Data Management: The Non-Negotiable Foundation for AI Success
AI is EVERYWHERE, and because of that, organizations are racing to implement artificial intelligence solutions to gain the perceived benefits using it provides. However, as a recent industry article highlights, many companies are putting the cart before the horse – diving in before you checked the depth – whatever metaphor you want – and pursuing advanced AI initiatives without first establishing proper data management foundations.
The Warning Signs Are Clear
The article cites alarming statistics from two separate studies that confirm what we at Solix have been emphasizing to our customers for years. According to these studies, 44% of financial services firms admit they’re “warehousing data in too many places or warehousing too much data.” Over 40% of companies report that more than half of their AI projects either fail or underperform, with data integration cited as the number one roadblock to AI success. Three-quarters of enterprises are sourcing data from 500+ different places, while 80% of data engineering resources are consumed just maintaining existing ETL pipelines.
These findings validate our long-standing message: Without proper data management, AI initiatives are destined to struggle or fail entirely.
The Dual Imperatives: AI Success and Regulatory Compliance
As the article aptly puts it, “When it comes to finding proper motivation, there are carrots and there are sticks.” AI innovation represents the carrot – the tremendous upside potential. Regulatory compliance represents the stick – the serious consequences of failure. I was in a recent conversation with an industry expert and talking about AI. The upside potential is what causes organizations to have FOMO about AI: Fear of Missing Out. The downside and risks AI introduces causes organizations to have FOMU about AI: Fear of Messing Up.
At Solix, we’ve built our solutions with both imperatives in mind, recognizing that data management is no longer optional – it’s absolutely essential for both innovation and compliance.
The Unstructured Data Challenge: An Untapped Asset for AI
Perhaps the most significant yet overlooked aspect of data management is the challenge of unstructured data. Despite accounting for up to 80% of all enterprise data and growing at 55-65% per year, unstructured data remains largely inaccessible and unused in most organizations. This includes text in reports and PDFs, spreadsheets, images, audio files, and videos – essentially all data that lacks a predefined model or schema.
Research shows that 60% of business leaders report that half or more of their organization’s data is considered “dark” – ungoverned and unknown. Even more concerning, one-third of organizations estimate this figure to be 75% or more. This dark data represents not just missed opportunities but also significant risks in terms of compliance, security, and operational efficiency.
Without proper classification and governance, unstructured data ages into obsolescence, with most becoming nearly inactive after just eighteen months. The result is massive stores of Redundant, Obsolete, and Trivial (ROT) data that incur storage and management costs while providing little value. However, with AI in the forefront, this situation has dramatically changed – these forgotten data assets now represent a possible critical new dataset for better AI responses.
The Path Forward: Preparing Data for the AI Era
We couldn’t agree more with the article’s conclusion that “investment in data management is non-negotiable anymore.” At Solix, we’re committed to helping organizations build the robust data foundations necessary for AI success through proper classification, governance, and preparation.
The first critical step is making your data AI-ready through proper classification and governance. Through data unification strategies and AI semantic layering, we can simplify data access, improve consistency and accuracy, and enhance the performance and reliability of AI applications. These approaches create a unified, business-friendly view of data that improves accuracy, limits hallucinations, and reduces unnecessary inference processing.
The companies that will be successful with AI will be those that recognize this fundamental reality and take action now. The right data management infrastructure doesn’t just reduce regulatory risk – it creates the conditions for breakthrough innovation and competitive advantage by unlocking the full potential of all enterprise data, especially the vast stores of unstructured information that have remained dormant for too long.
Whether you’re just beginning your AI journey or looking to improve your success rate with existing initiatives, start by evaluating your data management fundamentals. As the evidence clearly shows, this isn’t a step you can afford to skip.
This blog post references insights from a recent industry article highlighting the critical connection between data management, AI success, and regulatory compliance.
