Success Story

Blount Fine Foods Logo

See how a leading food manufacturer is improving operational efficiency—and increasing profit margins—by moving to a modern data stack.



Poor data quality and outdated technology drained the analytics team’s time and blocked strategic advancements.


Blount partnered with Data Clymer to advance its data maturity and lay the groundwork for digital transformation and growth.

Data Technology Stack

The Full Story

Driving Operational Efficiency in Manufacturing

Blount Fine Foods is one of America’s leading prepared foods companies. Perhaps best known for providing high-quality soups to grocery stores and restaurant chains, the family-owned business has seen massive growth since 1892. The company has quadrupled revenue over the past decade and grown by an average rate of 20% since 2000.

As part of its ongoing expansion, Blount invested in new manufacturing machines to drive operational efficiency. Blount’s analytics team, in turn, faced an opportunity to use the machine data to drive valuable business insights.

Building a Data Infrastructure to Support Growth

Before Blount could drive value from the machine data, however, its analytics team needed to overcome a substantial hurdle: overhaul the data architecture.

TJ Polak, Director of Analytics and Data Engineering, explained, “Our on-premise infrastructure just wasn’t scalable. We had a great deal of sprawl, and the outdated technology was causing multiple issues even without the massive influx of new machine data.”

30+ Lost Hours per Week

Outdated tooling made it hard to effectively manage and monitor data. Recurrent data failures had put TJ’s analytics team in reactive mode. They were often forced to spend their days tracking down issues rather than adding value through strategic projects. The tools also hindered innovation, which in turn damaged morale, retention, and recruitment efforts.

“Oftentimes, we were spending 30 hours a week trying to track down the root cause of data failures,” TJ commented.

No Trust in the Data

Worst of all, the organization’s data was unreliable, with duplicate entries and inaccuracies. Slow data load times and frequent error messages caused frustration across departments.

“No one was happy with the quality of the data,” TJ explained. “We were leveraging custom applications to try to make things work. But over time, the data was getting worse. Historical data became out of sync with our source system, causing issues with reconciliation and reporting.”

A Phased Approach to Data Maturity and Modernization

After recognizing the critical need to modernize Blount’s data architecture, TJ began evaluating technologies. That’s when a Snowflake representative suggested hiring a data consulting partner and recommended Data Clymer.

As a former data consultant himself, TJ knew that the right partner could provide the guidance and expertise they needed. After talking to Data Clymer, he felt a strong level of trust. Additionally, he valued the team’s approach: start small and establish value upfront.

“Data Clymer was honest and transparent, and I really appreciated the iterative approach to our partnership. We were able to identify initial milestones, prove out areas of value, and work toward a larger engagement.”

—TJ Polak, Director of Analytics

develop your playbookPhase 1: Develop a Roadmap

The partnership began with data strategy consulting.

  • First, Data Clymer evaluated Blount’s tools, goals, and the internal team’s skill set.
  • Next, they developed a roadmap to help advance Blount’s data maturity journey.
  • Finally, armed with a solid plan, the team set about building the new solution.

Data Clymer then stood up and configured the following technology according to best practices and business needs.

Using dbt and Matillion gave Blount the best of both worlds. Matillion is highly cost effective in the long run and can help with future reverse ETL workflows, while dbt meets industry dataOp standards and was extremely approachable for Blount’s team members who already knew SQL.

Matillion vs dbt: Evaluating the Best Data Transformation Tools

data modelingPhase 2: Ingest, Model, and Visualize Data

For the data ingestion process, Data Clymer built and configured new Matillion ingestion pipelines for a number of data sources, including a net new data source ingestion of HR data that required building a custom connector. The team also staged more than 80 tables from ERP data.

Using dbt for data modeling, the team then transformed the data, refactoring existing data models to enable analytics and reporting. They established production grade resiliency, including source freshness testing, data quality testing, idempotency, scheduling and alerting.

With an eye on helping Blount’s analytics team grow, Data Clymer also provided documentation and cross-training to empower them to maintain and extend the pipeline.

increased data trustImproved Data Quality and Trust

The new stack is far more reliable and has provided Blount with a much-needed window into data quality. Data Clymer helped develop standards and processes to ensure data quality each step of the way.

TJ says this alone has made his life much easier. While he used to spend his mornings manually double-checking that jobs and scripts ran correctly, he now has confidence that their data is safe and there’s an alert plan in place.

“I’m able to sleep at night knowing our data has quality checks. If something goes wrong, we know about it first, which has gone a long way in restoring trust in the data.”

—TJ Polak, Director of Analytics

Now, instead of wasting time chasing errors and addressing failures, TJ and his team can focus on building for the future. As a result, the team is not only more productive, but also filled with excitement about learning and advancing their skill sets with new tools.

And Data Clymer is there to help guide the team’s growth, providing expertise, resources, and collaboration to help them improve.

“The Data Clymer team is smart, hard working, and willing to help. They don’t just work on things and then pass it off. They have helped our analytics team learn and grow by providing the right exposure to ensure we understand the tools and implementation.”

—TJ Polak, Director of Analytics

Looking Ahead

TJ’s first priority is continuing to grow Blount’s analytics team. He recently hired a new team leader and plans to add two more data engineers by the end of the year. Another benefit of Blount’s new modern data technology: it’s far easier to attract strong talent.

Next on the priority list is getting the data out of the new manufacturing machines and centralized into one location. From there, Blount will have a standardized model for its data warehouse and reporting. Whereas Blount had been monitoring performance by hand for five generations, they are now laying the groundwork for a modernized digital approach.

This modernization will unlock future opportunities to use data to minimize waste and help inform decision making. For example, the team now has a PowerBI report to monitor weights for products and SKUs. Once all the machines are hooked up to IoT data transfers, they’ll be able to quickly track operational efficiency and monitor maintenance needs. The team is also working to integrate business metrics so the executive team can instantly understand the revenue impact of various scenarios and make smarter decisions.

“Data Clymer has been a great investment for our team. We’re starting to explore the value of using data from our manufacturing machines for things we never thought were possible. Now that we have data we can trust, the opportunities are endless.”

—TJ Polak, Director of Analytics

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