73% of ML decision-makers are concerned that headwinds could prevent them from making more ML investments

Forrester Consulting was commissioned by Capital One to conduct a research on the use of machine learning (ML) to enhance business performance throughout the organisation.

The 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%), according to research conducted by Capital One.

Dave Kang, senior vice president and head of data analytics at Capital One, stated, “Businesses see huge promise in deploying machine learning, but find headwinds in their data.” This can prevent firms from gaining useful insights and, paradoxically, discourage them from implementing ML solutions.

Impossible Situations Caused by Insufficient Data for Machine Learning

Data managers also face the challenge of overcoming data silos. Almost two-thirds (63%) feel that data silos within the company and among external data partners are a barrier to ML maturity, while 57% say that barriers between data scientists and practitioners stifle ML deployments. Some other major difficulties are:

  • Dealing with massive, heterogeneous, and chaotic data collections (36%).
  • Problems in bringing research-based models to market (39%)
  • mitigation of potential AI failure (38%).

Nonetheless, the study also shows that ML use is on the rise, with over 70% of CEOs aiming to enhance usage of ML throughout their firms despite these worries.

Automated anomaly detection (at 40%), automatic upgrades for transparent applications and infrastructure (39%), and compliance with new privacy and security regulations for ethical AI (39%).

Aware of ML’s potential

According to the survey results, those in charge of managing data are optimistic about the potential of artificial intelligence and machine learning to expand their companies, but they recognise the importance of breaking down barriers between departments and teams if they want to keep improving their ML applications.

To better demonstrate ROI to executives, they must also improve the process by which academic models are translated into deployable solutions. Decision-makers can show the key results of operationalizing ML to senior leadership, such as efficiency, productivity, and better customer experience (CX), by enlisting partners with direct knowledge and being ruthlessly focused on the commercial promise of ML.

Methodology

Forrester Consulting was hired by Capital One to conduct a survey of 150 North American data management decision-makers to learn more about their companies’ machine learning (ML) initiatives, hurdles, and plans for operationalization.

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