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.

Part 3 — The AI Bottleneck Nobody Is Talking About

Viviota and Rockfish Partner to Accelerate AI-Driven Engineering with Real and Synthetic Data

Over the next few weeks, I’ll be sharing a short series of observations from CES 2026 across three themes:

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.

Barry Hutt co-founder Viviota

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According to a recent survey, 47% of companies today consider AI/ML as a top priority in 2024. Yet, according to Harvard Business Review “cross-industry studies show, on average, less than half of an organization's structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or even used at all.” ¹

This statistic should concern R&D or Engineering decision-makers. Companies are drowning in data, and very few companies can leverage their data because they are stuck trying to find, access, and connect various data sources. So first ask yourself these three questions:

According to a recent survey, 47% of companies today consider AI/ML as a top priority in 2024. Yet, according to Harvard Business Review “cross-industry studies show, on average, less than half of an organization's structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or even used at all.” ¹

This statistic should concern R&D or Engineering decision-makers. Companies are drowning in data, and very few companies can leverage their data because they are stuck trying to find, access, and connect various data sources. So first ask yourself these three questions:

Are You “Digital Transformation-Ready”?

So, your customers are committed to a Digital Transformation (DT) journey. How does this affect the engineering organization? Engineering executives expect integrating digital technology into all aspects of the business will deliver great value. They expect DT to fundamentally change and improve business operations. As part of the DT journey, engineering teams want to unify and update systems, organizations, and processes to support next-generation product development cycles. Their engineering tools requirements, infrastructure ,and processes will be viewed through this lens. Are your offerings ready to support your customers’ DT projects with scalable, digital enterprise-ready architecture and capabilities?

Harvard Business Review[i] reported that cross-industry studies show, on average, less than half of an organization's structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or even used at all.