“I compile the relevant data for companies.”

Data is the commodity and the gold of the future. Unfortunately, companies often lack the resources or expertise to derive added value or concrete benefits from the existing data.

Storing large quantities of data is also a real challenge. That’s because the larger the volume of data, the more memory is necessary to file it away efficiently. Traditional database systems are not designed for that – which is why new technologies, such as cloud computing, are of the essence. Offerings such as platform as a service (PaaS) that allow companies to “rent” virtual platforms, so to speak, could provide a solution.

A second challenge is the speed of accessing data. In order to make real-time decisions, data always has to be up to date. That means it has to be transmitted and processed quickly, since old data can provide an incorrect basis for interpretation.

We’ve seen that the level of maturity in terms of AI differs tremendously from company to company. Some companies are still at the very beginning of their journey and first need to develop a basic understanding of AI and its value.

Other companies are already far more advanced. In some cases, they even have the necessary IT and cloud infrastructure in place. However, they often face other problems, such as working with a massive amount of personal data that they need to protect carefully due to the GDPR.

Exactly. We take an integrated approach to advising clients and cover all aspects of implementation. That is one of the tasks of the Data Insight Lab at LHIND, a center of excellence that supports companies in becoming data-driven businesses. At the Data Insight Lab, I work with colleagues from Business Analytics, IT Security, Data Science and Data Architects on scenarios and specific use cases to derive the greatest benefits for clients from their business data.

Exactly. The advantage of being a Technology Consultant is that you can assume a variety of different roles depending on the project and the client’s needs. One time, I acted as a cloud expert and developed the entire infrastructure for a business in the cloud. Another time, I acted as an agile project manager, leading various teams while consulting with the client and different stakeholders. The wide range of roles keeps the job interesting and exciting, since every day is different and you never stop learning.

The starting point for data analysis is always a solid basis of data. Because that is the only way to make substantive and well-reasoned statements, you always have to define which data is relevant for a decision first and determine where it can be found.

Once I’ve reviewed the data, I prepare a specific use case for each company using analytical methods from the field of machine learning. Based on the results of the analysis, I draw up forecasts for the future that businesses use as the foundation for their decisions.

It is always important to make sure that data is not provided only to a single department, such as IT. Instead, it has to be available to every department so that other people who work at the company can access the data and share their knowledge. That is the key to making the right business decisions.

The volume of data is rising in big data, which makes it more and more difficult to use simple data analysis methods. That’s why we work with machine learning. It lets us filter out important information from the sea of data more efficiently, spot patterns or make predictions.

I use various different machine learning methods depending on the problem we’re tackling or the data available.

If you want to learn more about customers’ buying behavior so that you can categorize them into certain buyer groups, for example, then supervised learning is a good choice, since the method is primarily suited for classification and regression analysis. To do that, I need representative, labeled data, which I run through an algorithm that detects patterns and correlations and provides me with an appropriate model.

However, since the model constantly needs to be fed with up-to-date data in order to learn automatically from experience and improve itself, it needs guaranteed access to new data and data points. That’s because the more high-quality data points we have and the more often the model learns, the better the results for future decision-making.

These days, nearly every company is sitting on a treasure trove of data. So I believe that all industries will benefit from machine learning in the future by using computer vision to identify defects or by employing natural language processing (NLP) to mechanically process documents and texts, to name just a few examples.

I also think that AI and machine learning have tremendous potential in health care and medicine in particular – not just on a financial and economic scale, but also for society as a whole. Technology can help detect illnesses earlier, provide people with better treatment or make work easier for medical professionals. AI, for example, is perfect for diagnostic imaging involving techniques such as X-rays. A major problem for medical professionals is sifting through the huge quantities of information, such as medical procedures, pathology reports or imaging data. The algorithms analyze data automatically and efficiently. In turn, medical professionals can then use this analysis to make their decisions. However, the health-care sector deals with a huge amount of personal data, which makes it very difficult to create a solid data foundation.

E-commerce and the logistics sector also especially stand to benefit from AI – particularly in combination with robotics.

AI is a general-purpose technology. It can be found in every industry and has made inroads into nearly all aspects of life. Our society and our world are diverse. That’s why we need a technology that leverages and supports this diversity while strengthening it further. AI has the potential to do just that.

Although we’re a very diverse society, we face many societal problems. We have bias, for example. Depending on how it is fed, AI can further exacerbate bias. As so often, the people who suffer are minorities or underrepresented groups. AI points out our societal problems. That’s certainly frightening, but it’s also fascinating.

Yes, I volunteer my time in the “Women in AI” community. The main focus there is on empowering women who want to gain a foothold in the industry or who are already experts in the field of AI but simply lack visibility. The community offers these women a platform. I also act as a mentor at schools or to women from other sectors who are looking to enter the world of IT.

However, I don’t want to just support women per se, since it’s just as important to help people from the other side as well. Leadership isn’t just male, female or other – it’s a combination of everything. Balance is the key to making sure it works properly.