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Organizations Position Data Science as a Key Driver of Strategic Business Initia…
Data Science Purchases Entail a Hefty Collection of Considerations
ML Model Building and Management Evolve as Challenges Linger
Data Science Initiatives Reveal an Intricate, Collaborative Web of Stakeholders
Personnel Are Highly Motivated to Improve Skills—and Their Organizations Are…
Conclusion
Research Methodology
Respondent Demographics
Research Report: Decoding the Data Universe: The State of Data Science and Machine Learning
Jan 12, 2024
by
Mike Leone, Christian Perry
Organizations continue to hone their strategic focus on data-driven decision-making, in turn driving widespread deployment of data science and ML initiatives. However, numerous challenges can prevent the successful integration of data into models and overall organizational strategies and mindsets. The inherent complexity of data science and ML initiatives fuels a rising need for improved agility, efficiency, and performance, along with well-planned risk reduction and compliance measures. Rapidly evolving strategies increasingly reflect a highly diverse data science ecosystem.
To assess the state of data science and ML in today’s organizations, Enterprise Strategy Group surveyed 366 professionals in North America (US and Canada) involved with data science and ML technologies and processes, including potential responsibility for strategizing, evaluating, purchasing, building, and managing these technologies.
Page Count: 25
Table of Contents
Executive Summary
Report Conclusions
Introduction
Research Objectives
Research Findings
Organizations Position Data Science as a Key Driver of Strategic Business Initiatives
Data Science Purchases Entail a Hefty Collection of Considerations
ML Model Building and Management Evolve as Challenges Linger
Data Science Initiatives Reveal an Intricate, Collaborative Web of Stakeholders
Personnel Are Highly Motivated to Improve Skills—and Their Organizations Are Obliging