IBM acquired SPSS in late 2009 and has been investing steadily in the business as a key component of its overall business analytics portfolio. Today, SPSS provides an integrated approach to predictive analytics through four software packages: SPSS Data Collection, SPSS Statistics, SPSS Modeler and SPSS Decision Management. SPSS is also integrated with Cognos Insight, IBM’s entry into the visual discovery arena.
Predictive analytics is a hot term in business today, but there is still some debate about what it means. My blog entry on predictive analytics discusses findings from our research and the idea that the lines between predictive and descriptive analytics are becoming blurred. IBM provides an interesting take on this conversation by discussing predictive analytics in the context of data mining and statistics. Data mining it sees as more bottom-up and exploratory in nature (though it can also be predictive) and statistics as more of a top-down hypothesis-driven approach (though it can also use descriptive techniques).
Our benchmark research into predictive analytics shows that companies are struggling with two core issues: a skills shortage related to predictive analytics and integration of predictive analytics into their information architecture. A preliminary look at IBM’s SPSS software makes it obvious to me that IBM is putting its full weight behind addressing both of these issues.
If you use SPSS Modeler you don’t have to be a data scientist to participate in predictive analytics discussions. Once data is loaded into the modeler and a few preliminary questions are answered about what you are trying to do, SPSS Modeler presents a choice of analytical techniques, such as CHAID, CART and others, and suggests the best approach based on multiple variables, such as number of fields or predictive power. This is a big deal because business managers often have some idea of clustering, regression and cause-and-effect-type functions, but they don’t necessarily know the intricacies of different techniques. With SPSS Modeler you don’t have to know the details of all of this, but can still participate in these important discussions. SPSS Modeler can bridge the gap between statistician and a day-to-day LOB analyst and decision-maker, and thus help bridge the analytics skills gap facing organizations today.
Another challenge for organizations is integrating multiple streams of data including attitudinal data. Built-in survey data collection in SPSS Data Collection can fill in these blanks for analysts. Sometimes behavioral data reveals knowledge gaps that can only be filled with direct perceptual feedback from stakeholders collected through a survey instrument. Trying to tell a story with only behavioral data can be like trying to tell the actual contents of a file based only on metadata descriptors such as file size, type, and when and how often the file was accessed. Similarly, social media data may provide some of the context, but it does not always give direct answers. We see LOB initiatives bringing together multiple streams of data, including attitudinal data such as brand perceptions or customer satisfaction. The data collection functionality allows managers, within the context of broader analytics initiatives, to bring such data directly into their models and even to do scoring for things such as customer or employee churn.
I have not yet discussed IBM SPSS integration with decision systems and the idea of moving from the “so what” of analytics to the “now what” of decision-making. This is a critical component of a company’s analytics agenda, since operationalizing analytics necessitates that the model outcomes be pushed out to organizations’ front lines and then updated in a closed-loop manner. Such analytics are more and more often seen as a competitive advantage in today’s marketplace – but this is a separate discussion that I will address in a future blog entry.
SPSS is ubiquitous in academia and pervasive in the market research industry, a market that in and of itself is estimated to be over $30 billion globally, according to the Council of American Survey Research Organizations. By leveraging IBM and SPSS, companies gain access to a new breed of market research to help merge forward-looking attitudinal data streams with behavioral data streams. The academic community’s loyalty to SPSS provides it an advantage similar to that of Apple when it dominated academic institutions with the Macintosh computer. As people graduate with familiarity with certain platforms, they carry this loyalty with them into the business world. As spreadsheets are phased out as the primary modeling tool due to their limitations, IBM can capitalize on the changes with continued investments in institutions of higher learning.
Companies looking to compete based on analytics should almost certainly consider IBM SPSS. This is especially true of companies that are looking to merge LOB expertise with custom analytical approaches, but that don’t necessarily want to write custom applications to accomplish these goals.