New Forrester report on operationalizing machine learning
ML is beginning to drive business impact, with automated anomaly detection as the top priority in the next one year to three years
Machine learning (ML) applications have the potential to supercharge data science and improve analytics, enabling organizations to make data-driven decisions quickly. Successfully leveraged ML applications can boost business goals, improve customer experience (CX), and in turn grow revenue.
In a study commissioned by Capital One, Forrester Consulting surveyed 150 data management decision-makers in North America about their organizations’ ML goals, challenges, and plans to operationalize ML. Respondents revealed that ML is beginning to drive business impact, with automated anomaly detection as the top priority in the next one year to three years.
Some additional key highlights:
- 53% of respondents plan to improve business efficiency by leveraging ML.
- 73% of respondents find transparency, traceability, and explainability of data flows challenging.
- 67% of respondents intend to leverage partnerships to fill ML staff gaps.
According to the Forrester study, the next few years are crucial to ML operationalization. Organizations must move past experimentation with ML to fully realized automation and deployment of applications with tangible results that can drive success across the business.
We invite you to read the full study Operationalizing Machine Learning Achieves Key Business Outcomes.
