The Biggest Blocker in Doing Data Science
The biggest problem of doing data science or data projects in general is not the lack of technical know-how within the company or within the technical team.
The biggest problem in doing data projects or starting data projects is actually the lack of communication between the management team and the data team.
The quarrel between the management team and the technical team
The technical team often only looks at their own problems only. They care only about the tasks given to them and they just want to get on with the work. The management team view them as naïve. They might feel they know how the company finances work so they are more important. This is not great for the progress of the company and when tough times hit, the management is likely to get rid of people from the technical team.
What I have seen work really well is a close relationship between the technical team and the management team. The management team keeps the technical team well informed of the business pain points and the technical team uses all its energy to solve those business problems. The technical team keeps the business team well informed about the challenges and all the problems that can occur in the near or the distant future.
The story of Lisa Su – The Top-notch Engineer CEO
Sometimes technical people can see solutions that non-technical people simply cannot. A great example is Lisa Su. AMD used to be a joke. Then, it got the best semi-conductor engineer to be its CEO, now with less than 10% of the budget of intel, it is challenging Intel in the PC chips market for power and performance and will soon likely overtake Nvidia in the graphics market. The next-gen PS5 and Xbox have AMD chips inside.
How to build a relationship between technical and management team.
This relationship building between the technical and management team can be done very well through a person who understands the technical language and can translate it to a non-technical language.
How to find commercial pros and cons in technical team’s language
For example, technical team may talk about a technology being outdated and usage of this technology is cumbersome for them. The technical team may only think of technical reasons, but it’s very easy to see the commercial pros and cons behind their technical reasoning. The translation of this into non-technical language would be that this technology is expensive or requires more time to implement, it will cost the business a new developer to write or to maintain in the future.
Another example would be where technical team may say let’s rewrite the legacy codebase in a newer language that we are using everywhere else. The management team may think the technical team is being lazy and does not want to look after the legacy codebase. However, the commercial benefits are quite clear.
The cost of not communicating with the technical team
If the company keeps to the legacy codebase, it would have to have two technical teams maintain two systems. The legacy system and the new system. When the legacy codebase breaks, the company would have to spend time (think money) and developers’ mental energy (think resources that could have been used elsewhere) to fix it. If the company had upgraded, a single team would look after a single system. A separate team won’t be needed.
Sure it would cost money to upgrade, however, that’s investing in fixing problems before they arise.
If you’re looking for a person who can assess your technical needs within data science or you need help with a data strategy for your data project, get in touch with me.