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5 Effective Data Visualization Techniques for Sharing Business Ideas

Eli Cohen

Eli Cohen

Data visualization

Data visualization has become an essential tool for communicating ideas and insights from statistical data engagingly and intuitively.

The amount of data created daily runs into zettabytes (where one zettabyte equals a trillion gigabytes).

Everyone and every organization now generates and analyzes data. Thus, effective data visualization techniques today need to be able to extract meaning and tell compelling stories from complex data.

More so, they need to be able to represent complex data in easy-to-understand formats. You are probably familiar with bar graphs, line charts, scatter plots, pie charts, etc. which are basic – though powerful – techniques for visualizing data.

However, this article introduces you to techniques that are just as powerful and do a better job of condensing a lot of information into a simple graph or diagram. Even if you don’t work with data, you will have to come across some of these techniques, and knowing how to interpret them is critical.

1. Correlation Matrix

In a correlation matrix, each cell contains the correlation coefficient between the variables on the corresponding row and column. 

Hence, the diagonal entries always have the maximum positive correlation of 1; since the row and column are the same variables, they exhibit perfect correlation.

The lowest possible figure, indicating a perfect negative correlation is -1 and it means neither variable is a factor of the other by any means.

Correlation matrix example. Source: Wikimedia Commons

A correlation matrix can be used to summarize a large dataset and provide a quick overview of the linear relationships between all the pairs of variables.

In most cases, you present in the form of a table, with each cell color-coded according to the strength of the correlation between the corresponding row and column.

Data scientists, machine learning engineers, and other AI experts find correlation matrices useful for regression techniques including linear regression and multiple linear regression, where one has to predict a dependent variable based on several independent variables.

Statisticians have a mantra: correlation does not imply causation. Hence, while it might be useful to determine which pairs of variables have the strongest relationships, that should not be taken as evidence that one directly causes the other.

The purpose of a correlation matrix is to show the multiple associations that each variable exhibits.

2. Choropleth Map

A choropleth map is one of the most useful visualization techniques for analyzing geographical data. Using colors or shades of colors, it shows statistical data assigned to specific locations on a map.

Each zone on the map is shaded or patterned in proportion to the measurement of the statistic being displayed on the map. Data such as population density, per capita income, election results, poverty rates, etc. across cities, states, or nations are best represented using a choropleth map.

Choropleth map example. Source: Wikimedia Commons

Choropleth maps are easy to understand and effective for representing thematic data on a large scale. Color/shading differences make it easy to identify clustered distributions, as well as outliers – the zones with the strongest or weakest shades of color.

They are less effective at smaller local scales and in such situations, a simpler bar or line graph is typically preferred.

It is standard practice that raw data are not represented using choropleth maps. They should only be used to represent standardized comparative rates or ratios.

Otherwise, the visualization becomes misleading. For instance, when showing population density, larger areas might appear darker even when they don’t necessarily have a higher density.

3. Bullet Graph

Bullet graphs are like regular bar charts, except that they have extra tick markers that show how each measure compares to certain targets or benchmarks. One interesting use case for this is checking, for instance, if employees or teams are meeting their KPIs.

Besides measuring progress toward a goal, they can also show deviation from a standard or requirement. Bullet graphs may also be used for comparing quantitative performance with one or more qualitative targets. Thus, when you need to represent large information in a compact visual, a bullet graph helps you to achieve this easily.

Bullet graph example. Source: Wikimedia Commons

Bullet graphs were introduced by data visualization expert Stephen Few to replace dashboards that contained too many discrete meters and graphs. Instead, with a bullet graph you can easily represent the current data on performance as well as the benchmarks in a single compact image.

More so, each bar in the graph can be color-coded to show which variables meet the target and which ones are lagging. Some bullet graphs also used layered shades of color to represent multiple qualitative targets.

And of course, you can combine many bullet graphs into a dashboard to track multiple targets at once. To make bullet graphs effective, though, they shouldn’t be used to represent too much information at the same time, lest they get overwhelming.

However, they can be used to represent much information about a small data set. That’s why they make such effective replacements for traditional dashboards.

4. Heatmap

Heatmaps are graphical depictions of data using color as the only (or primary) representation of statistical information. Alternatively, a grid could be used to present various values. A heatmap can also be layered on a screen containing information it is meant to represent.

For instance, marketers and SEO specialists often use click maps, which are a type of heatmaps, to show which areas on a webpage have the most clicks and adjust the user experience as appropriate.

Heat map example: Source: Wikimedia Commons

In addition, heatmaps are a way to quickly find clusters of relevant information. For instance, in a disease outbreak or armed conflict, a heatmap can be used to quickly determine areas where the problems are most concentrated – that is hotspots of the outbreak or conflict.

They are also used in statistics of field sports to track which areas of the field experience the most action by a player or the entire team. Interactive heatmaps add further information such as filters and tooltips for more in-depth insights.

Sometimes, it’s easy to confuse highlight tables for heatmaps. With the latter, data is represented in strictly tabular format, although they use similar color shading as heatmaps.

However, while highlight tables are great for representing simpler data, they don’t have the advantage of representing spatial context, which heatmaps encode in addition to color shades.

5. Network Diagram

According to its name, a network diagram depicts relationships and connections between nodes in a network. You might be familiar with network diagrams already from representations of computer or telecoms networks – they are very often used in the IT world. They are also used for organizational charts.

The nodes represent entities or objects that are connected in some way, with lines indicating relationships or interactions between connected nodes. Sometimes, the lines are directional to show the direction of relationships.

Network diagram example. Source: Wikimedia Commons

Now, you might be wondering if network diagrams have any place in the visualization of statistical data. They do. You can use network diagrams to represent correlation, multivariate, factor analysis, and causal inference networks.

Other kinds of networks that they can represent include Bayesian and time series networks. All of these rely strongly on statistical data. You can also draw network diagrams on top of a graph to represent associations between various nodes of data.

Statistical network analyses help discover hidden patterns and answer questions that may not be evident from inspecting all variables individually. Thus, the graphical format aids active interpretation.

Improve Engagement and Authority With Data Visualization

For data visualization to be effective in enabling you to make data-driven decisions or to enable AI models to improve marketing strategies, it must be much simpler than the complex data or ideas it represents.

The techniques discussed in this article will empower you to share compelling stories and improve your engagement with appealing visual data and stats.

Featured Image by Luke Chesser on Unsplash

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