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27 November 2024 | 6 min read

A storyboard communicates a story through a sequence of images that chronologically maps the story’s main events. They are common in film, animation, or comic books, but can also hold great value for product design. This is how I created a storyboard using Midjourney to support our product strategy and roadmapping process. We used the outputs in workshops with commercial and delivery teams to align on a shared vision and ensure user-focused decisions.

Great session. I really like the AI generated images to bring the Ideal Customer Profile to life. — Account Manager

Engaging and helped me to better understand how our offerings are targeted towards our target personas and wider strategy. — Technial Operations Manager

Using storyboards in UX or product design has various benefits:

• Communicate ideas visually. Using stories and images makes a storyboard easy to understand and remember. It can communicate solutions to complex issues in an engaging way.
• Create focus and alignment. Storyboards help to communicate an overall vision without getting hung up on details. They can serve as ‘north star’ in roadmapping and prioritisation.
• Put the user first. A storyboard can bring a persona to life and the problems they are facing. They are great for building empathy and ensuring user centric designs.
• Provide context. Storyboards show end-to-end user journeys, are based on real-life scenarios, and can provide additional context to tech teams and stakeholders.

There are three common storyboard elements:

1. Scenario: Storyboards are based on a user story with a clearly defined persona and problem to solve.
2. Visuals: Each step is represented visually in a sequence through sketches, illustrations, or photos on the environment, quotes from users or the product itself.
3. Captions: Each visual has a corresponding caption to describe the user’s actions, environment, emotional state, etc. They should be as concise as possible.

The process for creating a storyboard usually includes steps like data gathering, defining a persona and problem/scenario and a storyline that follows good practice storytelling. You can follow one of these useful frameworks from Nielsen Normal Group, UX Collective, UX Design Institute or Fresh Books.

Once you got your storyline and content for each panel, it’s time to create more refined visuals. After a bit of research, I decided to test out Midjourney as it seemed more capable in creating a series of images with a consistent character than other tools.

A prompt is a short text phrase that the Midjourney Bot interprets to produce an image. More advanced prompts can include one or more image URLs, multiple text phrases, and one or more parameters.

Find out more about how to craft your prompt in the official Midjourney documentation.

A good tip for creating consistent characters is to ask Midjourney to build a character sheet first. In film and animation, these are used in making concept arts of characters to show their emotions, posture, gestures etc. I found this guide and used the following prompt to generate my main persona for the storyboard.

Midjourney will output four slightly different versions based on your prompt. Here are mine.

Using the character I just build, I started to generate scenes following a similar structure.

• Text describing the scene
• The character image I just built in Midjourney to use as a character reference
• An image with the overall composition
• An image for a style reference

The prompt would look like this:

And here are some example outputs.

Unfortunately, not all images will come out exactly as you want. I came across several limitations that required some post editing in other tools.

• Strange/undesired content in the background or on monitors.
• The main character showing up multiple times in the image or on a screen, some of those really made me laugh.
• When I tried to incorporate a second person, Midjourney ended up mixing the two character references into one and showed the same person twice.

For the final touches, I used Figma and Photopea (a free Photoshop like application) to remove some of the AI weirdness and create the sequence of scenes. This is also where I added captions and speech bubbles.

• Using the Vision Storyboard to keep teams aligned and focused
• Product visioning through storytelling
• Storytell your Product Vision with a storyboard
• How We Used Storyboards to Make Our Company Vision More Tangible
• How To Create a Storyboard: A Guide for UX/Product Designers
• Storyboards Help Visualize UX Ideas
• Advanced Storyboarding with AI
• I just tried Midjourney’s new consistent character feature – here’s how it looks

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28 June | 4 min read

In 2020, I became tired of not being able to do a single shopping trip without buying and consuming plastic. So I decided to track our plastic waste for a whole year and see how much plastic we (my partner and I) were actually consuming. Luckily, I found MyPlasticDiary to help me record plastic use everyday. For each item you select a category and then record its weight. The app allows you to track your consumption against a weekly target and also gives scores (a combination of item type and weight). Six months into 2022, I finally managed to analyse my data and write up what I learned.

