Are You Using the Right Charts to Visualize Your Data?

Big data is vital to the success of every business today. Firms generate huge amounts of information, and these volumes are growing all the time. In 2010, for instance, global data volumes reached around five zettabytes, yet by the end of 2021, this is expected to reach 79 zettabytes, before growing even further to 131 zettabytes by 2025.

With so much potential data available, if you aren't utilizing this though advanced analytics strategies, you're missing out. But how do you ensure you're getting the best insight from this?

Big data analytics programs will often succeed or fail based on how the information is presented - both within the data science team itself and, crucially, when briefing other teams. If business units can't interpret the data correctly, they won't be able to make the best decisions using it.

This is why it's essential to understand data visualizations. The majority of people respond much better to visual information than data presented simply as figures or text summaries. That's why including charts and graphs in your data reporting is a must.

All good big data platforms will have a wealth of options for distilling large datasets into visual graphics. However, with so many choices available, it can be easy to get this wrong. Poor use of charts can make data appear misleading or inaccurate, so matching the right visualization tools to the right information is vital.

4 types of data story - and the best visualization tools for each

This starts by understanding the type of data you have and what story it's telling. Data-driven storytelling is critical in turning information into actionable insight and using it to improve decision-making.

Every chart you create needs to have a clear purpose - whether it's to illustrate key trends for your industry, show the impact of a change in product strategy or assess the performance of your employees. It's essential you know what message you want to send to your audience and which tools will get the point across most clearly.

With this in mind, here are some of the most common types of storytelling you'll come across when working with big data analytics, and how to translate this into a visual form.

1. Relationships between data sets

Examining how two or more sets of disparate data relate to each other is essential in spotting patterns and anomalies. Finding connections and correlations between sets is much easier if you're using the right charts. Good solutions for these types of story include:

• Scatter plots: Consisting of multiple data points plotted across two axes, a scatter plot is one of the best ways at showing correlations or spotting outliers. They're best suited to numerical data.
• Bubble chart: These are similar to scatter plots, but instead of being represented by a single point, they introduce a third variable, with each plot point appearing as a circle or bubble that corresponds to its size or importance.
• Spider charts: If you have three or more variables to include in your comparison, spider charts - or radar charts - can be highly useful. Use them when you have multiple easily-quantifiable metrics to look at, but be careful not to add more than five or six, as you'll lose readability.

2. Trends over time

Monitoring the progression of data points over time is one of the most common uses for any big data analytics. This helps you spot things like seasonal variation or the impact of a specific event or change on performance. Useful charts for these stories include:

• Line charts: One of the most familiar and easy-to-understand charts, line graphs display quantitative values over a continuous period. They show clearly how data changes over time, making it easy to spot trends. Just be sure to keep them simple - cluttered and overlapping lines can quickly become confusing.
• Bar/column graphs: The key difference between a bar chart and a line graph is that while a line chart shows continuous trends, bar and column graphs split them into distinct segments. This makes them useful for comparative ranking. For example, while a line graph can show you how sales rise and fall over a year, a column chart is more useful for telling you at a glance which quarter performed best.
• Area graph: Related to the line chart, an area graph fills in the area under the line to show a more detailed breakdown. For example, if you have four divisions, you can use different colored areas to see how much each contributed to total sales.

3. The composition of your data

If you want to show how individual segments of data combine to make a whole, or split up your data into constituent parts, there are a few ways to do this. These include:

• Pie charts: One of the most famous yet often maligned visualizations, pie charts still have a place when used correctly. If you want to break down a total into percentages and you don't need huge precision, they're a great way of getting the point across. If you want to see at a glance who the major players are in a market, for instance, they're ideal, but you can't use them for finer detail.
• Treemaps: If you need to break down a total into a larger number of subcategories - for example, sales data that covers many product lines - treemaps offer an in-depth way of visualizing data by both category and quantity, segmented by hierarchy.

4. How data is distributed

Distribution charts are useful in understanding how variables are split within a single data set. They give you insight into trends and outliers and can show which elements are performing best. These include:

• Histograms: A form of bar chart, histograms depict the frequency of data points within a set. For example, they can show what percentage of user reviews are assigned to each point on a ratings scale. This illustrates where your data points are concentrated and where anomalies lie.
• Density plots: Similar to a histogram, these charts offer insight into data where there are continuous possible data points rather than several discrete options. These depict a smoother visualization than histograms, making it easier to see the distribution shape, and can be used to overlay two or more variables for direct comparison.