Data's dark secret: Why poor quality cripples AI and growth

Data is the foundation of innovation, agility and competitive advantage in today’s digital economy. As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality. Your role in addressing this challenge is crucial to the success of your organization.  

Fragmented systems, inconsistent definitions, legacy infrastructure and manual workarounds introduce critical risks. These issues don’t just hinder next-gen analytics and AI; they erode trust, delay transformation and diminish business value. Data quality is no longer a back-office concern. It’s a strategic imperative that demands the focus of both technology and business leaders.  

In this article, I am drawing from firsthand experience working with CIOs, CDOs, CTOs and transformation leaders across industries. I aim to outline pragmatic strategies to elevate data quality into an enterprise-wide capability. These strategies, such as investing in AI-powered cleansing tools and adopting federated governance models, not only address the current data quality challenges but also pave the way for improved decision-making, operational efficiency and customer satisfaction. Key recommendations include investing in AI-powered cleansing tools and adopting federated governance models that empower domains while ensuring enterprise alignment.