Big data in production automation – from data analysis to competitive advantage

Big data might sound like a buzzword, but it’s far more down-to-earth than you’d think. Today, data has become one of the most valuable assets for any organization. And yet, paradoxically, most manufacturing companies fail to tap into its full potential. Studies show that companies utilize only 20% of the data available to them. That means 80% of valuable information that could transform the way a business operates goes to waste.

So, how can you turn digital noise into a real competitive edge? The answer lies in conscious big data management – a key to efficiency, innovation, and security in modern production plants.

Whether your organization has 50 or 5,000 employees, decision-making based on intuition alone is no longer enough. Executives, managers, and operational leaders need hard data: financial, operational, production, sales, or quality. Effective data management enables predictable, scalable growth.

Data comes from many sources: internally (machines, ERP, MES systems), from the business environment (suppliers, customers, market), and even directly from consumers (e.g. social media). Its volume is growing exponentially while storage and processing costs are dropping. But having access to data is not enough – understanding and using it in the right context is what really matters.

The simplest definition of big data is: „a lot of data”. But that’s just the start. Big data also means:

In manufacturing, big data includes machine and sensor data, management and logistics systems, market and environmental info. Big data is not just about tools – it’s an entire philosophy of data management, from collection to storage to decision-making.

Raw data from ERP systems, sensors, or machines has no value on its own. Only context and interpretation turn it into a useful business tool.

Think of it this way: if someone asks you whether 50°C is „hot”, what’s your answer? Everyone at explitia would say: „It depends”. That perfectly illustrates the importance of context. If we’re talking about body temperature – it’s dangerously high. If it’s milk pasteurization – not nearly enough. It’s the same with production data.

Big data in industry is a complex ecosystem of technologies that must work together harmoniously to enable effective data management.

Modern sensors collect data with unprecedented precision and frequency. They allow you to monitor everything – from temperature and humidity to energy consumption and machine vibrations. These are the foundations upon which all analytics are built.

Data from various sources must be structured and contextualized. Energy management systems, MES, or ERP provide business meaning to data and enable cross-analytics.

The cloud drastically reduces the cost of storing and processing data. It also allows for resource scaling depending on needs.

AI analyzes patterns, predicts trends, and provides actionable insights. However – as experts emphasize – AI is only 10% of the success. The other 90% lies in properly prepared data and infrastructure.

Production data analysis helps identify bottlenecks, minimize downtime, and improve scheduling. This allows companies to increase efficiency without costly investments in new lines or machines.

Data from sensors and systems allows predicting failures and planning maintenance before downtime occurs. This not only saves money but also enhances safety and extends equipment lifespan.

Big data enables real-time quality monitoring, rapid detection of deviations, and immediate responses to potential problems. Trend and root cause analysis help eliminate recurring errors and improve production standards.

By analyzing data across the supply chain, companies can better forecast demand, manage inventory, minimize the risk of shortages or overproduction, and improve delivery timeliness.

Monitoring energy and material consumption allows for more efficient resource use, reduced waste, and the implementation of eco-friendly solutions. Global leaders show that big data implementation can yield millions in annual savings.

Big data fuels the development of new products, offer personalization, and the implementation of modern business models (e.g., predictive analytics, digital twin). Companies that can quickly analyze and apply insights from data outpace competitors and better meet market needs.

Implementing big data isn’t just about technology – it requires a cultural shift. That’s why the process should be approached holistically with an optimal strategy. Learn from others’ mistakes and avoid the traps below!

One of the biggest traps is trying to plan every detail of a big data project from the start and rigidly sticking to that plan. IT projects are unpredictable—new challenges and variables always arise. We recommend an agile (phased) approach where you have a vision of the whole, but execute it piece by piece, learning from each stage.

Many companies postpone data collection until they know how to use it. This is a major mistake. The cheapest option is to collect data continuously, not to recreate it years later when the system is no longer available, and employees don’t remember implementation details.

The best technologies in the world are worthless if people don’t use them. It’s crucial to engage teams early in the design phase and show them tangible benefits.

A key mistake is creating a universal dashboard “for everyone.” Different organizational levels need different information:

Don’t start with the technology – start with the problem. Examples:

Check what data you already have or can easily obtain. Often, companies already possess 70% of the needed information.

Think long-term – how can big data help across different areas of your business? Even if you start with one issue, it’s worth having a comprehensive vision.

Plan the project in stages and allow for adjustments after each phase. This enables you to respond to new challenges and opportunities.

Even if you’re not yet sure how you’ll use it – start collecting. It’s the cheapest investment in your company’s future.

In practice, implementing big data in production involves:

Regularly reviewing and updating the strategy based on feedback from implementation

In the era of AI and automation, companies that can effectively use data will have a huge competitive edge. Big data is not the future—it’s the present. The real question is: is your company ready to harness this potential or will it continue wasting 80% of its available information?

Remember: every day you delay is another batch of unused data that could already be improving your production efficiency. Time to act!