Our expertise

The analysis of large amounts of data has been one of Comma Soft’s core competencies since our founding. Thanks to targeted pattern recognition and a deep understanding of specialist and business processes, we can identify previously undiscovered patterns and generate comprehensive added value from existing internal and external data.

Our business intelligence product INFONEA is based on modern in-memory technology for fast data analysis and enables high-performance interactive analyses and reports on extensive, complex and highly interconnected data. Experience Big Data live. We call this “making data discussable!”.

For the operationalization of analytics topics, INFONEA delivers numerous possibilities. For example, users can map complex business logic using reports, which in turn can be used as an interface for other applications. In this way, business experts can directly participate in creating and expanding complex workflows, even without programming knowledge. All data can be connected and used employing modern Rest-API interfaces and various other possibilities in the background. An integration of INFONEA into heterogeneous IT landscapes is thus possible without any problems.

Our offer

  • High-performance networking of extensive, highly complex data
  • Intuitive exploration of large data sets, “Making data discussable!”
  • Integration of individual Machine Learning/Data Science developments
  • Self-service reporting and analytics in a modern, mobile-enabled interface
  • Customization options in common scripting languages (Python, Javascript, d3)
  • Extensive role and rights management
  • Detailed metadata management and automatable data management paths (ETL)

Selected use cases

  • Complexity management and interactive reduction of variant diversity at an automotive manufacturer. By networking all data from development, production, logistics, finance, etc., end users interactive analyses with detailed filtering and simulation options become reality.
  • Support of complex planning processes with INFONEA by integrating into existing applications: Highly specific existing web applications can be supplied with real data from INFONEA in the background, enabling simulations on planning data based on real data from INFONEA. Furthermore, the simulation results can be transferred back to INFONEA and used there for a “what-if” analysis.

Project references

  • Complexity and variant management at Audi, awarded the BARC Best Practice Award 2016 for the “Best Group Solution”.
  • Automatic quality control in the production of an automotive supplier with the possibility of interactive error detail analysis by the production experts
  • Standardization of the release process for new passenger cars and automated “pixel-perfect” board reporting at an automotive group
  • Prediction of customer preferences in the optimization of investment products at an insurance company
  • Management of the office and field sales force at a leading German insurance company using comprehensive sales dashboards and customer analysis tools
  • Optimization of weekly reporting at a large IT service provider including operational dashboards for staff scheduling in service centres with integrated machine learning modules
  • Fundraising support at a non-profit aid organization

 

Contact

Dr. Markus Knappitsch
Head of Data Business Consulting Insurance & Banking
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Dr. Knappitsch is responsible for the Insurance and Banking divisions at Comma Soft AG. He also acts as a lead consultant in a controlling capacity. His focus is on the optimization and digitalization of processes in the insurance environment with the help of statistical methods and machine learning. The focus is always on combining relevant domain knowledge with modern data analysis methods.
Contact us!

Dr. Knappitsch is responsible for the Insurance and Banking divisions at Comma Soft AG. He also acts as a lead consultant in a controlling capacity. His focus is on the optimization and digitalization of processes in the insurance environment with the help of statistical methods and machine learning. The focus is always on combining relevant domain knowledge with modern data analysis methods.