The field of Data Science and the role of a Data Scientist are hot commodities right now. IBM predicts the demand for Data Scientist will increase 28% by the year 2020. [14] Demand is so high in fact, that in the screenshot below, I show an advertisement from one of many companies that offer data science/data scientist training with the guarantee that they will find you a job immediately after you complete your training or get your money back!

In this blog post, I will first explore what the field of Data Science is, and then discuss the role of a Data Scientist. Sources used have been noted and provided at the end of this article.

Data Science Defined

In 1998, Chikio Hayashi defined Data Science as a “concept to unify statistics, data analysis, machine learning [added to the definition later] and their related methods” in order to “understand and analyze actual phenomena” with data. It includes three phases, design for data, collection of data, and analysis on data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science. [1]

Turing award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge. [2][3]

Data Science – The New Buzzword

After Mr. Hayashi’s paper was published, the term “data science” instantly became a buzzword. Back in 2012, the Harvard Business Review called the Data Scientist “The Sexiest Job of the 21st Century”. It is now often used interchangeably with earlier concepts like business analytics, business intelligence, predictive modeling, and statistics. Hans Rosling, featured in a 2011 BBC documentary with the quote, “Statistics is now the sexiest subject around.” Nate Silver referred to data science as a sexed-up term for statistics. In many cases, earlier approaches and solutions are now simply rebranded as “data science” to be more attractive, which can cause the term to become “dilute[d] beyond usefulness.” While many university programs now offer a data science degree, there exists no consensus on a definition or suitable curriculum contents.

Data Scientist Defined

Back in April 2015, Jojo John Moolayil, IoT & Data Science author, provided a nice, simple definition on Quora of what a data scientist is. [11] Mr. Moolayil stated:

“The area of study which involves extracting knowledge from data is called Data Science and people practicing in this field are called as Data Scientists.”

Mr. Moolayil points out that businesses generate a large amount of data which includes “transactional, inventory, sales, marketing, customer, external and many other dimensions.” This data has enormous value embedded in it, but the data is latent (today, we may think of this latency in terms of a data lake). There are many trends and patterns that can be mined from this latent data, so the business can make insightful, actionable decisions.

So, here is where the data scientist comes in. The process of extracting information and meaning from this data is a monumental task. It requires a variety of skill sets which are interdisciplinary in nature. 

The data the data scientist has in front of them was probably accumulated across a variety of disparate data sources. The data may be structured, unstructured or semi-structured. Amalgamating all these data sources coherently into a proper dataset on which analysis can be performed requires a lot of technical skills. [11] Technologies like R Programming, Python, Java, Perl, C/C++, Hadoop, MapReduce, SQL, Hive, Pig, Apache Spark, Data Visualization, Machine Learning and AI, Unstructured Data (“Dark Analytics”), etc. are the ones most widely used. Yet, this list of toolsets is far from exhaustive.

Now that you have the data in a format that you can manipulate, you will need mathematical and statistical skills to begin analyzing the data. The types of analysis to obtain more information from the data may include, but is not limited to, exploratory data analysis, statistics tests, regression models, etc. Your toolsets to do this may include machine learning, deep learning, statistical inference, etc.

Finally, you have results you want to share with your business partners (e.g., the people that requested the results). This is where the data scientist needs solid skills in communications, the ability to present the data so it is understandable and meaningful, the ability to persuade, design thinking, problem solving, data ethics, data visualization, etc.

So, we have crossed several disciplines here regarding the kinds of skillsets a data scientist needs. We have technical skills, mathematical and statistical skills, and human centric & investigational skills.

[NOTE: This classification term, human centric & investigational skills, was provided to me by Bridget Cogley, Senior Consultant at Teknion Data Solutions and Data Ethicist. Neither of us liked the term “soft skills” since these skills are often difficult to achieve and more inherent in some people than others, and not everyone can easily learn them. We also did not like the term “non-technical skills” as these skills have an important cohesion and symmetry with a data scientist’s technical skills.]

Becoming a Data Scientist

As of the writing of this article, on Google, there are 146 Million search results for Data Scientist Training. Here are a few example screenshots, from the Google search I did, on how you too can become a Data Scientist.

Summary

On several occasions, I have heard IT managers and recruiters refer to the data scientist as a unicorn. That is, they believe they exists, it is just really, really hard to find one. The Data Scientist’s skills cross several disciplines requiring technical skills, mathematical and statistical skills, and human centric & investigational skills. Some of these skills can be learned through proper education and hard work. Other skills, like the human centric & investigational skills, come with experience or are a natural gift that some possess. Hopefully, with many colleges and universities developing curriculums in Data Science, organizations requiring this special skill will find their unicorn sooner than later.

Sources

[1] Hayashi C. (1998) What is Data Science ? Fundamental Concepts and a Heuristic Example. In: Hayashi C., Yajima K., Bock HH., Ohsumi N., Tanaka Y., Baba Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo.

[2] Stewart Tansley; Kristin Michele Tolle (2009). The Fourth Paradigm: Data-intensive Scientific Discovery. Microsoft Research. ISBN 978-0-9825442-0-4.

[3] Bell, G.; Hey, T.; Szalay, A. (2009). “COMPUTER SCIENCE: Beyond the Data Deluge”. Science. 323 (5919): 1297–1298. doi:10.1126/science.1170411. ISSN 0036-8075.

[4] Wikipedia, Data Science, https://en.wikipedia.org/wiki/Data_science.

[5] KDNuggets, 9 Must-have skills you need to become a Data Scientist, updated, KDNuggets.com, May 2018, https://www.kdnuggets.com/2018/05/simplilearn-9-must-have-skills-data-scientist.html.

[6] Browne-Anderson, Hugo, What Data Scientists Really Do, According to 35 Data Scientists, Harvard Business Review, August 15, 2018, https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists.

[7] Cearley, David W. and Brian Burke, Samantha Searle, Mike J. Walker, Top 10 Strategic Technology Trends for 2018, October 3, 2017, Gartner ID: G00327329

[8] Idonine, Carlie, Citizen Data Scientists and Why They Matter, Gartner Research Blog, May 13, 2018, https://blogs.gartner.com/carlie-idoine/2018/05/13/citizen-data-scientists-and-whythey-matter/.

[9] Loshin, David, Empowering the Citizen Analyst: Agile Techniques for Enhancing Self-Service for Data Science, Knowledge Integrity, TDWI Webinar, October 11, 2018.

[10] –, Gartner Says More Than 40 Percent of Data Science Tasks Will Be Automated by 2020, Sydney, Australia, January 16, 2017, https://www.gartner.com/en/newsroom/pressreleases/2017-01-16-gartner-says-more-than-40-percent-of-data-science-tasks-will-beautomated-by-2020.

[11] Moolayil, Jojo John, What is a data scientist?, Quora, April 27, 2015, https://www.quora.com/What-is-a-data-scientist-3.

[12] Smith, Stephen J., The Demise of the Data Warehouse, Eckerson Group, July 19, 2017, https://www.eckerson.com/articles/the-demise-of-the-data-warehouse.

[13] Harris, Jeremie, Why you shouldn’t be a data science generalist, Toward Data Science, November 1, 2018, https://towardsdatascience.com/why-you-shouldnt-be-a-data-science-generalist-f69ea37cdd2c.

[14] Columbus, Louis, IBM Predicts demand For Data Scientists Will Soar 28% By 2020, Forbes Magazine, May 13, 2017, https://www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/#137cc51c7e3b.

Lindsey Poulter Bio

Lindsey Poulter is a data visualization and Tableau enthusiast. She enjoys combining business strategy with design best practices to build innovative dashboards. Currently, she works at Wells Fargo as an analytics consultant, where she is responsible for creating dashboards for executives and customers within the IT department. She has been using Tableau for 4.5 years and has worked across various industries, ranging from pharmaceutical marketing to financial software. In her free time, she enjoys blogging about new ways to use Tableau or creating visualizations about (mostly) basketball. Lindsey currently resides in Kansas City but is planning to move to New York City in January.

Questions

Link: https://public.tableau.com/profile/lindsey.poulter8872#!/vizhome/SetActionDrilldown/ChooseaMetric

Michael: Hi Lindsey, Happy New Year! On your blog, http://www.lindseypoulter.com/, you have been discussing Set Actions quite a bit lately. Can you talk a bit about Set Actions and how they will help us in our development of data visualizations in Tableau?

Lindsey: In my opinion, set actions are the most powerful new feature since the level of detail (LOD) expressions were released in 2015. However, like LODs, the use cases for set actions and the new capabilities they provide weren’t immediately apparent to me. But, once I was able to wrap my head around how to apply them, I couldn’t stop thinking about all the use cases!

On its face, set actions allow the end user of a dashboard to choose which values are in a set and have the dashboard dynamically update depending on the selections. Previously, there was no way for a user to choose from a list of values which to include in the set. The selections however can only be made from a worksheet and not through dropdowns or radio buttons like filters. This isn’t inherently a bad thing, as interactivity is what takes a lot of Tableau dashboards to the next level. Set actions introduce new ways to have a dashboard react to the clicking or hovering on a data point. I like to think of set actions being the same as filter actions, however, instead of placing the selected value(s) on the filters shelf, the value(s) can be used in calculations, on the marks card, on the rows/columns shelf, or as a filter.

This capability opens a whole new world of possibilities. It allows for new functionality through the ability to take the value a user has selected and perform some function- such as showing additional detail or similar data points. For example, Corey Jones put together this example on his blog about using a parameter to show an additional level of information for only one category.

He also included an example of how to create the same table using set actions. So, instead of having to use the parameter dropdown, directly clicking on one of the categories will show the details.

Set actions expand the toolbox of possibilities when deciding the right user experience. Does it make more sense to use a parameter or to have the user click to expand? What I particularly like about set actions in this example is that the user doesn’t have to break their flow to go somewhere else on the dashboard to change the value.

