BI Project Scenario

 

Scenario Review the following scenario. Then complete the step-by-step instructions. You are a BI analyst for a grocery store chain that monitors dietary trends affecting in-store purchases. Your company wants you to examine which types of Hass avocados are purchased most often. The avocados are categorized as one of four sizes: small, medium, large, and extra large. In addition to the average price and total volume of each avocado, the date of each sale is also recorded. Using this data, you will create a historical table that illustrates how indexes and partitions work. This will allow you to practice creating partitions and clustered tables and demonstrate how to use them. Your goal is to use partitions and clusters to answer the following question: What is the distribution of avocado sales from 2015 to 2021?

To begin, create a new table without a partition or cluster. This will serve as a baseline to compare to the partitioned and clustered tables. Name it avocados.

Then, in the Editor tab, copy and paste the following SQL code and click Run. 

this is the result:

Next, create a table partitioned by an integer range (the years 2015 through 2022). Name it avocados_partitioned.

Return to the tab you entered the SQL code into. Delete that code then copy and paste the following SQL code. Click Run.

this is the result:

Next, create a table partitioned by an integer range and clustered by type. Name it avocados_clustered.

Return to the tab where you entered the SQL code. Delete that code, then copy and paste the following SQL code. Click Run.

Query the tables and compare performance

Query the Table without a partition or cluster

Query the partitioned Table

Query the partitioned and clustered Table

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 crucial for making informed decisions. As a business intelligence (BI) professional, you play a pivotal role in gathering and organizing data from various stakeholders across different teams. BigQuery, a powerful data warehouse, serves as an indispensable tool for querying, filtering, aggregating, and performing complex operations on large datasets. In this blog post, we will explore how Aviva, a BI professional, uses BigQuery to merge data from multiple stakeholders to answer important business questions for a fictitious coffee shop chain.

The Problem: Identifying Popular and Profitable Seasonal Menu Items

Aviva is tasked with helping the coffee shop chain's leadership identify the most popular and profitable items on their seasonal menus. By doing so, they can confidently make decisions regarding pricing, strategic promotions, and determining which items to retain, expand, or discontinue.

The Solution:

Data Extraction:

Aviva begins the data extraction process by identifying relevant data sources and preparing them for transformation and loading into BigQuery. To do this, she adopts the following strategies:

Meeting with Key Stakeholders: Aviva conducts a workshop with stakeholders to understand their objectives, the specific metrics they want to measure (e.g., sales metrics, marketing metrics, product performance metrics), and the sources of data they want to use (e.g., sales numbers, customer feedback, point of sales).

Observing Teams in Action: Aviva spends time observing stakeholders at work, asking questions about their activities, and understanding why certain information is essential for the organization.

Organize Data in BigQuery:

After completing the data extraction process, Aviva transforms the gathered data and loads it into BigQuery. Utilizing BigQuery, she designs a target table to consolidate and organize the data. This target table acts as the foundation for creating a final dashboard that stakeholders can review.

The Results:

The dashboard created using BigQuery provides stakeholders with valuable insights. They discover that peppermint-based products on their seasonal menus have experienced a decrease in popularity over the past few years, while cinnamon-based products have grown in popularity. Based on this data, stakeholders decide to retire three peppermint-based items and introduce new cinnamon-based offerings. Additionally, a promotional campaign is launched to highlight these new items.

Key Findings:

The use of BigQuery allows BI professionals like Aviva to obtain answers to critical business questions. By consolidating data in a target table and presenting it through an intuitive dashboard, stakeholders can easily access and understand the information, leading to more informed decisions on improving services, products, and seizing new opportunities.

Conclusion:

BigQuery's capabilities as a data warehouse provide BI professionals with the tools they need to derive actionable insights from vast and complex datasets. Aviva's success in using BigQuery to address the coffee shop chain's business questions exemplifies the value of this robust data analytics solution. As the landscape of data-driven decision-making continues to evolve, the role of BI professionals and tools like BigQuery will remain instrumental in driving business success.

Remember, data is the fuel that powers smart decision-making, and BigQuery is the engine that propels your organization forward into a data-driven future.