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Your measuring data is too valuable to remain unused.

In the process of industry 4.0, numerous major corporations and medium-sized companies ...

have come to make more use of measuring sensors for their products, components and productions lines, and in doing so, initially find themselves confronted with a huge and ever-expanding sea of digital data. In your sea of data, information treasures are hidden that can be interdisciplinary found using measurement data processing in combination with big data technologies. The prerequisite is technical engineering know-how coupled with solid IT skills to meet the value of your data assets.

Product development takes a long time and is cost-intensive.

Digitalization is also changing conventional industrial development cycles. ...

Therefore, shortening product development cycles has lately become the strategic goal of many companies. The development process is dominated by costly and multiple designing loops until a product can finally go into mass production. Processes in the fields of construction, simulation and testing that have been valid before must be reshaped to be able to compete on an innovative level in the future.

Predictive potential remains unused.

Apart from development processes, avoiding failure symptoms, condition-based maintenance or ...

even lifetime extensions are key topics for many departments. Profound and extensive smart data analyses make it possible to develop tendencies, predictions and recommendations for action for your product or component. However, all too often the predictive potential of your data remains unused. Proper measurement data processing allows you to add value beyond what has been measured.

Industrial big data need technically oriented solutions.

Processing measuring and sensor data in modern systems holds special challenges ...

which in their data volume are neither transparent nor easy to process. Often, “standard solution methods” focusing on IT and statistics instead of engineering signal processing are not sufficient or productive. An ever-growing complexity and variety of products and systems creates greater demand for sophisticated data analysis methods for individual systems to efficiently and systematically add value. That is the only way your measuring data can be understood and interpreted correctly.

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