#data16 - My Summary

Every year I mean to write this post. In the seven Tableau conferences I have been to, I think I hit this aim once. Let's make this two shall we? So here's a small collection of thoughts on a range of subjects whilst they are all 'fresh' in my head:

The Product

• Tableau 10 is going to rock. 10,000 people are on the beta programme and I love everything that I have seen and tinkered with so far. Speaking to Francois Ajenstat this week, I admitted that the volume of 'newness' can be overwhelming at times but woah does it make the jobs we have exciting. 
• The developments are needed. I love the tool but it is still quite new and has grown so rapidly so there are definitely things that need to change. People are still struggling with Blending and need more Enterprise level control to appease more of the stakeholders who are blinking demanding. v10 is delivering a lot of these requests. 
• Some demanded developments aren't being delivered. There is a lot of demand for sunburst, sankeys and network diagrams. I'm still glad that non-best practice charts are being held back. I like to highlight that if you really want to build these you can but there are reasons why you shouldn't. This challenge will never go away.

The people

• Great to chat to so many familiar faces. I couldn't walk anywhere for more than a minute before I'd stop and chat to some great people who I only get to see a few times a year. I love the community and the conference is a chat to talk to people in more than 140 characters
• Meeting lots of people who I have trained and are loving the tool. Special shout out to Claudia who I taught in the Netherlands but so many more from clients and Tableau Public training sessions. It really motivates me to keep trying to make my training sessions better 
• Putting a few of those Twitter profile photos to real faces. You know who you are!

The Tableau team

• Absolutely cracking job by the conference team to run a conference for a 1,000 people. This is no small event so great job to everyone involved. You guys allow us to just enjoy and that speaks volume
• Great to meet so many new Tabloids.
• Bethany Lyons maintained the rule of 'Attend any session at a conference that Bethany presents'. Freaking amazing and thought provoking work that keeps me humble (even if you did offer a me Women's sized t-shirt)

The Information Lab team

• I have so much fun working with this team of passionate gurus who clearly make such an impact on the community. I love what this team adds in and also how much we still have to learn.

So thank you all for making it a great couple of days and see you in Munich or Austin soon.

#data16 - Artilize This - Bethany Lyons

Balancing Art and Analysis in Tableau

Bethany takes some crazy challenges including creating Matt Miller's shoes in Tableau. Mission successful.

This session is not just about Matt's shoes, it's about the mix between quantative analysis and attractive design. 

if you have four measures to create a weighted score for a country-by-country weighting, then a bar chart is easy. But it's not effective. If two countries are the same weighting, it doesn't mean they score the same in each of the individual scores. 

Averaging loses a lot of the context - it's Bethany's Quartet now and no longer Anscombe's! 

Share price data is also true for this across a lot of stocks. A histogram of the daily rate of change is a nice way to look at this.

Stop fighting with the calc to form a certain type of chart and change the chart to make life easier.

Bethany shows an example of overlapping compaign dates and actually when talking through the way she fomulated the calculation logic, she broken it down bit-by-bit. A great lesson of how to solve the seemingly impossible / improbable to solve but do you need the level of precision the calculation is asking you to get to. 

No way I can blog the individual vizs without the data so hopefully Bethany will post these great examples / walkthroughs up! [Hint, hint]

#data 16 - Maria Konnikova - The Confidence Game

1951 in Canada and there is a huge shortage of doctors as no one wants to volunteer as they will be taken to be part of the Korean War. The Recruiting Officers were thrilled when an experience surgeon turned up out of the blue. The ship was on passage to Korea when they came across a ship of Korean injured soldiers. Ferdinand Demarra was actually the great 'imposter' and didn't even have a high school diploma - just a few text books on medicene. He actually undertook the surgery and somehow no one dies on the operating table. The Doctor Seer was actually in Canada and had his identity stolen when at a Monestary from a Brother Monk.

The real Dr Seer gets prosecuted! Demarra actually gets discharged with honours. He actually carried on practising medicene into the 1980s and nearly built a bridge in Mexico. He had an biographer who wrote a book on him but then the biographer had his ID stolen by Demarra. 

So what is it that makes us give our confidence (trust) to others?

Maria thought that sceptics wouldn't be caught out. The research proved that as a species we are pretty bad at working out if we are spotting lies. As most people are not out to get you, you get an implicit trust if you are not looking at them. Every single day we lie and we actually accept it as pure truth telling with be pretty miserable. "How are you?"... "Well let me tell you..." Uh oh! People with higher IQs actually trust peoople more. Stronger social connections often lead to longer lives. We have not evolved to spot them. Maria basically wanted to lock herself away as questioning the truth telling lead her to be more sceptical about society and all those around her. 

