Creating a data visualisation is an integral part of any data analyst’s job. I have been an analyst for more than six years now and in my career I worked on a lot of data visualisation projects. In my opinion, developing any data visualisation, you need two types of skills: first is programmatic expertise and second is to show value and communicate a story from your data visualisation.

Programmatic expertise comes with practice, do a few courses online and you will be expert in knowing which library/code to use to create any chart. Even if you haven’t worked on the tool before, you can still get the job done.

I would like to elaborate on the second expertise i.e. showing value and communicating a story from your data visualisation. Many analysts either don’t spend the time on this or do it after creating a dashboard. I recommend to put a dedicated time with your stakeholders before jumping on the tool to start creating a data visualisation. It needs deliberate thinking and brainstorming session to visualise the dashboard. Use pen and paper to run the brainstorming session or if you’re doing it virtually then use any available whiteboard tool to draw sample charts.

I have put together three guiding principles that you must answer in the brainstorming session before starting any data visualisation exercise. I’m also sharing few tips that can help you to create a compelling dashboard with a great story to show maximum impact.

Who is the primary audience and what are they most concerned about?

Make a list of the people who are going to refer to your dashboard. understand what is their primary job and what they are most concerned about. Is your primary audience sales team who is most interested to know their target attainment or company’s marketing head who wants to know the number of customers acquired on the back of recent marketing campaigns?

You have to be super clear about the job of people who are going to refer to your dashboard and what they are most concerned about. Once that is clear you’ve to make sure they get answers to their most frequent and important questions in an easy to consume format. Any data visualisation project is meant to answer ‘What’. If your dashboard is able to land ‘What’ clearly, consumers of dashboard themselves would investigate ‘Why’ as they have access to qualitative information in business. That’s why knowing your audience job role is most important to create an impact with your data visualisation.

What is the key message or story that you want to convey?

As an analyst you have the power to convert numbers to stories. We sometimes invest so much time in transforming data into charts and leave little time to position our story or message in the right way. Make sure you spend sufficient time to frame your story and insights that you want to communicate. Here are a few tips that will help you to land your message clearly to your audience.

1. ‘Less is more’ keep things simple and emphasise (label and write it in simple words) the most important insight you want to communicate.
2. Always mention all the assumptions/ exclusions / inclusions in footnotes to avoid any confusion.
3. To inspire action, show direct cause and effect relationships and mention data point to a direct action to take.
4. Contextualise numbers by putting benchmark or any previous period numbers as baselines.

What’s the best way to visualise the data?

There are plethora of charts or templates readily available in all the tools and we want to use them to create a fancy-looking dashboards but sometimes a simple table with numbers deliver your message in a more impactful way than any other ‘cool’ chart. This is linked to previous two principles, If you’re super clear on the first two principles, you would be able to choose the right visualisation. So, spend a good amount of time and have a brainstorming session with the team to get clarity on the above two questions. I would recommend few resources and tips to know the best way to choose right visualisation methods:

1. If putting numbers in a simple table communicate a clear message, then go for it, don’t put any chart; show numbers clearly and tell the story!
2. To choose a chart, check the library of the tool that you are using and consider the suggestions. Do few online searches also to see the recommendations. I found a chart suggestion guide that i would like to share. This is in the Harvard CS-109 extension program and I find it useful.
3. Make sure you make your charts aesthetically pleasing. Use monochromatic colors if there is no comparison or good bad dichotomy to show.

Chart suggestion guide

One of the last thing that I would recommend is to take feedback from your team and incorporate it before taking your project to your stakeholders.

I hope these guiding principles are helpful, If I missed any important point, please comment & share.

All the best for your next data visualisation project!