Frolicking with Finance

Hiatus over…..Team #VisualisingHE breaking the drought with some fun with HE Provider Financial data.

Back in March 2019 HESA ‘sprung’ a never-before-public release of HE provider institutional finances. Prior to 2019 this level of institutional data has been available to key contacts within institutions through HEIDIPlus platform within institutions – and paying customers. However as part of HESA’s open data programme we can access and visualise more than ever before.

Finance data release

HESA’s new release of Finance data, is the first time HESA has released its full Finance record as open data, freely available for re-use under the Creative Commons 4.0 licence. As well as income and expenditure analysis the release includes HE providers balance sheets, cash flow and capital expenditure.

What do HESA collect from Providers?

HESA collect financial data (submitted in the winter of each academic year in templates provided by HESA) from universities , colleges, and other HE providers in the UK and covers income, expenditure, balance sheets, statement of gains and losses, capital expenditure, senior staff pay and Key Financial Indicators. details on what is collected under each of these broad terms are on HESA’s definitions pages.

Where to start?

Team #VisualisingHE have pondered this data release for some months, and whilst there is a whole lot of juicy figures to dive into, by way of introduction to the HE Finance data, we thought that we should focus some of this post on ‘introducing’ the finance data and the first publication of the Key Financial Indicators (presented as a three year time series).

Key Financial Indicators or KFI’s for short, are compiled using information provided to HESA as part of the HESA Finance record. The KFI’s defined and shown below are a set of ratios extracted from the finance record. They are not performance indicators and take no account of provider-level characteristics such as the range of subjects taught or the types of provision provided.

What are the 9 KFI measures?

  • Days ratio of Total net assets to total expenditure
  • External borrowing as a percent of total income
  • Net cash inflow from operating activities as a percent of total income
  • Net liquidity days
  • Premises cost as a percent of total costs
  • Ratio of current assets to current liabilities
  • Staff costs as a percent of total income
  • Surplus/Deficit as a percent of total income
  • Unrestricted reserves as a percent of total income

For definitions of the KFI’s detailing the numerators/denominators see here

What have team #VisualisingHE done?

Team #VisualisingHE have focused on trying to present these data as simply and cleanly as possible. A #TableauKISS you might say.

Adam came come up with a KFI scorecard, heavily critiqued by both Elena and Dave to settle on the dashboard below, where a single provider can be chosen, the benchmark be selected (sector, country or region) and the dashboard simply presents a provider overview of indicators:

  • Current (2017/8) figure
  • Year on year percent change
  • Provider vs Benchmark comparison trend
  • Sector distribution (and percentile on tooltip)

KFI OverviewV2_final.png

Interactive Viz: Key financial Indicator (KFI) Scorecard

Dave decided to have a ‘rustle’ around the ‘Russell group’ to see what he could find…..

Whilst playing around with the income and expenditure data we were struck by how much Russell Group institutions dominated with nearly half the income going to the small number of Russell Group institutions compared to the large number of non Russell Group institutions. These is also increasing over the time period.

HESA Finance Russell Group and the rest

Interactive Viz: “The Russell group and the rest – Income in UK Higher Education”

Additional resources

My go to: WONKHE gives HE data a timely on the button exploration of data releases. Here David Kernohan put together an excellent first exploration of this data within moments of this data being released for the first time and is always my go to for initial headlines and points of analysis. HESA institutional finance 2019 release: KFI is going to rock you published in March 2019.

Thanks for reading, and as always we would love to hear your thoughts and critique.

We love to viz, write and discuss all things HE, but most of all we love to learn, so let us know what you think.

Adam, Dave and Elena.


21 Posts to date | Which have been Adams favourites?

Team #VisualisingHE have had a lot of fun and learned a lot over the last 21 months and I thought I would kick off my reflective post covering some of my favourite bits.

We kicked off the project in March 2017, to-date we have created some 21 posts, we have challenged ourselves to numerous new charts including; Sankey’s (Apr 18), radial charts (Nov 17), and even a chord diagram (Apr 18), to name just a few. Explored many open HE datasets, I have learned a ton about remote communication and working together, peer editing and critique, not to mention furthering my Tableau skills and bettering my fairly poor writing skills.

So what have been my favourite bits?

  1. Overall favourite blog post
  2. Favourite Viz – Elena
  3. Favourite Viz – Dave
  4. Favourite Viz – Adam
  5. Favourite solo blog post

Overall favourite blog post

Having written this paragraph a few times whilst in draft, it became obvious to me I have a few blog posts that I have really enjoyed (for various different reasons).

However I have settled on one standout post for me, of which I wasn’t personally involved, but admire a lot, it is the NSS 2017 – one dataset many dataviz approaches post, where Dave and Elena opened up the concept of #VisualisingHE to the #MidlandsTUG,  the result was a fantastic post showcasing the TUG’s many and varying vizzes.


My favourite viz – Elena

Elena’s contribution to the project has been invaluable. Her style is also unmistakable and has earned her quite a few Tableau public ‘viz of the day‘ accolades! My favourite is without doubt her solo post ‘destination Europe‘ a topic that really sang with her and it showed in her viz and really came over in her superb blog post.

Inflow & Outflow (6)

Interactive viz

My Favourite Viz – Dave

Dave consistently knocks out meaningful and insightful dashboards with care and thought. I love his style, ever clean and simple, which is, as we know often more difficult to achieve than the most complicated and intricate of vizzes.

