As we become better at collecting and analyzing data, we also have to become better at explaining what it all means. Data visualization is the use of algorithms to create images (graphical and pictorial) from data so that humans can more effectively understand and respond to that data. It is important because it enables us to digest large amounts of complex data that would otherwise be overwhelming or difficult to understand.
To better understand data visualization, there are several questions that we will answer:
- What are the benefits of data visualization?
- What are leading data visualization techniques?
- What are best practices for effective data visualization?
- What are the challenges of data visualization?
- How is AI transforming data visualization?
Benefits of Data Visualization
With clear graphical representations of business information, better conclusions can be drawn – ultimately leading to better decision making. Some other benefits associated with data visualization include:
- More rapid problem solving via faster access to business insights
- Identification of relationships and patterns
- Pinpoint and track emerging trends
- Ease of communication between parties
- A better understanding of operational and business activities
- Direct interaction with data
Data Visualization Techniques
There are a wide range of factors that influence what data is used and how it is visualized. Several common techniques for differing types of data visualization include:
Two-dimensional data: Often geospatial representations on maps. A few common examples are:
- Dot distribution: Dots represent the desired variable
- Distance or area cartograms: Focus on a portion of a map for a certain variable
- Choropleth: Using different colors to represent different levels of a variable
Multidimensional data: Covering multiple dimensions or variables. Some of the most common visualizations that we use to represent this data include:
- Histogram: Comparing time periods with a defined variable
- Pie chart: A representation of amounts by percentage of a whole
- Scatter plot: Demonstrates correlations between two different variables
Some multidimensional data can also be considered high-dimensional, which can be understand as similar to having a table with lots of columns. In this case, more advanced techniques such as the following can be useful:
- Parallel coordinate plot: For comparing many numerical variables and seeing the relationships between them
- Scatterplot matrix: A collection of scatterplots organized into a matrix
Hierarchical data: This type of visualization aims to compare subordinate relations between datasets. Some types of hierarchical visualizations include:
- Selection tree: ‘Nodes’ are connected by ‘branches’ which represent the relationships between the nodes.
- Sunburst charts: Each ring or circle represents a level of hierarchy, with the innermost circle representing the top
- Dendrogram: Similar to a tree, but instead utilizes clustering
Best Practices for Effective Data Visualization
Image source: Column Five
It doesn’t matter how good your data is if the people viewing it don’t understand what it means. There are a number of best practices that can be helpful in ensuring that your data visualization is clear and understandable.
Always focus on your requirements first: Start by asking “what decisions do we want the reader to be able to make based on this visualization?”.
Remember who your audience is: Use familiar terminology and avoid adding extra insights and visuals that may distract from your point. Remember that works for one group may not work for another, so consider having different versions of the same data when needed.
Have a consistent methodology: Build a scalable and repeatable process for obtaining your data and design visuals.
Context is key: Showing your metrics in a performance vs goal manner can go a long way in clarity and understanding – especially with the use of color.
Emphasize data quality: If the data you are using is of poor quality, the best visualization in the world won’t make it useful.
Profile your data: There are a few types of statistical data:
- Numerical: Meaning as a measurement (also called quantitative data)
- Categorical: Represents categories that don’t have mathematical meaning, such as personal preferences (also called qualitative data)
- Ordinal: A mix of numerical and categorical data that logically go together and have a sequence
It is important to understand the difference in data types because different visualizations will be more effective for different types.
Choose the appropriate visualization technique: There are a wide range of techniques that can be useful in your visualization. Strive to keep your visualization as simple as possible.
Data visualization ranges from simple (line graphs, pie charts, scatter plots and similar) to considerably more complex. This is particularly true when it comes to big data or high-dimensional data, where the process and visualizations understandably become more complicated.
Image source: UIE
Remember that visualization is not BI: Data visualization is the process that we undergo to take data and make it into graph and other visual representations of our data. BI refers to the strategies and technologies that are used by companies in order for data analysis. The two are often used in closed relation, so it is important to remain clear on the distinction.
Be an effective storyteller: Chances are that if you’ve taken the time to put together a data visualization, it’s because you want to achieve something. Never underestimate the power of the right story in order to give context to your data and help it achieve your goals.
Following these best practices will better help you end up with the best possible visualization for your data.
Most Common Application of Data Visualization: Dashboards
Image source: Maria Nowo
It’s not uncommon to hear speak of dashboards when the topic of data visualizations comes up. The key difference between the two is found in the frequency that data is updated. Visualizations are generated from data, whereas dashboards are updated more regularly to demonstrate changes within a dataset.
In other words, where a dashboard may be seen as a collection of resources, a data visualization is a single representation of sets of defined information. With a dashboard, there is often an interactive factor that enables users to get the right information that they need; making them particularly useful for business users.
Challenges with Data Visualization
Like with many modern tools, data visualization isn’t without its own challenges:
- Tools show but they don’t explain, sometimes assuming that viewers understand more than they do
- Different users can draw different conclusions
- Implicit bias of whoever is managing the data – no matter how small
- A false sense of security – sometimes graphs aren’t enough to tell the whole story and we don’t always take stock of this
Data Visualization and AI: The Way for the Future?
Data visualizations help build complex AI systems
Aside from helping organization make better business decisions and similar, data visualization can help users analyze AI model results in the ways explained below. These tools are critical for increasing trust in AI outcomes:
- Data visualizations help data scientists understand data before they start modeling. Most Kaggle notebooks, which show data scientists’ working process as they build a model, are full of data visualizations.
- Data visualizations can explain why AI models makes certain predictions
- Some AI models such as decision trees and forests can be visualized with some simplifications. These visualizations help business users to understand how the model works
- AI models need to be audited at several levels to ensure that their bias and variance are within limits specified by needs of the business. Visualizing model results can help with such audits
This last benefit (auditing) requires design and business thinking, but can be crucial in helping business users understand model performance. For example, a speedometer reading which is essential for a driver is less valuable for understanding a self-driving cars’ performance. However, when combined with other data such as speed limits and traffic levels, speed data can be powerful. This can lead to a number of practical uses, such as easily checking if a self-driving car exceeded the speed limit.
AI systems can prepare new data visualizations
Over time, there will be an increasing volume of data visualizations supported by AI, such as with AI systems that can draw realistic images based on text descriptions and other data. Already, companies like Narrative Science automatically prepare explanations for complex data.
Data visualization, when executed correctly, has the capacity to lead to positive change throughout an organization. However, its success is reliant on a wide range of factors that require both creators and users of the visualizations to be careful, thorough, and accurate in their analyses.
Want to see more about data, AI, and other related topics? Be sure to check out our blog
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