Consultant's Diary - Filter Customisation in Tableau

I'm going to try and share some of the techniques and methods I use as a consultant. Using Tableau & Alteryx daily means that I've found I take small techniques, tips & tricks for granted. Hopefully this will be useful for you all.

One of the things I love about Tableau is that I never stop learning new things. Recently, I was supporting my colleague Luke Stoughton teach Tableau to a group of university students - and I learned something about filters I'd never used or heard about.

When on a dashboard, there are a few methods to add interactivity - you are able to show different filters from your respective views/sheets, and apply them across multiple worksheets. Alternatively, and perhaps more obviously, you are able to use dashboard actions.

Reminder!

There are 3 kinds of dashboard action; Highlight, Filter and URL. These can be set to be triggered when the end user hovers, selects or 'clicks the menu' of the respective dashboard element. This can then be set across source sheets (ie. The sheet which you click) and target sheets (ie. The sheets which will filter upon these actions). A step further is to select the fields (dimension or measure) to filter.

So what did I not know?

When you create a filter a action, there is an arrow to right - which I've never paid much attention to. When you click this arrow, you are presented with a number of fields.

So let's take this really simple dashboard - Superstore Sales, a map and a bar.

There are two ways of creating actions - on a dashboard using the little funnel icon (only for filter though, with no customisation) or using the menu bar - Dashboard > Actions..

This then pops up a window, and if we select Filter, for example, this window pops up.

The arrow alongside the 'Name' field is similar to adding a <Parameter> field to your titles - it dynamically updates depending on the field. I'd never heard of this or even thought to use it before. In terms of user interface, what it can do - for example if you select your Filter action to be run on a "Menu" selection - it will update depending on the field you've selected. Here's an example:

This level of customisation can really help your visualisation be more user friendly and dynamic. A great example of this in the wild is Luke's Hygiene Rating visualisation (here: https://public.tableau.com/profile/luke.stoughton8750#!/vizhome/FoodHygieneRatings_0/FoodStandards). In this, Luke allows the user to click a URL action which changes depending on the establishment you select:

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The BBC Sport Chronicles: Part 3

Friends, I regret to inform you that BBC Sport is at it again. [http://www.bbc.co.uk/sport/football/43632176]

The above charming graphic is indeed proof that a bar chart, while the doyen of all charts, is not always the best choice. Let's talk about why.

What we see here is a comparison between Mohamed Salah and Kevin de Bruyne - easily the two best players in the Premier League this season. The chart attempts to depict Goals (a whole number), Assists (a whole number) and % shots on target (a percentage) on the same axis.

 Similar to the previous chart from BBC Sport which I deconstructed, this is ripe for a makeover.

A fellow data viz connisseur, Charlie Hutcheson, mentioned that while he 'could' read it, he wasn't sure he liked it. My thoughts were similar, if not erring on the stronger side of non-appreciation of this chart.

So let's go through my makeover steps.

Name	Value	Metric
Salah	37	Goals
Salah	10	Assists
Salah	62	Shots on Target %
Salah	38	Shots off Target %
de Bruyne	11	Goals
de Bruyne	20	Assists
de Bruyne	58	Shots on Target %
de Bruyne	42	Shots off Target %

I created my data table in Excel, and added an additional field called shots off target % - more on that later.

 

I then took this one a step further, creating a mini visual which is an alternative view to the currently published one.

Is it better? You decide. Have a go creating your own version!

 

My viz can be found here: https://public.tableau.com/profile/scribblr.42#!/vizhome/BBCChroniclesPartIII/FinalDashboard?publish=yes

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The BBC Sport Chronicles: Part 2

Just wanted to write a quick blog on the gift which keeps on giving, the BBC Sport data visualisation team.

Drawing from this article by Phil McNulty (http://www.bbc.co.uk/sport/football/43463551), there is a section where McNulty focusses on the conundrum around England goalkeepers, and who should wear the number 1 jersey at the World Cup.

And as part of this piece, was this wonderful bar chart;



The data is from opta, and each goalkeeper is grouped by the relevant number used. However;

1. How easy is it for me to compare the goalkeepers for clean sheets?
2. How easy is it for me to remember which colour bar refers to which metric?
3. What is the takeaway message here?

Of course, I then had a go at "re-vizzing" this, using the following data structure.

