Big data, like so many things in life, is a double-edged sword. If it’s properly channeled, we can ride high on this flood of data instead of drowning. For example, development processes and monitoring operating states generate immense volumes of data because of the different, unrelated testing and measuring systems involved. These are riddled with different formats and result in data redundancy. If this data is stored unsystematically, it’s bound to cause chaos – and big data becomes a curse. But a startup from Hannover is determined to call time on this waste of meticulously collected data. innoSEP GmbH is exhibiting its big data analysis model for measurement and sensor data at the Lower Saxony pavilion during CeBIT 2017.
The team has developed a state-of-the-art, efficient and thoroughly customized tool in the form of an end-to-end analysis model designed to handle the big data generated in industrial applications. It uses efficient data storage and professional system and data analysis as the basis for digital signal data processing, numerical model validation and intelligent database interpretations with regard to predictive/prescriptive analytics and machine learning. Besides acquiring profound system understanding, this solution aims to derive predictions, trends and recommendations for maintenance and prolonging service life as a means of optimizing development processes and operating states.