MicroPosts

• How big things get done

  How big things get done published the following breakdown:

  • 100% of the mega-projects analyzed
  • 47.9% finished on budget
  • 8.5% finished on bugdet and time
  • 0.5% finished on budget, time, and produced the expected benefits

  Later, the book analyzes what are the prerequisites to get into the 0.5% Olympus:

  • Know why all the time: is this code rewrite benefitting the product?
  • Think slow, act fast:
    • Make mistakes in the planning phase when screw-ups are cheap
    • The more time the execution phase takes, the more you expose yourself to black swans (ie, catastrophic and unexpected events)
  • Mitigate risks:
    • Spike code to understand if and how something can be done
    • Ask people who worked on similar features what went wrong/could have been done better
  • Estimate using anchors:
    • Hofstadter’s Law: “It always takes longer than you expect, even when you take into account Hofstadter’s Law.”
    • We always estimate with the happy case in mind and, even the most paranoid, cannot prepare for unknown unknowns
    • Ask others what was their experience building a similar thing and use their numbers as a starting point
  • Lean on experience:
    • Involve people who've worked on similar features in the past
    • Learn from other similar projects—and no, you are not working on anything so unique that cannot draw from experience
    • Use boring tech: "remove the words custom and bespoke from your vocabulary"
  • Build a team
  • Iterate:
    • "we're terrible at getting things right the first time"
    • "I have not failed ten thousand times," Thomas Edison said. "I’ve successfully found ten thousand ways that will not work."
    • "If something works, you keep it in the plan. If it doesn’t, you “fail fast,"
    • When talking about the success of the Empire State Building, "workers didn’t build one 102-story building, they built 102 one-story buildings."

  The following sentence made me stop and think about my fuck-ups:

  When delivery fails, efforts to figure out why tend to focus exclusively on delivery. That’s understandable, but it’s a mistake, because the root cause of why delivery fails often lies outside delivery, in forecasting, years before delivery was even begun.