I think my favourite viz of his is found in our September 18 post ‘A looksie at UCAS‘. It’s clean, simple and effective, uses a minimal colour palette but packs a punch.

UCAS subjects 2009 to 2018 (1)

Interactive viz

My Favourite Viz – created by me

For me, my favourite viz is probably one also featured in our latest post (Sept18). I have tried to develop my concept of #Coffeetableviz over the last couple of years, and this ‘looksie at UCAS viz’ gets close to the effect I have been trying to create. It isn’t totally finished and has a few elements I should tweak, however I think it packs a punch, gets the BANS over to the viewer, has a playful side, provides a takeaway but also encourages the user to take a deeper look at the interactive viz (and that’s what I wanted to achieve).

A looksie @ UCAS placed Applicants

Interactive viz

Given this post is about ‘my favourite bits’, I feel it justified to dwell on another post to which I enjoyed vizzing AND writing (I don’t always enjoy the writing bit as much as the vizzing if I’m honest!).

My favourite post and viz was my post on LEO – #Realtableau to funviz .

Why? Well I took a viz I crunched for my Exec team and Employability directorate and had a little fun with it over a few evenings at home. I really enjoyed documenting my journey of exploration, my battle with creating the radial chart and the iterative nature the viz took whilst working remotely with Dave and Elena. They have both always been on hand to comment on my viz outputs, suggest improvements and laugh with me, whilst I bash out expletives during the struggle to get a viz to behave!

The main viz for the post finished up looking like this:


Interactive viz

Thanks Dave and Elena, I have really learned a lot over the last 21 months.

Can’t wait for the next tranche of open HE data sets to emerge.

Thanks for reading.


A Looksie at UCAS placed Applicants 2018

So this month we decided to take looksie at UCAS again. This time focusing on the recently released ‘Placed Applicant’ data as at 31/08/2018, which is part of the daily clearing analysis 2018 statistical releases.

There has been a lot of talk around the demographic dip which will give Universities fewer 18 year olds, so we wanted to see where it has had an impact.  We also wanted to see to what extent International students felt welcome and the impact on areas, such as nursing, which had a change in funding arrangements.

International Interest

A looksie @ UCAS placed Applicants.png

A looksie at UCAS placed Applicants

Interactive viz

In the last year there has been a slight positive shift to both International student groups (Non EU and EU, excluding UK).  Both areas have increased in numbers as well which is a positive sign that International students still regard England as a place to come and study.  This has also offset some of the decline in the number of Home placed applicants.

Subject split

We started out by looking at how things have changed over three separate time periods (1 year, 5 years and the full 9 years in the data set).  What this showed was that despite the decline in the last year over the time period of the data set there has been an increase in nearly all the subjects with only one big subject seeing a decline.

UCAS subjects 2009 to 2018 (1)

Interactive viz

We then started looking at grouping up the subjects which showed that the majority of the placed applicants reside in the science and social science groups and these are also where the biggest increases over the time period are observed.  There has been a downward shift from Arts & Humanities over the last 9 years.

UCAS placed applicants by subject2.png

Placed Applicants by subject group

Interactive Viz


Nursing has changed shape in the HE setting quite a lot over the time of the data set.  There has been a shift to it being only accessible via a funded degree then they have moved to remove the funding.  It has always been a challenge for HE providers as there needs to be placement in place but ultimately it does lead to employment in most cases.  Looking at the data there looks to be a shift with 18 year olds over taking 20-24 year olds for the first time.  They still lag behind the 25-and-over, though, who make up the vast majority of the placed applicants for nursing.

B7 nursing placed applicants2

Placed Applicants – B7 Nursing only

Interactive Viz

So hopefully that was an interesting window into the latest UCAS placed applicants data.  Do let us know what you think.

Adam and Dave

Finding a Story in Europe’s Over-qualification Rates

What is the data about?

The data used in this post relates to information brought together by Eurostat in their Experimental statistics on ‘Skills’. Specifically, the “over-qualification rate by economic activity for the period 2008 to 2016”.

Rather than paraphrase the Eurostat definition, you can read it here. However, to briefly set out why we thought it was an interesting dataset for #VisualisingHE to have a quick play with, we’ve taken a few sentences from the Eurostat preamble about why we may need indicators about skills:

The European Commission communication ‘A New Skills Agenda for Europe‘ defines among others policy priorities and sets out actions to be undertaken with the aim to make better use of people’s existing skills. It should be ensured that the skills available in the labour market match the needs of businesses and the economy.

This undertaking includes finding ways to improve the matching between skills and labour market needs as well as bridging the gap between education and work. Skills mismatch indicators should measure the gap between demand and supply of skills (macro-level) as well as conditions of workers, jobs or vacancies (micro-level).

Where does the data come from?

The indicators are derived by combining available figures using EU Labour Force Survey (EU-LFS).

The use of a single source ensures consistency, comparability and reliability, and the methodology behind is based on the literature in the field (‘The skill matching challenge: Analysing skill mismatch and policy implications’ – Cedefop 2010) and on existing empirical exercises (‘Employment and labour demand’ – Eurostat 2016 and EU-LFS ad hoc module 2000).

Full methodological description including data.

What does the original data look like?

The image below is an example of one tab of the 8 occupational job categories, 2008-2016 by European country: Wholesale and retail trade, repair of motor vehicles and motorcycles


How might we present this data for better consumption?

