Predictive Analytics

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1 Predictive Analytics Improving Performance by Making the Future More Visible Benchmark Research Research Report Executive Summary Sponsored by Aligning Business and IT To Improve Performance Ventana Research 2603 Camino Ramon, Suite 200 San Ramon, CA (925) Copyright Ventana Research 2012 Do Not Redistribute Without Permission

2 San Ramon, California February 2012 Ventana Research performed this research to determine attitudes toward and utilization of predictive analytics. This document is based on our research and analysis of information provided by organizations that we deemed qualified to participate in this benchmark research. This research was designed to investigate practices and needs of individuals and organizations and the potential benefits from improving their existing processes, information and systems. This research is not intended for use outside of this context and does not imply that organizations are guaranteed success by relying on these results. Moreover, gaining the most benefit from implementing or improving the use of predictive analytics requires an assessment of your organization s unique needs to identify gaps and priorities for improvement. We certify that Ventana Research wrote and edited this report independently, that the analysis contained herein is a faithful representation of our evaluation based on our experience with and knowledge of predictive analytics, and that the analysis and conclusions are entirely our own. Ventana Research 2012 Page 2

3 Executive Summary In recent years business intelligence (BI) capabilities, once exclusively the province of technically astute analysts, have been embraced by a wider range of users in various parts of organizations, particularly the lines of business. Software vendors have made their tools easier to use, and analysis of data to assist decision-making is increasingly a mainstream business activity. As the use of BI and analytics have spread, innovators looking for the next competitive edge have begun to work with predictive analytics, which shifts the focus from backward-looking historical analysis to forecasting the future and providing a range of potential courses of action. As a technology predictive analytics has existed for years, but tools have been expensive and required sophisticated skills that included mathematical understanding and the ability to create, deploy and update analytic models. Enterprises in a few vertical industries and specific lines of business have been willing to pay large sums to acquire those capabilities, but they constitute only a fraction of the organizations that could benefit from them. For the untapped majority, technology has now advanced to a stage at which the potential of predictive analytics can be realized. To date, that has not occurred. In our recent benchmark research on business analytics, more than 2,600 organizations ranked predictive analytics only 10th among technologies they use to generate analytics; only one in eight of those companies use them. Ventana Research undertook this current benchmark research to learn about organizations that do use predictive analytics and to acquire real-world information about levels of maturity, trends and best practices. It explores how they do this now, how people at various levels feel about the current processes and tools, plans they have to change or improve them, and benefits they hope to gain by doing so. Two-thirds of research participants are satisfied (45%) or very satisfied (21%) with their use of predictive analytics. Of the organizations that participated in this research, two-thirds use predictive analytics. Larger ones as measured by their number of employees use it more often: Nearly three-quarters (73%) of very large companies and two-thirds of large ones do, compared with about half of midsize (48%) and small (53%) companies. More people in business roles (65%) said their organization uses it than did those in IT (49%), who may not be fully aware of deployments elsewhere in the organization. Among those at companies that have deployed predictive analytics, the research finds largely positive attitudes about the concept, use and results. More than half (58%) indicated that it is very important to them. Two-thirds of participants are satisfied (45%) or very satisfied (21%) with their use of predictive analytics. And three-quarters (74%) reported that they largely trust the results, while an additional 10 percent trust them totally. One reason for the importance assigned to predictive analytics is that most organizations apply it to core functions that produce revenue. Marketing (65%) and sales (59%) are the most common of those. Here also larger companies use these analytics for sales more often, probably because they have large enough volumes of Ventana Research 2012 Page 3

4 data to make the analysis revealing. The top five sources of data tapped for predictive analytics also relate directly to revenue: customer (69%), marketing (67%), product (55%), sales (54%) and financial (51%). In expanding their use, the largest share of companies (40%) are evaluating or plan to use social media data, which has implications for marketing and customer service. Appropriately, nearly three-quarters (72%) of organizations currently use the results of predictive analytics for forecasting, almost as many (70%) apply them to marketing analyses, and customer service (45%) is the use that ranks third. The largest companies examine results in all these areas more often than others; however, small companies have the highest percentages in using predictive analytics for product recommendations (61%) and social networking analysis (57%). Those efforts can contribute to a competitive edge, which is the business benefit the most organizations (68%) have realized to date. As well, more than half have used it to create new revenue opportunities (55%) or increase profitability (52%). Although participants are using predictive analytics for important purposes and are generally positive about the experience, they do not minimize its complexities. While now usable by more types of people, this technology still requires special skills to design and deploy, and in half of organizations the users of it don t have them. For example, 58 percent don t understand the mathematics required. This situation is more common in larger organizations; having fewer resources, small (61%) and midsize (56%) companies by number of Most organizations apply predictive analytics to core functions that produce revenue. Marketing (65%) and sales (59%) are the most common. employees more often have those who use predictive analytics design and deploy it, which suggests that their users may be limited to the technically astute. One-third employ specialized data scientists or statistical or data mining resources for design and deployment, and about as many use business intelligence and data warehousing teams. Thus, here more often than for most business software deployments these days, skilled personnel are necessary. A related issue is training and support for users of predictive analytics, which this research finds is often lacking. Fewer than half of organizations provide adequate training in any of three key areas: concepts and techniques, their application to business problems, and use of products. Even fewer (31%) provide adequate consulting resources or adequate help-desk support (24%). In general, lack of resources is the most common barrier to implementing changes in predictive analytics, cited by three in five (59%) overall and more in small and midsize companies; two-thirds of management (that is, vice presidents) and executives said this is a barrier. Lack of awareness of the value of these analytics (45%) ranked second. Management and executives generally have a rosier view of things than users, who are much more often not satisfied with their predictive analytics; for the upper levels the useful results loom larger in their vision than does the effort it takes to produce them. Models are central to predictive analytics, but over time they get stale and produce inaccurate results. Thus to ensure effectiveness it is important to update predictive Ventana Research 2012 Page 4