How do you take in data from the world around you and judge yourselves?

The 'above average effect', the 'exceptionalism bias' People think they are naturally above average on desirable traits. Being bad at 'understanding people' is not something we expect.

Al Capone - one fine day Victor Lustig (fine conartist who sold the Eiffel Tower - twice) Al Capone met with Lustig even though Capone knew his background and 'skills'. Lustig gets $20k to double Capone's money. Lustig just used a safety deposit box to hold the money but just gave the $20k back to Capone and said his ideas didn't hold out. Capone expected to either 1. Double or 2. Lose it all. Capone actually just gave him $6k to get him out of his financial difficulties. 

To avoid a con 'if it seems to be good to be true, it probably is'. But it's very easy to tell this if you are judging others. Not so easy when we assume we are exceptional ourselves, so wouldn't get caught out. When you are experiencing something about you and emotion - you are no longer as logical. If you get someone involved within the story, they critique the data less. Yale researchers proved this by comparing the same story in a newspaper compared to a stronger narrative. 

Hacking is about 'Humanint' - Human Intelligence - and we are sharing lots of our Human Experience that are giving hackers the keys to what we do and therefore making data available to bad actors. 

The best magician is the best storyteller and distractor rather than the best technical skills.

The reason why we don't have good data on cons as we don't want to admit we have been conned. 

Data16 - Storytelling Through Comics - Irina Porodnova

Comic Theory:
1. Sequential Art
2. Visual Grammar / Visual Language
All to apply Comic Theory to Visual Analytics

Comics add single images together to add context to that singular event. This is an obvious link from a single worksheet / chart to good storytelling with dashboards, pages or Storypoints.



McClouds transitions:
A. Moment to Moment transition - very simple progression. The pages shelf allows you to do this in Tableau simply and you can walk through the story step-by-step or moment-to-moment.
B. Subject to Subject - A series of changing subjects within a single scene - Multiple panes within the same view but of the same chart.
C. Aspect to Aspect - different aspects of a place, idea or mood - multiple filters or storypoints to investigate the different angles.
D. Non Sequitur - No logical relationship between the panels - a lot harder to find the story as there is no logic here. Classic for someone's first Tableau data visualisation!
Key takeaways from Transitions: A create logical relationship. B/ Images develop meaning when placed sequentially. C. Can serve as a replacement for written words

Cohn - Visual Language of Comics
Frame Types:
A. Establisher - setup of the story
B. Initial - build up of the story
C. Prolongation - increase tension and increase anticipation
D. Peak - the moment of the story
E, Release - exit - maybe the punchline

Great framework for putting together storypoints in Tableau.

When a comic is in the wrong order, comic storys become very difficult to follow yet we do this with data visualisation often.

The neuroscience of comics - in research, out brain expects a story to flow so when it doesn't the brain kind of gives up and doesn't try harder to understand. Therefore, avoid suprising your audience as the hardest the brain works is at first in setting up the basic understanding of the story in the Establisher phase.

Will be interesting to try the different structures of stories:

Great takeaway: Simplicity creates a Great Impact all on it's own

#Data16 London - A Laguage for Visual Analysis - Jock MacKinlay keynote

In the 1980s Jock started his research in to data visualisation and lots of this is built in to Tableau. 

Visual Analysis uses the power of visual language and visual analysis to combine to be very powerful. 

Jock reading Orwell's 1984 gave him the idea that to protect yourself from Big Brother was to use Data. Playfair's Balance of Trade visualisation gave him the idea to build a programme to allow people to make these kind of charts. 

Bertin's 'Semiologie graphique' (translated in to English from French) became the spine of Jock's phd. The book focuses on the syntax. The main figure allowed Jock to understand what the key aspects to visual analysis were.

Selection: 

A. Association 

B. Selection

C. Order

D. Quantity

Jock joins Association and Selection in to 'Categorical' data The horizontal axis of size, value, texture, colour, orientation and shape allows you to make the association. The language aspect comes from taking visual components and their use is the equivalent of turing words in to sentences.

