After discussing 3 competences I could carry over from mechanical engineering to digitalisation and data science, I would like to reflect on 3 new aspects I have learned on the way. Let’s go!
1. Iterative mindset
For me, a fascinating aspect of IT or data solutions is that they inherently allow for a very iterative way of working. If you, for example, want to solve a specific technical problem by analysing collected data, it is often quite simple and quick to re-process the data for your analysis with refined processing parameters. By that you might be able to improve your data analysis’ outcome from good-enough to great over time. In the physical world, if you would look, for example, at building a bridge, the freedom in later phases in the project is normally significantly lower. Really important is that you can play this possibility for iterations as a strength in your working mode. You can generate working prototypes very early in the process, discuss it hands-on with end users continually, and therefore develop step by step into the right direction.
2. Software competences
In future, more and more companies will become software companies. Executives of many (traditional) businesses have realised this and set their organisations’ paths in this direction. For me personally, when leading digitalisation/data science initiatives, the same is true. At the end of the day, data science and especially its realisation is also about mastering software.
Throughout my eduction I was never in touch with any input on software engineering. Of course, you find proven concepts for great design, development and maintenance in the field of software engineering, too. While it is not the task of a program manager to dig into all details, a basic conceptual understanding for software versioning, test-driven development, CI/CD pipelines, or DevOps - only to name a few topics - is a crucial competence for being able to communicate effectively in your teams and make the right decisions.
Side note: especially in the field of software engineering and software mangement is really simple to build up the basic competences, as countless free online courses (MOOCs) and good YouTube videos are available.
Finally, the digitalisation business is also a people business. Therefore, you will always deal with communication challenges …
3. Communicating innovation and expectation management
Let’s start with a trasitional engineering example: Everybody can relate to the functionality and utility of a car or a power plant. When it comes to digitalisation and data products, things get more difficult. It is really hard to explain the value of a technical solution which is not yet existing, sometimes very complicated and overall … intangible. If people are used to solving “physical” problems, as it is the normal case for people in industry, you have to find new ways of explaining the value, and to manage expectations in an appropriate way. You will not get a buy-in if you underpromise, but it will also certainly lead to frustration if you start overselling your ideas. Open and very tailored communication and use of multiple media, creative design workshops with specialised methods, focus on continuous prototyping, etc. can be very helpful in this regard.
If you would like to read more of my blog, check out the list of posts here!