In today's world, processing and analysing large amounts of data is becoming increasingly important for the efficient operation of industrial enterprises. Large volumes of data, called "big data" or "Big Data", contain valuable information that can be used to make strategic decisions, optimise processes, and increase productivity. In this article, we will look at the role of big data in industrial analytics systems and how it can help improve production processes.

• Data collection and storage:
  The first step in using big data in industrial analytics is to collect and store a large amount of data. Monitoring systems and sensors located on equipment and in production processes collect data on various parameters such as temperature, pressure, humidity, speed, etc. This data is collected in real time and transmitted to a centralised database for further analysis.

• Analysing and extracting insights:
  Thanks to powerful algorithms and analytical tools, industrial analytics systems can process large amounts of data and extract valuable insights. This allows businesses to obtain detailed information about the status of equipment, production processes, and supply chains. For example, big data analytics can detect deviations in equipment performance, predict possible breakdowns, and schedule preventive maintenance, which helps to avoid unexpected production stoppages and reduces costs.

• Forecasting and optimisation:
  Big data allows you to create predictive models based on the analysis of a large amount of historical data. This helps businesses forecast product demand, optimise inventory, and plan production capacity. In addition, analytical systems based on big data help to identify process efficiency, identify opportunities for improvement and reduce losses.

• Improving product quality:
  The use of big data analytics in industrial systems helps to improve product quality. By analysing data on material quality, production parameters, and resource consumption, companies can identify factors that affect product quality and take steps to improve processes and avoid rejects.

Conclusion:
Big data plays an important role in industrial analytics systems, helping to improve the efficiency and productivity of enterprises. By collecting, analysing, and using large amounts of data, companies can make fact-based decisions, predict trends, and improve processes. The use of big data in industrial analytics systems is becoming an essential element of the successful operation of modern enterprises.

The use of analytical systems based on big data contributes to competitive advantage, increased efficiency and reduced costs. The optimal use of big data allows businesses to understand market trends, adapt to changes and make informed decisions, which contributes to their success in today's business environment.