misunderstood, AI does not magically turn it into truth. It simply gives uncertainty a smoother voice.


Cultural change is hard and that is precisely why it matters. The organisations that invest patiently in culture create capabili- ties that competitors cannot easily replicate. Anyone can buy the same software. Few can replicate years of embedded behaviours.


Data culture is competitive advantage When organisations talk about competitive advantage, the conversation usually centres on products, patents, technology or artificial intelligence. These are undoubtedly important, but they are increasingly becoming available to everyone. The latest AI model, cloud platform or analytics tool can often be purchased by your competitors just as easily as it can by you. Consider two organisations with identical technology stacks. Both have modern cloud platforms. Both have AI capabilities. Both have high-quality dashboards. Both employ talented analysts. Yet one consistently makes faster, better-informed decisions while the other struggles to generate value from the same investment. Why?


Because one organisation has developed behaviours that allow technology to flourish. Its leaders ask for evidence rather than opinions. Its departments willingly share information instead of protecting it. Its people trust the data because they have confi- dence in its quality and understand its limitations. Its teams see mistakes as opportunities to learn rather than reasons to assign blame. This is where the greatest return on investment is often found, not in buying another platform, but in enabling people to use the platforms they already have more effectively.


The AI trust paradox


There is a strange paradox emerging in many organisations. People will challenge a dashboard, question a report and doubt the accuracy of a dataset, yet readily accept an answer generated by AI, even when that answer is built on the very same data they did not trust in the first place.


Perhaps AI makes data feel more human because it gives us language rather than rows and columns. Perhaps confidence sounds more convincing when it is written in complete sen- tences. But if the underlying data is incomplete, inconsistent or


The good news is that hope is not lost. Building a data culture is possible, but it requires intention, patience and practice. In Why Data Culture Matters, I offer a practical blueprint for doing exactly that, combining thought-provoking tips, simple techniques and engaging games that help teams move from abstract ambition to everyday action. Because when it comes to data culture, per- haps Yoda said it best: “Do, or do not. There is no try.” IP


l Order your copy of Why Data Culture Matters www.facetpublish- ing.co.uk/page/detail/why-data-culture-matters/?k=9781783308705


‘‘ August-September 2026


This is why data culture becomes even more important in the age of AI, not less. The challenge is no longer just whether peo- ple trust the data. It is whether they understand enough about the data to question the answers AI produces from it. This is not a theoretical risk. In 2024, Air Canada was found liable after its customer-service chatbot gave a passenger mis- leading information about bereavement fares. The customer relied on the chatbot’s answer, even though the correct policy appeared elsewhere on the airline’s website, and the tribunal ordered Air Canada to compensate them. The lesson is uncom- fortable but important: when AI gives a fluent, confident answer, people may trust it more than the underlying policy or source data, yet the organisation still owns the consequences. If we do not trust the data going in, why are we so willing to trust the answer coming out?


A final thought


Peter Drucker famously observed that “culture eats strategy for breakfast”. In the world of data, I believe we have reached an even more important conclusion.


Data culture does not just eat strategy. It determines whether your data strategy ever gets off the ground.


Organisations don’t become data-driven because they buy bet- ter technology. They become data-driven because people think differently. Because they ask better questions. Because they trust evidence. Because they collaborate. Because they understand that everyone has a role to play in data.


Only then does technology become transformative rather than simply expensive. Perhaps it is time we stopped asking, “What platform should we buy next?” and started asking a much more difficult question. What kind of data culture are we trying to build?


The challenge is no longer just whether people trust the data. It is whether they understand enough about the data to question the answers AI produces from it.


INFORMATION PROFESSIONAL 23


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