The design process behind ‘The pursuit of faster’

Last Friday I posted my new data visualisation project titled ‘The Pursuit of Faster‘. This was a project submitted for the Olympics contest.

This visualisation project explores the evolution of male and female medal winning performances across all Olympic Games since 1896. It portrays the patterns of improvements in the results of time-based events where speed is the measure of success, whether it be on foot (track athletics), in water (swimming) or on water (rowing and canoeing). By choosing a sport and selecting an event you can see how Gold, Silver and Bronze winning times have changed over the years, for both men and women, as they strive for that ultimate pursuit of being faster than the rest.

In this follow-up post I wanted to explain more about the design process and decisions behind this project.

Purpose and Parameters

The background to the project is framed by the contest and the opportunity to collaborate with a good friend of mine, Andrew Witherley, who is a skilled programmer and somebody with whom I have worked for a number of years in the past. We’ve been looking for an opportunity to work on a piece together in a non-client environment and so this was ideal.

The contest’s brief was very open – “We challenge you to use data and design to visualize the Olympics, helping us understand and enjoy as we watch” – and so this gave us a blank canvas to decide on the data story we wished to focus on.

There were a number of ideas flying around but one of the most fascinating (and achievable) directions surrounded the curiosity of how much faster people are these days compared to athletes down the years. How have enhancements in coaching, technology, sports science, training and physique influenced the improvement in performances?

And so the initial purpose of this project was to create an elegant, exploratory interface that allows users to learn about how medal results for different speed-based Olympic sporting events have improved across every summer games in which they have been held.

In terms of the influencing parameters surrounding the project, the main factor was the timescales for the contest. Whilst it was open for about 5 weeks, we really only had chance to work on things for the latter two weeks and even then it was rather irregular. The intended target audience would really be anyone with an interest in the Olympics but specifically those with a more analytical slant looking to learn something new rather than just digesting eye-candy – after all the Games would be (and has been) flooded with so many visualisation and infographics pieces.


Acquisition – With a clear idea of what we required, the best source of athlete, result and event based data was found on the excellent databaseOlympics, which has huge repository of data for all Olympic Games from 1896 to 2008. To extract the data we imported it from the source URL using Google Spreadsheet and the formula ‘=ImportHtml(“”; “table”;0)’, repeating for each year and sport.

We extracted data for all sports that involved time-based measurements for the results, rather than those that are based on points, distance measurements or tournament style contests. This narrowed down the sports to: Track Athletics, Canoeing, Cycling, Rowing, Swimming and Triathlon.

Examination – An inspection of the data revealed a few issues that we would have to deal with early. The different sports and the events within each sport have changed over the years so there were some inconsistent datasets. We decided to eliminate any event that wasn’t held in 2008 and those that have been held fewer than on three occasions (as there wouldn’t be much comparable data to work with).

Also, there were pockets of records for which no results were recorded or the results were estimated and a few that seemed questionable in accuracy (perhaps input errors of wrong times or years). Another issue was the challenge of handling time data (especially formats) which always seems to bring so much baggage with it as it moves from  one tool to the next. This was particularly a problem with the problem of dealing with the date 1896 in Excel and Tableau (I understand this can be resolved but it did cause a problem initially). A further problem, which materialised too late to undo, was the failure of Google’s Spreadsheet import routine to cope with non-standard text characters such as á or ö in many athletes names.

Transforming Data – a certain amount of data cleaning was undertaken to resolve some of the quality issues identified above, except for, at this stage, the athlete name problems (which affected 400+ records) – due to the time restrictions it was decided that this would be tackled later if possible.

Further transformation was undertaken to put the data into shape fit for analysis and fit for connection to the web. This included forming a calculated field of the result time in seconds to facilitate the creation of an ‘index’ which would reveal the % improvements across an event based on changes compared to the slowest result. This index therefore enabled comparison within and between events and sports. Team-based events (with multiple medal winners) were combined in to a single record.

After all the data treatment we were left with just four sports: Athletics, Canoeing, Rowing and Swimming.

Consolidation – We decided not to acquire any other data but we did seek to consolidate our raw material with a bank of images for country flags and event posters, which would be intended for use in the final design.

Visual Analysis – This was conducted using Tableau to familiarise with the range and distribution of values for each event/sport, understand design factors such as axis parameters, learn about which events were for both genders/just one and generally learn about the data in a more visual setting.

There were a few curious results (such as the men’s 3000m Steeplechase) which, after investigation, were down to inconsistent distances being run in some of the earliest events. The most confusing data surrounded some of the Canoeing events which revealed time inconsistencies around the 2000 Sydney games (much slower) but there were no apparent answers for this so we decided to leave them unchanged.

We were now very clear about the physical properties of the data we had to work and an early concept for how each variable would be deployed was soon apparent: Year (axis), Country (for analysis and detail for annotation), Sport (selection), Event (selection), Gender (filter), Athlete (detail for annotation), Result Time (axis, detail for annotation), Medal (data marker, series, filter).

Data Stories

There were several different ways of slicing our analysis and visualising the data on this subject so we needed to demonstrate a clear editorial focus. Some of the best visualisations are based on the type of questions that might pop up in a pub conversation between friends.

