Predictive Analytics

Transcription

1 Predictive Analytics Improving Performance by Making the Future More Visible Benchmark Research White Paper 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 VENDOR SPONSORS MEDIA SPONSORS Ventana Research 2012 Page 2

3 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 3

4 Table of Contents Executive Summary... 6 About This Benchmark Research Methodology Qualification Demographics Company Size by Number of Employees Company Size by Annual Revenue Geographic Distribution Industry Job Title Role by Functional Area Key Insights Maturity varies widely among organizations using predictive analytics Predictive analytics is an important technology to organizations that use it Most organizations are satisfied with the analytics they use and trust their results Revenue-generating functions most often apply predictive analytics Forecasting and marketing are the most common uses for the findings of predictive analytics The most frequent benefits of predictive analytics are gaining competitive advantage and new revenue sources Organizations employ a variety of algorithms to support analytics Deploying predictive analytics requires special skills Data scientists and data warehousing teams usually handle predictive analytics implementations Lack of resources and lack of awareness are the biggest obstacles to further deployment Most purchasers acquire predictive analytics as a stand-alone tool Most also prefer to deploy predictive analytics on-premises Organizations want predictive analytics integrated with other tools and applications Integration and access are common challenges Organizations need more timely results from predictive analytics Most don t update their analytic models frequently enough Most organizations do not provide adequate support and training for predictive analytics projects Funding for predictive analytics projects usually comes from the lines of business Organizations expect further predictive analytics to have a positive impact Led from the top, organizations are planning to deploy more predictive analytics What To Do Next Apply predictive analytics to revenue-related data to serve business Evaluate the maturity of your organization in using predictive analytics Determine how satisfied people in various roles are with these analytics Ventana Research 2012 Page 4

5 Consider using predictive analytics for forecasting and marketing Identify the business benefits you desire from using predictive analytics Become familiar with the variety of algorithms that support analytics Realize that deploying predictive analytics requires special skills Decide who will handle your predictive analytics implementations In starting out, find resources for deployment and raise awareness about the value of predictive analytics Decide how you wish to acquire and deploy predictive analytics Identify other tools and applications with which predictive analytics should be integrated Prepare to deal with technical challenges Explore the potential of gaining real-time results from predictive analytics Plan to update analytic models frequently Be sure to provide adequate support and training for predictive analytics projects Make clear who will fund, recommend and approve predictive analytics projects Involve your leadership and ascertain the benefits they expect Assess whether predictive analytics can enable new activities How Ventana Research Can Help About Ventana Research List of Figures 1. Participants by Company Size (Number of Employees) Participants by Company Size (Annual Revenue) Participants by Region Participants by Type of Industry Participants by Job Category Participants by Functional Area Ventana Research 2012 Page 5

6 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 6

7 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 7

8 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 8

9 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. Ventana Research 2012 Page 9

10 About This Benchmark Research Methodology Ventana Research conducted this benchmark research over the Web from October through December We solicited survey participation via blasts, our website and via social media invitations. invitations were also sent by our media partners and by vendor sponsors. We presented this explanation of the topic to participants prior to their entry into the survey: The application of analytics has become a top priority for businesses, according to our benchmark research. In particular, it is now technologically feasible to harness the potential of predictive analytics. But companies have been slow to embrace this new analytic technology; only 13 percent have adopted it. This benchmark research is designed to identify the obstacles preventing organizations from embracing these advanced techniques and using them in their analytical processes. It will also identify the benefits and best practices in those organizations that have adopted them already. The following promotion incented participants to complete the survey: What's In It For You? All qualified participants will receive a report on our benchmark research findings. In addition, all qualified participants will be entered into a drawing to win a benchmark research report of your choice valued at US$995 or 732. If you re one of the first 50, we ll even throw in a Starbucks card loaded with $5. All individuals who cannot receive a gift for participation will instead have a donation made to a nonprofit organization. Qualification We designed the research to assess the use of and plans for predictive analytics across organizations and industries. Qualification to participate was presented to participants as follows: The survey for this benchmark research is designed for business analysts, data scientists, IT personnel and others involved with the purchasing of technology for this area. Solution providers, software vendors, consultants, media and systems integrators may participate in the survey, but they are not eligible for incentives and their input will be used only if they meet the qualifications. Incentives are provided conditional on provision of accurate contact information including company name and company address. Further qualification evaluation of respondents was conducted as part of the research methodology and quality assurance processes. It entailed screening out responses from companies that are too small, questionnaires that were not materially complete, or those where the submission is from an inappropriate submitter or appears to be spurious. Ventana Research 2012 Page 10