Having brought the data together using Easymorph, Adam wanted to understand the distribution in proportions of over-qualified roles by occupation types for a single year (2016) to establish which sectors where hotbeds for skills mismatches across Europe and whether the data presented any areas with differing deviations.

The following chart highlights Transportation and storage as the occupation type with the highest median proportion surveyed, with Spain the country with the highest rate @ over 70%.


[Link to interactive viz]

This visualisation of the data provides the ability to grasp all of the proportions for all of the countries, for all of the occupation groups in one visual. The interactive viz also enables you to delve into where countries appear in the distribution of proportions.

How might we present the idea of change over time?

Wanting to recreate elements of the original data, a focus on the percentage point change in proportion over qualified between 2008 and 2016 followed:


[Link to interactive viz]

Again, presenting the change for all countries and all occupations using the box plots enables you to contextualise a particular countries change against that of the other countries.

Focus on a single occupational grouping

Could we create a simple highlight table only, the success of this is wholly down to Chris Love of the Information Lab, he helped me work through the calculations needed to rank by a single year, which turned out to be a little more complicated than initially thought. blog post here.

highlight table

[Link to interactive viz]

How could we present all the data in one visual?

Could we go one further and present all of the year data, all of the country data and all of the occupational data in one chart? And what would you get out of it?

Having a bit of fun with presenting change with Radials?  This view does draw out the changes so that you can see that wholesale and retail have had an increase in virtually all countries where as something like construction has had some big decreases and a fair few more moderate increases.

radial of change

[Link to interactive viz]

What about trend over time?

Elena took a more focused approach and only produced one visualisation that expands on the idea of change over time.

By utilising a slope graph and Tableau’s latest out-of-the-box style if step line charts, Elena’s dashboard allows the user to see how a country’s overqualification rate changed from 2008 to 2016 in each of the eight occupational areas whilst also allowing the reader to see the fluctuation over time. The colour indicates whether the rate for the country in the given year was above or below the median.

Overqualified in Europe.png

[Link to interactive viz]

So….. Did we find a story?

The data threw up plenty of small nuggets but no overall narrative so for the moment this post will be a demonstration of what data is there and how you can visualise the data in a few ways to present the proportions and change in proportions in more ‘graspable’ ways.  This also allows the viewer to identify some of the outliers and find trends in either a particular occupation type, or country.

Thanks for reading!

Team #VisualisingHE

(doing a post on something a little wider this month – Education, moreover over education!)

A tour around the Estate….

A bit of background…

This blog post comes straight off the back of a well-attended and thoroughly enjoyable HETUG (Higher Education Tableau User Group) Conference this week held at the University of Nottingham, at which Dave and I were lucky enough to present our wares.

Dave gave a fantastic presentation of his work on an approach to a ‘Matrix of Metrics’ – a topic whereby the key is in the ladders!!! He discussed how through engagement in data he has embedded targets and solid awareness of sector performance through clear and informative dashboards.

Myself, I wanted to demonstrate first and foremost the wealth and quality of information held by HESA accessible to HE providers through the HEDIPlus service. The HEIDIPlus service is a resource which enables a much-needed access to benchmarking data at provider level on many and varied HE topics – an enabler for providers to set appropriate targets for continued improvement in their strategic goals.

A key thread to my presentation was to highlight the flexibility Tableau (as a data visualisation tool) offers for enabling creative approaches to gaining engagement from non-data-savvy users and therefore facilitating the sharing of insights to a wider audience. My approach floated the notion of developing data awareness and building a data driven decision making culture through more infographic style visual dashboards.

And so, to this month’s post..

In this month’s post we celebrate a few nuggets from my explorations into, for many, the under explored HESA Estates Management Record (EMR) data set. What a treasure trove of figures this is, calling out for a bit of #VisualisingHE attention!

The freely available data broadly includes information about grounds and buildings, water and energy usage, waste management, transport and other environmental measures.

Within these broad areas hide some 228, YES, that’s 228 measures of estates related loveliness! In this post we have taken a few measures that caught the eye, and have given them a #VisualisingHE makeover.

First up is an interactive viz which presents a visual of the TOP30 providers in England boasting the highest number of buildings on their estate. This viz was inspired by Simon Beaumont after seeing something he knocked up for a #MakeoverMonday viz some months ago [Wk22 – world most expensive prime property], whereby he visualised the property as a city skyline. After stumbling over this no. buildings stat I thought I would apply a similar visual to this Estates dataset as a potentially approachable way to contextualise the volume of buildings a provider is maintaining on their estates.

In this viz each bar line denotes a provider and the height of the bar denotes the number of buildings a provider has. This viz simply uses a dual axis chart: one a simple bar chart and the other –  a custom shape to give the ‘buildings’ a top. A darker colour = more buildings.

Which Providers boast the highest number of buildings..

No. Buildings[Interactive Viz]

Next to peek my interest was the desire to understand the size of the estates providers manage. The visualisation below attempts to provide visual cue to area, by using a tree map chart type to express the area.

The top chart shows the sum of area in hectares by UK region, the one below displays the total site area by provider. You can click each region in the top chart and an action will filter the provider chart to provide an overview of the distribution of estate by provider in the selected region.

