Lisa Ventura MBE
FCIIS, Founder, AI & Cyber Security Association (AICSA
Good data governance, not clever algorithms, turns AI from a costly gamble into a genuine business advantage.
Gartner predicts organisations will abandon 60% of AI projects through 2026 because the data behind them isn’t ready. 1 A separate Gartner survey found 63% of data leaders lack the right data management practices or are unsure they have them. 1 Good governance changes the picture.
Governance builds digital trust
Under UK GDPR, the accountability principle set out by the Information Commissioner’s Office requires organisations to demonstrate, not merely claim, responsible data handling. This discipline protects customers and pays for itself.
Gartner research puts the average cost of poor data quality at $12.9 million a year for a typical organisation. 2 A Forrester study found over a quarter of organisations lose more than $5 million a year to poor data, with 7% losing $25 million or more.3 Strong governance cuts this risk and builds customer trust.
Data good enough to make any algorithm worth using is the real win
What good governance looks like
Good governance starts with clear ownership of data, defined quality standards and honest audits of what an organisation holds. Appointing a named data owner in each team assigns accountability, rather than leaving governance to chance.
Training staff to treat data as a shared asset, not an afterthought, matters too. A short data audit, run twice a year, catches problems before they reach a live AI system. Regular audits and clear accountability for data quality deliver results faster than any algorithm alone.
Organisations getting governance right gain genuine value from AI: faster decisions, fewer errors and systems people trust. Staff spend less time firefighting bad data and more time on work worth doing. The prize isn’t a smarter algorithm. Data good enough to make any algorithm worth using is the real win. Good governance isn’t a barrier to innovation, but the foundation innovation stands on.
[1] Gartner. (2025). Lack of AI-ready data puts AI projects at risk. https://tinyurl.com/y76xx7vb.
[2] Gartner. Data quality: best practices for accurate insights. https://tinyurl.com/4642xpnp.
[3] Forrester. (2024). Millions lost in 2023 due to poor data quality, potential for billions to be lost with AI without intervention. https://tinyurl.com/mr53zy4h.
