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Agile metrics at scale: Gaining InSITe

Agile metrics at scale: Gaining InSITe. Data visualization is like photography. Impact is a function of perspective, illumination, and focus. Larry Maccherone. @ LMaccherone. Strategy fails one small decision at a time. ODIM Framework. Time In State InSITe Visualization.

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Agile metrics at scale: Gaining InSITe

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  1. Agile metrics at scale: Gaining InSITe Data visualization is like photography. Impact is a function of perspective, illumination, and focus. Larry Maccherone @LMaccherone

  2. Strategy fails one small decision at a time

  3. ODIMFramework

  4. Time In State InSITe Visualization

  5. Control limit misapplication Control is an illusion, you infantile egomaniac. Nobody knows what's gonna happen next: not on a freeway, not in an airplane, not inside our own bodies and certainly not on a racetrack with 40 other infantile egomaniacs. ~Days of Thunder

  6. Control chart example

  7. Are you normal?

  8. What if you are not normal? • Assume Gaussian to set SLA • For data set in slide 2, mean + 2 standard deviation is 25.4 days • If you assume Gaussian, then you’d assume that roughly 98% of all occurrences are below 26 days • In reality, 26 days only covers 94% of all cases • That’s 3x the risk • To get to 98% coverage, you need 39 days

  9. If the shew(hart) fits… • Some in the kanban world say to use Shewhart’s method • That’s 7x the risk for our data set!!!

  10. Pick your Poisson • Assuming log-normal or Poisson… • 2.25x the risk

  11. It’s ODIM not MIDO

  12. Heavy tail phenomena are not incomprehensible… but they cannot be understood with traditional statistical tools. Using the wrong tools is incomprehensible. ~ Roger Cooke and DaanNieboer

  13. What does this tell us?

  14. Almost all major scientific discoveries, historical events, and artistic accomplishments are “black swans”… undirected and unpredicted.

  15. Coloring outside of the lines • Control limits say, “don’t color outside of the lines” • What if the most valuable work is done outside of the lines? • Interactivity  Answer questions

  16. Purpose of measures • Feedback (focus of outcome measures) • Evaluate your prior outcomes after the fact to inform current decisions • co-location is/isn't worth the "cost" in a particular situation • paying for coaching pays for itself in productivity • TDD has this quality benefit at this cost • Diagnostic/status/red-flag • What's going on? • Why? • Forecasting • As a lever (potentially “evil” use of outcome measures)

  17. Are you blocked?

  18. Use measures to find… CORRELATIONS LEVERS EARLY INDICATORS

  19. When to NOT take a shot? • Good players? • Monta Ellis • 9th highest scorer (8th last season) • Carmelo Anthony (Melo) • 8th highest scorer (3rd last season)

  20. Larry Maccherone: LMaccherone@rallydev.com Isaac Montgomery

  21. Productivity (output/cost) • output: $ income, Value, Points, Items, SLOC, etc. • cost: $ cost, teams*time, man hours, etc. • other speed/flow: throughput/velocity, flow-efficiency • Predictability • say/do, velocity/throughput variation, etc. • Time to market (controlled arrival) • cycle-time from decision time to end of development • Responsiveness (surprising arrival) • lead time from arrival to delivery • Quality • defect trend, defect density, test coverage • Customer satisfaction • Employee satisfaction/engagement

  22. Do it fast(er) • Productivity • Time to market • Do it when expected • Predictability • Responsiveness • Do it right • Quality • Customer satisfaction • Do it with a smile • Employee satisfaction

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