A message for the readers

I have been working in the field of Business Intelligence and reporting, and my primary focus has been on creating visual dashboards. I developed a knack for data visualization as a student of Business Analytics, one because it was much simpler and fun to create visuals without writing code, and also because I understood that the key consumers of business reports are not data-scientists but businesspeople. There is no point in showing them which model has been used for statistical analyses, quite simply because business-owners, managers, marketers, sales-people just don’t need to (and don’t want to) know all that. The key function of data visualization is conveying the message that the data shows to the right people in the right way. I decided to write this book for all the people who are learning data analytics or business analytics, are starting their careers and even those who are not data-literate but wish to start leveraging some form of data analysis for their business or to help take their careers to the next level. I intend to share the most important things that I have learned while creating data-visuals to help you get the jumpstart in data visualization and visual analytics by taking the guesswork out of the game. I promise you that data visualization is the most fun thing to do with data, especially because you can create amazing visuals and uncover trends and hidden insights without undertaking a long and tedious process of statistical analysis. In this book, you will learn about data visualization in greater detail by digging deep into the purpose of visualizing data,

visual data analytics, dispel common myths about data visualization and most importantly - creating effective visuals based on scientific knowledge about human perception and the technical limitations as well as limitless possibilities of different charts. You will learn the most important skill of storytelling to most effectively communicate your discoveries from data. You will take a step (or many steps) ahead of using standard ‘Excel’ charts and almost instinctively start using advanced charts based on the type of data you analyze and the story you create. There is a graphical chart type for every purpose, and there is a wide range of charts within each ‘category’. We will work on creating a workflow by understanding chart types and variations. Then we will explore the use of colors in visualizations, setting the right context for your charts and some do’s and don’ts in data visualization. Finally we will explore some techniques to create effective and visually appealing dashboards using different charts to tell a story. I sincerely believe this book will help you get that jumpstart in understanding and using data visualization and give you a competitive edge to propel your career or grow your business. I hope you enjoy learning from the book as much as I enjoyed creating it. Let’s get you started on your journey to data visualization mastery!

AAKASH GOHIL

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DISCLAIMER This book has been created for the purpose of helping people enhance their skills and advance their careers. All the images, examples and data used have been sourced from the public domain, and all creatures have been credited wherever possible. The content written and produced herein is the work of the author intended to educated the readers. No copyright infringement or plagiarism is intended.

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Contents What is Data Visualization and Visual Analytics? .......7 Dispelling Common Data Visualization Myths .............14 Visual Analytics is only for big companies ..............................15 Data Visualization is a modern fascination.............................17 Data Visualization means making pretty charts ................18 A good data visual is just too easy to create ..........................20 Data Visualization is the last step of the journey ................21 Visualizing Data is not Data Science ...........................................22 Creating dashboards does not require data skills .............23

Storytelling with data ..................................................................24 How humans understand data.......................................................24 Charts with purpose ...............................................................................28 Comparisons ......................................................................................................29 Trends .....................................................................................................................34 Proportions .........................................................................................................37 Relationships .....................................................................................................39 Distribution .........................................................................................................43 Deviation...............................................................................................................44 Geographical ......................................................................................................45 Text............................................................................................................................47

Using charts the right way .................................................................48 Grab attention with colors and shapes .....................................60 Setting context for effective communication .......................65 Creating effective dashboards ........................................................69 1. Understand the data ....................................................................72 2. Create an overview of the dashboard ...............................72 3. Understand the audience .........................................................73

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4. Set the visual parameters ..........................................................73 5. Blueprint ...............................................................................................74 6. Set the requirements for the data.......................................74 7. Build .........................................................................................................75

How to design a dashboard ..............................................................76 Design in grids ..................................................................................................76 Design with the flow ....................................................................................77 Avoid TMI ..............................................................................................................79 Prioritize goals...................................................................................................79 Ink to Data ratio ...............................................................................................80 Color uniformity ...............................................................................................81 Visual consistency ..........................................................................................82 User Experience consistency ..................................................................83 Iterate ......................................................................................................................84

Developing the mindset for Data Visualization ...........85 Understanding data structures ......................................................85 Visualizing data ..........................................................................................87 The art of visual design .........................................................................90 The art of creating stories with data............................................93 Steps to create a data story: ..............................................................95

Summary ............................................................................................97 Additional resources ....................................................................99 Blogs and websites .................................................................................99 Data Visualization Techniques ..............................................................99 User Experience and Graphic Design ...............................................100

People to follow on LinkedIn ............................................................101 Online Groups and Communities .................................................102 YouTube Channels ...................................................................................103

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Chapter 1

What is Data Visualization and Visual Analytics? And what is Business Intelligence?

