What are the differences between big data, business intelligence and business analytics and how do I move from a sea of data to added data value in the company? Data intelligence plays a central role here, as it bridges the gap between pure data collection and intelligent, automated value creation.

Unstructured data – i.e., text, videos, images, social media posts, and more – makes up the bulk of the world’s data, with 80-90% of all data falling into this category. As such, it represents a virtual gold mine of insights, just hiding and waiting to give savvy businesses a leg up on the competition.

In the rapidly changing IT landscape, companies need flexible data integration tools. The integration of cloud and on-premises systems in real time is becoming increasingly important. Five key functions ensure a successful, future-proof data strategy.

If data is the oil of the 21st century, most companies are sitting on huge deposits that they can no longer extract on their own. In order to gain truly valuable insights from the growing masses of data, they need efficient analyses - such as data mining.

From predicting the measurements of a patient in an intensive care unit to forecasting the movement of economic rhythms - forecasting makes it possible to predict the future with a certain degree of certainty and plan accordingly.

In times of climate crisis, companies want and need to operate more sustainably in order to reduce their carbon footprint. To achieve this, most organizations are actively working on sustainability strategies or already have them in place.

Today's companies are faced with a constantly growing flood of data. In addition, many are struggling with a large number of different monitoring tools in order to get to grips with these volumes of data and prepare them for well-founded decisions.

Data-Centric AI (DCAI) represents a pioneering approach in AI by focusing on the quality and relevance of data to optimize machine learning models and system performance.