Unlike other industry verticals such as finance, retail and technology, which have historically more effectively used data and data analytics to optimize their businesses, manufacturing has traditionally been a laggard. If a recently released survey is an accurate indication, that’s about to change.

Interestingly, manufacturers are awash in an abundance of data. “It’s like The Rime of the Ancient Mariner – Water, water everywhere, and not a drop to drink. Manufacturers have an ocean of data, but much of it is not useful to them,” says Devin Yaung, senior vice president of Group Enterprise IoT Products and Services at NTT.

The reason why so much data held by manufacturers isn’t valuable for them is, according to Yaung and others, because so much of their data is siloed, lacks high quality, or the manufacturer doesn’t have the data infrastructure and scalability or management framework to extract value from that data.

“Because there are so many data sources, I think a lot of organizations are currently struggling with how they build the infrastructure, the framework,” says Doug Bellin, World Wide Head of Smart Manufacturing at AWS.

According to the Manufacturing Leadership Council’s survey report, Data Mastery: A Key to Industrial Competitiveness, less than half of survey respondents rely on a financial metric that has been validated to establish the value of their data. Instead, the survey found that organizations use “second-order metrics, including the impact on operational performance, as proxies for the value of data.”

The most significant challenge respondents cited in this survey is coordinating data among organizational systems.

Manufacturers are getting in front of data security and privacy policies, with 84% having a formal policy on data security and 72% on data privacy.

Data is Essential to Competitiveness

One of the most critical findings from the study was that an overwhelming majority of manufacturers (86%) believe that the effective use of manufacturing data will be “essential” to their competitiveness. Another 14% said that data will be “supportive” for their competitiveness by 2030. Clearly, manufacturing leaders understand data’s role in enabling them to be flexible and agile in a time of seemingly constant disruption.

Still, according to the survey, at 28%, not even a third of respondents have a formal policy on data quality, although an additional 35% have a partial data quality policy. That figure indicates signs of more formal policies to come and that higher-quality data will make it possible to attain the full potential of their data and support advanced activities, such as GenAI and digital twins.

Despite the unprecedented availability and rise in analytics tools, most manufacturers still depend on spreadsheets for data analysis. However, the survey of manufacturers sees this changing rapidly, with the number of manufacturers able to perform predictive analytics will double by 2030.

Manual tasks will need to be replaced by automated intelligence. “The game here is how do manufacturers go from data capture to intelligence without having hundreds of people that have to tough [the process],” says Bellin.

Other notable findings within this survey include that 61% of respondents said their companies have a corporate-wide data governance plan. That may be so, but only 15% of respondents said there is a gap between their data and overall business strategy and that their strategies are fully aligned. However, 31% believe their data and business strategies are closely aligned, and 25% are partially aligned.

Data use within manufacturing is expected to soar. By 2030, roughly one-third of survey respondents expect their data to grow by 500%, while another 18% see data use increasing between 200% and 500%.

By 2020, the survey found that attention will shift from using data to understanding operations, which will be active by most respondents by then, to 70% focusing on optimizing and 60% on predictability.

Survey respondents expect data to play a growing role in automation going forward, with only 15% of respondents saying that automation is a primary objective for manufacturing data initiatives. Survey respondents expect that number to reach 33% over the next six years.

Bellin explains that more manufacturers will start to capture data when generated and make automated decisions on that data. Today, many such decisions are still made by people. “Right now, the human is in the loop because [automation and AI] are so new people don’t trust it yet,” says Bellin.