11 Demographics We designed the survey used for this research to be answered by executives and managers across a broad range of roles and titles working in organizations of various sizes. We deemed 198 of those who clicked through to this survey to be qualified to have their answers analyzed in this research. In this report, the term participants refers to that group, and the charts in this section characterize various aspects of their demographics and qualifications. Company Size by Number of Employees We require participants to indicate the size of their entire company. Our research repeatedly shows that size of organization, measured in this instance by employees, is a useful means of segmenting companies because it correlates with the complexity of processes, communications and organizational structure as well as the complexity of the IT infrastructure. In this research, participants are divided almost evenly among those who work in very large companies (having 10,000 or more employees), large companies (with 1,000 to 9,999 employees) or small companies (with fewer than 100 employees); about one-sixth work in midsize companies (with 100 to 999 employees). This distribution is consistent with prior benchmark research and our research objectives and provides a suitably large sample from each size category. Figure 1 Participants by Company Size (Number of Employees) Very Large 28% Small 28% Midsize 16% Source: Ventana Research Large 28% Ventana Research 2012 Page 11

12 Company Size by Annual Revenue When we measured size by annual revenue, the distribution of categories shifted downward in each of the two halves, larger (53%) and smaller (48%; percentages are rounded). By this measure, 5 percent fewer are very large companies (having revenue of more than US$10 billion), but 2 percent more are large companies (having revenue from US$500 million to US$10 billion); 5 percent fewer are midsize companies (having revenue from US$100 to US$500 million), and the biggest group now is small companies (with revenue of less than US$100 million), of which there are 9 percent more. This sort of redistribution is typical in our research projects when we measure by revenue instead of headcount. Figure 2 Participants by Company Size (Annual Revenue) Very Large 23% Small 37% Large 30% Midsize 11% Source: Ventana Research Ventana Research 2012 Page 12

13 Geographic Distribution In this research more than half of the participants were from companies located or headquartered in North America. Those based in Europe accounted for 16 percent, in Asia Pacific for 13 percent, in the Middle East for 6 percent and in Central and South America or Africa for 3 percent each. This result was in keeping with our expectations at the start of this investigation, since organizations participating in our research most often are headquartered in North America. However, many of these are global organizations operating worldwide. Middle East 6% Figure 3 Participants by Region Africa 3% Central and South America 3% Asia Pacific 13% Europe 16% North America 59% Source: Ventana Research Ventana Research 2012 Page 13

14 Industry The companies of the participants in this benchmark research comprise a broad range of industries, which we have categorized into four general categories as shown below. Companies that provide services accounted for more than half of the total. Those in manufacturing accounted for a bit more than one-fifth, and those from finance, insurance and real estate for about one-sixth. Government, education and nonprofits accounted for the balance. Figure 4 Participants by Type of Industry Government, Education, Nonprofit 9% Manufacturing 22% Services 53% Finance, Insurance, Real Estate 16% Source: Ventana Research Ventana Research 2012 Page 14

