Data scientist: a strategic profession at the service of management

More and more data to process

In the digital age, the coexistence of social networks, websites of all kinds and online businesses implies large volumes of data that must be managed or archived. It is possible to classify this data to identify societal trends. It’s easy to determine what people like best so you can target them and get their attention to encourage them to take action.

To achieve this, we can for example use data concerning online purchases, the traffic of a site on the net, the behavior of its users or the information shared on social networks. This massively collected data represents “big data“. They are analyzed and used for commercial purposes, but not only.

The importance of machine learning for data science

These data analyses are mainly done through technologies in line with machine learning, a subfield of artificial intelligence that has emerged thanks to the exponential increase in computer computing power. Thus, it is possible to analyze and classify information, but also to make predictions in real time.

To do this, it is essential to know how to correctly use the various data and the results that emerge from them. This is where the data scientist is specialized: he helps those who call upon him to process the information and extract the essential elements.

Data scientist: a job that’s on the rise

Data scientists are an indispensable aid to corporate decision-making. Their job is to organize and analyze large amounts of data. To achieve their goal, they have at their disposal more and more sophisticated computer programs based on “machine learning” and very often participate in the design of customized information systems to further their activity.

A long working process

The work of the data scientist begins with the establishment of the process of collecting the relevant data and ends with the decisions that are based on the results of the analyses performed. As explained earlier, there are many different sources for collecting data. The first thing to do is to put in place tools to collect and sort them.

Structured data is obviously the easiest to manage. These come from your accounting data, GPS coordinates or traffic data from an Internet site, for example. Unstructured data is for example from customer reviews, emails or videos. More work is needed to make sense of the latter data.

One objective: to make sense of the data

Whether the information is structured or not, data scientists are able to process data and make sense of it. The final results of an analysis must be simple enough to be understood by all stakeholders, especially those working outside the field of new technologies.

A data scientist’s approach toinformation analysis depends not only on the industry, but also on the specific needs of the company or department in which they work. For a megadata specialist to make sense of structured or unstructured data, business leaders and managers need to talk about what they are looking for.

How is a data scientist trained?

Until a few years ago, there was no specific curriculum for data scientists. As a result, the positions were filled by computer engineers, individuals with a master’s degree and PhDs. Doctors were the first people who were really competent in the field, as they paved the way by working on the subject and publishing a large number of studies.

Over time, universities have been able to draw on this group of teacher-researchers to develop specific courses. Initially, options in data sciences were set up in the different curricula. Comprehensive studies in the field have emerged in parallel with a growing demand from companies. Currently, this demand is still higher than the supply.

A multidisciplinary profile with complementary training that is highly appreciated

In most cases, a deep knowledge of data science, artificial intelligence or applied mathematics is unfortunately not enough to make a good data scientist. Indeed, some sectors require the mastery of skills in additional areas. For example, in highly specialized industries, very specific knowledge will be required. The skills required by the marketing industry will be different from those required to work in healthcare or education. Additional training is therefore very often appreciated.

Also read in our “project governance” file:

• Project comitology: the governance bodies of an IT project and their roles
• Steering and governance of an HR project: which profiles should be involved?
• Steering and governance of a Finance IS project: which profiles should be involved?
• Steering and governance of an IT project: which profiles should be involved?
• Project governance: what role for the steering committee?
• The actors of a project team: organisation, role and skills
• The IS manager at the heart of the development and evolution of systems
• HRIS Manager: what role in the evolution of HR Information Systems?
• IS project manager : what role and responsibility in an IS project?
• Functional consultant: a role close to the business processes
• Technical consultant: a profession at the heart of technological development
• Solution architect : a profession that manages development and deployment
• DevOps Consultant: role, missions and development skills
• Data Protection Officer (DPO): what roles and missions?
• CISO: a key job within the business for system security
• The service delivery manager at the heart of team management
• Scrum master, a key profession for Scrum project management
• Data scientist: a strategic profession at the service of management
• MOA / MOE: how are the roles divided on an information system implementation project?