Optimizing a Global Supply Chain with Automated Data Discovery at Samtec

When you’re a global manufacturer with more than 7,000 employees, 40 worldwide locations, and over 5 million part numbers to manage, data quickly becomes one of your most valuable and most challenging assets. For Samtec, a leading provider of electronic components used by companies like Google and Tesla, data powers every part of the business. It helps optimize the supply chain and supports Samtec’s commitment to “sudden service,” delivering world-class customer support at speed.

Despite that focus, Samtec’s data teams were spending weeks ramping up on new projects. With decades of siloed systems and critical knowledge locked in the heads of longtime employees, analysts and engineers often struggled to find and understand the right datasets. This added delays, created bottlenecks, and cost the company tens of thousands of hours each year. Samtec knew it needed a better way to capture institutional knowledge and make data discovery faster and easier across the business.

95%

time saved on impact analysis (from hours to minutes)

90%

reduction in data discovery bottle necks

Industry:

Manufacturing

Company size:

7,000 employees

Integrations:

Amazon Redshift

Power BI

Challenge

Untangling Decades of Data Silos

Samtec’s data team was responsible for supporting a global business but their institutional knowledge lived in silos. With a 50-year history and data spread across disparate systems, it was difficult for analysts and engineers to discover, understand, and trust the data they needed.

Several key pain points emerged:

Tribal Knowledge Bottlenecks: Understanding where data lived and what it meant required consulting long-tenured employees. This slowed down onboarding and created constant dependencies on a handful of experts.
Project Ramp-Up Time: Even experienced analysts needed weeks to scope cross-functional projects. They often had to track down table definitions, interpret undocumented schemas, and find the right stakeholders to explain data relationships.
Siloed Systems and No Single Source of Truth: With disconnected databases and no centralized catalog, teams lacked visibility into how data was being used or how changes would affect downstream reports.
Manual Impact Analysis: Power BI dependencies were tracked manually. Just understanding which reports used a given field could take hours, creating risk and rework during schema changes.
Limited Self-Service: Business users and even data-savvy teams couldn’t confidently explore or reuse existing datasets without help from the core data team.

Samtec needed a way to reduce time-to-insight, preserve expert knowledge, and give both technical and non-technical users an easier way to navigate their complex data environment.

Even for people who had been at the company for years, starting a new project in a different domain felt like being a new employee. You had to find the right database, figure out what the tables meant, and track down someone who could help interpret it all. It could take weeks just to get oriented before Select Star.

Zach Grimes

Data Science Manager, Samtec

Solution

Creating a Self-Service Data Foundation

Samtec selected Select Star after evaluating ten different data catalog vendors. The team prioritized ease of use, strong lineage and ERD capabilities, and visibility into SQL queries being used across their data stack. Select Star stood out for its intuitive interface, speed of implementation, and contextual AI that actually helped analysts find and understand the right data.

The implementation focused on several key areas:

Search and Discovery: Select Star gave users the ability to search across their data stack without needing to write SQL queries. Analysts could quickly find relevant tables, explore existing queries, and understand how others were using the data— all without having to schedule meetings or rely on tribal knowledge.
Data Lineage and ERDs: Built-in data lineage and entity relationship diagrams allowed teams to visualize how tables connected and how data flowed across systems. This was especially helpful for cross-domain projects and onboarding new team members.
Power BI Impact Analysis: Previously, understanding how schema changes would affect Power BI reports required hours of manual effort. With Select Star, Samtec could now run instant impact analyses to see which reports depended on specific fields or tables, significantly reducing the risk of breaking key dashboards.
AI-Powered Recommendations: Select Star’s AI helped surface relevant datasets and identify popular joins and filters, giving analysts a strong starting point for new projects. It even supported debugging SQL queries by understanding Samtec’s data environment.
Centralized Documentation: By capturing metadata and documentation directly in Select Star, Samtec began building a knowledge base that was accessible across the organization. This shift helped move critical insights out of individual heads and into a shared system that supports self-service.

Select Star’s ease of use meant that even teams outside the core data function, like the innovation group, could explore and answer their own data questions without needing constant support. For a centralized team supporting a global organization, this shift was a major unlock in both productivity and scalability.

‍

We tested a lot of tools, but Select Star was the only one where we didn’t need a walkthrough to figure it out. It just made sense. The ERDs, lineage, and AI search were exactly what we needed to give our team a real head start on every project.

Zach Grimes

Data Science Manager, Samtec

Result

Faster Projects, Fewer Bottlenecks, Better Decisions

With Select Star in place, Samtec’s data team has significantly reduced project ramp-up time, improved documentation coverage, and made self-service analytics more accessible across the business. The benefits have been both technical and operational:

Faster Project Kickoffs: Analysts now start projects with confidence, using Select Star to explore relevant tables, understand relationships, and review popular queries. What used to take weeks of meetings and manual research can now be done in days or even minutes.
Efficient Impact Analysis: Select Star’s automated lineage and Power BI integration make it easy to assess how schema changes will affect downstream dashboards. Tasks that previously took hours can now be completed in seconds.
Knowledge Sharing at Scale: By documenting key datasets and capturing institutional knowledge in Select Star, Samtec has begun to break down silos and reduce reliance on individual experts. The platform helps ensure that knowledge is preserved and accessible for future projects.
Scalable Time Savings: Cross-functional work that once took weeks of searching and stakeholder meetings now takes hours — saving analysts 20+ hours per project on average by leveraging documented assets that accelerate future work.

Empowered Teams: Non-central teams, including Samtec’s Innovation group, can now answer more of their own data questions without constant support from the core data team.

Select Star has become the go-to resource for understanding Samtec’s complex data landscape, enabling faster insights, more scalable processes, and better outcomes for both analysts and stakeholders.

Looking ahead, Samtec is continuing to roll out documentation for more of its critical systems. As this effort progresses, the team expects even greater gains in speed, consistency, and institutional knowledge, laying the groundwork for more scalable and impactful data work in the future.

Previously, data projects at Samtec were often delayed by weeks due to data discovery bottlenecks. With Select Star, analysts now get the context they need in hours or days, cutting project scoping timelines by up to 90% in some cases.

Zach Grimes

Data Science Manager, Samtec

Optimizing a Global Supply Chain with Automated Data Discovery at Samtec

Challenge

Solution

Result

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