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Add More Complexity to Your Questions:

At this point, simple questions should be easily answered at a frequency necessary to meet the demand. There should be a high degree of confidence in the data that supports these questions, and minimal friction in accessing this data. Now is the time to begin adding additional depth to these questions. It is important to recognize that as the complexity and depth of analysis increases, so does the importance of team communication. This is the point at which your team will begin to identify trends, patterns, relationships etc. It is necessary to reliably and efficiently answer these questions as a mechanism to understand the impact of intervention.

  • Ask the font line staff what their observations or hypotheses are. These may be things like: “Wednesdays are busy” or “Every time it gets really cold…”
  • If the data are related to people, what are the demographic characteristics (i.e. age, race, gender, socioeconomic).
  • What are the locational characteristics of the data? Are there geographic phenomena, patterns, or groupings?
  • If the data are related to transactions such as permit processing or renewal or applications for benefits, consider the average processing time, which ones took the longest, which were the shortest. What might be impacting this?

Conduct some exploratory analysis, and then review the results with the team. Review the results against a sample of individual records alongside front line staff.

As the questions become more complex, the possibility exists that they cannot be answered, or that the answers may not be reliable. Therefore the questions and the data used to ansewer them must be aligned with the following criteria: Knowable, Understandable, Relevant, Present, and Valid.

  • Knowable: Is the question actually able to be answered with the data;
  • Understandable: Do we actually understand 1.) The question and the impacts of its answer and 2.) the data sufficiently enough to determine whether it's capable of answering the question;
  • Relevant: Does the question and it's potential answer(s) actually provide us with insight into what we are seeking to understand
  • Present: Does the data actually exist and is it sufficeint to answer the question.
  • Valid: Are the questions reasonable or logical and are the data based on facts.