More CIOs and CTOs are seeking actionable insight from the new technologies that are driving digital transformation within the industrial and manufacturing sectors. Manufacturing plants generate mountains of data throughout the day, every day. That data is a valuable asset.
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 spreadsheet capabilities.
According to the latest worldwide market study by ABI Research, manufacturers and industrial companies will be spending $19.8 billion on data management, data analytics, and associated professional services by 2026.
Data Management and Analytics Market Development
"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," said Michael Larner, principal analyst at ABI Research.
According to the ABI assessment, the findings need to have a meaningful impact on operations and so manufacturers need to take a step back and devise precise objectives for data analytics projects.
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 of manufacturing systems is critical for avoiding production downtime and improved employee safety on the factory floor. At the same time, video inspection software typically captures defects with a greater degree of accuracy than the human eye.
As the use cases expand, the IT supplier ecosystem evolves to meet them. For example, Bright Wolf, InVMA, and Dploy Solutions marry technological and consulting expertise to help their respective clients achieve 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.
Moreover, the advancements in Artificial Intelligence (AI) and machine learning mean that IT suppliers can no longer merely report on captured data, they must also predict outcomes and suggest recommended actions for their customers.
Outlook for Data Analytics Use Case Growth
The orientation for action makes for compelling value propositions, and when combined with data visualization platforms embed data in many different roles. The advent of no-code and low-code platforms don't require IT staff to be data scientists to utilize software 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.
Clearly, there is a growing need for data analytics professional services within the industrial and manufacturing sectors. Business and IT leaders would greatly benefit from information and guidance that would enable operation teams to discover and apply the lessons learned by similar organizations.
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 spreadsheet capabilities.
According to the latest worldwide market study by ABI Research, manufacturers and industrial companies will be spending $19.8 billion on data management, data analytics, and associated professional services by 2026.
Data Management and Analytics Market Development
"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," said Michael Larner, principal analyst at ABI Research.
According to the ABI assessment, the findings need to have a meaningful impact on operations and so manufacturers need to take a step back and devise precise objectives for data analytics projects.
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 of manufacturing systems is critical for avoiding production downtime and improved employee safety on the factory floor. At the same time, video inspection software typically captures defects with a greater degree of accuracy than the human eye.
As the use cases expand, the IT supplier ecosystem evolves to meet them. For example, Bright Wolf, InVMA, and Dploy Solutions marry technological and consulting expertise to help their respective clients achieve 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.
Moreover, the advancements in Artificial Intelligence (AI) and machine learning mean that IT suppliers can no longer merely report on captured data, they must also predict outcomes and suggest recommended actions for their customers.
Outlook for Data Analytics Use Case Growth
The orientation for action makes for compelling value propositions, and when combined with data visualization platforms embed data in many different roles. The advent of no-code and low-code platforms don't require IT staff to be data scientists to utilize software 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.
Clearly, there is a growing need for data analytics professional services within the industrial and manufacturing sectors. Business and IT leaders would greatly benefit from information and guidance that would enable operation teams to discover and apply the lessons learned by similar organizations.