Category Archives: spss

Business analytics reduces cardiac surgery mortality rate by 50 percent

A wonderful case study came out last week of how Sequoia Hospital is using business analytics software to inform heart surgery doctors on when to operate and what is the best post- and pre-op care option.

The hospital is using IBM SPSS predictive analytics software to sift through heavy data spanning healthcare databases, medical precedents and real-world medical cases:

"For instance, the software revealed that an anticoagulant drug often given to patients after a heart attack dramatically increases the chances of serious postoperative bleeding. Based on that information, Sequoia was able to put a protocol in place to stop the drug at least five days prior to surgery to allow the patient’s platelets to recover and significantly reduce bleeding events."

Why can business analytics play such an important role in healthcare?

Statistics is at the core of modern medicine. Whether it is measuring the response of a sample group against a control group to decide whether a new drug outperforms the placebo, or whether it’s tracking through a national database of asthma sufferers in search of factors leading to increased instances of attacks, the medical profession relies heavily on statistics-based predictions. Software such as IBM SPSS solutions have the tooling to crawl through this data and help medical professionals make more informed decisions.

As more hospitals and medical facilities switch to electronic record systems, the amount of medical data that computing systems can access mushrooms. This requires more sophisticated, powerful applications (such as those that can tie together unstructured data from various sources). The payback is a larger sample group, diminished margin of error, and a performance increase in the delivery of healthcare. Whereas in the past a hospital would have only had its own records as evidence when deciding on a course of action, now state-wide or nation-wide information can be mined.   

More information on how IBM SPSS solutions is transforming the medical profession

Using structured data analytics to make better business decisions

imageIn the current edition of Analytics, a cross-brand team from IBM (Irv Lustig, Brenda Dietrich, Christer Johnson and Christopher Dziekan) explain IBM’s view of the structured data analytics landscape.

Key to this model are three categories of structured data analysis:

1. Descriptive Analytics: A set of technologies and processes that use data to understand and analyze business performance
2. Predictive Analytics: The extensive use of data and mathematical techniques to uncover explanatory and predictive models of business performance representing the inherit relationship between data inputs and outputs/outcomes.
3. Prescriptive Analytics: A set of mathematical techniques that computationally determine a set of high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the goal of improving business performance.

As the authors explain, this model can help businesses make better decisions, rather than just simply automate standardized processes.

Let’s use the example of a fictional global shoe manufacturer we’ll call ‘Footloose’ to see how each category could be used to increase business performance.

Descriptive analytics

These are your flexible dashboards that let you focus in on key areas of the business. For Footloose, this could be all the standard operations dashboards eg. like the one showing monthly shoe sales by region. Footloose should be able to see how actual sales fared against the forecast. Where there are deviations (say the sales of sandals in Spain has gone through the roof), they can use descriptive analytics to drill-down into the data. They may see that the growth is coming from the Madrid and possibly related to a major marketing push during a hot spell in that region.

IBM Cognos solutions offers this kind of descriptive analytics (including business intelligence) that can be implemented to measure and explore how a company is performing.

Predictive analytics

Here we use data from the past to make predictions about the future. For Footloose, this could include combining seasonal sales variations for a sports shoe with the longer term uptrend they have been seeing for the last few years. Footloose can also use predictive analytics to improve their web presence: they can launch a recommendation engine to suggest what a visitor might want to view next based on what they (and people like them) have looked at in the past (like the book suggestion service Amazon offers).

IBM SPSS offers a set of predictive analytic tools which allow business users to employ predictive insights at the point where decisions are being made.

Prescriptive analytics

How can we achieve the best outcome, whilst addressing any uncertainty in the data? Prescriptive analytics can help us answer this question. Let’s say Footloose has made its prediction about what shoe sales are likely to be over the coming year. Now they just need to figure out how to respond to those predictions. Sales of sandals are expected to remain high in Spain so they need to increase their distribution channel there. How should they achieve this? Increase the fleet of vehicles or buid more (costly) distribution centers.

Footloose can plug the data into an optimization model (costs of building a new plant, buying new trucks, gas) to calculate what would be the most efficient supply chain to deliver the extra required capacity.

IBM ILOG Optimization has technologies specialized for these kind of calculations where there are large data sets with potential uncertainty.

I’ve used this example to present a simplified view of IBM’s approach to structured data analysis and how IBM technologies can be used in tandem to improve business performance. A key advantage of these technologies is that their utility stretches across various industries and applications.

For a fuller explanation of this field, I’d definitely recommend reading the full article in Analytics Magazine

Critical issues in business analytics: James Taylor

Decision management expert and consultant James Taylor was ‘cornered’ recently at the IBM IOD conference and asked to explain the present and future of business analytics. An eloquent speaker and veteran in this field, James does a great job of highlighting the current growth and energy in this space, some of the confusion this has engendered, and the questions you should be asking yourself in determining whether analytics are right for you:

He also highlights three critical issues on which I’ll share my viewpoint:

Where can analytics be employed?

Decisions take place across the organization: from the CEO deciding who to appoint as the new sales director, down to the customer service rep asking if you want to take out a maintenance plan when you buy a new computer. At not all points does it make sense to employ analytics to inform the decision process. If you have highly automated business processes in your organization, then company-wide business analytics may make sense. Alternatively, it may just make sense to use analytics to sharpen up one department such as the marketing operation.

