Good to Great
Intro
As always, I don’t agree with or use everything I read, but I will still try and summarize it here. If you are new here, read why I put my semi-personal book notes online.
This is another one of those books that almost everyone I know in the startup space has read. I appreciate the amount of work that went into the book from a data perspective, but reviewing my notes recently I think it is relatively dated. For this book, I’ll give a very brief summary of the main points and won’t dive too much into the detail. This book has concepts that are focused on bigger companies and teams but also apply to startups.
As with many books in this genre, always be aware that correlation isn’t the same as causation. This book seems to make that mistake often by not looking at the opposite cases of those that this book presents. And also focuses on personal success stories presented as a set of steps that can be followed by anyone but without considering the context/background of that person/situation.
Summary
The book performs a study with a 21-person research team who looked through 2,000 pages of interview transcripts totaling 384 MB. (The fact that such a small amount of data is used as a proxy for value shows just how dated this book is). The author focuses on a few specific companies and asks the question, “what allowed these companies to transition from mediocrity to success”. Now, while the companies listed here are no longer impressive, I do think there may be something we can learn from this.
Leadership
The author thought the most charismatic and famous execs would be the ones who brought the company to excellence but that was an incorrect assumption.
Exceptional leaders are humble and driven. Those with both characteristics will do what’s best for the company. The Power of Positive Leadership would agree
Exceptional leaders who only care about success regardless of accolades.
Exceptional leaders who will mentor others to become leaders. Laws of Leadership gives the same advice in rule #13.
Exceptional leaders who give recognition to the team when things work out. I remember reading this elsewhere but I can’t seem to find it.
Right People
Get the right people on board, and then figure out where to set sail. I think this is often what early-stage investors look at as well; is the team capable since that’s what you are investing in. The book mentions the best and brightest which I may agree with. However, that does mean you need to look outside the box. In addition to the typical Stanford CS graduate, find that person that is always creating side projects. Those side projects are as good of a proxy (if not better) as an ivy-league diploma.
The author also says you need to let the wrong people go. In the startup community, there is a saying, “slow to hire, fast to fire” (or some variation thereabouts) and I think that’s very relevant here.
Look at the Truth
Understand and accept the current situation, even if it’s bad. The author mentions that regardless of the situation, the successful ones accepted their circumstances. Only once accepted, can you commit to making a change.
The first step in solving a problem, is to recognize it exists. Zig Ziglar
Good leaders don’t blame others in those negative circumstances.
No matter how bad things, leaders stay optimistic. I think the balance of staying optimistic but not lying to yourself is a very delicate balance. The book, Power of Positive Leadership has a great section about that.
Hedgehog
Be a hedgehog, not a fox. A fox knows how to do many things and is clever. A hedgehog only knows how to do one thing deeply and with focus. Companies that don’t tend to win act like a fox, they do many things but lack consistency. I know that most startup books would say start off by focusing on a niche and doing that better than anyone else before spreading out.
The author says that any action taken by a person in the company needs to do the following 3 things.
- Identify your passion.
- Identify what you can do better than anyone else.
- Identify how you can capture the most value from a market.
Find the overlap of all three on give up on any idea that doesn’t fit those requirements.
Discipline
This does not mean that leaders will become tyrants. It means all employees have a level of discipline where they can lead themselves to some extent. Still giving freedom and responsibility to work autonomously is essential.
Leverage Technology
This section would seem to go doubly for companies who are in the tech space. Technology without purpose is indiscernible from art. This can also apply to developing a product for a customer and not just leveraging technology within the company as the author suggest. I see a lot of engineering leads implement technology for the sake of technology; I used to be guilty of this myself. However, a feature that doesn’t serve a customer is not valuable and a waste of engineering and product resources.
The author mentions that it should fit the “Hedgehog Concept” mentioned above. I would add that using a lean product approach (not agile in this case) is also a good way to assess the value of a piece of technology. The same way your organization uses the lean product development methodology for a customer could be recycled for use when selecting a technology internally. Even the author mentions a de-risking technique commonly used by startups practicing lean product development.
Long Haul
This isn’t a quick process. Even following the above theories it will likely take years of dedicated and disciplined work. The road to get to excellence will take a long time to traverse. So, “be prepared to invest years” as Peter Thiel says in Zero to One.
Outro
One of the benefits of such a dated book is we get the benefit of seeing the future performance of some of the companies referenced here, without waiting. This lets me see if those companies changed to fail (going against the information presented in the book). Or if the author was just wrong. From this, I can decide how much I trust the author in the future, which is handy. Think of it like the test-dev set in a machine-learning algorithm.
