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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%)
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%).
Believing in the promise of ML
The survey reveals that data management decision-makers believe in the promise of AI/ML to grow their businesses, but in order to continue to evolve their ML applications, decision-makers need to overcome silos among both people and processes.
They must also find better ways to translate academic models into deployable products to better illustrate ROI to executives. By leveraging partners with firsthand experience and remaining relentlessly focused on the business promise of ML, decision-makers can prove the key outcomes of operationalizing ML like efficiency, productivity and improved customer experience (CX) to executive leadership.
Capital One’s commissioned study by Forrester Consulting surveyed 150 data management decision-makers in North America about their organizations’ ML goals, challenges and plans to operationalize ML.
Read the full report by Capital One and Forrester.