We wanted to create an exploratory tool that allowed users to pursue queries such as “I wonder how much faster the 100M medallists are now compared to the past?” and be able to find out further information about all the relevant events, how the patterns compare for the different medals, what % improvement in time has been experienced, which events have improved the most and the least, who were the athletes who won the medals etc.

Additionally, we wanted to facilitate comparison between the progress of men and women: is one improving more than the other? Whilst we knew from the data that women were slower compared to men across the events, were they showing evidence of getting closer to men in any event?

Design Concepting

Data representation – The data representation was intentionally straightforward, presented using the visual variable of position on a common scale to maximise the clarity of the visualisation. The markers would resemble medals (albeit transparent) and coloured accordingly, then each point would be joined into a line chart to show the pattern and progress of improvement. Below are a couple of the early concept sketches.

The x-axis would be the years of the Olympics games and the vertical y-axis would be the result times. The main decision made here involved the plotting of smaller values towards the top. Whilst this is counter-intuitive for most displays of line charts, actually for issues of speed it is more natural to think of better as being higher. Metaphorically it also ‘feels right’ that the faster results appear to reach the x-axis before the slower ones, as if you were looking at the finish line.

We packed the results into a frame based on a dynamic axis which just had a buffer of 1% above and below the fastest and slowest results.We experimented with different medal sizes and line widths to get a balance between visibility and resolution of the data.

There was a technical barrier differentiating in marker shape between male and female results but because the times were always so different (and women’s results consistently slower than men’s) this didn’t prove a problem ultimately.

As well as the data on the line chart, we wanted to include details of the races for users to lookup in a pop-up box and provide a range of summary statistics to form some deeper analysis of the events and across the sports:

Colour and background – The colour scheme was designed to be a very limited palette, with little or no decoration. The obvious temptation for an Olympics piece is to follow the colour of the five rings but this didn’t mean anything of worth for this design. Most of the background is pure white and the fonts and chart apparatus are subtle grey. Initially a concept for the title was created based on a montage of all the Olympic Games’ posters but this proved to be unnecessarily distracting.

The colours of the medals were used for the line/medal markers on the main chart and a non-patronising (the lazy use of blue vs. pink) palette of dark blue and orange was chosen for the summary stats to differentiate genders. The grey-shaded panels just helped to frame the individual charts without battling for attention with the data.

Interactivity – The main interactive features involved allowing the user to change their sport, event and data selection, with toggle filters for medals and genders. Additionally, a hover (athlete, result) and click (pop-up of race details) event was programmed for interacting with the main chart. Here’s an early technical version to get the concept working initially:

In an ideal world, with extra time, we intended to create a sort button to switch between ‘by year’ and ‘by fastest time’ view and we would have liked to have incorporated a visual ‘ruler’ when you hovered over a marker to create a horizontal line allowing you to see the faster and slower results easier.

The summary statistics and a view of the raw data were included using a neat concertina device to show/hide these extra elements.

Arrangement – The main decisions around arranging the design involved whether to go for a portrait (years on the Y, times on the X) or landscape layout (years on the X, times on the Y). We correctly (in my opinion) settled for the latter. We also had to decide where to position the selection tabs and filters, eventually placing them across the top. All this would be packed into a central web page dimension of 960px wide to maximise screen compatibility.

As mentioned above, gridlines, tickmarks, axes and axis labels, borders and tabs were all reduced as much as possible in terms of their visual prominence.

Annotation – The annotation was important to maximise the user assistance. We included a brief introduction to allow people to get on with using the visualisation but then included a further ‘about’ page with much more detail about the function, intention and background to the project. The font throughout was ‘Myriad Web Pro’ apart from the title which was created using ‘Kozuka Gothic Pro’.

Construction and Evaluation

In terms of the tools used to construct this piece:

Aside from the time pressures, in terms of judging ourselves when we felt it was completed we established the following criteria to measure our own satisfaction:

We felt we had achieved all these reasonably well. Then we considered the criteria set out by the contest:

Instinctively, once again, we feel we will have delivered on the first (important) issue of understanding. Originality in this piece isn’t particularly achievable because the visualisation method (ie. essentially a line chart) is the best way of conveying our data story, and so any alternative approach would have potential lost this message. The style is really a personal judgment but I think this conveys a certain elegance through its clarity.

‘Partly Cloudy’ weather app

Last month I profiled a project titled ‘Energy technologies visualisation for the IEA‘. Well, the Raureif folks have been busy and have just this week launched a stunning new data visualisation design in the form of a weather app for the iPhone called ‘Partly Cloudy‘.

Partly Cloudy’s visualisation shows a weather forecast in the form of a radial display like an analog clock. The weather is portrayed using color, rather than via lists of digits and icons which allows you to sense an entire day’s temperature patterns at-a-glance. Extra layers of detail are represented by a blue pattern from the centre indicating the forecasted rain fall and there is also a pattern for the expected wind strength.

You can modify the view to show options for 12 hour, 24 hour and 7 day views but the real beauty is the rotating slider as it reveals new details of the weather forecast coming up.

Partly Cloud is not the first venture in to the iPhone App market for Raureif’s, they have also developed the Virtual Water project and the EcoChallenge app.

