It’s the Christmas edition of the Sweet Spot! That wraps up 12 months of, what I hope, have been inspiring and entertaining things to read/watch/listen to, all somehow related to helping people see and understand data. For the final issue of the year, some fun stuff. Still relevant. But fun. See you in 2018!
Numberphile is one of my favourite YouTube channels. This video, by the wonderful Cliff Stoll, provides a brilliant gotcha to teach you a lesson about data collection. It’s a simple lesson but one with big implications: how do people learn to trust their data? Should they trust it blindly? Are they measuring things correctly?
Our visual system is the most powerful of our senses, but our brain’s a little lazy and doesn’t remember everything with complete accuracy. This campaign from Signs.com asked people to draw logos. The results are really interesting, and they did some great data analysis and storytelling with the results. (shared by Louis Archer in Product Marketing)
“A virtual cult of the spreadsheet has formed, complete with gurus and initiates, detailed lore, arcane rituals – and an unshakable belief that the way the world works can be embodied in rows and columns of numbers and formulas.” That’s my favourite quote from this piece celebrating October 17’s Spreadsheet day (marking the 35th anniversary of Visicalc. When Tableau is 35yrs old, will people look back and reflect in the same way? (That’s 2038, by the way!)
Clearly there’s life in the old spreadsheet! This article strikes an almost romantic tone as it describes the depth and complexity of a spreadsheet that models NYC’s transport system. Read it and marvel at how far we’ve come with Tableau. I’m amazed at how this is so anachronistic and yet, somehow, appropriate? Go download the spreadsheet itself and marvel at the charts, and the maintenance nightmare this must be. Surley we could model this with a good database and Tableau?
Shall we map the entire world, every day? Why yes, why not? We’ll need a bunch of our own satellites to create 1.4 million images daily (that’s 6 petabytes of data, each day!). That’s what Planet did. Now they’ve built it, think of the possibilities of this dataset. Their promo video is crazy: imagine measuring a country’s economy by measuring the number of ships in a port each day? Or tracking natural disasters more accurately than ever before? Sure beats the Excel datasets described above! Their promo video really sets the scene.
Today Andy Kirk and I duel it out in the annual Data Debate. We tackled big and small issues in the field of data. The recording will be available here. This post contains links and further reading on the topics covered.
Are politicians emboldened to say whatever they want? If they are, is that changing society’s attitude to data? Is social polarization accelerating this trend? I fear it is.
Sir David Norgrove, Chair of the UK Statistics Authority, was driven to write to Boris Johnson in September warning him of his “clear misuse of national statistics.” William Davies writes on this in the Guardian, using GDP as an example of the end of statistics. Simon Kuper offers a glimmer of hope with advice on how to take on the populists in the FT. However, his main argument is to ditch the facts and lead with emotion and the story. Sure, that might win people over, but if there’s no role for information and data, have we lost the fight?
The case for animation
My main argument is that animation is exceptional for presenting data in a dramatic way. It enables storytelling and creates tension, surprise, and drama.
The master was Hans Rosling, who astounded us in 2006 with his first TED talk on health data, and later with his Joy of Stats
But I see a lot of people fixate on the chart they want to build rather than focus on the question or the data itself. Watch this video. The end result is a chart you’d never find this in a chart directory. But, if your question is: In which years does B outsell A, it’s a wonderful way to represent it (it’s not the only way, but it does work).
Also consider the two charts below. One shows drought index in the US and the other shows US Road Fatalities. The structure of the data is the same (month, year and state). Only one is a successful chart (the drought chart on the right). Consulting a chart directory, there’s a risk you’ll pick the small multiple and publish. But of course, the data itself drives the appropriate chart type.
Big Ass Numbers: awesome!
Which of the above dashboards conveys a headline better? The one with the BANs or without the BANs?
I came to appreciate BANs through the writing phase of The Big Book of Dashboards. I consider them the Headlines for your dashboard. A well defined set of BANs will capture your KPIs in a way you can interpret instantly. Clever use of colour or other visual indicators can show, straight away, whether they are above or below target. Once the headline is digested, then you can decide if you need to devote time to interpreting the charts in the rest of the dashboard.
This was fun! What I like about defending “bad” charts is that it emphasizes the lack of “rules” in dataviz. There are only guidelines and any binary argument of right or wrong is a failed endeavor.
My defense is simple. If your primary goal is to show the total of all categories, then a stack is fine. Yes, that comes at the expense of being able to accurately see the changes over time of the individual categories. If the primary goal is to see the individual categories, a stack is a poor choice. I believe you should consider primary/secondary goals in all assessments of charts. Remember: All charts are a compromise.
The same defense applies here. If the goal is to give the gist of the data, as they tried to in the BBC article on Apple’s Tax Bolthole, then you’re ok with bubbles. A bar chart will provide more accuracy, sure. In the case of the BBC article, I’m not convinced readers miss out by their inability to accurately see if Apple is precisely 30% bigger or smaller than another bubble.