Recording plastic everyday for an entire year was very time consuming. Even though MyPlasticDiary includes preset categories and gives you approximate weights, I often weighed items on our kitchen scale and put in the exact measure (why wouldn’t you?). Some days I forgot to record items and included them the next day. I also didn’t record while away on holidays. So, gaps in our data didn’t  necessarily mean that we didn’t consume any plastic that day.

Using predefined categories in MyPlasticDiary, I could look at our plastic consumption by types of products. Unsurprisingly, bottles and pots/trays made up the biggest amount in terms of weight, but bags and wrappings accounted for 40% of all items we threw away. And I could even look at subcategories, for example we threw away 251 Tetra paks, 78 yoghurt pots, 60 carrier bags and 39 shampoo bottles.

I didn’t just want to track our plastic consumption but also reduce it. Some things we changed (or at least tried):

• Stop grocery delivery to have more control over our shopping
• Use cotton bags for loose vegetables
• Buy larger package sizes for things like yoghurt, bottled water, rice, pasta
• Use reusable coffee cups again (shops didn’t accept them at the height of the pandemic)

We’re still buying some bottled water but also use a water filter for tap water. We didn’t want to start spending significantly more money, eg. in special shops that don’t use plastic. So in the end our plastic consumption decreased only slightly, even though we were even more aware of our shopping behaviours.

While I tried to record our plastic waste as best as possible, it is still an underestimate of our actual consumption. It is missing things we forgot to track, plastic we consumed when being out and about or plastic we didn’t know was plastic (eg. in clothes). For example, I often forgot to record bin bags themselves (each bin bag weighs around 7g). Our data also excludes items where it was unclear how much plastic they are made of, eg. crisps bags which look like foil but actually contain metallised plastic film. Another thing I didn’t track consistently were menstrual products. But recording parts of it made me finally switch to using a menstrual cup.

It isn’t just hard to consistently track your own plastic consumption, but also to find data to compare yourself against. First of all, there are different ways of looking at it. Most sources either focus on single-use plastic waste while some studies have tried to estimate the total plastic waste per person, including single-use plastic as well as waste from businesses, industry and agriculture. Considering that some of our data is missing, it’s not surprising that our consumption is below both the EU (31kg) and UK (44kg) average for single use plastic waste per person. The latest estimate on total plastic waste claims that the average person in the UK produces 99kg of plastic per year.

Plastic is destroying our nature, wildlife and human health. If we really want to tackle plastic pollution, we need a system change, better data and stricter policies.

I still can’t believe that companies aren’t obligated to publish details about their packaging, which would make it easier to get this kind of data. While you can scan barcodes to get calorie information, the same doesn’t yet exist for plastic because we simply don’t know enough about plastic in packaging (how much, recycled or not, what type, etc.). MyPlasticDiary is currently developing a service to help retailers show information about plastic packaging online.

Consumers alone can’t be responsible for solving the plastic crisis. We need reforms that encourage recycling on a large scale and also acknowledge the role of corporate polluters. More importance should be placed on alternative packaging solutions and ban single use plastic beyond plastic straws. If you want to read more on this topic, check out this article: More recycling won’t solve the plastic pollution – it’s a lie that wasteful consumers cause the problem and that changing our individual habits can fix it.

Most of the analysis is based on my own plastic use data tracked using MyPlasticDiary. The app itself gives you some plastic use stats to see if you are on track against the goals you set. At the moment you can’t export data, but the team behind the app was super helpful in sharing a csv file with me when I got in touch. Data for plastic in period products was sourced from wen and the European Commission.

There was no need for extensive data cleaning and I made only small adjustments. I changed the weight recorded for Tetra Paks to 20% of their full weight, as this article suggests 14% of Tetra Paks are plastic sheets and 6% bioplastic caps. As I only started recording on the 6^th of January 2021, I used data from January 2022 for those first few missing days.

Analysis was done in python as well as most of the visualisations using matplotlib and calplot. All my code can be found on Github.

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26 July 2021 | 4 min read

As part of my job as a Data Analytics Lead I am helping my company to upskill around all things data. So a few months ago, I started sharing #ThursdayTools with my team each week and I thought these might be useful for others too. So here is a list of open source, non-code toools that I use for data viz, data for good, charity data, maps, infographics and more.