New visualisation design project: The pursuit of faster

After a frantic couple of weeks I have managed to submit an entry on time for the latest data visualisation contest, this time around the London 2012 Olympics. This is an exploratory joint submission in collaboration with a friend of mine, a talented young chap by the name of Andrew Witherley, of whom I hope you’ll be hearing more of in the future.

Entitled ‘The Pursuit of Faster‘, this design project explores the evolution of male and female medal winning performances across all Olympic Games since 1896.

It portrays the patterns of improvements in the results of time-based events where speed is the measure of success, whether it be on foot (track athletics), in water (swimming) or on water (rowing and canoeing). By choosing a sport and selecting an event you can see how Gold, Silver and Bronze winning times have changed over the years, for both men and women, as they strive for that ultimate pursuit of being faster than the rest.

You can also access a range of summary analysis and visuals to compare the % improvements of medal-winning times, see which events have witnessed the largest improvements and how these patterns compare between men and women, which countries have won most medals for each event and what the largest winning margins were.

The purpose of this visualisation is to consider how enhancements in coaching, technology, fitness, training, physique and sports science in general have influenced the improvements in performances? How much closer are women to reaching the performance levels of men? How much faster do the trends suggest athletes can go?

I will be posting some more detailed narrative and explanation to go alongside this project, but in the meantime you can explore it here.

Monitoring live energy usage at London’s Olympic landmarks

Arriving in time for the start of the Olympics today, Power the Games Live is a new development from Applied Works for the official energy provider EDF, which draws from many thousands of data records from a multitude of measuring devices to create a transparent and accessible display of energy usage during the games.

Using a monitoring dashboard called ‘Visi‘, this is a real-time tool that visualises energy consumption at the key Olympic venues including the Olympic Park, the Acquatics Centre, Velodrome, Basketball Arena, the London Eye and London Bridge and see the current energy consumption in real-time.

You can modify the display for daily, weekly, monthly and annual usage and also overlay the usage with contextual information such as the outside temperature, rainfall and daylight/competition hours. Notice the subtle design of a darker background for night time hours.

During the Games, Visi will be on show in the EDF Pavilion at the Olympic Park. The Visi product will remain at the venues after the Games to enable future energy managers to understand and reduce electricity consumption at the Olympic Stadium, Aquatics Centre and Velodrome.

You can read more about the project on this Applied Works case-study and learn more through this video:

Interviewed for the SND website

I was recently interviewed by Kyle Ellis for the excellent Society of News Design (SND) website, to discuss data visualisation and the Olympics.

Anyway, if you haven’t had quite enough of my thoughts and opinions of late, then do take a look!

(Many thanks to Kyle for the conversation!)

WSJ’s new project: ‘Political Moneyball’

The Wall Street Journal have tonight released a massive, immersive and ambitious data visualisation project entitled ‘Political Moneyball‘, which visualises over a million records to reveal the networks and relationships that exist behind political contributions.

The interactive tool, developed by the always-brilliant duo of Sarah Slobin and Andrew Garcia Phillips, creates a powerful, flexible interface to explore the political donations as declared to the US Federal Election Commission.

By navigating around the many different political committees, parties and, of course, candidates, you can learn about the volume of donations made and received by all these political ‘actors’ as well as by those individual funders not part of this committee scene.

One word that best describes this piece is ‘heavy’, and that’s is in the best possible sense: it is heavy through the weight of data records and also heavy in the richness and variety of routes into the data, with so many dimensions and interactive features (such as filtering, view switching, following pre-discovered headline stories). There is simply so much going on with this work it is incredible and I urge you to have a go and spend some time interacting with it to truly appreciate its size and depth.

As befitting a visualisation and data framework of this vastness, this is not a project that you would or should expect to be instantly ‘graspable’. It requires the user to invest time in understanding how to use the features and how to read the findings. The entrance to the interactive is an explanatory screen which gives user an introduction in to what they are about to see, describing the basics of the interactive and offering a link to a video tutorial, shown below.

This level of care for the user’s experience is wonderful, it shows respect for the complexity of the visualisation’s context as well as the reader: the designers clearly care about us users being able to understanding how to navigate, explore and interpret the visualisation. This is supported, once you get through to the main screen, by a detailed methodology and a host of annotation elements which really equip us with all the information and guidance required.

All in all a hugely impressive piece and one that a 30 minute free wifi pass in Munich Airport won’t allow me to sufficiently explore right now!

Problems with interpreting multiple-colour legends

Just a quick post to respond to a piece I’ve just been sent because it reinforces one of the issues I point out in my training sessions. The graphic was produced by the BBC in reaction to Bradley Wiggins’ victory at the Tour de France, in an attempt to highlight the UK’s long wait for a winner. Well, that’s the title, but the graphic actually goes into a slightly different direction with an analysis of the prevalence of English speaking competitors in the top 10.

If you can’t be bothered to count, there are actually 27 different countries represented by the colour legend, but can you easily tell the difference between Russia and Slovenia in the chart? How about the US and Ireland or Germany and Mexico?

There is plenty of evidence in colour theory text (particularly see Colin Ware, ‘Visual Thinking for Design’, chapter 4) that any colour scheme representing categorical values (like countries in this case) that going beyond 12 classifications will make it difficult for the viewer to easily and reliably interpret the differences thereafter. Ideally you will stick to a maximum of about 8 but certainly the usage of 27 categories puts unnecessary extra cognitive burden on the viewer and increases the amount of visual searching, navigating between the legend and the cells to make out the meaning.

