The author starts by pointing out that some paradigm changes are self-discovered and others are forced from the “outside” and he further explains this statement with some examples like Pepsi/Coke/SunnyD, the airline industry, loyalty cards in supermarkets, or the internet and how it affected retail. He also touches on the topic of how R&D is different in universities and in companies and how these two different “worlds” interact. Then he continues with the traits of leadership where he emphasizes the importance of developing empathetic relationships with your team, being honest and trustworthy, and other traits and actions that will help you lead and grow your team. One interesting point he makes here is to have a team member who is a “back channel” to your team. Often times this is a person that others don’t feel is a threat to their own ambitions. Another interesting point was to encourage the use of distinctive vocabulary and terms to describe team members and their ideas. By doing so your team is creating its identity or “brand”.

When the author talked about behavior, rationality, and decision-making he leaned on the work of Daniel Kahneman Thinking, Fast and Slow and he strongly recommended picking up his book. He summed up heuristics and biases that you can read more in-depth in Kahneman’s book, and then he continues with the need to provide enabling frameworks in order to encourage people to improve themselves. He continues by emphasizing the need for people working in the data field to accept the “code of conduct” as they may find themselves dealing with personal data or creating models that can affect people’s lives. One important trait when working with data is also presenting the data and how the development of successful presentation skills is a never-ending journey, not just a destination.

In his third chapter, he goes on about the importance of protecting intellectual property when creating models and algorithms and how that is an uphill battle if not impossible. Also, an excellent point he makes in this chapter is that you should never confuse activity with progress, and how to cover your basics when doing an interview with your clients, prospects, or your outsourced partners. For example when I worked in IT and when we were pitching projects and when most was already agreed upon we never discussed the possibility of key stakeholders walking away from a project or even either company. When reading this, and thinking back it would have been smart for both parties to make sure to know the succession of each key stakeholder if it would happen that any should leave. I’m definitely taking this advice for future projects. The author then continues by talking about loyalty and for whom employees (who work with clients) mostly work for? Is it the company that employs them? In many cases actually no, but for themselves. They work for their reputation which will afford them better employment in the future. What can you do about that, if anything? Also when leading a team remember that the blame game doesn’t do anyone any good, it only weakens the team. And one more thing is weakening your team, and that is underperformers. The author makes a good point on how keeping people on your team when they underperform even though you put into place frameworks for team members to improve starts to erode the trust and engagement of other team members that do perform. So it is best to do regular overviews of your team and let go of people who are holding the team back.

In the last chapter, the author goes over the hype and the adoption curve that new technology can bring and how that affects the usage of this new or previous technology. As an example, he provides blockchain technology and AI. In the end, the author gives us his perspective on what the future hold for data science and from where new data scientists and analysts will come and what will their mission be.