As a data professional, I am just amazed by all the recent developments in the area of generative AI.
While some call it hype and are willing to quickly write it off as just another tech trend, others are convinced it is a game-changer.
Regardless of which stream you support, it is hard to ignore the transformational possibilities generative AI can bring to the future of education and the workplace.
To back up this statement, it is enough to mention that Harvard University is introducing an AI chatbot into classrooms this fall (fall 2023) to approximate a one-to-one teacher-student ratio. The students will use the Harvard-developed chatbot to guide them to solutions rather than to provide them with straightforward answers.
For me, this is a clear indicator that Harvard is triggering a wave of change in how the new generations will learn and, consequently, work.
Meaning, generative AI is not just a passing trend, and we need to start finding a way to adapt our working environments to it.
Despite my enthusiastic view of generative AI, I have never had such FOMO before.
In other words, although I have navigated through various data roles in the past 12 years and gained knowledge of machine learning concepts, I am not able to keep up with the new developments in the generative AI area.
The new terminology, the concept of prompt engineering, the development of new large language models, numerous apps and solutions built on top of them, new e-learning courses, and the sheer volume of posts on this topic — all of this is simply overwhelming.
Moreover, I can’t shake off the unsettling feeling that some of my data skills are now just, well, obsolete.
The idea that my business colleagues will replace my hard-earned query skills with a few keystrokes is scary.
However, when giving it a second thought, I have to admit that I don’t even mind the fact that some (but only some) of my skills will be replaced. Executing ad-hoc queries several times per week to answer the same repetitive business questions is something I never liked to do.
Among others, I am aware that “me” being in between the data stored in the data warehouse and the generation of business insights is just slowing down the decision-making process.
The other thing I am aware of is that this transition, i.e., my substitution, won’t happen overnight.
First of all, the current development environments need to be adapted, i.e., they need to be more “business-user friendly”, and less “developer-friendly”.
Second, the business users will need to gain a technical understanding of what is “behind the hub”. The freedom to generate analytical insights from natural text entries comes with the same issues.
Problems like slow insight generation, incorrect insight generation, enrichment of the insights without new inputs (new data sources), and the technical process of insight quality checks will still exist.
And someone will still need to handle and “fix” these problems for the business users.
In other words, generative AI won’t be able to easily replace fundamental data knowledge.