Bertins actually started out as a Cartographer and hand drew his maps. Understanding the visual system allowed him to make them as clear and impactful as possible. Bertin's built physical tables with rods to allow for sorting (Permiatations Matrices)

At Bell labs, Cleveland and McGill were scientifically researching human performance that validated Bertin's work. Here is there findings:

And Jock's combination of the two:

Jock managed to turn this thinking in to software. Here's a photo of the recording of his 'apt' tool.

Jock got to work at one of the top technology parks (Xerox Park) on the west coast. Dr Stuart Card developed the first mouse as a pointing device.

And some of the first developments the team made:

Human Visual analysis pipeline was created in the late 1990s and forms the basis of normal visual analysis now and how the tools are designed this flow. Tableau uses this flow that there is complex backtracking as the process isn't purely linear as you make analytical decisions:

When Pat Hanrahan transfered to Stanford, Jock got to spend time with Pat and Chris Stolte at Xerox Park. Pat turned Chris to Jock's dissertation. Jock's work was missing the link to databases and that was what really lead to Tableau. 

Show Me is Jock phd research in action. Here is the logic behind the scenes:

Jock's demo of wildlife strikes is great. He experienced a birdstrike that damaged the front radar and therefore his favourite dataset to demo became a lot more pertinent. 

The present - Research. Tableau has a strong research team to do fundamental research as it is really trying to understand a lot of visual language aspects. Here is some of the research that is currently happening. 

Humans and computers are different in their skills. Language makes Humans cooperative. Humans are intuitive, computers are not but they are great at diving deeply in to the data. A few researchers have dived more deeply in to this. The 30-40 categorical data fields can be analysed a lot faster than humans so could Show Me be developed to find the signal within the data? 

In organisations, we need to cooperate. This can be tough to do so can computers show who has what data more effectively? 

...more to come

#Data 16 - Brian Cox keynote

How data helps us study some of the biggest questions of our age?

The Theory of General Relativity shows how thinking about fundamental questions can lead to massive examples. Einstein challenged the way we think about gravity and the forces in the universe.

Freefall is a state of rest and a rewrite of Newton's laws. We see this in the International Space Station where everything stays still when released. It's why sitting is hard work because we are under force from gravity.

Einstein replaced force with geometry. Newton's law of attraction was replaced with Geometry (Space Time) - Einstein's Theory of General Relativity. Using Einstein's theory about gravity, you get the idea that the Universe is moving and therefore there was "a day without a yesterday" (Lemaitre). Using the equations therefore predicted what was in the universe before it was discovered and proven.

Huble taking pictures of Andromida in 1923 and understanding behaviours of types of stars proved there were other galaxies to our own. Huble went on to use 'Red Shift' to show the expansion of the universe in 1929.

This data is essentially wrong so he was wrong. The data was represented properly but his data was actually wrong (disproven with modern data)! Basically, Huble was measuring the galaxies were too close.

There are 350 billion galaxies in the observable galaxy. There seems to be patterns and therefore, what lead to that sort of pattern is one of the big unsolved problems. If you held a 5p coin to the sky, 75 feet away you would still get 10,000 galaxies in that tiny piece of sky. That means there is a lot of data to analyse.

Gravitational measurement of the stretching and the squashing of space /time. One detector is in Louisiana and one in Washington State. There was a detection made of distruption because of two large black holes merging. The actual event happened over 0.05 seconds. It went from 1/3rd to 2/3rds the speed of light in a 1/10th of a second. It left an object the mass of 60 times the mass of the sun. This is the triumph of Einstein's Theory. It's the first time that light wasn't used and gravitational waves that were.

The idea of the moon before we started exploring our Solar System was of a barren moon (due to our own). This is far from the case. The rings of Saturn are just 2 meters thick but 100km wide. One small moon of Saturn hass water and is very active. These water vents (pictured) is very much like our own start of life on Earth.

The Large Hadron Collider is producing huge amounts of data and is mimicing the initial collisons that were present at origin of the universe. Dr Brian Cox works on the Atlas detector. It captures Proton / Proton collisions and this actually captures a Higgs particle (Muons are the red lines)

And extra findings like (not statistically proven yet) but there are particles that are not currently recognised that could be dark matter or signs that additional dimensions actually exist. My mind is offically blown right now so apologies for the words after this part...

The 90 billion light years across can be measured back to 10 x-22 of a meter across. This distribution helps to predict with precision about the distribution of particles. The eternal inflation model shows that there could be multiple big bangs as part of a longer cycle so there could be an infinite number of universes. Welcome to the Fractal Multiverse!!

