What do McDonald’s, Nathan’s Famous Hot Dogs, and Data Science have to do with one another? More than you might realize. Stay with me here. McDonald’s and Nathan’s knew something businesses have yet to learn when it comes to data.
These businesses were the epitome of efficiency. Nathan’s came first and took a page from Henry Ford’s playbook to create systems. The idea was to have one person in charge of one thing. Hello, assembly line. McDonald’s built on that idea. Everything must be the same, in every store, everywhere, no matter what. Now, we’re cooking!
In Data Science, people imagined people in white lab coats who knew everything about everything. Except, they didn’t and don’t. Data Science was a new shiny object for businesses to get ahead of the curve and in keeping up, threw a wrench into what was once siloed, then became teams, and needs to be, well, not siloed exactly. Just separated a bit. And Data Scientists could name their price. It was the sexiest job of the 21st century after all, right? Well, the beginning anyway.
Data Grows Up and Up and Up
Data has evolved and so has Data Science. We’re much more specialized than we have ever been and yet, one industry and one type of professional within that industry is tasked with a wide array of projects or problems which perhaps should be left to those who are more hyper-focused on the problem. Otherwise, you’ll get McDonald’s making filet mignon. It may take longer, the ingredients are more expensive, it’s cooked differently. You get the idea.
More Data equals more problems equals more Data Scientists. But who are Data Scientists? They are the Business Analysts, Statisticians, Data Engineers, Data Architects, and so much more. To solve a problem, though, you need to know first what you want to solve and that can help you figure out who to hire to help you solve it.
As advancements scaled, so to did business problems, and to solve problems you need people from different backgrounds for different, new, and innovative ideas. As things scaled, the role of the Data Scientist became more ambiguous and unwieldy, and Big Data embraced Machine Learning and Artificial Intelligence. So, rather than be lumped in with one title, the titles and their job descriptions shifted the world of Data Science and its Scientists into hyper-focused roles such as Machine Learning Engineer or Artificial Intelligence Engineer. This shift challenges the status quo much like Data Science did in its earlier days.
If you’ve ever heard the phrase, jack of all trades, master of none, you may realize the scope of Data Scientist has become too broad. Below are 3 hyper-focused in-demand skills for the future:
- Artificial Intelligence (AI) and Machine Learning (ML). These jobs could include Computer Vision Engineers, Robotics, ML Engineers, Researchers, and Developers.
- Cloud Computing. Think Amazon AWS Engineer or Microsoft Azure professional.
- Natural Language Processing.
Be specific and focused. Focus on specific and tangible areas where problems are yet to be applied and solved. Avoid generalizing people and roles. Even in cybersecurity, wouldn’t you rather have a professional focused on the security of your business with experience in the cloud? Enter Chief Security Officer who may suggest a Security Engineer or Ethical Hacker to test your safety protocols.
So, what makes McDonald’s and Nathan’s so special? They wanted to create a unified experience and have done so. Your McDonald’s experience in San Francisco is the same as in Ohio is the same as in New York is the same as in London. But to create this experience, these two fast food chains knew it would require one person focused one job working as one unit. A team all working toward the same goal. Maybe it’s time to take a page from their playbook?
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