Total site area…

total site area

[Interactive Viz]

The viz below is the viz I used in my presentation at the #HETUG. This viz focuses on the total water consumption used in 2016/17 – overall and by provider. I have chosen to create this viz because the environment is a very topical issue and water consumption plays a significant part in the impact on the environment. I wanted to express and try to contextualise the seemingly vast consumption of water the UK HE sector consumes in a potentially engaging infographic.

Water Consumption…

Water consumption_Coffetable[Interactive Viz]

In the spirit of encouraging the ‘green’ ethos I thought I would wheel in a cheeky viz of the number of cycle spaces UK providers have installed on their campuses/estates to facilitate safe parking and encourage green and healthy commuting for both their staff and student bodies. The viz below highlights the TOP10 providers provision.

Build the ‘Cycle’ spaces & they will come…

no of cycle spaces

[Interactive Viz]

And finally for this months blog we focus on a key by-product of all the amazing stuff HE providers do in their cycle and that is waste mass! Question is what do we do with it all?

Tonnes of waste mass by type…

As part of Cole Knaflic’s monthly #SWDchallenge (June) she challenged the community to explore the use of slope charts (SWD’s June blog). For this I pulled together a viz on the UK HE’s total waste mass in tonnes by type between 2015/16 and 2016/17.

UK HE Waste mass by type

[Interactive Viz]

Let me know whether these visualisations encourage you to take a closer look at the data available on how UK HE providers manage their estate.

Many thanks for reading!

Adam and the #VisualisingHE Team

The curvy side of UCAS

Our HE dataset in focus this month is the recently published 2018 entry – March deadline UCAS applicant figures.

Spring has put a coil in our tails and we have left best practice chart types tucked in the winter coat pockets and all gone a little bit curvy! Why? Well we thought we would flex our Tableau muscles and have a go at a few chart types we haven’t yet had a bash at to see if we can make ’em work for this project.

Adam took a liking to a Chord chart, Dave some circles and Elena got sooper curvy with a lovely no data prep Swankey Sankey!

The Data

UCAS publish a set of statistical pdfs and csv files full of all sorts of delicious figures describing applicants and applications from the 2018 UCAS cycle as at the March 24th deadline. Why? March the 24th is the deadline for some Art and Design courses, and here at #VisusualisingHE towers, we didn’t want to forget these creative types missed in the UCAS Jan deadline data releases (most known in the publication calendar). Hence, we waited especially for this release to capture them as well.

The tables published include applicant numbers by age, sex, country of domicile, ethnic group, POLAR3, POLAR4, SIMD 2016, country of institution applied to, and institution type (higher, medium, and lower tariff), as well as the number of applications (choices) by subject group.

Key takeaways from the main UCAS domicile headlines:

  • Applicants by domicile – Non UK applicants up 8% compared to previous cycle, EU (excluding UK) up a lesser amount of 2%, UK down by 3%, overall all domicile down by 2%.
  • Applicants by UK domicile – Northern Ireland displaying the largest percent change in applicants (-5%), England (-4%), Wales (-3%), overall UK down – 3% on previous cycle.
  • Applicants by English region of domicile – The North East showing greatest volatility with -8% applicants, however the North East do form the smallest population of English domiciled applicants (2018- 15,820).
  • Applicants by declared country of domicile – China remain the stand out country of domicile applying to study in the UK, accounting for 20% of all non EU applications (13,070 / 65,440).

Our Vizzes

Adam has been wanting to have a bash at a chord chart for a while, so headed straight to a fantastic instructional post by Noah Salvaterra called DIY chord diagrams in tableau that has been saved in his favourites for sometime.

In his blog post Noah basically guides you through how to prep your data and helpfully and very generously shows you how to clone his tableau file and replace it with your own source data.  From then on it’s up to you to get creative with the look and feel of the viz.

It is fairly rare in HE data that you get a dataset pop up that is perfect for this chart type, given that you need matching dimensions to show a ‘to and from’ (country of domicile of applicant and country of provider). All I had to do was add some blank rows to help scaffold the non UK applicant data (because I didn’t want to exclude them).

Adam settled on vizzing Table 7 contained in the UCAS overview which takes a look at the country of domicile of applicants and the UK country location of the provider.

UCAS 2018 March deadline_Domicile of Applicants

Interactive viz: UCAS 2018 Entry | Domicile

Dave, well he got creative with circles, and came up with a novel way of presenting the data set, in a FT style. He also opted for the big picture vs. high level of precision, so where Adam and Elena spent time in getting those numbers in the visible space or the trendy #VizInTooltips, Dave kept it simple – no numbers as values are encoded in the relative colour and size of the circles.

Dave’s viz definitely grew on us quite quickly even though it’s kinda a bubble chart… which is generally a big ‘no no’ in the #Dataviz community. But here, it just works because it is simply not trying to say too much.

Dave focused on the country location of both the applicants and the the HE providers. His method elegantly shows whether applicants chose to apply to providers based locally, and if they choose the go elsewhere in the UK, whereabouts they chose to go.

So what are the key insights? The largest proportion of each group of applicants, based on their location, apply to institutions in their own country. Not many students outside of Northern Ireland choose to apply to NI providers but NI applicants don’t necessarily choose to stay locally as they also apply to study in HEPs in England and Scotland, too.  It was also interesting that the majority of applicants to Welsh HEPs by volume were actually English.


