When AI multiplies errors
"In the past, a data error might have led to a single bad decision," explains PwC study. "Today, that same error can be multiplied thousands of times through automated systems, creating a cascade of issues." This multiplication effect transforms minor data discrepancies into potential business disasters.
The emergence of AI and machine learning has added a new urgency to data quality discussions. As Forbes explains "AI systems are like highly efficient amplifiers’’. They'll amplify your successes, but they'll also amplify your data quality issues at a massive scale."
The future of data quality
Imagine a large company preparing for a critical product launch. Everyone’s set, from marketing to analytics. But a minor data error goes unnoticed (ex: incomplete customer data, duplicative customer records, etc.), gets multiplied in different systems and becomes a major risk to the whole project. Such a scenario happens more often than we think. And this is the reason why companies today move towards real time proactive data quality. Such systems can detect and correct inconsistencies early on, helping companies to avoid disruptions and make data-driven decisions with confidence.
A culture of data quality
Today leading companies are embedding data quality into every corner of their operations. This means investing in the newest technology and establishing solid data governance standards. It means also developing a culture where the company has fully understood that data quality is ineluctable.
The companies that will thrive in the next years are the ones that can ensure their data is accurate, integrated and actually usable. Building a culture of data quality means integrating quality checks throughout the whole data lifecycle. According to BCG's global study, such companies will achieve 20% higher revenues and 30% higher margins than competitors.
The ultimate prize
The organizations that master data quality won't just avoid errors – they'll gain the power and the certainty to get value out of their data. In a world where data drives nearly every business project, this combination of quality and autonomy is a must. As one CTO recently noted, "The question isn't whether we can afford to invest in data quality. The question is whether we can afford not to’’.
Written by Michal KOLATAJ
