Gen Z Is The Key To Scaling Up Data Science

The question of tomorrow is not how we will utilize data science properly, but rather, how we will scale up data science initiatives. In specific, the "scaling up" that I'm referring to is the optimization of the data gathering process and machine learning algorithms. There's more than meets the eye to answering this question––in fact, it's not just pure, raw numbers that matter, even though we happen to be talking about literal data. We also need to take into consideration the inputs behind those metrics: humans. The most common flaw associated with the failure to scale up data science initiatives is when enterprises fail to think about the people. Without a human-centric focus, and one that champions diversity at its core, data collected will resemble biased junk and will produce useless, even harmful outcomes for the enterprise. So how can this problem be fixed? How can we scale up data science? By focusing on people and non-homogenous teams, which is exactly what Gen Z brings to the table.

The importance of human-centricity

A people–oriented society is paramount in an AI-driven world, and in order to achieve this objective, data science implementations must be scaled in accordance with the notion of human-centricity. The convergence of AI and humans calls for personalization, as the premise of the future is unity with technology––not merely having artificially intelligent systems living alongside us, but with us, fully integrated. Therefore, data science measures must fall in line with the people-oriented approach, or the societal objective for a digital future will not proliferate, as personalized AI and machine learning algorithms will fail to operate at optimality. Gen Z, however, champions the human-centric approach, and will enter data science campaigns in this manner, hence scaling up data science efforts that are necessary in order to champion an AI-driven society.

Moreover, one of my Gen Z peers said the following when prompted about this topic:

“I don’t think that we can have a future without having AI and people work together. AI should be a for the people thing, so all of our new ideas and innovations with AI should focus on helping people and society. That’s the point of advancement. And obviously making sure our data science follows that thought is key to any future success.”

Branching off of the “for the people” thought, why do we collect data in the first place? We gather data because it helps us learn from our past in the hopes of better projecting the future––we collect it to help us accelerate progress and toaid us,humans. This is why Gen Z is so powerful in scaling up data science––it understands the great significance of the human-centric approach.

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