Metric	Player	Value
Appearance	Butland	27
Clean sheets	Butland	5
Goals conceded	Butland	49
Appearance	Hart	15
Clean sheets	Hart	3
Goals conceded	Hart	33
Appearance	Pickford	30
Clean sheets	Pickford	7
Goals conceded	Pickford	49
Appearance	Pope	27
Clean sheets	Pope	10
Goals conceded	Pope	22

Here's my process of revisualising this data.

The above changes are simple and help clear the purpose of the visualisation up - As well as helping the reader interpret the data more quickly and easily.

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Reflections: 2017

2017 is coming to a close, and I wanted to break my blogging hiatus to reflect on the last 12 months.

Working abroad

One of the best parts of the Data School was having the opportunity to not only work in the UK, but also abroad - and I was lucky enough to spend my final placement working between the UK and mainland Europe. The experience was really good fun, and the work exposure was awesome. I learnt about the trials and tribulations of working remotely, and across time zones - something I again was able to do later on in the year.


I also met these fun dudes while wandering around.

In fact, personally & professionally it’s been a fun year - I’ve visited 8 countries in total (OK, this list also includes Scotland…)

Uncharted territory

I joined the Information Lab permanently after finishing my 2 years as part of the Data School (but being a part of the Data School is forever, right?!) - and Carl Allchin (a great colleague, friend and mentor) encouraged me to shadow our Tableau Server guru, Jonathan McDonald. I’d had barely any experience of managing or administering a Tableau Server on my three placements, and while I knew some basic concepts, it was a whole new world to me. I’d thrown myself in the deep end with Alteryx Server on my final placement, and learned on the fly, but never had had the opportunity to do the same with Tableau.

Met some swans innit.

However, learning from JMac, I got more and more involved with Tableau Server - and I’m proud to say that between April and December, I pushed myself to pass both the Tableau Server Qualified Associate exam AND the Certified Professional exam. If you’d told me this on the 31st of December 2016, I probably would have laughed - Servers were a field and a concept I was alien to.

Marry this up with passing the Tableau Desktop Certified Professional exam, boom - I’m fully Tableau Certified! This is one of my biggest achievements of 2017.

Expeditions & conferences

I also was able to attend the Tableau Conference in Las Vegas this year, sandwiched between some travelling of the US - a bunch of us from DS1 road tripped across from Oakland to San Francisco, down to spend a few nights in Yosemite before driving through Death Valley to Las Vegas. The trip ended after the conference, where we spent some time around the Grand Canyon.

It was stunningly beautiful and super fun - and the Conference did not disappoint. Meeting & conversing with a vast range of people enthusiastic with Tableau was awesome, and developing my own knowledge was incredibly useful. My highlight, though? Meeting and speaking with Devs. Simply awesome.

So what’s next?

I’m never one to goal set across a long period of time - because both personal and professional life is fluid - but guidelines can be fun. And my only guideline to myself is to continually reinvent & develop my own skillset and knowledge. And of course, have a ton of fun on the way.


I hope to pick up my blogging routine and branch out across other media - and most importantly share more about my ideas for Tableau, for Alteryx and as much as I can in this space (with some room for sports analytics, of course)

I hope you, Reader, the very best health, wealth and happiness for 2018!

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Why your understanding of ‘elite mentality’ is false, and how data can help solve this problem.

In recent years, there’s been a rise in the usage of the term ‘elite mentality’ - particularly in the world of football (soccer). The application, in the way the term is used, is to describe someone who is a good player, but not ‘elite’ because they don’t have a winning mentality. This is measured by the number of finals they’ve won, and medals they have etc etc yada yada yada.

I disagree.
Like big time.

Cavani, Higuain, the Argentinian national team, Tottenham Hotspur… often are often all called bottlers who don’t have this “elite mentality.” Players who aren’t good enough; aren’t champions; aren’t

Poppycock.

Sport is about winning, ultimately. But sport is wonderful, beautiful and engrosses billions. And for many, an “elite mentality” is where a player, individual or team are able to win regularly, consistently and in style.

However - If an elite mentality exists, in my eyes, and perhaps even in a coach or even player/ex-player, it doesn’t come from what you win. It comes from how you grow through your career.

