Observations, Data, Analysis - Identify What's Important
Observe with impartiality. Observe and document the behaviour without any pre-judgement or jumping to solutions or conclusions before a complete analysis.
What's Important within the context and parameters of the issue you are examining.
Based on observations, what questions do you have? How can you explain what is happening and why? What is the likely impact of the issue you are observing.
Data Collection, Analysis.
Complexity in the organisation (scale)
- who uses the data and what do they use it for
- information that has not been included in the data collection
- dependencies - bonuses are dependent on sales
- An example of scaling: 300 stores x 65 week lead time x 65K SKU's
Case study: a supermarket employee was asked to report on the amount of bread on the shelf at the end of the day. The employee instead logged the amount of bread during the afternoon when he had time, because he wanted to leave when his shift finished. The result was a constant under-supply of bread to that store.
Value of Data
Focus on data in motion - fresh / wide data and using the right tools to classify, automate, simulate and optimise data.
... observation, natural conversations, interviews of various sorts, checklists, questionnaires, and unobtrusive methods. Participant observation is characterized by such actions as having an open, nonjudgmental attitude, being interested in learning more about others, being aware of the propensity for feeling culture shock and for making mistakes, the majority of which can be overcome, being a careful observer and a good listener, and being open to the unexpected in what is learned (DeWALT & DeWALT, 1998).