Data mining, in eleven steps

Most analysis begins with somebody opening a spreadsheet and looking around. This is the alternative — a documented process where each step has questions that have to be answered before moving to the next one. It is what we work from.

	Step	What actually happens
1	Translate the business problem	Turn a sentence like “our marketing is not working” into something measurable. Two questions first: how will the answer be used, and how will it be delivered
2	Select the data	Build a wish list, then find out what actually exists. Expect different systems, different formats, missing fields and no documentation
3	Get to know the data	Examine the distributions. Compare real values against what the documentation claims. Validate assumptions. This is the step everyone rushes and where every later problem starts
4	Create the model set	One row per thing being studied. A balanced sample. Multiple timeframes. Split into training, validation and test
5	Fix the problems	Outliers, skewed distributions, missing values, categories with too many levels, and identifiers whose meaning changed at some point in the past
6	Transform the data	Reduce variables, capture relationships that are not linear, turn counts into proportions, handle inputs that move together
7	Build the model	The step everyone imagines is the whole job. It is one of eleven, and usually the fastest
8	Assess it	Accuracy, yes. But also: how comprehensible is it, and how stable. A model nobody understands does not get used
9	Deploy it	Move it into the place it will actually run, and fit it to how people work
10	Assess the results	Measure what happened. Not what the model predicted — what happened
11	Begin again	The process is iterative. Going back two steps is normal