The AI data problem nobody talks about: Why more information isn't making better decisions

In today's information age, businesses have been relentlessly gathering data from various sources like customer data, sales data, inventory data, and more. The belief that more information leads to better decisions has guided this data-gathering process. However, many companies are now facing a hurdle where more data isn't translating into improved outcomes.

The issue lies not in the availability of data, but in its organization and interoperability. AI models require structured data to function accurately, yet businesses have been providing them with unstructured, incomplete, and non-standardized data. This has resulted in AI struggling to understand and synthesize the data, despite the exponential increase in its quantity.

The problem is further exacerbated by the fact that different systems used by businesses store data in varied formats and operate on different logics. While some systems offer import and export options to translate data, there's a lack of a universal logic to tie everything together in a way that AI can easily understand. This fragmentation of data slows down decision-making, and AI systems that can effectively handle this issue are becoming increasingly valuable.

Industries like real estate, where numerous systems need to communicate and cooperate to appraise and list properties, are witnessing the impact of this data issue. To address this, AI models need to prioritize context and connection, while companies must also pay more attention to how they store and format their data. The technology is still in its infancy, and the focus on quantity rather than quality needs to shift.

As more success stories emerge, highlighting the benefits of automating manual processes, AI's potential in driving decision-supporting insight will become more apparent.