Leveraging BigQuery for Data-driven Insights: A Coffee Shop Chain Case Study

Introduction

In today's data-driven business landscape, having access to accurate and comprehensive insights is essential for making informed decisions. As a Business Intelligence (BI) professional, you play a key role in gathering and organizing data from multiple stakeholders across different teams. BigQuery, a powerful cloud data warehouse, enables fast querying, filtering, aggregation, and complex operations on large datasets.

To better understand how BI professionals use modern data tools, you can also explore how data warehouses support BI workflows.

In this post, we explore how Aviva, a BI professional, uses BigQuery to merge data from various stakeholders to answer important business questions for a fictional coffee shop chain.

The Problem: Identifying Popular and Profitable Seasonal Menu Items

Aviva is tasked with helping leadership identify which seasonal menu items are both popular and profitable. These insights will guide pricing decisions, promotional strategies, and choices about which items to retain, expand, or discontinue.

The Solution

1. Data Extraction

Aviva begins by identifying relevant data sources and preparing them for transformation and loading into BigQuery. This step aligns with the core principles of ETL pipelines, which you can review in this overview of ETL concepts.

Meeting with key stakeholders: She conducts a workshop to understand objectives, required metrics (sales, marketing, product performance), and available data sources (sales numbers, customer feedback, POS data).

Observing teams in action: Aviva spends time with stakeholders to understand their workflows and why specific information matters to the organization.

2. Organizing Data in BigQuery

After extraction, Aviva transforms the collected data and loads it into BigQuery. She designs a target table that consolidates all relevant information. This table becomes the foundation for the final dashboard reviewed by stakeholders.

If you're interested in how target tables fit into BI modeling, see Dimensional Modeling in Business Intelligence.

The Results

The BigQuery-powered dashboard reveals clear trends: Peppermint-based products have declined in popularity over recent years, while cinnamon-based products have steadily grown.

Based on these insights, leadership decides to retire three peppermint items, introduce new cinnamon-based offerings, and launch a promotional campaign to highlight the updated seasonal menu.

Key Findings

BigQuery enables BI professionals like Aviva to answer critical business questions by consolidating data into a single, analysis-ready structure. Presenting this information through an intuitive dashboard allows stakeholders to quickly understand trends and make informed decisions about products, services, and new opportunities.

Conclusion

BigQuery provides BI professionals with the tools needed to derive actionable insights from large and complex datasets. Aviva’s success in supporting the coffee shop chain demonstrates the value of this powerful analytics platform.

As data-driven decision-making continues to evolve, both BI professionals and tools like BigQuery will remain essential to business success. For more on choosing the right BI tools, explore how BI teams select metrics and tools.

Data is the fuel that powers smart decisions — and BigQuery is the engine that drives organizations into a data-driven future.