Mapping geographic data often means working with postcodes. Luckily David Kane has built Find That Postcode, a tool that lets you upload a list of postcodes and select what geo data to match, including longitude/latitue, Local Authority, MSOA, and much more. It’s such a timesaver and super easy to use.

Community Lens was developed by the Data Collective – a community for people using data in the social sector. It is another great tool to explore local needs. Upload a list of postcodes, e.g. from beneficiaries, and get an output map with your pins as well as heatmaps based on measures of deprivation you select.

The British Red Cross Covid Vulnerability Index is a fantastic resource to explore local needs. It was created similar to the Index of Multiple Deprivation but includes different dimensions (clinical, health, economic, social, socio-economic). You can download data on Ward, MSOA and Local Authority level or use the interactive map.

If you are interested in socio-economic data on cities in the UK, the Centre for Cities has built an interactive tool that lets you select from a range of indicators (eg business, population, housing, employment) for 63 cities. You can look at a point in time or a time series. Download data or as image.

Parli-N-Grams built by Guiseppe Puntofisso displays the frequency of words in UK parliament debates. You can search for words or phrases in the House of Commons or House of Lords and get a timeseries starting at 1919. Also lets you compare different terms against each other. Super interesting to look at trends over time.

360 Giving is a charity setup to help grant makers in the UK publish their data in a standardised way and make it accessible to everyone. You can use grantnav or 360Insights to explore and download grantmaking data from over 197 funders.

If you are interested in data on charities in the UK, head to Find That Charity built by David Kane. You can search for charities by using their name or charity number, upload a list of charity numbers and match data against it, get links to other official registers, etc.

Another great tool to explore charities in England and Wales is CharityBase created by Dan Kwiatkowski. You can search for charities or directly access the API. Much more user friendly than the Charity Commission register itself.

DataBasic is a suite of easy-to-use web tools that introduce concepts of working with data. The tools let you upload data and explore data in a fun and interesting way. You can use it to count words in a text, find out what’s going on in your csv file, compare differences between two texts or run a network analysis.

Turning your data into a stunning data visualisation doesn’t have to be time consuming. Krisztina Szűcs has developed Plot Parade – an amazing data art project providing a number of layouts ready to use, just pop your numbers in and export as an image.

If you got geographic data to map and need something quick and easy, Dark Horse Analytics have have built Map in Seconds which is literally that. Select a region and copy/paste your data, map done. Export map as pptx or png file. Unfortunately, because it is so easy to use, the number of regions are fairly limited/high level.

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8 May 2021 | 6 min read

With the start of the pandemic, many charities lost big chunks of income basically overnight. While there were lots of individual stories, it was much harder to predict the impact for the charity sector as a whole. In the early months, a national campaign pushing for government support foresaw a £4.3bn loss in income for charities. However, this estimate was based on historic, two-year old data and a fair amount of assumptions on which income sources would be affected most. These data gaps led to the creation of a Data Collective aiming to help charities and other civil society organisations to access, analyse and share data. Others, such as Pro Bono Economics started advocating for better data on charities.

Working with charity data for more than five years now, I felt one piece of the puzzle was missing. Where do you actually find data on charities in the UK? And what are the limitations? This blog lists some of the most important data sources, largely for my own benefit, but also to help others navigate the charity data jungle. For more links and resources take a look at this list collated by the Data Collective.

Charities registered in England and Wales (169,814) account for 84% of all UK charities (201,861). The Charity Commission in England & Wales (CCEW) is one of the most important data source for the sector and holds a lot more historic data than the other two regulators. It can be accessed in three ways:

1. A website
2. A data download
3. An API

In 2020, the Charity Commission published a new public register (website) which provides information on the sector as a whole and details for individual charities. It also offers a free data download that contains all current and removed charities as well as historic financial data. Previously, the data dump came only in the form of .bcp files, a specific database format. Since March 2021, data is also available as csv files which will supersede the legacy extract. For more info on how to access the legacy data files, read this guide. In addition, the Charity Commission maintains an API that can be accessed with a developer account.

