Why engineering data preparation is the hidden ROI driver
Engineering data preparation is the systematic process of cleansing, structuring, enriching, and tagging raw test and sensor data so engineers can find, trust, and use it quickly for analytics and AI. Done well, it turns chaotic files and logs into a reusable asset that cuts time-to-insight from days to seconds and unlocks predictive models.
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Topics:
Big Data,
Analytics,
IIoT,
Electric Grid,
F1,
COTA,
Autonomous Vehicles,
Analog Data,
ADAS,
Hybrid Cars,
Electric Cars,
ESS,
ANC,
Edge Computing,
Factory 4.0,
Intelligent Data handling,
Build Back Better Act,
EV,
Digital transformation
AI Dog CES 2026
Part 3 — The AI Bottleneck Nobody Is Talking About
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Topics:
Test,
Big Data,
Analytics,
Viviota,
SAP,
Sensors,
automotive industry,
Real-time computing,
COTA,
U.S. Grand Prix,
Autonomous Vehicles,
Sensor Data,
Machine Learning,
aerospace,
Analog Data,
ADAS,
Hybrid Cars,
NIWeek,
ESS,
ANC,
Sensor Data Management,
data cleansing,
simulation,
webinar,
Smart Factories,
Manufacturing,
Intelligent Data handling,
Software,
LabVIEW,
Build Back Better Act,
Digital transformation
Viviota and Rockfish Partner to Accelerate AI-Driven Engineering with Real and Synthetic Data
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Topics:
Engineering Data,
Big Data,
Analytics,
automotive industry,
Electric Grid,
Autonomous Vehicles,
Machine Learning,
aerospace,
Hybrid Cars,
Electric Cars,
Sensor Data Management,
Edge Computing,
Factory 4.0,
Manufacturing,
Intelligent Data handling,
Software,
Digital transformation
Walking the floor at CES this year felt like stepping into a future that suddenly arrived all at once. Everywhere you turned, there were robots, intelligent machines, autonomous systems, and increasingly lifelike interfaces. But unlike past years, many of these technologies no longer felt like fragile demos or research experiments.
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Topics:
Engineering Data,
Big Data,
Analytics,
Autonomous Vehicles,
Analog Data,
Digital transformation
Over the next few weeks, I’ll be sharing a short series of observations from CES 2026 across three themes:
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Topics:
Engineering Data,
Big Data,
Analytics,
IIoT,
Autonomous Vehicles,
Sensor Data,
Machine Learning,
Analog Data,
Electric Cars,
ANC,
Sensor Data Management,
Edge Computing,
Factory 4.0,
Manufacturing,
Intelligent Data handling,
LabVIEW,
Digital transformation
Engineering teams possess an underutilized goldmine in today’s fast-paced industrial landscape: their data. From sensor readings and vibration logs to video, sound, and test metadata, engineering data holds critical insights that can accelerate innovation, reduce costs, and safeguard operations against failure. Yet, a significant portion of engineering time is wasted merely searching for, organizing and wrangling data to make it usable.
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Topics:
Engineering Data,
Big Data,
Analytics,
Viviota,
IIoT,
automotive industry,
Autonomous Vehicles,
Sensor Data,
Machine Learning,
aerospace,
Analog Data,
Sensor Data Management,
data cleansing,
simulation,
Edge Computing,
Manufacturing,
Intelligent Data handling,
Build Back Better Act,
EV,
Digital transformation
I was reminded yesterday of how cars play a role in our identity, and separately how new auto safety regulations for electric and hybrid vehicles create an opportunity for auto manufacturers to be creative. I was picking up my 2011 Kia Optima Hybrid from a car stereo retailer in Austin, and while I waited, I glanced at the stacks of sub woofers waiting for owners. They ranged in price from $149 to over $1000. Admittedly, my family has installed high-end stereos with sub-woofers in cars ranging from a 1992 5.0 Mustang GT to a Toyota Sienna mini-van, however a low-rider in the showroom brought to mind low-decibel vibrations at intersections when, by chance, I was sitting at a red light alongside a low-rider. The car and the sub-woofer are an expression of the driver and their identity.
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Topics:
Autonomous Vehicles,
ADAS,
Hybrid Cars,
Electric Cars
The automotive industry is working toward a revolutionary event, a truly autonomous vehicle—one in which a human driver is no longer required. To accomplish this, automotive companies have focused R&D teams, whose mission is evolving and growing onboard automated safety systems. Today onboard safety systems include features such as airbags and anti-lock brakes. For autonomous driving to become a reality, more dynamic safety systems are needed, for example collision detection/avoidance, and smart cruise control with vehicle-controlled lane changing.
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Topics:
Autonomous Vehicles,
Sensor Data,
aerospace,
ADAS