This is the final installment of the blog series, “The Top Challenges of Visualizing Time-Based Measurement Data”.
Engineering data collection is often a repetitive or iterative process. Typically, data is acquired during some sort of a test that may be run over and over again while making incremental changes. This results in the acquisition and storage of dozens, hundreds or even thousands of data sets over time. Additionally, data acquired about today’s model of “widget” may need to be compared to data from last year’s model, so the management of legacy data is a concern.
Compounding this challenge is the fact that engineering data is often acquired and saved in a variety of different formats. In any given engineering test system, instruments may have their own individual way of saving data that differs from each other component of the system. Data may be stored in data files or databases, and being able to consume and visualize data from these different sources presents a pain point.
Look for intelligent data visualization software that is able to handle a variety of input sources. Rather than being restricted to loading data from a set number of data sources, scalable data visualization software must be modular in its interfaces so that additional future data sources may be added to the system. In the ideal case, once data is loaded into the software environment, data visualization will be agnostic to the type of source from which the data was loaded.
– Derrick Snyder email@example.com
Derrick Snyder is a product manager for NI DIAdem measurement data processing and visualization software at National Instruments. He received a bachelor’s degree in Computer Engineering from Vanderbilt University.