15 Job Title We asked participants to choose from among 17 titles the one that best describes theirs. We sorted these responses into four categories: executives, management, users and others. Slightly less than two-thirds identified themselves as having titles that we categorize as users, a grouping that includes senior manager or manager (26%), director (12%), analyst (15%) and staff (6%). The higher levels account for not quite one-fourth: executives (12%) and management (11%), by which we mean vice presidents. The Other category accounted for the balance. Figure 5 Participants by Job Category Other 12% Executive 12% Management 11% Source: Ventana Research User 65% This is how we aggregated the 17 title response options: Executive CEO or President COO or Head of Operations CIO or Head of Information Technology or MIS CFO or Head of Finance Chief Marketing Officer Other CxO Management SVP or EVP VP User Senior Manager Manager Ventana Research 2012 Page 15

16 Director Analyst (Business, Financial, etc.) Statistician Staff Other Consultant Professor or Teacher Student We concluded after analysis that this response set provided a meaningfully broad distribution of job titles. Role by Functional Area We asked participants to identify their functional area of responsibility as well. Given the especially technical nature of this research topic, we divided them into those in the lines of business, who comprised nearly two-thirds, and those in IT, who comprised almost one-fifth. We view these as suitably large samples of each category. The remaining participants made up the Other category, as listed above. Figure 6 Participants by Functional Area Other 13% IT 23% Business 64% Source: Ventana Research Ventana Research 2012 Page 16

17 Key Insights Our benchmark research yielded the following important general findings and key insights regarding organizations use of and plans for predictive analytics. (We discuss the maturity levels of this market in the Maturity Index portion of the full research report; the actual questions asked in our survey are in the Appendix to the research report.) Maturity varies widely among organizations using predictive analytics. Our Maturity Index analysis in this benchmark research found that in the use of predictive analytics more than one-third (35%) of organizations are at the Tactical level, the lowest of four levels by which we rank maturity. In contrast, however, the next-largest group (28%) of organizations is at the highest Innovative level. Somewhat more than one-third (37%) are at the two middle levels. These findings indicate a gap in the use of predictive analytics; at the low end is a group that likely includes newer users while at the top is a much more mature group that has benefited from first-hand knowledge and learned from others that have used predictive analytics longer. In the four categories in which we assess maturity, the Information and Technology segments closely track the overall maturity levels while the People category indicates less maturity, with 44 percent of companies at the Tactical level and only 5 percent at the Innovative level. The research shows that predictive analytics requires specialized expertise and that most organizations have not cultivated that expertise. In the Process category maturity indicates that organizations are more evenly split in their adoption of processes to support predictive analytics, ranging from 20 to 32 percent. Predictive analytics is an important technology to organizations that use it. Two-thirds of the organizations that participated in this research use predictive analytics. Larger organizations as determined by their number of employees use the technology more often than smaller ones; 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. Geographically, European organizations (77%) most often use predictive analytics. Among the organizations using predictive analytics, almost three in five (58%) indicated it is very important to them. Most organizations are satisfied with the analytics they use and trust their results. Two-thirds of participants deploying predictive analytics are satisfied (45%) or very satisfied (21%) with their use of it, but there is still room for improvement. Participants we categorize as users are more often not satisfied (21%) than are managers (12%) or executives (8%); we view this as suggesting that the results of predictive analytics (consumed at the higher levels) are valuable but that producing them may require more effort than users expect. Satisfaction is inversely related to company size by number of employees: 30 percent of small organizations are very satisfied but only 16 percent of very large organizations are. By industry sector Finance, Insurance and Real Estate (FIRE, 77%) is most often satisfied or very satisfied. Ventana Research 2012 Page 17

