The lean startup and the LION way: elective affinity

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While reading “The lean startup” book by Eric Ries, I had a strong déjà vu impression, a sensation that what I was reading was part of my previous experience.
The Lean Startup principle advocates a pragmatic, data- and experiment-driven method to create successful startups (“organizations dedicated to creating something new under conditions of extreme uncertainty”).
Summarizing the content of this fresh and inspiring book: forget about boring and speculative business plans with growth figures ranging into the next three years, start with a business idea, quickly build a “minimum viable product” (MVP), define relevant metrics and be serious with the measured data (avoid “vanity metrics allowing entrepreneurs to form false conclusions”). If the data do not show clear signs of growth, learn what went wrong, pick a new plan (“pivot”) and repeat the whole process. The faster the build–measure–learn feedback loop, the less money and time are wasted. The Lean Startup method is ultimately an answer to the question “How can we learn more quickly what works, and discard what doesn't?” (Tim O'Reilly).

The first elective affinity (“already seen”) is in my opinion with the classic method guiding experimental science, where the build–measure–learn is called “Scientific method” and is thus implemented:

  1. Design an experiment — scientific discoveries do not happen by chance; in spite of popular stories of apples falling on somebody’s head, experiments need to be designed with strategy and intention;
  2. Measure relevant quantities and treat the measured data with maximum attention and respect;
  3. Build (“learn”) a pragmatic model —no philosophy involved— of how the measured quantities are related.
At this point, the model provides better insight on the experiment design, and the process can start over with a new experiment — and an improved model if new measured data cannot be explained by the previous one.

As Galileo Galilei marked an important step towards the eventual separation of science from both philosophy and religion in 1600, in a way, Eric marks a step towards the separation of startup methods from the philosophy of giant established companies and the religion of standard accounting practices.

Galileo was born too early to have access to computers and he could not ride the horse of the exponential Moore’s law.

The second relationship (“already seen”) is a more recent one with the “LION way” approach. The LION way deals with connecting two topics which are in most cases separated: machine learning (the creation of flexible models from data) and intelligent optimization (the automated creation and selection of improving solutions).

In particular:

  1. Experiments can be designed by software (Design of Experiments — DOE) in a strategic manner, to derive the maximum amount of useful information in the shortest time;
  2. Measurements can be online and super-fast, think about an e-commerce service measuring all visitors behavior in real-time to quickly adapt to user preferences;
  3. Models can be learned by machine learning starting only from —possibly abundant— data, no costly and slow philosopher involved!
Last but not least, the whole reiteration of the three steps models can be implemented by (semi-–)automated schemes to design better and better solutions (intelligent optimization).
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It is precisely when models are fed to optimizers (learning and intelligent optimization) that the real source of innovation and continuous learning starts.

More and more innovative and bold (LIONhearted?) people can now master the source of power arising from LION techniques to solve problems, improve businesses, create new applications.
More about LION at the LION way website.