Data governance is not bureaucracy – it's the missing first step in every data and AI strategy – iTWire
While AI is the hot buzzword right now, the message from major data and analytics vendors has been remarkably consistent. Whether you’re talking to SAS, Snowflake, Databricks, Confluent, Cloudera, or any number of others, the advice is the same: “before you attempt advanced analytics or AI, you need to get your data in order.” Getting your data in order is usually shorthand for data governance. And yet, for many organisations, governance remains misunderstood, under-scoped, or quietly avoided.
You might want to achieve big things with AI. Every vendor is telling you if you aren’t already using AI then you need to, because your competitors are. However, it’s not that simple. To do something with AI you need a target. And you need fuel. The fuel for AI is data, and it’s a safe bet your business is full of data. But where is it? Who can access it? What’s classified? As much as you might be itching to get onto the tools and make something fabulous – all in the name of “doing some AI” – you won’t get far, at least as far as you want, without data governance.
What data governance really means (and what it doesn’t)
Let’s start by removing a few myths.
Data governance is not:
At its core data governance is how an organisation decides who is accountable for data, how it is defined, how it is trusted, and how it is allowed to be used.
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In practical terms, governance answers questions executives ask every day:
If those answers are unclear, governance doesn’t exist – regardless of how modern the technology stack might be.
Why governance has become a C-level and board Issue
Historically, poor data quality was inconvenient. In an AI-driven organisation, it is dangerous.
Three forces have changed the stakes:
Put simply, AI does not introduce new data risks – it magnifies existing ones.
This is why many boards are now treating data governance as a risk management capability, rather than a technical nicety.
Governance as the foundation of a data and AI strategy
A useful way to think about this is as a dependency stack:
Without governance:
With governance:
This is why it is a big deal, and when you speak to the big data and analytics companies, they’re going to want to take you back a step. You might ask them to build this big shiny thing, but they’re going to ask about your data governance first.
The 90-day data governance startup plan (what executives need to know)
The biggest mistake organisations make is trying to govern everything at once. Successful programs start small, visible, and business-led.
Days 1–30: Establish ownership and scope
Days 31–60: Define what “good” looks like
Days 61–90: Make Governance Operational
This approach delivers tangible results inside a quarter – which is why it resonates with executives.
The one-slide view every executive understands
At board and C-suite level, governance should be framed simply:
This is why governance increasingly appears in risk, audit, and AI discussions, not just IT roadmaps.
Making governance practical: what IT and Data Leaders actually do next
For IT Managers, Architects, and Heads of Data, governance becomes real through everyday practices, not policy documents.
Here’s what iTWire recommends you put in place:
1/ Start with ownership, not tools
2/ Govern the things people argue about
3/ Embed governance into delivery
4/ Keep it lightweight
Check out our iTWire explainer on data governance here:
Why vendors keep emphasising Governance (and why they’re right)
When vendors like SAS talk about decision intelligence, or Snowflake and Databricks talk about AI-ready data platforms, governance is the unspoken prerequisite.
These platforms can enforce quality rules, track lineage, manage access, and support explainability. They do a great job of this. But they cannot decide what data means, or who is accountable for it. That responsibility always belongs to the organisation.
Governance is not the goal – value is
Now, getting your governance right is critical. However, the most successful organisations don’t talk about “doing governance”. They talk about things like trusted numbers, faster insights, confident AI, reduced risk, and better decisions.
Governance didn’t make these results; it is simply how they get there. And increasingly, it’s not optional.
If your organisation is serious about data and AI, the question is no longer whether to implement data governance – but how quickly you can make it practical, visible, and business-owned.
The good news? You don’t need years, armies of consultants, or massive tools programs.
You need clarity, ownership, and the discipline to start. Will you be the one to bring it?
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David has been computing since 1984 where he instantly gravitated to the family Commodore 64. He completed a Bachelor of Computer Science degree from 1990 to 1992, commencing full-time employment as a systems analyst at the end of that year. David subsequently worked as a UNIX Systems Manager, Asia-Pacific technical specialist for an international software company, Business Analyst, IT Manager, and other roles. David has been the Chief Information Officer for national public companies since 2007, delivering IT knowledge and business acumen, seeking to transform the industries within which he works. David is also involved in the user group community, the Australian Computer Society technical advisory boards, and education.
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