Poor data quality hampering businesses and their AI plans | MarTech

"More than three-quarters of business leaders say they're under growing pressure to drive business value with data, according to a Salesforce report, but many leaders say their data is often outdated, incomplete or low quality. Salesforce's " State of Data and Analytics " report (registration required) found that while business leaders are eager to use AI for surfacing insights and augmenting employee efforts, technical leaders say new data and analytics strategies are needed. This isn't a new problem."

"63% of business leaders describe their organizations as data-driven, up 10 points from 2023. 63% of data and analytics leaders say their companies struggle to use data to drive business priorities, highlighting a gap between perceived and actual data maturity. 49% of business leaders say they can reliably generate timely insights. 49% of data and analytics leaders say their companies occasionally or frequently draw incorrect conclusions from data lacking business context."

"The report also found that while 67% of data and analytics leaders feel pressure to implement AI quickly, 42% lack full confidence in the accuracy and relevance of AI outputs. The risks of feeding poor data to AI are significant. The report found that 89% of data and analytics leaders with AI in production have experienced inaccurate or misleading outputs. Among those training or fine-tuning their own models, 55% report wasting significant resources due to bad data."

More than three-quarters of business leaders say pressure to drive value from data is growing, while many report data is outdated, incomplete, or low quality. 84% of data and analytics leaders say data strategies need a complete overhaul for AI ambitions to succeed. 63% of business leaders call their organizations data-driven, yet many data leaders say companies struggle to use data to drive priorities. 49% of business leaders can reliably generate timely insights, while 49% of data leaders say teams sometimes draw incorrect conclusions from context-lacking data. 67% feel pressure to implement AI quickly; 42% lack confidence in AI outputs, and inaccurate outputs plus wasted resources from bad data are common.

Read at MarTech