Rob Ashe and Mychelle Mollot kicked off the IBM Business Analytics Analyst Summit 2011. Rob began by pointing out that IBM is just 2 weeks away from its 100 year anniversary. IBM has focused on several themes for its 100 years he says including focusing on the science of information, on reinventing the corporation and making the world a better place. As IBM looks forward it sees analytics as a key element of its future. Analytics is a cross-IBM initiative, in services as well as research, software and optimized hardware. It is one of the four growth platforms (the others being smarter planet, growth markets and cloud). IBM’s analytics strategy involves:
- Best of breed solutions
- Domain and industry talent and skills
- Unparalleled research – like Watson for instance.
- Broad synergy
- Major capital commitment – $20B – for inorganic growth
To date they have spent $14B on acquisitions, 8,000 consultants in the BAO group, 300 researchers and more. As a result they feel they have complete platform from workload optimized hardware to information management to business analytics (BI to predictive analytics) to industry and cross-industry solutions.
IBM’s business analytics team is talking less these days about its platform and more about the capabilities of that platform. Focusing on capabilities like assembling and interacting with relevant information, spotting and analyzing trends, predicting potential threats and opportunities, what-if scenarios, measuring and monitoring behavior, aligning strategic and operational decisions. These capabilities can be used by groups across the enterprise.
Based on their recent survey with MIT Sloan on analytics they see that companies adopting and using analytics get 3x performance and that top performers are 5.4x more likely to be analytics users. Analytics driven organizations show big improvements in EBITDA, Revenue, return on invested capital and more. And analytics-driven companies are increasingly in every industry not just the traditional strongholds – Rob gave examples from banking, government, food, telecommunications and more. These companies have moved from “aha” moments to what IBM is calling an Analytics Quotient or AQ (I once said that analytics was not about aha moments, now it seems IBM agrees).
Mychelle came on to talk about AQ. AQ is realized analytic value divided by analytic potential. Companies, she says, with strong AQs have some characteristics:
- Aware in that they notice things quickly
- Aligned with collaboration across the business
- Agile so they respond quickly
- Focused in that they have a clear vision
AQ is supported by an AQ maturity model that measures decision-making “savvy”, readiness and capacity to leverage analytics and mastery of information. Levels:
Rely on spreadsheets and historical views
- Builder (where most customers are today)
View into results within silos. Sharing is within silos and little view of what’s driving results
Strategy drives investment, starting to look forward and aligning things across silos
Top-down goal setting, insights move between departments and everything is managed coherently across the groups
The maturity model seems to have a bit of a split personality though – sometimes it seems like being more sophisticated moves you up it and sometimes it seems like a broader more enterprise-level focus is key. For instance, someone using very sophisticated analytics in a powerful way in one silo could be a Master or not depending on how you read the model.
To help clients assess their AQ IBM has developed a micro site and some assets including a quiz based on the SPSS survey tool. Some good questions in this quiz but it is very focused on “decision makers” which immediately focuses people on strategic and tactical decisions not operational ones. Regardless, once you know what level you are at you can get access to case studies and additional materials relevant to someone at your level. It’s a great idea though and I will definitely check it out.
Rob came back to drive into IBM’s strategy. Clearly they see competing with analytics becoming the new normal. This means a balance between business and IT when it comes to managing and exploiting information. I like to say that analytics, business and IT teams are the three legs of analytic success but IBM seems not to think that analytics people (data miners, predictive analytic modelers) are not a separate group. Still very BI-centric it seem to me….
Need he says to ensure that analytics is for
- All people
Start in one group, expand to others, add different kinds of users throughout and outside the organization. Of course many of these people are not “decision-makers” in any sense of the word and so graphical and interactive tools are not the right way to push analytics to them. Demo was of a unified workspace which is great for analysts but not for operational folks.
- All decisions
From very collaborative to completely automated – strategic, tactical and operational. Nice to see the explicit identification of automated, operational decisions (the focus of my work in Decision Management) as a kind of decisions to be supported. Focused also on very specific industry solutions, which is good too as “decisions” is a hard concept without specificity. This time the demo is of SPSS Decision Management and Modeler which is a great Decision Management product. Nice to see the use of business rules in conjunction with predictive analytics to drive decision-making and so put predictive analytics to work.
- Analytic freedom
This means consistent user interfaces across devices including mobile ones with interactive tools for executives and analysts that are not on the desktop but on mobile devices. Not sure this is really different from all people except that it focuses on not just supporting those people on their desktops. Disconnected use is clearly a big deal for IBM which is reasonable.
IBM has been building out its “Big Data” solutions recently and integrating these with the rest of their business analytics platform. Check out this review of their latest big data platform announcement. IBM’s analytic investments going forward will clearly include a strong focus on mobile analytics as well as some more business-oriented data discovery tools that can pull in lots of data sources without a lot of IT investment. More use of the SPSS predictive analytics toolset clearly on the cards too.
IBM wants their customers to have a high “AQ” or Analytic Quotient. They want them to be using analytics to improve every decision – strategic, tactical and operational. And they want them to deploy this widely on a lot of different devices.