Report: 73% of ML decision-makers are worried headwinds may hinder further ML investments

by | Oct 26, 2022 | Technology

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Capital One’s new commissioned study by Forrester Consulting reveals the biggest challenges, concerns and opportunities facing companies when leveraging machine learning (ML) to improve business performance across the enterprise.

At a time when organizations are increasingly investing in and prioritizing ML deployment, Capital One’s study finds that a majority of data management decision-makers face key operational roadblocks that may inhibit ML deployment, including transparency, traceability and explainability of data flows (73%) and breaking down data silos between internal departments (41%).

“Businesses see massive potential in applying machine learning, but encounter headwinds in their data,” said Dave Kang, SVP and head of data insights at Capital One. “This can hinder businesses from seeing actionable insights, and perversely shy away from adopting and operationalizing ML solutions in the first place.”

Machine learning data obstacles

Another key obstacle for data managers — breaking down data silos. More than half (57%) believe internal silos between data scientists and practitioners inhibit ML deployments, and 38% say data silos across the organization and external data partners pose a challenge to ML maturity.

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Other top challenges include:

Working with large, diverse, messy datasets (36%)Difficulty translating academic models into deployable products (39%)Reducing artificial intelligence (AI) risk (38%)Image source: Capital One.Still, despite these concerns, the data also reveals that ML adoption continues to rise, with nearly 70% of executives planning to increase use of ML across their organizations. Top ML deployment priorities over the next three years include automated anomaly detection (40%), receiving transparent application and infrastructure updates automatically (39%), and meeting new regulatory and privacy requirements for responsible and ethical AI (39%).

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