Manufacturers to Spend $19.8B on Data Management and Analytics by 2026
May 28, 2020
plants generate mountains of data throughout the day, every day.
Traditionally, data has been noted on paper or analyzed in spreadsheets.
However, today it can be collected automatically via sensors and
analyzed with tools that far exceed spreadsheets’ capabilities. ABI
Research forecasts that in 2026, manufacturers and industrial firms will
be spending on US$19.8 billion on data management, data analytics, and
associated professional services.
“For many manufacturers, there is an appreciation that operational
decisions need to be based on empirical evidence rather than guesswork.
The challenges are not necessarily capturing and analyzing data, rather
what to analyze in the first place,” explains Michael Larner, Principal
Analyst at ABI Research. “The findings need to have a meaningful impact
on operations and so manufacturers need to take a step back and devise
Manufacturers should engage suppliers to help them prioritize activities
and shape projects. For example, is the priority to increase production,
reduce waste, improve quality, or to fully understand whether a piece of
machinery needs to be serviced? Predictive maintenance is critical for
avoiding downtime and improving safety on the factory floor. At the same
time, video inspection software captures defects with a greater degree
of accuracy than the human eye.
the use cases expand, the supplier ecosystem evolves to meet them. For
example, Bright Wolf, InVMA, and Dploy Solutions marry technological and
consulting expertise to help their respective clients appreciate digital
transformation from a business perspective. Davra looks to ensure
manufacturers are using clean data, Relimetrics focuses on video
inspections, Altair on analytics capabilities to support digital twins,
and Senseye on predictive maintenance.
The advancements in Artificial Intelligence (AI) and machine learning
mean that suppliers’ cannot just report data but also predict outcomes
and suggest recommended actions. The orientation for action makes for
compelling propositions, and when combined with data visualization
platforms embed data in many different roles. The advent of no code/low
code platforms allows staff not have to be data scientists to utilize
analytics in their roles.
“While manufacturers have spent decades refining their physical
production lines, today they need to expend effort in optimizing their
processes for collecting and analyzing data. But data should not be
collected just for the sake of it,” Larner concludes.