Goal Setting

[‘Big Brother’ surveillance in elite sport is pushing a culture with a machine mentality](http://theconversation.com/big-brother-surveillance-in-elite-sport-is-pushing-a-culture-with-a-machine-mentality-34214)



The above article talks about how surveillance is focussing on tiny flaws to ridicule managers. This is of course a by-product of any data - that with the right context or framing, it can be used to push a certain narrative.

But data can be incredibly useful - particularly when it can enable sports professionals to begin to quantify the unquantifiable - which in turns adds another layer of information to consider when diving into player or team analysis.

Let’s take an example.

I am a coach of a professional football team’s academy, and I have 6 players I’ve earmarked for progression into the first-team. But all have flaws.

-> The striker attempts to dribble for as long as possible and usually ends up being dispossessed due to trying flair moves
-> The wide midfielder in my academy enjoys cutting inside and shooting speculatively, rather than looking for the pass.
-> The central defender always thumps it clear, rather than starting an attack.

All of the above can be coached, but with the emergence of data, this progress can be tracked to see which of these players possesses the mentality & hunger to continually progress.

By this, I mean a player who not only is looking for coaches to tell him his strengths and weaknesses, but is one who will consistently and constantly look to improve.

Data can be combined with human coaching to show a player situations where they should change their decision making process, and even for coaches highlight areas where approaches can be gamified in training sessions.

Get the first player to pass at least twice and show him the value he can bring when 1-on-1 with more space to exploit.
Coach the second player to think twice before letting fly with the rocket, with a ‘points’ related coach (merits for ‘good’ locations, demerits for ‘less good’)
Work with the third player to improve their own the ball confidence and technical ability.

Talent Identification (TID) is the intricate art of wanting to use data to help highlight the right time to bring a player through the ‘funnel’ to first team football. Based on precedent, it’s then possible to build an understanding of what key factors are in tagging development, and how to best nurture talent.

Harry Kane

The strange rise and rise of Harry Kane was highlighted by the contrasting views - on one hand, you had the Kane hype train which was highlighting the second coming of a true world class striker; the others were saying it was a freak season due to the previous ‘failings’ of the striker on loan spells at Norwich, Leyton Orient and Leicester.



But amongst these loan spells, coaches spotted something in his game which they wanted to nurture. If you recall, Kane often started as a CAM, as well as a CF when he came through the academy - which is strange now looking back given his propensity to be a baller when it comes to finishing chances.

How can we find players with Kane’s mentality? Who have a hunger to develop, and which cannot be spotted in their on-field outputs, but simply from their responses to coaching, viewing how they apply recommended changes and their drive to improve.

What can clubs do now?

A super duper smart analytical approach will combine the expertise, experience and nous of a coach/scout with the vast amounts of data being collected in order to learn to be smarter with decision making.

Are there markers to help spot talent?

Can training session events be logged, and used in tandem with on-pitch output to track progress toward goals?



How can the qualitative match the quantitative to improve conversations had internally? Can this ensure a club doesn’t pay over the odds for an average player by simply bringing a existing expertise together with existing data?

The dream, as they say, could be getting to a level of data and competence where key markers are used to time purchases of players to capture the upward peak of their career.

Now that would be elite.

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Visualisations in the Wild - Sky Sports can do better

5 days ago, Sky Sports posted this image on Twitter. Among the soccer/football analytics community, this was excellent to see given the usage of Sky Sports of Expected Goals - a metric which has been actively pushed amomg bloggers, and indeed also used within clubs.

While enthused by the progess, I was less than excited by the visualisation which Sky Sports used to display this metric, among others. It took a while to compute what was going on. Let's break it down…

http://www.skysports.com/transfer/news/12691/10939046/alexandre-lacazettes-scoring-stats-compared-to-arsenal-team-mates

So the above graphic's objective is to investigate Arsene Wenger's claim that Lacazette is a good finisher. So it compares 3 metrics, and minutes played as a contextual figure among 7 of Arsenal's forwards.

Now, I'm a fan of donut charts (well, two segment donuts with a number in the middle as shown here) - however, the way it's used above is misleading. Let me explain why. It took me a while to compute, but the 'blue' full donuts are highlighting the highest among the 7 players, and the 'completed' segment of the donut (ie. The red part) how close this is to the highest in that metric.

