Jurgen Appelo has an interesting theory; when people have invested time and energy in a model (tool, framework, method), people have a tendency to make their models more and more complicated. “Let’s add another dimension.” “Let’s deepen the domains.” “Let’s add some columns or swim lanes.” “Let’s draw an extra diagram.”
The main approach to solve Big Data challenges is to take out the complexity of the data sets.
Complexity itself is anti-methodology. It is against “one size fits all.”
- Tom Petzinger, Interaction of Complexity and Management
This means it makes more sense to use multiple simple models instead of one complicated model. Having a toolkit of methods and frameworks, which each fail in their own way, is a smarter approach than relying on one method or framework to deal with all situations.
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