Data science teams are stymied by disorganization at their companies, impacting efforts to deploy timely AI and analytics projects. In a recent survey of “data executives” at U.S.-based companies, 44% said that they’ve not hired enough, were too siloed-off to be effective, and haven’t been given clear roles. Respondents said that they were most concerned about the impact of a revenue loss or hit to brand reputation stemming from failing AI systems and a trend toward splashy investments with short-term payoffs.
These are ultimately organizational challenges. But Piero Molino, the cofounder of AI development platform Predibase, says that inadequate tooling often exacerbates them.
“The major challenges we see today in the industry are that machine learning projects tend to have elongated time-to-value and very low acc …
