Revamped Data Architecture Provides Clarity on Business Growth Trajectory – Blount

Before:

Outdated Data and Technology Capabilities Hinder New Machine Investments

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

Blount’s data and technology capabilities were outdated, draining the analytics team’s time and blocking strategic advancements. Blount was losing 30+ hours a week in trying to manage and monitor their data structure.

Outdated tooling made it hard to effectively manage and monitor data. Recurrent data failures had put Blount’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.

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.

Needing to solve their ineffective data architecture, they turned to Data Clymer, a Spaulding Ridge company, to help.

Solution:

A Phased Approach to Data Maturity and Modernization

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.

• Snowflake for data storage and compute
• Matillion for data ingestion
• dbt for data transformation

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.

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.

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.

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.