{"id":32236,"date":"2021-05-11T11:41:36","date_gmt":"2021-05-11T10:41:36","guid":{"rendered":"https:\/\/digileaders.com\/?p=32236"},"modified":"2021-05-13T12:03:39","modified_gmt":"2021-05-13T11:03:39","slug":"presenting-data-5-tips-for-making-your-data-understandable","status":"publish","type":"post","link":"https:\/\/digileaders.com\/presenting-data-5-tips-for-making-your-data-understandable\/","title":{"rendered":"Presenting data: 5 tips for making your data understandable"},"content":{"rendered":"

Catherine Hope, Digital Performance Analyst at DWP Digital presents her 5 top tips for presenting data and making it accessible to all.<\/strong>\u00a0<\/span><\/p>\n

I\u2019ve been passionate about presenting data clearly for over 10 years now, and recently took the lead of a cross-government group of \u201cPresentation Champions\u201d. We share best practice for designing charts and tables, and we\u2019re learning about writing for the web to help everyone understand the huge amount of data that government publishes.<\/span><\/p>\n

In this role and in my day job as a statistician for DWP Digital, I tell stories with data.<\/span><\/p>\n

Sometimes these stories help teams to make decisions about whether a change they made to a process has worked, or if they need to try something else. Or sometimes the stories make government data available to the public in an easy-to-understand way.<\/span><\/p>\n

In all cases, the most important thing is to write for everyone, and to tell the story in a way that your readers understand. With that in mind, I\u2019ve pulled together some tips on presenting data that I hope will help you to make it as open and accessible as possible.<\/span><\/p>\n

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You are not\u00a0your\u00a0audience<\/h2>\n

It\u2019s really easy to write something or present data in a way that you understand. But you have a lot of baggage: your subject knowledge of the thing you\u2019re presenting, its historical context, your technical knowledge of the way you did the analysis, and what conclusions can be made from the analysis. It would be very unusual for everyone seeing your data presentation to have exactly the same knowledge as you. Otherwise, what would be the benefit of sharing that knowledge with them?<\/span><\/p>\n

It\u2019s essential to think about who your audience is going to be, and to write for them. If you don\u2019t know who your audience is going to be, like when I present government data for the public, you can\u2019t make any assumptions about what they already know.<\/span><\/p>\n

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Keep it simple<\/h2>\n

The use of plain English will make your analysis easier to understand for everyone – even those as steeped in jargon as you are. Nobody ever read a simple sentence and thought \u201cwell, that was too easy to understand.\u201d<\/span><\/p>\n

Jargon and acronyms, which are often used in government, can alienate people. You might be presenting your work to a team you know really well and assume everyone knows what HRTs and JBCs are. (habitual residence test and the Joint Biosecurity Centre if you were wondering). However, if one member of the team is new to the role, you risk making them feel excluded because they can\u2019t keep up.<\/span><\/p>\n

Explain your acronyms, and give a bit of context about the subject.<\/span><\/p>\n

This is the case with technical concepts and language too. When I analyse whether a change to a process has made a difference, I use statistical tests to see whether that change might have happened by chance, or whether it was a \u201csignificant\u201d change. When I present the results to the team, I will always talk about what the test means and what conclusions we can or can\u2019t make.<\/span><\/p>\n

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Use annotations to help tell the story<\/h2>\n

I\u2019m steeped in the detail of the things I present analysis about, but my audience might not be. I try to tell the story and give enough context so that people can understand why the data I\u2019m showing them is important.<\/span><\/p>\n

Simple annotations can really help this. Below are two charts I made from data about the social security benefit Universal Credit (UC). It is a payment to help with your living costs. You may be able to get it if you\u2019re on a low income, out of work or you cannot work.<\/span><\/p>\n

The title of this chart tells you what the chart shows: the number of claims to UC. It shows a massive peak during the year, and that the number has gone back down again. But it doesn\u2019t say what caused that peak.<\/span><\/p>\n

Simply by adding an \u201cactive title\u201d to the second chart, I\u2019m explaining more: that claims to UC peaked when lockdown began. I\u2019ve also added an annotation directly onto the chart to point out where 23 March is – I\u2019ve not assumed that you know when the lockdown happened in the UK.<\/span><\/p>\n

Doing this helps people to think \u201coh, I see\u201d and it helps me to know that they\u2019ve understood from the chart what I wanted them to. I\u2019ve told my story.<\/span><\/p>\n

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Accessibility = Inclusivity<\/h2>\n

Accessibility isn\u2019t (just) a legal box to tick, it helps us make sure that everyone can use government statistics and data, regardless of the technology they use. This is for everybody.<\/span><\/p>\n

Making data easy to understand is only one of the ways of making it accessible: information published on the internet needs to meet all of the\u00a0<\/span>Web Content Accessibility (WCAG) Guidelines<\/span><\/a>.<\/span><\/p>\n

In the cross-government group of presentation champions that I lead, we\u2019ve been focusing on how to make government statistics meet these guidelines. WCAG asks that content should be:<\/span><\/p>\n