The simplest use of analyzing data is to describe ‘what has happened’ or ‘what is happening’ from what is called raw, unprocessed data. Usually in the field of data visualization we come across tabular data, which is much more organized than unstructured data such as image/audio/ video. Raw data by itself is not good enough to effectively derive insights from, even if it is properly structured and tabulated. A data-table is still just a table with columns and rows. And with hundreds or thousands or even millions of rows, how could anyone identify if the sales of a company are growing or declining - or whether one or more ad campaigns are not actually producing any returns - or how much has a company’s stock price grown/fallen? There is only one way to identify these insights - by visualizing data in charts and graphs.

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The very first thing any person looks at while looking at stocks to invest is the trend chart of stock price over time to see has the price been growing or falling over a certain period. The quickest way for a sales manager to see which products have been selling well is to look at a bar graph of the total sales of all products. If you use a smartwatch or any activity tracker, you are shown the details of your daily activity as well as information about your vitals, all on a tiny one-inch screen. The weather reports that show a colorful cloud over a geographic map is smart data visualization. Data Visualization means interpreting data into a visual format with the purpose of communicating insights effectively and efficiently. It is a graphical representation of data. The process of visualizing data is transforming quantitative (numerical) data into qualitative (descriptive information) that is easiest for the human brain to understand. And this could be the most engaging and enjoyable process in the whole field of data analytics. Data visualization is not too technical once you know the most basic concept, tools and tricks - it is the easiest to learn and exciting to work with. Data Visualization grew as a part of statistical analysis in the academic and research fields, initially only focused on descriptive analytics - meaning understanding and explaining what has already happened. Just like showing that the price of a certain stock “has grown in the last few months” or a particular product/product category “has been sold the most in the last quarter”. By the nature of it’s application and intended use, data visualization was usually the step after cleaning and prepping data, even after 8

statistical analysis. However, the application of data visualization grew in the order of operations in analytics, and statistical graphs started to be used before statistical analysis, as soon as data is cleaned and prepped. This helped in understanding the data and setting a path for statistical analyses toward a desired or expected outcome. This is where the term Visual Analytics comes from - data visualization as an analytical process. With modern computing capabilities, we can have any chart showing real-time data, that too with additional information that was not traditionally shown with graphs. The simplest example is the trend chart of stock prices - if you hover the mouse pointer over a data point on the chart you are shown the stock price as well as the percentage change over previous day, 15-day and 30-day moving average AND you can also change the level of detail of the chart by changing everyday stock prices to moving average chart or even adding Bollinger bands for added visual context. There are many Data Visualization tools out there - some are high-priced, enterprise level software and some are not as expensive and some are free to use. In similar fashion, some tools are highly technical, with a larger learning curve while some are extremely user-friendly and non-data-literate people. If you have had zero experience with data visualization, you may think that it is a difficult process to get started with and learn the craft. However, the truth is just the opposite of that. You can get started with learning visualization tools and techniques even without the knowledge of statistical data analytics methods. And once you get started with one 9

of the tools, you will automatically start developing instinctive tendencies towards different types of visuals, and you will start developing complex visuals sooner than you may anticipate in the beginning. Tools like Tableau and PowerBI have free versions for personal use and learning, and are quite easy to learn and start exploring data visually. There are tools like QlikView which are not available for free but are relatively cheaper even for business use. Then there are data visualization tools offered by large analytics solutions developers like SAS, which are enterprise-level tools in terms of both price and complexity. Anyone could very easily start their data visualization journey for free by downloading the free Tableau Public software and learning from YouTube and the many blogs and forums dedicated to data visualization as well as Tableau. I personally love working on Tableau because of it’s beautiful UI and visuals. At the same time, PowerBI gives a few more chart options. What you will notice once you start using one or more of these tools is that each has a unique set of strengths, while each has different limitations in terms of both visualizing data and handling larger amounts of data. Today data visualization is used as a method of exploring the hidden story behind data before and during the analytics process, but it is most widely used to create dashboards after statistical analysis to tell a story about the trends and patterns in the data as well as the insights derived through the analytics process. Dashboards are groups of charts and graphs put together in a single view with the purpose of communicating a certain 10

