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Predictive analytics is concerned with trawling through historical data to find useful patterns which might be used in the future. As such it employs data mining techniques to find the patterns, and once found and verified they are applied via some scoring mechanism, where each new event is scored in some way (e.g. new loan applicants are scored for suitability or not). The data mining platforms compared in this article represent the most common alternatives many organizations will consider. The analysis is high level, and not a feature by feature comparison – which is fairly pointless in our opinion. The five criteria used to compare the products are:
Capability – the breadth of the offering.Integration – how well the analytics environment integrates with data, production applications and management controls.Extensibility – very important and a measure of how well a platform can be extended functionally and how well it scales.Productivity – the support a platform offers for productive work.Value – likely benefits versus costs.
This form of analysis creates some surprises, but you need to look at the full review to see why a particular offering does so well.
Via Alex Kantone