The ability to know what value the user has selected, and then use that in calculations, allows for a lot of flexibility. In this example, showing a three-month moving average for 2018 sales, hovering over any point shows the actual values that make up the moving average. A calculation can be written to return what value is in the set, such as October 2018, and then an additional calculation can be written to also return the two prior months.

As with all new features, set actions can be used in ways Tableau probably didn’t intend them to be. One way is to create a set on a dimension, such as a region, and tie those values to something unrelated. If the central region is IN the set, show the sales metric, if the east region is IN the set, show the profit metric, etc. This allows for the ability to create custom buttons to switch between metrics or dimensions!

Have I convinced you yet that set actions expand our toolbox of possibilities when developing Tableau visualizations?

Michael: Can you discuss how you use Tableau at work? What pain points with data do you help your business community solve using Tableau?

Lindsey: If I was on 2018.3 at work I would have a lot fewer pain points! We are currently on 10.5, but our dashboards rely heavily on navigation buttons, collapsible filters, and hierarchies… all things that would be much easier to accomplish with the new built in features!

As for what I do at work, I am responsible for creating automated dashboards that allow various stakeholders within the company to monitor the areas they are in charge of. The goal of most dashboards is to allow individuals to quickly find the areas that need improvement, allowing them to swiftly take action. I am never the end user of a dashboard, so I work alongside the subject matter experts to understand the business need, the data nuances, and how to provide an end product that will allow them to be successful. I am very fortunate in my position in that a lot of the dashboards I work on start out as proof of concepts (verse the stakeholder needing it right away!) and allow me to be creative. Innovative solutions are highly encouraged, which allows me to try out ideas I’ve read about from the Tableau community. My team is often approached with an idea or a data set and given the space to find the best way to present the data. After we decide on an approach and test it out, we roll out the methodology or idea to be on all applicable dashboards we produce.

I am never the end user of a dashboard, so I work alongside the subject matter experts to understand the business need, the data nuances, and how to provide an end product that will allow them to be successful.

As with most companies these days, Wells Fargo has a vast amount of data, from many different systems. The biggest pain point I help solve is finding ways to display all the disparate information in one place, in a way that makes sense. Normally, stakeholders want an aggregated, high-level view of their data but still be able to drill down into it. Often a lot of the dashboards I build are to diagnose data quality issues!

Michael: Your blog web site is fairly new. What motivated you to start your own blog?

Lindsey: I have been active in the Tableau Twitter community for almost three years. In March of 2017, Matt Chambers reached out to me encouraging me to start blogging and sharing my ideas. I set up my domain name, header, and blog and started my first post about my data visualization journey. However, I never finished that post and wasn’t sure how I could contribute something new or original to the Tableau community. Fast forward to the 2018 Tableau Conference. It was my third conference, but my first time making an effort to connect with the people I knew from Twitter. Through conversations with others and hearing so many amazing speakers, I was inspired to share my thoughts and ideas on a medium other than Twitter. I finished up the blog post I started in March of 2017 as my way of announcing that I, like many others after TC, was starting a blog. I wasn’t sure what my next blog post was going to be, but it didn’t take long for me to begin experimenting with set actions and wanting to share my methods with everyone else!

Michael: Besides Set Actions, can you tell us three of your favorite Tableau Desktop tips and tricks?

Lindsey: I have a lot of favorite Tableau Desktop tricks! However, if I had to choose, I would say these tricks are things I constantly use in my day job. Almost every dashboard I make has large KPIs displayed at the top, so I enjoy any tip that makes it easier/faster to create them!

My favorite trick is using custom number formatting to add text or alt codes to numbers. I frequently use it to add triangles to percent change values or abbreviations after values, such as Month-over-Month (MoM). It eliminates the need to create additional calculations. The format is positive formatting; negative formatting; zero formatting.

My second favorite trick is hiding data. To build off the last example, if I wanted to only show the most recent month, December 2018, I couldn’t apply a December 2018 filter because it would remove November 2018 data (and make the MoM not work). So, hiding the data allows it to still be used in calculations but not be visible in the view. I like to add a calculation called LAST()=0, which will evaluate to true for the value that is the last row in the view. Then, right-clicking on false and selecting hide will “hide” (and not filter) every row that evaluates to false.

The end result is a one row table showing the most recent month.

To play on this even further, my last tip is formatting the KPI. Right clicking on the LAST() calculation and unchecking show header will remove that column from the view.

Double-clicking in the columns shelf and then typing “Month over Month Change” (including quotes) will then add a title above the number (just be sure to hide field labels for columns!).

Or, the KPI can be formatted in a more visual way. This can be achieved by switching the order date to the columns shelf, adding discrete sales to the columns shelf, and adding the percent difference in sales to the color mark.

These steps create a KPI using one worksheet and very minimal calculations!

Michael: You previously worked with Ryan Sleeper and participated in the Kansas City Tableau User Group he ran. Can you tell us how Ryan has influenced your career and what tricks he has taught you?

Lindsey: I learned of Ryan the same day I learned about Tableau. Ryan was very well known in the Kansas City analytics world and when my boss assigned me, an intern, the task of experimenting with Tableau, he also passed along Ryan’s website. So, right off the bat, Ryan was a helpful resource for me. His blog was a combination of how-tos but also included posts on things such as color theory and dashboard design. I had bounced around between accounting, graphic design, and web development, so Ryan’s writing helped me realize that data visualization was the sweet spot for all my interests.

Fast forward two years and Ryan and I ended up working together for two months before he started his own company. I had the best time working with Ryan and continued to learn from him. It was invaluable to have someone to bounce ideas off of and discuss everything from color to chart types to how much white space a dashboard should have. One of the best tricks Ryan has taught me is the power of producing clean, simple dashboards. One really tiny trick Ryan taught me was that on a PC if you right click on a measure and drag into the view, you can choose the aggregation before the pill is added into the view!

One really tiny trick Ryan taught me was that on a PC if you right click on a measure and drag into the view, you can choose the aggregation before the pill is added into the view!

Michael: Your local NFL team, The Kansas City Chiefs, has an amazing quarterback in Patrick Mahomes. Are you a fan of the Chiefs and do you think they can win the Super Bowl this year?

Lindsey: Growing up in Kansas City, the Chiefs and the Kansas Jayhawks were never really good at football. Therefore, I devoted most of my sports watching to a winning team…. KU basketball. Every single year when the Chiefs did have a promising start, and I would get my hopes up, I was always disappointed by a terrible playoff loss. So, admittedly, this year, I’ve been really cautious to get on the bandwagon or think the Chiefs will even make it to the Super Bowl. However, the vibe around KC feels much different than previous years… largely due to Mahomes.

Michael: What is next on your “To Do” list? What can the Tableau community expect to see from you in the near future?

Lindsey: Going into 2019, I want to continue to consistently blog and share my ideas with others. I’m still really excited about set actions, so I plan to continue experimenting with new ways to use them. On my Twitter, I have frequently shared ideas for ways to use set actions, such as a hierarchical filter, but didn’t blog about how to due to the end result being less than ideal performance wise. So, my goal is to find new ways to engineer these ideas so that others can use them. I also have a few ideas for visualizations I want to build, specifically around the Oscars and women’s running shoes options, so I hope to be able to share more original visualizations. Lastly, I plan on turning my set actions posts into a presentation that I can speak at different Tableau User Groups.

Tableau Public

Link: https://public.tableau.com/profile/lindsey.poulter8872#!/

Karen Hinson is a Lead Analyst on Chick-fil-A’s Field Analysis & Performance Reporting team. Prior to joining Chick-fil-A in 2013, Karen was a Senior Data Analyst at Invesco. At Chick-fil-A, Karen is responsible for producing reports and dashboards that measure the health of the business.

Karen was part of the team that launched Tableau Server at Chick-fil-A. Today, she continues to champion Tableau to elevate analytics across the organization. Karen earned her B.S. in Finance from the University of South Carolina and her M.B.A. from Georgia Tech. She enjoys sharing her passion and enthusiasm for data and analytics, speaking at universities, Data + Women events, Tableau User Groups and industry conferences.

Visual/Tableau Resume

QUESTIONS

Michael: Hi Karen. So, I need to start this interview by stating the fact that my boss is totally in love with Chick-Fil-A. Two years ago, for his birthday, I got him the Dept 56 Eat Mor Chikin sign, and this year, for Christmas, I got him one of your TC18 T-shirts.

So, with that said, I want to begin the interview with asking you what you do for Chick-Fil-A and what kind of data problems do you help them solve?

Karen: That’s awesome! I hope your boss enjoys the “Data & Chicken” shirt!

Chick-fil-A is a wonderful place to work. My job on the Field Analysis and Performance Reporting team is all about using data to “tell the story” behind our menu, sales and profits. Here are a few examples of Tableau dashboards I’ve created at work:

The one above was built for our Executive Committee and is focused on 12 KPIs.

The one above tracks Sales, Transaction Count and Check Average Growth for the prior week (since we’re closed on Sundays, a week for us is the past 6 business days).

To increase business acumen, all CFA staff receive a subscription to this dashboard on a weekly basis.

The view above displays data for the prior week broken out by daypart (can be filtered for a specific region/service team/operator team/market).

This month, I’m working to produce the 2018 Annual Performance Report (above is a look at the APR I built last year – entirely in Tableau).

Michael: Can you tell my readers how you first started using Tableau?

Karen: I began working for Chick-fil-A in April 2013. My boss at the time had recently discovered Tableau and introduced me to it. I was instantly impressed; I had never used a software that made it so easy to make sense of and visualize data. I made a conscious decision to invest a LOT of time & energy toward developing my skills and becoming an expert. Everything I was asked to build, I used Tableau to build it. Over the next two years, my boss and I convinced the business to invest in Tableau Server. Today, Tableau dashboards push data and insights to the masses at Chick-fil-A.

