Part 10/11:

The Role of Technology and Future Directions

Emerging AI and machine learning tools are revolutionizing data quality landscapes. Features to look for include automated profiling, rule definition, anomaly detection, and self-healing capabilities. It is essential, however, to use AI judiciously—seeing it as an enabler rather than a magic wand.

The future envisions quality by design, with solutions that are embedded in architecture from the outset, along with self-healing systems capable of automatic correction. Organizations should aim for predictive data quality, anticipating issues before they occur, thus elevating their data maturity and trustworthiness.

Implementing Data Quality: Practical Steps

Finally, the presenter shares pragmatic advice: