The modern oracles of our digital and networked age are called Artificial Intelligence (AI), Big Datadata analysis and predictive analytics. Data collectors like Google, Meta, Alibaba and Amazon measure the world, create personality profiles and quickly comb through huge amounts of data for patterns and correlations in order to make predictions in real time. The data analysis methods promise a targeted look into the “crystal ball”. States, research institutions and commercial companies hope that this will provide precise forecasts of future developments in order to minimize the risks of their own actions and to be able to better assess the opportunities of future actions.
According to current estimates, more than 32 billion connected devices will be in use around the world in the Internet of Things by 2030. With an estimated population of 8.2 billion people, this corresponds to around four “Internet of Things” gadgets per person on earth. If it is now taken into account that only less than one percent of all basically network-capable devices are connected to the Internet, the projections of more than 80 billion IoT devices in the next few decades are anything but utopian. And these devices, from connected lighting, fitness trackers, smart clothing (Smart Clothes) to the coffee machine to the car, the door lock and the approximately one billion surveillance cameras worldwide will constantly generate data that can be used for data analysis.
How can decision-making processes be optimized from data?
But it’s not just the sheer volume of data that’s causing a boom in the context Artificial Intelligence (AI) and Machine learning (ML). Another driver is the miniaturization of the increasingly powerful microprocessors that are now found in every smartphone, for example. Since 1994, the number of components on a microchip has increased by at least a factor of 10,000, as has computing power. In the mid-1990s, the most powerful supercomputers could handle around 100 billion computing operations per second – every good smartphone can do that today. And at the same time, electricity consumption fell to less than a 100,000th. At the same time, every smartphone and every car has a variety of sensors: high-resolution cameras, rotation and acceleration sensors, measuring devices for magnetic fields and ambient light, satellite tracking, fingerprint sensors, microphones and much more.
Added value is only generated from the exabytes of data when new insights are derived from them or decision-making processes are optimized. This is where moving methods from the world of “data analytics” helps us. For example, one of the biggest problems with the application of AI the bias that can arise from faulty data or faulty analysis methods. A deep understanding of data analytics methods allows us to identify and correct potential sources of bias in the data. This is crucial to ensure that AI-Systems work fairly and unbiasedly. TO-Models, especially those based on complex algorithms like Deep Learning based, can be difficult to interpret. A solid understanding of data analysis methods helps make these models more transparent and better understand their decisions. This is particularly important in areas where AI-Decisions can have a significant impact on people’s lives, such as in healthcare or the financial sector. Companies that understand and apply the methodological foundations of data analytics can discover new ways to use their data and develop innovative products and services. This can represent a significant competitive advantage.
Data analytics is like solving a puzzle
Data analytics is like solving a puzzle. Imagine you have a large amount of puzzle pieces (data) in front of you and you want to generate a clear picture (information) from them to make a decision based on it. Data analytics helps us sort, analyze and combine these puzzle pieces to see and understand the picture.
Data analysis includes a variety of methods and techniques to examine large amounts of data and identify patterns, trends and relationships. These methods include statistical analysis, Machine learningdata mining and predictive models. In the Risk management Data analysis serves to reduce uncertainty and minimize the probability of negative events (downside risk) or to reduce the probability of positive events (upside risk) to increase.
“If we want qualified risk managers and business decision-makers in a modern technological society, then we have to teach them a few things: data literacy, communication skills, psychological and intercultural competence, empathy and, above all, statistical thinking, that is, a sensible approach to risks and uncertainties.”
Risk comes from not knowing what you’re doing
We have outlined the requirements in our book “Data Analytics im.,” which was published a few days ago Risk management” formulated (Romeike, Frank / Wieczorek, Gabriele (2026): Data Analytics im Risikomanagement – Descriptive Analytics – Diagnostic Analytics – Predictive AnalyticsSpringer Verlag, Wiesbaden 2026). In addition to our book, we offer the intensive seminar “Data Analytics and Quantitative Methods in Risk Management”. With the book and the seminar we would like to make a small contribution to helping people understand more about the future. The following quote comes from Warren Buffett: “Risk comes from not knowing what you’re doing.”
We are overwhelmed with a tsunami of useless knowledge every day. The amount of information increases by 2.5 quintillion bytes every day, but that’s just not the amount of useful information. Many people find it increasingly difficult to perceive meaningful signals in the general noise. In the corporate world, making decisions and acting under conditions of uncertainty is commonplace. Managing directors, board members and politicians have to deal with uncertain scenarios every day and make a decision at the end of the day.
And the world of data analysis and the Stochastics provides us with valuable tools and makes our knowledge more multifaceted and diverse, but not less precise. However, for this we need skills in the area of mathematical and statistical methods.
Data analytics opens up new perspectives to recognize hidden connections, make well-founded decisions and make complex developments understandable in a data-driven world – and this is precisely where its fascination and relevance lies. Let’s go on a journey together…
Further information and registration
date: 2026-02-15 07:36:00
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