Challenge #3: It’s Hard to Visualize Data when It Comes from Different Sources

Contributed by Derrick Snyder | Austin, TX

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 derrick.snyder@ni.com

Connect with Derrick on LinkedIn

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.

Challenge #2: It’s Hard to Visualize Data When You Need to Correlate It

Contributed by Derrick Snyder | Austin, TX

This is the second installment of the blog series, “The Top Challenges of Visualizing Time-Based Measurement Data.”

Often, data is being collected about a number of different phenomena at the same time. Though visualizing one data set alone is certainly valuable, incredible results come from the ability to discover relationships between different types of data. For example, when engineers test the ability of an airplane’s wing to withstand the violent forces that could occur in times of turbulence, they may simultaneously measure the force being applied to the wing, the strain (flex) of the material in the wing, the vibration on the wing, the noises that the components make when they finally reach their breaking point, and high definition video of the entire test being performed. Looking at a simple graph of the strain data over time is beneficial, but the ideal goal for engineers performing this test is probably to characterize how the strain and vibration of the wing are affected when exposed to different levels of force over time for a given design (all while watching and listening to the event played back). To do this, simple static graphs or images are insufficient.

Today’s advanced data processing software must be dynamic. Not only should graphs be able to depend on or interact with one another (for example, to provide cursor visualization coordinating one graph’s data values in time as compared to others), but in fact, graphs alone are no longer adequate visualization tools. To properly visualize engineering measurement data, it needs to be able to be correlated with alternative information such as video, GPS positioning or timestamps, sound, and more – you never know when the interrelationship of information may be the key to the next breakthrough in understanding.

– Derrick Snyder derrick.snyder@ni.com

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About Derick Snyder

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.

The Top Challenges of Visualizing Time-Based Measurement Data

Contributed by Derrick Snyder | Austin, TX

I’ll put this bluntly: in industry today, data is our single most valuable resource. The more we can understand data, the more potential we have to improve. Whether we’re driving process efficiency or reducing product cost, trending public opinion or observing environmental patterns, data enables us to make decisions that better our world.

In the not-so-distant past, the biggest challenge with data was simply obtaining it. Today, data is all around us; it’s in the air we breathe, the social networks we update, and the conversations we hold. Today’s challenge is no longer in obtaining data – it’s in effectively managing and visualizing data so that we can make sense of the all the data we’re able to obtain (and do so faster than our competitors).

In the next few blog posts in the coming weeks, I will be addressing the top data challenges faced by scientists and engineers who seek to acquire “data” that can be measured of some sort of phenomenon over time; for example, measuring temperature fluctuation in a temperature chamber for the duration of a given test, or monitoring the voltage being supplied by a battery. Collecting this type of data often presents a unique set of challenges when it comes to visualizing acquired data.

Stay tuned!

– Derrick Snyder derrick.snyder@ni.com

Connect with Derrick on LinkedIn

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About Derick Snyder

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.