Over the next two years, my boss and I convinced the business to invest in Tableau Server. Today, Tableau dashboards push data and insights to the masses at Chick-fil-A.

Michael: Can you tell us three of your favorite Tableau Desktop tips and tricks?

Karen:

#1 Sure, this one is really simple but useful… using shortcut keys like ALT+Shift+R or ALT+Shift+C to add a field to rows/columns.

#2 Want to use a special (non-supported) font in a viz you’re publishing to Tableau Server or Tableau Public? Save the font as an image and insert the image in your viz.

#3 This is a trick I’ve been using for years involving complex filters. It’s pretty complicated but has certainly proven very helpful!

Here’s the scenario… Have you ever wanted to create a view like the one below that allows users to select a specific location (in this case, a restaurant) and compare that location against a relevant peer group (in this case, that restaurant’s Service Team)?

• First step is to create a parameter allowing the user to identify the specific location (my parameter is called Location Number… but this could be a store number/employee number/customer number/etc).
• Next step is to create a Compare Against parameter with choices. For example, perhaps you want users to be able to select from these 3 options:
  • Chain
  • Region
  • Service Team

If you’ve ever attempted to do something like this, you know the difficult part is… when a user selects “Chain,” they want to see how their location compares to all other locations across the chain. However, if a user selects “Region” or “Service Team,” they want to see how their location compares to others in the applicable Region or Service Team.

Which means…

• You must identify the correct Region or Service Team (based on the Location Number parameter) and
• Return data only for those locations within that specific Region or Service Team

How?

Create calculated fields to identify the correct Region and Service Team locations. These formulas will be used to filter Region and Service Team fields in the data.

Next step: Drag Region to filter. Click on Condition and set Region = the “correct” Region value by adding a formula like this to the By Formula section:

Drag Service Team to filter. Click on Condition and set Service Team = the “correct” Service Team value by adding a formula like this to the By Formula section:

Why does this work?

Region is filtered (Region = Calculate Correct Region value). Same for Service Team.

Which means…

Michael: Last year, you attended TC18. Can you tell us a few of your favorite sessions or events you attended and why they were your favorites?

Karen: Yes! I look forward to attending the conference every year. This was my fourth one! Here’s a picture of me and my co-workers out in New Orleans.

This year, I really enjoyed Mike Cisneros’s You are an Artist session! Definitely worth checking out… it was brilliant!

Another highlight from TC18… Fanalytics (primarily geared toward Tableau Public authors). Loved it.

This was my second time speaking at the conference. My session was called Vizspiration (How Chick-fil-A Transforms Ideas into Business Value).

Last thing… Really enjoyed the morning devotionals organized by my buddy, former Zen Master Nelson Davis

Great fellowship! I love spending time like this with others in the Tableau Community!

Michael: I use to go to Atlanta a lot for my previous company as my entire team was located up in the Alpharetta/Roswell area. It seems to me that a lot of big companies in your area use or build business intelligence & analytics tools (for example, I remember from my past life that Home Depot used MicroStrategy). Do you feel Atlanta is becoming a hub for IT companies similar to Silicon Valley in California?

Karen: I certainly hope so. There are a handful of Fortune 500 companies headquartered in Atlanta, and there’s a LOT of tech talent in the area. Did you know the Atlanta Tableau User Group was the first TUG to originate in the United States?

Michael: What is next on your “To Do” list? What can the Tableau community expect to see from you in the near future?

Karen: One week from today, I’m traveling to NYC with Tableau to present at the 2019 NRF conference.

I’m also expecting baby #4 next month! So I will be on a hiatus this Spring. However, if this maternity leave is anything like the last one, I will likely create vizzes like this in my “spare time.” J
 

Tableau Public

Link: https://public.tableau.com/profile/karen.hinson#!/

On the last day of 2018, I took and passed the Tableau Desktop Specialist Exam. This exam focuses on the foundational functionality of Tableau Desktop and a basic understanding of the product.

It costs $100 to take the exam and it is recommended you have 3 or more months of Tableau Desktop experience. Tableau also recommends that you have previously taken the Desktop I – Fundamentals class.

So, I have been using Tableau for a while, and you might wonder why I decided to take the Desktop Specialist Exam (DSE) versus the Desktop Qualified Associate Exam (DQA). There were two reasons.

The first reason I took this exam now was that Tableau offered a special price of $50 to take the exam if you purchased it before the end of 2018. I saw it as an additional resource in my practicing to take the DQA exam. Also, it gave me a feel for how taking an exam virtually worked (and I was glad I did!).

The second reason was that, at work, we want to be able to internally teach a DSE prep class for our business partners so they could obtain this certification to help them in their career path within the City and for them to add to their accomplishments list for their yearly performance reviews.

The Exam Format

The exam consists of 30 questions and you have 60 minutes to complete the exam. So, this is two minutes per question. This may sound daunting, but you really will spend more time (2-3 minutes each) on the “hands-on” questions versus the multiple-choice questions (maybe 10-30 seconds each).

At the end of this blog post, I provide some resources to help you prepare for the exam. I also purchased a practice exam from Learning Tableau for $9.99. Overall, this exam was very helpful, but I felt one of the “hands-on” problems was not worded properly, and the problem and the solution did not feel in-sync. Things like this can diminish your confidence in taking the exam. I e-mailed them about the question and, hopefully, they will correct it in the near future.

Here are some topics you may see on the exam.

My very first question on the exam was a “hands-on” Tableau workbook problem. I am used to exams starting slowly and increasing in difficulty as the exam progresses. I am unsure if the questions are randomly ordered, but this was a bit of a surprise for me. I was expecting a simple multiple-choice question as my first problem.

I believe I had 6-8 “hands-on” Tableau Workbook problems I needed to solve. One of the questions had you build a simple dashboard. Basically, they wanted you to use one worksheet as a filter to the other worksheet.

Read the Visual Analytics Cookbook as recommended by Tableau. Understand the differences between chart types, recommended best practices for creating each chart, and their idiosyncrasies.

Work through all of the questions and “hands-on” problems in the Tableau Exam Prep Guide. The solutions to the questions and problems are included in the same PDF file at the end of the file. I recommend you first try to work the “hands-on” problems on your own, then look at the solutions. This will help you determine areas of Tableau Desktop you need to put a bit more practice into.

Understand the Tableau Order of Operations. Think of other use cases besides the typical Context Filter one (trust me on this!).

If you have taken the Tableau Desktop I – Fundamentals class, I recommend you redo all of the exercises in the class workbook (included all of the Bonus Problems). Again, I recommend you first try to work the exercises on your own, then look at the solutions. This will help you determine areas of Tableau Desktop you need to put a bit more practice into.

Read Marian Eerans blog post on how to prep for the Tableau Desktop Specialist Exam. I have provided the link in the Resources section at the end of the post.

At least 3-6 questions required you to select two or more choices via checkboxes. These can throw you off if you don’t get all of them correct. I am unsure if you get partial credit for the ones you selected correctly or if it is an “all or nothing” scenario. I personally hate these types of questions because I overthink them and probably pick some wrong choices “just in case.”

TIP: You will be able to do a Google search. Here is a search format I used that specifically searches the Tableau Online Help Site.

[Keyword1] [Keyword2] [Keyword3] site:onlinehelp.tableau.com

So, if I wanted to search the Tableau Online Help Site for topics related to bar charts, I could use this search string.

bar chart site:onlinehelp.tableau.com

Regarding taking exams, I would consider myself a “bitter-ender.” That is, I work on the exam until the clock runs out even if I am done. If this describes you too, I would recommend you watch the time clock during those last few minutes to ensure you click the Submit button before time runs out. I am not sure if time did run out they would just automatically submit the questions you answered or if they would say you did not submit any answers to the questions, so you did not pass. So, watch your time!

Taking a Virtual Exam

The exam is administered by Loyalist Exam Services. I want to warn you now that if you are taking the exam virtually, get yourself psychologically ready to not start the exam immediately. You will have at least 30 minutes of setup tasks to do.

My exam was scheduled for 9:00am. However, it took 31 minutes to validate and setup before I actually started the exam.

The proctor had me do several validation tasks. Here is a list of some of them.

I had to carry my laptop around the room I was in with the webcam on. She did not like the following items.

• I had unused Post-It notes next to my laptop. I had to put these on a shelf about eight feet away from where I was sitting.
• I have a desk shelf of books about six feet from my laptop. She wanted me to remove it from the desk. It would have been a somewhat laborious task to do so. I showed her none of the books related to Tableau, and she was good with that.
• You will need to show her some form of a photo id (e.g., driver’s license) via the webcam.
• I had my wallet and iPhone on my desk. I had to also put these on the shelf eight feet away from my desk.
• I was not allowed to wear a headset. The thought here is they do not want someone feeding you answers via the headset.
• I had to show, via the webcam, that no one else was in the room, and there were no other computers, tablets, phones or computer monitors, etc.
• I talk to myself a lot while I work. As I was taking the exam, I started talking to myself. She told me I could not talk to myself during the exam. Both myself and I were not happy about this! Again, the thought here is that you might be talking to someone using an earbud or something, and not allowing you to talk (even to yourself) ensures no one is feeding you answers.

Once you finally are allowed to enter the virtual site, you will have a copy of Tableau Desktop (v2018.2 in my case), Windows Explorer (where the folder of data files and Tableau Workbooks were kept), and Google Chrome. You are not allowed to go back to your computer during the exam.

About halfway during the exam, Tableau Desktop froze in the virtual computer. She had to talk me through how to reset it. It was not a big issue, but not something you want to happen during a timed exam.

The proctor was always online and helped me immediately when I needed it.

During the exam, you have the option to mark questions to complete later after you get settled in or your brain is clicking more. It was very easy to go back to those questions, answer them, and continue on with the exam. If you are not immediately sure of an answer, mark it and go on. Another question on the exam may jog your memory or answer the questions you were unsure of.

Once you press the Submit button indicating you have completed the exam, the proctor will have you complete 4-5 questions related to how she did as a proctor and what you thought of their services. Then, your score will be displayed telling you if you passed or not.

About 5-10 minutes later, a PDF of your certificate will be e-mailed to you along with percentages of how you did on each section of the exam.

Resources

Tableau Exam Prep Guide

Visual Analysis Best Practices Cookbook

Tableau Desktop I: Fundamentals Course Description

Learning Tableau Practice Exam #1 (Purchase – $9.99)

Marian Eerens – Getting Ready for the Tableau Desktop Specialist Exam blog post

Tableau’s Order of Operations Online Help

Source: ESPN, January 8, 2019.

Last Wednesday, The Washington Post ran an article about The History of U.S. Border Wall Apprehensions. There were several interesting data visualizations in the article, but the one that really caught my eye was their heatmap (or density map) showing apprehensions at the U.S.-Mexico border, which is along the southern portion of the United States (see screenshot below).

In Tableau, heatmaps or density maps can be created to reveal the patterns or relative concentrations that might otherwise be hidden due to an overlapping mark on a map. Tableau creates a heatmap by grouping overlaying marks and color-coding them based on the number of marks in the group.

In The Washington Post heatmap, the fence type is represented by a red line if it is for pedestrians and a black line if it is for vehicles. The density color (grouping of marks) for border patrol apprehensions in the fiscal year 2017, becomes more Orange in color and deeper shades of Orange for the more apprehensions that occurred (see two screenshots below). NOTE: Sources used for this article are listed at the end of this blog post.

As you can see in the screenshot below, for my home State of Arizona, Nogales is an area of high activity for border patrol apprehensions.

I thought this heatmap excellently showed you quickly what type of fence was present at a particular point along the border, as well as the number of apprehensions at any particular point.

Some Background on U.S.-Mexico Border Patrol Apprehensions

According to numbers released by the Department of Homeland Security, nationwide apprehensions of migrants entering the country without authorization are at some of their lowest numbers in decades. The U.S. Border Patrol states on its website that these numbers do not include individuals met at ports of entry looking to enter legally, but are determined to be inadmissible, or individuals seeking humanitarian protection under U.S. law. [1]

In 2018, U.S. Border Patrol took int custody just over 400,000 people illegally entering the United States, down from the second-high of 1.67 million in 2000. [1]

The Washington Post Fact Checker Salvador Rizzo reported that most of these declines have come, “partly because of technology upgrades; tougher penalties in the wake of the 9/11 terrorist attacks; a decline in migration rates from Mexico; and a sharp increase in the number of Border Patrol officers.” [1]

The first iteration of current fencing along the U.S.-Mexico took place during the 1990s where the administrations of Presidents George H.W. Bush and Bill Clinton authorized the construction of fencing along the California-Mexico border. Then, in 2006, President George W. Bush expanded the border fence by signing the Secure Fence Act into law, which authorized the construction of a fence along 700 miles of the U.S.-Mexico border. [1]

Apprehensions of unaccompanied minors at the border from El Salvador, Honduras and Mexico began to decline in 2016 and 2017 from previous highs in 2014, according to Border Patrol statistics. Guatemala, however, has seen a large increase in apprehensions of minors at the border, reaching a high of just over 22,000 in 2018, the largest of any country within the past five years. [1]

The rise of violence in some Central American countries has caused migrants and asylum seekers to head to the United States. According to a U.N. High Commissioner for Refugees report from 2015, “increasing violence in Guatemala, El Salvador and Honduras has led to a fivefold increase in pending asylum cases — now 109,800 — in Mexico and the United States since 2012.” [1]

Detainments along the U.S.-Mexico border saw an overall decline of 81.5 percent from 2000 to 2017. The border fence near the Rio Grande Valley is the only border crossing that has seen an increase in apprehensions within that same time frame (see screenshot below). [1]

Sources:

[1] Brittany Renee Mayes, Aaron Williams and Laris Karklis, The history of U.S. border apprehensions, The Washington Post, January 9, 2019, Updated: January 10, 2019, https://www.washingtonpost.com/graphics/2019/national/trump-border-wall-arrests/?utm_term=.11ba1329299d.

[2] PHOTO: Berlinger, Joshua, Mexican lawmaker climbs border wall in stunt aimed at Trump, CNN, March 3, 2017, https://www.cnn.com/2017/03/03/americas/mexican-lawmaker-border-wall-trnd/index.html.

[3] PHOTO: WKRG, Immigrants climb over U.S. Mexico border wall, YouTube, FOX 10 Phoenix, September 6, 2016, https://www.youtube.com/watch?v=1dkLJcQrvLY.

Readers:

I have been a big fan of Charles Apple’s work for a long time. I have blogged about him and his work in the past (see “Charles Apple” in my Categories on the right or do a search for “Charles Apple” on my blog).

Charles Apple (photo, right) is a visual journalist, instructor, and blogger. 

Charles is a big Clemson football fan and created a great infographic titled A History of Clemson Football. I have included the infographic as one complete view. Then, I broke the infographic into three larger sequential pieces so you can view it better.

Mr. Apple posted this infographic on Facebook yesterday. His comments on this post were as follows.

This was a bit of an afterthought — I probably should have finished it earlier and I probably should have tried to market it to more papers. This shows Clemson football wins, losses, national rankings and national championships going back to the start of their program in 1896. My pals “back home” at the Greenwood (S.C.) Index-Journal were kind enough to give this one a home.

I hope you enjoy Mr. Apple’s creative infographic as much as I do. Congrats, on the big Clemson win!

Best Regards,

Michael

Dr. Klaus Schulte is a Professor in the Department of Economics of Münster University of Applied Sciences in Germany. His teaching and research area is Business Administration with a focus on Management Accounting.

In less than a year, Dr. Schulte went from being a Tableau newbie to becoming the European IronViz Champion.

His other work areas include:

• Cost accounting and cost management (traditional procedures, planning processes)
• Strategic management accounting (strategic performance indicator systems, e.g. Balanced Scorecard)
• Functional management accounting (in various functional areas such as procurement, marketing, logistics)
• Cross-functional management accounting (e.g., investment and risk controlling)

QUESTIONS

Link: https://public.tableau.com/views/IronVizDeconstructingtheBigMacIndex/Dashboard1?:embed=y&:display_count=yes&:showVizHome=no#1

Michael: Hello Klaus. Last year, you were selected as the IronViz Europe Champion with the entry I show above.

Can you please tell my readers the process you used to find the data you needed, design the dataviz, and finally, build it in Tableau?

Klaus: Sure! Initially, we were given the data from The Economist’s Big Mac Index some weeks ahead of the competition. The Big Mac Index points to potential over- and undervaluations of national currencies against the dollar all around the world by comparing a national big mac price in dollar to the big mac price in the US.

Very quickly my attention went towards Europe where I found it quite interesting, that the Big Mac Indices of the European countries all look quite similar, but also have these differences in value, although huge parts of Europe are part of a common market and a lot of countries even share the same currency. So, my first choice was to focus on Europe.

Then my second choice was to play with the product – the Big Mac – itself and I was lucky to find Eurostat data of indices related to the ingredients of a Big Mac.

By bringing in this data I wanted to answer two analytic questions:

• Does a weighted ingredients index based on Eurostat data differ from the Big Mac Index? (The answer is: basically not!)
• And can I take an ingredients index as an indicator for over-/undervaluations in countries where you don’t have McDonald’s and a Big Mac like for example in Iceland. (The answer is: Yes!)

In the next step, I wanted to find a design that is able to communicate this analysis. With the design, I also wanted to play with the Big Mac/burger theme. When I was googling around and when I saw a burger box and realized that they are made out of trapezoids and rectangles, a great viz by fellow German Heidi Kalbe came to my mind.

In this viz Heidi created a trapezium tile map. While she used trapezium shapes in a grid, I wanted to use polygon data due to the special #IronViz situation, having no time to figure out the right size of the shapes to make the distances between the shapes look really accurate. With a polygon map, I would be able to just bring in the data, set a border and go for it (a step-by-step instruction on how to build it can be found on my blog).

To build my map I first sketched it in PowerPoint:

Then I needed just a little bit of geometry. I built my trapezes out of three equilateral triangles what allowed me to calculate the coordinates of my trapezes in Excel very easily. The rest was just bringing the data to Tableau and creating the viz.

With this map, I was able to communicate my analysis very effectively. I went for a big map for the Economist’s Big Mac Index and for several small maps for the ingredients. And the best thing was that I was able to create these charts very quickly.

Michael: Can you tell my readers how you first started using Tableau, how you are using it in the classes you teach, and what kinds of projects your students are doing with Tableau?

Klaus: I was introduced to Tableau by Thierry Driver from Tableau’s academic team who did a workshop at our business school in November 2016. In the follow-up of this workshop, I downloaded the product, but only started using it since summer 2017, when I decided to use Tableau in class from winter term 2018/2019 on.

The idea was to bring data visualization closer to my accounting students because management accountants are often not too familiar with data visualization. I also wanted to include a Tableau Training, because as a University of Applied Sciences we strongly believe that applying things we talk about in the classroom is the best way to learn. Since I had no BI experience so far, all my Tableau work was necessary to prepare myself, because as you can imagine, it’s always good to be some steps ahead of your students.

I also wanted to include a Tableau Training, because as a University of Applied Sciences we strongly believe that applying things we talk about in the classroom is the best way to learn.

To learn Tableau, I participated in a lot of community projects such as #makeovermonday, #sportvizsunday, #vizforsocialgood, and #workoutwednesday. What also helped me a lot was to visit a two-day Tableau Intermediate training in Amsterdam where I got a deeper understanding of how Tableau actually works.

Today I’m using Tableau in my courses at the master’s level in our Accounting, Controlling & Finance program. Actually, there are two courses, one is a lecture on data visualization in the first semester where a Tableau training is included. In this lecture I want my students to create visualizations on clean datasets like provided by #makeovermonday.

The second lecture in the third semester is more like a project, where my students visualize data from real-world companies. These projects can focus on improving already existing (static) reports but also on answering their analytic questions for example in the areas of procurement, marketing, or logistics.

Michael: Can you tell us three of your favorite Tableau Desktop tips and tricks?

Klaus:

1. Dragging a table calculation from a(ny) shelf to the measure shelf to create a new calculated field.
2. Synchronized scrolling for multiple sheets (Link)

3. Copy and paste worksheet formatting

Michael: Last year, you attended TC18. Can you tell us a few of your favorite sessions or events you attended and why they were your favorites

Klaus:

1. Mike Cisneros: You are an artist | How and why to get started making public data visualizations (YouTube)

Very inspiring, funny, profound! Mike is a world-class presenter and there were lots of nuggets to take away from this presentation!

2. Tableau Conference 2018 Opening Keynote

It was my first Tableau Conference. I really liked the opening part!

3. Data Night Out

I had a blast at Data Night Out at a spectacular venue. After leaving the Superdome we went to the French quarter where I met a lot of people from the community!

Michael: What is next on your “To Do” list? What can the Tableau community expect to see from you in the near future?

Klaus: I really like thinking about how to use Tableau in innovative ways and to blog about it, like for example my custom zoom control for multiple maps, the combination of background videos and dataviz or just recently the integration of sparklines/sparkbars into a hex map. This is something I want to continue.

  Viz | Blog

Viz

Viz | Blog

I also enjoyed a lot collaborating with Ludovic Tavernier when creating our data essay about tennis legend Boris Becker. We have already some ideas about future projects so there might be more to come.

Viz | Blog

Furthermore, I would like to continue participating in community projects like #makeovermonday, #sportsvizsunday, and #workoutwednesday and attend both conferences. I’m in particular looking forward to TC Europe in Berlin. It will be great to meet my friends from the community on home soil.

Tableau Public

Link: https://public.tableau.com/profile/klaus.schulte#!/

Spencer Baucke Bio

Raised in Loveland, OH, Spencer Baucke has a B.A. in Economics from the University of Colorado and an M.S. in Finance from the University of Cincinnati. Spencer is a Sr. Analytics Engineer at GE Digital in Cincinnati, OH. His work experience includes several years in banking as well as a stint at the City of Cincinnati’s Office of Performance and Analytics. He has also worked closely with the education non-profit StriveTogether in a consulting role for the past few years. Mr. Baucke is a regular contributor to Tableau Public and the larger Tableau community via Twitter. He also is a co-founder of the Tableau/Twitter initiative, #SportsVizSunday. 

In his free time, Spencer enjoys staying active in the community. For the past five years, he has had the immense honor of coaching varsity football at Shroder High School. He is also a Public Allies mentor and is active in his local community council. 

Spencer noted that he would be remiss if he didn’t mention his wife Nia, who has put up with him spending way too many Saturdays in front of his laptop learning Tableau and other data analytics tools. He also notes that she is the most amazing wife in the world! They also welcomed their son, Nolan, to the world in September, as you can see by the bags under their eyes.

QUESTIONS

Link: https://public.tableau.com/profile/spencer.baucke#!/vizhome/SportsDynasties/SportsDynasties

Michael: Hello Spencer. I really like your Sports Dynasties data visualization you published on Tableau Public.

Can you please tell my readers the process you used to find the data you needed, design the dataviz, and finally, build it in Tableau (including the tricks you used to create this unique dataviz)?

Spencer: Thanks, Michael! I first got the idea of looking at the dataset that Corey Jones loaded to our data.world site for January’s #SportsVizSunday challenge. Most of my sports vizzes start off as bar arguments, this one being no exception. The word dynasty is thrown around a lot during sports conversations so I wanted to see if I could somehow quantify a dynasty using the data set Corey had put out. There was some good back and forth on Twitter with some people from the Tableau and #SportsVizSunday communities about what a dynasty was so I took that and ran with it.

As far as building it, I first laid out my criteria and identified the teams that met it in Excel. Then I went back and researched each team so I could name the dynasty appropriately. I think that’s some of the fun of sports vizzes though because it was so fun to go back and see some of the great teams that I had either forgotten about or didn’t live to see. I think part of that is growing up a Cincinnati Reds fans where you’re indoctrinated to have a strong sense of reverence for the Big Red Machine and all they did.

Once I curated the data I used calcs from Toan Hoang’s Tableau magic blog post on arch charts. Design wise, I have a tendency to want to put too much data into a viz, so I was looking for a more simplistic way to display data. I really feel like Ken Flerlage does that better than almost anyone, so I definitely spent some time looking at his Tableau Public page when making this viz.

Michael: Can you tell my readers about your Tableau/Twitter initiative, #SportsVizSunday?

Spencer: #SportsVizSunday is a twitter/tableau community of sports and data lovers that provides a way for people to engage and share sports related data visualizations. Every month we post a data set on our data.world site (data.world/sportsvizsunday) for people to work with. When you complete a viz please post it on the data.world site as well as share on Twitter tagging James Smith (@sportschord), Simon Beaumont (@Simon Beaumont04), and myself (@jsbaucke) and use the hashtag #SportsVizSunday. This way we make sure to see your viz and are able to share it with the broader #SportsVizSunday community while providing feedback where appropriate. In addition, you can also share a viz using the #SportsVizSunday hashtag regardless of day or month to promote a sports-related viz that you want to share with the community.

We have been really fortunate to have some big name in the Tableau community including a few Zen Masters, Tableau Ambassadors, and other well-known community members supporting our initiative. Our first year (2018) culminated in a speaking slot at Tableau Conference in New Orleans. The support there was overwhelming. We were just hoping that a few people actually showed up to hear these three goofy dudes talk, but we actually ended up having to turn people away due to capacity. At the end of the day, it’s the participants that make this thing go, and we are so appreciative of all of them!

The line for #SportsVizSunday’s presentation at TC18 above. The three of us below.

Michael: Can you tell us three of your favorite Tableau Desktop tips and tricks?

Spencer: Honestly I’m a horrible tips and trips person (lol-ing literally). I still look up Ryan Sleeper’s blog post on trellis charts every time I make one, so I’m generally not one of those people who finds some cool feature in Tableau no one knows about. Most of the time its the opposite, I have to read a Lindsey Poulter blog post on Set Actions ten times before I understand it.

If I had a trick it would be to start bringing in your fields using a right click instead of left to eliminate time for how you want that field to show.

Hard work and time are undefeated.  

If I had a tip it would be to spend a lot of time in Tableau. Set out and try to understand how different chart types are built. If you see a viz on Tableau Public that makes you go “wow, I could never do that,“ download the workbook and figure out how they made it. Hard work and time are undefeated.  

Michael: I am very impressed with how involved you are in your community and all you do for social good. Let‘s discuss each topic one at a time.

Can you tell my readers about StriveTogether and how you are involed with them?

Spencer: StriveTogether is a local non-profit that partners with local school districts and service providers to try and ensure every child succeeds from cradle to career. They do a lot of outstanding work with Cincinnati Public Schools and the northern Kentucky districts, Newport Independent School and Covington Independent schools. The past few years they have been heavily leveraging Tableau as their primary data analysis and visualization tool to help them understand things from testing results, school and community survey data, and regional/districts trends. As a consultant, I help them maximize Tableau’s potential by building out template dashboard and ad-hoc dashboard as well as helping out with any problems they may have visualizing their data in Tableau. 

Funny enough, StriveTogether is where I got my Tableau start. They originally had me helping with some of the data work they were doing in Excel when the person who I reported to at the time, Geoff Zimmerman, told me I should check out this cool data tool called Tableau. He sent me to a training class at the University of Cincinnati lead by none other than Zen Master Jeff Shaffer, and the rest is history. Jeff’s energy and passion about Tableau were so inspiring that I knew I wanted to pursue learning more about this tool. Between Jeff’s training and using Tableau with StriveTogether, that’s really where I built my foundation of Tableau knowledge.

Michael: In addition to working a full-time job in IT, you also coach varsity football at Shroder High School in Cincinnati. Wow, that must be a lot of work. Can you tell my readers what it is like to coach varsity football and how you apply what you do in business intelligence and analytics to coaching football?

Spencer: For those not from Cincinnati, high school football is huge here. Friday nights for me as a kid meant going with my dad to see the local high school teams play, so it has always been a big part of my life. I feel really fortunate that I’ve had the opportunity to coach such fun and hard-working kids. Getting texts from old players and having them drop by on break from college makes all the time invested seem worth it.

During my time at Shroder, we hosted the first home playoff game in Cincinnati Public School history, which had to be my proudest moment as a coach. We ended up losing the game but being outsized, outnumbered, out funded, out everything’d, and the fact that my players didn’t back down when times got tough taught ME a lot.

We definitely tried to use analytics during one season especially, but with my time crunched as it was we never truly got to use it as much as we would have liked.

Michael: You mention that you use other programs such as Spotfire and OBIEE along with Tableau at work. Can you compare and contrast the three programs?

Spencer: I feel like I could write a 10 page paper on this topic. Maybe I should blog about it or something. The three programs are so different, and they all have their strengths. During my time at GE I have used all three on the same data sets as well so I’ve gotten an apples to apples view of it. I’ll try and do my best explaining the pros and cons.

User Interface: In my opinion Tableau is by far the most user-friendly. Even the layout itself of okay, here are your worksheets which you build individual charts and metrics into, then you take those and build a dashboard, that makes sense to me. Spotfire’s user interface is similar, but it’s also greatly different. Every tab is a dashboard, so you add dashboard elements to a tab instead of adding worksheets to a dashboard. A lot of the formula syntax is similar as well. It took me about two months to be functional in the program having already known Tableau. OBIEE is completely different and the least intuitive from a user perspective. OBIEE’s interface is much different than the other two but conceptually you’re able to build different views of your data then bring those together into a dashboard view. Navigating through the different screens in OBIEE is also way less intuitive in my opinion.

Viz Capabilities: Tableau is the best in this category by far. Google “best Tableau viz,” then do the same for Spotfire and OBIEE and you’ll understand what I mean. The flexibility of Tableau is unmatched with Spotfire coming in a solid second. If you really want your Spotfire viz to come to life you will need to use Java scripting as some basic charts types like pie charts are not able to be done with default chart types. I should note that I’m not endorsing pie charts as a preferred chart type, but it is often requested from process owners and so you need to at least be able to do them. OBIEE again is third on this list for me. Their presets are limited and in general, the charts are not aesthetically pleasing. Having said that, I did get to play around with Oracle‘s newest Data Visualization (name of the tool) edition and it is very comparable to Tableau in its ease of use. If you are going the Oracle route I would suggest the DV Desktop.

Data Sizing/Render Times: The biggest hurdle I’ve jumped through with Tableau is its time to render large data sets. When I say large I’m talking about querying a data lake view of 100M+ rows that is several hundred columns wide. I have read a ton of blogs and seen conversations about this topic on Twitter about how a lot of Tableau’s rendering ability is based on the back end architecture of your view. While I completely agree with this, I have tested all three products on the same data sets and concluded that Tableau takes the longest time to render a view of the three. Unlike Tableau, Spotfire is able to cache results of queries so when you run a repeat query the viz renders almost immediately. OBIEE, which is strongest for running table-like reports (often for export), can query these rows fairly efficiently as well. In terms of ranking them on rendering time I would go Spotfire, OBIEE, then Tableau.

Obviously, each person has their own preferences and experiences, and each tool has different use cases in which it would excel, but those are my overall thoughts. And Tableau obviously rocks!

Michael: Since you are very knowledgeable about sports, I have to ask you this burning sports question I have. I am a life-long Detroit Lions fan (I grew up in Detroit). What do the Lions need to do to be playoff contenders?

Spencer: As a Bengals fan, I am part of a fan base that can say they understand your pain. I am actually a part-time Lion’s fan myself as my father-in-law played for the Lions for several years in the late 80’s (Jimmy Williams, trading card below). To be honest, I have no idea what they need to do!! Win more would be my answer lol. I know a lot of analytics people would say don’t invest in running backs, go for it on 4th down, and go for 2 pt conversions more often.  

Michael: What is next on your “To Do” list? What can the Tableau community expect to see from you in the near future?

Spencer: I guess this platform is as good as any to announce that I am actually taking a new job at Tessellation starting in February! I am super excited to be joining Luke Stanke, Steve Fenn, and Co. at this amazing company.

In terms of goals for 2019, I want to blog more about Tableau. I will continue to help run #SportsVizSunday and in my free time, I want to keep focusing on vizzes that I am passionate about. I’m also working on setting up a data analytics after-school club at the school I coach football at, so hopefully, that gets off the ground soon. Also, if anyone knows the secret to being a Featured Author on Tableau Public let me know, that’s on my list of goals for 2019.

Thanks so much for interviewing me Michael, I really appreciate everything you do in the community!

Tableau Public

Link: https://public.tableau.com/profile/spencer.baucke#!/

Readers:

I have been really busy at work the past week or so and have not been able to post any blogs. Yesterday, I was looking at transit maps on the great site, http://transitmap.net. I thought I would share ten of them with you that I found interesting.

Enjoy!

Michael

Historical Map: BART System Map and Planned Extensions, March 1, 1989

Historical Map: Map of Kyoto and Vicinity, 1920

Historical Map – Bus and Streetcar Lines on the Benjamin Franklin Parkway, Philadelphia, c. 1935

Historical Map: Lines of the Lincoln, Nebraska Street Railway, 1892

Historical Maps: Melbourne Tram Destination Posters by Vernon Jones, c. 1930s

Historical Map: New South Wales By Train Information Wheel, 1938

Historical Map: Strassenbahn and Stadtbahn Map, Vienna, Austria, 1933

Historical Map: Map of Interurban Lines and Trolley Observation Trip, Portland Electric Power Company, c. 1923

MUNI Route Changes, January 27, 1982

Montreal Street Railway System, 1893

Readers:

Back in 2013, I did a multi-blog post on the 1854 cholera epidemic in London. Most of the reference material I used was based on Steven Johnson’s riveting book, The Ghost Map: The Story of London’s Most Terrifying Epidemic–and How It Changed Science, Cities, and the Modern World (see book cover below)

What I want to discuss today are three key topics.

1. The World has Left Yemen to Die, which is excerpted from an article in National Geographic written by Nina Strochlic, a staff writer covering culture, adventure, and science for National Geographic magazine. And, Matteo Bastianelli, a documentary photographer, and filmmaker based in Rome, Italy. Many of his long-term projects center on the consequences of war. This is an exclusive story and photos which gives a rare look inside the country of Yemen, where civil war has trapped civilians in a life of violence and disease. [1]
2. I will revisit John Snow’s now famous Ghost Map, which showed how Dr. John Snow and Reverend Henry Whitehead were able to pinpoint the cause of the cholera epidemic in 1954 London. I will discuss the breakthroughs made by John Snow, but also discuss what he was not able to achieve. [2]
3. Finally, I will discuss the other less known mapping of cholera in England in 1854. This was excerpted from the book, All Over the Map, A Cartographic Odyssey, by Betty Mason and Greg Miller. The book discusses physician Henry Wentworth Acland who tracked an outbreak in Oxford that affected 290 people that year. His work, which resulted in the 170-page Memoir on the Cholera at Oxford and an accompanying map, was “perhaps the most comprehensive study of an urban disease of its day,” according to Koch. [3]
   

Best Regards,

Michael

Note: See references to the source material at the end of this blog post.

The World Has Left Yemen to Die (National Geographic)

In 2019, 165 Years after the 1854 cholera epidemic in London, which produced the now legendary Ghost Map, cholera still exists in our world. Since 2017, Yemen has seen more than a million suspected cholera cases—the worst outbreak in modern history (see the two maps from National Geographic below). [1] One NGO ordered a shipment of medication in July 2017. It didn’t arrive until April 2018.

In Yemen, doctors and other health workers at public hospitals haven’t been paid since 2016. Humanitarian groups are supporting the health ministry with salaries and supplies. But a Saudi-led coalition blockade on the country’s airports and ports in an attempt to stop supplies from reaching the rebels has arbitrarily delayed or diverted aid shipments, says Kristine Beckerle, with Human Rights Watch, adding that both sides “are weaponizing aid.”

If you are dying, one elderly man told Bastianelli, you have to pay to be pronounced dead.

Many of Yemen’s doctors have moved to private hospitals or fled the country, leaving a shortage of medical professionals. Those who stay behind train their neighbors to treat wounds in case of an emergency overnight, when it’s too dangerous to travel. The private clinics cost more than even a middle-class civilian can afford. If you are dying, one elderly man told Bastianelli, you have to pay to be pronounced dead. The other option is to drive across the front lines to one of the country’s two open airports. Few can afford the cost of fuel—or the risk. “They’re locked in Yemen. No country is giving them asylum or making a humanitarian corridor,” says Bastianelli. “They count the days and wait to die.”

The Ghost Map: The Story of London’s Most Terrifying Epidemic–and How It Changed Science, Cities, and the Modern World.

In 1831, cholera hit the United Kingdom and began killing tens of thousands during successive waves of terrifying outbreaks. The disease was fatal for as many as a quarter of its victims, often within just days of exposure. Many scientists of the day embraced the theory that epidemics were caused by the foul air, or miasma (from open cesspools, raw sewage, and rotting rubbish), that hung over large swaths of most major cities. Many people believed that cholera was an airborne disease that you contacted from breathing the air. An English doctor named John Snow documented an outbreak of cholera in his London neighborhood in 1854 that contradicted this theory.

Dr. Snow, with the help of Reverend Henry Whitehead, carefully mapped the locations of the victims’ homes and demonstrated that the deaths were clustered around a public water pump on Broad Street in the Soho district. By interviewing the victims’ families, they were able to trace nearly every case back to that water pump, bolstering Snow’s theory that cholera is a waterborne disease and convincing the local authorities to remove the pump’s handle. The story has become legendary, and Snow’s map is often portrayed as a breakthrough moment in both cartography and epidemiology.

However, history can be cruel in the way the truth is told. Dr. Snow did indeed do excellent work that helped advance the science, and his map still rightfully stands as a shining example of medical cartography. But it wasn’t until long after Snow’s death in 1858 that his theory was proved correct and his work was hailed as a turning point. “His map has become an icon and Snow himself an almost mythic figure,” writes medical geographer Tom Koch in Cartographies of Disease. “Few focus, therefore, on Snow’s failure to convince his contemporaries of his argument (Miasma Theory where it was an airborne disease versus Snow’s theory that cholera was a waterborne disease), the limits of his thesis in the context of his time.”

Dr. Snow wasn’t the only one mapping cholera in England in 1854. The physician Henry Wentworth Acland tracked an outbreak in Oxford that affected 290 people that year. His work, which resulted in the 170-page Memoir on the Cholera at Oxford and an accompanying map, was “perhaps the most comprehensive study of an urban disease of its day,” according to Koch.

The Topography of Disease

Many experts found Acland’s work more convincing than Snow’s, partly because of its breadth and thoroughness. But Acland’s research had another big advantage: Its conclusions supported the prevailing miasmatic theory of disease, which had been developed over centuries. Snow, on the other hand, was bucking the mainstream with his waterborne-disease theory.

While Snow’s analysis was focused on one possible explanation for the outbreak (it was a waterborne disease), and his argument rested on the visual clarity of his map, Acland took a more statistical approach that considered many potential disease factors. In addition to mapping victims, Acland included sites that had previously been deemed unhealthy (brown dots), those that had subsequently been cleaned up (brown circles), streams that were unpolluted, and those that had been contaminated (dashed lines), including point sources of the contamination such as outflows of raw sewage (see map below). Areas with poor drainage were shaded green.

Dr. Snow was content to stop mapping the cholera deaths that occurred after he thought his case had already been made. By contrast, Acland mapped the entire list of victims in 1854, as well as those of two previous outbreaks. He used different symbols for the locations of victims’ homes from 1832 (blue dots), 1849 (blue bars) and 1854 (black squares and bars). And, most important for his argument, Acland mapped the physical topography of the town with five-foot (1.5 meters) contour lines. His map, together with his statistical analysis, showed a clear correlation between elevation and the disease. In each of the three outbreaks, people in low-lying areas suffered a much higher rate of infection and death. Even the higher spots that had unhealthy brown dots fared better than the lowlands.

Acland failed to see the whole picture, making his a cautionary tale, she (Este Geraghty) says. “You have to determine what things mean, not just the outcome and the correlation.”

Acland’s map neatly backed up the miasmatic theory, suggesting that the toxic air would collect and remain in low areas with less wind. “His statistics are showing that he had an excellent argument and evidence,” says Este Geraghty, chief medical officer for the mapping software company Esri. But Acland failed to see the whole picture, making his a cautionary tale, she says. “You have to determine what things mean, not just the outcome and the correlation.” As data visualization journalist Alberto Cairo has noted in numerous presentations of his I attended, “Correlation does not necessarily equal causation.” I fondly remember he once said, “The sending of Christmas cards does not cause Christmas to occur.”

Note: Cairo is also the Knight Chair in Visual Journalism at the School of Communication of the University of Miami.

Acland saw polluted water as a potential contributor to pestilent air, not as a medium for the spread of an invisible agent of disease. Consequently, he hadn’t paid attention to sources of drinking water. Instead, like many of his contemporaries, he looked to the weather for clues to how elevation could be influencing the disease.

“That there is a connection between the state of the Atmosphere, or of the imponderable agents of the globe, and the existence of the Epidemic, is scarcely doubted by those who have carefully attended to its history,” he wrote.

Acland meticulously charted the timeline of the 1854 outbreak against a host of local climate variables, including temperature, barometric pressure, wind, rain, humidity, cloud cover and ozone levels (below). But he was unable to find anything that waxed and waned precisely with the number of new cholera cases. What Acland was able to demonstrate, with copious data, is that 1854 was an abnormal year for weather in Oxford. Comparing it with the 25 years prior, he found that rain was abnormally low, as was wind speed. The list of things that were abnormally high included temperature range, pressure, thunder and lightning, days with hail, and appearances of the northern lights.

He couldn’t quite put any of these variables together with elevation to form a reasonable explanation, especially considering that the previous outbreaks didn’t follow the same pattern. But Acland was still confident that if another epidemic were to occur, “the rapidly advancing science of Meteorology” would be able to use data to clarify which of the abnormalities played a role.

Acland’s study was more comprehensive, and, at the time, more convincing than Snow’s, but it had one glaring flaw: His conclusion was definitively wrong.

Acland’s study was more comprehensive, and, at the time, more convincing than Snow’s, but it had one glaring flaw: His conclusion was definitively wrong. “Like much of the science of every era, it missed an intervening vector,” Koch writes. What Acland failed to see was that at higher elevations, water typically came from wells or streams, while lower areas mostly relied on rivers that were often polluted with sewage. But, Koch notes, “the mapping—and here, the mapmaking—were clear, consistent, and, if ultimately incorrect, still rigorous.”

Snow’s theory was largely dismissed during his lifetime, but he ultimately triumphed long after his death. Conversely, Acland enjoyed recognition from his peers for his work on cholera but lived to see the theory it supported disproved. Still, his work is worth more than a footnote, argues Alberto Cairo.

“The myth of the hero who singlehandedly wrecked miasmatic theory obscures the fact that those who held onto it were also thoughtful fellows,” Cairo writes. “It is unfortunate that we don’t study them further, as we humans learn much more from our mistakes—both individual and collective—than from our successes.”

Sources:

[1] Nina Strochlic and Matteo Bastianelli, The World Has Left Yemen to Die, National Geographic Magazine, August 2018, https://www.nationalgeographic.com/magazine/2018/08/dispatches-yemen-health-crisis/.

[2] Steven Johnson, The Ghost Map: The Story of London’s Most Terrifying Epidemic–and How It Changed Science, Cities, and the Modern World, Riverhead Books, October 2, 2007, ISBN: 1-59448-925-4.

[3] Betty Mason, The Topography of Disease – A 19th-century doctor famously mapped cholera’s toll to try and understand its origin and spread—but that’s only part of the story, Scientific American, January 29, 2019, https://blogs.scientificamerican.com/observations/the-topography-of-disease/.

Sophie Warnes Bio

My name is Soph and I’m a huge nerd and trained journalist who loves using data to help people better understand the world. I’m a Londoner living in Cardiff and working for the Office for National Statistics as a Senior Data Journalist. I also publish a data journalism and data visualization round-up newsletter called Fair Warning every Sunday.

QUESTIONS

Michael: Hello Soph. You are a Senior Data Journalist with the Office for National Statistics. Can you tell my readers a bit about what kinds of projects you are involved with on a day-to-day basis? How are your projects selected?

Soph: I see my role as helping to bring statistics to ordinary people who might not necessarily already understand them or why they are important. It’s my job to make statistics interesting enough that people engage with data, basically!

I work in a multi-disciplinary team, alongside designers and data visualization producers, and we always have to have statisticians involved as they need to sign off on how we use their data. I generally work on multiple projects at a time and they can take anywhere from a few weeks to a few months, so it really varies.

We select projects based on a whole variety of things but what’s really key is that we think it will help people in some way.

One of my favorite recent projects was a collaboration between my team and the Data Science Campus, which is an incredibly exciting and new-ish part of ONS where data scientists investigate how we can use data science techniques for the public good. The project was about how green your street is and it was a brilliant project where the dataset was created using machine learning, and we really had all hands on deck with all elements of the team working together to build something incredible. We don’t usually use 3D maps but we settled on using this way of visualization as a way to give people a sense of the richness of the greenery that would surround them if they were walking along the street. I’m so proud of it.

Michael: What are the primary tools you use to create your data visualizations to help tell your stories? Can you compare and contrast them a bit?

Soph: I don’t generally create and publish data visualizations as part of my role, as it’s more focused on the editorial/writing side of things. It’s about finding the story in the data and writing the headline and the article that results from finding the data.

The data vis team use JavaScript libraries like D3 normally. But, in looking at the data myself I will play around a little with data visualizations because I find it helps me to explore the data quite in-depth. I started using R to interrogate data towards the end of last year and I find it really useful for quickly scoping out any potential data vis we might want to use, but also in helping me answer questions like, what is the story here?

I personally try to get quite involved in the data vis and design elements of my projects because I have experience in visualization myself and I always have an opinion! I really enjoy the creative aspect of getting around a table and discussing how we’re going to do something or improving designs etc. I find that really exciting – I love bouncing ideas off of people and collaborating in that way – it’s one of my favorite parts of the job!

Outside of my work, I love playing around with data and creating visualizations and maps and all that sort of stuff. I am a real nerd!

Link: http://www.sophiewarnes.com/shop/sheep-in-wales-colour-full-headings

Michael: You also sell some of the maps you have created on your blog. I really like your Number of Sheep in Wales map. Can you discuss why you feel this is an effective way to tell this story?

Soph: The funny thing about this map is that I made it about two months before I eventually moved to Wales, having never been there before. I had no idea I was going to move, and I’ve told people that before and no one believes me!

The idea for this came from one of my first editions of my newsletter Fair Warning. I had included a map of Australia (here) that had shaded areas to show sheep and wheat, and bits of it were marked “no sheep” and “some sheep” – it just made me laugh. Someone suggested I do one for Wales so that’s where this one came from.

Most of my professional work had been fairly straight-forward up until this point, basic charts and graphs, nothing too beautiful or whimsical. But I had been inspired by people like Mona Chalabi, Giorgia Lupi, and Stefanie Posavec to try to do something hand-drawn that was a bit arty and different. I’m quite a whimsical person really, and there’s this long-running joke of Wales having more sheep than people (this is absolutely true by the way!), and I thought doing it in a cartoony way would really kind of capture that absurdity somehow.

It wasn’t really so much a story as a random thought – I didn’t sketch it out at all, I just found the data, used my MacBook Pro as a lightbox, hand-drew the outline of Wales in a black pen ON my MacBook, and then took it off and started drawing little sheep icons. I actually drew every single one of them out by hand! I scanned it in and cleaned it up in Photoshop and that was it. It took me about an hour to make, but then I went back to it later and made adjustments like adding color etc.

Michael: I really like your Fair Warning newsletter (Link: https://www.getrevue.co/profile/FairWarning). I find it very informative and eclectic. Can you tell us a bit on how you prepare each edition of your newsletter? What makes a story newsworthy to include in your newsletter?

Soph: Thank you! I have been a bit inconsistent lately, mostly because I’ve been really busy pursuing hobbies and being invited out etc, so I feel like I am barely at my flat long enough to sit down and write it. It can take anything between two and four hours, I would say. It depends on how much success I have in finding things immediately. I used to try and gather links during the week and then sit down to write it on Saturday, scheduling for Sunday, but I’m barely in during weekday evenings so that’s stopped.

