Humans have been keen visual learners and communicators since the dawn of their existence - data visualization encapsulates this in our modern era.
Data visualization refers to any sort of graphical or illustrative display of data and other information. Data visualizations and graphics simplify surveying trends, patterns, and other informational features.
There are many ways to visualize data, and most people are likely familiar with graphs and charts such as bar charts, flow charts, line charts, pie charts, scatter plots, histograms, etc. Data can also be visualized in tables, maps, infographics, and dashboards.
These more ‘standard’ graphs and charts are still used at the very cutting edge of data science and analysis, but flexible data visualization tools such as Python’s Matplotlib, Seaborn, and Plotly to robust BIs and visualization suites such as Sisense, Microsoft Power BI, Tableau have expanded the possibilities.
Today, data visualization is involved in practically every digital process that requires front-end visualization and analysis of data. Of course, the many uses of data visualization are not just confined to the private sector, and business environments but are also integral to science, demographics, healthcare, and academic research purposes.
The importance of data visualization
Data visualization is an excellent way to present data and information. Data practitioners can use visuals to explore patterns and trends and find correlations between different variables.
Data visualization can help people better understand the a topic or subject's complexities by making it easier to see the relationships between different pieces of information. By animating and visualizing data, data insights become a mutually intelligible, communicable, and transferable resource.
- Data visualization makes data intelligible and easier to understand and analyze
- Charts, tables, graphs, etc., are transferrable, communicable resources that can be passed from one party to another.
- Huge datasets can be converted into fairly simple graphics.
- Visuals enable easy exchange of information between key individuals and stakeholders.
- Data visuals enable comparisons between different phenomena by charting changes and relationships throughout time. They also allow people to chart the progress of a process throughout time (e.g., the progress of a marketing campaign or the accuracy of a machine learning model).
If you work in a customer or client-facing role that regularly uses data visualizations, or otherwise communicate using data in your working life, it's really worth putting time into building the best visualizations.
Building quality visualizations involves technical, communication and design skills. While you don't need to be a graphic designer to create excellent visualizations, it's still essential to understand how to communicate data effectively through a visual medium.
The benefits of data visualization
The benefits of data visualization are numerous and diverse, but we can summarize them into two primary categories.
- First, data visualization helps people better understand the information that is being communicated.
- Second, data visualization is a powerful tool for making complex information more accessible and easier to understand.
As they say, “a picture is worth a thousand words”; that’s why graphs, charts, tables, and other forms of data visualization have become so interwoven into our daily lives. Humans are excellent visual learners, and we’re very efficient when dealing with visual representations of data and information.
Not all data needs to be visualized on the front end, e.g., if it’s being piped somewhere else and no one needs to look at it in situ. However, data visualization is necessary for every situation where data needs to be broken down or assembled into visual media, making data more intelligible and usable.
Data visualization is required for the following:
- Someone or something collects data on a topic, theme, subject, problem, etc., and wants to visualize that data together as one.
- For comparative purposes, e.g., when comparing data from different years, populations, products, etc.
- To communicate ideas. For example, infographics are often used to exchange visually-enriched data. Graphs and charts can be passed from one person or department to another without affecting the data itself.
- When complex or large datasets need to be curated into simpler, easier-to-understand formats. For example, governments around the world used data visualization methods to inform populations about the coronavirus pandemic.
- When data needs to be sorted into different visual formats to draw trends and analyze relationships.
The disadvantages of data visualization
Not all datasets produce coherent, meaningful visualizations. Some of the disadvantages of data visualization are that it is not always clear what the data is trying to say, and it can be challenging to understand advanced charts.
Also, the apparent presence of a trend or pattern in a graph may be misleading as simply ‘seeing’ patterns in the data is not always sufficient to determine that those patterns truly exist objectively. As such, it’s sometimes necessary to perform data analysis to validate trends and patterns in the result.
Humans are excellent at seeing patterns and trends, which is a double-edged sword when dealing with data visuals. It’s tempting to see what you want to see or put forward the favored argument.
Favorable results can be ushered to center-stage, and given more emphasis in the visuals. Furthermore, since data visualization occurs in the front-end, it’s susceptible to human manipulation, both intentional and unintentional.
Data visualization for marketing
Data visualization is used in marketing all the time.
Firstly, to build visualizations, you'll need to integrate some data with some of the tools below. You could do this using an ETL tool, obtaining data using APIs, webhooks, UTM paramaters or even IoT sensors. Sources of marketing data include CRMs, Google Analytics, Shopify, social media, etc. You could also analyze scraped data, search data, or data from open or paid datasets.
Building visualizations allows markets to:
- View customers using certain criteria for the purpose of segmentation analysis
- Identify trends, outliers and changes in data over time
- Measure the effectiveness and impacts of campaigns
- Build marketing mix models (MMMs)
- Use MMMs to model different campaigns, their ROI, etc, which helps expose metrics such as diminishing returns
- Tap into new trends in the market
- Identify changing sentiments surrounding a brand's products, campaigns, etc
- Measure search and SEO data
Let's not forget that building flashy marketing visualizations and graphics helps marketers communicate with CEOs and other stakeholders. For example, visualizations might help a marketer explain why doubling investment into campaign is unlikely to yield double the results, at the point of diminishing returns is approaching.
Visualizations also make it easier to identify cause and effect, e.g., sales from this demographic increased when we ran more of Y paid ads. Getting to grips with the following range of visualizations will enhance your marketing career. Not only are visualizations extremely useful for finding things out - they're also useful for communicating ideas, concepts and results between customers, clients and team members.
Data visualization: Top 10 charts and graphs
It's essential to have multiple data visuals in your repertoire. Selecting the right graphs for the right reasons will make your data much more effective.
