Today's business intelligence solutions have to be forward looking - and this requires a whole new breed of mathematics.
"To remain competitive, companies can no longer rely on a rear-view mirror approach to business intelligence," says Andr'e Zitzke, Solutions Specialist, of SAS Institute SA, leaders in business intelligence. "They need to predict everything from what customers will want to buy tomorrow (so that they can be sure to stock it) to which customers will move to the competition, so that they can retain them if they are profitable."
Sophisticated predictive analytics, based on advanced mathematics, is therefore an essential component of leading business intelligence solutions.
"The new rash of compliance legislation, for example the operational risk component of Basel II, has opened up a totally new field for mathematicians and statisticians," says Zitzke. "New maths is having to be developed to meet the demand."
According to Zitzke, in the past when new mathematical concepts were developed, leading organisations were quick to examine them, trying to see how they could be utilised to do better business.
"Today, however, businesses have leapt ahead in their requirements, and the maths is having to catch up in order to solve new business problems quantitatively, as well as to cope with the vast volumes of data being generated today. In the past the question was always: Do we have enough data to do the analytics?" he says.
For example, large financial services organisations now have to analyse and predict their operational risk to comply with Basel II.
Basel II (New Basel Capital Accord) is a set of broad policy guidelines that each country's supervisors can use to determine the supervisory policies they apply. The new framework is intended to align capital adequacy assessment more closely with the key elements of banking risks, and to provide incentives for banks to enhance their risk measurement and management capabilities.
Operational risk is not a finite, tangible area that lends itself to traditional analysis. Instead, it involves a disparate mix of everything from business areas and functions to material loss events and physical security.
Enterprises face the challenge of investing in the right business intelligence solution that will enable them to obtain forward-looking analysis that can accurately predict operational risk.
SAS has a large numbers of PhDs - actuaries, statisticians and mathematicians - working in its research and development department. These experts either develop new maths themselves, or are well versed in the latest mathematical developments globally to ensure that SAS utilises these advancements to help businesses solve their current problems.
"The good news is that SAS users themselves need no knowledge of the sophisticated mathematics built into its solutions," says Zitzke, who adds that there are basically two groups of SAS users.
The vast majority are in business or finance, and use the results of analysis to perform their functions. They can use business intelligence to look at specific scenarios, asking 'what if' questions such as 'if the interest rate changes, what impact will this have on the future financial state of the company, and what are the risks associated with it, and how can an organisation optimally hedge this risk?'
"The other group are the quantitative analysts who understand the maths and stats behind the solutions, and develop statistically sound models that produce the results needed by the end-users," he says.
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