Welcome to the Sweet Spot. What’s the Sweet Spot? As Tableau’s Evangelist, I need 3 things to do my job well:
I need to know Tableau inside out
I need to know my day-to-day job
But I also need to know how data impacts and is impacted by the world around us.
Keeping on top of all of these gets me in my Sweet Spot. To stay there, I read a lot of stuff. And now: It’s time to share it, fortnightly, with you all.
This week – 3 things to challenge assumptions about AI (hint: robots ain’t coming to kills us. It’s much more boring, in a good way, than that). Thanks to everyone for feedback on switching up the format of this, and for links you’ve been sending me. I’ll include some of those in future mails.
AI is being hyped more than Big Data was. That is deceptive and misleading; it’s time to refocus.
Why read and share? AI is really exciting. But the media swoons over Musk, Zuckerberg and others and their sci-fi prophecies. This brilliant article by Chad Steelberg urges us to dismiss the hype and focus on the reality. Think about our own roadmap: the Data Interpreter and Recommendations are machine learning, in our products now. That’s not killer robots: it’s just a branch of AI doing mundane (but still amazing) stuff to make our jobs go quicker. Ignore the attention-seeking tech titans, and instead get on with augmenting your own intelligence.
Superintelligence: it’s going to destroy us and take over the galaxy. Or is it? Watch this and decide.
Why you should read and share this? Personally, I think talk of Superintelligence is an interesting philosophical game, but closer to science-fiction than reality. The Future of Life Institute do think about this stuff, though. Watch their overview. Then decide: when talking about AI to customers/colleagues, should you focus on mundane machine learning applications of killer robots? [Hint: it’s not robots.]
Want an overview of the current opportunities and risks of AI? This podcast has them all.
Why you should read and share this? AI is coming, and it’s part of Tableau’s roadmap. It’s up to all of us to educate ourselves about the pros/cons and realities. We all need to get on top of it. This podcast is a superb overview of the benefits and risks from the BBC. It’s balanced and doesn’t succumb to the usual hype. It features lots of diverse opinions, including a great interview with Cathy O’Neil, author of Weapons of Math Destruction.
Happy reading! More to come in a couple of weeks. I’ve been toying with ways to do this (LinkedIn, here, YouTube). What do you think? Do you like the content? How would you best like it delivered?
A great pleasure in life, is conuming amazing content across the web. Here’s 3 great things I’ve read in the last two weeks which help me connect data with things happening in the wider world. I hope you like them!
Why should you read this? First because it’s data-driven diversity and second because it teaches us how to have better conversations with others.
tl/dr: An Uber exec resigned over a sexist comment (Paraphrased: “More women on the board? They’ll never stop talking”). The Economist looks to see if any data supports that: it doesn’t. However, the data does reveal fascinating differences between gender’s conversational styles and goals. This is great information for all of us to empathise with others.
Why should you read this? This is about iteration with data: the only way to insight
tl/dr: It is simply not possible to get the best articulation of your data without exploring it and teasing out the best view through iteration. This article excellently describes the iteration process and how it helped develop the best possible end result.
Why should you listen? Because AI and machine learning aren’t good enough to threaten humans yet.
tl/dr: Fitness gadgets suffer from Dead-End Dashboard problems but they’re getting better. This podcast pits them against a real fitness trainer. The conclusion? Like most AI applications, they’re far from fully functional and for the foreseeable future. The point is that for now, augmenting human ingenuity is the right path forward for AI and ML.
Let me know your thoughts – do you have any comments? Do you want to see more posts like this?
Last week, Stephen Few critiqued lollipop charts and I wrote a post in their defense, in which I claimed I coined the term “lollipop chart” when I first wrote about them in 2011.
Stephen Few claims he’d seen them several times before I made them in Tableau. I accept that’s entirely reasonable. I remember at the time I thought my idea was original, and if I’d seen them prior to my post, I hadn’t consciously registered them.
Stephen also sent me a link to the lollipop graph, on Wolprham Mathworld. One of his readers had googled the term lollipop chart back when I wrote my post. The purpose of the lollipop graph is different to that of the lollipop chart, but coining cutesy names is certainly not something exclusive to me (tadpole or barbell graph, anyone?
Which all renders my claim to inventing them hanging on by a thread! I probably wasn’t the first to make a lollipop chart. I wasn’t the first to come up with a cute name. Maybe, just maybe, I can claim the ever-shrinking privilege of coining the lollipop chart!
Problem: A bar chart with many bars of a long length are unpleasant to look at.
I believe lollipops create a visual experience which is easier to look at. In his post, Steve used an example using 7 bars with a large range. I didn’t create the lollipop for that situation. While I believe a bar or lollipop works for that kind of data, criticizing the lollipop without addressing the original intention is disingenuous. I did consider fat bars and thin lines, too, in seeking a solution. Fat bars are just too inky, and the thin lines don’t have enough definition. Lollipops are an attractive compromise to solve the problem.
*Did I invent lollipop charts? Alberto Cairo credited me with their invention in his book The Truthful Art, and I’m not going to argue against that! The images on my original post are no longer available, unfortunately.