Without the correct toolkit, organizations will struggle to unlock the efficiency and sustainability benefits of real-time operations data, says Harpreet Gulati, Senior Vice President and Head of PI System Business, AVEVA
Data has often been called the richest resource of the 21st century and industrial enterprises increasingly rely on it to maximize business value. But like other materials, this key industrial resource only becomes useful when it is extracted, processed, and delivered to the right people at the right time –securely and in context.
Overall, the size of the global datasphere is growing at a compound annual rate of 23%, and is likely to hit 184 zettabytes by 2025.
For businesses in every sector, analyzing – or mining – this data provides several benefits. Industrial enterprises, for example, can use it to reveal deeper insights about their critical operations, which helps them increase efficiency, decrease waste, partner more closely with suppliers, and make more informed, data-driven decisions.
But that’s only if the data is interpreted correctly. Research shows that data users can spend up to 40% of their time searching for, validating, and preparing data for analysis.
Bring the right data management system to the job, however, and this data yields a productivity and efficiency dividend that can also reshape the way the organization collaborates and innovates. And when extended to partners, suppliers and service providers, data delivers a value boost across the industrial ecosystem.
According to Gartner, businesses that effectively share data throughout their ecosystem will achieve 50% higher growth than those that retain a siloed approach.
Industrial organizations across the globe are already leveraging data for business gains in different ways.
Faster time to value
In many industries, operations are increasingly distributed and decentralized, making data collection and analysis a challenge. But with the use of low-cost sensors, better software, and improved connectivity, organizations can now collect and track real-time data securely and reliably across a wider footprint. This information can then be transmitted to a central hub in the cloud, where it can be analyzed to predict possible outcomes and used to alert business leaders of possible courses of action.
The City of Salem in Oregon, for example, uses data analytics to protect its drinking water from harmful algal blooms on Detroit Lake, which supplies the city with its drinking water.
Authorities realized the lake was a repository of information. To tackle the problem, they dropped sensors from a pontoon into the middle of the lake to measure everything from algal activity and toxin and allergy levels to weather data. This disparate data is transmitted to an enterprise-class operations data system. From there, ecologists and statisticians can use the data to build artificial intelligence models that automatically notify city authorities in advance of likely algal blooms.
With this information, officials can prepare appropriate responses ahead of time to circumvent possible water shortages. The entire operation takes place in real time, saving expensive manhours while maintaining water quality.
Share industrial data securely
In another use case, industrial operators are teaming up with equipment manufacturers and service providers to operate complicated systems, monitor asset performance and ensure compliance and safety. Getting access to client data in real-time has helped providers deliver more compelling products, minimized their capital investments, and helped them get to market faster.
The Iowa-based Renewable Energy Group, for example, partnered with South Carolina-based Allied Reliability to reduce their centrifuge failures. The two companies built a bidirectional highway of real-time and historical information using cloud data-sharing.
With vibration analysis and recommendations based on real-time performance data, the partners were able to anticipate the need for centrifuge maintenance, reducing equipment downtime by as much as 90%.
Deliver new services
With the ability to share real-time data securely over the internet, companies can now collaborate dynamically and create new business models. A service provider, for instance, can be given a complete and accurate view of a client’s operating environment from a web browser, making it easier to design and deliver new services in response, creating new revenue streams.
US power leader Dominion Energy has been gathering and sharing power data across its North American grid network using a scalable cloud data management platform. Their secure, cloud-native data management solution makes it easier to turn grid data into a new competitive advantage.
Dominion can now share its energy generation and performance data with customers, who can verify its use of low-carbon sources. In turn, they can use this information as evidence of their net-zero actions for their investors and auditors.
Delivering this valuable operations data is now a selling point for the company, encouraging customers to select Dominion over other energy providers.
Unlock data-led growth
These are just some of the many business outcomes possible when trusted, high-fidelity operations data streams are aggregated and analyzed across assets, companies, and geographies. Integrated, edge-to-cloud data management solutions can help companies in many ways. They can improve asset reliability, reduce unplanned downtime, improve collaboration, securely share timely and relevant information, and facilitate the development of new – and often customized – products and services.
Industrial enterprises are generating more operations data than ever before. The challenge is to collect the data reliably, aggregate it as needed, quickly prepare it for analysis, and make it available to all the teams that can use it to derive business value.
When harnessed correctly, businesses can realize competitive advantages from industrial data while unlocking growth and sustainability across the industrial ecosystem.