Before you start creating a visualisation of your data, it’s a good idea to invest some time in understanding what your metrics mean. This means checking what each column or number represents, where the data comes from, and any limitations it may have. If you don’t understand what your data is telling you, it’s unlikely that your audience will understand.   

It’s important to first think about who will be using the visulaisation and why they are looking at it.  Building a visualisation with the needs of your audience in mind is important because their needs will affect the kind of visuals they find most helpful. Asking for feedback early in the development phase of a prototype is a great way to make sure you stay on track. Keep in mind that different versions may need to be created for different groups.  

A strong visual needs reliable data behind it. Invest time in cleaning your dataset before you start work on a visualisation. This includes checking for errors, missing values, wrong data types, or duplicates. Clean and accurate data leads to reliable visualisations.  

There are many tools available to design and deploy your visualisation such as Tableau, DOMO, Data Studio, R Studio, Power BI, Python. Each tool with their own pros and cons. Ultimately, the technology you choose will be dependent on your specific data and audience. Before deciding on a tool, map out how the tool will connect to your data and how it will be updated and shared in time. This will help to safeguard against wasting time, and licensing fees in some cases, on technology that is not fit for your purpose.