The depressed Data Science Unicorn
Let’s assume we’ve found one.
The one and treasured unicorn in the data science universe. That one with the perfect match of hard & soft skills, you need to convert data into cash finally—the Special One who could do all on his own. Starting from ideation of a data science use case with the right business impact, establishing a robust data pipeline producing quality ensured data features, providing the optimal model solving the problem, and ensuring performance monitoring for sustainability.
Leaving out the in-between tasks, like stakeholder management, presenting results, and roadmap design and keeping it even more simple, let’s also assume (in that unicorn world) a fantastic ready-to-use data science tech stack is available too.
Given these perceptions and assumptions, what do we then expect from this rare beautiful creature?
Do we expect to have that lonely unicorn being competitive in a harsh business reality where the data product’s time-to-market also defines the success of data science use cases?
If yes, poor little thing! But let’s play this one out:
That bespoken unicorn develops an idea for a data product and pitches it in front of decision-makers. The idea gets approval, and everybody is excited about the promised optimization of a business-critical marketing process nurtured with model insights the unicorn has conceptualized.
The only thing which does not suit us as a stakeholder is the expected time-to-market the unicorn has provided. So what could happen next are three possible scenarios:
In the first scenario, we could say: “The idea is extraordinary, let’s go for it and take the risk of a long time-to-market” — which in the worst case (but not unlikely) could lead the competition to catch up having the same idea but implementing it in a shorter time. The hoped-for advantage would be gone.
Unhappy unicorn, unhappy stakeholders.
Second, we could say we’ll go for the idea but decide for a quick-n-dirty approach and deprioritize essential quality ensuring initiatives. But this causes the so-called hidden technical debt, which will slow us down in the long run, and the hoped-for advantage would be gone.
And again, unhappy unicorn, unhappy us.
Third, we decide everything suggested should be implemented by the unicorn but in a shorter time. Translated into work-life-balance, that means: Massive workload under time-pressure leads to a burned-out unicorn.
Have you ever seen a burned-out unicorn? They refuse food intake, lose the joy of life, and become a spirit-broken ordinary horse over time. Eventually, the unicorn will leave us and willingly joins the bull covered in fire to get a reunion with other unicorns in the sea. And we will never see The Last Unicorn again.
In those three scenarios, which are not exhaustive, the unicorn has no other choice than to become depressed.
But maybe there is one scenario left, which might work for us and the unicorn. If we have a unique data product idea, where the odds are not against us, our competitors will shortly develop an equivalent idea. And in case our concept is so unique that even the long time-to-market will not harm the success of that idea, maybe then we will have a chance of a romantic ending. And even this probably will only be sufficient to produce a prototype or minimum viable data product. As soon as it comes to operationalizing and scaling everything up, we’ll need a functioning data team again. To accelerate and keep our competition in the distance.
In sum: A rare data science unicorn can only survive — but not thrive — in a rare business environment. For a while, at least.
Well, now I am depressed too.
A herd of unicorns is called a blessing
So why looking for a data science unicorn at all if we anyway will need a fully specialized team at the end? Indeed, there seems to be no realistic scenery where a unicorn as a solo-player can get happy working with us.
I do not doubt the existence of unicorns — I saw some of them during my career, but they all ended up in one of the above fortunes. I believe unicorns are not rare because there are only a few existing out there. They are rare because they are hiding. They are aware of what could happen to them if they show up. We should take that fear from them and open up new visions and perspectives in which a unicorn can thrive.
The only reason I can imagine looking for a unicorn is to don’t want it to perform unicorn-alike. I want a unicorn because I know it can cause miracles when it comes to leading a data product team. They oversee the entire data product development process, understand what should be tackled first, and could wait for the next development iteration. Instead of letting them implement every aspect of the data product by themselves in a non-reasonable time, we should encourage them to enable data product team members to become the best version of their own and deliver appropriately. It can organize data engineers, data architects, data scientists, and all other data professionals in that team and allow them to become specialized unicorns. With this in mind, we could build a herd of unicorns.
People use to say, a herd of unicorns is called a blessing; likewise, a well-established team of specialists is a blessing too, no matter the existence of the beloved unicorn. When you had the chance to find a data science unicorn, take the bull by the horns and help to understand its influential role as a team member and support it to become a team leader.
Sounds like a plan! But what if the unicorn just wants to be a data scientist? Well, yes. That might happen, and actually, it is very likely to occur since that unicorn applied to become a data scientist, right?
In this case, we should forget everything written above, stop looking for unicorns, and instead hire a product manager comfortable creating AI Products who can lead a team of data specialists. And actually, to be honest, this is even the best solution for all parties. Since a product manager is precisely meant to organize, coordinate, challenge, and encourage a great team of specialists to do their own tasks, even it takes responsibility for the in-between tasks mentioned.
Wait! What? — Yes.
I love Unicorns, but we don’t need them because we can not offer the fairy tale environment in which a unicorn can thrive. So let’s leave them free. They will show up when they are ready to take new heights and dimensions. And after all, a well-coordinated team of specialists is a blessing too, right?
So why even bothering?
That was fun writing my very first medium article — an excerpt of my thoughts & experiences. I hope you liked it, too.
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