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Link to Best Practices for Deploying and Scaling Industrial AI

Best Practices for Deploying and Scaling Industrial AI

Artificial Intelligence (AI) is transforming industrial operations, helping organizations optimize workflows, reduce downtime, and enhance productivity. Different industry verticals leverage AI in unique ways.

Link to The Center for Adaptive Edge Intelligence

The Center for Adaptive Edge Intelligence

Adaptive edge intelligence brings real-time decision-making to the point of data creation, whether from sensors, machines, or cameras.

Link to The Value of Vehicle Electrification

The Value of Vehicle Electrification

Electric vehicles (EVs) present automakers with many design, engineering, and manufactu ring challenges.

Link to Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT

Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT

Digital transformation is empowering industrial organizations to deliver sustainable innovation, disruption-proof products and services, and continuous operational improvement.

Link to Smart Manufacturing for Automotive

Smart Manufacturing for Automotive

Leading a transportation revolution in autonomous, electric, shared mobility and connectivity with the next generation of design and development tools.

Link to Center for Data Pipeline Automation

Center for Data Pipeline Automation

As businesses become data-driven and rely more heavily on analytics to operate, getting high-quality, trusted data to the right data user at the right time is essential.

Link to Center for Automated Integration

Center for Automated Integration

The goal of automated integration is to enable applications and systems that were built separately to easily share data and work together, resulting in new capabilities and efficiencies that cut costs, uncover insights, and much more.

Link to Continuous Intelligence: Insights

Continuous Intelligence: Insights

Digital transformation requires continuous intelligence (CI). Today’s digital businesses are leveraging this new category of software which includes real-time analytics and insights from a single, cloud-native platform across multiple use cases to speed decision-making, and drive world-class customer experiences.
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Self-Service Data Prep: a Gartner Guide

Self-Service Data Prep: a Gartner Guide

Self-service data preparation offers agility and competitive advantage to a business. But a lot can go wrong.

Written By

RTInsights Team

Jan 18, 2016

A variety of new tools exist to capture and prepare data from emerging sources such as the IoT, social media, and smart meters. The growing sophistication of such data preparation tools allows the average business user to analyze and visualize the data, often with no coding chops required.

There’s a dilemma, however, in embracing self-service data preparation. Businesses that don’t use such tools, or govern their use with a strict IT hand, lose agility and could be at a competitive disadvantage. Opening the floodgates on self-service data prep, meanwhile, could lead to chaos and business mistakes from faulty analysis — the kind of spreadsheet errors that caused a major bank to purchase toxic mortgages or helped cause an economic collapse.

In this special report you will learn:

Four use cases in which self-service data preparation is preferable to centralized data warehousing
Four classes of data preparation products and industry-leading vendors in each class
How to get the right tool to the right user, including recommendations for data governance and business roles.

RTInsights Team

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Why Satellite Connectivity Sits at the Heart of Enterprise Network Resilience

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Why Satellite Connectivity Sits at the Heart of Enterprise Network Resilience

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On a Trust-Building Trajectory: AI in Network Automation

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On a Trust-Building Trajectory: AI in Network Automation

Those who approach AI with discipline rather than hype will be best positioned to benefit as the technology matures.

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Five Reasons Why DataOps Automation Is Now an Essential Discipline

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Five Reasons Why DataOps Automation Is Now an Essential Discipline

The ability to deliver AI-ready, trusted data depends less on new tools and more on how effectively DataOps are automated, governed, and observed.

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Real-Time RAG Pipelines: Achieving Sub-Second Latency in Enterprise AI

Artificial intelligence technologies

Real-Time RAG Pipelines: Achieving Sub-Second Latency in Enterprise AI

To operationalize sub-second RAG, the author proposes the FLASH Framework (Fast Latency Architecture for Streaming Hybrid retrieval), a 4-step methodology distilled from deploying 50+ enterprise pipelines since 2024.

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Real-time Analytics News for the Week Ending February 14

Real-time Analytics News for the Week Ending February 14

In this week’s real-time analytics news: Apache Software Foundation (ASF) announced that Apache HugeGraph has become a Top-Level Project (TLP).

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Feb 15, 2026

Why Satellite Connectivity Sits at the Heart of Enterprise Network Resilience

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Hyperscale low-bandwidth IoT satellite constellations will soon power remote asset tracking, environmental monitoring, and agricultural telemetry.

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Cleaning up the Slop: Will Backlash to “AI Slop” Increase This Year?

Artificial intelligence technologies

Cleaning up the Slop: Will Backlash to “AI Slop” Increase This Year?

While AI may offer marketing teams the promise of innovation and greater productivity, it also brings with it potential consequences for their public image, particularly among younger consumers who treat AI with a greater level of scrutiny.

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How Data Hydration Enables Scalable and Trusted AI

Data management

How Data Hydration Enables Scalable and Trusted AI

Data Hydration covers the activities that support AI models being supplied with an adequate supply of essential information to allow them to thrive.

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Analysis and market insights on real-time analytics including Big Data, the IoT, and cognitive computing. Business use cases and technologies are discussed.

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