I can see what was trying to be shown here, clustering the regions and languages of countries to highlight the different eras of dominance, but it would have been better to reduce the colours and bulk some countries together into regional labels.

Discussion: Is data visualisation gender blind?

Over the past few months its been hard to ignore the quantity of stories, incidents and awful mis-judgments that highlight a certain under-representation and sub-standard treatment of women amongst the science and technology sectors.

One term that seems to pop-up in most of these accounts is ‘brogrammer‘, a fusion of words to signify a specific cohort of programmers with frat-boy characteristics, and this culture is something that appears to have been developing in recent times, particularly in parts of the tech world.

Seeing these stories emerging in fields that overlap or have a natural association with data visualisation made me wonder if there were any gender-related issues prevalent in our subject area. I knew we still had issues in society at large (recent evidence 1 and 2) but I was curious if there were any specific community or subject-related issues in data visualisation and infographic design.

However, going beyond a basic curiosity and turning it into something constructive was difficult. I honestly wasn’t sure how to handle it: was it even something I should look into? As a freelance male in the field without any personal experiences or observations of poor treatment towards female I’m not exactly best placed to offer observations about this issue after all. Furthermore, by simple drawing attention to this issue would it prove to be a positive action or could it run the risk of being clumsy, patronising, tokenistic and just generally ill-judged, creating noise about an issue that didn’t need it?

Never-the-less, the matter of whether data visualisation was ‘gender blind’ felt like an important and interesting topic and I did want to put the question out there to find out if there are any observations from within the field where women have felt they get a rough deal, feel like they get treated differently to men, experience more limited opportunities etc.

So I decided that I didn’t want to write it myself but simply facilitate a discussion to see if there are any issues that surface, find out what they are and see what can be done about them, if not, we move on. I invited a few participants to offer their thoughts on the topic in the hope of collating a sample of perspectives. This invite to participate wasn’t an exclusive golden-ticket, by the way, just a few people I either know in the field or whose names happened to pop up on my twitter timeline at a time when I was thinking about it. A couple of people I approached politely declined the offer which is absolutely fine.

Anyway, the final piece is presented below as a mixture of responses to the questions I posed and more open-styled responses to the general subject. I was going to split them up over a number of posts to reduce the content in a single article but that would mean potentially fragmenting the discussion so I’ve piled everything in to one.

I hope this creates a positive reaction because its intention is simply to ask a question and trigger a discussion. I therefore hope to see some constructive observations in the comments section from anyone, male or female.

Finally, I want to sincerely thank all who contributed to this article. If you think about it, its a bit of a tricky call deciding to take part in a discussion like this but every respondent has been superbly honest, open and constructive in their support for this piece and I wish them all the very best!

And so, in purely random order, and presented verbose, as received…


1) What would you describe your role/position/title or discipline in the field?

I’m CEO and Co-founder of Periscopic, a socially-conscious data visualization firm. I have a computer science background.

2) How long have you been working in the field?

I’ve been working in the interactive field for almost 20 years and specifically in data visualization for 8 years.

3) What barriers to entry have you seen or felt in this field?

The data visualization field (in its current incarnation) is nascent and that, combined with the trendiness of it, contribute to a spirit akin to the wild West. It seems to be a free for all of how to do it, what is possible, who’s able to do it, etc. I’ve seen people from sociology, political, mathematics, design, psychology, and other backgrounds entering the field of data visualization. More structure may emerge as we all figure out this field, but currently there seem to be few barriers.

However, I do believe the media isn’t painting a picture of parity in the data fields. Journalists currently seem infatuated with most of our male counterparts whom are interviewed time and again for their expertise in our trendy discipline. There are a lot of women in the field. I would like to see them lauded in the media as much as the men.

This lack of inclusion in the media can create the perception that the dataviz field is male-dominated and can lead to fewer women feeling compelled to enter it.

4) In general how do you perceive the culture/community of data visualisation?

I think it’s incredibly supportive and welcoming. It seems to be driven more by the desire to bolster the practice of visualization rather than by business. As such, there can often be very spirited discussions and critiques of visualizations, but I see these as helping the discipline as a whole become better, more exacting, meaningful, and important. I’m frequently impressed by the collective knowledge, insight, good humor, and openness of the community.

5) Have you experienced (observed or directly been involved) any negativity surrounding gender issues…

(a) …through the evaluation/feedback/discussion of a design project?


(b) …through the evaluation/feedback/discussion of a technology/programming solution?


(c) …through the evaluation/feedback/discussion of analysis/stats?


(d) …through the evaluation/feedback/discussion of a specific subject matter?


(e) …through involvement/participation/attendance at conferences?

No, quite the contrary. I feel I get preferential treatment as some conference organizers, such as O’Reilly, specifically look for diversity in their speakers.

(f) …interactions over social media?


6) Have you ever felt compelled and comfortable to act on anything you have witnessed or experienced?


7) Finally, how would you contrast your experiences in this field with any experiences or observations you have made about other related fields such as science, technology, maths/statistics, design or industry?

