RTInsights

Many industrial organizations have pilot AI projects that work flawlessly—but when it’s time to scale? Everything stalls. Why? Because AI in isolation doesn't scale. 🔍 The problem: AI is often deployed in silos—one plant, one line, one use case. Data is fragmented, messy, or trapped in proprietary systems. Teams lack shared practices, and models don’t “speak the same language.” 🛠️ The solution: To unlock real ROI, AI needs a foundation—not just a proof of concept. That means: ✔️ Unified, high-quality, contextualized data ✔️ Models trained on your own operational data ✔️ Open platforms that enable collaboration across teams ✔️ A human-AI loop with explainable insights and trust ✔️ DataOps + upskilling = speed and scale When AI becomes part of the fabric of operations—not just a side project—it can transform decision-making, reduce downtime, and drive enterprise-wide value. How do you get there? Find out in our new hub, "Best Practices for Deploying and Scaling Industrial AI," sponsored by Cognite. Visit now: https://lnkd.in/eH3whwPP #IndustrialAI #DataOps #AIstrategy #DigitalTransformation #OTdata #AIatScale

Today, most manufacturers use IIoT solutions to track and monitor their equipment and production environments, while edge computing primarily serves high-priority applications that require minimal delay. However, these technologies are on a path of rapid convergence as factories scale up their IIoT networks and demand faster, more autonomous decision-making. Read more➡️ https://lnkd.in/egBfVt7T #Manufacturing #IIoT #EdgeComputing sponsored by Volt Active Data

Real-time visual intelligence leverages AI and computer vision to process and analyze visual data instantly. Learn about its four main use cases in the full infographic. Download now ➡️ https://lnkd.in/eEW_7r_B sponsored by Volt Active Data

When organizations move from relying solely on IoT sensor data to incorporating real-time video data from cameras, they significantly enhance their predictive capabilities across several industrial domains. In specific, integrating video analytics with IoT data makes predictive models more accurate, contextual, and proactive. Here's real ways organizations across industrial sectors can derive significant value from real-time visual intelligence👇 sponsored by Volt Active Data

Increasing complexity in automotive production requires enabling the workforce to embrace digitalization. New skill sets such as knowledge of advanced manufacturing and data science are critical elements in the transformation of the automotive industry. Learn more in this infographic➡️ https://lnkd.in/epjnmr3Z #AutomotiveInnovation #WorkforceTransformation #DigitalSkills sponsored by Siemens

Organizations are facing data quality issues that have a growing impact as they increasingly leverage data in their products and services. Those quality issues are driven by problems with data entry, data capture, and data pipeline failures. Traditional approaches to data monitoring and observability have limitations in that they are not scalable, not proactive, and only provide partial visibility into the data pipeline. RTInsights sat down with Kyle Kirwan, CEO and Co-founder of Bigeye, to discuss these issues, the need for data observability 2.0, and more. Read the interview here➡️ https://lnkd.in/evH6VCH4 #DataObservability #DataLineage #DataQuality sponsored

RTInsights is now part of the TechnologyAdvice family! Expect our same commitment to quality content and more. Read the announcement here: https://lnkd.in/eFUKRW2D

Unlike traditional datasets, which often consist of numerical and text data, visual data from intelligent video applications provides a depth of context that traditional data cannot match. Here’s why real-time processing of visual data is not just beneficial but essential for enterprises👇 Learn more: https://lnkd.in/eYiY4VKX #AI #IoT #RealTimeAnalytics

Building effective multimodal trading infrastructures demands specialized technologies capable of ingesting, processing, and analyzing diverse data streams in real time. These systems must handle both structured data (like market quotes and trades) and unstructured data (such as text from news articles or social media posts) while maintaining consistent low-latency performance. The foundation of such infrastructures typically includes: ▪️ High-performance temporal databases They can efficiently store and query time-series data across multiple frequencies, from nanosecond-level market data to lower-frequency alternative signals. These databases must support complex temporal queries that can align signals across different time scales while maintaining historical context. ▪️ Streaming analytics platforms They are capable of processing continuous data flows in real-time, applying complex analytics as data arrives rather than in batch processes. These systems enable the immediate detection of significant patterns or anomalies across multiple data streams. ▪️ Vector database technologies They can represent diverse data modalities in a common mathematical space, enabling similarity searches and relationship discovery across different data types. These databases have become particularly crucial as trading firms incorporate more unstructured data into their decision frameworks. Learn more: https://lnkd.in/e52EcMBf #CapitalMarkets #FinTech #DataAnalysis #AI sponsored by KX

There are many communication protocols that are used in industrial settings. However, MQTT and OPC UA are probably the two most commonly used ones in such environments. Here's a look at how they are used in industrial automation👇 #Manufacturing #DigitalTransformation #IndustrialAutomation