LogisticsSourced

Case Study: How Pitney Bowes Saves 30+ Hours/Month with Select Star's Automated Data Cataloging

Pitney Bowes Case StudySourced & dated by Case Study Desk

Key facts · TL;DR

Company

Pitney Bowes

Industry

Logistics

Challenge

Manual ETL analysis across a data mesh slowed decision-making

Headline result

Pitney Bowes' data-management team saves more than 30 hours a month and improved data-asset cataloging efficiency by up to 67% using Select Star across its data mesh.

Key results

30+ hrs

Saved per month on data troubleshooting

67%

Improvement in cataloging efficiency

up to 67%

12+

Teams using Select Star

The challenge

Pitney Bowes operates a data mesh with many independent teams producing and consuming data. Understanding how data assets connected required manual ETL query analysis, which was time-consuming and delayed strategic decisions.

The solution

Pitney Bowes implemented Select Star's data discovery platform on AWS for automated cataloging, lineage tracking, and governance.

“I'm saving at least 30 hours a month easily just by automating schema changes.”
VS
Vishal Shah
Data Architect Manager, Pitney Bowes

The results, in context

The data-management team saves more than 30 hours a month by automating schema changes and cataloging, and improved data-asset cataloging efficiency by up to 67%. More than 12 teams now use Select Star, and the team accomplished in one month what had taken a year with its previous catalog.

Products used

Select Star Select StarSelect Star Automated Data CatalogSelect Star Column-Level Lineage