Of all the computer science related sub-disciplines I’ve been a part of, data visualization is by far the most friendly. I’ve been involved in the fields of gaming, physical computing, web development, and IT. Some of these more than others are more male dominated, competitive, and almost antagonistic, making them hard or at least intimidating to break into for women. I once launched a small website boycotting Duke Nukem because of its sexism and received a lot of hate mail including people telling me to die. It was striking to me that there was such aggression directed at me as a woman over a simple call to action they could have easily ignored. Some men in male-dominated fields, apparently, feel that being in the vast majority allows them to be more domineering or wanton. I once had a boss that told me I had to wear skirts to work. You can see similar cases of this such as the recent Secret Service scandal.

Perhaps my familial and welcoming perception of the dataviz field is due in part to social media. Nowadays it’s easy to connect with fellow practitioners, academics, and even celebrities in a given field. Pre-Twitter, we had to rely on introductions and cold calling/emailing to make these connections. Additionally, social media in some ways holds people accountable because their identities are attached to them as opposed to the days of BBSs and forums where anonymity reigned, making it far easier to behave in malicious ways without repercussion.

Whatever the case may be, I’ve encountered nothing but support and encouragement from the dataviz community, although I’m still waiting for my profile piece in Fast Company or the New York Times. 😉


My first rule of being a woman in tech is don’t talk about women in tech, but I’m very bad at following rules, and I’m so happy to see Andy tackling this issue, so here I go (again).

For me, the “women in tech” issue is more usefully understood as the intersection of two greater problems: the women in public problem, and the diversity in tech problem.

Diversity in Tech

I think “women in tech” is the safe discussion of diversity, and leaves out: race, class, more. So I want to quickly get a little unsafe with you: if you think women are underrepresented in tech, look around for people of color. I do this when I feel lonely at conferences; it’s a good practice in perspective.

There is invisible diversity that is almost never discussed. For instance, class. How lucky we are if we have the resources to be at a conference in the first place, to buy these shiny objects we make our work on. How many interested talented people have no hope of being able to attend, of having the tools we take for granted.

Women in Public

Last October at VisWeek I was walking around downtown Providence to get dinner, the lone woman out on the street, and the vibe was a little hostile. I tweeted “One of my favorite things is wandering around alone in strange cities. Sometimes this makes me wish I was a man.” Last week, after running a meetup in Portland, I was walking along a busy street just before 10pm. A man in a black guv rolled to a stop next to me and asked the classic “do you want a ride?” Usually this sort of thing is a bad joke, but he was a little more threatening than normal. I noticed I was the only woman on the street, and at that point my night was over: retreat to the hotel.

These are blog post friendly examples; the threats and acts against my safety in public go far beyond this. This is not to make you feel sorry or particularly concerned for me. I’m a grown woman. I know what I’m getting into. There’s no reason public spaces have to be this way, but they are. Walking around at night by myself is both a pastime and a conscious act of resistance against public spaces without women.

At the beginning of every tech conference, when the “there are so few women here” alarm is sounded, I want to say: Let’s look around more often and be honest. How many women do you see in male public spaces in general? Women on the street at night? Female business travelers in the airport?

Professional Experiences: Data Vis & Math

A listing of unfortunate events that have happened to me professionally as a woman in math and data vis.

I’ve realized, halfway through a semester, that my voice sounded weird in class because I was the only woman who had ever spoken. I’ve been the only woman in a class where the professor started every new proof with “If a guy wants to….” This is how I learned that “guys” isn’t as neutral as we pretend it is. I’ve been in classes where no one spoke to me the entire semester (and were then shocked to find out who ruined the curve).

I’ve been seated at the wives and girlfriends table. I’ve been asked many times who my boyfriend is by people searching to explain my attendance at an event.

I’ve been ignored. I’ve been introduced as an afterthought or not at all. I’ve watched, on many occasions, women in professional partnerships with men be treated by the press and public as invisible appendages.

I’ve been called out publicly as the only woman at a table and asked to respond immediately as to why more women weren’t in attendance. (Some of you reading this were there.)

I’ve been mistaken for other women. Molly Steenson and I gave very different Ignite talks at Eyeo, but a non-trivial number of men started conversations with me thinking I was Molly. (A tiny sample, but no woman mixed us up.) Techy gentlemen, let me tell you: if I can tell you apart, if I can recognize an individual in a sea of black rimmed glasses and shaggy hair, I know you can discriminate two blonde women.

Stop – did you notice the part of my Eyeo story where men were approaching me to talk about my work and ideas? Yeah. That does not happen often. Despite the mixups, an explicit recommendation: Women, come to Eyeo next year, you’ll be happy you did. Eyeo is the by far the best event I’ve been to in terms of being listened to and not ignored.


The story is too long and sad to tell without a drink, but math as a field was ruined for me by experiences that happened because of my gender.

I decided I would never waste my energy that way again – if, after a few knocks at the door in good faith, a field doesn’t want me, a person or group ignores me, I walk away.

If you flick through who data vis people follow on Twitter, you won’t see many female faces, and you’ll see almost no color. I make a daily decision to be in public online, to have my face as an avatar, to engage, to be visible in the stream. I choose to be visible online consciously, and at some risk, in the same way that I choose to be in male public spaces offline.