While it took a while for me to understand that, it's not overly intuitive. I saw a really nice summary from  Steve Wexlar which said "The goal for your viz: allow the largest number of people to get the greatest degree of understanding with a minimum amount of effort." - Given that it took longer than expected to understand the chart, I don't think this is the optimal chart for the message that Sky Sports want to convey.

So what can be done?

After a couple of tweets with Mark (@everyteam_mark) and Danny (@DannyPage), I hopped into Tableau and built out the following examples as alternatives to the visualisation.

The most basic ways I thought this could be re-done was using dots and bars. The bar chart is the most intuitive way of viewing data as the length is easy to compare - however, it's deathly boring and super functional. A dot plot is similar to a bar, but instead of encoding length, it uses position along an axis. Instead of an axis here, I've used labels so it's easier to attribute a value to the mark.

An immediate alternative which came to mind was a bump chart - this is effectively a way rank a player along the four metrics. However, this does mean that the detail is lost within the raw difference between each player as it only highlights the order and ranking. It does, however show that for all the metrics, that Lacazette is second - which supports the claim the article was making - but as mentioned before, the detail is lost.

My personal favourite alternative was this one - it effectively pivotes the dot plot above, but as (at least in the western world) we read left to right, it's easier to compare the position of the dots, and Tableau also allows us to highlight two players at once. It also shows rank, comparison and also raw difference between the players.

These are just a few examples of how I'd quickly do a makeover of what Sky Sports showed - I also really liked how Simon (@analytic_footy) showed an alternative to the bump chart, highlighting Lacazette in red, with the others greyed out; which was a great touch and added some great context. I am definitely a fan.

How would you visualise this data?

Find it below, and have a go - and let me know how you get on!

Player	Mins played	Shot conversion %	Goals per 90 (exc. Pens)	xG per 90 (exc. Pens)
Lacazette	2,408	33.3%	0.67	0.47
Sanchez	3,223	18.6%	0.61	0.42
Giroud	1,194	30.8%	0.9	0.51
Walcott	1,925	16.1%	0.47	0.36
Welbeck	746	10.5%	0.24	0.38
Perez	265	11.1%	0.34	0.27
Iwobi	1,462	7.69%	0.18	0.18

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Hey, where'd that 'Style Guides' series go?

I've been super lax about blogging over the last year or so.

A combination of work, life and learning have overtaken the many series' I planned to follow over the last 12 months. Even my quantified self series fell off after a couple of months as I kept missing my deadlines - making time for this development is crucial, which I will endeavour to change.

I have, however been doing a lot of thinking.

And this thinking has been around the data field as a whole, as well as the space which Tableau & Alteryx sit in - the self-service, data to the people kind of space. And that's been a driver in why my series on style guides did indeed fall off the wagon, or get put on a back burner.

OK - explain.

The idea behind the series was simple. Talk about how and why enterprise organisations & general corporations use & create style guides, and discuss further how this can translate into Tableau - with the audience being anyone who was looking to scale out Tableau Desktop and make it the norm.

Now the motivations were sound - but I've been reflecting and reading a lot into a lot of Tableau's strategy, mission & of course, their awesome Drive methodology (it's a really good read for anyone in this space, in my opinion.)

When speaking at the Keynote for Tableau Conference 2014, Christian Chabot talked about innovation, creativity and empowering the users. In this keynote, Chabot talks about the great painters, that da Vinci didn't create his work by filling out a painting request form, or that Jackson Pollock didn't go round and show people how to splatter paint etc. He also mentions that traditional BI software was full of templates and wizards, but in reality insight comes from a creative flow. 

So now I'm in two minds.

I completely agree with Christian - the creative and iterative flow is fantastic; but I also think that it's best when the user has a good solid base of what they should be looking to emulate. So does a style guide help, or restrict this? Whilst on my second placement, I had regular (and occasional heated) debates about this with my colleague Jules - who thought that restriction of any form defeated the object of Tableau. My thinking was, and to an extent still is, that Tableau shouldn't be shackled, but some direction (be it through colour palettes, icons/shapes which are custom for the company, fonts etc) should be shared.

But what about layouts?

One of my first blogs in this series was about homogenising style. Indeed, that's what the series should be about right? And when I look back at it, again I'm in two minds. On one hand, a unified approach to report building has always been the norm, and makes sense due to the familiarity and instance recognition. On the other - don't we want to disrupt this with a new way of thinking?

