In the latest edition of the Digital Economy Dispatch, Prof. Alan. W. Brown dives into a pressing issue at the heart of the AI revolution: the paradox of poor quality data in an era abundant with information. Titled “AI’s Data Dilemma: Confronting the Paradox of Poor Quality Data in the Age of AI”. In this article, which followed on from Alan’s recent DDRC podcast of the same title. Alan offers an exploration of the challenges and opportunities posed by the current data landscape.

The dispatch reflects on a podcast, led by a panel of experts including Yvonne Gallagher, Christine Ashton, Stefan Crossfield, and Rashik Parmar, highlighting the growing disconnect between the vast volumes of data being generated and the quality of this data. Key findings from the Data Orchard’s 2023 review, along with insights from various sectors, highlights the alarming trends in diminishing data quality, widespread data illiteracy, and eroding data security confidence.

This issue goes beyond mere statistics; it raises fundamental questions about our preparedness for the data demands of the AI age. How can we bridge the gap between the data we have and the data we need for ethical and impactful AI? The discussion emphasises the importance of data quality, relevance, and effective utilisation as the keys to unlocking the true potential of AI insights.

But this is more than just a discussion—it’s a call to action. The article urges digital leaders, teams, and organisations to engage in this vital conversation, addressing questions about data maturity, management processes, and governance structures. It’s about raising our data quality game to ensure a responsible, equitable, and thriving AI-powered future.

For a deeper understanding of this critical issue and to join the conversation, read the full dispatch in Digital Economy Dispatch #168. This is not just a technological challenge, but a societal imperative that requires our collective attention and action.