I make this effort because I believe that increasing the diversity of visible voices in data vis will make the community stronger.

I knocked (loudly, a lot) and the data vis community opened its doors to me. So many people publicly, privately, and in invisible ways, have supported me in making massive changes in my life this past year. So many that I can’t list them, but can say: you know who you are, and thank you.

Lately I’ve been thinking about this: “You can have anything you want, you just have to pay the cost.” If we want to increase diversity in data vis, we can make it happen; we have to decide to pay the cost. The cost is dedicated individual effort, it is you remembering someone’s name; you remembering to name the female partner in a partnership; you introducing people who should be introduced; you asking people about their work; you recommending people to clients; you talking to people who don’t look like your friends.

Something to make it easier: you don’t have to get it right every time. I’ve put my foot in my mouth terribly, though I’m aiming for less regularly. We all make mistakes, we all have thoughtless moments. One moment doesn’t make a person, but somehow all our collective moments do add up to make a community. If everyone who reads this decides to make the effort, the moments will add up. What a community we can have if we decide to pay the cost.


1) What would you describe your role/position/title or discipline in the field?

Seminar leader, short course instructor, speaker, author on presenting data clearly

2) How long have you been working in the field?

15 years

3) What barriers to entry have you seen or felt in this field?

None. (I’m self -employed)

4) In general how do you perceive the culture/community of data visualisation?

I don’t think that there is a single culture/community of data visualization but rather separate communities of graphic artists, statisticians, computer scientists, journalists, etc.

5) Have you experienced (observed or directly been involved) any negativity surrounding gender issues…

I did in my days in industry but not since I’ve started my second career.

(a) …through the evaluation/feedback/discussion of a design project?

(b) …through the evaluation/feedback/discussion of a technology/programming solution?

(c) …through the evaluation/feedback/discussion of analysis/stats?

(d) …through the evaluation/feedback/discussion of a specific subject matter?

(e) …through involvement/participation/attendance at conferences?

(f) …interactions over social media?

None of the above

6) Have you ever felt compelled and comfortable to act on anything you have witnessed or experienced?


7) Finally, how would you contrast your experiences in this field with any experiences or observations you have made about other related fields such as science, technology, maths/statistics, design or industry?

Experiences in all these fields have changed over the years. Years back there was much more discrimination.


1) What would you describe your role/position/title or discipline in the field?

Graphic designer / information designer / data illustrator (and everything in between 🙂

2) How long have you been working in the field?

For about 6 years, while also spending much of that time working as a book cover and book designer.

3) What barriers to entry have you seen or felt in this field?

I don’t really think there are barriers to entry in this field but I think that it sometimes feels harder for people coming in from a graphic design background as opposed to a more technical background because of all the distrust/fatigue around many information graphics on the internet (and the general debate about the balance between aesthetic and information)

4) In general how do you perceive the culture/community of data visualisation?

I do meet and know many helpful, charming, and lovely folk who are within this community, but often I find that many people are quite happy to chat about what others are doing wrong instead of what others are doing right, which can sometimes be intimidating. Perhaps it’s a graphic design sort of issue…I wonder if people coming from a graphic design background are possibly more sensitive to criticism and how it is worded as opposed to other fields. I find many of my graphic designer friends and I tend to attach to our work in a more emotional way than other industries.

5) Have you experienced (observed or directly been involved) any negativity surrounding gender issues…

(a) …through the evaluation/feedback/discussion of a design project?

(b) …through the evaluation/feedback/discussion of a technology/programming solution?

(c) …through the evaluation/feedback/discussion of analysis/stats?

(d) …through the evaluation/feedback/discussion of a specific subject matter?

(e) …through involvement/participation/attendance at conferences?

(f) …interactions over social media?

I haven’t answered any of these because I haven’t felt this in any specific way. Sometimes the differences feel more intangible, I guess. I produce quite different work than other data practitioners, and I often feel as though there is a barrier between myself and the more technically-minded, though it’s hard to know whether this is just down to the difference in our work or the difference in our gender, particularly as there *are* more men in data visualisation anyhow. One thing I do notice, however, is that after I speak at conferences it sometimes feels that more women approach me after conferences than men. Again, I’m not sure why this is.

I have felt the gender imbalance more greatly at conferences, both with speakers and with the attendees, though this might differ by the conference (Eyeo felt more balanced, whereas at See conference in Germany there were more male speakers)…at a dinner after the See conference this year there were at least 30 men and only two women! Of course, all the people in these various settings are all good, lovely people. But in a situation where you feel like the odd one out it can make you feel more self-conscious or more shy, less confident to speak your view, depending on your personality. I can feel shy in situations where I don’t know anyone and this is amplified in an all-male environment.

6) Have you ever felt compelled and comfortable to act on anything you have witnessed or experienced?

I don’t think I feel comfortable to take to Twitter or social media to discuss this, but that might just be my nature. I am happy talking about these sorts of things with other data practitioners, however.

7) Finally, how would you contrast your experiences in this field with any experiences or observations you have made about other related fields such as science, technology, maths/statistics, design or industry?

In the design world I feel like there is still a ‘boys club’ mentality in some cases, but it’s tempered by the fact that I know a large group of successful female designers and illustrators. In comparison to the design world (and the digital world) the data world feels more obviously male, and harder to access/engage with, but again: I’m not sure if this is either down to the nature of data folk or their gender.