Is there room for both?

For a while I was working with a client helping build a Centre of Excellence - and during my research (which lead me to Tableau Drive.. seriously, if you want a great manual for enterprise strategy in this space, do not look any further) I found that style guides aren't that prevalent - but examples are. 

A few of the CoE leaders I spoke to mentioned a 'Gallery' which effectively sounded a bit like Tableau Public, but for business/company specific dashboards. A user who was looking for layout, theme or general inspiration would be able to log onto a project on a Tableau Server and see what was going on.

So that's where I am - In two minds about whether a series on data visualisation style guides is useful, or whether it's something which is best in a hybrid approach with the worry being that a hard and fast rule would encroach upon Tableau's beliefs.

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Long-form Tableau Visualisations - The New Normal?

I've never made it a secret. I'm by and large against the principle of long-form visualisations.

My reasoning has been that while they're very useful to simply scroll through as an image on a mobile, there's a lot to consume when viewed in browser.

The hashtag #saynotolongform which I've sporadically used is designed to be provocative - There are some excellent examples of long & thin dashboards. But for me, this is sparking a real change in the way that the construction and consumption of this format - particularly on the side of a designer. And increasingly it's "data visualisation" which has taken the back seat with a more 'poster' style of dashboard taking the lead.

Edward Tufte talked about the data-ink ratio, but perhaps a new phenomenon with the rise of long form visualisations in Tableau is the data to image ratio. 

Lots of logos, images, shapes, custom lines, fancy borders/background images are married to stunning colours to create some simply gorgeous visualisations. But a lot of the information ends up being hidden in text; either with BIG STATEMENTS, large numbers or just integrated in descriptions.

The visual analysis which Tableau champions, where bodies of text and tables are replaced by the 'right visualisation for the message' - is this slowly disappearing in the public space, in favour of eye candy?

Money where my mouth is

So in lieu in finding out (if you excuse the phrase) how the other half live, for the Iron Viz Europe feeder contest I actively decided I wanted to create a long-form visualisation. And perhaps it showed why I wasn't a fan, as I really didn't enjoy the experience of building one. I've also sometimes found that some long-form visualisations are designed in such a way that when scrolling, I can forget or lose the underlying message - or worse, the story or the message that I thought a visualisation was telling is not the one the author is allegedly attempting to convey.

I feel as though, if Tableau are going to feature and focus upon long form visualisations in the public sphere, perhaps there should be an effort to also make it easier. This said, there's also scope I think for some developments on dashboarding in general, and updating this to fit the use cases people use Tableau for - both as part of their day-to-day jobs as well as for data hobbyism.

The interesting thing is that long-form seems to be something which has ramped up in the last 12-18 months - perhaps even coinciding with the 'Device Specific Dashboarding' feature which dropped along with Tableau 10.0 - And while I can see it's benefit for a mobile viz, I'd even go as far to say that I am yet to see a fantastic mobile dashboard, with rich amounts of analysis, insight & a clear story (And please, if you're reading this post, do send me your favourite long-form vizzes, and why you like them!)

But designing for the medium of consumption is something which is absolutely must be considered. This is where I feel that 'dashboard design' in itself, is not a skill. It's a part of a wider collection, and I think we as a community should stop using it as a blanket term for specific data visualisation decisions made with purpose. The current connotations just seem to be pure 'design' related, than about good dashboarding, especially when it comes to different approaches to storytelling.

Championing Long-Form

I think the difficult part of the Iron Viz contests is looking beyond the aesthetics of a visualisation (by this I mean using a lot of colour, text & images to assist/add context to the data) and focussing on analysis. This is where I think the data to image ratio comes into play - tables of data have now been replaced with text, large bold statements & other image-based clutter.

Indeed, design comes as one of the 4 sections that's being measured by the Tableau Public team - Design, Storytelling, Analysis & Overall Appeal being the categories - but it seems as though there's been a shift, or even a skew by the dashboard designers in the Community to use long-form to emphasise the design aspect, perhaps even more than the data visualisation.

Why is this?

In my mind, the reason for this is essentially because of the 'storytelling' section of Iron Viz. With over 60 entries for the first Iron Viz feeder, in order to stand out it can almost become a pre-requisite to create a visualisation which has a lot going on. By this I mean a dashboard which follows the 'flow' of a story, which each 'section' getting small nuggets of analysis.

