Quality Data Critical for Process Efficiencies
· news
The Quality Conundrum: Can Process Industries Balance Big Data and True Insights?
The recent OPTIMIZE 26 conference in Houston highlighted a crucial distinction in the industrial complex: quality data versus big data. Industry experts warn that it’s not just about sheer volume, but rather relevance and accuracy that matters. As Nuno Pacheco, a senior optimization engineer at Repsol, noted, “Poor quality or randomly dumped data will lead to bad insights.” This is not just a matter of semantics; the consequences are far-reaching.
AI models trained on subpar data produce unreliable results, which can have disastrous effects in industries where precision is paramount. The dichotomy raises questions about the foundation of process industries’ digitalization efforts. Adriano Alfani, CEO of Versalis, credits high-quality data gathering and analysis with enabling his company to respond to operational climate changes with agility.
This led to a transformation plan that ensured competitiveness in a challenging market. However, this success story also highlights the limitations of relying solely on big data. Most industries recognize the importance of quality data. As Engro Fertilizers’ Muhammad Zaghum Riaz points out, “AI-enabled fully autonomous plants remain our ultimate goal.” Achieving this requires more than just collecting vast amounts of data; it demands a strategic approach to gathering and analyzing relevant information.
The industry’s emphasis on quality data has sparked innovation in software solutions. Emerson’s Aspen Technology business is at the forefront of this movement with its Inmation OT data fabric, which unifies and contextualizes industrial data, making it more scalable, flexible, and enterprise-ready. The market for data fabrics is growing, forecasted to reach $16 billion to $19 billion by the middle of the next decade.
This growth is not just about technology; it’s about transforming industries. AspenTech’s Inmation OT data fabric provides a platform for high-quality data management, catering to the needs of process industries. As automation and AI advance, the importance of quality data will only intensify. It’s not just about efficiency or cost savings; it’s about staying competitive in an increasingly complex global market.
Industry leaders must prioritize accurate data collection and analysis to unlock true potential. The quality conundrum will persist until industries can strike a balance between big data and meaningful insights. The clock is ticking, and the question remains: will they be able to achieve this delicate equilibrium before it’s too late?
Reader Views
- RJReporter J. Avery · staff reporter
It's striking how often "big data" gets trotted out as a silver bullet for process industries' efficiency woes without considering its potential pitfalls. The article highlights the crucial distinction between quality and quantity, but what about ownership? Who bears responsibility when poor-quality data leads to subpar decision-making? The industry would do well to establish clear accountability measures alongside these emerging software solutions, lest we see more of the same cycle: AI models trained on dubious data producing unreliable results, followed by finger-pointing and costly rework.
- CMColumnist M. Reid · opinion columnist
While the industry's focus on quality data is a welcome shift from the big-data-for-the-sake-of-it approach, we mustn't overlook the human element in all this: the operators and engineers who are ultimately responsible for implementing these digitalization initiatives. As we develop more sophisticated software solutions to manage industrial data, we risk exacerbating existing skills gaps among plant personnel. Companies would do well to invest in training programs that complement their new technology stacks, ensuring that workers have the expertise to effectively interpret and act on the insights generated by these systems.
- ADAnalyst D. Park · policy analyst
The OPTIMIZE 26 conference highlights a crucial distinction in industrial complex digitalization: quality data versus big data sheer volume. Industry experts warn that poor quality or randomly dumped data can lead to bad insights and unreliable AI models. However, another issue is the lack of consideration for the "last mile" of data adoption - where high-quality data meets operational needs. In other words, simply collecting and analyzing relevant information is not enough; it must be translated into actionable decisions that drive process efficiencies, a gap that software solutions like Inmation OT still need to address effectively.