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So you want to be a Data Rock star?

Zhiwei Jiang
By Zhiwei Jiang, CEO, Insights & Data and Ron Tolido, CTO, Insights & Data, Capgemini

Fine. We all agree that “data is the new oil” is getting a bit worn out. ‘Smart energy’ works a lot better as a metaphor. It’s settled. Now, about that other cliché in the IT industry: “Data Scientist is the sexiest job on Earth.” Is it really still better than being the lead singer in a band?

You wouldn’t necessarily say so, if you look at recent publications. AI and do-it-yourself tools are coming, rendering the role of a Data Scientist potentially obsolete through intelligent automation and through augmenting power users at the business side. And then there is a quickly growing number of off-the-shelf analytical and AI solutions as well, ready to be used by just adding water: your own data.

If we look at the latest projections of the World Economic Forum in their Future Of Jobs report , we see – of course – the evolving need for skills in the technology field, combined with critical, analytical thinking and creativity, and – arguable due to COVID-19 – active learning, resilience, stress tolerance, and flexibility. And look at that top-three job roles in demand: Data Analysts and Scientists, AI and Machine Learning Specialists, and Big Data Specialists. Without diving into what exactly might be the difference between these job roles (or why ‘big data’ is still a thing, for that matter), it is obvious the outlook is not so bad for anybody setting their mind on being an ace in data.

But it’s gonna take time, a whole lot of precious time. And a very particular set of skills, that have quickly been changing positions. Here’s our take on the top 7 skills that we believe need to be mastered to become the leader of the data band. We have combined it with some tips for entertainment – safely to be enjoyed at home of course – to get you inspired.

1.     You do the math

Yes, AI will definitely assist and augment more and more in the heavy lifting of data science and analytics. But having a deep affinity with algorithms and logic, even when they go into new areas (such as deep learning and reinforcement learning) is key. You need to appreciate what is going on under the hood in order to make informed decisions about which ways, approaches, and tools to use for the problem at hand. After all, a fool with a tool is still a fool. You may not want to go as hardcore math as Alan Turing, but watching The Imitation Game is always illustrative.

2.      OK Computer

Admittedly, in the end it is all about creating outcomes by the data-powered enterprise. Technology is only a way of achieving that. But every business now is a technology business: it’s technology that brings us a surge in real-time data points from so many more sources; it provides us with the means to collect it, store it, integrate it, and analyze it. Technology enables us to visualize insights at any point of action and take intelligent, automated action. It’s not for everybody to become a nerd such as in the IT Crowd series, but you don’t want to be a Jen Barber either.

3.     I see you

The WEF puts emphasis on problem-solving and analytical skills in its outlook. And rightfully so, it’s a complex technology business world that needs a solid IQ level. But it is also a humans’ world, and humans aren’t algorithmic, data-driven, intelligently automated beings. Understanding the problem is more crucial than solving it, hence you better work on upping your EQ – creating empathy, conversational capabilities and the ability to respectfully balance the objectives of being data-powered and being human. Jada Pinkett Smith’s Red Table Talk will bring you a healthy shot of EQ.

4.     Let’s get down to business

In a technology business, the best use of data is typically made far from central IT and data management, right in the middle of the business. In order to thrive there, your sector or domain knowledge needs to be nothing less than substantial. And then, when you master that sector or domain, make sure you have your own list of relevant external and open datasets – and increasingly also algorithms – to bring in into every new project. It will be the litmus test for your industry insight. Want to get a flavor of a domain, e.g. Marketing? Mad Men is our killer recommendation.

5.     What’s your story?

Few things are more difficult to bear than a poorly understood, unappreciated Data Scientist. Still, it happens regularly that even the most imaginative, deeply smart insights and predictions do not land in the business, let alone that they are being acted upon. Cold hard facts do not often do the trick, you see. Proper storytelling and visualization skills are needed to tempt your clients into the data-powered journey. Have a look at the Fargo series and notice how it over and over comes back again with another compelling “true story”: bingeworthy avant la lettre.

6.     Just be good to me

Being “data-powered” seems tempting and rewarding, but pronounced in a certain way, it suddenly sounds eerie. It’s up to every practitioner to seriously understand the ethical considerations of data and AI, and then live and breathe them every single day. Data the good way, data for good purposes. There is no shortcut, no workaround. Stay tuned, as very soon we will introduce you our code of seven principles for Ethical AI. Want to see an all-too relevant ethical dilemma evolving? The Circle with Emma Watson and Tom Hanks brings you right there.

7.     I can do it in the mix

Want to “Be Like Water,” ultra-agile, ultra-adaptive, and ultra-responsive? You’re going need tightly integrated, multi-disciplinary teams that rapidly bring solutions to operations. No times for silos or egos. Software development brought us DevOps, DataOps was the response of the data community. And it’s only the beginning, as all skills mentioned here must be put in the same cocktail, both within the team and within the individual team members. Specialization is bliss. Fusion is better. Ask Debbie Ocean about multi-skill teams and see how that works out in Ocean’s Eight.


Zhiwei Jiang