<p>Artificial intelligence (AI) is advancing at a pace few organisations anticipated. As the conversation shifts from whether to use AI, to how to scale it safely and responsibly, <a href="https://www.gartner.com/en/data-analytics/topics/data-governance">data governance</a> is now firmly in the spotlight. This is revealing gaps many Australian business leaders didn’t know existed.</p> <p>The quality and trustworthiness of data now directly determine whether AI delivers value or introduces risk. Yet data governance frameworks have often failed to keep up, leaving organisations exposed as expectations around trust and accountability accelerate.</p> <p>While this sounds like a technical challenge, data governance is rarely about technology alone. It most often fails because of people, not tools. Low data-driven maturity, difficulty demonstrating business value and limited understanding of data, analytics and AI across the business remain the biggest inhibitors.</p> <p>Gartner predicts 60% of organisations that fail to address the cultural challenges associated with <a href="https://www.gartner.com/en/data-analytics/topics/data-analytics-strategy">data and analytics</a> governance by 2027 will fail to govern AI successfully.</p> <p>In addition, many still treat culture and governance as separate priorities, resulting in process heavy approaches that struggle to engage stakeholders or inspire stewardship. This leads to declining participation, as well as increased risk exposure and diminished returns from AI investments.</p> <p><b>Data governance needs leadership</b></p> <p>As AI raises the stakes for data governance, many organisations are discovering that their current approach is no longer sufficient.</p> <p>Too often, responsibility sits within IT even as success now depends on coordinated action across the business. Without broader authority and buy-in, technology-led teams struggle to influence senior stakeholders or drive the behavioural change that effective governance requires.</p> <p>This is where executive sponsorship becomes essential. Overcoming cultural challenges, such as disengagement, competing priorities and reluctance to change, demands authority and credibility at the highest levels.</p> <p>A senior executive sponsor, whether the CEO or a non-technology leader, brings influence across business units and can clearly articulate why data governance matters, providing both mandate and motivation for meaningful engagement. This can unlock participation across functions, resolve tensions and reinforce that governance isn’t optional but a strategic imperative.</p> <section class="section main-article-chapter" data-menu-title="Making data governance work"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Making data governance work</h2> <p>Data governance often struggles due to how it’s positioned, not because the rules are wrong.</p> <p>When it’s still seen as control heavy or IT‐owned, engagement quickly drops. In an AI‐driven environment, those perceptions don’t just slow progress, they actively limit the value organisations can get from their data.</p> <p>What makes a difference is how governance connects to the business. At its core, governance is about trust in data and how it’s used.</p> <p>That trust underpins better decision making, stronger compliance, operational efficiency and AI initiatives that can scale with confidence. But this only resonates when governance is framed in terms of business outcomes, not data quality tasks or technical controls.</p> <p>It also requires a shift in how responsibility is shared. Governance isn’t something one team owns. It relies on coordinated effort across policy setting, enforcement and execution, spanning both business and technology. </p> <p>The goal isn’t more structure or bigger committees, but clearer alignment to priorities that matter. When people understand how governance supports their goals, rather than seeing it as additional work, participation improves and governance begins to stick.</p> </section> <section class="section main-article-chapter" data-menu-title="Focus fast and prove value"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Focus fast and prove value</h2> <p>One of the fastest ways to derail a data governance programme is trying to take on too much.</p> <p>When priorities aren’t clear, organisations often default to bottom‐up data hygiene, such as cataloguing, cleansing and documenting data in isolation from real business needs. This takes time and delivers little visible value upfront, leaving governance efforts exposed to disengagement and defunding.</p> <p>A more effective approach is to start with specific outcomes, not data management issues. Governance should be anchored to a small number of business priorities, such as regulatory risk, AI initiatives, or workflows mature enough to deliver results quickly. Even an initial view of priorities helps frame discussion, demonstrates business understanding and builds momentum. The goal isn’t perfection, but alignment. </p> <p>Done well, data governance becomes a process of continual improvement. By focusing on a manageable set of outcomes and embedding governance into existing business workstreams, organisations can deliver early wins, build confidence and expand over time.</p> <p>This not only accelerates value, it reinforces the cultural shift needed for governance to stick, making it part of how the business operates rather than a standalone initiative.</p> <p><i>Sally Parker is a senior director analyst at Gartner, focused on master data management, data & analytics strategy, data governance and data-driven culture</i></p> </section>
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