With more information than ever before, data visualization has never been more important.
Stats provide a good overview of your organization. All that data makes it possible for organizations to effectively determine causes of problems. And when harnessed, data can also be used to predict sales trends, retain employees, prevent customer churn, and so much more.
But your data is only as powerful when you can understand it. Without visual representation of those stats, you can't get clear takeaways.
In this blog post, we'll discuss the power of data visualization and provide examples of how important it is to visualize your results.
What is Data Visualization?
Data visualization is the process of making data easy to understand by translating it into a visual format, such as a graph, chart, or diagram. This visual representation helps businesses to understand what their data is telling them, and the faster they can do that, the faster they can make intelligent decisions.
There’s a lot of data out there. Your business produces tons of it, everyday. But simply having a lot of data doesn’t make a business rich with insight. You need to be able to understand what’s behind those numbers. You also need to be able tell its story so you can communicate its value to others.
For instance, rattling off numbers to your boss won't tell them why they should care about your data. But if you show them a graph of those numbers, like how much money could be saved, suddenly that data becomes much more valuable and is sure to capture their attention.
There's a reason for that, and it's not just because data shared via text is bland.
As humans, our brains can only process a very limited amount of information at a time. If that information is presented to us visually, we are more likely to quickly process that information. That's because our brains are programmed to understand images much faster than text: it processes information 60,000 times faster than the time it would to decode text. In fact, when paired with visuals, information is 70% more memorable than it would be if it were lone text.
The Risk of Relying on Numbers Alone
Let's take a quick look at the of Anscombe’s Quartet.
This is a group of four data sets that are nearly identical in simple descriptive statistics. It has varying x and y values and was created by the statistician Francis Anscombe in 1973 to demonstrate both the importance of graphing data before analyzing it and the effect of outliers on statistical properties.
In this table below, you can see that the summary statistics indicate that the means and the variances were identical for x and y across the groups:
As with all raw data, it's hard to gain any insight without further analysis. So let's take a look at the statistical summary.
Summary statistics for each dataset in Anscombe's Quartet:
- Average x value is 9
- Average y value is 7.50
- X variance is 11 and y variance is 4.12
- The correlation between x and y is 0.816
- A linear regression (line of best fit) follows the equation y = 0.5x + 3
You may think: Great! This summary tells me what I need to know and I can see that the results of these datasets are all very similar.
But what happens when we visualize the same datasets? When plotted on a chart these four datasets tell quite a different story.
Data Visualization in Action
The quartet showcases why it's dangerous to only report on summary statistics. This type of analysis often doesn’t give a clear picture of your data and makes it hard to spot trends and outliers.
The charts that visualize datasets I & II demonstrate how visual analysis can help to identify trends and relationships between variables. While the charts that based on datasets III & IV show how visual analysis makes it easy to spot outliers that would otherwise disappear into statistical data.
As you can see from the graphs above, data visualization portrays a much different story than what the quartet originally showed us. We can see that:
- Dataset I (top left) appears to have clean and well-fitting linear models
- Dataset II (top right)is not distributed normally
- Dataset III (bottom left) show a distribution that is linear, but the calculated regression is thrown off by an outlier.
- Dataset IV (bottom right) shows that one outlier is enough to produce a high correlation coefficient.
This is not to say that summary statistics are useless, but that they are only part of the full picture. For example, a business owner’s sales statistics will summarize quantities sold, profits and other variables. This is a good starting point, but visualizing the data enables a deeper dive into the analysis.
Data Visualization is Important for Your Business
Whether you're using basic bar graphs or intuitive charts, data visualization helps you to make confident moves, influence decisions, and ask the right questions.
Once visualized, you can investigate statistical anomalies and correlations that appear in your data. These steps are a necessary part of the decision-making process, and help businesses to avoid repeats of past failure, and work towards replicating and building on success.
Effective data visualization is the crucial final step of data analysis. To avoid missing insights from your business data, it's imperative you're equipped with the right tools to help you visualize all those insights that can find trends and outliers that traditional reporting can miss. Learn more about data visualization by checking out our guide to charts.