"Data quality is essential for AI." It sure is. But don't expect that observation to suddenly get everyone fixing it.
Getting data in order isn't a revolutionary idea that suddenly emerged alongside chatbots and machine learning. It's been the foundation of good decision-making for decades. Yet many companies still treat data management like flossing: they know it's important, commit to periodic bursts of discipline, but ultimately fall back into neglect.
Now, with AI promising to transform everything from customer service to supply chains, there's a renewed scramble to fix shaky data foundations. It's as if the business world collectively stepped into a house of mirrors, where every reflection reveals another unflattering angle of their neglected data practices - each view more unsettling than the last.
But merely rushing to quickly get the data in shape is rarely sustainable. The roots of poor data management run deeper than "nobody realized it was important." They're embedded in how we structure our organizations, allocate resources, and measure success.
Good data management is hard. It isn't just about a one-time cleaning effort. It's ensuring that data flows smoothly across the organization while maintaining consistency and compliance. This isn't achieved with quick fixes - it's more akin to healthy living, requiring regular attention and care.
Compounding this is the challenge of misaligned incentives. In most processes, data producers and data consumers aren't the same. With this fragmented responsibility and conflicting interests, data maintenance becomes everyone's problem but nobody's priority - until something breaks.
And when data goes bad, the resulting AI failures can become spectacular - amplifying and exposing both the cracks in your data foundation and the underlying weaknesses in your organization.
Breaking this cycle requires treating data management not as a back-office chore, but as a strategic imperative for survival. This means establishing clearly defined ownership of data quality all the way up to the C-suite, investing in continuous improvement processes, and tying data quality directly to business outcomes.
Tomorrow's intelligence depends on today's discipline. Companies that treat data as a strategic asset won't just be ready for today's AI - they'll be ready for whatever comes next. After all, relentless execution of the basics has always been a solid foundation for innovation at the edge.