Data and algorithms: Lecturer Ana Moya heads up Data Science at Handelsblatt

I am responsible for processing the figures in the company, which on the one hand help to determine the future development of a company and on the other hand help to set the course for the company's strategy. Specifically, it's about customers and their behavior in relation to products or services.

In my case, it's the subscribers of Handelsblatt, Germany's largest business and financial newspaper published in German every weekday. The task here is to extract what can be deduced about the needs of customers from the 'sea' of data.

In the past, we only had statistics as an instrument for this. Today, we have more sophisticated tools at our disposal. We don't just want to find new clients, but first and foremost to keep the ones we've won. We want to ensure their satisfaction. The data and analyses obtained help us, for example, to create emails with personalized content based on the reading habits and interests of subscribers.

To do this, we analyze which articles are read, how long readers stay on certain pages and which topics are particularly popular. Interactions with customer service are analyzed, as is the progress of subscriptions: renewals, cancellations, switching to other subscription models and also demographic and geographical data on readers.

The profession has changed accordingly: We now speak of "data engineers", "data architects", data scientists and data analysts. In fact, we refine the data.

• The data engineer?develops data pipelines that collect and cleanse data from various sources.
• The data architect?designs the infrastructure that enables efficient and scalable storage.
• The data scientist?analyzes complex data sets and develops predictive models, while the data analyst interprets the data and creates reports.

You have to have an entrepreneurial approach to the company for which you are preparing such data, you have to know the business and the different business areas, and you also have to empathize with the customers. We therefore also work at interfaces with strategists, sales people, product developers, marketing experts and the editorial team.

Data scientists must therefore also have soft skills, handle data sensitively, keep an eye on the interests of the respondents and communicate sensitively about them.

Ultimately, you also need to know the algorithms and the methods behind them.

They are very interested in innovations and experiencing with them. They are really curious. I love exploring research questions with them. For example, we recently asked ourselves how AI will affect different industries and what opportunities and challenges are associated with it.

With regard to the pending question of how AI will affect us, I can reassure you: In-depth data knowledge will remain. AI can help us analyze data and recognize patterns, but interpretation and decision-making should not be left to AI alone. Human reflection and critical thinking are essential to understand and make sense of the results of data analysis. This is an area where human intelligence is irreplaceable and can never fully replace AI.

The question always remains: What happens to this data in the short and long term? Data storage must be ethically regulated.

Many companies collect and store data and believe that they are already "data-driven". A role concept is important here: it regulates access and what may be done with it. Above all, access to raw data must be very restrictive. Absolute data protection compliance must be observed.

The final stage of data processing is to consider what measures should be taken on the basis of the data information. This requires not only compliance with existing legal requirements, but also consideration of ethical principles. Some actions should prohibit themselves, as laws regulate the overarching spirit and not each individual step.