From supply chain management to customer experience, the organisations pulling ahead are those that treat data as a strategic asset rather than a by-product. And now, as artificial intelligence becomes the engine of decision-making and operational efficiency, the quality of that data will determine the potential of every AI initiative.
Yet too many enterprises still underestimate this reality. Only a small fraction have mature data management practices, with 97% admitting that poor data quality undermines day-to-day operations. This is not simply a technical hurdle; it is a critical weakness in digital readiness.
Data quality is a prerequisite for AI
AI systems can only be as good as the data that feeds them. When information is incomplete, inconsistent or trapped in silos, the insights and predictions those systems produce become unreliable. The risk is not just missed opportunities but strategic missteps that erode customer trust and competitive positioning.
That urgency is magnified by the scale of today’s data explosion. Global data creation has leapt from just 2 zettabytes (ZB) in 2010 to an estimated 149 ZB in 2024 and is projected to surpass 180 ZB by 2025. This dramatic growth offers extraordinary opportunities for innovation, but only for organisations that can separate the signal from the noise.
Companies with a strong digital foundation are already ahead in AI adoption, and those without risk drowning in information while starving their AI models of the clean, reliable inputs they need. But before any organisation can realise AI’s full potential, it must first build a resilient data foundation, and the enterprises that place data quality at the heart of their digital strategy are already seeing measurable gains.
By investing in robust governance, integrating AI with data management and removing silos across departments, they create connected teams and more agile operations. Recent research from Harvard Business Review reinforces this hypothesis, with data-driven, AI-supported organisations expected to achieve a 5% uplift in productivity and 6% in profitability compared with intuition-based approaches. In competitive markets, those percentage points often mean the difference between leading and lagging.
Building a resilient data architecture
Raising data quality is not a one-off exercise; it requires a cultural shift that calls for collaboration across IT, operations and business units. Leaders must set clear standards for how data is captured, cleaned and maintained, and champion the idea that every employee is a steward of data integrity.
The long-term challenge is to design data architectures that can support scale and complexity and embrace distributed paradigms that support interoperability. These architectures do more than maintain order. They enable AI systems to deliver real business value that, in the end, will offer significant rewards.
Ultimately, high-quality data powers AI systems to make faster, more accurate decisions, from optimising supply chains and personalising customer experiences to predicting market shifts. It enables teams to work from a single version of the truth, reduces duplication and accelerates innovation. Most importantly, it transforms data from a passive record of business activity into a proactive source of competitive advantage.
Digital transformation is no longer about adopting new technologies in isolation; it is about building a data-driven organisation that can adapt, learn and grow in real time. AI will not succeed without high-quality data. Businesses that make data quality a strategic priority will not only improve efficiency and productivity but will also set the standard for the next era of intelligent enterprise.
Sachin Agrawal is the managing director of Zoho UK
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