Datacor embeds Amazon Quick Sight to give rental customers self-service analytics

What happened

Datacor, which sells the TrackAbout solution for industrial gas and welding distributors, integrated the Amazon Quick Sight embedded analytics capability within Amazon Quick into TrackAbout, per an AWS Machine Learning Blog post.
The post says TrackAbout customers collectively manage 27.5 million assets and over 725,000 bills per month, and that rental billing is a major revenue stream for them.
The reported problem: rental data lived in TrackAbout's operational systems but could only be extracted through Datacor's support team, which fielded requests for custom SSRS reports or raw exports and returned static answers after days or weeks, so customers couldn't iterate on follow-up questions.
Datacor's answer, per the post, was an automated cross-cloud data pipeline plus embedded interactive dashboards and a natural language search bar, letting business users explore rental performance data and ask questions directly instead of filing IT tickets.
The post quotes Datacor Chief AI Officer Sundar Kuppuswamy saying the unglamorous work underneath — cross-cloud ingestion, row-count validation, and an auditable tenant-isolation model — is what earned the right to put natural language querying in front of customer revenue data.
Kuppuswamy also framed the return on the investment as putting decisions back in customers' hands, citing a distributor asking about a five percent acetylene rate increase and getting an answer in the same meeting.

What happened

Datacor, which sells the TrackAbout solution for industrial gas and welding distributors, integrated the Amazon Quick Sight embedded analytics capability within Amazon Quick into TrackAbout, per an AWS Machine Learning Blog post.
The post says TrackAbout customers collectively manage 27.5 million assets and over 725,000 bills per month, and that rental billing is a major revenue stream for them.
The reported problem: rental data lived in TrackAbout's operational systems but could only be extracted through Datacor's support team, which fielded requests for custom SSRS reports or raw exports and returned static answers after days or weeks, so customers couldn't iterate on follow-up questions.
Datacor's answer, per the post, was an automated cross-cloud data pipeline plus embedded interactive dashboards and a natural language search bar, letting business users explore rental performance data and ask questions directly instead of filing IT tickets.
The post quotes Datacor Chief AI Officer Sundar Kuppuswamy saying the unglamorous work underneath — cross-cloud ingestion, row-count validation, and an auditable tenant-isolation model — is what earned the right to put natural language querying in front of customer revenue data.
Kuppuswamy also framed the return on the investment as putting decisions back in customers' hands, citing a distributor asking about a five percent acetylene rate increase and getting an answer in the same meeting.

Sources