New TDWI Research Report Explores How Organizations Using Predictive Analytics Are Making It Work
Report reveals best practices and recommendations for successfully implementing predictive analytics and machine learning in organizations.
SEATTLE, WA, June 14, 2018—TDWI Research has released its newest Best Practices Report, Practical Predictive Analytics. This original, survey-based report looks at how organizations using the technology are making it work and how those exploring the technology are planning to implement it. It looks at the organizational, technology, process, and deployment challenges enterprises face and offers best practices and recommendations for success.
According to the TDWI survey, predictive analytics is on the cusp of widespread adoption, but it remains elusive. Previous research, together with these survey results, indicates that had users stuck to their plans, three-fourths of them would have already adopted predictive analytics. In reality, slightly more than a third have done so.
Report author Fern Halper, vice president and senior director of TDWI Research for advanced analytics, points to three practical considerations for making predictive analytics efforts successful: skills for predictive modeling, planning for model deployment, and infrastructure. Lack of skills ranked as the biggest barrier to adoption. She explains that to address this challenge many enterprises are looking to increase the skills of their employees as well as use some of the new breed of automated, easy-to-use predictive analytics tools that contain embedded intelligence. For those enterprises that use predictive analytics, model development and deployment remain challenging.
The report also delves into the use cases for predictive analytics and the new technologies (including automation and open source) that assist in predictive analytics and machine learning. Best practices, including understanding business problems and maintaining high data quality, are also explored.
This comprehensive report reveals:
- Common uses of predictive analytics are direct marketing, retention analysis, and cross-sell/up-sell
- Nearly a third of respondents believe automated tools for predictive analytics will allow more people to build models (31%) and will cut the time required to build a model (24%)
- Nearly half of respondents (45%) use APIs to export models into production; a third (34%) rewrite the model
Halper describes the impact of new trends and best practices in data and analytics infrastructure, including data lakes and the cloud. She examines the importance of satisfaction with and measuring value of predictive analytics projects, and concludes with 10 recommendations for successful predictive model implementations. This research was sponsored by Hortonworks, SAP, and Tellius.
About the Author
Fern Halper, Ph.D., is vice president and senior director of TDWI Research for advanced analytics. She is well known in the analytics community, having been published hundreds of times on data mining and information technology over the past 20 years. Halper is also coauthor of several Dummies books on cloud computing and big data. She focuses on advanced analytics, including predictive analytics, text and social media analysis, machine learning, AI, cognitive computing and big data analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead data analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her by email (firstname.lastname@example.org), on Twitter (twitter.com/fhalper), and on LinkedIn (linkedin.com/in/fbhalper).
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