story derived from the data through the process of analysis. Dashboards, when connected to live data, serve as user interfaces that can be used to display and track metrics and key performance indicators (KPIs). A dashboard may be built to monitor the performance of a business as a whole, or a specific business process or any other application which may not be related to business at all. A quick search through the web will reveal hundreds of dashboards created about the 2020 Covid pandemic, and thousands of different dashboards created as beautiful infographics on the Tableau Public Gallery. There are a few rules in data visualization which help make the process of creating visuals and dashboards streamlined and take the guesswork out. That is not to say that it is a tedious task, but it is important to understand the idea of form against function while getting too carried away with creating the most beautiful visuals and ignoring the effectiveness of communicating the story behind the data. “The main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights i n t o a ra t h e r s p a r s e a n d c o m p l e x d a t a s e t b y communicating its key-aspects in a more intuitive way." as said by Vitaly Friedman, the co-founder and editor-in-chief of Smashing Magazine and User Experience expert.

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Data Visualization has become mainstream Data Visualization has reached maturity. It went from zero to one, and now all developments are in the areas of improving UI/UX, adding features, integration with other platforms and mainly in widening the application of data visualization in fields outside of business, reaching the lives of the average people - giving the power of visual analytics to everyone. Business Intelligence, also called simply as BI, is a field of work that combines extracting data, preparing data, managing data and then using data visualization tools. The purpose of the entire process is to enable the stakeholders (clients / business heads / functional heads etc.) to efficiently and effectively identify and understand actionable information without having to look at raw data. The desired end result is enabling quick, data-derived decision-making for business success. The most common output of the BI process, before extracting actionable insights, is creating dashboards used by business analysts and data-driven business leaders. “Data visualization is also its own standalone endeavor with more and more professional roles and whole organizations dedicated to it…….. it is becoming less of a tech company rarity and more a part of everyone’s everyday life.” - Elijah Meeks, Data Visualization Engineer at Apple and Executive Director of the Data Visualization Society. Modern data visualization has evolved from being optimized for creating charts of corporate executives to becoming a part of the modern socio-cultural fabric. Elijah Meeks also 12

says that modern ‘data visualization is personal stories, small businesses, data science, political campaigns, human resources, community building’. The rapid expansion of its applications has brought visual analytics and data visualization to the forefront of tech capabilities, with companies now investing heavily in tools that offer new possibilities. In 2019, SalesForce acquired Tableau for $15 billion and Google purchased Looker for nearly $3 billion. Amazon developed a data visualization tool named Amazon QuickSight to integrate with its AWS platform. There are hundreds of forums and groups online dedicated to the ‘Data Visualization Community’, and there are bloggers and YouTubers who gather about as much fame as other online influencers. The ease of use and easy availability of data visualization tools and data itself has also contributed to giving birth to new terms like Citizen Data Scientists, Data Activism and Data Journalism. Having said that, it is still important to understand that even though visual analytics or data visualization is easy to learn and practice, there are some rules about effectively visualizing data and telling the story through those visuals. The next chapters of the book aim to take you through the journey of understanding these rules, principles, and tips and tricks of effective data visualization.

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Chapter 2

Dispelling Common Data Visualization Myths

“Designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information” ~ Vitaly Friedman Okay so Data Visualization has gone mainstream and it is quite easy to learn and practice on your own, without investing a load of money into the tools and lessons. So is it the simplest of all data analytics processes? There do exist some myths related to the field of data visualization and the process of data visualization. Dispelling these myths should make learning and practicing data viz easier and streamlined.

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Visual Analytics is only for big companies Based on what was explained in the previous chapter, you should know by now that this is definitely not true today. Yes, in the past dat viz was seen as technical, difficult to implement and time consuming, not to mention expensive. This was because of the fact that data visualization tools were originally developed and sold by software companies which produced enterprise-level business analytics software. With newer platforms like Tableau, the field of data visualization and visual analytics became democratized to an extent. This is why the field grew its applications in media, journalism and even as a leisure activity for data visualization geeks.

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