Part Two: The Four Data Visualisation Types for Executives and Managers
I love strategy. It’s about doing the right things rather than doing things right. In the last article of our five-part series on data visualisation, we introduced the four basic types of data visualisation that are created when you ask these two simple questions:
- What data do we have?
- How do we want to use that data?
In this article, we’re going to dive deeper into each of the four data visualisation types, their application and best practices, and how you can use them to get value from your visualisation efforts.
Idea illustration can be called the consultant’s corner. It’s all about communicating and simplifying complex ideas by using a human’s ability to understand metaphors (trees, pyramids, etc) and design practices (hierarchies).
Examples that come to mind are organisational charts and decision trees. For example, imagine you’re trying to decide whether or not to approve a development budget for an improved product within your organisation. You might draw up a decision tree to determine the different investment amounts, their probability of success or failure, and the bottom line impact on your business.
The decision tree helps you determine the best course of action for your organisation by helping you visualise which alternatives at any particular point will yield your organisation the highest monetary gain.
Whenever you have a process or framework of data, an idea illustration chart will be the best way to visualise the information. Your goal with these type of graphics it to teach, simplify, or explain a concept.
Since there are no “datasets,” the skills needed for this type of visualisation are design skills. It’s about understanding how to use space in design, the colours to use, and other design-oriented topics.
This data type is what most of us think about when we talk about data visualisation. It’s the whale curve that shows customer profitability. Or the revenue chart for the last eight quarters. They are the basic bar graphs, line charts, and scatter plots that many executives and managers paste into a formal presentation or a spreadsheet.
Everyday DataViz deals with information that is data-driven, like our profit and loss. Our purpose is to communicate a point.
For example, let’s assume you’re trying to communicate the importance of retention and how it has affected your organisation. You might upload a chart like this into your presentation:
Image Credit — Think Growth
This chart shows the growth of a company’s monthly revenue rate. Each colour is a separate cohort being stacked on each other. With a chart like this, you might show managers or executives that retention is doing good. You’re growing, and unlike most products, your retention isn’t suffering as you grow.
The primary skill is storytelling. You want to get really good at making the right statement. Ask yourself, “What do I want people to gain from this chart or graph?” It should take your reader no more than a few seconds to digest this information. Simplicity is key.
In the chart above, assuming you understand cohorts, monthly-revenue-rates, and retention, then you should have an easy time digesting what this data is telling us.
Often disregarded as a form of data visualisation, this category is used as a tool to brainstorm solutions and structure. You will be using whiteboards, butcher paper, or the back of a napkin. It takes place in an informal setting and during strategy sessions.
Its purpose is to solve new problems, innovate and discover insights. A simple example might be sketching the layout of the manufacturing plant for efficiency. Or maybe the warehouse.
It’s used to answer challenges related to restructuring the organisation, coming up with new business processes or making decisions.
This is the area organisations struggle with the most. This category often involves analysing large amounts of data and dynamic data sets for insights. It’s about being able to identify patterns, trends and anomalies and testing your hypotheses.
With this data type, you’re looking to see if what you suspect is true and what are the ways you can depict the idea.
Suppose someone believes there are times of day customer shops their site on mobile than desktop. He loads data into a tool. Begins playing with the data, working fast. The design really doesn’t matter here. That’s why we mentioned in the last series not to leave it to your designer. This type of data visualisation is best left to managers or analysts.
The skill set needed with this data visualisation type is prototyping, manipulation of data and spreadsheets, and knowledge of programs.
There is an ongoing obsession with process over outcomes in the data visualisation world. What we’re trying to do with data visualisation is move people, change their minds, or discover new insights in our organisation that cause action. The chart or the colour we use is just a function of that goal. The best way forward is to start with your strategy and identify which type your information falls into and create the best visualisation for that.
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