18 Despite design and deployment issues, organizations mostly trust the results they get from predictive analytics. Three-quarters (74%) of organizations reported that they largely trust the results, and an additional 10 percent trust them totally. The benefits of these analyses appear to justify their use even if users can t fully understand how they operate. Revenue-generating functions most often apply predictive analytics. The research shows that predictive analytics most often is applied to revenuegenerating functions including marketing (65%) and sales (59%). When we divide participants functions into business and IT, those on the business side more often than IT reported that they are currently using predictive analytics; we conclude that IT may not be aware of what the business units are doing in this area. Smaller (52%) and midsize (50%) companies as measured by annual revenue use it less frequently than average (57%) in the sales function, likely reflecting an insufficient amount of their sales data needed to produce accurate results using predictive analytics. Customer data (69%) and marketing data (67%) are the most common categories of data to which organizations apply predictive analytics, but at least half each also are analyzing product, sales and financial data. All of these sources are directly related to revenue. More than one-quarter (27%) of organizations are evaluating the application of predictive analytics to social media data, which the research shows is the category with the greatest growth potential. In terms of size, very large companies by annual revenue most often use or plan to use sales data (88% vs. 76% for all sizes), product data (89% vs. 70%) and financial data (82% vs. 73%). These organizations are likely to have the largest amounts of data, which may make predictive analytics more attractive. The research also finds that more very large companies by number of employees (23%) have technical challenges in using predictive analytics due to the volume of data than do small companies (13%). Forecasting and marketing are the most common uses for the findings of predictive analytics. Nearly three-quarters (72%) of organizations currently use the results of predictive analytics for forecasting, and almost as many (70%) apply them to marketing analyses. Customer service (45%), product recommendations or offers (43%) and fraud detection (34%) are the next-most-common areas in which results are applied. In general, future deployments will continue to focus on these areas in about the same proportions as today with the exception of two growth areas: Social network analysis (38%) and predicting prices in the supply chain (36%) are on the rise. Participants categorized as users reported the most use of predictive analytics for marketing analyses (76% vs. 70% of all participants) and less than other job titles for fraud detection (27% vs. 34%), intelligence or surveillance analysis (22% vs. 28%) and social network analysis (19% vs. 27%). Users may not be charged with doing these analyses, which specialists often are brought in to conduct. Some differences emerge by company size as well. Very large companies by number of employees do more forecasting (79% vs. 72% for all), marketing (85% vs. 70%), customer service (58% vs. 45%) and fraud detection (48% vs. 34%). Bigger companies have more data to analyze in each of these areas, and the potential financial benefits are large enough to justify more investment in them. Small companies more often than others use predictive analytics for product Ventana Research 2012 Page 18

19 recommendations (61% vs. 43%) and social networking analyses (57% vs. 27%), efforts that can help produce a competitive edge. The research understandably finds predictive analytics used for tasks relevant to the specific industry. FIRE (86%) and Services (80%) do the most marketing analysis. FIRE also does more fraud detection (79% vs. 40% of all) and surveillance or intelligence analyses (50% vs. 31%), areas in which this sector is vulnerable to large losses. Manufacturing is more focused on predicting supply chain prices than others (31% vs. 19%) and mechanical failures (25% vs. 10%). In a regional difference of note, North American organizations use predictive analytics more for scientific or clinical research (30% vs. 17%) and healthcare decision-making (26% vs. 16%) than those elsewhere. The most frequent benefits of predictive analytics are gaining competitive advantage and new revenue sources. Organizations that have deployed predictive analytics most often have benefitted by gaining competitive advantage (68%). As well, more than half (55%) have used it to create new revenue opportunities and increase profitability (52%). Small companies by number of employees are most enthusiastic about it as a path to greater competitive advantage (81%), reflecting one of their key challenges; in contrast, very large companies least often have created new revenue opportunities (35%) from its use. IT professionals see different benefits than those in business functions. They more often reported improved customer service (61% vs. 43%), freeing up of analysts time (33% vs. 26%), better compliance (33% vs. 18%) and a reduced load on IT (28% vs. 13%). These differences suggest a need for better communication within the organization about the benefits achievable so that IT can understand and meet the needs of the lines of business. Organizations employ a variety of algorithms to support analytics. Predictive analytics requires a variety of algorithmic approaches. More than 90 percent of participants each use decision trees and linear regression; 69 percent use decision trees frequently and 66 percent use linear regression frequently. Logistic regression, association rules and K-nearest neighbor analyses are all used by four out of five organizations. Support vector machines (SVM), survival analysis and Monte Carlo simulations are used least frequently, but even so all are used by 40 percent or more of organizations. Very large companies make most frequent use of logistic regression (72%) and use it even more often than linear regression. Small (44%) and midsize (50%) companies make more use of K-nearest neighbors than the average (36%). By industry, Services uses logistic regression more frequently than average (71% vs. 60%). Manufacturing organizations are likely to use K- nearest neighbors (57%) and association rules (53%) more frequently than logistic regression (33%). These variations in frequency of use can help organizations understand the different techniques available and prioritize their own investments in developing or acquiring the capabilities they need as well as prioritizing training on how to use them. Deploying predictive analytics requires special skills. There s little self-service in predictive analytics because few users have the skills they need to develop it. In half (51%) of deployments the people who use predictive analytics are not the ones who designed and deployed it. However, this fact is not well understood by executives and managers, 65 percent of whom said their users Ventana Research 2012 Page 19

