Every year, BARC asks close to 2,000 data, BI, and analytics professionals which trends are shaping their priorities. In the 2026 edition of the Data, BI & Analytics Trend Monitor, 1,579 respondents delivered a clear and consistent verdict: data quality is the number-one trend, followed by data security and data culture.
At first glance, that ranking may not seem surprising. In fact, it is remarkably stable compared to previous years. But that consistency is exactly what makes it important. While the industry conversation continues to shift rapidly toward AI, automation, and next-generation analytics, the people working closest to the data are pointing to something far more foundational.
That message matters, particularly for EM360Tech’s audience of enterprise technology leaders, architects, and decision-makers.
Right now, organisations are under intense pressure to deliver on AI. Budgets are being reallocated, boards are demanding measurable ROI, and vendors are racing to embed generative and agentic AI into virtually every product category. The narrative suggests that success depends on adopting the latest tools as quickly as possible.
But the reality on the ground looks very different.
The Trend Monitor reinforces what many practitioners already know but often struggle to prioritise: none of these initiatives succeed without a trustworthy data foundation. AI is only as good as the data it is trained on and operates with. Without high-quality, well-governed data, even the most advanced models will produce unreliable, biased, or unusable outputs.
The numbers back this up. In a related BARC study, 45% of respondents say the data they can access is not in the quality they need for AI use cases. That is nearly half of organisations effectively hitting a ceiling before they can even begin to scale their AI ambitions.
This has real consequences. AI initiatives stall, proof-of-concepts fail to move into production, and expected efficiencies or insights never materialise. In many cases, organisations misdiagnose the issue as a tooling problem, when in reality it is a data problem.
Alongside data quality, data security remains firmly in second place. This is particularly relevant in a European context, where tightening data sovereignty regulations and growing concerns around data governance are reshaping how organisations manage and store information. As AI adoption increases, so too does the risk surface. Ensuring data is not only accurate but also secure and compliant is foundational.
The third-ranked trend, data culture, adds another critical layer to the conversation. It highlights a point that is often overlooked in technology-led strategies: tools alone do not create value. Organisations need people who understand data, trust it, and are empowered to use it effectively in decision-making. Without that cultural alignment, even the best data platforms and AI capabilities will fall short of their potential.
Taken together, these three trends paint a clear picture. The industry may be focused on what is next, but success still depends on getting the basics right.
For technology buyers, platform evaluators, and enterprise architects, the takeaway is direct: before investing heavily in the next wave of AI capabilities, it is worth taking a step back and assessing the strength of your data foundation. Are your data pipelines reliable? Is your data governed, accessible, and trusted across the organisation? Do your teams have the skills and confidence to use it?
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