A beginner’s guide to the basics of data for manufacturers.
It’s no secret that manufacturing is quickly becoming a data driven environment. With that, the ability to collect, move, and make sense of information from machines and systems is becoming a core skill for engineers. From tracking machine performance to automating quality checks, data is no longer just a byproduct—it’s a strategic asset. At the heart of this shift is the data pipeline: the invisible but essential infrastructure that moves information from where it’s generated to where it can be used.
If you’re a manufacturer only just opening your eyes to the world of industrial data, this article will walk you through what a data pipeline is, how it works, and why it matters.
What is a data pipeline?
A data pipeline is a series of steps or processes used to move data from one place (the source) to another (the destination), often with some form of processing or transformation along the way. Think of it as an automated conveyor belt for information, designed to reliably carry data from machines, sensors, or systems to dashboards, databases, or analytics platforms.
In a manufacturing setting, a data pipeline might start at a temperature sensor on a CNC machine, pass through an edge device or gateway, and end up in a cloud-based dashboard where a plant manager can monitor operations in real time.
To understand how a data pipeline works, it helps to break it down into its basic parts. Data starts at the source. In manufacturing, common sources include sensors, machines, control systems and human inputs. Each source generates raw data such as numbers, states, or measurements that provide insight into how the equipment or process is performing.
Once data is generated, it needs to be collected and moved. This is often done using industrial protocols. OPC UA is a common standard for industrial automation systems. MQTT is a messaging protocol often used for sending data from edge devices. Modbus or Ethernet/IP are industrial stalwarts used to communicate with legacy equipment.
At this stage, edge devices may act as the bridge between your OT (Operational Technology) equipment and your IT infrastructure. Most of the time, this raw data needs to be cleaned, formatted, or enriched before it’s useful. This processing could involve filtering out noise or irrelevant data, averaging values over time, tagging data with machine IDs, timestamps, or batch numbers and detecting anomalies or generating alerts. All of this can occur at the edge, in a local server, or in the cloud, depending on the application.
After processing, data is stored or delivered to its final destination. This could be dashboards for real-time monitoring or databases or data lakes for long-term analysis. Manufacturing Execution Systems (MES) or ERP platforms are a prime destination for almost all manufacturing data and machine learning models will use the data for predictive analytics. The key is that data ends up somewhere it can be digested and acted upon, whether by people or machines.
Why data pipelines matter in manufacturing
Data and connectivity on the shop floor has been a reality for many years, but most of that time cost and complexity of the technology meant it was adopted by only the largest manufacturers. But advances in chip technology, AI and cloud connectivity mean even small manufacturers can implement these powerful technologies. As competition, complexity, and customer expectations grow, so does the need for smaller manufacturers to invest in connected, data-driven operations.
There are key benefits of implementing data pipelines that help manufacturers see quick return on investment, such as real-time visibility of what’s happening the shop floor instead of after a shift ends; noticing warning signs of failure before breakdowns occur; catching defects early using data from sensors and vision systems; monitoring usage patterns and energy requirements, and; tracking every part and process for compliance and recall readiness.
Indeed, even a basic data pipeline can replace clipboard checklists and Excel spreadsheets with automated, actionable insights. You don’t need to employ an army of data scientists to implement much of the current technology—most of it is designed with manufacturers’ deployment needs in mind, and low-code options are growing rapidly.
Start small, think big
If you’re new to data pipelines, the key is to start small. Pick one machine, one sensor, or one metric that matters. Build a basic pipeline that helps you see something you couldn’t before—then grow from there.
As factories become smarter and more connected, manufacturing engineers who understand how to harness data will be at the forefront of process innovation, quality improvement, and operational efficiency. Next time you look at a machine, don’t just see it as a tool, see it as a source of insight waiting to be unlocked.
