How do you visualize time? A line chart with time on the x-axis, right? We do this unthinkingly, but have you stopped to consider why we default to using an x-axis? Or more generally, the very “direction” of time itself? I find this topic fascinating and since just about everyone has time in their data, it’s relevant to Tableau. After reading these links, you’ll think differently every time you see or build a line chart!
I’m a dataviz history nerd, and this (long) article explores cultural directions of time, with particular focus on the first ever horizontal timeline chart, Carte Chronoraphique, by Jacque Barbeu Dubourg. This was a 16.5m (54ft) long chart of human history. It was literally ground zero for the timeline: the first time anyone put time on an x-axis. If you’re at all into the history of our field, I cannot recommend enough that you make the time to read it.
If you show Spanish and Swedish people animated lines that lengthen at different speeds, the Spanish can perceive the different rates, but Swedes cannot. Why? Because the two culture perceive time differently (Spaniards see time as “volume”, Swedes as “length”). Does this apply to animations in dataviz? Do different culture see timelines differently? That would be fun to research…
I don’t normally plug Tableau in this column, especially not my own work, but visualising time is one of the fundamental things Tableau users do. If they stick to timelines, they will miss valuable insight. The whitepaper shows different ways to visualize time: everyone exploring data should look at data from as many different angles as possible to find and articulate the best insight.
Welcome to the Sweet Spot. This week: three links that tell me we’re seeing the start of Artificial Intelligence’s drop from the top of the Hype Cycle. This is a good thing – going past the peak of inflated expectations to the trough of disillusionment is necessary for any maturing technology. The conclusions of each of these are that AI will succeed only with significant oversight from people. Further proof is in this week’s UK House of Lords level-headed report “AI in the UK: Ready, willing and able?”, which I’ll cover more next time.
Elon Musk has had to roll-back production targets for his Model 3. He cites over-reliance on too much technology, including failed attempts to automate too much. “Humans are underrated,” he tweeted. This is Tesla, world leader in automation, rolling back.
I banned my kids (8 and 10) from YouTube this month. I’ve seen for myself the descent into weird and unpleasant videos that the algorithms lead them to. And now YouTube themselves have tacitly acknowledged the problem: they are rewriting their Kids app to be human-curated because algorithmic recommendations are unsafe. This is Google, world leader in AI, rolling back. James Bridle’s essay from November 2017 is a highly recommended, disturbing, read.
Lewis Hamilton started on pole position in in F1’s season-opening race, but lost the lead due to an error in the team’s data models. The models incorrectly indicated a big enough lead to allow for Hamilton to take a pit stop. Vettel snatched first place as a result. “When you are relying on so much data, so much technology to come out with the strategy or whatever, I wish it was more in my hands,” said Hamilton. Few sports are as data-driven than Formula 1, and here it is, making hash of it.
Today I’m sharing a video on how I created a Maestro flow to blend two datasets. The blend could be done in Tableau, but I wanted to show how the same, and more, can be done in Maestro.
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.
Go check out the Fitbit dashboard. It will change your life, initially. And if you get more life out of one than I did, let me know.
“Springboard” not dashboard
Governed data sources (which are useful for enabling self-service while retaining control. We use them to encourage account managers to build personalized dashboards
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
Tristan Guillevan won Iron Viz at least in part because of his use of animation to tell a compelling story about US house prices.
Bloomberg’s climate warming chart is, I believe, one of the best animations in dataviz. It reveals the creeping, then accelerating, trends in temperature in a way that stops and makes you think.
Finally, I ran a terribly weak poll on Twitter. The poll is bogus, of course, but the thread of conversation was excellent.
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.
Stacked bars
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.
As part of the dashboarding series of webinars for Tableau, I presented “Design Tricks for Great Dashboards“. I hope you enjoyed it. Let me know your thoughts below or on twitter (@acotgreave)
Here is the list of resources and ideas I mentioned in the talk.
Affordances and signifiers. Don Norman, of course, has excellent resources on this aspect of design. I’ve written previously on how this applies to dashboards.
Amy Alberts and I hope you enjoyed our talk at #data17 about eye-tracking and what it can teach you about building better dashboards.
This post is a list of links and resources we talked about in the talk:
The dashboards we’ve used in the study come from my book, The Big Book of Dashboards. Go check it out if you want to see great examples of real-world dashboards you can apply to any scenario.
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?
Reuters/Bryan Snyder