Interactive Viz: UCAS 2018 Entry | Location of Applicant vs Location of HEI

Elena took on a Sankey, tried it many different ways: old school, hard way… the data prep way… several times… Unfortunately, this didn’t quite work, so she then took a punt at the Information Lab’s no data prep method. After a battle involving an undisclosed number of attempts, a few choice expletives and a delicate navigation of the nested table calcs, she struck gold and mastered the Sankey build in a record time of 7 mins (this still includes following the steps outlined in the blog post religiously)!

One word of advice: Ian Baldwins blog post is fantastic – it is written very clearly and has extremely useful screenshots of what your table calcs should look like. Just remember, when ha syas ‘make sure your calculations look exactly the same’, then make sure they are EXACTLY the same!

So what’s so special about the Sankey diagram? Well, Sankeys can show movement, a flow. It is frequently used to show poll data to show the proportion of voters parties loose or gain between two elections.

In this case, Elena chose to show what proportion of the total applicants chose each subject and how that differs between the gender and country region of the applicants. The best insight is seen when hovering over the right arm of the Sankey on either chart, or on the subject titles in the table at the top. This will reveal how popular this subject was for each group of applicants.

UCAS 2018.png

Interactive viz: UCAS 2018 Entry | Gender & Subject Comparison

The minute it was published it got Viz of The Day! 

As usual thank you for reading and we hope you enjoyed exploring our vizzez!

Dave, Adam and Elena

Team #VizualisingHE

#SWDchallenge |Feb18| Education

Whats this all about then?

In Cole Knaflic’s own words…

“To raise awareness and celebrate Black History Month, storytelling with data is collaborating with data.worldTableau Public, #MakeoverMonday, Viz for Social Good, and Data for Democracy to ignite the imaginations and talents of our respective community members around the data sets and data stories connected to Black History. Each week’s focus is on a different sub-topic. I’ve decided to make this month’s #SWDchallenge to be centred on education, specifically the access, benefits, opportunities, and ignorance-curbing power. Create a visual with this in mind and let’s use data to recognise the importance – today perhaps more than ever before – of education in our society.”

We at #VisualisingHE HQ thought this was too good an opportunity to miss, so we have decided to pull a few vizzes together to support this great initiative.

The @storywithdata TIP:

When it comes to creating effective visual stories: be thoughtful in your use of colour and words.

and more detailed brief:

Colour, used sparingly, is one of your most strategic tools when it comes to the visual design of you data stories. Consider not using colour to make a graph colourful, but rather as a visual cue to help direct your audience’s attention, signalling what is most important and indicating where to look. Note that for this to be effective, the use of colour must be sparing. If we use too many colours, we lose the ability to create sufficient contrast to direct attention.

Words used well will both ensure your visual is accessible as well as indicate to your audience what you want them to understand in the data. There are some words that must be there: every graph needs a title and every axis needs a title (exceptions will be rare!). Don’t make your audience work or make assumptions to try to decipher what they are looking at. Beyond that, think about how you can use words to make the “so what?” of your visual clear. I advocate use of a “takeaway title”—meaning, if there is something important that you want your audience to know (there should be), put it in the title so they don’t miss it. Also, when your audience reads the takeaway in the title, they are primed to know what to look for in the data. When I’m putting a graph on a slide, I’ll use the slide title for the takeaway (and put a descriptive title on the graph). When the graph is on its own, I’ll often title with both—typically “descriptive title: takeaway.”

So what did we knock up?

Dave stacked a delicious selection of waffles highlighting which subject areas are most diverse in UK HE (It’s not all that we expected):

Which subjects are more diverse (1)

Dave’s interactive viz is here

Adam slid in with a viz using UCAS end of cycle data resources focusing on acceptances of UG UK ethnic groups over time, highlighting the percentage change in ethnic groups and gender since the introduction of fees in 2006.

% change in student accepts to UK HE_tdsktpREVISED

Adams Interactive viz is here

Elena slammed it with some lesser spotted HESA staff demographics with a specific focus on the numbers and proportion of black staff and students in academia – both from the academic and research student points of view.


Elena’s viz is here

Thanks everyone,

Keep tuned for more from #VisualisingHE soon.

Dave, Elena & Adam

LEO – #RealTableau to Funviz

flow diagramv2

What is this post about?

LEO is back again as the topic of this #VisualisingHE blog post. Why? Tableau Zen Master Chris Love nicely pricked my attention with his tweet:

chris tweet

Charlie Hutcheson among many others have engaged with this great conversational starter and call out for some ‘real life’ examples of Tableau in the wild. For Charlie this ended his blog posting hiatus and encouraged him to write a great post Hiatus over thanks to #RealTableau along with sharing a flurry of superb examples of #RealTableau dashboards on Twitter.

For me, this prompted me to share one of my viz by day V’s viz by night Tableau endeavours. Often my #VisualisingHE vizzes are spawned from #Dataviz by day dashboards. #Dataviz by night explorations both deepen my understanding of a dataset and further my own data visualisation skills (often allowing my more creative streak to get an airing). The LEO data set is becoming my favourite of late, it is interesting and very topical in the current HE environment due to the introduction of TEF. The LEO dataset is public and therefore lends itself perfectly for this blog post about my flow from #RealTableau to a Funviz, showcasing vastly different approaches to data visualisation.

This blog post focuses on telling the story of the evolution of an idea, highlighting how much I value the #VisualisingHE project which encourages me to practice my Tableau skills and benefit from constructive critique.