5 models frequently, but because this is not a task that many users can undertake, it constitutes another demand on resources. The research shows that this activity does not get the attention it deserves: Almost two-thirds (63%) of organizations update their models only monthly or even less often. In this regard, the organizations that are most satisfied with their analytics come from the 24 percent that update models daily or more often. Here is another instance in which those higher on the organizational chart are more optimistic about how things work: One-quarter each of management and executives told us that updates are happening constantly, but only 8 percent of users reported that models are updated that often. An area in which predictive analytics can deliver value is in scoring new records as quickly as possible for example, to assist customer service representatives or make product recommendations based on items in online shopping carts. In some cases, such as fraud detection or online ad selection, real-time scoring is essential. But only 30 percent perform real-time scoring on a regular basis, and as many never score records in real time. Once more we find a relation between an effective process and satisfaction with predictive analytics: 88 percent of those that use real-time scoring regularly said that they are satisfied or very satisfied, as did 73 percent that use it occasionally. When real-time scoring is used infrequently or not at all, satisfaction rates drop below 50 percent. Perhaps the biggest challenge associated with predictive analytics (cited by 55%) is to integrate them with the organization s information architecture. Participants also want it integrated with other tools, especially with business intelligence (58%) and within business Lack of resources is the most common barrier to implementing changes in predictive analytics, cited by 59 percent overall and more in small and midsize companies. applications (56%). A related issue is accessing source data from other systems, which 35 percent experience difficulty in doing. The disparity between those using predictive analytics effectively and those struggling to do so is reflected in our Maturity Index analysis. More than usual in our benchmark research, the largest portions of participants are found at the lowest of four maturity levels (Tactical, 35%) and at the highest (Innovative, 28%). Those at the two middle levels together account for only 37 percent. In the four categories in which we assess maturity, organizations generally are least mature in People: 44 percent rank at the Tactical level and only 5 percent at the Innovative level. The lack of resources, training and awareness, noted above, have large impacts here, and so does the absence of technical skills among many users. Maturing is critical because of the ways in which organizations choose to fund, acquire and deploy predictive analytics. Two-thirds fund these projects from general business budgets or a line of business s IT budget, so these parts of the enterprise will expect to benefit from it. Participants named usability being able to meet business needs as the top criterion for selecting predictive analytics software; 70 percent said that is very important. A majority (57%) prefer on-premises deployment of it, which requires in-house support, although more small companies will consider on-demand (26%) or hosted (17%) approaches. A plurality (44%) prefer a stand-alone tool, although 37 percent want predictive analytics embedded Ventana Research 2012 Page 5

6 within other technologies, most often business intelligence (20%). The challenge of integration is germane here. Despite the various issues impeding maturity in predictive analytics, organizations have high expectations for their future use of the technology: 86 percent asserted that predictive analytics will have a major positive impact on their organization. Moreover, nearly one-third (32%) indicated that impact could be transformative in enabling them to do things they couldn t do before. The research also shows that organizations are planning to invest more in predictive analytics in all areas. We believe it bodes well for increasing awareness and resources that executives and management are strongly positive (more than users) in a variety of benefits derived from these analytics, such as increasing profitability and creating new revenue opportunities. We conclude that predictive analytics is a promising technology that businesses will embrace increasingly over the coming years. Most face challenges in taking full advantage of it, but these can be overcome. There is sufficient advantage in understanding and improving processes, performance and markets to make the effort worthwhile. This Executive Summary is drawn from the full Ventana Research Benchmark Research report. The full report is available for purchase, payable by check or credit card. For more information about the full Benchmark Research report or assessment of your organization using the Maturity Index, please contact us at Ventana Research 2012 Page 6

7 About Ventana Research Ventana Research is the most authoritative and respected benchmark business technology research and advisory services firm. We provide insight and expert guidance on mainstream and disruptive technologies through a unique set of research-based offerings including benchmark research and technology evaluation assessments, education workshops and our research and advisory services, Ventana OnDemand. Our unparalleled understanding of the role of technology in optimizing business processes and performance and our best practices guidance are rooted in our rigorous research-based benchmarking of people, processes, information and technology across business and IT functions in every industry. This benchmark research plus our market coverage and in-depth knowledge of hundreds of technology providers means we can deliver education and expertise to our clients to increase the value they derive from technology investments while reducing time, cost and risk. Ventana Research provides the most comprehensive analyst and research coverage in the industry; business and IT professionals worldwide are members of our community and benefit from Ventana Research s insights, as do highly regarded media and association partners around the globe. Our views and analyses are distributed daily through blogs and social media channels including Twitter, Facebook, LinkedIn and Business Week s Business Exchange. To learn how Ventana Research advances the maturity of organizations use of information and technology through benchmark research, education and advisory services, visit This material is copyrighted. Reproduction or distribution of this research in any form without prior written permission is forbidden. Ventana Research 2012 Page 7

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