Everyone loves Maps - Andy Kemp

1. Why use maps?
A. I have geograhpic information
2. It's nice to see on the map
C. People ask for it
D. People question maps less*    *100% true according to Andy
Maps add context to the information presented.

When should you use a map? Basically when you start the question 'WHERE'?

2. Mapping Basics

Your data + Geocoding + Background Map gives you mapping in Tableau. 

You can tell Tableau to give a geographic role and if you aren’t using any geographic data, Tableau doesn’t do any geocoding to remove the performance overhead.

Cities with populations of 15,000 people or more have a stored longitude and latitude in the Tableau geocoding database.

Tableau now supports the same hotkey shortcuts as Google Maps and the other major mapping tools.

Pairing up dashboard actions with maps can be insanely powerful. I always forget how much context this adds to the dashboard. 

New definitions for unknown options: 1. Show at default positions - umm place a mark in the middle of the map. 2. Filter data - ahh, just get rid of them! Lovely descripitions. 

3. Next Level Techniques
A. You can add your own geocoding for data specific to your own organisation
B. Background images - floor plans, campus buildings, transportation maps etc

C. Mapbox - you can create your own background mapping (and horrendousness too!)
D. Don't forget about the power of WMS mapping. The Netherlands has a lot of the best free WMS services available worldwide.

Nice explanation of Polygon mapping too (to build Choropleth maps)

4. What's New?
A. 9.3 Support for polygon boundaries for postal code for 42 European countries. Restrict end user support
B. Coming soon - being able to put shape files straight in to Tableau
C. In 10 - being able to roll up from 5 digit postcodes to 4 digit postcodes to 3 digit etc

Openning Tableau Keynote - James Eiloart and Francois Ajenstat

People have come from all over the world to be here (thanks Fi Gordon for making your way from Oz).

Keynotes from Dr Brian Cox, Dr Jock MacKinlay and Maria Konnikova (author of the confidence game)

The opening slide of the conference maybe one of my favourites ever:

James Eiloart

Tableau is being used to solve more of the world’s most serious and largest problems. We are in an era of disruption where start-ups are challenging the large, status quo players.

2ns half of the 19^th Century – huge amount of disruptors. There were established players but new innovations were still challenging that status quo. The rules of the game and competition was very different

Nikola Tesla – developed ‘Alternating Current’ (and the start of a great band name – editor’s addition). He was a prolific inventor. In Croatia, he was inspired by Thomas Edison. They were quite the opposite in terms of characters. Tesla had OCD. Edison was disorganised and had quite a mean streak. 1884 Tesla travels to visit Edison. Edison is making a fortune out of selling ‘Direct Current’ so Edison was pretty protective of his idea. Edison offers Tesla $1m (in modern value) but when Tesla delivers the work, Edison refuses to pay up. Tesla gives up and takes a job digging ditches in Manhattan. Tesla gets backed by a bunch of investors and conceives a lot of remarkable innovations. The light bulb actually got discovered by Tesla. Edison pays for demonstrations of why Tesla’s innovations are so dangerous – ie electrocutes an elephant. At the World’s Fair 1893, Tesla demos the latest technologies and shows how safely AC can be by sending it through his body. 

There are 8 million doctors, 21 m teachers and 2m journalists so how can technology unlock the innovation of those professionals. 

Matt Francis (Welcome Sanger Trust), Henrik Falldin (Skanska) and Rob Radburn (Leicestershire County Council) are all using Tableau to allow data lead decisions to be made to develop genomics, architecture and empowering social workers (to name but a few). Tableau's job is to build the best analytical canvas to unleash that creativity. 

Francois Ajenstat
"Data is the electricity of the 21st Century"

The history of Tabluea
Started at Stanford as 'Polaris' a formal language to describe table based data visualisations. Polaris had three breakthough innovations: 1. VizQL - it allowed an infinate number of visulisations to be created. The VizQL language can be compiled in to a database query. 2. 

Francois shows the underlying VizQL and how simple it can be to build charts direclty using VizQL

Francois shows what has been added to Tableau since the last London conference. v9.3 gets the fastest adoption rate. He highlights the popularity of visualisations within Tableau Server.

Tableau 10 has 10,000 customers using the Beta at the moment. Completely new design, font and colours.



1. Any Data
A. New connectors to Google Sheets etc
B. Data Integration at the Row level from multiple sources. Just click on add on the top of the data connection pane in the data preparation window to get to it. Blending now look a lot more like a normal join.

