Episode 13

Management by Exception in Data Dashboards

October 9th, 2019

14 mins 34 secs

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About this Episode 

    <p>This podcast serves as a Part II to an earlier episode. In Part I, titled, <a href="https://demystifyingdata.co/making-better-decisions-with-data/" target="_blank">“Making Better Decisions with Data (Episode 9)”</a> Chris outlined basic thinking around how to develop and execute a data project by keeping cause and effect in mind. </p><p>In this second part, Chris continues along this same vein of logic to describe a dashboard reporting philosophy defined best as a Management by Exception approach to reporting. In situations where your outcomes metrics are a score, rating, or otherwise continuous, there will be normal fluctuation. Knowing when something unique is happening becomes critical to effective reporting in these circumstances.</p><p>In this episode, Chris addresses how to develop management by exception approach to your data dashboards by addressing the following key points:</p><ul>

  • Variable Type: Not all metrics are created equal. Make sure you understand cause versus effect, inputs versus outcomes; and record the data appropriately.

  • More is More: When using a “management by exception” approach to reporting, it’s okay to have a lot of metrics because you’re not going to be trying to react to everything. You’ll only be reporting on metrics that are important and unique.

  • Charting Data Over Time: In many cases you’ll be charting metrics over time as either a count or average reading. Connecting these measures over time with a simple line chart will reveal a natural ebb and flow that represents the distribution of the metric.

  • Unique Variation: As such, we can use a static called Standard Deviation to identify the likelihood that a given measure of this variation is “normal” or “unique.” The more severe the implications, the less sure we need to be that variation is unique before it warrants our attention

  • Management by Exception: A Management by Exception Dashboard is only highlighting those situations where the threshold of tolerance for a metric is reached, and a measure is a “unique occurrences.”


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