How important is big data and where can it be applied to improve operations?

There are few phrases in the industrial lexicon more widely used but less widely understood than ‘big data’

It would be a mistake though to write the term off as an over-used buzz phrase rather than a concept that can bring tangible value to any enterprise. Understanding and properly leveraging big data is mission-critical to the success of every business. In the context of the capital-intensive industries, big data arms organisations with the resources and information to enable data-driven decisions and improve business-related outcomes.

However, these organisations often don’t know how to manage big data or what exactly to use it for. When analysed properly, big data can deliver enhanced real-time visibility and better decision-making, and it can support higher levels of productivity and innovation. Doing all this well is a differentiator for any organisation.

How COVID-19 focused minds on data challenges

The pandemic accelerated many industrial organisations’ digitalisation journeys, down to the way they store and access data. However, it also revealed the limitations of traditional industrial data models where data is siloed across teams, sources and locations. This data gatekeeping hinders visibility, ensuring only certain individuals with unique access or domain expertise can understand or access data sets that could otherwise be relevant to other business decision-makers.

With many employees continuing to work remotely, this model of siloing industrial data storage and access has proved counter-productive. What happens, for example, 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 changing, static enterprise data access, workflows, and reporting limit the organisation’s real-time visibility into employee safety; business value and growth.

COVID-19 highlighted that capital-intensive organisations would benefit from rethinking how they store data and make it accessible across the enterprise. With more enterprises adopting hybrid working, there is an increasingly urgent requirement to use solutions that provide continuous and democratised big data access across all users.

Democratising industrial data with a next-generation 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 they can end up sitting on troves of unused, unstructured, unoptimised data. More data can yield less visibility.

Making industrial data actionable and valuable requires a next-generation data historian to identify and elevate data based on relevancy. This intuitive data wrangling from different assets across the enterprise, from sensors to the edge and 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 to make 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. As veteran employees with years of expertise retire, replaced with younger employees with much less 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 or silo.

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 be about helping with predictive maintenance so supervisors can schedule plant downtime to repair assets before unexpected costly breakdowns occur, providing anomaly detection to alert workers to small deviations from the norms of quality, and predicting with greater certainty around supply chain management challenges.

It could also 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, capital-intensive businesses must switch their focus from mass data accumulation to more thoughtful, strategic industrial data management – homing in on data integration, mobility, and accessibility across the organisation. By deploying technologies like next-gen data historians and Industrial AI, these businesses can unlock new, hidden value from previously unoptimised and undiscovered sets of industrial data.

About the Author

Bill Scudder joined AspenTech in 2015 and serves as Senior Vice President and General Manager, AIoT Solutions. AspenTech’s AIoT Hub provides the foundation for industrial AI, including flexible data mobility and integration from on prem to the cloud, and empowers our customers to get actionable insights faster than ever with our next-generation Industrial AI solutions. He previously served as the company’s Senior Vice President and Chief Information Officer, and remains responsible for the company’s IT organization.