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

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Capital One’s new commissioned research by Forrester Consulting reveals the largest challenges, considerations and alternatives going through corporations when leveraging machine studying (ML) to enhance enterprise efficiency throughout the enterprise.

At a time when organizations are more and more investing in and prioritizing ML deployment, Capital One’s research finds {that a} majority of knowledge administration decision-makers face key operational roadblocks that may inhibit ML deployment, together with transparency, traceability and explainability of knowledge flows (73%) and breaking down information silos between inside departments (41%).

“Businesses see massive potential in applying machine learning, but encounter headwinds in their data,” mentioned Dave Kang, SVP and head of knowledge 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 studying information obstacles

Another key impediment for information managers — breaking down information silos. More than half (57%) consider inside silos between information scientists and practitioners inhibit ML deployments, and 38% say information silos throughout the group and exterior information companions pose a problem to ML maturity.


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Other prime challenges embody:

  • Working with massive, numerous, messy datasets (36%)
  • Difficulty translating educational fashions into deployable merchandise (39%)
  • Reducing synthetic intelligence (AI) danger (38%)

Image supply: Capital One.

Still, regardless of these considerations, the info additionally reveals that ML adoption continues to rise, with almost 70% of executives planning to extend use of ML throughout their organizations. Top ML deployment priorities over the subsequent three years embody automated anomaly detection (40%), receiving clear utility and infrastructure updates robotically (39%), and assembly new regulatory and privateness necessities for accountable and moral AI (39%).

Believing within the promise of ML

The survey reveals that information administration decision-makers consider within the promise of AI/ML to develop their companies, however with a view to proceed to evolve their ML purposes, decision-makers want to beat silos amongst each individuals and processes.

They should additionally discover higher methods to translate educational fashions into deployable merchandise to higher illustrate ROI to executives. By leveraging companions with firsthand expertise and remaining relentlessly centered on the enterprise promise of ML, decision-makers can show the important thing outcomes of operationalizing ML like effectivity, productiveness and improved buyer expertise (CX) to govt management. 


Capital One’s commissioned research by Forrester Consulting surveyed 150 information administration decision-makers in North America about their organizations’ ML objectives, challenges and plans to operationalize ML.

Read the full report by Capital One and Forrester.

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