Tag Archives: statistics

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

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

Blogs: more visitors than Facebook

Possibly a bit premature given that the year is not even over yet, but MediaPost have pulled together a number of statistics on how many blogs are out there, how many people reads them, and how much money they make.

According to comScore, blogs had 77 million unique visitors in the US for August 2008 – as opposed to Facebook, which only had an audience of 41million. I didn’t expect these figures to be quite so high. Another impressive figure is that 77% of active internet users read blogs. Quite impressive given that the medium has only been around for about the last 5 years.

There is a lot more information in the MediaPost research brief. Read more…