There is a political dimension to this which also has to be considered. It could be that the marketing department has a tight agency relationship who strongly pitch an analytics solution highly tailored for the marketing operation. Whilst this may be able to drive up efficiencies in marketing, it won’t help the support decision process (or possibly cross-sell or up-sell opportunities).

On the other hand, it could be that the IT department, in the interests of cost-cutting, prefer to go with a centralized solution with a narrower maintenance footprint. 

These considerations (which tend to be aggravated in larger organizations) need to be taken into account in addition to the theoretical/modeling questions:

"What are the decisions that drive my business? how do I apply analytics to make a better decision or drive my metrics in the direction I want?"

Figure out how to align Business, IT and Analytics

In the past it was tough enough to engage business and IT departments (which can be heavily siloed and have the kind of relationship you see between Siamese Fighting Fish). But now you’re throwing an analytics team into the tank.

Although I’d suggest that this analytics team can provide the glue that holds together those creative types in marketing and the IT logicians. It’s not unusual to find the analytics practitioners sitting somewhere in a department such as the corporate office. Whilst they may have strong knowledge of the tools, they are also plugged into the business imperative. As long as the importance of their role is realized and they are given due authority, they may well be able to spearhead the implementation of a business analytics solution and its systematic application.

Begin with the decision in mind

Why? So you don’t end up drowning in data that will do nothing to drive your business. James points out that you need to understand what is a good and bad decision (for instance be clear on what a positive or negative outcome looks like).

Just like the scientist needs to understand there is inherent bias in the questions she asks, so you should realize that the decisions you choose to focus on can have a profound effect on your business. For instance just applying analytics to short term decision making (such as maximizing quarterly sales) could pull you out of synch with any strategic objectives and hurt you in future years. If you go overboard using predictive analytics to decide what to offer individual customers next on your website based on their past behavior, you may end up looking like a creepy stalker. Keep an eye out for symptoms of unintended consequences!

James is one of the most prominent/prolific bloggers in the decision management space and can be found at JT on EDM and ebizQ.

In the video James references these IBM technologies: Cognos business intelligence, predictive modeling from SPSS, and business rules and optimization solutions from ILOG.

How CIPCO use IBM SPSS Statistics to improve energy supply

imageSPSS have put out a case study showing how the Central Iowa Power Cooperative (CIPCO) switched from using Excel spreadsheets to IBM SPSS Statistics to optimize those decisions an energy utility provider has to make: capital planning, utility rate setting, power purchases, emissions tracking and more.

Given that energy requirements: supply, demand, price can shift on an hourly basis, tracking these across the 3,000 power nodes CIPCO provides is no trivial matter. As CIPCO’s Lisa Sell points out, "IBM SPSS Statistics gives us the power and flexibility to keep track of everything, with very little manual manipulation." The growing wind farm industry in Iowa adds even more uncertainty into the equation. Sell and her team use Statistics to analyze the dynamic pricing of wind-generated energy and its effect on the rest of the system.

In addition to forecasting and planning the IBM SPSS solution also helps CIPCO keep compliant with the annual power generation and cost reporting required by government agencies, as well as calculating the profitability of the various plants.

Read the full case study

IBM’s marketing automation solutions: a primer

Christopher Hosford over at BtoB Magazine ran an interesting piece on IBM’s foray into the field of marketing automation focusing on the recent spate of acquisitions here at IBM. I thought it would be worth expounding on how each of these acquisitions fits into the notion of a holistic marketing automation solution – using an example that hopefully most of us can relate to: internet retail.

Coremetrics

Internet retailers use web analytics to explore which parts of their site are most effective, which channels are driving most visitors and what are the common paths taken by visitors who buy. Conversely, analytics can also highlight problem areas such as product lines that receive heavy traffic but little conversion to sale, expensive marketing channels that provide little revenue-generating traffic and navigational bottlenecks. You can take this further using a solution such as Intelligent Offer, which exposes the analytics to the visitor: much like the recommendation engine used by Amazon bookstore on their individual listing pages to say ‘if you like this book, you may be interested in these books too’.

Unica

An internet retailer that exploits different marketing channels, eg. email, web, social networks, can use Unica’s Interactive Marketing solution to track responses across the different channels and use this data on past behavior to tailor future messaging. It also allows you to uncover those prospects that have been most responsive and are more likely to cross over and become customers.

Netezza

Netezza can help the internet retailer wherever there are large sets of structured or unstructured business data. For instance you can use Netezza for bid price optimization of search marketing campaigns where you might have 100s or 1000s of keywords covering product inventory, coupled with multiple text ads and landing pages, leading to millions of permutations. Predictive analytics can help you determine what is the optimal paid search campaign structure.

Sterling Commerce

When it comes to order processing, Sterling Commerce can help internet retailers ensure consistency across different channels (eg. keep consistency across different web sites with different experiences). As one example, the system can help dealing with coupons and the correct application of discount codes across all channels.

I should point out that these are only individual examples. Each of these acquisitions have plenty of other offerings, many of which touch on different components of marketing automation.

I’d be remiss not to mention Cognos, SPSS and ILOG, all of whom offer business analytics offerings that can be customized in a marketing automation context.

IBM’s Business Analytics solutions are set to mature as these acquisitions are woven further into the fabric of each other and the expansive IBM quilt of offerings. Early indications are positive however, as IBM’s Business Analytics revenue has grown 12% over the last year to a net income of $3.6 billion. This would suggest we’re in for some interesting times ahead!

BtoB Magazine article on IBM’s marketing automation solutions