6. Data Capture and Root Cause Analysis

Now we get to measuring success of the Enablement program.

The world is drowning in data, some of it organized. We are also playing catch up with analytics tools and skills. How do we turn data into information, insight and actionable knowledge? There are multiple steps or stages to turning raw numbers into meaning. This is a bit like panning for gold.

Each one of the following activities has its own scope and challenges:

  1. Meaningful metrics need to be defined
  2. Data needs to be generated, sourced, scrubbed and collected
  3. Data needs to be dis-aggregated to reveal detail
  4. Trends and correlations need to be discovered across datasets, using scatter plots and correlation analysis
  5. Hypotheses need to be created as to causes and effects, and tested
  6. Statistical techniques like identifying assignable causes through control charts, and regression analysis need to be applied
  7. Conclusions need further and ongoing validation such as A/B and control group testing
  8. Action should flow from the preceding and changes re-analyzed over time

A likely first conclusion: we need to do ALL THAT? The key here is being smart about what we measure and quickly eliminating irrelevant data. These steps are somewhat like a sales funnel- we need to quickly qualify what to work on and ignore the rest.

I'll have more to say about specific approaches to analytics in future blog posts.