Sweet Spot: when the World Cup meets analytics

Welcome to the Sweet Spot for 13 July. The World Cup final is on Sunday, so I thought I’d look at how data has influenced comment on this year’s tournament, and what that means for the rest of us.

I love football because it’s the opposite of science (The Guardian, 4 minute read)

Jorge Valdano, former Argentinian striker, laments the growth of analytics in football. “Big data and mathematical projections are making their way on to the field of play to tell us things I don’t want to know,” he says. I say he’s wrong. He makes the fatal mistake of thinking that data removes intuition, and that data represents a bigger truth than anything else. He says that knowing that someone ran 15km in a match isn’t on its own a measure of success. I agree. But this data adds to a conversation the coach can have with players. Fans love the data in football. It adds flavour, just like it does in business: no decision should be 100% based on data; it helps to be informed by data.

Running at the World Cup (More or Less, 9 minute podcast)

The Russians ran further than any other team in the Group Stages. Some say this is evidence of doping. The analysis in this is very interesting, but the program fails to point out the main issue: running distance is just one factor in hundreds that would indicate doping. Correlation doesn’t equal causation; there may be many reasons Russian players ran so far. It is not possible to use one data point to make such a big conclusion. Either way, this is a fun episode to listen to.

Can Big Data Analytics Save Germany’s World Cup? (Forbes, 2 minute read)

I share this for the irony of PR stunts going wrong. It’s about the German team’s “Virtual 12th man”: SAP’s Big Data and AI-driven analytics platform which is used for player and game analysis. Sounds AMAZING. I’m sure it is. Except…. Germany got knocked out in the Group Stages for the first time since 1938! Which *definitely* proves that data is just one part of an equation in running any team, or business. 🙂

I’m supporting France on Sunday. Vive la France!

Sweet Spot, May 4th: Which way is time?

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!

History on the line (Stephen Boyd Davies, 30min read)

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.

The language you speak changes your perception of time (PopSci.com, 10min read)

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…

New Ways to Visualize Time (Tableau.com, 5min read)

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.

Sweet Spot, 20 April: Artificial Intelligence is starting its descent into the Trough of Disillusionment

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.

Tesla rolls back automation. “Humans are underrated,” says Elon Musk (7min video/5 min read, CBS)

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.

YouTube Kids to release a non-algorithmic version of its app (2 min read, Buzzfeed. Also: an important 21 min read, Medium)

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.

Reliance on data lost a Formula 1 race (3 min read/watch. ESPN and The Guardian)

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.

Paper, logos and cognac: Sweet Spot Dec 12 2017

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!

Secrets to measuring a piece of paper (5 min video, Numberphile)

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?

A New Master Blend: Giorgia Lupi and Kaki King (3 min video, Hennessy)

Dataviz, music and cognac: a Christmas gift seemingly designed for the data geek in your life. This is a great project from three inspiring people.

Branded in Memory (15 min read, Signs.com)

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)

Sweet Spot Dec 1: Visicalc, Excel, and Planet-Sized Data

A Spreadsheet Way of Knowledge (20 min read, Backchannel)

“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!)

Meet the Spreadsheet That Can Solve NYC Transit (10 min read, Vice Motherboard)

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?

Mission One Complete! (5 min read/watch)

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.

Sweet Spot: popping the hype balloon of Artificial Intelligence

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.

We need to shift the conversation around AI before Elon Musk dooms us all (3min, Quartz)

Reuters/Bryan Snyder

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.

Myths and Facts about Superintelligent AI (4min video, minutephysics)

minutephysics, YouTube

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.]

The AI revolution (30min podcast from BBC’s The Briefing Room)

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?