Raw sensor readings and industrial IoT streams do not automatically translate into meaningful insights. This data has to be cleaned, contextualised, and structured before the AI can deliver accurate, real-time insights.
The Path to AI-Ready Data
Manufacturing data often starts as a firehose of unstructured information from sensors, machines, and control systems. On its own, raw data provides little context - one temperature reading or vibration value is meaningless if you don’t know which machine it came from, when it was recorded, or whether that reading is normal or a red flag. This lack of context or gaps in the data can lead AI models astray, causing them to miss crucial signals or generate false alarms. For example, you might encounter unit confusion where, for instance, a reading of ‘75’ might be safe if it’s 75°C in a furnace but alarming if it’s 75 psi for a pressure valve!
A robust data platform with quality controls and data governance prevents these issues, ensuring that the data you feed your AI is accurate and reliable. Making raw data suitable for AI requires a strong data foundation, involving a few critical steps:
1. Data Integrity
Data must be accurate, consistent, and complete. As well as cleaning the data to fix errors and handle missing values, this includes validating sensor outputs, removing duplicates, and setting up automated checks that flag abnormalities. Including contextual metadata in your reporting (e.g., machine ID, timestamps, batch numbers) ensures that AI systems interpret the readings correctly and can understand what’s going on.
2. Data Governance & Security
It’s important to keep your data well managed so insights can be taken efficiently. Start by clearly defining ownership (who manages each data stream?), access rights, and compliance rules. Then implement a data catalogue so stakeholders know what data is available and how it’s structured.
Be sure to guard your data from cyber-attacks by use role-based access controls and encryption to protect sensitive operational information.
3. Unified Data Models
Gathering data from multiple sources (sensors, machines, ERP systems) into a central location can break down data silos between operational technology (OT) and information technology (IT) and unify the information. You can standardise key definitions (like “downtime” or “defect rate”) to avoid confusion across teams and systems. Consider building a unified namespace or knowledge graph to link equipment, processes, and sensor streams.
With these steps in place, raw industrial data evolves into a structured asset (sometimes referred to as a digital twin) ready to feed advanced analytics and machine learning algorithms.
Enabling real-time insights in manufacturing
Once you’ve cleaned and contextualised raw manufacturing data, the next challenge is where (and how) to store and process it. Legacy databases and nightly batch processes cannot keep pace with the speed of Industry 4.0. This is where modern data architectures step in - enabling real-time insights for high-stakes environments like production lines, supply chain operations, and maintenance schedules.
From Data Lakes to Lakehouses
Traditionally, a data lake is a centralised repository storing vast amounts of raw data in its native format. This is ideal for capturing every sensor reading or machine log for deeper analysis. However, data lakes can turn into “data swamps” if there’s no governance or structure.
Enter the data lakehouse. This combines the scale and flexibility of a data lake with the governance and schema management of a data warehouse while ensuring that both data scientists (who often thrive on raw, unstructured data) and business analysts (who want well-structured tables) work from one unified platform.
With a lakehouse, manufacturers can store historical data cheaply while also imposing structure where needed, fostering collaboration and speeding up analytics.
Real-Time Data Streaming
In a factory setting, data loses value if it arrives late. If a critical machine overheats, you can’t wait 24 hours to see that in a report. Real-time streaming technologies can fix this problem by ensuring there’s a continuous flow of sensor data into the platform.
An added benefit of this includes fault detection, which immediately detects anomalies in temperature or vibration data. Additionally, production dashboards can give operators minute-by-minute insights into throughput and quality metrics. These streaming pipelines can also trigger automated alerts or process adjustments, further reducing downtime and scrap.
Edge-to-Cloud for Smart Factories
Many modern manufacturing architectures process all the data by following an edge-to-cloud model:
- Edge: Lightweight computing devices on or near the factory floor which handle immediate tasks - like local inference for anomaly detection or filtering sensor noise
- Cloud: Massive compute and storage for large-scale analytics, historical trend analysis, and advanced AI model training
This hybrid approach offers low latency at the edge, while still tapping into the limitless scale of cloud systems. It’s particularly useful for predictive maintenance: the edge device does the real-time monitoring, and the cloud refines AI models using aggregated data from multiple locations.
Time to use your strong data foundation for quality AI insights
Now your data is prepped for adding in AI technology, you can start reaping the benefits! Manufacturers are using AI and machine learning in multiple ways, such as:
- Predictive Maintenance: With a robust data foundation, machine learning models can predict failures more accurately, reducing unplanned downtime and maintenance costs
- Quality Assurance: AI vision systems can detect defects in real time if they’ve been trained on clean, labelled images and integrated seamlessly with production line data
- Energy Optimisation: Unified data on machine usage, temperatures, and utility costs helps AI automatically balance loads and identify savings, sometimes up to millions of dollars per year
Ready to get started? Here’s some tips when trialling new AI models in your business:
1. Start Small, Scale Fast: Launch a pilot project with one production line or AI use case. Show quick wins, then expand the data platform to other lines or facilities once you know it’s effective.
2. Leverage Edge and Cloud: Use edge devices for real-time control loops and the cloud for enterprise-wide data aggregation and analytics.
3. Measure ROI: Keep track of your KPIs (downtime, quality, throughput, costs). Then be sure to communicate your results to get buy-in from leadership and frontline operators alike.
Interested in finding out more about data readiness for AI in manufacturing? If you’ll be attending Smart Manufacturing Week in Birmingham this June, join Nicholas Lea-Trengrouse for his speaker session all about this topic. He'll explore how data architecture, storage, and compute impact AI performance, ensuring models deliver accurate, real-time decisions.
