Data visualisation process

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Step 1: Understand the business issue

Prior to beginning a data project, you need to understand the overall scope of the work, the objectives, the information the stakeholders are seeking, the type of analysis required, and the project’s key deliverables (outputs).

Step 2: Understand the data set

You can use a variety of tools to understand your data. For smaller data sets, you can use Excel. For larger data sets, you should use tools such as Tableau, R, or Python, among others.

At this stage, make sure you go through the data to identify any errors (e.g. omitted data, data duplications, data that logically does not make sense, etc).

Step 3: Data preparation

Once you have organised and identified all the variables in your dataset, you can input missing variables, create data categories, and others.

Step 4: Data modelling

Build data models to find answers to the questions and fulfil objectives of the data. Determine which data models best suit your analysis (eg regression analysis).

Step 5: Validation

Ask the following questions to validate the data analysis and visualisation efforts:

1. Did the data models work properly?
2. Does the data need more cleaning?
3. Did the data analysis provide the desired outcome or answer the required questions?

Step 6: Visualisation and presentation

Data visualisation is critical to communicate your findings to the stakeholders. Interactive visualisation tools such as Tableau are useful to illustrate your conclusions to clients.

Next, with the steps provided, you will attempt to prepare a data visualisation using Excel.