Indeed, while browsing through the entries this morning, I noticed a few which had 2 charts on the dashboard. While I understand & am an active fan of 1-chart dashboards, my view is that the annotation layer (this post is great on this topic) should drive the storytelling.

What is the limit? When does it become a problem to have more images & headers over insight into datasets?

From 2 charts/sheets/views to 10-15 on another entry - is that an overload? Whereby in order to tell a compelling story, multiple charts need to be used just to 'add' more data visualisations, and have something to support the text?

Clutter is still the devil.
If 'dashboard design' IS a skill, then where does the ability to hold a narrative and not overly clutter come in? This brevity and ability to be concise is perhaps even more important than having a beautiful, colourful visualisation.

Perhaps this is an indicator to uphold a strong narrative the 'one screen' which I read - but my question is - is adding this level of 'scrollytelling' taking 'one screen' a bit too far? Should each segment of analysis be done using tabs instead? (Not story points, as I think this feature needs a bit of work - but that's for another post!)

The right tool?

One of the most brilliant things about Tableau is the simplicity to create something - it's quick, intuitive & once you 'get' the software, there's a lot more depth you're able to bring in. However, I've seen a few members of the Tableau community start dabbling with d3.js - which I think does scrollytelling correctly; the marriage of text and visualisation works well when it's integrated in the user experience - the first chart on this visualisation is a case & point of that.

So what's next?

I absolutely don't think that this trend will slow down at all.

The process of designing something 'beautiful' is inherently linked with long-form visualisations, because of the real-estate given to add a whole host of elements. However, this also means the shift away from some of what I believe to be core Tableau concepts - the championing of data visualisation, the ability to have some - but not total - design control.

What I ultimately want to do here is start a conversation. Given the statements and assertions I've made above, I want to hear more about what the community think - particularly on where this is going & whether it's something which should be discussed more.

As always, I welcome all thoughts, feedback & challenges - Comment, or get in touch! I would love to chase this down the rabbit hole further.

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The Data School: 2 years on

This post was originally published on The Information Lab: https://www.theinformationlab.co.uk/2017/06/23/data-school-2-years/

On the 23rd of June 2015, there was a spark in Central London.

And two years later, this spark is now a burning flame, which has been a roaring success.

I am, of course, talking about the Data School.

An idea started by Tom Brown, Craig Bloodworth and (perhaps the most important piece of the jigsaw) Andy Kriebel ended up in eight complete strangers taking a leap of faith and joining an immersion programme like no other. It promised four months of training in two market-leading data analysis applications, followed by 18 months of consulting at clients of the Information Lab.

And it worked.

It really, really worked.

Quantify This.

5 cohorts trained (DS6 are currently in training)

702 blog posts

1238 Tableau Public vizzes

18 Viz of the Days

4 Tableau Ambassadors

The Experience

For me, coming out of university, I had no idea of the size and impact that the Data School would have. In fact, Tableau was simply a tool which made my soccer charts look better, and made them more dynamic. Anything more than that (I thought at the time) involved some crazy coding - so I endeavoured to up-skill in the summer after university anyway, but in the end I was lucky enough to be offered a job with The Information Lab.

Between the numerous client projects, the public presentations (I've now done 7 public presentations - 3 webinars and 4 in person live) and the amazing expansion of my personal and professional networks, the opportunity to work and consult with the Information Lab has been great. My three placements were at BCG, Deloitte and Novartis (who are based in Switzerland!), and each experience lent itself to another learning experience.

At BCG, I built, ran, and developed training materials whilst balancing case work where we supported consultants. At Deloitte, I experienced the breakneck speed of being part of a consulting team with hard deadlines, iterating on the fly. At Novartis, I learned about strategically building out a Centre of Excellence, and supporting the development of enterprise scale rollouts of both Tableau and Alteryx, as well as the challenges that come with that.

And alongside all of this, the continued personal development, time management and extra-curricular activities have been paramount to getting me where I am and making me who I am today.