20 are also designing and deploying the systems. The users themselves, not surprisingly, have a more accurate view: Only 42 percent said they handle design and deployment. Smaller companies are different, though; having fewer resources, small (61%) and midsize (56%) companies by number of employees more often have the same resources do both. By region Europe seems to be most self-sufficient with 64 percent doing both. Users most often don t produce their own predictive analytics because in 84 percent of organizations they don t have the skills; more than half (58%) don t understand the mathematics required, and 40 percent can t access the necessary data. Management and executives don t fully recognize this issue only half said that mathematical knowledge is an obstacle. Very large companies by annual revenue perhaps devote more resources to training; in them skill declines as an issue to 57 percent, but that is still more than half. A more substantial issue for the biggest companies is data access. With a greater diversity of data sources and more need for interdepartmental cooperation, 71 percent of very large companies by annual revenue cited access to data as an obstacle to users producing their own analyses. Data scientists and data warehousing teams usually handle predictive analytics implementations. Even IT often lacks the skills to implement predictive analytics: Only 6 percent of general IT resources and 2 percent of line of business IT are primarily responsible for designing and deploying predictive analytics, although these two do it in 30 percent of small companies by number of employees. For more than 60 percent of projects, specialized data scientists or statistical or data mining resources (32%) are responsible or business intelligence and data warehousing teams (31%) are. Midsize companies are most likely to rely on the business intelligence and data warehousing team or line of business analysts; in them each is primarily responsible for 36 percent of the deployments. By industry, Services (40%) and Manufacturing (41%) use specialists more while Government (50%) and FIRE (36%) use business intelligence and data warehouse teams more. When we asked how satisfied organizations are with their use of predictive analytics, the projects led by specialized resources such as data scientists produce the highest levels of satisfaction (70% vs. 65% overall) while 41 percent of those at which business intelligence and data warehouse teams are in charge said they are only somewhat or not satisfied. These and other findings show that predictive analytics requires skills and knowledge beyond those found in many business intelligence implementations. Lack of resources and lack of awareness are the biggest obstacles to further deployment. The research shows that a lack of resources is a barrier to implementing desired changes in predictive analytics in more than half (59%) of organizations, and lack of awareness of the need for them is significant also (cited by 45%). One-third (35%) said it takes too long to assemble and deploy applications, and nearly as many (32%) said too much training is required. Technology, which only 10 percent said is not suitable, ranked least important as a barrier. Management and executives cited lack of resources more often than the average of all participants (67% vs. 58%) rather than objecting to functional aspects. Two-thirds (65%) of IT participants were Ventana Research 2012 Page 20