A little intro to the dataset

In case you haven’t read my previous blog on Introducing LEO’s Graduate Earnings, the Longitudinal Education Outcomes (LEO) data enables us to know how much UK graduates of different subjects at different universities are earning now, either one, three or five years since graduating. It does this by linking up tax, benefits, and student loans data”. (WONKHE beginners guide to LEO).

#RealTableau to Funviz

#Dataviz by day

1. #RealTableau viz. This standard functional data visualisation is designed to give a user an overview of an single entity (HE provider) performances against a sector benchmark (median annual earnings post graduation), display the difference against the benchmark (£) and clearly highlight the subjects performing above and below the benchmark. (blue v’s grey). #barchart

Interact with the viz on Tableau Public:

LEO Provider vs GB Sector earnings_#RealTableau

Provider vs Serctor Median Ann Earnings by Subject area_realTableau

1 – #RealTableau

2. #RealTableau viz. This visualisation provides insight into the distribution of earning differences in relation to benchmark earnings for a particular subject. The business wished to understand a providers positioning within the range of differences in earnings across the sector, the viz allows for the selection of a specific provider (displayed as a larger dot in the plot). #boxplot

UEA difference

2 – #RealTableau difference

Punch the time card and head for home.

Que #dataviz by night

3. Searching for a better vantage point to see the data. Whilst the established #RealTableau vizzes provide insight into the relative performances of a single HE provider against a benchmark (per subject area), I wanted to get a better sense of the whole sector in one visual, so I started to try a few things out and explore the data.

This working viz provides you with actual earnings, difference AND the provider names, yet still this only looked at a single subject area of study at a time. I really want to get an understanding of the differences in salaries by provider and region in one visual.

sample bar

3 – Searching for the stat

4. Looking for inspiration I posted a few visuals to my friends and fellow #VisualisingHE comrades to see if they had any bright ideas.

Dave posted back with a short and sweet…. “I’ve got an idea but it will mess with your head”… #radialbarchart

daves radial idea

4 – Looking to #VisualisingHE for inspiration

He was absolutely right, a lovely challenge and a chart type I haven’t yet tried in Tableau, this should be perfect for presenting a ‘difference’ in earnings. Slightly less functional than the bar (4) but much more fun.

5. OK I know why I haven’t done one before… Math! Luckily Rajeev Pandy has written a fantastic blog about it and Charlie Hucheson had done a great take-apart Tuesday on a Radial Bar chart which should, in theory, allow for some pretty crude blind method copying.

Evening 1: didn’t go so well – more reading required! Confirmation that I need to understand these calculations rather than simply employing the blind copy and paste method! #notabigloverofunderstandingmathbecauseimreallyjustamusicanandloverofstorytelling

Frustrations messaged to team #VisualisingHE, met with encouraging words and support! Cheers peeps!

Que a bit more googling and a little read of Charlie’s blog to see if I pick up any pointers as to what I really need to understand and what I can just gloss over!

6. Evening 2: and the light bulb moment…………The issue was not the calcs, but simply that when reshaping the data – duplicating it up adding a reference id of 1 and 0 (necessary for this chart type), tableau brought the 0 and 1 identifier in as a number, you need to make this a dimension not a measure. Once this was on the worksheet, I got magical fireworks going off. #Wine


6 – Light Bulb moment

Happy faces and gifs galore from #VisualisingHE

7. Understanding the calcs and working out what they are doing in the chart (inner and outer radius). #justplayingandmakingprettypictures


7 – Pretty pictures

8. Utilising the ‘combine fields’ functionality in Tableau to get control of the lines and groupings of the data.

region coloured

8 – Controlling colour

9. Had a bit of a play with sorting. Firstly I wished to see if I could bring to life Dave’s drawing (4) but also wondered if it would read better if the differences were sorted by +/- sector median. #seashellaccidentalviz


9 – seashells and sorting

10. One big picture… Explorations in trying to present all the subject level data and the regional grouping splits together in one viz? #onebigpicture #fireworks


10 – One big picture

11. I love accidents! Whilst playing with the viz messing about changing the chart type I stumbled on changing the line chart to be a square, oh how pretty! #accidentalart #uselessbutpretty

when vizzes go funky

11 – #Accidentalart

12. Back on focus. Having settled on presenting the regional groups by use of colour, I set out on multiplying the sheets, to create a grid of radial bar charts in small multiples. I did need to unpick the ‘normalising calc’ in the viz to set it to a max difference for all the charts, so that they could be compared together in one viz.


12 – Pretty Happy with the look and feel

Next steps: A message out to #VisualisingHE seeking thoughts and comments on what’s missing in the viz. And help sizing the viz.

13. The value of friends to critique your viz is something I never underestimate. Dave sketched out his thoughts on the need for a top section of the viz to allow me to annotate the viz, describe how to read the chart and allow for space for the interactive highlights and filters to have their space in the viz. Elena on hand for #Vizoftheday advice on longer form vizzes and feedback. #whatthisprojectisfor

how about this final iterations

13 – Feedback and further thoughts

14. Ideas and comments. (13) Brought to life in Tableau.


14 – Added context

15. Funviz. The finished viz | LEO Provider vs GB Sector earnings_Infographic (click to interact with viz in Tableau Public)


15 – Fun viz

Hope this post has been interesting, and you have enjoyed following my flow of ideas and iteration with this particular dataset #RealTableau to Funviz

Adam #VisualisingHE



Times Higher World League table | Pillar Talk


#VisualisingHE does International League tables…

A laymans introduction to the pros and cons, and uses and abuses of league tables.

