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Eric Sigler
May 23, 2017
Technology
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Instrumenting The Rest Of The Company: Hunting For Metrics
Presented at Monitorama 2017, video at:
https://youtu.be/wnjCNBfH3kg?t=3h3m35s
Eric Sigler
May 23, 2017
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Transcript
@esigler Instrumenting The Rest Of The Company: Hunting For Useful
Metrics Eric Sigler, Head of DevOps, PagerDuty
@esigler Alternatively: ”Lies, Damn Lies, and Hacky Scripts"
@esigler
@esigler Engineer Eng Engineer Eng? Manager Mgr Manager
@esigler (No stock photos harmed in the making of this
talk.)
@esigler "We have problem $foo, so we're going to do
$bar."
@esigler "What data did you use to understand $foo? And
how will we know if $bar improved anything?”
@esigler “We can’t really measure either $foo and/or $bar.”
@esigler “Without data, you're just another person with an opinion.”
- W. Edwards Deming
@esigler
@esigler
@esigler
@esigler (Turns out other managers do this too.)
@esigler
@esigler "We have a problem with people not knowing what
the chatbot does, so we're going to write better documentation."
@esigler
@esigler ?
@esigler
@esigler “If only there was some way we could track
events, and show them over time.”
@esigler
@esigler
@esigler
@esigler Outcome: Writing a smarter help function in the chat
bot. (And simplifying some commands).
@esigler
@esigler Takeaway: Reuse existing tools when it makes sense.
@esigler
@esigler "We have slow tests in CI, so we're going
to complain a lot about it.”
@esigler “Define slow.”
@esigler Local != CI
@esigler
@esigler
@esigler
@esigler
@esigler “Tests take forever to start.”
@esigler
@esigler ?
@esigler
@esigler Outcome: More workers. (And, knowing how many to budget
for.)
@esigler Takeaway: Look for ways to reverse engineer existing metrics.
@esigler
@esigler "We have to ship code faster, so we're going
to reorganize."
@esigler
@esigler
@esigler But it doesn’t show where the bottlenecks are.
@esigler Pipe GitHub metrics into &
@esigler
@esigler
@esigler
@esigler Then start making changes.
@esigler
@esigler
@esigler Outcome: Productivity success! (With massive organizational change to enable
it.)
@esigler Takeaway: Look for proxy metrics
@esigler Potpourri: Data collection (chat, email, calendars) Cross-validation of metrics
(“Sniff test”) Cognitive biases around metrics Plotting against organization events
@esigler Takeaways: Useful metrics are everywhere You aren’t alone in
digging for metrics Existing tools can be repurposed Look to reverse engineer your way to a metric Look for proxy metrics (but choose wisely)
@esigler Thank you!
@esigler Image credits: https://commons.wikimedia.org/wiki/File:Staff_meeting.jpg https://blogs-images.forbes.com/kellyallan/files/2015/06/Deming-in-Tuxedo-DEM-1078-Dr.-Deming2-1940x1130.jpg (Wherever I grabbed that
screenshot from Pulp Fiction, my apologies I am a terrible person for not capturing the URL)