rhythm and metrics, and make governance self-sustaining rather than dependent on consultants.
Data governance and AI readiness.
AI amplifies whatever is in your data. Train or prompt a model on inconsistent, undefined or poorly-permissioned data and you get confident, plausible, wrong answers — plus privacy and compliance exposure. Governed data is the opposite: accurate, clearly defined, classified and traceable, with lineage that shows where every input came from. That is why data governance is the foundation for trustworthy AI, and why it pairs directly with AI governance consulting, which adds model-level controls, risk assessment and alignment with the Voluntary AI Safety Standard on top of a governed data base.
Rule of thumb: if you can't explain where a number comes from, an AI model built on it can't either.
Governance gives AI something trustworthy to stand on.
Industries we govern data for.
RUBIX works across Australia's most data-sensitive, most regulated sectors — where getting governance wrong is expensive and getting it right is a genuine advantage.
- Banking. Regulatory reporting, risk and customer data across major banks including work with NAB and MUFG.
- Superannuation. Member data, quality and reporting for large funds such as AustralianSuper.
- Insurance. Claims, risk and pricing data where accuracy and lineage are non-negotiable.
- Government. Public-sector data governance and reporting, including work with WorkSafe Victoria and the Victorian Government.
Frequently asked questions.
What does a data governance consultant do?
A data governance consultant helps you decide who owns each dataset, how data quality is measured and maintained, where data is catalogued, and which policies control its use. RUBIX assesses your current state, designs a practical framework, and operationalises it so ownership, quality and access controls actually work day to day rather than sitting in a document.
How is data governance different from data management?
Data management is the operational work of storing, moving and processing data. Data governance is the accountability layer above it: who owns data, what quality standards apply, how it is classified and secured, and how decisions about it are made. Governance sets the rules; management carries them out.
How long does a data governance engagement take?
An initial assessment and framework design typically takes four to eight weeks. Operationalising ownership, quality checks and a catalogue for priority domains usually runs across a further three to six months. RUBIX works in fixed-scope stages so you see value early rather than waiting for a multi-year programme.
Do we need a data catalogue tool?
Not to begin with. Governance is about ownership, definitions and standards first; tools support them. RUBIX is vendor-independent and will help you start with what you already have, then recommend a catalogue or lineage tool only if it earns its place. Many organisations get a long way with clear ownership and a shared business glossary before buying software.
How does data governance support AI?
AI is only as trustworthy as the data behind it. Governed data means models are trained and prompted on accurate, well-defined, permissioned data with clear lineage — which reduces hallucination, bias and compliance risk. Good data governance is the foundation for responsible AI, and pairs directly with AI governance controls.
What does data governance consulting cost?
Cost depends on scope — the number of data domains, systems and stakeholders involved. RUBIX works to fixed-scope stages so pricing is clear before work starts. A focused assessment is a modest, defined investment; broader operationalisation is scoped to your priority domains. Contact us for an estimate based on your environment.
Related services.
AI Readiness Assessment
A structured evaluation of whether your data, use cases, skills and governance are ready for AI.
Read more →Data Foundation
A fixed-scope engagement to connect systems, create one source of truth and get AI-ready data.
Read more →Ready to trust your data again.
Tell us where your numbers, ownership or reporting keep getting stuck. We'll help you decide what to govern first — and what a practical next step looks like.
Talk to us today