21 concerned about both lack of resources and lack of awareness. More of them also said that implementation takes too long (47% vs. 35% of all participants). Shortage of resources is a more intense problem in small (64%) and midsize (75%) companies by number of employees when compared to the overall 59 percent. But while their size may restrict the available resources, it may also make it easier to raise awareness of predictive analytics. Small companies suffer less from lack of awareness (32% vs. 45% of all). By industry, Services companies most often lack resources (67%), and Manufacturing least so (38%). In FIRE lack of awareness (54%) is most common, and those in Services most often complain about projects taking too long (40%). Most purchasers acquire predictive analytics as a stand-alone tool. The predictive analytics market is highly fragmented; participants named more than 40 products, and 70 percent use more than one product. A substantial portion (44%) prefer to purchase predictive analytics as stand-alone technology. Somewhat fewer (37%) prefer it embedded within other technologies, but no particular type of applications in which it should be embedded stands out; the most common choice (for 20%) was within a business intelligence product. There is approximately the same level of interest in purchasing it as part of an application (8%) or embedded within a database (7%). Most also prefer to deploy predictive analytics on-premises. Consistent with the preference to control their predictive analytics as a stand-alone application, more than half the participants (57%) prefer on-premises deployment of it; fewer than one-fifth (18%) prefer an on-demand arrangement, and only 6 percent want it hosted by the supplier. Recognizing the demands of installing new software, one-third of IT participants prefer an on-demand or hosted option. And having more limited resources, small companies by number of employees most prefer the ondemand (26%) or hosted (17%) approach. By industry, FIRE had the strongest preference for on-premises (77%) and Manufacturing the weakest (35%). Organizations want predictive analytics integrated with other tools and applications. Participants would like their predictive analytics integrated with other tools, especially with business intelligence (58%) and within business applications (56%). These two integration destinations are not mutually exclusive: Three-quarters of those who want predictive analytics embedded in business intelligence tools also want them embedded in business applications. The point is that organizations want to deliver capabilities to end users; thus, the research shows lower but still significant demand for integration with the database as well. In this regard participants would like in-database development of predictive models (45%) and externally calculated results loaded into the database (39%) as well as in-database scoring of new records (38%). Executives embracing the concept of integration are most interested in delivering predictive analytics to end users via business intelligence tools (82%) and business applications (73%). IT, on the other hand, is much less interested than all participants (29% vs. 56%) in delivering via business applications. This reduced interest may reflect a lack of understanding in IT of the role these analytics can play in daily business decisions. Ventana Research 2012 Page 21

22 Integration and access are common challenges. More than half (55%) of participants cited integration as a technical challenge in using predictive analytics. Another technical challenge of integration is that more than one-third (35%) cannot access the necessary source data. The technical challenges vary by company size and industry. Very large companies by annual revenue most often have integration issues (63%) and difficulty accessing the data (47%). Navigating the labyrinth of the organization and variety of data sources makes these challenges even more difficult in those companies. By industry, the most participants in FIRE (75%) and Government (67%) face challenges with integration. Manufacturing organizations most often complain predictive analytics is too hard to use (33% vs. 18% on average), perhaps because modeling their operations requires more knowledge of advanced algorithms than in other industries. North American organizations more often than those in other areas have challenges with integration (61%) and data access (42%). Organizations need more timely results from predictive analytics. The sooner results of a predictive model can be applied to data, the sooner appropriate action can be taken. Best, of course, is scoring new records in real time. In some cases, such as fraud detection or online ad selection, real-time scoring is essential. However, few organizations are proficient at this: Only 30 percent perform real-time scoring on a regular basis, and an equal number don t do it at all. The research makes clear that this capability influences an organization s 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. The research shows that very large organizations by annual revenue have the least adoption of real-time scoring with two-thirds (65%) indicating infrequent or no usage. By industry, Services has the highest regular usage (36%) and the fewest (23%) that don t use it at all. This finding likely is influenced by the inclusion in Services of online commerce and media businesses, which must operate on real-time data. Most don t update their analytic models frequently enough. Inevitably, models get stale and produce inaccurate results. Yet the research shows that almost two-thirds (63%) update their models only monthly or even less frequently. Only one-quarter (24%) update models daily or more often, and this group shows the most satisfaction, with 50 percent indicating they are very satisfied compared with 22 percent overall. It also reveals that executives and management are not aware of how often models are being updated. One-quarter of management (that is, vice presidents, 25%) and executives (27%) each said that updates are happening constantly, but only 8 percent of users reported that models are updated that often. Despite having additional resources, very large companies by number of employees least often update models constantly (4% vs. 12% on average) and most often do so on a monthly basis (23% vs. 14%). By industry, Services organizations, despite leading in real-time scoring, least often update models constantly (9%); FIRE, an industry familiar with the changing dynamics of the marketplace, does it most often (29%). Manufacturers most often update quarterly (31%). The capitalintensive portions of these businesses may not require more frequent updates, but the commercial aspects can change in a moment and thus need timelier predictions. Ventana Research 2012 Page 22

