Siu is a web analyst at Code. His job involves implementing tracking, making website data accurate, producing and analysing reports and visualising data. Clients will enrol his help if they have data but no one to analyse it, too much data or no data at all.
It's clear from talking to him for just a few minutes that he's smart and knows his stuff but he's been thinking a lot recently about how he can transfer his knowledge more effectively.
Being able to collate, analyse and visualise data isn't enough if the insights aren't translated to the people who make the decisions. A web analyst can be effective on their own but they are much more effective when they understand the business they are analysing and the business understands their data.
"When I first started to deliver reports to clients, I was a lot more technical,'' he says.
"In my first meeting, I noticed there weren't many questions from the client. It was more a case of people in the room nodding and wanting to move onto the next slide. I felt like they weren't taking it all in."
He sought advice from his colleagues at Code about how they present and collated the feedback into actions for his future presentations.
So, here are his top tips for how to present data:
Adapt your language -Using a common language that everyone in the room understands is one of those ‚blindingly obvious in theory yet often missed in practice' things. Even if both sides are data-savvy, there will often be instances where a common term means various things to different people and so being clear about what is meant and coming to common definitions is still important here.
Take the simple approach - Keeping things simple isn't simple. Finding the balance between making something easy to understand without it losing its meaning or important context is a real challenge but one which is worth doing if the data is going to do its job.
Use headlines - Where Siu may have used paragraphs before, he'll now often use a headline‚ they're much easier to take in.
Tie it back to the original purpose - It's easy to get lost down rabbit holes when analysing data but it's important to make sure everything ties back to the original purpose of the analysis, the question that it set out to answer.
Give context - This can be done by tying related data together. For example, rather than keeping device and form completions in separate sections of a report, combine them and give a recommendation if they give an important insight i.e. 80% of people come to the site on their mobile phones but they struggle to fill out the form compared to people who are on desktops. We should concentrate on improving those kinds of journeys.
Use colour - Siu's had to break a few rules with our style guide in order to make sure that colours are distinct and therefore easier to read. Making the data understandable is more important than making it look pretty but if you can do both, great.
Play with size - If there's an important point to make, make it the biggest thing on the page and don't bury it in the small print.
Use templates - Templating certain aspects of reports means that there is consistency for clients (and internal team members too).
Always summarise - Siu now adds a summary and talks briefly through what he's covered after every distinct section of the report. This helps people to digest what they've just seen and heard so that they are much more open to listening to the rest of the report, instead of trying to hold everything in their heads.
Make it people-centric - Talking about people rather than numbers is a powerful way of making your audience connect with and care about data, which is just a reflection of what people are doing anyway. So instead of saying this form had a dropoff rate of 90%, you could say one in ten people who started to fill out this form didn't complete it.
Take time to reflect - Perhaps the most important but most often missed aspect of an analysts role is taking time to reflect. When you've spent so long in the data, things will start to seem obvious to you and you may lose sight of how it might not be so obvious to others. Siu now comes back to his presentations and asks himself a simple question: 'will my audience understand this?'
As he sits before his screens which show an array of bewildering charts and numbers, he says he's not finished yet by a long way, emphasising the importance of continuing to reflect on his work.
A good sign of his progression though is that implementing these things seems to be really working. When I go back to the same clients now, there are lots of questions about the data. They ask if they can get more of it or look further into certain aspects. They're much more keen to understand each slide rather than moving quickly on to the next one.
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