I tend to check the same websites/sources each week, but I feel like increasingly that isn’t going to be as interesting for people, so I spend quite a bit of time looking at other sources, forums, hashtags, etc to see if anything really cool comes up. Most people will have seen stuff that the Washington Post publishes, but they might not have seen some random person’s tweet about a cool visualization they’ve made. So I try to balance it between what ‘mainstream’ data journalists and vis producers are doing, and what other people outside the field are doing. The audience for Fair Warning is surprisingly diverse; I am constantly amazed at the different people that subscribe to it.

I would honestly say that in picking links to go in, I just go with my gut. I pick things that I like for whatever reason, sometimes because the data itself is new or interesting to me, or I have some kind of emotional response to it. Sometimes the visualization feels really technically advanced and different to anything I’ve seen before (I loved this NYT visualization about racial inequality). Sometimes I just like a visualization because it’s pretty or aesthetically pleasing. The map of rivers across the US is a great example of this. It completely blew me away. It’s just stunning, but on the face of it doesn’t seem like a big deal.

I try to maintain a balance in Fair Warning but it can be quite hard and some issues are (to me) not great, but I think that’s always going to be the case when you push yourself to do something on a regular basis. I feel quite strongly that it should be an extension of me and my personality rather than just a boring link round-up, and I think that slightly quirky tone is why a lot of people like it. I’m not trying to be anything I’m not, I am really honest and transparent generally, and I like that that comes across in Fair Warning. I would hope if people met me they’re like “Yeah, that’s the Soph I know!”

Michael: I think you are a great storyteller. What recommendations can you tell my readers on how to become an effective storyteller, whether it is written or visual?

Soph: I think the key thing about storytelling is that you are ultimately telling stories to humans, and humans are fascinated by other humans. So, if you try to make your stories about people in some way, then you’re onto a winner. The ‘how green is your street’ piece is ostensibly about greenery on streets, but actually what it’s asking of people is ‘do you know how green your street is?’ – how well do you know your area? A few people filled out a survey on the article and said that they disagreed with our figures because of course their street is more green than this number! And you think, ok, I probably did a good job with this if people are moved enough to bother writing to me about it.

The way I conceive of an article, personally, is I try to think, which ‘angle’ in the data would elicit an emotional response from people? Would someone read this thing and feel surprised? Angry? Will they immediately Google to see if it’s correct? Will they feel moved or connected enough to share it? It’s all about trying to connect people to data, by giving them the information they don’t know about themselves, or where they live, or the people they know. I like to think that people want to learn and be challenged.

It’s the same thing with data visualization – we’ve found that it’s quite good to let people place themselves in the data somehow, to provide personalized information back to them.

It’s the same thing with data visualization – we’ve found that it’s quite good to let people place themselves in the data somehow, to provide personalized information back to them.

I’ve gone on for a bit… I’ll strip it back to three recommendations:

• The best stories are about people, so think of that aspect first
• Be mindful of how someone who is ‘new’ to whatever you’re making might interpret it (ask a friend to tell you what they get from it!)
• Familiarise yourself with rules about writing and different styles of writing, if you’re serious about it. Always write active sentences, write as if you’re telling a story to someone, and re-read what you’ve written to make sure it flows. Too many times I’ve seen people chop and change the structure of their story (which is fine) but they forget that doing that will completely change how the piece flows.

Link: https://medium.com/@sophiewarnes/a-week-in-caerdydd-2260995da60b

Michael: On Medium (link above), you discuss your recent move from London to Cardiff. I live in Arizona (suburban Phoenix) where houses are primary detached homes, and in most older areas, you have some land too (1/3 to 1+ acres). From reading your posts, it seems that affordable housing in London and Cardiff is similar to New York City where people tend to own small apartments or lofts, but pay high premiums for living in the city. Is that a fair characterization of what housing is like in London?

Soph: In London, for sure. The housing market there is bonkers. Absolutely bonkers. Young people are being priced out, and anecdotally lots of people I know have moved or considering moving. The problem is that London is where certain industries are based – journalism as an example, most national news outlets are based in London. If you want to go into specific careers you are kind of putting yourself at a disadvantage if you DON’T go to London. I think what we will see in the future is a greater pattern of people in their 20s and 30s just leaving London, perhaps living in commuter belts. I grew up in London so I love it very much but it’s a complicated city to have a life in, and I think what with it being so expensive…you are basically paying a premium to have a lower quality of life, and I think people will wake up to that soon.

Cardiff, although it’s a capital city, it is really tiny. And sure, bits of it can be really expensive – I was looking at buying in the city center and you obviously don’t get as much bang for your buck – but it’s a hell of a lot cheaper than London. The flat I have would have cost probably twice as much in London, and it’s absolutely gorgeous. But Cardiff was (or is?) one of the fastest growing cities in the UK if not Europe, and it has a lot going for it.

Michael: What is next on your “To Do” list? What can the data visualization community expect to see from you in the near future?

Soph: This is a great question! I don’t have any plans set in stone but I have a lot of aspirations! I would love to do more experimenting with data and visualization in my own time – in fact, I have taken my first baby steps in joining the #TidyTuesday community which is a great way to find people doing similar things, access R scripts and see how different people tackle the same dataset. The problem, as always, is finding the time!

I really enjoyed my experimental look at encoding data in jewellery and other physical representations of data. I’ve had vague plans for a while to go back to doing some more experimentation, but haven’t managed to yet. It’s quite time-consuming!

Last year I did a few talks and workshops throughout the year and I’ve already given a short talk to some university students, introducing them to the basics of data visualization. I’d love to do some more talks or workshops this year. They make me nervous but I love sharing ideas and skills and getting new perspectives on things.

I’m also going to NICAR in Newport Beach in March, so am hoping to meet a few more people in the data vis/data journalism community.

Source: Bed, Bath & Beyond

Source: CandyStore.com

Continuing my week of data visualizations and infographics related to Valentine’s Day, here is one from http://www.candystore.com depicting a map of the United States showing which candy is the favorite Valentine’s Day candy for that State.

Source: RichMedia

Valentine’s Day… a day to celebrate your loved ones, indulge in chocolates and satisfy your craving for heart-shaped candy! Whether you’re part of the 54% that plans to celebrate Valentine’s Day or someone whose more on the get-that-arrow-away-from-me-cupid side of things, you’ll probably be surprised to learn just how much we spend on this love-filled day (bet you didn’t know that 21% of people get their pet a gift!).

Source: Wallace, Irma, Valentine’s Day – Facts of Love, Infographic Journal, Feb 12, 2016.

February 14th is celebrated all over the world as the day of love, exchanging chocolates, flowers, and gifts with loved ones. However, it’s not just about the romantic stuff, but also about the amazing facts associated with it that makes the day quite an interesting one. Here’s the most enjoyable and fascinating collection of Valentine’s Day statistics from Picovico to absolutely amaze you.

Source: –, Client Highlight: How White Supremacy Attempts to Make Slavery and Segregation, “Soooo Long Ago.”, Zerflin.com, April 6, 2016, https://zerflin.com/2016/04/06/client-highlight-how-white-supremacy-attempts-to-make-slavery-and-segregation-soooo-long-ago/.

Text describing this graphic tattoo is excerpted from the Zerflin.com web site.

Back in 2016, Zerflin was commissioned to create a piece of art that gives an account of America’s history with slavery. This graphic tattoo is a timeline that highlights key points in Slavery of the African Diaspora in America. A timeline that is too very often dismissed and disregarded.

“…but slavery was sooo long ago.”

The folks at Zerflin heard this quote over and over throughout the course of modern American history. In an attempt to urge black people to “move on” and to recognize just how good they have it in America, this dismissive and tone-deaf statement attempts to transform relatively recent history into ancient history or myth.

In an attempt to urge black people to “move on” and to recognize just how good they have it in America, this dismissive and tone-deaf statement attempts to transform relatively recent history into ancient history or myth.

However, when looking at this graphic, it is very clear that American slavery and segregation was not so long ago. In fact, it is very possible to have conversations with many African Americans who have vivid memories of Jim Crow South and the racist and subversive practices in the North.

America cannot escape its past. This country’s history is stained with the blood of thousands and its foundation built on the backs of enslaved men, women and children. America’s complete history cannot be told without including the horrors of slavery and its long-lasting effects.

The enslavement of African peoples by Europeans began in 1441 with Prince Henry the Navigator of Spain. However, because this tattoo is specific to the enslavement of the African Diaspora in America and because it includes American segregation, Zerflin felt that it did not make sense to include a full timeline of the entire diaspora.

For this graphic tattoo, here’s what Zerflin included:

1619: Arrival of “20 and odd” Africans in late August, on an English warship the “White Lion.”

1865: Slavery abolished in the United States of America.

1954: Supreme Court Declares School Segregation Unconstitutional in Brown v. Board of Education.

The colors; Red, Black, Yellow, and Green, are colors used in PanAfrican symbolism and design. As the client is an African American, Zerflin wanted to represent that history and culture in the colors themselves as they wrap around the wrist.

There are DOZENS of things that could be added. Because it’s a tattoo design, they felt there was only so much they could add. But that is exactly why the green area is untitled. Green doesn’t necessarily mean good. The untitled green area recognizes that full equality for African American still has yet to be fully realized. When it comes down to it, people are STILL fighting for equality; whether it’s for city services, fair treatment by police, education, or wages.

To suggest that an entire community of people forget the stain of slavery and its adverse effects is a selfish attempt to absolve this country of its sins.

To suggest that an entire community of people forget the stain of slavery and its adverse effects is a selfish attempt to absolve this country of its sins. That is why Zerflin was happy to create pieces like this graphic tattoo because these pieces help to keep this country accountable for its legacy of slavery. Without looking backward and acknowledging the horrific past, we cannot fully move forward.

I highly recommend you visit the Zerflin web site. Here is a link.

Source: https://www.livingcities.org/