He recommended that IT leaders

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Discussing the findings, David Kang, senior vice president and head of Capital One Data Insights, said, “Governance and automation are important aspects of a well-managed data ecosystem.” In his experience, once machine models are in production, automation can help companies ensure continuity of model prediction services. “Automating the monitoring and training of an ML model can ensure that it is healthy when it goes into production and help teams make more accurate decisions about when to retrain a model,” he said. Such automation, Kang said, gives engineers confidence in consistent reproducibility and support.

provide human oversight of automated models, such as through a centralized governance body that can manage processes, controls, monitoring, and technology infrastructure to help them scale ML responsibly while providing greater transparency into developments.

A robust governance framework is also needed to australia mobile database the processes and data platforms required by companies that want to embrace AI and be data-driven. Forrester warns that some data and IT teams may be too controlling, which can limit the capabilities of platforms and processes. The analyst firm recommends a governance framework in which trust is implicit and business decision makers are empowered to do what they need to do without having to worry about permissions or follow procedures.

A recent Forrester report, “Insights-driven: A foundational principle of customer obsession,” found that cultural issues are often more prevalent than technical ones. The report’s authors noted that data teams sometimes deliver dashboards and reports that no one uses. Forrester attributes this to the low literacy of business users and executives, who use data selectively when it validates a decision. In such cases, analysts recommend using comprehensive training and communication to close data literacy gaps.