New Forrester study: Democratization of machine learning
Business leaders are ready, but usability is a bottleneck to machine learning democratization
The biggest bottleneck to machine learning (ML) democratization is usability with LOB decision makers indicating that current ML capabilities are too technical.
Becoming an advanced insights-driven business — ‘one that uses data, analytics and software in closed, continuously optimized loops to differentiate and compete’ — provides significant top- and bottom-line benefits. Democratizing machine learning (ML) by making readily available ML-powered tools accessible to roles across the business, including line-of-business (LOB) and operations, is critical to speeding up and scaling the contributions of data science to business success.
In a study commissioned by Capital One, Forrester Consulting surveyed 100 data science and 81 LOB leaders at North American companies about democratizing ML and the opportunities it presents to its firms. According to the study, LOB roles are ready and excited to use ML but may not completely understand what still needs to happen to support democratization.
Additional Forrester study key highlights:
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86% of respondents intend to measure the success of their business initiatives by their capacity to improve their firm’s ability to make insights-driven decisions.
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The top challenges to democratization are deploying ML models in a business context (54%), optimizing models in a timely manner (54%) and breaking down organizational data silos (51%).
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Cultural components are keys to ML democratization success including collaboration, communication and training with 64% of respondents agreeing that lack of training is slowing adoption of ML workflows.
According to the Forrester study, focusing on usability, adopting low-code/no-code interfaces wherever possible and applying governance policies that do not interfere with the actual data that users want to see can propel initiatives forward.
