he amount of information processed by computer networks every day is measured in terabytes and petabytes. At our current rate, we are thought to be generating 2.5 quintillion bytes of data every day, and this number is growing every day. This pace is accelerating with the growth of the Internet of Things (IoT). When the statistical data of the devices connected to the Internet of Things (IoT) in the world are analyzed, an exponential growth is predicted. While the number of connected devices is estimated to be around 23 billions today, this number is estimated to increase to around 30 billions in 2020 and 75 billions by 2025. By 2020, there will be four connected devices for every person in the world. This shows that the number of data generated will reach incredible proportions.
It is estimated that 90% of the total data in the world today has been generated in the last two years alone. This is due to the exponential growth in the number of connected devices. Data generation at this scale is measured in four ways; the variety of information available, the volume of information generated measured per second, and the speed and accuracy with which information is generated.
Much of the data we create generates striking statistics. The Internet, social media, communication channels, digital services, the Internet of Things, the Internet of Things, all reveal stunning results about the amount of data we create every day, every minute and every second.
The ability to use and manage this information represents a completely new paradigm for almost all organizations. Industrial automation technologies are at the beginning of a new era of innovation. Global modern industrial systems combine the power of machines, computing, cyber physical systems, the Internet of Things, automation, cloud and data exchange, and Big Data opens up new horizons of opportunity in these systems.
In this context, the ability to obtain this data and use the right data in the right way will undoubtedly lead to a change in the balance in the new industrial age. The ability to obtain and use data will be the unstoppable power of organizations' effectiveness in the big data era.
Big Data Analytics
Big data analytics has inevitably become a prominent discipline with the rise of big data. The evaluation of valuable data obtained and extracted from industrial systems is of great importance in industrial planning and decision-making processes. Fraud analysis, recommendation systems, industrial failure analysis, production analysis are the core areas of industrial big data analytics.
Data extraction from different physical systems requires fast and instant data analysis. In the context of Industry 4.0, data representations are required in different ways. For example, while a machine operator needs visual representations of data, log data of processes and machines are needed for fault detection. In addition, management units need to see the data with different reports.
When big data applications are considered within the scope of Industry 4.0, business intelligence, product quality improvements, machine health predictions, production planning and smart factories stand out.
Knowledge extraction from production data is known as production intelligence. Online analytical processing, benchmarking, data mining, performance analysis, predictive and deterministic analysis are the functions of business intelligence. In addition, the processing of industrial big data prevents production stops and delays that may arise due to machine failures. Businesses can have the ability to predict failures before they occur thanks to the amount of data they have and smart applications.
