The challenges in data analysis


The trade press is full of articles on digitalization. Technology providers see their chance and occupy fields such as blockchain, big data, and many others. The current trend or mainstream is vehemently pushing for a development towards the data economy.

How can data and information be developed into knowledge?

In the field of education, however, Professor Dr. Julian Nida-Rümelin, for example, Philosopher at the Ludwig Maximilian University in Munich regards it as one of the greatest challenges to transform this trend into a development towards a knowledge society. In my opinion, this challenge also exists in the world of business.

Companies of all sizes, from all industries, need to modernize their system architectures and to implement digitalization technologies. The focus is on process digitalization, networking, and automated data acquisition. The question remains: “How can we make use of this enormous flood of data and information?”.

In our daily work with customers we continue to frequently find classic ERP architectures, supplemented by – sometimes insufficiently – integrated BI systems. They are usually set up for ad hoc reporting and the business areas represented are primarily management, finances, and sales. The fields of production and logistics often take second place here due to their complexity. Qualitative data analyses are complex and time-consuming to perform and therefore often fail due to limited IT-resources and/or the lack of an overall view of the data stocks. The use of external data analysis experts on the other hand requires a long and arduous familiarization process for understanding the customer’s systems and business world. In order to avoid theoretical solutions that are simply divorced from reality and therefore yield no benefit, you should therefore predominantly use providers who understand your business in production, logistics, or marketing and can therefore draw the right conclusions for creating knowledge out of your data.

With the present state of the art, it is also possible to use high-performance analytical tools in the cloud and limited to a certain period of time. This saves licensing and maintenance costs for these systems.