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

ISBN-13: 9781484254967 / Angielski / Miękka / 2019 / 202 str.

Tim Costello; Lori Blackshear

Opis

Intermediate user level

Spis treści

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 mind when we are joining different files that might be at a different levels of detail.

Example: Trying to join together a file where the data is stored at the level of individual transactions by day with a file where the data is stored at the level of summary of the transactions by day will create problems.

In the data tool we can aggregate the data in the file stored at the level of the transaction. This would make joining to the file summarized at the daily level easier and give more predictable results.

 

Part IV: Additional Data Sources

Chapter 8: Joins: Bringing It All Together

Here we look at why you would want to join together data from different sources and:

·         How you can have a data flow with multiple inputs,

·         How you can join some or all of those inputs together

As well as the mechanics of those joins. We discuss LEFT, INNER, and RIGHT joins and look at how the fields chosen to join each data flow can have a big impact on the results of the join. Tableau has some really interesting ways to visualize the effect of using different join types and different join fields. We will look at these join profiling visualizations in detail.

 

Chapter 9: Union Joins: Joins, Only Better

Where the previous chapter looks at how to join multiple data sources to add additional fields, this chapter looks at a join type that adds records to the results.

Example: Think of five data files covering sales in a retail location for five months. Each file has the same format but different data. A union join lets us combine the contents of those files into a new output so they can all be treated as one data source.

Tableau Data Prep has some powerful for combining files. We look at these options in detail.

 

Part V: Output

Chapter 10: The Output Step: This Is What It’s All About

The Output Stop creates new files that contain data that has been modified in the Tableau Data Flow. We look at how these new files can be saved as .csv, Tableau Data Extracts or a Tableau Hyper Extract files. I walk through when and why you might want to create additional output files for troubleshooting or review. We share advice from our experience in similar tools to help avoid overly complex data flows.

 

Chapter 11: Saving and Sharing Data Flows: Sharing Is Caring

At this point we have walked the reader through all the major functionality of the Tableau Data Prep tool. Here, we look at how to save the data flows we have created, how to share them and how to use them to keep our Tableau workbooks refreshed with clean, well-shaped data.

 

Part VI: Prepping Data in Tableau Desktop

There are a lot of things we can do to join, clean up and reshape data in Tableau Desktop without ever going into the Tableau Data Prep tool. In this section we look at the things that should be considered when connecting to any data source, even those that come from the Tableau Data Prep tool.

 

Chapter 12: Connecting to Data: Sometimes Simple Is Better

Connecting to data in Tableau Desktop is very much like connecting to data in the Tableau Data Prep tools. In this chapter we quickly walk through the different options, focussing mainly on the differences in this tool as compared to the Data Prep Tool.

 

Chapter 13: Cleaning Data: Your Checklist for a Solid Start

Most of the things that can be done in the Cleaning Step in the Data Prep Tool can be done in the Connect To Data screen. In this chapter we show the most common cleanup tasks and how they are accomplished in the Connect to Data screen. This section will include brief coverage of how to do the following in Tableau Desktop:

·         Rename fields

·         Change data types

·         Split fields

·         Combine fields

·         Create custom calculations

·         Pivot data

 

Chapter 14: Metadata Management and Setting Defaults: Do These Things In Every Connection, You Can Thank Me Later

Metadata is data about data. In this section we look at how to add metadata that will be visible in tooltips in the Tableau Desktop design interface. These details are extremely helpful in documenting the intended use of individual fields and the details of how calculations are implemented. We also look at when and how to set default values (default colors, shapes, calculations) to make things more consistent within a Tableau Workbook and between tableau workbooks.

 

Chapter 15: Saving and Publishing Data Sources: Sharing is Caring (Second Verse, Same As The First)

No one wants to repeat their work. In this section we look at how to save the connections, changes to individual fields, custom calculations and default values to local files and Tableau Server. Once saved, others can begin working more quickly knowing that all the prep and cleanup steps have been taken care of in advance.

 

A Word About Blends (Blend with Caution)

Blends are an extremely powerful way to combine data from different data sources in memory and then use data from each without having to combine the data files with another tool. Prior to the release of the Tableau data tool this was sometimes the only way to get data from different data sources to work together in the same Tableau view. The dark side of blends in Tableau is that they are often used without a full understanding of how they work and in some cases they can create results that are difficult to predict, difficult to troubleshoot and worst of all look accurate when they might in fact be misleading. In this section I will discuss blends in some detail, show a way to avoid blends using the connect to data screen and show an even better way to avoid blends by joining data from multiple sources using the Tableau Data Prep tool.

O autorze

Tim Costello is a senior data architect focused on the data warehouse life cycle, including the design of complex ETL (Extract, Transform, Load) processes, data warehouse design and visual analytics with Tableau. He has been actively involved with Tableau for almost 10 years. He founded the Dallas/Fort Worth Tableau user group. He has delivered hundreds of Tableau classes online and in person all over the USA and Canada.

When Tim isn’t working with data, he is probably peddling his bicycle in circles around DFW airport in Dallas, Texas. He aspires to be a long distance rider and enjoys going on rides ranging over several days and hundreds of miles at a time.

Lori Blackshear is a senior business process architect and expert at facilitating meaningful and productive communication between business and technology groups. She has deep experience in healthcare (human and veterinary), software development, and research and development in support of emergency services.

Lori served as a paramedic in Fort Worth, Texas and Nashville, Tennessee before shifting careers to helping people solve problems with data. When Lori isn’t pondering business processes, she is active in the Fort Worth Civic Orchestra (violin) and the East Fort Worth Community Jazz band (tenor saxophone).

Blurb

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
  

You will:

• 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