Book Image

Data Storytelling with Google Looker Studio

By : Sireesha Pulipati
Book Image

Data Storytelling with Google Looker Studio

By: Sireesha Pulipati

Overview of this book

Presenting data visually makes it easier for organizations and individuals to interpret and analyze information. Looker Studio is an easy-to-use, collaborative tool that enables you to transform your data into engaging visualizations. This allows you to build and share dashboards that help monitor key performance indicators, identify patterns, and generate insights to ultimately drive decisions and actions. Data Storytelling with Looker Studio begins by laying out the foundational design principles and guidelines that are essential to creating accurate, effective, and compelling data visualizations. Next, you’ll delve into features and capabilities of Looker Studio – from basic to advanced – and explore their application with examples. The subsequent chapters walk you through building dashboards with a structured three-stage process called the 3D approach using real-world examples that’ll help you understand the various design and implementation considerations. This approach involves determining the objectives and needs of the dashboard, designing its key components and layout, and developing each element of the dashboard. By the end of this book, you will have a solid understanding of the storytelling approach and be able to create data stories of your own using Looker Studio.
Table of Contents (17 chapters)
Free Chapter
1
Part 1 – Data Storytelling Concepts
5
Part 2 – Looker Studio Features and Capabilities
10
Part 3 – Building Data Stories with Looker Studio

Building the dashboard- Stage 2: Design

In this next stage of the process, you define any key metrics needed, assess the data preparation needs, select the appropriate visualizations, and design the dashboard layout at a high level. The current analysis only requires simple aggregations such as counts and calculating the percentage of total counts for specific attribute values as key metrics. Turnaround time, which helps measure the efficiency of the CFPB, can be defined as follows:

The other key metrics include the volume of complaints, the percentage and volume of untimely responses, and the percentage and volume of in-progress responses.

The complaints database contains most of the required data in usable form. On closer inspection, you discover that the ZIP code data is not clean – with non-digit and extra characters.

Analyzing complaints at both the state and ZIP code level provides a zoomed-in and zoomed-out view of the distribution of...