In today's working world, everyone is confronted with more and more data volume and data types. All this available data needs to be processed, analyzed and reported in order to transform it into meaningful information. Measurement data often comes in a wide variety of formats and with different data models.
This presents users with some challenges:
- Too much time needed to achieve the desired results: In their daily work, users want to be able to focus primarily on interpreting data (e.g., measurement data, simulation data ...) to make informed decisions about how to proceed, not on time-consuming coding for reading out of data.
- Overwhelming amount of data: There is often more data than users can process in their daily work due to time constraints, lack of resources and insufficient computing power.
- High complexity in handling data due to complicated tool usage: Users have problems with reading and processing different types and formats because the user is not able to use the tool intuitively and the tool is missing capabilities.
- Lack of flexibility in the use of existing tools: For special problems, users need the freedom to simply define new calculations and algorithms to gain more or deeper insights; many tools do not allow this or only to a very limited extent.
There are many tools that can process and analyze data, but they often have limitations in terms of the amount of data or types of data that can be processed. Programming skills are often required
to get the expected results. Automating processes is also often cumbersome and reusing generated analyses is rarely possible.
