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Capital One’s new Forrester study, Operationalizing Machine Learning Achieves Key Business Outcomes, reveals the top challenges, concerns, and opportunities data executives experience as they leverage machine learning to improve business performance. The report finds that data management decision makers are concerned about key operational challenges that could slow ML deployment and maturity, but the data also indicates that adoption continues to grow. 67% of Leaders plan to increase the use of ML across their business to the extent that: for the next three years.
Key findings include:
- Data decision makers face major challenges in leveraging ML to improve business outcomes
- When asked about the main ML challenges facing their organizations, 73% of decision makers cite transparency, traceability and explainability of data flows as key issues.
- Breaking down data silos is also a major obstacle for data managers. 57% believe internal silos between data scientists and practitioners are hindering ML deployment, and 38% say data silos across their organization and external data partners pose challenges to ML maturity. I believe there is.
- Other key challenges cited by respondents include working with large, diverse, and messy data sets (36%), the difficulty of converting academic models into deployable products (39%), and AI risks. mitigation (38%).
- Despite these challenges, data decision makers plan to increase ML deployments across the enterprise
- A majority (61%) of data decision makers plan to add new ML capabilities and applications to their organizations over the next three years.
- Top priorities for ML deployments over the next three years include automated anomaly detection (40%), automatic receipt of transparent application and infrastructure updates (39%), new regulations for responsible and ethical AI and privacy Includes meeting requirements (39%).
- More than half of respondents (53%) now prioritize leveraging ML to improve business efficiency.
- To meet these challenges, data leaders are investing in strategies and partnerships to accelerate ML maturity.
- Nearly two-thirds (57%) of respondents intend to leverage partnerships to fill gaps in their ML staff.
- Data leaders say they plan to invest more money and resources in ML technology (63%)
- Nearly 40% currently partner with a third party for ML model development, training, or data sourcing and plan to expand that partnership (37%)
Conclusion
Data management decision makers believe in the potential of AI/ML to grow their business. To maintain executive buy-in, they are moving the organization out of the experimental stage and towards operationalizing their ML strategy. To keep their ML applications evolving, decision makers must overcome the silos between people and processes. She must also find a better way to translate her academic model into a deployable product to better explain her ROI to executives. By leveraging partners with first-hand experience and a relentless focus on the business promise of ML, decision makers can realize the key outcomes of ML operationalization, including improved efficiency, productivity, and CX. You can prove it to management. With leadership buy-in, organizations can pivot to scaling and operationalizing ML applications.
Read the full survey here.
methodology
This Opportunity Snapshot was commissioned by Capital One. To create this profile, Forrester Consulting supplemented this research with custom survey questions asked of her 150 data management decision makers in North America. The custom survey started and completed in July 2022.
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