One of the world's most iconic brands — millions of customers daily across thousands of locations

Starbucks is one of the world's most iconic consumer brands, serving millions of customers every day across thousands of locations globally. To maintain operational excellence at that scale, store-level data needs to be fast, accurate, and accessible — not locked inside central data teams.

The challenge isn't just volume. It's proximity: the people who most need the data — store managers — are the furthest from the systems that hold it.

Store managers waiting on central teams — slow decisions, delayed insights, heavy overhead

Starbucks faced a structural challenge: empowering store managers to make data-driven decisions without creating heavy dependencies on central data or IT teams. Manual workflows were slow, and inconsistent reporting formats created delays and inefficiencies that grew with every new location.

Every report request that flowed through the central team was a delay. Every inconsistent format was a source of error. At Starbucks's scale, these inefficiencies were multiplied thousands of times over — and the infrastructure cost of maintaining them was significant.

Self-serve operational dashboards — built for store managers, not data teams

Mammoth gave Starbucks the platform to put data directly in the hands of the people who need it:

• Enable store managers to access and customise operational dashboards without data team involvement
• Reduce reliance on central data teams for everyday reporting and performance tracking
• Maintain data consistency and governance at scale across all locations
• Speed up report generation and decision-making across thousands of locations
• Deliver measurable cost savings by reducing infrastructure overhead and manual effort

53% cost reduction. 75% self-serve. 1400% ROI.

• 53% reduction in monthly infrastructure costs — leaner data operations at scale
• 75% of store managers now access data independently — no central team required
• 1400% increase in ROI — operational efficiency gains multiplied across thousands of locations
• 60% reduction in manual reporting workload for the central data team
• 5× improvement in operational data accuracy and 3× increase in manager engagement with data