How to: Create low-fidelity index measures to fill progress data gaps
We come across a scenario often, when teams are subjectively estimating their progress (overly optimistically), and then missing the mark on their ultimate outcome goals.
In the OKR biz, that's called a watermelon metric scenario: our performance is "green" on our estimated progress all quarter and then flips to "red" when the actual data comes in at the end of the term. Green on the outside, red on the inside = the metric “went watermelon.”
When we have this pattern, or where we have gaps in measurable data, we can work toward improving our predictive capability about progress estimation by experimenting with a low-fidelity index measure, so that we can:
Watch that measure more often (as often as we want / need) to see what our progress might be? and
Fill gaps where we just do NOT have data.
In this video, I walk through an anonymized example scenario, sharing the questions I ask and the steps we follow to help clients identify this helpful type of experimentation with measurement.
Questions? We'd love to hear from you via email. And if you're interested in more support around developing No-BS OKRs, check out our new course that launches in November at http://findrc.co/nobsokrs.
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The Evolutionary OKRs Playbook?
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This free download is a draft excerpt from the manuscript in progress that includes only the essential pages to help you learn essential words and meanings of Evolutionary OKRs, see examples of completed OKRs, and gain early access to some of our draft worksheets to help you form your own OKRs.
