Data Scientists Are in the New Professional Bubble

Nowadays, all organizations are affected by the digital transformation of their activities. New selling techniques, promoting products differently, actively animating your community of customers, and producing products with more and more customizations are some of the many avenues where transformation is possible. 

As companies undergo production transformation, the need for managerial transformation is critical.

The two main objectives of managerial transformation should be: to grow their teams and promote employee empowerment. “Managers of the future” should be able to create motivating working conditions for their colleagues. They should also help employees continuously acquire new skills and become expert users of new technologies.

Yesterday’s economy supported the belief that knowledge embodied power. We believed that we were turning more and more to an economy of talent. In fact, the first workers to adopt new technologies into their daily life quickly overtook their colleagues. A young engineer who used the first big data tools with ease often made more progress in his factory than a conservative expert who camped on his historic positions. 

But now, it is increasingly clear that talent is not our economy’s greatest power: data is the new power, in the form of business information. When a company masters capturing and enhancing its data, it finds itself in a position to rapidly gain strategic intelligence and be a competitive force in the marketplace. 

The onset of data transformation usually leads to standardization of key goals throughout the company: 

  • collect everything so as to not lose any opportunities; 
  • enhance raw data by recalculating different strategies (via digital twins) such as factories, products, and services;
  • benefit from data as much as possible thanks to new applications that offer quick and easy control of AI.

So in the context of a company’s complete transformation—including management and information systems—how can we explain Industry’s craze for data scientists? 

Technology has advanced beyond maximizing open-source data solutions or using Python code for prototyping software applications. These tools alone will not facilitate a complete transformation of a company’s information system. 

These days, we understand that raw data is a strategic asset.

How can we invest in teams of data scientists so they guide us in the key stages of transformation? 

During a time when both intellectual and physical tasks are becoming automated, we’re asking talented craftsmen to create the company’s future operational standards from scratch. But you don’t dig a building’s foundation with a shovel! 

If the opportunity to run trials, tests, or prototypes to better understand new technologies, then it is delusional to ask a few employees, however brilliant, to redevelop technologies that already exist: powerful, professional applications. 

Should we use open-source bricks to build a massive cybersecurity infrastructure like Microsoft Azure or AWS? Should we manually code all the case-by-case AI that will allow us to solve complex prediction or prescription problems? Should we blindly trust a few isolated individuals who are, in essence, exposed to cognitive biases or human interpretations?

No, no, and no!

Digital transformation presents an immense opportunity.

Contextualized information deserves a standardized process; it is a precious, intangible asset that cannot be understated.

Data scientists are going to disappear. Their job is to create applications that, for the most part, already exist. Additionally, these professional applications are more efficient than the rapid prototypes that data scientists create in-house. 

In order to guarantee your business’ sustainability, you need to design a solid and efficient information system based on the best application solutions that are already readily available. These solutions guarantee a maximum level of cybersecurity and the absolute integrity of the company’s strategic information. Even more so than machine learning, a transitioning company must learn from qualified and mastered data to quickly reach its futuristic vision.

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