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Our benchmark research on business analytics finds that just 13 percent of companies overall and 11 percent of finance departments use predictive analytics. I think advanced analytics – especially predictive analytics – should play a larger role in managing organizations. Making it easier to create and consume advanced analytics would help organizations broaden their integration in business planning and execution. This was one of the points that SPSS, an IBM subsidiary that provides analytics, addressed at IBM’s recent analyst summit.
Predictive analytics are especially useful for anticipating trend divergences or spotting them earlier than one might otherwise. For example, sales may be up compared to a prior period, but is it simply month-to-month variability or the start of an upward trend? Better analytical techniques can help distinguish between normal variation and the beginning of a new trend. By using analytics, one might even discern that while the revenue numbers have been positive recently, the underlying data contains warning signs that point to diminishing volumes, lower prices or both in the future.
Predictive analytic models are created using a top-down or a bottom-up approach, or some combination of the two. SPSS offers tools to handle both. The top-down approach involves creating a statistical hypothesis based on business observations or theories and then testing that hypothesis using statistical methods. IBM SPSS Statistics enables users to build a relevant picture from a sample, as well as test assumptions and hypotheses about that picture. A bottom-up approach unleashes automated data mining techniques on data sets (typically large ones) to distill statistically significant relationships from them. SPSS Modeler is designed for use by experienced data miners but also business analysts to speed the creation and refinement of predictive models. Often, companies employ both approaches iteratively to refine and improve models.
I used to joke that the main value proposition of SPSS was that while its chief rival, SAS, required its users to have a Ph.D. in statistics, SPSS could be used by anyone with a master’s degree. Applying predictive analytics techniques is simple in concept but far from simple to integrate into day-to-day business beyond its traditional roles such as market research. This partly explains why so few companies have woven predictive analytics into their planning and review cycles. It’s possible to create relatively simple predictive models, but for many business issues, such models may be too simplistic to be useful. And they may not be reliable enough because they generate too many false positives (people spend too much time chasing non-issues) or false negatives (missing important developments or breaks in trends).
Beyond the data and technology challenges posed by advanced analytics, there are significant people issues that companies must address to make their use practical. These can be more difficult to tackle than most business/IT issues because of the experience and skills that are needed that our benchmark in predictive analytics still finds lack of adequate resources. Automating general business processes, for instance, requires bringing together business subject-matter experts with people who understand IT. Advanced analytics, however, requires three sets of skills – business subject-matter expertise, IT and statistics – that are rarely found in any single individual. Communication among sets of individuals who have these skills often is difficult because they have a limited appreciation of the others’ domains and often have difficulty expressing the nuances of their own area of expertise.
Today there’s greater focus than ever on analytics, partly because an explosion of available data has made it possible and even necessary to make sense of it. As part of IBM, SPSS has been benefitting from the parent’s “smarter planet” marketing theme. SPSS also has taken steps to expand demand for its tools by reducing the people barriers to adopting advanced analytics. One step has been to automate data preparation for use in Statistics and Modeler. Another is an automated modeler that takes several different approaches to analyze a set of data in a single run and then compares the results. Yet despite these steps, I expect advanced analytics to require specialized skills for many years.
Therefore, I also expect adoption of advanced analytics to happen slowly. Most executives at the senior and even middle levels of corporations have limited familiarity with advanced analytics. Many may have had their last formal education with statistics as a required business school course. To spur broader adoption of predictive and other advanced analytics, IBM and others must foster a “pull” approach to marketing analytics. Business executives need to know that advanced analytics are available and of practical value, especially outside of traditional statistics-heavy realms such as consumer research and fraud detection. Sales planning, financial planning, enterprise risk management, maintenance and customer service are all areas ripe for use of predictive and other advanced analytics. We found all of these as future use of predictive analytics in our benchmark. It’s easy to convince analysts like me of the value of analytics; it’s much harder to get business executives to incorporate them into day-to-day practices. It would be helpful for its own cause if IBM SPSS were to identify promising uses of advanced analytics by function and industry and provide a canned blueprint that can serve as a starting point. Such a blueprint would incorporate a business case illustrating the problem, the suggested steps for addressing it and the scope of benefits that can be realized.
The continuing explosion of data will give rise to an increasing number of ways that business and finance executives can use information to their advantage. But first they have to know that they can.
