Big data is a valuable resource, provided that the data collected is given meaning and value.

It is the data scientist’s role to sort through and interpret this data so that it can be used to guide the business. As a reminder, big data refers to all the digital data collected by websites, apps, physical sensors, social media and so on. This can include written information as well as videos and images. Artificial intelligence relies on data to improve its performance.

Overview and responsibilities of the Data Scientist

Their work is absolutely vital to a company’s management, as the data provides operational and strategic guidance (HR, management, marketing, etc.).

For example, in the telecommunications sector, thanks to usage metrics and forecasts, the sales department is able to offer personalised packages. Often confused with the data analyst – who specialises in a specific category of data relating to a business or strategic issue – the data scientist has a more holistic view and a more cross-functional role.

Business implications

Data scientists work across a wide range of topics and in diverse sectors: energy, telecommunications, media, music, fashion, banking, insurance, tourism, healthcare, transport, finance, agriculture…

Big data is a veritable tidal wave sweeping across all sectors, which have fully grasped its strategic and financial implications.

Ethical implications
The GDPR, the legislation governing data retention and protection, has caused quite a stir.

There are indeed numerous ethical issues related to data (data breaches, cybercrime, commercial incentives, environmental impacts, etc.).

Data scientists must, of course, be aware of these and take them into account in their work. Legal experts in data and AI, alongside ethics officers, provide solid support for specific and sensitive cases.

aivancity’s programmes incorporate all aspects of artificial intelligence and the challenges it presents – whether technical, technological, commercial, ethical or legal – into their curriculum. These are comprehensive, blended learning programmes that enable future engineers to acquire the highest possible level of knowledge and a broad range of expertise.

Key skills

A data scientist is an excellent statistician and a very good analyst. They are proficient in at least one programming language (Python, Java, Perl, C/C++, etc.) and machine learning. Algorithms are right up their street.

In data science, curiosity is not a bad thing: asking questions enhances one’s level of expertise. Identifying the problems to be solved and the opportunities that data can offer are essential skills.

Patient and adaptable, the data scientist knows how to present their findings in a way that is accessible to all their stakeholders. Keeping abreast of technological developments is essential, and experimentation helps drive the evolution of practices within the profession.

Trends and drivers of change

In small businesses, data scientists are often required to take on other roles, such as data miner and data analyst. They may also work for a service provider, such as an IT services company.

In a larger organisation, they work with the big data team. Becoming a Scrum Master – that is, a big data project manager – is one possible career path. Managing a team and a department is a logical next step in their career. Entrepreneurship as a consultant is another.

The systematic automation of data collection and sorting processes is expected to save data scientists 80 per cent of their time. This shift in their responsibilities should leave them more scope for creativity and strategy.

With its enormous potential and significant challenges, big data is a sensitive resource. Only experts such as data scientists can handle it whilst balancing technological advances with ethical considerations.