RESEARCH & RESOURCES

Executive Summary: Predictive Analytics for Business Advantage

To compete effectively in an era in which advantages are ephemeral, companies need to move beyond historical, rear-view understandings of business performance and customer behavior and become more proactive. The solution is predictive analytics.

To compete effectively in an era in which advantages are ephemeral, companies need to move beyond historical, rear-view understandings of business performance and customer behavior and become more proactive. Organizations today want to be predictive; they want to gain information and insight from data that enables them to detect patterns and trends, anticipate events, spot anomalies, forecast using what-if simulations, and learn of changes in customer behavior so that staff can take actions that lead to desired business outcomes. Success in being predictive and proactive can be a game changer for many business functions and operations, including marketing and sales, operations management, finance, and risk management.

Although it has been around for decades, predictive analytics is a technology whose time has finally come. A variety of market forces have joined to make this possible, including an increase in computing power, a better understanding of the value of the technology, the rise of certain economic forces, and the advent of big data. Companies are looking to use the technology to predict trends and understand behavior for better business performance. Forward-looking companies are using predictive analytics across a range of disparate data types to achieve greater value. Companies are looking to also deploy predictive analytics against their big data. Predictive analytics is also being operationalized more frequently as part of a business process. Predictive analytics complements business intelligence and data discovery, and can enable organizations to go beyond the analytic complexity limits of many online analytical processing (OLAP) implementations. It is evolving from a specialized activity once utilized only among elite firms and users to one that could become mainstream across industries and market sectors.

This TDWI Best Practices Report focuses on how organizations can and are using predictive analytics to derive business value. It provides in-depth survey analysis of current strategies and future trends for predictive analytics across both organizational and technical dimensions including organizational culture, infrastructure, data, and processes. It looks at the features and functionalities companies are using for predictive analytics and the infrastructure trends in this space. The report offers recommendations and best practices for successfully implementing predictive analytics in the organization.

TDWI Research finds a shift occurring in the predictive analytics user base. No longer is predictive analytics the realm of statisticians and mathematicians. There is a definite trend toward business analysts and other business users making use of this technology. Marketing and sales are big current users of predictive analytics and market analysts are making use of the technology. Therefore, the report also looks at the skills necessary to perform predictive analytics and how the technology can be utilized and operationalized across the organization. It explores cultural and business issues involved with making predictive analytics possible.

A unique feature of this report is its examination of the characteristics of companies that have actually measured either top-line or bottom-line impact with predictive analytics. In other words, it explores how those companies compare against those that haven’t measured value.

Actuate, Alteryx, Pentaho, SAP, and Tableau Software sponsored the research for this report. 

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 co-author 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 (fhalper@tdwi.org), on Twitter (twitter.com/fhalper), and on LinkedIn (linkedin.com/in/fbhalper).


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