Robert Kugel – SVP Research
We started Ventana Research a decade ago, with the objective of providing the highest quality in business-focused information technology research available. We were particularly interested in offering fact-based market research that would focus on the practical needs of getting the most value from technology in business and IT functions. Since then many of the observations we make and much of the advice we offer are grounded in our benchmark research. In a field that often blurs the distinction between fact and opinion, we stress the former. The quality of our research stems from the methodology and the processes we use in benchmarking organizations’ performance. We believe our approach makes our research highly credible and worthy of your attention.
I suspect that only a relative handful of people who read research from various firms pay close attention to where their findings come from. Being a skeptical, numbers-oriented guy, I am routinely appalled when I look at the demographic data behind some of research I see published especially when I am included in the research. I’ve seen purely finance-focused research surveys (on the financial close, for example) in which the large majority of responses came from individuals who don’t work in finance. Or in the case of issues that mainly affect larger organizations, responses that come mostly from companies with fewer than 100 employees. This sort of research is fundamentally flawed and should not be used for any education or decision making purposes.
It should be common sense that as a rule people who work in IT departments, for example, know little about the specifics of how line-of-business and finance departments function, and vice versa. For that reason, we are careful to qualify those who participate in our research. We ask individuals only questions that they can reasonably be expected to answer knowledgeably or voice opinions rooted in first-hand experience. If our research topic is a significant issue for larger companies, we eliminate the responses from small and midsize businesses before we compile our findings.
At times we like to highlight the differences between groups of participants, which are revealing. For example, we have examined what IT departments think is important to line-of-business users and, in the same research, compared it what line-of-business users say they think is important. As well, we have comprehensively compared and contrasted the requirements and attitudes of small and midsize businesses with those of larger ones.
We refer to our research as “benchmark” because it examines the intersection of organizational functions from business and technology perspectives to identify points of friction and challenges in utilizing information and technology to manage performance. Our benchmark research reveals an organization’s maturity in its ability to execute key functions and can serve as a guide for improvement. Another differentiator of it is the experience we apply to devising our questionnaires that cover each topic comprehensively in a relatively short set of questions. Of course, there’s some value in doing surveys that simply catalog what technologies organizations are using, what issues they face and what plans they may have for the future. But we believe there’s substantially greater value in research such as ours that connects the dots between how companies use information technology in their important functional processes and how successful they are in executing them. Our benchmark research also assesses the maturity of organizations in how they handle these important core processes, and the underlying maturity of their specific people, process, information and technology elements as well as the benefits they achieve or do not achieve. Companies can use this approach to begin assessing how they compare to a benchmark and how they compare to similar companies.
The benchmark research is the foundation of everything we do. Combined with the experience of our team of industry experts, it provides the framework and the facts for our advice, observations and assessment of applications and technology to help organizations improve performance. Sometimes the research reveals facts that contradict the assumptions of conventional wisdom (especially when this wisdom is driven by bias or wishful thinking). For example, to be truly successful, sales and operations planning (S&OP) is supposed to involve a broad spectrum of business functions. Yet our S&OP benchmark showed that only 21 percent of companies involve four or five of the five key functional areas in the planning process – namely executives, manufacturing, operations, finance and sales. Nearly half (45%) involve either none or just one of them. Thus we were able to conclude that one important reason why S&OP initiatives are not achieving their objectives in many companies is a lack of broad participation.
In another example, our recent benchmark research on trends in the financial close revealed that rather than achieving a faster close, companies on average take longer today to complete this core financial function than they did five years ago. We hear companies pay lip service to closing fast, but a majority of them are taking longer than they should (five or six business days) to complete their monthly or quarterly accounting cycle.
I’m writing this partly because I want to make clear why I believe our research and methodology is of the highest quality, why it’s worth your consideration and why spending time participating in our benchmark research is worthwhile. I also bring this up because my email in-box is inundated daily with requests from other analyst organizations to participate in some bit of research. The question sets they send are basic, and not surprisingly, their findings are often unremarkable and at times just wrong, in my judgment. The main purpose for their research, unfortunately, is not to achieve a better understanding of technology users’ needs and practices, but to generate leads that are sold to technology vendors. (Full disclosure: We offer this service to sponsors of our research, but we send along contact information for only those participants who indicate they want to be contacted by a specific technology vendor.)
I have no doubt there are differences in the quality of business-focused IT research offered by different companies. Ventana Research is dedicated to creating and communicating the highest-quality research possible that drives education on best practices and the available avenues for improvement.
Robert Kugel – SVP Research