Why should technologists care about belief?

Lean and Agile are based on belief

The build-measure-learn cycle. Image from https://steveblank.com/2015/05/06/build-measure-learn-throw-things-against-the-wall-and-see-if-they-work/
  • Purpose is the ‘why’ of an organization, and acts as the common vision that everything in the organisation works towards (Daniel Pink’s video describes this well).
  • Customer value is what the customer needs instead of the things we think they’d like to have. Concentrating on customer value reduces waste in the organisation.
  • Iterative learning is about learning in small increments so we can adapt quickly when markets or needs change, and don’t bet too many resources on an idea that might not work out.

Lean value trees link beliefs together

Mock-up Lean Value Tree for Bannon’s brain
  • The organisation’s purpose (aka mission): who is it, and what its vision is for itself. This is a stable belief, owned by the organisation’s executive.
  • Goals that support the purpose, framed as desired, stable and measurable outcomes.
  • Strategic bets on those goals: different ways that we could satisfy a goal, with metrics associated with them. Stable in the medium term, owned below executive level.
  • Promises of value: value-driven initiatives against those bets. Stable in the medium-term, owned by the product team.
  • Hypotheses: measurable, testable guesses that support or refute the promises of value. Experiments on these are very short term (often quick and dirty) ways to learn about the problem space and potential solutions, and are owned by the development team.
HDD card (from Barry O’Reilly post on HDD)

We’re really optimising on other peoples’ beliefs

Lean practitioners talk a lot about customer value, and about including users in its feedback loops (either directly, or by tracking the changes in user behavior after changing the system). This optimizes systems not on an organisation’s beliefs, but on its beliefs about other peoples’ beliefs, filtered through those other peoples’ behavior. This is why things like good survey design and execution are difficult, with many types of observer effect (e.g. people doing and saying what they think you want to hear, responding differently to different ways of asking etc) that need to be allowed for.

Image: http://www.rosebt.com/blog/descriptive-diagnostic-predictive-prescriptive-analytics

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