/thyssenkrupp_logo_claim_d.svg){kind=logo}
How data analysis makes our processes more efficient
Innovations and modern technologies are changing the way we work and live - and creating new jobs. AI, big data and robotics are no longer dreams of the future. They are part of the daily lives of our digital experts at thyssenkrupp. One of these digital minds is Sarah Händler, Head of Analytics Data at thyssenkrupp Steel. We asked her how digitalization influences her work.
As Head of Analytics Data at thyssenkrupp Steel, Sarah Händler is responsible for the collection, preparation, analysis and provision of all key corporate information. Together with her team, the expert prepares data for the evaluation purposes of the various departments at thyssenkrupp Steel - centralized in a single platform, the tera information platform. Linking the data there provides a holistic view of the processes involved in steel production, order processing, purchasing and sales processes, transportation and much more.
Sarah Händler: From Intern to Head of Analytics Data
Originally, Sarah Händler wanted to become a journalist. She did an internship in public relations at thyssenkrupp Steel while still at school. As she found working analytically on complex problems particularly exciting, she ultimately decided to study mathematics. However, she wanted to return to thyssenkrupp Steel because: "Anyone who has ever had the opportunity to see steel production up close knows how complex, multi-layered and deeply impressive this process is."
Studying mathematics awakened an interest in Sarah to develop practical, tangible solutions from theoretical problems that can help optimize processes in everyday work. As Händler explains, "Big Data analyses, artificial intelligence, and Industry 4.0 are just a few of the terms that sparked my interest in digitalization topics." With a focus on digitization, she applied again at thyssenkrupp Steel in 2017 and started in the Analytics Data Team after a traineeship
More than a collection point for data: tera is the first fully integrated data warehouse.
The expert now heads the data analytics team at thyssenkrupp Steel. Central to her work is the tera data warehouse, which is one of the components of the tera information platform alongside a lakehouse. tera stands for “thyssenkrupp Steel efficient reporting and analytics” and is therefore the central information platform. With the exception of personal data, all of the data collected from all of the smelter's important operational IT systems is stored there and processed for reporting tasks, but in particular also for process control and decision-making. "Steel production is an extremely complex and time-consuming process," explains Sarah Händler. "Each piece of material passes through a multitude of processing steps, production facilities and locations." In the data warehouse, data from all these processes - from the first inquiry from the customer, through production, shipping of the finished piece of material, to invoicing or quality assurance - is mapped in full.
The tera data warehouse is the basis for almost all digitization projects at thyssenkrupp Steel.
Digitization - a project that affects processes in all trades.
If you ask Sarah Händler which aspect of her work she finds most exciting, she doesn't have to think long: "We work on a lot of different topics, are in contact with almost all of the processes at the smelter, constantly deal with new tasks and learn something new virtually every day." She names the mapping of this complex world in the data as the biggest challenge. Many of the more than 30 supplying IT systems feed the data warehouse almost in real time. This means that unlike a classic data warehouse, which is usually only loaded at night, the tera data warehouse is supplied 24/7.
tera was created in response to growing reporting requirements as well as developments in digitalization, such as new data sources and new analysis techniques and tools. Today, the team no longer just uses data for retrospective reporting, but increasingly for process control tasks and decision-making. For this reason, tera is being continuously developed and supplemented by an innovative lakehouse, among other things.
Not only collecting and analyzing data - failure is also part of the process
The collection of data is made possible by over 500,000 sensors that measure thicknesses, temperatures, coatings, and much more. "It's only through these sensors that we get over 500 million metrics a day, which we make available to our users in a timely manner."
Since she and her team work strongly with the users, Händler can also use her experience in public relations in her job. The technical tasks have to be discussed and approaches to solutions have to be developed together. Being able to explain the results of data analyses and complex technical processes to target groups is also an important skill.
"We have the freedom here to simply try things out. We don't focus on failure - but analyze what we can do better next time and then start again." Says Händler about the error culture at thyssenkrupp.
And what happens when something doesn't go smoothly? Händler emphasizes that even in the Data Analysis Team, especially with innovative approaches, it happens again and again that the original idea doesn't (yet) work. That's part of the process. "You can't be afraid to try out new things here and thus also to fail sometimes."
Sarah Händler on the future of digitization
For the next five to ten years, Händler sees many new developments that will continue to shape digitization, such as cloud technology, machine learning and artificial intelligence, and even green IT. "In my view, however, the Data Driven Company is at the forefront of these developments: We have all understood in recent years that data is the gold of the 21st century. The Data Driven Company approach consistently takes this idea further."
Händler is convinced that data will increasingly become the focus of decision-making and that all relevant corporate decisions will in future be made as objectively as possible on the basis of this corporate data. For her, the advantages are clear: "We are increasingly moving away from reactive reporting to data-based forecasts that help us identify risks in our environment earlier and counter them adequately."