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Mohamed Ben Hamoudine   |     |  Read Time 5 mins


Welcome to another article in our data science series. We will discuss the challenge that companies are facing right now. We already know that artificial intelligence is gaining budget in companies and a part of it goes into creating a team. But after that, the team is released into the wild in the hope that they will produce expected results when, in reality, failure is almost unavoidable with this approach.

At Zifo, our expert team has provided recommendations and consulting to a variety of research-based organizations. Therefore, this is what we will discuss today around a simple question: What team to create internally to carry out your data projects? It is indeed worth asking how companies can, in a thoughtful way, make data teams that are genuinely productive.

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The term “Data Scientist” has received relatively recent media coverage. This function existed before this appellation, wherever data needed to be managed and analysed. So, if you want to build a specific data team, you must know that it is not just a matter of hiring the best data scientists. It is also about making the best use of the skills of the employees who are already present in the company, whether they have received dedicated training in data science or otherwise. They can add their knowledge of processes and people to guide the team and ensure that it remains in direct contact with all business lines and real business goals.

One question now may come to mind: How do we hire the right people?

As we develop machine learning skills, we must ensure that the group and people do not become disconnected from the business. We are looking for people who will produce “the magic” and help develop a data-driven project mindset. Still, we must ensure that the people involved do not “innovate” in the wrong direction and work in areas that are not of value to the rest of the organisation.

After having taken care of this potential problem, it is then all about getting the right balance. In general, the most successful data science teams – and one could even say, in general, the people who are most successful in their professional life – know how to find a harmonious balance between the pleasure of doing the work and the concrete expression of their creativity, while carefully weighing the risks and rewards involved in such an approach.


Part of the success of young and motivated data teams can be attributed to their enthusiasm and agility. They are willing to win and are still small enough in number to maintain the required flexibility and know-how to think outside the box. But these powerful data teams can also fall into an unproductive trap, which consists of focusing too much on the “fun” and creative aspect and lacking discipline.

The consequence will be that the team will come up with risky solutions that undermine the company or the sector activity they are supposed to help.

One leading solution to help with this matter is to adopt a data culture in your company. Spreading the data culture and making data-driven decisions is a responsibility shared by everyone within the company (and not just the data team’s responsibility). It can eventually manifest itself in the development of a “self-service” data team.

Automation, predictive analytics, machine learning, and artificial intelligence are essential and positive business developments. In other words, it does not mean cutting jobs or taking responsibilities away from other people.

To find out more about how Zifo can help with AI, ML, Deep Learning, and scientific Data Sciences, please email us directly at info@zifornd.com