Industrial IoT and data platforms

Almost every AI project in a plant starts with the same problem: getting reliable data out of the machines. PLCs, CNC controllers, drives and sensors speak dozens of protocols, tag names follow no shared pattern, and the same value can mean different things on two lines. Industrial IoT and data platforms exist to collect that data, give it structure and context, and pass it on to the tools that analyze it.

The tools here work at different layers. At the edge, Litmus Edge and HighByte Intelligence Hub connect to equipment, normalize tags and model the data close to the machines. Ignition from Inductive Automation is a SCADA and application platform that many plants use as their data backbone. AVEVA PI System is a long-standing historian that stores time-series data for years. Higher up, Cognite Data Fusion, Sight Machine and Seeq turn that data into models, analytics and investigations for engineers. MachineMetrics focuses on machine monitoring for CNC and discrete shops, ThingWorx gives developers a platform for building industrial apps, and Samsara brings sensors, cameras and a mobile app to equipment, sites and fleets.

Most plants end up combining several of these. A common pattern is an edge layer that talks to machines, a historian or data platform that stores and models the data, and analytics or AI tools on top. Before choosing, map what you already have: historians, SCADA, MES and the networks between them. Buying a new layer that duplicates one you already run is a common and costly mistake.

Context is the hard part. A temperature value means little until you know which machine, which product and which step of the process it belongs to. Ask vendors how they model assets, lines and production runs, and how much of that work your team will do. Look for support for open standards such as OPC UA and MQTT, which make it easier to swap tools later.

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9 AI manufacturing tools in industrial iot and data platforms

Litmus Edge

by Litmus

Industrial edge platform that connects to PLCs and machines, normalizes the data and passes it to analytics and cloud systems.

Can run on premises

Sight Machine

Manufacturing data platform that models production lines and gives engineers analytics and AI on process and quality data.

Runs as a cloud service

AVEVA PI System

by AVEVA

Operational data historian that collects and stores time-series data from plant equipment for analysis and reporting.

Can run on premises

Samsara

Connected operations platform with sensors and cameras for equipment, sites and fleets, plus a mobile app for frontline teams.

Runs as a cloud service
Has an API
Mobile app

HighByte Intelligence Hub

by HighByte

Industrial DataOps software that models and contextualizes machine data at the edge before sending it to other systems.

Can run on premises

Ignition

by Inductive Automation

Industrial application platform for SCADA, HMI, MES and IIoT projects, licensed per server with unlimited tags.

Can run on premises

Cognite Data Fusion

by Cognite

Industrial data platform that connects OT, IT and engineering data into one model, with AI assistants for search and analysis.

Runs as a cloud service
Has an API

MachineMetrics

Machine monitoring for CNC and discrete manufacturing that tracks utilization, downtime reasons and cycle times automatically.

Runs as a cloud service

Seeq

Advanced analytics for process manufacturing data in historians, used by engineers to investigate and monitor operations.

Runs as a cloud service

Security belongs in the first conversation. These platforms connect to control networks, so involve your OT security team, follow segmentation rules, and know which connections go out to the cloud. Plan who will maintain drivers, tag mappings and models after the project team moves on, and test any new software on a non-critical line before it touches production.

Data volume and retention need planning as well. High-frequency vibration or power data can fill storage quickly, so decide what to keep at full resolution, what to summarize, and for how long. Cloud platforms often charge by data volume or by the number of connected assets, which changes the cost of sending everything upstream.

Naming standards help more than any single product. A shared model for sites, areas, lines and machines, such as the hierarchy described in ISA-95, lets new tools find the data they need without a fresh mapping project each time. Write the standard down and review new tag names against it.

The payoff is reuse. Once data from a line is clean and modeled, the same feed can serve predictive maintenance, energy monitoring, quality analysis and daily performance boards, instead of every project building its own connection to the machines.

Questions about industrial iot and data platforms

What is a data historian, and do I still need one?

A historian stores time-series values from equipment, such as temperatures and speeds, for years and makes them fast to query. Many plants keep one, like AVEVA PI System, and add newer platforms on top for modeling and analytics.

What does "edge" mean in industrial IoT?

Edge software runs at the plant, near the machines, instead of in a distant data center. It collects and cleans data, can run local analytics, and keeps working if the internet connection drops. Litmus Edge and HighByte are examples.

Which protocols should an IIoT platform support?

List the controllers and devices in your plant and check drivers for each. OPC UA and MQTT are widely used open standards, and support for them makes it easier to connect new tools later.

How do we keep IIoT projects secure?

Treat them as changes to the control network. Involve OT security early, segment networks, limit outbound connections, manage credentials centrally and patch edge devices. Test on a non-critical line first.

Can one platform do everything?

Rarely. Most plants combine an edge or SCADA layer, a historian or data platform, and analytics tools. Choose pieces that use open standards so you can change one without rebuilding the rest.