Table of contents for Information on Demand 2011
- Opening Keynotes at #iod11
- Business Analytics Optimization Keynote #iod11
- Analytics Keynote #iod11
- Transformation in the era of big data and analytics #iod11
- Business Analytics – the power to meet your priorities #iod11
- Decision Management Orchestrating Consistent Enterprise-wide Decisions #iod11
Eric Yau kicked off the business analytics keynote today, saying that they are going to focus in on the 3+3 areas discussed yesterday and with that he introduced Deepak Advani. Deepak introduced some key IT trends – rising data volumes, high volume decisions that require decision automation and LOB demanding more flexibility and independence – and some business trends – consumers voice is deafening, finance is under more pressure and risk issues are exploding. This leads to the 3+3 bets IBM is making:
- Big Data Analytics
- Decision Management
- Analytics goes personal
For Customers, Risk and Finance.
IBM has been busy identifying industry use cases over the last few years to drive scale by finding repeatable patterns across Retail (market basket, assortment management), Financial Services (risk management, customer retention), Telecom (churn, Next Best Action) and Government (tax analytics, crime prevention).
A focus on customers is becoming central to more companies – replacing a focus on channels or products. You need to attract, grow and retain customers and maximize their lifetime value. The growth of social networks, search and the internet more generally has completely changed how many companies interact with companies with more channels and more interactions before purchases. How people use products have changed too, with search replacing manuals and a sea change in when people call the call center. Advocacy, net promoter scores, are a new and important element in all this for many customers. A complete range of analytic technologies to drive better customer interactions is critical to IBM’s analytic strategy. This encompasses everything from reporting and dashboards to decision management and predictive analytics.
Jason Verlen came up to demo IBM’s stack in a customer development scenario:
- Using SPSS Modeler to build customer segments based on customer behavior
- Cognos Consumer Insight to analyze advocates social media data about products, drill into the details etc.
- Unica to execute the campaign designed to address the failings found in the social media data
- Back to Cognos Consumer Insight dashboard to see if the campaign is working
- Coremetrics to see how much traffic is being driven to the website
- SPSS Modeler to show a causal relationship between the improved social status and the sales results
Finance is another focus area for IBM with a set of capabilities to support planning, reporting and alignment across the organization. This includes reporting and dashboards, profitability modeling, forecasting and consolidation. New products include the rebranded Clarity product (Cognos FSR) as well as TM1 and Cognos Controller updates.
Risk is the third area of focus. You have to be able to identify, measure and manage risk. IBM’s platform very focused on the monitoring of risk with reporting, workflow, staff assessment key risk indicators etc. Embedding the risk management strategy into operational decisions is also critical.
Moving on to the platform side, Deepak started talking about Big Data Analytics. Cognos Consumer Insight is an example based as it is on IBM BigInsights’ hadoop platform. Cognos is tightly integrated with Netezza for big data analysis and increasingly so is SPSS Modeler which can push modeling activities back into the database. InfoSphere Streams is also being leveraged with both Cognos and SPSS Modeler being integrated with it for streaming analysis and scoring – allowing a billion scores a day for instance!
Deepak moved on to talk about Decision Management and nicely plugged my new book! He went on to talk about building Decision Services using the combination of business rules and predictive analytics – operational and analytical decision management in IBM’s terminology. And of course what-if analysis – impact analysis and simulation – is critical to make sure the way decisions are made is what you want. As Deepak says, analytics need to be injected into every process, every system to make better operational decisions. Couldn’t have said it better myself.
Jason came back to give a demo of SPSS Decision Management in a Telco call center. This call center does not just display data about customers though, it displays predictions about customer value and churn. It also uses decision management to make an appropriate offer and adds that to the environment. Jason showed the one click modeling facilities on SPSS Decision Management, the integration with SPSS Modeler to upgrade models, the rules editor to add rules and the simulation environment to make sure the new approach will work. The demo also shows how text notes can be analyzed and fed into the models, driving the predictive dashboard to show changes and updating the offers based on what the analysis says. Jason went on to show how external data, social media data and survey data, can be integrated with the internal data to drive better offer decisions.
Deepak discussed how IBM sees the integration of rules and events (in the new Operational Decision Management product) and the use of optimization as well as continuing integration with ILOG rules in SPSS Decision Management over time.
The folks from Cognos came back to talk about personal analytics but I had to leave to go sign books…