There are few phrases in industry that are more thrown around, and simultaneously less understood, than “Big Data”. That can make it tempting to dismiss big data as a carelessly repeated buzzword, more than a concept of real, germane value to any enterprise, and particularly process engineering businesses — but that would be a mistake. Understanding and properly leveraging big data is mission-critical to the success of any modern enterprise.
Knowing how and where industrial enterprise IT can precisely apply and leverage big data to their benefit relies on a few key factors, including the need to democratise big data; implement a next-generation data historian; shift gears from a strategy of mass data accumulation to strategic industrial data management; interweave big data with industrial AI-enabled technologies; and understand just how these benefits can play out in a post-pandemic landscape.
How COVID-19 forced a rethink in data storage and access
The COVID-19 pandemic accelerated many industrial organisations’ digitalisation journey, right down to the way they store and access data. This has revealed the limitations of traditional industrial data management models, where data is siloed across teams, sources, and locations. This data gatekeeping significantly hinders visibility, ensuring that only certain people with unique access or domain expertise are able to understand or access data sets that could otherwise be relevant to others across the enterprise.
In a pandemic setting where many employees were forced to work remotely, this model of siloing industrial data storage and access was extremely counter-productive. What happens when access to particular data is kept by an employee, but that person is working remotely? In a fluid situation like a pandemic, where public health guidance is constantly shifting, static enterprise data access, workflows, and reporting severely limit the organisation’s real-time visibility into the safety of its employees, much less business value and growth.
COVID-19 proved industrial organisations need to rethink how they store data and make it accessible across the enterprise. With more organisations adopting a permanent hybrid approach to working on-site and remotely, there is a pressing need to use solutions that provide continuous and democratised big data access across all users.
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Democratising industrial data with a next-gen data historian
Big data can be a double-edged sword. More data means, theoretically, more inputs to analyse for more efficient, productive outputs. The more you know about how teams operate, the better those insights can be leveraged for greater productivity, time savings, cost efficiencies, and business growth. But more data doesn’t always mean better data. Often it’s the opposite: businesses accumulate more data than they use or know what to do with. This mass data collection approach means industrial organisations end up sitting on troves of unused, unstructured, unoptimised data. More data ends up yielding less visibility.
Making industrial data actionable and valuable requires a next-generation data historian for identifying and elevating data based on relevancy. This intuitive data wrangling from different assets across the enterprise, from sensors to the edge and to the cloud, establishes a universal baseline for formatting and securing data. Rather than siloed data going through different formatting and security stages based on their source or team, all data across the enterprise is assigned identity tags and placed in the same formatting standards, opening data visibility and access across the organisation.
Strategic industrial data management
Rather than collecting data en masse and dumping it into unstructured data swamps, a strategic industrial data management approach utilises data historians and industrial AI solutions to make industrial data more visible, accessible, and actionable across the enterprise.
This isn’t just about cleaning up data lakes or making data actionable. This strategic data management approach also helps to bridge a growing skills gap in the industrial workforce. As veteran employees with years of domain expertise continue to retire, replaced with younger employees who don’t have the same level of experience, an AI-powered, data historian-driven strategic data management approach ensures that critical, historic knowledge is preserved and shared widely across the organisation – regardless of team, silo, or an individual worker’s retirement.
Big data will continue to play a mission-critical role in arming industrial organisations with the resources and insights needed for making data-driven decisions tied to concrete business value outcomes. This could mean anything from optimising production lines to providing real-time process visibility, all to help teams become more productive, effective, and innovative. But to reap the most value from big data and apply it meaningfully to industrial applications, process engineering businesses must switch their focus from mass data accumulation to more thoughtful, strategic industrial data management – specifically homing in on data integration, mobility, and accessibility across the organisation. By deploying tools like next-gen data historians and industrial AI solutions, industrial organisations can unlock new, hidden value from previously unoptimised and undiscovered sets of industrial data.