In addition to the regulators, there are a number of data tools making use of the official data, for example:

• CharityBase: Free and open source database, API and web app with data on charities in England and Wales. It uses the Charity Commission data but adds in grant making data from 360Giving. Data can be accessed on individual records or downloaded in bulk.
• Find that Charity: A database of UK non-profit organisations that draws on all three charity regulators, as well as other regulatory sources. In addition to charities, it provides information on Community Interest Companies, Registered Societies, Universities, Local Authorities, Sports Clubs and NHS Bodies. Data can be accessed on individual records or downloaded in bulk.
• 360Giving: Initiative to support grant making organisations to publish their grants data in an open, standardised way. Data can be downloaded for individual grant makers or as a whole.

There are a range of other applications such as the Charity Explorer for England and Wales and the Scottish Charities App. However, they are not directly linked to the original data sources and depend on manual updating. Hence, the data can be out of date very quickly.

For anyone who is interested in the sector but doesn’t want to undertake analysis themselves, there are a couple of resources too, namely:

• UK Civil Society Almanac: An annual publication on the state of the UK voluntary sector. In addition to regulatory sources, it draws on a sample of 10,000 charities for financial details and also gives insights on the sector’s workforce and volunteers. Some of the sample data can be accessed through the UK Data Service.
• Small charities data: A digital research hub that brings together the latest and best available data on small charities in the UK, those with an income below £1m. It uses a range of data sources, including CharityBase, the UK Civil Society Almanac, 360Giving and many others.
• Foundation Giving Trends: Annual report providing key facts and figures on giving, income and assets in the top 300 UK independent charitable foundations.
• Third Sector Trends Survey: A longitudinal study of the voluntary sector in North England. It is based on surveys and draws on total population estimates from the UK Civil Society Almanac.
• TSRC: Third Sector Research Centre at the University of Birmingham undertaking various research on charities.
• VSSN: Voluntary Sector Studies Network, a community of researchers studying the voluntary sector.

Official data is patchy and limited. While all three charity regulators offer free data downloads,  data differs between them. In addition, charities have 10 months to submit their annual accounts, so official data is always slightly outdated. Smaller charities usually have to provide little information, so even fewer data is available. There is also a mismatch between data that’s available through the public register versus the data downloads.

Data does not cover all social organisations. While there is quite a lot of data on registered charities, it does not cover all civil society organisations. Data for some other types, e.g. social enterprises, community benefit societies or cooperatives, can be found on Companies House or the Mutuals register. While there is very little information on below the radar organisations.

Data is often too broad. While knowing the total number of charities and their income can be helpful, understanding the local picture or specific subsectors is often more useful. However, there is no single classification for subsectors and geographic breakdowns are always impacted by a head office effect, i.e. where charities are registered is not necessarily where they operate.

For more limitations and what’s needed, take a look at the latest report by Pro Bono Economics or this article as part of the Data Collective.

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February 2021 | 4 min read

Well, 2020 was a challenging year, that’s for sure. For many, the pandemic meant struggling with health problems, finances or even loss of loved ones. But there was also (re)discovery. With millions confined to their homes, people had to find different ways to fight ‘lockdown boredom’. Old hobbies were revived and new ones picked up, of which among the top trends has been bread making. In spring 2020, homemade bread started trending on social media as supermarkets recorded yeast and flour shortages, and bread making was even declared a meditative and empowering act.

As a sourdough lover and baker myself, I decided to explore lockdown baking in more detail. I wanted to get an idea of how many more people had been baking with sourdough and whether their engagement had changed during the first lockdown period. So I collected all posts in the Reddit sourdough community for 2020 and found some interesting trends.

In 2020, more than 16,800 Reddit users posted a total of 40,525 submissions on r/Sourdough. Both, the number of users and posts increased during March and peaked in April and May. This trend matches with other countries introducing tighter restrictions during spring to control the spread of Covid-19. Well over 100 countries had imposed either a full or partial lockdown by the end of March.

For accessing and retrieving data from Reddit, I used Praw and Pushshift. The official Pushshift Reddit API documentation and articles by the data science community helped me with this task, in particular Using Pushshift’s API to extract Reddit submissions and How to Scrap Reddit using pushshift.io via Python. These general tips for webscraping were useful to combat issues around server overload.

More documentation and code can be found on my Github.

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14 December 2020 | 4 min read

I recently left my job at NCVO and my lovely team gave me some great leaving gifts, including a book with 100 maps of London. Feeling inspired, I’ve been playing around with some mapping tools for geospatial data. This blog shows some of my experiments using QGIS, an extensive open source desktop software.