The best part? From day 1 with the team, I was assigned Brian Prestidge as my mentor - a true gentleman who spent nine years working in football using Tableau and Alteryx. The soft skills, application, and anecdotes from him were fantastic, and the opportunities which came from sharing my interests also helped my development. I attended two OptaPro forums, and developed my own style of sports dashboards which are both informative, beautiful, and applicable to different audiences, and of course, who can forget the Data School's #TeamIago who attended Hack MCFC.

Now.

Since finishing the Data School, I was lucky enough to be offered the opportunity to join The Information Lab as a core consultant. How has this changed what I do day to day?
In all honesty? Not all that much. I'm still consulting with clients, but instead of one client for six months, I'm working with three clients in a week, six in a month etc. It's a really fast-paced role, and fortunately there's still the opportunity and time for more learning. In fact, I think I've learned so much in the last couple of months, purely given the wide breadth of clients I've worked with, as well as having the time allowed to reflect and develop.

My final thoughts are this; in June 2015, along with eight strangers I embarked on a two-year journey.

At the end, I've gained friends, colleagues, and the job of a lifetime - and it's just the start.
A particular highlight is where I found myself, in the middle of July 2015 sitting working on a client project, or a dashboard and thinking - hey, three months ago, I was doing this for fun.

Now it's my career.

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The Data School; 2 years on.

On the 23rd of June 2015, there was a spark in Central London.

And two years later, this spark is now a burning flame, which has been a roaring success.

I am, of course, talking about the Data School.

An idea started by Tom Brown, Craig Bloodworth and (perhaps the most important piece of the jigsaw) Andy Kriebel ended up in eight complete strangers taking a leap of faith and joining an immersion programme like no other. It promised four months of training in two market-leading data analysis applications, followed by 18 months of consulting at clients of the Information Lab.
And it worked.
It really, really worked.

Quantify This.

5 cohorts trained (DS6 are currently in training)

702 blog posts

1238 Tableau Public vizzes

18 Viz of the Days

4 Tableau Ambassadors

The Experience

For me, coming out of university, I had no idea of the size and impact that the Data School would have. In fact, Tableau was simply a tool which made my soccer charts look better, and made them more dynamic. Anything more than that (I thought at the time) involved some crazy coding - so I endeavoured to up-skill in the summer after university anyway, but in the end I was lucky enough to be offered a job with The Information Lab.
Between the numerous client projects, the public presentations (I've now done 7 public presentations - 3 webinars and 4 in person live) and the amazing expansion of my personal and professional networks, the opportunity to work and consult with the Information Lab has been great. My three placements were at BCG, Deloitte and Novartis (who are based in Switzerland!), and each experience lent itself to another learning experience.
At BCG, I built, ran, and developed training materials whilst balancing case work where we supported consultants. At Deloitte, I experienced the breakneck speed of being part of a consulting team with hard deadlines, iterating on the fly. At Novartis, I learned about strategically building out a Centre of Excellence, and supporting the development of enterprise scale rollouts of both Tableau and Alteryx, as well as the challenges that come with that.
And alongside all of this, the continued personal development, time management and extra-curricular activities have been paramount to getting me where I am and making me who I am today.
The best part? From day 1 with the team, I was assigned Brian Prestidge as my mentor - a true gentleman who spent nine years working in football using Tableau and Alteryx. The soft skills, application, and anecdotes from him were fantastic, and the opportunities which came from sharing my interests also helped my development. I attended two OptaPro forums, and developed my own style of sports dashboards which are both informative, beautiful, and applicable to different audiences, and of course, who can forget the Data School's #TeamIago who attended Hack MCFC.

Now.

Since finishing the Data School, I was lucky enough to be offered the opportunity to join The Information Lab as a core consultant. How has this changed what I do day to day?
In all honesty? Not all that much. I'm still consulting with clients, but instead of one client for six months, I'm working with three clients in a week, six in a month etc. It's a really fast-paced role, and fortunately there's still the opportunity and time for more learning. In fact, I think I've learned so much in the last couple of months, purely given the wide breadth of clients I've worked with, as well as having the time allowed to reflect and develop.
My final thoughts are this; in June 2015, along with eight strangers I embarked on a two-year journey.
At the end, I've gained friends, colleagues, and the job of a lifetime - and it's just the start.
A particular highlight is where I found myself, in the middle of July 2015 sitting working on a client project, or a dashboard and thinking - hey, three months ago, I was doing this for fun.
Now it's my career.

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