Industrial Big Data: where challenges meet opportunities
Industrial big data range from the process dashboards, information gathered from the customers to gauge interest and forecast demand to the connected production machinery on a warehouse floor.
With the big data becoming a significant game-changer, the experts report that modern organizations tend to use data to enhance customer experience, reduce costs, and to improve targeted marketing to make existing processes more efficient. Moreover, recent data breaches have also made enhanced security an important goal that big data projects seek to incorporate.
Enormous amounts of industrial data are increasingly generated by manufacturing companies. Due to the rapid development and application of advanced sensor technology, computer science, internet, communication technology, big data, artificial intelligence technology (AI), internet of things (IoT), the manufacturing industry is facing a major leap forward.
When it comes to a data-smart factory, specialists in the field usually refer to the one that operates using advanced sensors and information technologies. So, tons of data are generated and collected in a smart factory, requiring big data processing technology to build an integrated environment in which the production process can be represented transparently, controlled and managed in a more efficient and secure way.
Reliability and safety are among the vital factors of the intelligent system. By offering substantial benefits, industrial big data will definitely improve the system performance of the manufacturing process, achieving near-zero downtime and ensuring predictive maintenance.
Industry-related challenges of Big Data
Increasing demand for natural resources including oil, agricultural products, minerals, gas, metals, has led to an increase in the volumes, complexity, and velocity of data that is a challenge to handle and process. Likewise, lots of data from the manufacturing industry is simply ignored. The underutilization of this extremely valuable information prevents the improved quality of products, energy efficiency, reliability, and better profit margins.
Big Data application in key industries
In rapidly evolving industries, big data enables businesses to solve today’s manufacturing challenges and to gain a competitive edge. With big data and analytics, companies have got a chance to make better real-time decisions about asset usage and operations scheduling. Nowadays, data can be generated by hundreds of thousands of machines and parts, from valves to monitors equipped with sensors and wireless capabilities.
In such a way, this data helps chemical and oil/gas manufacturers optimize production levels, reduce waste, improve accuracy, and manage energy consumption. With data flowing from multiple sources, data-driven industrial companies can now gain a better understanding of supply chain operations, resulting in streamlined processes and better distribution channels (e.g., data on exchange rates let the costs be reduced and suppliers optimized). As a result, distribution within large enterprises and to hundreds of destinations can run more efficiently.
Equally, in the oil and gas industry, large amounts of data that are constantly generated from oil and natural gas upstream, midstream and downstream processes can be quickly processed and analyzed to reveal new insights to prevent equipment malfunctioning and improve operational efficiency. For instance, by integrating IoT into offshore equipment, employees track and monitor lifespan and other elements that can affect production, such as wave heights, temperature, and humidity.
In like manner, once automation is introduced to remote-control systems, the personnel is removed from areas of risk, and time-critical responses are constantly generated. In other words, captured data is stored in an active database, linked to maintenance scope, and accessed through 3D representations of the platform, making it accessible to all authorized parties.
Video surveillance technology is another big manifestation of the industrial data presence. It’s crucial for manufacturing sectors to protect critical infrastructure that covers large areas. To encompass these spaces, numerous video surveillance cameras need to be deployed. Video analytics can help analyze the video streams of those cameras to provide real-time alerting, as well as operational insights for maintenance purposes.
What are data mining technologies used for?
Since the amount and type of industrial data are continuously changing, for a comprehensive information analysis via data mining one should understand the purpose of the use of the data. Data mining technologies are used to detect equipment/product design defects, to study production processing defects, as well as to analyze staff/customer behavior, habits, and demands.
However, only a small portion of valuable information can be extracted from the industrial big data. There’s still an accurate need in the knowledge and information collected from humans and the overall working environment that might also assist to reduce production costs and improve product quality. All in all, big data real-time analytics provides innovative opportunities to establish a more efficient manufacturing process, cost and risk reduction, safety improvement, more regulatory compliance, and better decision-making.
All this suggests that industrial big data is not about just “adding Wi-Fi" to one of the company’s devices - it's about building a smarter company infrastructure that blurs the line between the digital and the mechanical facets of the operational processes. High complexity, automation, and flexibility of data-driven industries bring new challenges to reliability and safety.
It goes without saying that gradual transition towards big data application may not be easy for many industries, for many of them still lack manpower and capabilities for hiring the required personnel that can handle big data. Personal and cybersecurity also need attention since this remains a considerable barrier in realizing the value of industrial big data analytics. All in all good expertise and strategic mindset while using big data tools, will surely ensure success and reduce the margin of error.