Report: 73% of ML decision-makers are anxious headwinds could hinder additional ML investments

Capital One’s new commissioned examine by Forrester Consulting reveals the most important challenges, issues and alternatives dealing with 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 examine finds {that a} majority of knowledge administration decision-makers face key operational roadblocks which will inhibit ML deployment, together with transparency, traceability and explainability of knowledge flows (73%) and breaking down knowledge silos between inner departments (41%).

“Companies see large potential in making use of machine studying, however encounter headwinds of their knowledge,” stated Dave Kang, SVP and head of knowledge insights at Capital One. “This will hinder companies from seeing actionable insights, and perversely draw back from adopting and operationalizing ML options within the first place.”

Machine studying knowledge obstacles

One other key impediment for knowledge managers — breaking down knowledge silos. Greater than half (57%) imagine inner silos between knowledge scientists and practitioners inhibit ML deployments, and 38% say knowledge silos throughout the group and exterior knowledge companions pose a problem to ML maturity.

Different prime challenges embrace:

• Working with giant, numerous, messy datasets (36%)
• Problem translating educational fashions into deployable merchandise (39%)
• Decreasing synthetic intelligence (AI) threat (38%)

Nonetheless, regardless of these issues, the information additionally reveals that ML adoption continues to rise, with almost 70% of executives planning to extend use of ML throughout their organizations. Prime ML deployment priorities over the following three years embrace automated anomaly detection (40%), receiving clear software and infrastructure updates mechanically (39%), and assembly new regulatory and privateness necessities for accountable and moral AI (39%).

Believing within the promise of ML

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

They need to additionally discover higher methods to translate educational fashions into deployable merchandise to raised illustrate ROI to executives. By leveraging companions with firsthand expertise and remaining relentlessly targeted 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. 

Methodology

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

Learn the full report by Capital One and Forrester.