In short: I am a graphic designer and lots of data folk are programmers / scientists. I don’t know whether I feel slightly intimidated due to the personality differences between the fields or if its because of gender!


1) What would you describe your role/position/title or discipline in the field?

I am one of the 4 co-founders of Dataveyes, a start-up specialized in data-visualization

2) How long have you been working in the field?

Since 2009 and my degree into media management.

3) What barriers to entry have you seen or felt in this field?

I have no seen any barriers to entry in this field, in France, probably because of the immaturity of this market in my country. When we began designing data visualization, we were in a way pioneers in France to blog about this subject, to give conference about this subject, to “market” our self as “dataviz” specialist, etc.

I come from the media field, which I studied during five years, that also give us some visibility, around the thematic of the data journalism, and made us different from the statisticians, cartographs, or other data-analyst that could have worked far before us on data visualization, without beneficing the same media cover.

I think our positioning also helped us enter this field: since the beginning at Dataveyes we mix technical skills, with an approached focused on user, information, and storytelling, and that was new in France.

4) In general how do you perceive the culture/community of data visualisation?

It is a very open minded community in my opinion. It is a passionate and inquiring community, always exploring, in search of novelty and discoveries about what we can visualize, always creating new way to draw this visual and interactive language to communicate information buried inside data. It is also a rigorous culture, seeking to combine logic and harmony, mathematics and aesthetics, analyse and intuition.

5) Have you experienced (observed or directly been involved) any negativity surrounding gender issues…

(a) …through the evaluation/feedback/discussion of a design project?


(b) …through the evaluation/feedback/discussion of a technology/programming solution?


(c) …through the evaluation/feedback/discussion of analysis/stats?


(d) …through the evaluation/feedback/discussion of a specific subject matter?


(e) …through involvement/participation/attendance at conferences?

No. On the contrary, in France, the new technology field is mainly masculine. When you’re a young woman, and when you are able to tell the story of your work, I have the feeling it is easier to be invited to conferences, because organizers aim diversity, rhythm, dynamism, etc., to make their event interesting. I did not feel any negativity against women. In France, I have the feeling that new technology community would like to welcome more women, but for cultural reason, there are less women likely to choose this field to make their career.

(f) …interactions over social media?


6) Have you ever felt compelled and comfortable to act on anything you have witnessed or experienced?


7) Finally, how would you contrast your experiences in this field with any experiences or observations you have made about other related fields such as science, technology, maths/statistics, design or industry?

Due to my young age, I did not have many experiences into other professional field, but I feel the data visualization field as an innovative & always moving group, where everything is possible.


Being a journalist has nearly ruined my ability to be candid. I fact-check myself when I speak, start my sentences over to add context, attribute ideas that aren’t mine and feel uncomfortable making definitive statements that I don’t know to be fact.

So I’ve been struggling with how to present my personal take on gender issues at work. If a man behaved badly should I also present an example of a woman behaving badly? Or for balance an example of a man behaving in a lovely manner?

It’s taken a while to arrive at the following framework:

Context: I’ve spent more than two decades at news organizations where women have had positions of power. My career has been graced by many female mentors who have made my rise possible. (I’m a visual journalist.)

Balance: The older I get, the more of a humanist I become. Gender is not a filter for how I view people. Also, I understand that sometimes there is a cognitive gap between perceived and actual offenses.

Caveat: I’ve had stellar colleagues, both male and female. I have experienced women behaving badly. This is not about that.

What this is: Some examples of negative experiences that I’ve had with men during my career + some Insight into why I found the behavior offensive. Note: not all bad behavior is equal and all this represents outliers within the subculture of these workplaces.

1) Meeting with an editor about a story, he told me to make sure to incorporate pictures of either “cocktails, meat or women, because that’s what draws readers to a page”. So, women, meat and booze are not equal. While I understood his logic, I also understood that this blatant objectification was meant to provoke me because I was a woman. Which was low, and annoying.

2) One morning the colleague I shared a cubicle with took out several bottles of cologne and asked me if I’d like to “choose his scent.” This guy was confusing proximity with intimacy. He also crossed the line into the world of physicality. (Sadly for him, he was later fired for browsing pornography at work.)

3) There was a sports editor I had to duck for almost a year because he kept asking me out. When I’d tell him I had a boyfriend, he’d suggest I do what he did and lie. Relationships at work are a bad idea, but I get that they happen. This was past the ‘take a hint’ stage though. When I’m at work, if you really want me to find you interesting — try working with me, not presenting yourself as a lying cad.

4) A friend of mine confided in her manager out of courtesy that she was pregnant, but not ready to share that information. The manager then pressured her to go public so he could line up a replacement for her. While I don’t expect men to understand pregnancy or how scary it can be, I don’t think it’s too much to ask someone to understand that private information is private. Also, when you choose your administrative tasks over your obligation to the human race everyone loses.

5) A colleague who fancied himself good-looking told many of us that we were his ‘work-wife’ so we’d deliver better work for him. “Everyone has a wife at home and a wife at work.” A) I never accepted the proposal B) His polygamist tendencies were pathetic C) He wasn’t all that

So have men gotten in my way at work? Sure. So have women. For balance though, and this is just me, you should know: I’ve been referred to as “a force of nature,” ‘the intrepid” and told “get it done, just don’t kill anyone.” So getting in my way is possible. Stopping me altogether … X or Y chromosomes make little difference.

