Spartan Nash

ETL Data Workflows: Spartan Nash boosts business logic through the modernization of ETL workflow

Spartan Nash is an American food, grocery products, and supplies distributor, serving independent retailers and military commissaries, with corporate-owned retail stores in 44 states of the US, Europe, Latin America, and the Middle East, listed on the stock exchange with over $9B in revenue for 2022. Spartan Nash's mission is to provide top-quality products and services that meet the needs of its customers while promoting the growth and success of its business partners. The company also has a solid international presence, delivering exceptional customer value through its distribution centers and e-commerce solutions.
3GB of optimized data workflows from over 15 departments
Real-time decisions based on reliable data across department
Snowflake tables built to store workflow output
refactoring workflows into easy-to-change silos

The challenge

Spartan Nash has business interests in various sectors related to the wholesale and retail distribution of food and consumer products. As a result, each area generated a vast volume of data each year, which was essential for gaining an overall understanding of the business.

BCG was brought in as 3rd party consultancy in order to help provide increased visibility into the operating business metrics. At the time, they produced a first iteration of an ETL pipeline based on Alteryx that would create these consolidated metrics for analysis. However, the existing architecture was outdated and resulted in data quality issues that entangled effective analysis and key business decision-making.

This unreliable data made it difficult for the organization to gain accurate insights and make informed decisions, leading to potential missed opportunities and decreased competitiveness in the market.

They needed a top-notch, resolutive tech partner who could assess their current workflow processes on Alteryx (ETL) to identify inefficiencies or areas for improvement.

The solution

The optimization ¡developed by Moonshot involved refactoring and modifying ETL workflows to make them more efficient and easy to maintain. Through collaboration with key stakeholders and extensive analysis, Moonshot proposed milestones to achieve significant improvements in data processing.

Source data from Snowflake and SharePoint

The team built the necessary Snowflake tables to store the Alteryx workflow output. With the partnership between team members from Moonshot and SpartanNash, they confirmed that the data loaded into Snowflake was accurately retrieved by PowerBI and met the necessary criteria for generating the required reports. 

Source Data from Snowflake and Sharepoint

Easy-to-change silos with workflows for each area. 

The workflows were refactored into easy-to-change silos, with each workflow responsible for a single area of business logic. Moonshot's team combined all the necessary steps in the correct sequence to deliver effective workflows that streamlined the Spartan Nash process.

Workflows to streamline Spartan Nash process

Proper documentation within Alteryx

The project delivered well-documented Alteryx workflows, explaining each business logic unit's purpose and method. The project team recommends that SpartanNash avoids using SharePoint inputs in Alteryx to minimize the time investment in configuration, maintenance, and workflow execution. 

Standardized Scheme 

Establishing a standard schema for data structures used by all departments is recommended to simplify the business logic and help optimize workflows.

The results

The optimization resulted in more efficient, easier maintenance, and well-documented workflows on Alteryx that explain the purpose and method of each unit of business logic. After the partnership, the team also identified potential risks, reported some of the current technology's limitations, and delivered detailed recommendations, technologies, and platforms to optimize future workflows.

"Good working with you guys – smart team." Benjamin Smith (Director of Data Analytics)

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