By Edgar Randall, Head of Europe, Dun & Bradstreet.
For years, business leaders have been told that data is their greatest asset. Collect more of it, connect more sources, and better decisions will follow. However, as organisations accelerate their investment in AI, analytics and digital transformation, this long-held assumption is starting to be challenged. The problem facing leaders today is no longer a lack of data; it’s almost the opposite. Many businesses now have too much data, spread across too many systems, with too little trust in what it actually says.
Our recent research surveying global data management leaders highlights the scale of the challenge. On average, firms manage more than 60 internal and external data sources, ranging from customer and supplier records to third-party risk, compliance and market data. Rather than delivering clarity, this explosion of information has created fragmentation, inconsistency and confusion. One in three leaders says they have too much data, struggle to find or access it, and don’t even know where to begin with analysis.Why Data Management Strategy Matters
The challenge, then, is no longer just about how much data organisations have, but how confidently they can manage and use it. Without a clear data management strategy, businesses are left trying to make decisions based on disconnected, duplicated or outdated information.
Building a strong data strategy requires structure first. Establishing consistent identifiers is essential to organising data around the entities that matter most and creating a unified, reliable view across systems. Without this foundation, fragmentation persists beneath the surface, even when platforms appear well integrated.
It also requires a shift in mindset. Not all data should be treated equally. Different sources vary in reliability, completeness and timeliness, yet many firms continue to treat them as interchangeable. Leading organisations are increasingly recognising the need to assess and prioritise data assets based on factors such as provenance, source quality and recency.
In this context, effective data management is not about collecting more information, but about ensuring the data that matters most is trusted, connected and fit for purpose.
When Data Becomes a Business Risk
Data overload isn’t just a technical problem; it is a strategic one. Poor or insufficient data management has real consequences across every sector. More than half of leaders surveyed say data issues have contributed to privacy or security lapses, while 40 per cent report regulatory violations or damage to brand reputation. Others point to delayed product launches and missed opportunities to grow the business.
When data is fragmented and inconsistent, trust quickly erodes. Leaders are forced to question the accuracy of reports, dashboards and forecasts, slowing decision-making at precisely the moment when speed and confidence matter most. Instead of empowering teams, data becomes a source of friction – one that limits growth rather than enabling it.
Why AI Raises the Stakes Even Higher
AI promises automation, efficiency, and deeper insight, but it is only as effective as the data that feeds it. Our research found that nearly 30 per cent of leaders have already experienced AI-related issues due to poor-quality data.
This creates a powerful contradiction: businesses are accelerating their use of advanced analytics and large language models, yet many are doing so without the stable, well-governed data foundations these systems require.
When underlying information is incomplete or inconsistent, AI can amplify errors at scale, producing outputs that appear authoritative but are fundamentally flawed. In this environment, trust in data is not a ‘nice to have’ but an essential.
Shifting the Focus From Volume to Value
Encouragingly, leaders recognise the need to change. Almost three-quarters of respondents say improved data management would directly translate into increased revenue, while 71 per cent expect better customer experiences and 70 per cent anticipate more actionable business decisions.
As a result, master data management has therefore become a top strategic priority, with 63 per cent of leaders ranking it as their number-one data focus for the year ahead. By providing a consistent, trusted view of core business entities – customers, suppliers, and partners – master data enables organisations to replace disconnected records with aligned, reliable information. The goal isn’t to collect less data, but to ensure the data that matters most is fit for purpose.
Clarity as a Competitive Advantage
Technology alone won’t solve the challenge. The research also highlights persistent gaps in data governance, skills, and accountability. Without clear ownership and standards, even the most advanced tools can create new silos and security risks.
This is why many organisations are turning to data management partners to help them simplify complexity, improve integration, and maintain data quality at scale. Over 90 per cent of leaders surveyed already work with a partner and credit these relationships with faster decision-making, improved agility, and quicker time to business value.
Success in a data-driven economy will not be defined by how much data you collect, but by how well you manage it. By challenging the myth that more is always better, investing in strong governance, and putting trust at the heart of digital transformation.
In a world where data underpins every strategic move, from AI adoption to customer growth, clarity, not volume, is the true competitive advantage.
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