Process industries are throwing billions of dollars at something far more valuable than sheer data volume: high-quality data. And it’s no surprise why.
Industrial AI needs precision, not just quantity
While everyone’s heard of big data, the process industries are zeroing in on a more nuanced concept: quality data. This isn’t about gathering huge amounts of randomly collected information, but about creating a structured and reliable dataset that AI can actually learn from. It’s a subtle but crucial distinction, especially in an industry where efficiency and accuracy can mean the difference between profit and loss.
Take a pharmaceutical plant, for example. AI can analyze production data to optimize batch processing and minimize waste. But if that data is incomplete, inconsistent, or tainted by human error, the results will be garbage in, garbage out. The same applies to oil refineries, chemical plants, and any other process industry where precision matters.
The importance of data quality in industrial AI
Industrial AI is a $15 billion market that’s projected to grow by 20% annually for the next five years. Companies like Siemens, ABB, and GE Digital are at the forefront of this trend, developing AI-powered software that can analyze vast amounts of data to improve process efficiency, predict equipment failures, and optimize energy consumption. But for all its potential, industrial AI is only as good as the data it’s fed.
Data quality is a problem that’s plagued the industry for years. A 2020 study found that 71% of companies in the process industries experienced data-related errors, with 45% attributing these errors to human error. That’s why companies are investing in data management platforms that can ensure data accuracy, consistency, and completeness. The payoff is tangible: a study by Accenture found that companies that prioritize data quality can achieve up to 25% higher revenue growth than those that don’t.
What this means
The bottom line is that process industries need quality data to succeed in the age of industrial AI. It’s not about collecting more data, but about gathering the right data in the right way. Companies that prioritize data quality will be the ones that reap the benefits of industrial AI, from improved efficiency to increased revenue growth. For those that don’t, the consequences will be stiffer competition, reduced profitability, and a shrinking market share.