’emoto’: Visualising the emotional response to London 2012

The London 2012 Olympic Games is nearly upon us and, naturally, in the lead up to such a major sporting event that will generate incredible quantities of data and create fantastic opportunities for analysis, there are a number of interesting visualisation and infographic developments making their way around the field. One of the most ambitious, unique, multi-faceted and fundamentally exciting projects will be launched next week, it is called ‘emoto‘ and is something you will be hearing a great deal about.

Emoto is a new artistic data visualisation project created by Drew Hemment (artist, curator and director of the FutureEverything festival), Moritz Stefaner and Studio NAND. In this age of social media and real-time availability of public perception, this project will be the first to capture, measure and artistically portray the ongoing online response and ’emotional tone’ surrounding the London 2012 Olympic and Paralympic Games. This real-time monitoring will allow anyone to see what events, athletes or particular stories are resulting in the most online chatter.

“This project… will bring to life the most compelling aspect of any Games, the emotional intensity of the audience response, the hot topics raised, the essence and the pulse of London2012. This is not a scientific representation but an artistic representation and it will be a unique contribution to the London 2012 Festival experience creating a shared experience for across the globe. This will change the way people consume major sporting and cultural events because for the first time you receive instant feedback from other people around the world who are sharing that experience.”

The discovered sentiments will be displayed in a series of infographics (sample below) which show whether the reactions are positive (orange) or negative (blue). This sentiment information is visualised in three ways:

  1. A ‘swarm’ in which animated geometric objects represent levels of interest around certain topics, such as the names of an athlete or event, which are frequently being mentioned online and whether the ‘chatter’ around these are positive (orange) or negative (blue)
  2. Sentigraphs, a form of scatterplot, will display the general mood over time based on the sentiment scores for social media messages – once again assigning them as positive (orange) or negative (blue)
  3. A ‘stream’ will display the actual content of the tweets and social media messages related to the games. Viewers/journalists will be able to ‘grab’ these comments to gauge the reaction to a particular event or news story that has got people talking online.

These infographics will capture the change in moods in real-time as the events unfold and as the key insights emerge, the team will be blogging about the most interesting findings and patterns emerging from the the digital visualisations.

Throughout the Games, residents and visitors to London will also be able to download the ‘emoto in London’ app for android; a mobile app, created by researchers at the SENSEable City Lab, which uses live twitter information to create a real-time augmented reality visualization of the online response to the Games on the handheld device.

Once the games have completed, the project will continue to evolve in a fascinating way. The raw material of the millions of online interactions will be transformed into a 3D artwork to create a landscape of the collected sentiment data. It will be milled onto 19 plates, each of which represents a record of the emotional response over one day of the event. The artwork will go on show at WE PLAY Expo from 7 – 9 September 2012 in Preston.

And that’s not all! The project will also culminate in ‘emoto in London’; a large scale public sound installation which translates the sentiment-analyzed Twitter data into a 48-channel harmonic experience, which will be on show in Trafalgar Square from August 29 – September 9.

All in all this is an amazing sounding project, one of the most creative and ambitious visualisation developments I can recall. You can read more about ‘emoto‘ once it is live on the dedicated site and follow developments on the newly set-up Twitter feed.

Announcing my next data vis training locations

Back in May I invited people interested in my data visualisation training courses to register their preferred location with me so that I could start to design my next schedule of events based on where the main clusters of potential interest were. Here’s an intermediate update to give people a sense of how things are looking.

Here are a few images with the completed (Green ticks), planned (orange circles) and “wishlist” (blue crosses) locations mapped out.





Next locations

So, just to clarify in writing. Over the comings weeks I will be arranging events taking place in Manchester (UK), London (2 events), Paris, Berlin, Seattle, San Francisco and Los Angeles. These will be scheduled for some time most likely between September and ideally before the end of the calendar year.

For the North American events I have clustered interest from Seattle, Portland and Vancouver in to a single planned event in Seattle (mid-point?). Likewise San Francisco will be designed to cover those based in Oakland and Los Angeles will hopefully not prove to be too far for those of you in San Diego. If there is a rapid take up of interest there may be scope to put on extra sessions but we’ll see how things go and whether the schedule allows it.

I will update the site with details of dates, venues and arrangements as soon as I have them. Before then, if you are keen to get your name down in pencil against one of the training sessions then just contact me, ideally via email.

Private training events

Just a reminder that this is about the schedule of public events but I also offer private training events for organisations for their colleagues to attend a customised on-site session. Such events offer the benefits of receiving a full training session but with the content and activities designed, where possible, to suit the client’s specific requirements. Once again, if you want to find out more about these opportunities then get in touch.

2013 events?

Just to finish off with an early idea of where I am hoping of visiting longer term. I feel the following locations are likely to be in my plans (or is it dreams?) for 2013: Madrid, New York, Boston, Houston, Sao Paulo, Rio de Janeiro, Buenos Aires, Mumbai, Bangalore, Hong Kong, Melbourne and Sydney…