C. Automatic Spreadsheet cleaning to improve the Data Connections
D. Wildcard unioning so you don't have to put everything together (Pattern based Union)

For Everyone
A. Tableau has K-Means clustering automatically built in. No phd required!
B. Data Highlighter - seeing your data in context. Dynamic search that shows up as highlighting within the product. It's like highlighting dashbaord actions on steriods!

C. Cross data source filters - you no longer need to set up the parameter to pass the where clause within the filter. 'Select all Related DataSources' on the filter.
D. Custom terriorities on the fly by selecting the group and removing the lower level of detail.E. Table Calculation dialogue box - 

Anywhere
A. Android app
B. Device Specific Dashbaords - Click 'Preview' to see how the device screen changes what Tableau shows. Click 'Add Layout' for when Tableau doesn't automtically resize the dashboard nicely. All under one URL.

C. Mobile Device Management (MDM) for enterprise
D. Deply server in more places: Back up from Windows and restore on Linux (not in v10) and Google Cloud Platform
E. Web Editing enhancements - Building dashboards within the browser including floating elements and dashboard actions. Formatting at the workbook level. Multiple data sources too.

Enterprise level new features
A. Version control for data sources
B. Subscribe other people
C. Better API management - getData() as an enhancement to the Javascript API. Add D3.js elements in to the webpage including adding in network diagrams
D. Extract data management - extract failure notification
E. Can favourite data sources on the server (and impact analysis coming in later versions just not 10.0)



Go to tableau.com/getbeta to get automatically added to the Beta programme. 

Why invest in yourself at the Conference?

Self-reflection is a great thing. Looking at yourself in the mirror and understanding what gaps you have in your skillset despite being considered good at what you do is really healthy. For all those Tableau Jedis and Ninjas, now is the time to do that… it’s conference season! Work out what those knowledge gaps are and hit the conference sessions that will fill those gaps (or canyons in some cases). 

Whenever I mention that I’m off to the Tableau conference, I normally get one response – “enjoy the party” and they are right, the partying is great. But attending the conference has some career benefits too.

1. Knowing what is coming soon

No Tableau Ninja worth their salt won’t have an eye to the future. Keeping your eye on what is getting voted up on the Ideas forum but seeing what is coming soon during the ‘Devs on Stage’ session at the conference is something else. Often, the features won’t be in the live version for some time but being able to explain what is coming can help your Clients or Colleagues plan for the future – do you need to invest in a different mobile reporting tool or will the app upgrade hit the spot. Is it time to polish up those Table Calculation skills or has Tableau just made life easier by redesigning their implementation.

2. Learning

Before I joined The Information Lab, the main learning opportunities I had was to attend the conferences to seek out new techniques, hints and tips. And those learning opportunities are everywhere. The Zen Masters’ sessions will show skills that are often a stretch for all but the other Zens but knowing what is possible will help you when you get that project that you’re not sure whether can be done in Tableau or not. The Product Consultants are also a great source of knowledge. They are likely to have spent the most time with some of the newer features so the Product Consultant sessions are great ones to attend. I think everyone still lives by Bethany Lyons Level of Detail sessions last year. The best thing is, what is learnt is often shared in snippets on Social Media.

3. What other companies are doing with Tableau

If you are a consultant, you get to see a lot of different organisations introduce and grow Tableau. This knowledge is valuable in understanding what to and what not to do. If you’re not a consultant or haven’t got the budget to stretch to getting some of our time, then hearing from customers’ experience is the next best thing. In my first Tableau conference (London 2013), I was lucky enough to get to present Barclays’ Tableau journey with Peter Gilks. Whilst it was fun reflecting on the work we had done, it was great to get feedback from the audience on what else we could look to do that they had found to be successful. I still get people come up to me and thank me and Peter for inspiring them to get behind their Tableau deployment (or give it a go) and love how much the hints and tips helped them to develop faster.

4. New Cities – new experiences – new friends

Getting to travel to the conferences gets you to see more of the world. Here’s Peter and I in New York before we headed Washington DC for the Global conference in 2014.

Little did either of us know that the global conference was going to be the chance to cement all the friendships we had started to form on social media. Data Geeks are often insular by nature but there is something about going and spending time with those who share the data visualisation passion that makes chatting to others easy. As you can see, we are a shy and retiring bunch.

  

So, what more could you want? Learning opportunities everywhere you turn, knowing what is coming next in your favourite data tool and the chance to meet some fabulous people. See you in London, Munich and Austin folks – come and say hello.