Huge archives of data of great interest to organizations are generated in real time. This can include feeds from social networks, information associated with loyalty cards and much more. Mastering the understanding of collected data is therefore a key issue for companies, and there are few professionals capable of reading and making sense of them. Find out more about the job of data scientist or data analyst, a multi-skilled megadata expert at the service of management. Project governance almost systematically requires a steering committee.
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 find out what web users prefer, so you can target them as effectively as possible and grab 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. In this way, information can be analyzed and classified, and predictions can be made 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. So the first thing to do is to put in place the tools needed 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 those involved, 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 also the first people with real expertise 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.
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