QGIS is free and relatively easy to setup. Once you downloaded the latest version, you are ready to go. The tool allows you to import and work with different kinds of geospatial data. It also comes with Open Street Map – an editable map of the world with underlying geodata for example about landuse or buildings. To access data for a specific area, I installed the QuickOSM plugin and downloaded data for my home town in Germany. I then filtered for different types of land (parks, forest, residential), rivers and buildings. This page lists all the map features that Open Street Map uses to describe and tag geographic data. Once I was happy with my map, I used the layout function to include a title and legend. Below is the final result.

With Open Street Map you can visualise an incredible amount of detail for a specific area. I came across this beautiful visualisation of Kew Gardens in London and thought I might try something similar. For my map I chose the Bergpark Wilhelmshöhe, a landscape park in Kassel, Germany. It is the largest European hillside park, and second largest park on a hill slope in the world. The construction began in 1689 and took about 150 years. Since 2013, it has been a UNESCO World Heritage Site. Although I am fairly happy with my final map, I didn’t even get close to what other GIS visualisation experts are able to produce.

Of course, I wanted to use QGIS to visualise some data. For testing purposes, I used a fairly simple data set to look at population statistics in London over time. I downloaded a shapefile (.shp) for London boroughs here and imported it into the software. Then I got the ONS population estimates and calculated the percentage change for each borough. I imported the data as a csv file and then merged the attributes with the shapefile based on the borough geo code. Finally, I created five categories based on the change values and mapped them using different colours.

The end result reveals some interesting findings. All boroughs have seen a rise in population between 2001 and 2019, except for Kensington and Chelsea where numbers have fallen by 4%. In general, the south west and south east have seen smaller increases than the north and north east.

There is endless data you can visualise on a map. Free GIS data lists a range of free geographic data sources including some country specifics for the UK. For the map below, I used data on broadband connections by Ofcom made available on CDCR. This data is collected on output area (OA) level and you can download the shapefiles for London here. With a bit more time, I could have mapped postcode data against the output areas for a more granular view like in this Financial Times article.

Obviously, there is a lot more you can do with QGIS. You can produce more complex maps, calculate fields, do 3D modelling or automate tasks by using QGIS and python. After a week of playing around with the tool, I think I got a good feel for the software and would definitely consider it for future projects.

If you are interested in creating maps, then I’d recommend taking a look at the #30DayMapChallenge. It is an annual cartographic challenge where participants from all over the world created beautiful maps on the theme of each day. You can find some great examples in the official repository or follow the hashtag on social media. The repository also lists a number of tools and resources to get you started with visualising geospatial data.

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3 December 2020 | 5 min read

Imagine whispering mummies, laughing shadows and invisible dogs … If you like the idea then the three investigators is for you: An American juvenile detective book series first published as “Alfred Hitchcock and the three investigators”. Three young boys investigating baffling phenomena by application of reason and logic. Created by Robert Arthur Jr in the early 1960s, the books were translated into many languages.

The series became particularly popular in Germany as ‘Die drei ???’ (the three question marks). So popular in fact, that from 1993 the German publisher Kosmos continued the series after the American version stopped. The German detectives even got their own audio drama, video games, graphic novels and theatrical plays.

As a huge fan of the series and data lover, I wanted to explore the audio drama in more detail while learning some new data science skills. Thanks to the fansite Rocky-Beach.com, I was able to collect data on each episode, and this is what I found.

In 1979, the German publisher Europa started a radio drama based on the book series. Since then, they produced at least one episode each year, with two exceptions. When the American titles stopped, the production had to pause for one year. A litigation between the book publisher (Kosmos) and the audio drama publisher (Europa) over naming rights caused the second gap in 2006-07. Today, with over 50 million sold copies, Die drei ??? is the most successful audio drama production worldwide.

Since all three investigators and the narrator are male, it’s not surprising that in most episodes the majority of characters are men. However, even if the main characters are disregarded, women make up about 29% on average of the side characters. There are 11 episodes with no female roles at all, while the episode with the highest proportion of women of all side characters is 75%. The proportion of female characters has varied over time, with no consistent trend line. However, the average went up slightly when the original book series ended in 1993.