The main reason I’ve been holding off vizzing a league table to date is that our eyes have been caught by the less obvious, lesser vizualised and, potentially, less contentious open data sources. We are currently experiencing very exciting times in the UK: the drive for open access to data has never been stronger. Thanks to bodies like HESA, DfE and HEFCE much previously locked away data is making its way into annual publication cycles – many thanks for these rich sources of data to get our teeth into!

This post takes a side step from some of the HESA and UCAS sources #VisualisingHE have been blogging about recently and turns to the wonderful world of league tables…..

From experience “You either love them or hate them”,  which camp do pitch your tent in?

For me, it’s been a way of life and work for the last ten years. Content drawn out of these ranking tables feed the HE provider Marketing machines with key headlines, but, more importantly, may provide benchmarking on competitors’ performance and key insights into global trends, they could also be used to evidence key areas for strategic investment and required focus for improvements. For a provider to stay afloat in this heavily competitive HE environment, they need to be clued up to what’s going on around them in order to stay in the game, whether this is the recruitment of students or ability to land key research bids for example.

Why use league tables, and what are some of the limitation?

On the Domestic league table front the primary league tables include; The Times and Sunday Times Good University Guide (paywall protected), Complete Uni Guide and the Guardian University Guide. Whilst the stated primary aims are to provide a guide to prospective applicants seeking information to guide their university applications, one could argue they are also a handy source of bench-marked performance data. Overtime the robustness of this data and the methodologies used to compile the metrics have improved, allowing them to be a valid reference source. Compilers are much more open to involving professionals from the HE sector in the process of building a rankings table, seeking their buy in and professional expertise to advise what metrics and methodologies are sensible and robust AND WHAT THE DATA CAN SUPPORT.

So what’s the crack on the International league tables? Quite simply it’s the International’ness’ of it all…. Getting yourself on a WORLD ranking stage is quite some accolade, and may mean big business and potential revenue. If you take a look at any UK providers’ mission statement/corporate plan, it will have an international strategy strand. A league table, like it or not, is a key source for international students, agents and funders alike, seeking the definitive TOP ‘X’ shortlist of providers.

It is also arguably, just for bragging rights for some providers and academics to boost their egos!

Some of the International tables look pretty on the outside, but in all honesty don’t have a lot going on in the mechanics to substantiate the actual rankings popping out the other end. They are to a lesser or greater extent heavily subjective depending on the editorial slant. And for the analyst in us, do not always warrant much deeper analysis or use than a promo email signature or throw away twitter brag.

With the caveats and subjective nature of league tables aside, a league table can provide real insights into common performances across the world. However, within the HE bubble and on the public International league table stage, only the brave wrangle with key issues like identifying ‘robust’ globally common metrics and take a peek into Pandora’s box, exposing varying data quality standards and governance processes. International league tables may therefore be challenged at the core, and wrangle with these limitations to execute a ranking table.

How does the Times Higher Education (THE) World University Rankings reach its final rankings?

  • Teaching (30%) – 15% survey driven 15% data driven
  • Research (30%) – 18% survey driven 12% data driven
  • Citations – (30%) data driven
  • International Outlook (7.5%) – 7.5% data driven
  • Industry Income (2.5%) – 2.5% data driven

THE weightingsFor a full breakdown and debrief on how the table is made up, check out the the THE methodology page.

One key thing to mention is that the ‘THE’ lists the TOP1000 providers in the world, however, anything outside the TOP200 is banded. Frankly, this is a little annoying for visualisation, so I have focused on a subset of the publication I am ‘most’ interested in – the TOP200.

Where does the analyst and the vizzer come in?

League tables are by definition a ranking data table, it’s their brief and they generally do it well… that’s great, but it isn’t very insightful nor visually appealing to investigate. Therefore, #VisualisingHE have taken some time to make it a little easier to explore what’s going on.

How? You may wish to try tools like or Google sheets ‘IMPORTHTML‘, they come in very handy, easymorph can also be a great tool if you need to spin things around.

Starting to question the data

Q1 – Which countries make up the TOP200?

map_location of TOP200.png

Q2 -How is the little old UK doing in the big wide world of Higher Education?

THE World University Rankings 2018  TOP200_UKvs.PNG

Check out the interactive Viz to flip through the TOP5 per Continent: THE TOP200


  • UK providers make up 16% (31/200) of the TOP200 world rankings
  • European providers holding 51% of the TOP200 rank

UK v’s the Rest of the World | Digging a little deeper…

Q3 – Which pillars do the continents excel on?

UK vs_overview

  • UK outperforms the rest of the world on two pillars; International criteria worth 7.5% and Citations worth 30%.
  • Industry criteria is an emergent shortfall in the UK’s balanced score card (worth 2.5%)
  • Teaching and Research carry most weightings and fall short of the rest of the world median scores – but it is the rest of the world TOP200 we are comparing here, lest not forget.

Q4 – Where is this pillar prowess distributed across the globe?

UK vs.png

  • It’s a mixed picture, and fascinating to spend a few moments teasing out key strengths and weaknesses emergent when grouped by continent.

Which leads me to the main visualisation of this blog post that sets up the stage with an overview of the whole TOP200 in one view, encourages the viewer to explore the overall ranking and relationship between the scores across the pillars of assessment, whilst also giving the ability to highlight a specific HE provider of interest.

