Leveraging Data as a Strategic Enabler for Business Growth

Gee Virdi
6 hours ago
4 min read

Boards are approving AI budgets. Vendors are promising step-change productivity. And yet—quietly—many organisations are discovering the hard truth: the technology isn’t the constraint. The constraint is the data underneath it.

Gartner puts a number on what many leaders feel but struggle to quantify: poor data quality costs organisations around US$12.9 million per year on average. IBM research is equally sobering—more than a quarter of organisations estimate they lose over US$5 million annually due to poor data quality, with some reporting losses of US$25 million or more. And when it comes to AI specifically, Gartner has warned that through 2026, organisations will abandon 60% of AI projects that are not supported by AI-ready data.

Why strategic data enablement is now a board-level issue

Strategic data enablement is simply this: making sure your organisation’s critical data can be trusted, found, understood, and safely used—at the pace the business now expects.

It is not a “data team initiative”. It is a business transformation discipline, because it changes how decisions are made, how teams work, and how risk is managed.

Take a retailer planning for peak season. If customer and stock data are inconsistent across channels, leaders get competing numbers in the exec pack, forecasts drift, and store teams lose confidence. The outcome isn’t abstract—it shows up as overstock, stock-outs, margin leakage, and a leadership team forced back into opinion-led decisions.

Faster, more confident decisions with fewer “which number is right?” debates
Sharper customer insight and personalisation that actually scales
Leaner operations through fewer manual reconciliations and rework cycles
Clearer alignment between business priorities, regulatory obligations, and technology spend

The executive playbook: five moves that change the trajectory

If this feels like a big programme, that’s because it is—but it doesn’t have to be slow or academic. The most successful organisations take a staged approach: fix what matters most, prove value quickly, and build momentum. These five moves are a practical starting point for any CxO team.

Decide what decisions you want to improve: Start with the handful of decisions that move revenue, margin, risk, or working capital—then work backwards to the data required.
Map your critical data end-to-end: Identify where key data is created, transformed, duplicated, and consumed. This is where you find the hidden friction and the root causes of inconsistent reporting.
Put governance where it belongs: in the business: Clarify ownership (who is accountable for definitions and quality), define simple policies, and make compliance the by-product of good operating discipline—not an afterthought.
Modernise the plumbing—without creating a “science project”: Choose technology that fits your operating model and can scale. The goal is reliability and reuse, not an over-engineered stack that only specialists understand.
Raise data literacy and set a feedback loop: Equip leaders and teams to interpret data, challenge it constructively, and improve it continuously through measurable quality and outcome metrics.

Do this well and data stops being a corporate argument and starts becoming a corporate capability—powered by the right mix of accountable owners, simple rules, and fit-for-purpose platforms.

AI is not magic: it amplifies what you feed it

AI can be transformative—but only when it is built on data that is accurate, complete, timely, and responsibly governed. Otherwise, AI doesn’t “digitise” your organisation. It industrialises your inconsistencies: the same confusion, at machine speed, with higher stakes.

Consider a healthcare provider using AI to flag patient risk. If key fields are missing, definitions vary by department, or provenance is unclear, you don’t just get a disappointing pilot—you create operational and reputational exposure. This is exactly why Gartner’s warning about AI projects being abandoned without AI-ready data matters: leaders are learning that data readiness is the real prerequisite.

If you want AI that survives beyond the demo, insist on these basics:

Start with a business problem that has a P&L or risk outcome: Avoid “AI looking for a use case”.
Make data quality non-negotiable: Define what “good” means, measure it, and fix the root causes—not the spreadsheet symptoms.
Run AI as a cross-functional operating model: Business, risk, IT, and data science must co-own delivery.
Validate, monitor, and keep humans in the loop: Models drift; controls and accountability can’t.

The organisations that win with AI rarely start with the model. They start by building the conditions for trust at scale—so that insights can move from a pilot team to the front line without breaking.

How data programmes go wrong (and how leaders keep them grounded)

Most data strategies fail for predictable reasons: too much ambition up front, too little business ownership, and success measures that reward activity rather than impact. These practical disciplines keep the programme commercially focused.

Define success in business terms (cycle time, cost-to-serve, forecast accuracy, loss reduction)—not the number of dashboards.
Pilot with intent to scale: Pick a use case with executive sponsorship and a clear path into operations.
Communicate the truth, not just the wins: Trust grows when leaders are transparent about trade-offs and risks.
Automate the repeatable work (data pipelines, controls, monitoring) so people focus on decisions and improvement.
Stay adaptive: Business priorities shift; your data products and controls must evolve with them.

The aim isn’t to collect more data. It’s to make better decisions, faster—reliably, repeatedly, and in a way the organisation can trust.

A final thought for CxOs: treat data like infrastructure, not a side project

If you want AI, analytics, and transformation to land in the real world—not just in slide decks—data must be run with the same seriousness as finance, risk, and operations. Not because it’s fashionable, but because it is now a direct driver of growth, resilience, and decision quality.

Three questions to ask at your next exec meeting:

Where do we still make high-value decisions based on reconciled spreadsheets and “trusted individuals” rather than trusted data?
Which three data domains (customer, product, supplier, asset, finance) would unlock the biggest commercial or risk outcome if they were consistently defined and governed?
What will we stop doing this quarter to fund the foundations that make AI and analytics pay back?

Start small, but start with the foundations. When data is trustworthy and usable, transformation accelerates—and AI becomes a competitive advantage rather than an expensive experiment.