Working in data hygiene, we know a thing or two about the importance of data quality. As more businesses look to harness the power of GenAI, they too are learning that data quality and hygiene are critical to success.

AI systems are only as good as the data fed into them, and generative AI (GenAI) is no exception.  BT’s Chief Data & AI Officer, Deepika Adusumilli, recently highlighted that while ideas for GenAI applications are plentiful, turning them into high-impact tools requires rigorous data quality standards. Without clean data, even the best AI strategies struggle—a reminder of the direct mail adage, “rubbish in, rubbish out.”

Direct Mail Lessons for GenAI: Data Quality is a Continuous Process

The direct mail sector has long understood that data hygiene isn’t a one-off project but a continual commitment. Lists must be routinely verified and refreshed to remain accurate, relevant, and compliant.

Similarly, BT’s approach to data quality shows that keeping data “clean” isn’t a one-and-done affair.  Supported by Google Cloud, BT has migrated 95% of its data to the cloud. Rather than bulk transferring everything, the company is prioritising datasets based on specific use cases—ensuring that data is not only accessible but optimised for the tasks AI will perform.

Establishing a Data Fabric and Data Mesh

To create an accessible, adaptable data environment, BT has implemented a “data fabric” and “data mesh” approach. This structure enables data to be catalogued, categorised, and transferred across applications as needed. Such a setup helps maintain data quality, reduce redundancy, and streamline access across use cases, making GenAI applications more robust and versatile.  Adusumilli believes that only when the right data infrastructure is in place can BT make meaningful strides in its AI projects.

Prioritising Use Cases for Maximum GenAI Impact

With 90 potential GenAI ideas, BT has recognised that only a portion could immediately progress to implementation.

By selecting twelve priority use cases, including customer service and sales enhancements, BT is focusing on initiatives where data quality and accessibility could make the most difference. This selective, intentional approach is key for any business wanting to maximise GenAI’s potential—prioritising data that serves specific, high-value purposes.

Data Hygiene: Essential for GenAI Success

Data hygiene is no longer optional in the AI era. Like the direct mail industry’s rigorous standards for data accuracy, GenAI relies on a clean, well-maintained data foundation. BT’s experience illustrates the benefits of an ongoing commitment to data quality, with systems in place to continually adapt and refine data as AI projects evolve.

For businesses looking to leverage GenAI, a clean data infrastructure is essential to success.