Q5 – How do the continents compare in the TOP200? And who excels in what Pillars of assessment?

THE World University Rankings 2018 TOP200_v2_overallscore

Go on.. Go beyond this screen shot and dive into to this interactive viz: TOP200 – How do the Continents compare


Continental ranking by median TOP200 rank

  • #1 – N America leads the way with a median rank of 63
  • # 2 – Oceania (80)
  • #3 – Asia (95)
  • #4 -Europe (125)
  • #5 – Africa (171).

#1 ‘Pillar’ talk (Continental Median score)

  • Overall score | #1 N America (67.5 out of 100)
  • Teaching | #1 N America (57.4/100)
  • Research | #1 Asia (63/100)
  • Citations | #1 N America (94.6/100)
  • International | #1 Oceania (90.6/100)
  • Industry | #1 Africa (88.5/100)

Hope you have enjoyed this little foray into International League tables, and how they could be used to glean a little more insight from the raw ranking tables by creating a few visuals and presenting the data a little differently.




Opinions and thoughts are mine and are not in anyway linked to that of my employer.

Teaching Excellence Framework | TEF

What is this framework of excellence you speak of?


#VisualisingHE investigates…..

The Teaching Excellence Framework (TEF) aims to recognise and reward excellence in teaching and learning, and help inform prospective students’ choices for higher education.

If you want to read up a little on the background to TEF check the HEFCE pages. If not, dive in and enjoy the ride…..

TEF in a nutshell:

The Inputs:

  • Feeding into TEF are a collection of standard metrics deemed core to identifying ‘teaching excellence’ in higher education.
    • The metrics include student satisfaction on teaching on the course, assessment and feedback and provision of academic support during studies, dropout rates and employment destinations.

These key metrics are sourced from an annual survey about student satisfaction (the National Student Survey – NSS), dropout rates of students from standard provider HESA returns and employment success rates sourced from an annual destination of leavers survey (DLHE). These scores are bench marked by HEFCE for a provider to aspire to at institutional level.

  • In addition to these data driven performances, a supporting statement is written by each provider highlighting what great things they have done to date and what they are working on to improve key metrics.

The Output:


In what I can only imagine to be a ‘X’ factor styled showdown…

  • An HEFCE panel of experts assembled to assess this provider level data and textual statements to come up with a rating of Gold Silver and Bronze per provider.

What happened next?

There was a medal ceremony (well the data was released on the 22nd June 2017 to the public)


  • Some people challenged this initial synopsis
  • Many people wrote about it
  • Many hours have been spent analysing the data and understanding what it could mean for the sector going forward.
  • NEXT….Subject level TEF looming #TEFstillhotontheHEagenda

In summary

TEF has caused one heck of a hullabaloo in the HE sector……… featuring heavily in strategic decision making and has reached to the core of HE operations.

Since the data was released in June 2017, many a HE analyst and journalist have been busily crunching the data, splicing, dicing and waxing lyrical. As an example, WONKHE alone have written 140 articles tagged as TEF to date, that is a lot devoted to one topic……

VisualisingHE investigates

In this post I make no attempt to try and summarise what has been written about over the last two months, but do take a look if you have the time as there are some very interesting articles to get your teeth into. What we would like to do is viz a few facts sourced from the data.

Therefore #VisualisingHE have put together a few headline vizzes which hopefully introduce TEF to anyone that reads our blog, and, encourages a dabble into provider level performance underpinning the metrics.

Hope you enjoy our vizzes and insights in Tableau:

Higher Education Provider Medal distribution

TEF_The rings


  • Wowza – 33% of HE providers got GOLD! that’s quite a statement for HE Education in the UK.

England – Higher Education Provider distribution by region

TEF by Region

Interact with the viz: TEF by region


The TOP3 regions rich in GOLD:

  • #1 East Midlands – 89%
  • #2 East of England – 44%
  • #3 West Midlands – 42%
  • The East Midlands is by far and away the hot spot for GOLD laying claim to more than double the proportions achieving gold in other regions.
  • The North East and Yorkshire and the Humber heavier in Silver and the London Universities slightly weightier than other regions in the proportions gaining Bronze awards

UK | HE Provider – distribution by region

Who is paved in GOLD|SILVER|BRONZE?

TEF Awards by Region_HEonly

Interact with the viz: TEF by region (provider) to view which providers gained gold silver and bronze in which region.

TEF Awards by missions group

TEF colours_higher education institutions

Interact with the viz: TEF by mission group


  • Across the Higher Education sector 32.8% of HE providers achieved GOLD status, 49.3% SILVER and 17.9% achieved BRONZE.
  • For the Former 1994 Group 36.4% of HE providers achieved GOLD, 45.5% SILVER and 18.2% achieved BRONZE
  • And in the revered Russell Group 38.1% bagged GOLD, 47.6% coming in in SILVER and the remaining 14.3% of providers picking up BRONZE

TEF awards – The Metrics and splits deep dive

Take a deep breath and have a look at the underpinning demographic splits also assessed in the TEF. These splits are also presented by category to help unpick areas for a provider to address and improve.



Interact with this viz: Teaching Excellence Framework – Awards


Knowledge is with the beholder…

Interventions in the hands of the provider…


For the good of the student

Hope this has been an enjoyable and informative read.

Adam #VisualisingHE