The Four Data Visualisation Types for Executives and Managers
The growing number of data visualisation tools makes it easier than ever for executives and managers to take their data and create stunning charts. An unintended consequence is that many organisation implement what I call a “click and viz” strategy, where little thought was given to purpose and strategy.
For example, why are we trying to create this chart? What are we hoping to learn or share?
Instead, we’re creating beautiful visualisations simply because we need something to put in our presentations. Automatically converting financial spreadsheet data into a line chart just visualises cells of a spreadsheet. It doesn’t tell us anything different.
To many organisations, the focus has become about the design. When executives and managers decided to get better at visualising their data, they start with learning the tactics. They wonder, what colours should I use? How can I get the right contrast to make the focus on a particular set of data? Would a scatter-plot, bar chart, or graph work better? Where should I place the key?
Starting with these questions is to put execution before strategy. It’s like building a product without first asking if there is any demand for it or placing a distribution warehouse in Sydney without asking if there is a need for it. We gravitate towards tactics because they’re easier than strategy.
This is part three of our five-part series on data visualisation. In the last article, we spoke about how de-prioritising the strategy aspect is the number one data visualisation mistake organisations make.
Today, I’m going to share with you how to make the strategy your focus — by making sure you’re communicating the right message before you dive into the weeds. This topic has been shaped by the learnings of Scott Berinato in a 2016 Harvard Business Review Article on how to communicate using data visualisation.
Two Data Visualisation Questions You Need To Answer
When it comes to determining your data visualisation strategy for a particular set of information, there are only two questions you need to ask yourself.
- Is the information conceptual or data-driven?
- Am I trying to make a statement or discover insights?
The first question is about asking yourself, “What kind of information or data do I have?” You either have information that is conceptual or data-driven.
Conceptual data is often qualitative data, like survey feedback or workflows. Specific examples of conceptual information include how your organisation is structured, the location of all your warehouses on a map, or your company’s sales process and the steps that must be taken before leads can be moved to the next stage.
A perfect example of conceptual data visualisation is the sales process infographic I found on Hubspot’s website.
Image Credit: Hubspot — Example of a Sale Process Conceptual Data
On the other hand, data-driven information is often qualitative, like the amount of revenue in the first quarter or customer profitability. Data-driven information is meant to inform and enlighten.
The data you have is usually an obvious answer. You either have ideas or statistics. Notice that we’re not deciding how we will show our data. Instead, we’re simply trying to determine the data or information we have.
What Are You Doing With the Data?
The next question forces you to ask yourself, “What am I trying to do with this data or information?” You will either be communicating or trying to make a discovery.
In most of this series so far, I’ve focused on using data visualisation to make discoveries. That’s because it’s the area most managers and executives get wrong. The biggest improvement and return on investment lies in getting much better at using data visualisation to discover insights.
Managers and executives rarely have an issue making statements with their data. They do it frequently and it’s the most common use of data visualisation tools. For example, a manager might show how the budget is broken up for each department by using a pie chart.
Using data visualisation to discover insights and confirm hypotheses is much harder. That’s why few companies do it. Examples of trying to use data visualisation strategies to make discoveries might include, seeing if your supply chain investments contributed to rising profits or trying to find out what would happen if you visualised your customer data by age, location, and history of purchase.
Let me take this a step further for clarification purposes. You show your CEO that the inventory accuracy rate has decreased. This is making a statement with your data. Let’s say your CEO wants to know why. You suspect it’s because of the increased seasonal orders, but you’re not sure. You will use data visualisation to explore, confirm, or refute your hypothesis. This is discovering insights with your data.
The Four Data Visualisation Types
These questions lead to four types of data visualisation that we’ll explore in the next article in future detail.
- Idea illustration: Declarative data that is conceptual
- Everyday Dataviz: Declarative data that is data-driven
- Idea Generation: Exploratory data that is conceptual
- Visual Discovery: Exploratory data that is data-driven
Notice that neither questions tell us what kind of chart or colours to use. Instead, we figured out what information we have and how we’re going to use it. Figuring out the tactical stuff is about aligning the answers to those two questions.
In the next article, I will dive deeper into the four major types of data visualisation that have been briefly introduced here and the best practices for each type. For example, what chart should you use if you have information that is conceptual and you’re trying to make a statement?
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