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IBM Encourages Adoption of Predictive Analytics

June 28, 2012 in Uncategorized | Tags: Analytics, Forecasting, IBM, marketing, modeling, Performance Management, Planning, predictive analytics, sales forecasting, SPSS | by Robert Kugel | Leave a comment

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.

Regards,

Robert Kugel – SVP Research

Infor Presents Itself as a Large Software Startup

Infor described this year’s Inforum user group meeting as a coming-out party for a large startup company. Such a debut was necessary because Infor had been operating in something of a stealth mode for the past three years: a limited marketing presence, no unified message and a weak, sometimes inconsistent brand identity. It also needed to formally introduce Infor to customers of Lawson, the ERP supplier it acquired last year. The “startup” designation is meant to signal that Infor has been able to render a decade-long consolidation of dozens of smaller companies into one cohesive entity.

I think the current management team has put Infor – particularly the pre-Lawson portion of the portfolio – solidly on the road to viability. This has been no small feat. It’s hard to amalgamate a mix of smaller applications software companies, none with substantial market share. Unlike bringing together a more homogenous set of businesses (say, metal fastener distributors), achieving operating efficiencies has required from Infor a clever dose of pragmatic technology, notably its ION middleware. Infor has had to create a software architecture that makes it easier for both the company and its users to maintain its applications. As well, ION makes it feasible for users of software to upgrade it or migrate to other portfolio applications and to add complementary applications without difficulty.

One of Infor management’s more important objectives for the conference was to get across to users its new strategy, which I covered in an earlier blog. The keynotes on the first day provided the long version of what I expect will be the company’s core message over the next year or so. To persuade its installed base to stay with it, Infor is promising they can keep what they have for as long as they want. Moreover, it will make it easier and less expensive for them to get a broader set of capabilities, such as analytics, performance management, social business applications and cloud-based applications designed to meet their specific needs from Infor rather than going to another vendor. None of these products breaks new ground; all are necessary for the company to remain competitive. For new customers, Infor is offering the promise of faster time to value and potentially lower implementation costs in its targeted verticals. I think this is the right message, but it will require frequent repetition and an aggressive marketing effort supported by a solid roster of willing customer references.

Serious challenges remain for Infor in the business environment and in executing the strategy. After a decade of modest, incremental improvements, the overall pace of technology change is accelerating. The software consolidators of the past decade benefited from limited technological evolution as they tried to stitch together their acquisitions. The slowdown extended the useful lives of enterprise applications, especially record-keeping systems such as ERP. Vendors have been able to keep customers paying software maintenance and had time to integrate the pieces of their portfolios. Now this is changing. For example, companies see that cloud computing can be a better choice for deploying software than doing it on-premises, whether functionally, economically or both. Increasing numbers of people are working with mobile devices, larger sets of data and more sophisticated analytics. Customers’ expectations of how to interact with systems are starting to change as vendors tout the “consumerization” of their business software offerings to make them more like the apps available on mobile devices and more appealing to the social media generation of business users. Infor is responding to each of these, including creating positions for Senior Vice Presidents of Cloud and Speed to promote rapid product development and innovation.

In addition, while Infor is now the third-largest enterprise software company, it is competing with a roster of cash-rich titans (notably IBM, Microsoft, Oracle and SAP). Infor’s recent recapitalization put it on stronger financial footing, but it is still highly leveraged. To the extent that it is successful in generating incremental revenue, it will be able to get cash to pay down its debt. Solid revenue growth also would give it the opportunity to raise equity in the public market and de-lever its balance sheet even faster. The downside to this virtuous circle, however, is that to the extent that Infor is unable to generate “escape velocity” revenue growth, it will crimp its competitiveness because of the need to service the debt. For this reason, how well the organization executes is very important.

In my opinion, Infor faces at least four substantial execution challenges in achieving its objectives. The first is to build broad awareness of the benefits of Infor’s approach and the value of its software. One of Infor’s key strengths is its installed base of midsize companies. These buyers want software that doesn’t require much customization and implementation effort because they have small IT staffs and limited budgets to buy, implement and maintain the software. Infor’s “micro-vertical” applications provide deep functionality built for specific types of businesses. For instance, this means not just the food and beverage vertical but milk producers, brewers or bakers, each with its own business-specific capabilities, units of measure and workflows built into the software.

A second challenge is to identify, define and refine a set of core sales offers that promote cross-sells or up-sells with the most revenue-generating potential. Infor offers a lot of options, but there are endless permutations of what customers can do using ION and individual pieces of Infor’s portfolio. To accelerate clarity of what’s possible and promote sales execution, I think Infor needs to define offers for the needs of specific sets of users. The new mobile and cloud applications utilizing ION are certain to be attention-getters and likely to be among the most quickly adopted, but I don’t expect them to be big earners.

A third challenge in this chain is that sales execution. Whether it’s inside or outside sales, in each of its traditional areas (ERP, analytics and other software categories across multiple micro-verticals) Infor is now offering a broader, more complex array of purchase offers than before. Training sales people to be able to understand customer requirements and communicate value propositions among all the options will not be easy. Simplifying this with a short menu of predefined offers can make it easier for the sales organization, but it’s just a start.

The fourth issue is demand, which is a function of buyers’ software budgets. Of course, the better Infor is able to articulate and demonstrate the value of its solutions, the easier it will be to close business. Moreover, the company is expanding its presence in rapidly growing countries such as Brazil and China. Yet however compelling Infor’s value proposition may be, other capital or operating budget priorities and spending constraints are likely to be a limiting factor, especially in its core market of midsize companies. This factor to some extent may be beyond Infor’s ability to change.

Infor’s attempt to rationalize and overhaul a sizable portion of the U.S. software business has been a big bet. I’m encouraged by what I saw and heard at Inforum. The real test is whether it will be able to sustain double-digit license revenue growth and its retention rate of existing clients in the mid-90 percent range over the next six to eight quarters. That’s a challenge for any large startup.

Regards,

Robert Kugel – SVP Research