23 Most organizations do not provide adequate support and training for predictive analytics projects. This research examines five different ways in which organizations can support predictive analytics, including three types of training, specialized consulting and help-desk resources. It reveals that most do not provide enough support. Fewer than half provide adequate training in any of the three areas: concepts and techniques, their application to business problems, and use of products. Fewer than one-third (31%) provide adequate consulting resources and less than one-quarter provide adequate help-desk support. Smaller companies by number of employees generally said they do a better job of training than did large companies. In the area of predictive analytics concepts and techniques, 57 percent of small companies said they provide adequate training, but only 26 percent of very large companies said so. Support can have positive impacts on user satisfaction. Among organizations that provide adequate help-desk resources and those that provide adequate training in predictive analytics concepts and techniques, 89 percent reported being satisfied or very satisfied compared to 66 percent of all. The second-most-important form of training is on the application of predictive analytics to business problems. When adequate training is provided here, 86 percent are satisfied or very satisfied. Funding for predictive analytics projects usually comes from the lines of business. Predictive analytics should be a business investment rather than an IT investment, and most participants recognize this. Two-thirds of organizations fund these projects from business budgets: 44 percent from general business budgets and 23 percent from a line of business s IT budget. FIRE is the only industry in which IT funds predictive analytics more often (54%) than the business side (46%) of the organization. By role, analysts are most often involved in recommending (53%) and evaluating (55%) predictive analytics technologies. Reflecting the concentration on applying predictive analytics to revenue-related activities, the head of marketing is the nextmost-influential role (33%) in recommending a solution. Due to the need for specialized expertise, consultants are also active participants (33%) in recommending solutions. In the evaluation process IT staff (44%) and IT management (42%) follow analysts as participants in the process. Reflecting the importance of these projects, approvals rise to the C level with the CEO (47%) most often involved, followed by the CIO (32%) and CFO (28%). Organizations expect further predictive analytics to have a positive impact. The vast majority of participants (86%) asserted that predictive analytics will have a major positive impact on their organization, with nearly one-third (32%) indicating it could be transformative (that is, enabling them to do things they couldn t do before). This sentiment coincides with the findings that organizations are planning to invest more in predictive analytics in all areas. More large (40%) and very large (41%) organizations by number of employees expect a transformational impact compared to the average of 32 percent. By industry FIRE is most likely to expect a transformational impact (47%). Ventana Research 2012 Page 23

24 Led from the top, organizations are planning to deploy more predictive analytics. Nearly all (94%) participating organizations are planning to deploy more predictive analytics. As with benefits already achieved, expectation of competitive advantage (77%), increased profitability (65%) and new revenue opportunities (59%) are driving these planned investments. Executives and management are bullish about predictive analytics. They are more likely than users to anticipate each of the following benefits: Increased profitability New revenue opportunities Improved operational efficiency through cost savings Increased workforce productivity Inclusion as part of a wider BI or performance management initiative Reduced risk Customer service complaints and issues handled Compliance requirements met. Small companies by annual revenue are most interested (72%) in the benefits predictive analytics can provide for new revenue opportunities and in cross-sell and up-sell opportunities (48%). By industry Services organizations are most interested in increasing profitability (71%) and cross-sell and up-sell (49%). Regardless of the reason, the interest in predictive analytics is nearly universal. Ventana Research 2012 Page 24