Five questions to ask before choosing an industrial data platform
Five questions that show what it takes to turn a reliable data streaming foundation into real-time insight and governed action.
Turn real-time manufacturing plant data into operational intelligence with the new HiveMQ Platform
HiveMQ's new platform helps manufacturers reduce downtime, improve efficiency and replicate proven operational improvements by getting the data foundation right.
From machine data to action: How HiveMQ Platform works
HiveMQ Platform turns fragmented plant data into real-time, trustworthy intelligence, so you catch problems while the line is still running and get your AI pilots into production.
Act: How to operationalize data for governed agentic AI-driven action
Learn how agentic AI in manufacturing turns trusted operational insight into safe action, with scoped agent roles, human approval and a full audit trail.
Analyze: Turning contextualized data into actionable real-time intelligence
Learn how an operational intelligence platform turns contextualized industrial data into real-time, governed insight for teams, applications and AI agents.
Why clean data is the real competitive advantage in industrial AI
Clean industrial data gives AI the context and governance it needs to scale. Learn how manufacturers can build a trusted data foundation beyond pilots.
Contextualize: Turning data into understandable operational information
Learn how industrial data contextualization turns connected data into trusted, governed information that people, applications and AI agents can act on.
Contextual modeling strategies to reduce manufacturing data volume
Efficient contextual modeling reduces manufacturing data volume without losing insight, using report-by-exception and Semantic Graph design.
Why do industrial AI teams still spend so much time on data preparation?
Industrial AI teams lose most of their time to data preparation. The real fix isn't more data engineering. It's closing the architecture gap upstream.
How to win an Industrial Data Innovation Award
Enter the Industrial Data Innovation Awards - recognizing excellence in MQTT, industrial AI and industrial data innovation.
Building a scalable data foundation for real-time operational intelligence
A technical white paper on the four-stage Connect, Contextualize, Analyze, Act architecture for turning fragmented manufacturing data into real-time operational intelligence and a governed foundation for agentic AI.
Why point-to-point data integration breaks industrial AI
Point-to-point integration buckles under industrial AI. Event-driven architecture and a Unified Namespace fix the integration complexity causing AI failure.
Why distributed intelligence must replace centralized data
Why centralizing operational data slows industrial AI at scale, and how a distributed intelligence model helps teams act on operational data in real time.
Data without context is just noise: The case for semantic operational data
Raw sensor data lacks context. See why semantic operational data, discovery and governance are the three capabilities that make industrial AI models reliable.
How to Design a Fault-Tolerant Data Pipeline for Industrial AI Workloads
How to design a fault-tolerant data pipeline for industrial AI: guaranteed delivery, redundancy patterns, failure recovery, and the failure modes that appear first in production.
Industrial IoT Solutions: A Guide to Platforms, Connectivity, and Industrial Data Architecture
How industrial teams connect OT and IT at scale: a practical guide to MQTT, Unified Namespace, and how to evaluate industrial IoT platforms for real-time data.
How to Compute MTBF, MTTR, and Availability in Real-Time Without a Separate Data Stack
MTBF, MTTR and availability are miscomputed because the definitions are hard, not the math. See how HiveMQ Pulse lets reliability engineers encode those definitions directly with no extra infrastructure required.
Don't Underestimate Industrial AI Data Quality Challenges, Says Gartner
Gartner warns that data quality challenges are consistently underestimated by manufacturing CIOs. Here is the three-activity framework for industrial AI data readiness, and how HiveMQ delivers each layer.
Why Every Industrial Company Needs to Become a Data Streaming Company
Why real-time data streaming is the missing foundation for industrial AI - and why enterprises that build it now will outpace those that wait.
Industrial AI Use Cases and the Data Infrastructure That Powers Them
Industrial AI use cases like predictive maintenance and process optimization scale only when three data infrastructure layers work together.
Why Manufacturing AI Projects Stall Before They Start - Hannover Messe 2026
Most manufacturing AI projects don't fail because of the model. They fail because the data backbone isn't ready. Here's what we heard from hundreds of manufacturers at Hannover Messe - and what it takes to fix it.
Closing The Industrial AI Value Gap
Real-time data streaming, governed operational data, and the right architecture. Learn the industrial AI playbook that turns pilots into platforms.
Industrial AI Pilot: Why 68% of Manufacturers Can’t Scale Past the POC
Why 68% of industrial AI pilots fail to scale: non-replicable data pipelines, missing ROI baselines, and unclear ownership kill production deployment despite technical success.
Building Ontology-Driven Intelligence for Industrial AI Agents
Learn how ontology-driven AI agents use semantic models, knowledge graphs, and structured data to enable reliable, scalable agentic automation in industrial operations.