A Food Solutions Company

About the Business 

This client is a food solutions company that delivers the ingredients for a better life. As a distributor, wholesaler, and retailer with a global supply chain network, the client’s customers span a diverse group of national accounts, independent and chain grocers, ecommerce retailers, U.S. military commissaries and exchanges, and the company’s own brick-and-mortar grocery stores, pharmacies, and fuel centers.

The Business Challenges

The food solutions company has data spread across more than 600 applications. They were looking to modernize their data and analytics infrastructure, which is currently centered around an architecture composed of Netezza, MicroStrategy, and Cognos.

The client wanted to migrate its current architecture to a modern data platform by performing a data and migration strategy, along with a proof-of-concept exercise. The goal of this project was to collaboratively develop a plan that will give the company a path forward to a scalable and flexible analytics architecture cost-effectively and efficiently.

Common Themes We Heard:

• Need to be nimble and agile to gain speed-to-value in building an analytics foundation
• Want to become self-sufficient in future development capabilities
• Provide knowledge transfer via partnering with the 2nd Watch team during the pilot
• Establish a pilot that can be leveraged as the foundation for the full migration
• Want to keep the architecture and technology simple and easy-tomaintain
• Leverage the pilot and go-forward strategy to develop an outsourced vs. in-house resource plan

The 2nd Watch Solution

The 2nd Watch team began by documenting business and technical requirements for the future state data platform to ensure ongoing extensibility, flexibility, and minimal maintenance as new use cases are identified and built. We provided a diagram and narrative description of recommended reference architecture and technology for their future state, for both data integration and analytics, as well as a roadmap to project success.

For this roadmap, we identified the client’s high-value use cases and requirements for a pilot phase to serve as the foundation of the future platform, along with an execution plan, success criteria, and AAR plan. We also developed a high-level analytics plan, including macro-budget estimates to implement the different phases of the roadmap to achieve their high-priority use cases; the budget considered what could be outsourced (onshore vs. offshore), what could be built via automation, and what could be built in-house.

We then presented a summary of all findings and recommendations to project stakeholders that support both short-term and long-term business goals. After this phase, our team was able to move into implementation.

For the implementation phase of this project, we used QlikCompose and QlikReplicate to replicate source data for sales; created a data model, data warehouse and data mart in Snowflake; and then conducted workshops to deep dive into the data model and Snowflake Data Cloud. Our team then set up workspaces for future reports, dashboards, and data sets in Power BI, testing Azure DevOps pipelines for continuous integration. We also conducted workshops around data modeling, reporting structure, and row-level security.

The Business Benefits

Our client has now outlined their long-term BI and analytics vision, as well as interim goals for their data platform initiative. They also documented executive priorities for data and metrics that are most useful for operational decisions. 2nd Watch helped the client understand their existing data sources, data relationships, and data process issues; and we then recommended the right tools, technologies, and templates to facilitate the IT and business teams’ growing demand for analytics. The roadmap we provided included budgets and milestones, as well as a pilot phase, to ensure goal attainment.

The greatest impact of this project was our client’s analytics expansion. We helped them identify high-value use cases by combining source master data into the new business layer, highlighting the benefits of an integrated data platform, meanwhile removing reporting silos to improve the availability and impact of analytics.