Prepare Your Data for Tableau: A Practical Guide to the Tableau Data Prep Tool

Focus on the most important and most often overlooked factor in a successful Tableau project-data. Without a reliable data source, you will not achieve the results you hope for in Tableau. This book does more than teach the mechanics of data preparation. It teaches you: how to look at data in a new way, to recognize the most common issues that hinder analytics, and how to mitigate those factors one by one.
Tableau can change the course of business, but the old adage of "garbage in, garbage out" is the hard truth that hides behind every Tableau sales pitch. That amazing sales demo does not work as well with bad data. The unfortunate reality is that almost all data starts out in a less-than-perfect state. Data prep is hard.

Traditionally, we were forced into the world of the database where complex ETL (Extract, Transform, Load) operations created by the data team did all the heavy lifting for us. Fortunately, we have moved past those days. With the introduction of the Tableau Data Prep tool you can now handle most of the common Data Prep and cleanup tasks on your own, at your desk, and without the help of the data team. This essential book will guide you through:

The layout and important parts of the Tableau Data Prep tool

Connecting to data

Data quality and consistency

The shape of the data. Is the data oriented in columns or rows? How to decide? Why does it matter?
What is the level of detail in the source data? Why is that important?

Combining source data to bring in more fields and rows

Saving the data flow and the results of our data prep work

Common cleanup and setup tasks in Tableau Desktop



What You Will Learn

Recognize data sources that are good candidates for analytics in Tableau

Connect to local, server, and cloud-based data sources

Profile data to better understand its content and structure

Rename fields, adjust data types, group data points, and aggregate numeric data

Pivot data

Join data from local, server, and cloud-based sources for unified analytics

Review the steps and results of each phase of the Data Prep process

Output new data sources that can be reviewed in Tableau or any other analytics tool



Who This Book Is For

Tableau Desktop users who want to: connect to data, profile the data to identify common issues, clean up those issues, join to additional data sources, and save the newly cleaned, joined data so that it can be used more effectively in Tableau

The first book to fully explain the Tableau Data Prep tool environment

Introduction

Part I: Getting Started

Before we can visualize data, we must understand that data. This book introduces the idea of looking at data in new ways, focusing on data quality and consistency. Specifically, we look at:

· The layout and important parts of the Tableau Data Prep tool (Chapter 1)

· Connecting to data (Chapter 2)

· Data quality and consistency (Chapters 3,4,5)

· The shape of the data. Is the data oriented on columns or rows? How to decide. Why it matters. (Chapter 6)

· What is the level of detail in the source data? Why is that important?(Chapter 7)

· Combining source data to bring in more fields (Chapter 8) and rows (Chapter 9)

· Saving the data flow and the results of our data prep work (Chapter 10 and 11)

· Common cleanup and setup tasks in Tableau Desktop (Chapters 12-15)

We will start the book with the end in mind, telling a story of connecting to data, cleaning that data, creating a dashboard to display insights from the data with Tableau Desktop and sharing those insights in Tableau Server.

Chapter 1: Getting to Know the Tableau Data Prep Tool

An introduction to the Tableau Data Prep tool environment with focus on key tools and menu options. A description of the source data that will be used for all demo in this book and links to where that data can be downloaded.

Part II: Connecting To Data

Chapter 2: The Input Step: Connecting to Data

A tour of the input step with discussion of the types of data that can be brought into to a data flow including examples of a simple data flow with one input and a complex data flow with multiple inputs.

Chapter 3: The Cleaning Step: The Heavy Lifting Happens Here

Cleaning is one of the most important phases of data prep. In this section we look at.

· Renaming fields

· Changing data types

· Splitting fields that contain multiple values into individual fields with one value in each field

· Combining multiple fields into one field

· Adding new fields that contain the results of calculations

· Removing spaces, numbers or any unwanted characters from a field value

Chapter 4: The Group and Replace Step: It's Like a Magic Wand for Inconsistent Data

Tableau has some very impressive grouping and replacing functionality built in to the Data Prep tool. In this chapter we will look at scenarios that discuss misspellings and common variations of data and how to handle them. Using this tool, we can group values within a field together manually. This gives us the ability to bring together values that we recognize as being the same but have been stored with minor inconsistencies. For example, in a field called [category] we could change the saved values

· Appointment

· Appt

· Apppointment ( misspelling intentional) so only the correct value of "Appointment" is saved, rather than any of the variations we have identified. This is an extremely valuable and easy to use data cleanup tool that we will explore in detail.

Chapter 5: The Data Profile Card: It's Like a Super Power Only Better

The data profiling step is critical in any data flow. In this step we look at summary cards and how to interpret them. This step helps us spot the following at a glance:

· Patterns in data with counts for each distinct value

· The occurrence and count of NULL in each field

· Outliers (data that appears significantly more or less often than expected)

Part III: Data Shaping

Chapter 6: The Pivot Step: Reshaping Data 101

In this chapter we look at the way the source file is layed out and how the layout can limit your visualization options in Tableau Desktop. The Pivot Step in the Tableau Data Prep tool is your go to resource for fixing this problem.

Chapter 7: The Aggregate Step: Group and Summarize Data

In this chapter we look at how to identify the level of detail for a source file and explain why it is important to keep this in min

Intermediate user level