New TDWI Research Report Explores Artificial Intelligence and Advanced Analytics
Report reveals emerging trends and early-adopter best practices that contribute to an analytics success cycle.
SEATTLE, WA – October 31, 2017 – TDWI Research has released its newest Best Practices Report, Advanced Analytics: Moving Toward AI, Machine Learning, and Natural Language Processing—This original, survey-based report looks at these technologies and discusses emerging best practices, enabling technologies, and real-world use cases.
Specific attention is paid in the report to the group who are currently using one or more of these technologies—referred to as the “early adopters”—from whom a number of lessons can be learned. One important conclusion is that these technologies are creating value. Early adopters are more likely to be satisfied with their analytics program than those just now investigating the technology. Twenty-one percent of those investigating AI, ML, and NLP are satisfied with their programs versus 52% of the active group—a significant difference. Additionally, 82% of early adopters have measured a positive impact, feel confident there is an impact, or believe advanced analytics is helping their organization.
Fern Halper, vice president and senior director of TDWI Research for advanced analytics, points out that this is part of an analytics success cycle. “As an organization gets comfortable with analytics, it gets better results; those better results drive further efforts and the company starts to build on its success, perhaps bringing in new analytics techniques. As management sees this success, the organization continues to build on its achievement. It is a virtuous circle.”
The report also delves into the tools and technologies that organizations are using to undertake their advanced analytics initiatives, including open source software packages and multiplatform data architectures. R and Python ranked high on the list of tools for use in ML, NLP and AI projects. As one respondent stated, “It is easier to find people who know open source rather than commercial tools.”
This comprehensive report reveals:
- The two most common use cases for early adoption of ML, AI, and/or NLP are operations (58%) and marketing (53%)
- Though organizations investigating these technologies are looking to business analysts to build models, early adopters typically employ data scientists to build them by a 2:1 margin
- A vast majority of early adopters (90%) are using open source tooling together with commercial packages for their efforts. For those with no plans to use these technologies, lack of support, lack of budget, and lack of skills were the top three reasons named
Halper concludes with a variety of suggestions and best practices that can guide user organizations through the successful implementation of advanced analytics programs, including investigating tools and platforms, and looking at the cultural needs for successful ML, AI, and NLP programs.
This research was sponsored by SAS, ThoughtSpot, and Vertica. · Download the report · Watch the webinar on-demand
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 (email@example.com), on Twitter (twitter.com/fhalper), and on LinkedIn (linkedin.com/in/fbhalper).
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