Or: How sensible decisions can be made using “large amounts of data”!
Ettlingen, July 23, 2013 – In light of the current debate about the U.S. wiretapping scandal PRISM, the topic Big Data in the context of corporate use has also moved into the spotlight – especially in connection with one’s own business processes. On one hand it must be protected by appropriate measures for risk prevention (keyword: industrial espionage), on the other hand the available “large amounts of data” (Big Data) have to lead to sensible decisions in everyday business.
However, the full potential of Big Data methods and tools can only be used effectively in businesses if the decision-making processes and information processes involved are adjusted accordingly. The availability of information – almost in real time – opens up ever new design possibilities for business processes. To ensure that the information can also be implemented quickly in decisions, decision-making processes need to be automated as much as possible. To do so, complex regulation systems have to be developed, and their controllability does represent a special challenge.
When using different data sources, semantic technologies are required in particular to link together data from various sources. The corresponding workflows to efficiently merge the data streams and data sources must be designed and built taking into account data quality requirements.
From the decision maker’s point of view, it is important to identify which business processes or parts of business processes are particularly affected by new Big Data technologies. Methods need to be provided in order to determine the need and benefits of Big Data technologies in business processes early and reliably.
Desirable are e.g. reference models or component models for the use of Big Data technologies in various application domains, such as those developed by PROMATIS in line with cooperative projects with the Karlsruhe FZI and AIFB Institute at the Karlsruhe Institute of Technology (KIT). In connection with the use of reference models, a systematic knowledge transfer of Big Data expertise can be carried out in companies.
Professor Andreas Oberweis (KIT) states in this regard: “Big Data opens new impressive ways to improve business processes. However, fears of the risks and dangers of Big Data among the population should not be underestimated. Ultimately, trust can only be created through transparency…”