On Can, Do, and Want – Why Data Culture and Death Metal have a lot in common
Recently, while working at statworx, I experienced a sense of déjà vu regarding the topic of data culture. As the Head of the AI Academy, my main responsibility is to convey my enthusiasm for artificial intelligence, programming, data, and cloud computing to my clients. This often requires projecting my passion for these subjects onto individuals who may have limited technical experience, and whose interests may not typically align with transformer models and functional programming
This tension reminded me of something that happened before my professional career.
All beginnings are difficult
Prior to my passion for data and artificial intelligence, I was already a very enthusiastic (hobby) musician – with a special passion for the genre of Death Metal (Note: I don’t want to bother interested readers with more detailed genre descriptions here 😉). During my studies, I was a singer and guitarist in a Death Metal band. For those of you who are not familiar with Death Metal, it may seem like all those “off-key notes” and “growling” don’t require real skills – but let me assure you, it takes a lot of talent, and many people in this genre have years of hard work behind them.
https://youtu.be/WGnXD0DME30?t=25
When you listen to or, even better, watch this music, you are quickly impressed by how fast the musicians today race across their guitar fretboards. However, it’s essential to recognize that every musician faces a challenging beginning. Those who have learned an instrument can attest to this reality. Initially, it can be demanding to navigate through prescribed teaching materials and maintain the necessary drive to acquire techniques, with the ultimate goal of performing a decent piece of music. At first, it was very difficult for me to get excited about notes, rhythms, and finger exercises or to stay on task with the appropriate perseverance.
Generiert mit DALL-E. Prompt: death metal concert with view from stage to crowd, guitar in the foreground with bokeh, photorealistic style
Let’s get creative
At the beginning, the songs were not particularly good or technically demanding, as I had not yet learned any significant guitar or singing skills. But then something happened: my motivation kicked in! I realized how these techniques and skills allowed me to express my own feelings and thoughts. It was as if I could create my own products.
I wrote more and more songs and almost unnoticed learned important skills on the fretboard. It became my personal mission to stoically master all the necessary finger exercises in order to be able to play ever more complex structures. At the same time, I became part of bands and a local music scene where we inspired each other at concerts and kept motivating each other to write more complex and better material. Here, we also inspired more, mostly younger, music fans to try their hand at this music. They joined in, listened, and thought, “I want to be able to do that too!” So they started writing their own songs, learning their own techniques, and becoming part of a creative cultural scene.
Skills alone are not everything
One may wonder what this little excursion has to do with data culture. The above theme has also been reflected in my work with data culture. In our AI Academy, we mainly focus on topics related to data literacy and related skills. Initially, I made the same mistake in my thinking that hindered me when learning my instrument: skills are everything – or with skills, everything else will somehow come.
I assumed that the skills taught are so important, so relevant, so productive, and especially so attractive to learners that after learning these skills, everything else will automatically follow. But that’s not the case. Over time, through our training, we have reached an ever-increasing circle of people, including those with different core competencies. These are people who cannot or do not want to be evangelists or enthusiasts for matrix algebra in their main activity.
The following questions are always at the forefront here:
“What does this have to do with me?”
“What does this have to do with my work?”
“How could this be valuable for me?”
And just like in my story about songwriting, playing concerts, or exchanging ideas within a music scene, I also had the same experience with data and upskilling. Some of our most successful training formats, the AI Basics Workshop and Data Literacy Workshop, enable the most important topics and learnings around data and AI to be made usable for one’s own company – with the possibility of generating their own ideas for the use of these technologies together with experienced AI experts. This is not only about learning how AI works, but also about interactive and guided exploration:
“What does this have to do with me?”
“How can I create value for my environment with this?”
“What problems does AI need to solve for me?”
Motivating ideas
At first, we noticed how enthusiastic training participants interacted with the content, and how the mood in our courses shifted much more towards a growth mindset:
Not focusing on what I can already do, but rather asking what I still want to achieve and what I want to achieve.
On the other hand, our courses quickly became popular with our customers’ employees. We were, of course, pleased with the word-of-mouth that contributed to the recognition of the high course quality and exciting topics. However, we did not anticipate that the ideas generated in the course would develop their own dynamic and, in many cases, generate even greater impact in the company than the course itself.
Similar to concerts in the death metal scene, new enthusiasts could also be won over here. They realized that the person who successfully drives a use case forward was also at the beginning of data and AI not too long ago.
“If others have achieved that, I want to try it too, and I’ll figure out how to learn the finger skills on the way.”
Can – Do – Want – A constant cycle in the organization
And so three important dimensions came together for us.
- Can – Mastery of skills such as good guitar playing, project management in data and AI, programming, or basic knowledge in data analysis.
- Do – Regular and ritualized work on the topic, conducting initial use cases, and exchanging ideas with others to learn the language interactively.
- Want – Creating sustainable motivation to achieve goals through initial successes, inspiring exchange, and a clear vision for the potential impact and value generation in the company.
The three dimensions form a cycle in which each dimension depends on the others and has a positive effect on the other dimensions. If I improve my guitar playing skills, it will be easier for me to develop new ideas and share them successfully with others. This creates further motivation to tackle more skills and challenges.
That is why data culture and death metal have a lot in common for me.
Let’s connect if you’re interested in diving deeper into the topic of data culture, including its three dimensions “Can”, “Do”, and “Want”.
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