Why Data Quality is critical for AI

The real cost of bad data

It’s been nearly three years since ChatGPT launched, and since then, AI has rapidly reshaped the business landscape. While the technology feels new, a fundamental rule remains unchanged: you only get out what you put in.

In the age of AI, feeding poor-quality data to sophisticated models results in what’s known as “AI slop” – inaccurate, biased, or nonsensical outputs.

This isn’t just a problem for open models. Many companies have internal data that is riddled with errors. The cost of this bad data can be significant, leading to issues from incorrect reporting and regulatory fines to duplicate or incomplete customer profiles that damage the customer experience – and the bottom line.

Your AI is only as good as your data

To succeed with AI, the quality of your data is non-negotiable. Corey Keyser, Head of AI at Ataccama, offered a stark summary of the issue: “If I point an AI or Large Language Model (LLM) at a piece of data, but I don’t understand the kinds of problems that data has or whether it’s high quality or not, it doesn’t matter how effective the generative AI is. The metrics will be wrong, the view will be wrong”.

During the webinar, we address answers to this, as well as:

• The role of Data Governance and Data Quality managers
• What agentic AI is and how it can be helpful
• What it means to have a culture and ecosystem that drive data quality and AI use
• How to measure data readiness for AI

We also talk about how organisations can scale AI successfully. As an example, leading companies are putting data quality front and centre. They treat it as a measurable KPI with incentives, transforming it from a purely technical task into an organisation-wide priority. This cultural shift is essential for building a reliable foundation for AI.

Putting theory into practice with Ataccama ONE

Understanding the problem is one thing; solving it is another. This is where tools designed for data quality and governance become critical. In the webinar, Scott Anderson, Principal Consultant at QMetrix, demonstrated the capabilities of the Ataccama ONE platform, including these features:

• An intuitive Data Quality (DQ) capability to efficiently deliver comprehensive data quality monitoring across data catalogues
• AI assisted Data Quality and Observability so you can understand the state of your data and validate its health
• Data Catalogue and Lineage for understanding your data and its flow across data assets

See the Ataccama ONE platform in action by watching the demo, which begins at the 29:51 timestamp of the webinar.

To build a future-proof AI strategy, organisations must first build a foundation of trusted data.

Ready to take control of your data? Talk to our Data Solution experts about how Ataccama’s powerful platform, combined with QMetrix’s expertise, can drive better business outcomes for your organisation.