What is Data Debt? Organizations are awash with data. And data has been growing at organizations for decades. Data is accumulating across many different systems, processes, and as organizations evolve, merge, and change. What comes out of this is the idea of data debt, which is the accumulation of data-related problems over time as a result of the accumulation of data systems. Additionally from data stored for a wide range of purposes, data processes, and other aspects. It results in quality, flexibility, governance, and security issues. In this episode of the AI Today podcast we discuss what debt from data is, why it occurs, and the problems with it. What is the difference between debt from data and technical debt? When people talk about debt from data, it's often coupled with he idea of “technical debt”, first articulated by Ward Cunningham in 1992. Technical debt happens when organizations make decisions about applications that defer future costs in favor of current benefits. There is a future cost – but the time-to-market benefit may make the trade-off of short-term gain vs long-term cost acceptable. When it comes to debt from data, it's the paradox that no matter how low the cost of storage declines, the costs of information capture, access and management keep on growing. We discuss this on the podcast in greater detail. Also, we provide come of the problems that debt from data causes. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification Free Intro to Trustworthy AI course Subscribe to Cognilytica newsletter on LinkedIn AI Glossary AI Today Podcast: The Critical AI and Data Skills Needed for AI Project Managers Top 10 Ethical Considerations for AI Projects