What does a data scientist actually do?

Organisations like to report that they are data-driven, use big data and engage in data mining. That’s all well and good, but what does it all mean? After all, data in its original form exists as a kind of raw material. Similar to crude oil, ore, salt or sand, they are indifferent to being processed further. Even before it is used, the question arises as to how and where data is collected, stored and structured.

This is often where the data programming or optimisation of a suitable tool begins. These activities are the responsibility of roles as diverse as data engineers, data analysts, business analysts, data business partners, data architects and data scientists. This blog post describes which skills data scientists need and which methods they use. What’s more, you’ll discover whether you need a data scientist in your company as well as how they can contribute to a company’s success.

The spread of the internet and digital technology means that copious amounts of data are generated. It is collected, stored and regarded as the currency of the digital age. However, this is where the misunderstanding begins: at first, data is nothing more than an accumulation of character strings. Taken by themselves, they have no cognitive value whatsoever.

On the other hand, information is understood to be systematic, analysed and visualised. In short, a meaningful string of characters. The highly specialised task of data scientists is to process data in this sense.

In order to structure data into information with a meaning that can be interpreted, the first step is to collect and clean it. The latter is important because raw data is never “clean”; it may have duplicates and redundancies, for example. Without cleaning, data scientists run the risk of producing distorted patterns and drawing misleading conclusions. Automation thanks to AI is increasingly helping with this mammoth task.

The process requires data scientists to be proficient in using tools and methods from statistics, software engineering and machine learning. A good dose of perseverance won’t go amiss.

Once the data has been brought into shape, it is then analysed. For example, the aim might be to better understand product use, design A/B tests and prototypes, or expand a service portfolio. Unlike traditional data analysts, data scientists not only summarise the past; they also try to predict the future.

At this stage, they need to be aware that they are working with colleagues and decision-makers who don’t necessarily have an abstract understanding of data. Therefore, one of their most important tasks is to communicate information in concrete terms using visualisations, dashboards, models and reports.

Data scientists analyse large amounts of data using computer programmes, so they should be familiar with the code of common languages such as Java, R, Python, SQL and others.

• Advanced computer science skills include understanding database systems, software architecture and human-computer interaction.
• Data scientists also need to be skilled in statistical analysis. Their task is to recognise patterns in data sets as well as anomalies.
• A prerequisite for using machine learning in this way is to be able to implement algorithms and statistical models that allow computers to learn in an automated manner.

Data scientists play a key role in corporate decision-making. Their skills must therefore extend beyond pure data modelling. Their soft skills must include business intuition, enabling them to understand business strategies and ideally develop them themselves.

This requires analytical thinking to counter abstract risks before they become real problems. Inquisitiveness and the courage to think outside the box help to identify, analyse and validate data (sources) for creative solutions. Finally, critical reflection prevents operational blindness and wrong conclusions being drawn.

The ability to communicate across all levels with all stakeholders of an organisation is absolutely indispensable. It is unlikely to be immediately obvious to most people why this or that algorithm, of all things, will lead to reliable strategies. The only thing that helps here is to explain it in a way that is both clear and comprehensible to the layperson. For example, a marketing expert does not need to know how customer data was generated. However, they must understand why it provides them with the right information for the next campaign.

The idea of a manager steering an organisation safely through all the various pitfalls based on intuition and experience is honourable – but perhaps less and less applicable. A large number of recent corporate successes have shown that data-driven business decisions often lead to more effective and economical results – just think of the “Big Four”. This may seem unromantic, but it is only logical in light of the digital transformation of business areas, institutions and research.

A few examples will illustrate this change clearly:

• Providers of goods and services are using data science and machine learning to develop targeted consumer products.
• E-commerce companies, for example, identify individual customer personalities based on purchase history and tailor their recommendation system accordingly.
• Banks use predictive analytics to help virtual assistants guide online banking users in a forward-looking way.
• Marketing has transformed from being a creative domain to one based on numbers, thanks to data science. Data scientists provide numerical answers to which leads are the most promising, which alternatives consumers are considering for a product and which other items consumers have in their shopping baskets. Marketing departments avoid unnecessary use of resources by evaluating this information and focus on efficient, personalised targeting.
• Other sectors where data scientists are indispensable include Industry 4.0, mobility, energy, health, human resources, public, politics, media, e-learning, science and, of course, data services themselves.

Leaders in these industries should consider whether data analytics is relevant to their business model, whether their organisation or data volume is large enough to justify employing a data science specialist and whether sufficient human and technical resources are available to make practical use of their expertise.

Those who answer “yes” to these questions will gain competitive advantages by hiring data scientists: managers receive decision-making support, corporate goals are set, best practices are defined, target groups are determined, optimisation opportunities are explored and, ideally, trends are set – all of which give you a head start.

To speak to Experis about hiring a data science specialist, get in touch today.