The three investigators are all about mystery and strange incidents, which is reflected in their book titles. Paranormal subjects are the most common theme, appearing in 48 titles. This includes words such as ghost, demon, vampire or devil. Haunted places such as castles, villas, mountains or canyons are also very popular. Fewer titles include animals, people or words about mystery and death. With five mentions each, the most common words are Schrecken (horror), Drache (dragon), schwarze (black), Rache (revenge) and Spur (trace/clue).

Most of the data is sourced from the extensive fan website Rocky-Beach.com. It lists all of the episodes with title and descriptions as well as meta data such as author, actors, publication date, duration and more. Using python, I was able to scrape this data from the website and combine all individual tables in one big data set. To determine the gender of actors, I used the Genderize API to predict gender based on their first names. Background information is largely based on information from Wikipedia.

All analysis code and details about the methodology can be found on Github.

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18 October 2020 | 5 min read

What are charities? How big is a small charity? What do charities do? If you are working in the sector, you might think these are trivial questions. Having spent more than four years researching on charities, I can tell you that there are no easy answers.

It’s not unusual to have different understandings of social concepts like charities, so this is not a problem per se. For research, however, shared definitions are useful when comparing data sets or making assumptions about different parts of the sector. If more researchers adopted the same definitions, the evidence base on the sector would grow immensely and, with it, the ability to demonstrate impact. This blog summarises some of the key issues when defining the sector.

Voluntary sector, charity sector, non-profit sector, third sector – there are many, often interchangeable names for the sector. For example, at NCVO many of my research outputs use the term ‘voluntary organisations’. However, when talking to the media or public, I often switch to ‘charities’ as it’s more commonly understood. This isn’t perfect, but it’s the best compromise.

Similarly, it’s often unclear what kinds of organisations are included in ‘the sector’. NCVO’s annual sector report – the UK Civil Society Almanac – refers to the ‘voluntary sector’, but mostly focuses on registered charities, as that is where most data is available. And even then, only certain kinds, for example, it excludes universities with charitable registration. NCVO accepts social enterprises as being voluntary organisations and hundreds of its members are social enterprises. It’s unclear whether most people would include, or are thinking of, social enterprises when thinking about ‘voluntary organisations’ – or whether they refer mainly to registered charities and unregistered community groups. I’ve often seen research projects in or about the sector that seem to be unclear about what kinds of organisations they are referring to or aiming at.

How big is a small charity? It’s a surprisingly contested question.

Most often, income is used to determine size of organisations but even then, different groupings exist. For example, the Small Charities Coalition defines ‘small’ charities as those with an income under £1m. While the NCVO Almanac uses a more granular approach and distinguishes between six different income bands. For example, ‘micro and small’ charities are those with an income under £100,000. There are a number of different groupings used by other infrastructure bodies.

Charities work across many different sectors. When charities register with the Charity Commission in England and Wales, they are classified based on three questions.

1. What does your charity do? (its purpose, eg. health, environment)
2. Who does your charity help? (its beneficiaries, eg. children, older people)
3. How does your charity operate? (its specific activities, eg. grant-making, service delivery)

One of the main problems is that charities self-select their own categories and can pick as many as they wish. Some categories are very broad (‘general charitable purposes’), while more specific activities such as food banks or homelessness services are not included. As a result, these categories are not very helpful for exploring how different sectors are doing or how many charities are active, for example, in mental health. The Charity Commission is currently rethinking their classification system, but it will take years until new categories are available for all charities.

NCVO uses a slightly amended version of the International Classification of Non Profit Organisations (ICNPO), which puts organisations into one single category based on their main activity. This is somewhat more helpful for looking at different subsectors. But some of the categories are still very broad, eg. ‘social services’, and often do not reflect the reality of many organisations that operate in more than one sector. For example, a community centre that also runs a sports club. Official surveys will often use other classification systems, such as the Standard Industrial Classification (SIC), while many other projects apply their own categories depending on their needs. This is particularly true for funders who might be interested in specific areas and therefore use their own subsector frameworks.

For many of these issues, there is no one size fits all. However, when collecting data on the sector, researchers should be clear themselves of the definitions they are using and why. Making use of existing frameworks allows for better comparisons while custom definitions might be more appropriate for very specific audiences. The more data can be brought together and matched up, the stronger the evidence base is for charities to make their case.

