
Two surveys highlight the need for consistent, high-quality data in building reliable AI systems.
‘Garbage in, garbage out,’ is the oft-cited refrain with respect to data as the foundation for AI systems, generative and predictive alike. According to Hansa Iyengar, Senior Principal Analyst Enterprise IT with Omdia, “AI thrives on quality data, and ensuring that data is clean, accessible, and ready to power intelligent systems is key to unlocking its full potential.”
A pair of reports released on Tuesday, October 15, 2024, highlighted some of the challenges associated with providing AI with good data. For example, Salesforce’s CIO survey found that security/privacy threats and the lack of trusted data were chief among CIO concerns. Theta Lake’s survey of financial services firms’ use of unified communications and collaboration (UCC) solutions found that siloed data sources is a key issue – 75% of its respondents reported challenges in identifying, locating or retrieving information for reviews/audits.
“Organizations often have data scattered across multiple systems,” Iyengar said. “To prepare for AI, they must consolidate their data into a unified platform—whether it’s a data lake, warehouse, or cloud solution—ensuring easy access for AI models.”