Guide: Is your industrial data ready for AI?
Gartner has identified AI as the most critical strategic technology for 2025. But before you can harness AI’s potential, you need to ask yourself a critical question: “Is your industrial data ready for AI?”
In this guide, Martin (Head of Industrial Data Science), will explore the key considerations for preparing your industrial data for AI and the steps you can take to maximize its value.
Written by: Martin Paczona – Head of Industrial Data Science, Novotek Austria
Why do AI need high-quality industrial data?
Let’s start from the beginning! AI models require structured and clean data including context information to generate meaningful insights. Poor data quality can lead to a number of problems, like biased models, incorrect predictions, made up facts, and limited usage. Here’s why data readiness matters:
- Accuracy and reliability: AI algorithms perform best when you feed them with complete, consistent, and high-integrity data.
- Efficiency in decision-making: Well-organized data allows AI to generate real-time insights for smarter decision-making and process automation.
- Interoperability across systems: AI thrives on integrated data sources from IoT devices, ERP systems, and production databases.
Checklist: Assess your industrial data maturity
1. Data Collection
- Are you capturing the right data with the right frequency from IoT sensors, production equipment, and ERP systems?
- Is your data collected in a structured and standardized manner?
- Is your data collection scalable?
2. Data Storage
- Is your data stored in separate silos, making AI adoption difficult?
- Can your data be accessed and processed across different departments seamlessly?
3. Data quality, preparation & cleaning
- Are there gaps, inconsistencies, or redundant data in your records?
- Have you implemented data cleaning processes to ensure accuracy?
- Does your data include context? Do you have a strategy to add it?
- Can you easily transform the data to the required format?
4. Real-time vs. Historical data
- Does your system support real-time data streaming for predictive insights?
- Is your historical data properly archived and retrievable for trend analysis?
AI adoption: Common challenges and how to overcome them
Prepare for the most typical challenges when it comes to data readiness:
Data silos and lack of interoperability ➡️ Solution: Deploy an integrated data management platform that consolidates multiple sources. An example is the Industrial DataOps solution HighByte Intelligence Hub.
Poor Data Quality ➡️ Solution: Utilize AI-driven data cleansing tools to eliminate inconsistencies and errors.
Resistance to AI Adoption ➡️ Solution: Educate teams on AI benefits and offer hands-on training to encourage adoption.
4 Use cases in Industrial AI:
Energy Optimization
AI predicts the heating process to lower the heat consumption by 15%.
Quality Prediction
AI predicts the quality KPIs of a cable extrusion machine in real-time this allows to reduce the scrap produced on the line
Soft-Sensor
AI predicts the aging of sensors in a wastewater treatment plant to increase the maintenance intervals of such plants
Predictive Maintenance
AI detects anomalies at a production robot by analysing process times which reduces the downtime and increases the product quality
AI Data Consultation
If you want help with evaluating how ready you are for AI or want to explore different use cases, you can always contact us!
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