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17 August 2020 | 5 min read

As part of my role at NCVO, I lead on the UK Civil Society Almanac – an annual research publication on the state of the charity sector in the UK. Since its first launch in 1997 as hardcopy only, the Almanac kept evolving and went online in 2012. However, when I started managing the project in 2018, the website and print publication felt outdated and incoherent while the content had almost become a copy and paste exercise. I was tasked with a redesign and this blog summarises what I learned.

In line with greater digitalisation, the Almanac is now mainly published online while the hardcopy has changed from 170 text heavy pages to a 50-page professionally designed summary report. Compared to many other research outputs our starting point wasn’t that bad, but at a closer look some issues emerged.

In 2012, the Almanac website was built by a member of the research team using WordPress. Even though this gave us control over content, it also meant that a lot of the team’s resources were going towards maintaining the website. Plugins like HighCharts and TablePress provided great flexibility for data visualisation but required skills in Json and CSS to create nice looking charts. The print publication, which was produced simultaneously, lacked connection with the digital version in terms of messaging and visual design. The print publication had been outsourced to the same agency for more than 10 years and did not meet data visualisation standards that had advanced in recent years.

It became clear that we had to come up with a more consistent narrative and bridge the gap between the different outputs. The publications should be driven much more by the needs of our audience.

You might think a research report is not a brand. This is probably true for one-off smaller publications, but it is highly effective for a project like the Almanac that consists of multiple outputs and channels. Over the years while the Almanac had changed with the times, it had not adopted a consistent communication strategy. I was able to commission Furthr – a branding and data visualisation agency – to run a three-hour workshop to get clarity on the Almanac’s goals, audience, benefits and tone of voice. With external support we were able to pin down what the main objective was and make hard decisions on our main audience and what they should take away from our research. As an overall goal, the Almanac should work for our core audience of senior stakeholders such as CEOs of charities, in terms of content and the way it is presented. This guided all our later decisions on our outputs and how we communicate.

At the same time, there was an organisation wide project on the way to create a new digital publication platform. This meant we could use the new content management system for our Almanac website and the development was led by the digital team. During this process, we met regularly to review the progress and ensure the tool would meet our needs.

For more specific research content, such as tables and charts, we had to find a suitable data visualisation tool that could be integrated with the website. So I signed up to various data visualisation libraries and compared their offers. We decided on DataWrapper as it’s easy to use, has a clean look and a lot of free features. Although it isn’t as flexible as HighCharts, it reduced the amount of time and skills needed to produce professional looking charts that were on brand. For the print edition, I found a few design agencies focusing on data visualisations and put together a design brief based on our goals. We then decided to go with Data Design Studios as they had a lot of existing data skills, including in-house researchers, and their proposal linked well to our brief.

While working on the technical development, we also reviewed our published content to make it more centred around what our users wanted to know. As a first exercise we created a Knowledge Board based on previous user research. Fortunately, we had a range of user data to draw from and drive our discussions, including stakeholder interviews, surveys with charities and Google Analytics data. This drove our decisions around the structure of the report, the topics we would cover and the prominence of each. Although, a lot was limited by our methodology, we also added more additional data sources to meet our users’ interests. We also added a wider range of content, including definitions we’re using and downloadable data tables.

While focusing on user needs, we also wanted to improve our content based on evidence and good practice in communicating research. Previously, most of the heading and chart titles within the Almanac were purely statistical. But research has shown that good headings and titles are more memorable when they are descriptive. The ONS digital team has done a great job in sharing knowledge of how and why they are changing the way they communicate statistical information. To help users understand the content more quickly, we started to focus on the key messages. We also prioritised them in terms of how important and useful they are for our main target audience – decision makers in charities – to reduce overall noise. We did this across all our channels (website, print and social media).

Our plans to create a more coherent content strategy, to change print designers and develop a new website in one year were very ambitious, but we did it. I was extremely pleased with the overall outcome and we received a lot of positive feedback. Since then, we have continued to improve things, including making use of Google Analytics to inform our content and homepage layout as well as adding spotlight sections on interesting aspects based on stakeholder input.