1: Bar and column charts
Bar and column charts are the classic comparison charts for relatively small data sets and are ubiquitous in education and society.
Though simple, bar charts are one of the best ways to represent multiple fields of quantitative data. Bar charts can also feature split bars to display proportion.
2: Scatter plots and bubbles
An excellent graph that uses Cartesian coordinates to display values for two variables (typically). Scatter plots also help visualize outliers and trends between variables. Bubble charts are variations on the scatter plot but add an additional dimension, creating a triplet of associated data.
3: Line graph
A classic graph which is typically used to plot a trend throughout time. The resulting line is easy to follow and allows the viewer to visualize how data fluctuates across the Y-axis as it progresses through the X-axis.
4: Area chart
Area charts are based on line graphs and show accumulated totals over time. They’re most effective when there isn’t too much overlap between the various areas depicted in the graph.
Histograms are great for viewing the distribution of a dataset. They look similar to bar charts but aren’t the same, as the x-axis should only be used to represent continuous (and typically numerical) data.
6: Waffle charts
Waffle charts consist of 100 smaller squares arranged in a 10-by-10 layout. These are great for visualizing high-level part-to-whole relationships similarly to a pie chart.
7: Box plot
Box plots are very useful for visualizing the spread of data. They can quickly display median values in relation to upper and lower extremes. Box plots are usually divided into a five-number summary containing the minimum, first quartile, median, third quartile, and maximum. A good choice for identifying anomalies and outliers.
8: Gantt chart
Gantts are most frequently used in project management. They allow users to visualize dependency relationships between activities and schedules. Gantts are also used in agile development environments.
9: Donut and Pie Charts
Donut and pie charts aid the visualization of part-to-whole relationships. These charts illustrate proportions for different sections of the pie/donut. These are some of the most uncomplicated and intuitive graphs of all and are excellent for communicating proportions.
A more complex graph type, heatmaps are graphical representations of data where values are depicted by different colors or color intensities. This helps visualize positive and negative correlations across a sample.
Top 10 data visualization tools and platforms
For Python, Matplotlib is an easy-to-use visualization library built on NumPy arrays. It can be used to create all manner of static, animated, and even complex interactive visualizations and consists of various plots like histograms, scatter plots, line plots, etc. Moreover, Matplotlib is free and open source.
Though fundamentally simple, Matplotlib is very customizable and flexible. It’s still primarily used for basic but quick plotting.
Seaborn is another Python data visualization library based on Matplotlib. It has a customizable but straightforward high-level interface and has comparatively simple syntax compared to Matplotlib. It also integrates Numpy and Pandas modules. Moreover, Seaborn is free and open source.
Plotly works with Python, R, Julia, and other programming languages and is essentially an open-source library that provides chart types and tools to build dashboards.
The Plotly Dash analytics framework enables the creation of powerful visualization dashboards. In addition, Plotly graphs are easy to share via URL, and users can interact with your data rather than decipher it in code form. Again, Plotly is free and open source.
Tableau is a BI and data visualization tool for creating powerful interactive graphs, charts, tables, dashboards, maps, etc. Connecting data sources and pipelines to Tableau is relatively simple, and it’s definitely geared more towards significant data visualization problems in SMB and enterprise environments.
With that said, Tableau Public is a free version of the software and enables people to explore, create, and share their data visualizations.
Tableau is designed for collaboration and teamwork, and multiple users can combine efforts on the same tasks. Tableau has been around for a while, and it’s an excellent tool for bridging the gap between those with high-level Excel knowledge and those with more complex data skills.
QlikView has attempted to innovate the data visualization space by incorporating advanced analytics and more sophisticated dashboarding tools. However, QlikView is more complex and verges on being overbearing when compared to Tableau, though some will disagree.
While users can dip in and out of Tableau to perform various visualization tasks, this isn’t as easy in QlikView. Overall, QlikView is more oriented toward mature organizations with larger data budgets.
Microsoft Power BI has tremendous scope and is used for all types of business intelligence, predictive analytics, reporting, etc. Many of the world’s most prominent organizations use it, but it’s also user-friendly and represents an excellent natural progression from Excel and other Microsoft products. It fits nicely into Microsoft business ecosystems and is a powerful centralized data visualization, analysis, and reporting platform.
Sisense is another data visualization/BI tool that resembles Tableau and QlikView. It’s great for dashboarding and has a powerful and intuitive drag-and-drop interface that allows users to build complex graphs and dashboards in mere minutes.
Sisense boasts several features that improve its credibility for Big Data applications, such as its in-memory databases (IMDBs). In addition, a sophisticated AI engine for predictive analytics also aids with complex data analysis.
8: Excel and Google Sheets
Excel and Google Sheets have improved dramatically over the years and are easy to use, presenting data and visuals in a logical format that many people can understand and use.
Datawrapper is a freemium data visualization tool that works in the browser. It’s super-easy to upload datasets and visualize them in Datawrapper, and the resulting charts and graphs are sharable and responsive. The free version has some limits, but they’re pretty generous.
10: Zoho Analytics
Another powerful BI and visualization tool which allows organizations to connect their data sources into one customizable, powerful visualization and dashboard platform. Zoho’s advanced AI assistant helps users analyze datasets, and it has some great collaboration and sharing tools for reporting.
Summary: Data visualization
Data visualization is fully-integrated into almost every digitizable industry, sector, activity, and process worldwide. As a result, most of us probably see graphs, tables, and charts every day of our lives, whether in a newspaper, magazine, TV, street billboard, or, of course, on our PCs and smartphones.
Visualizing data helps us understand and analyze information. The results are easily intelligible and communicable between various parties, whether that’s students working on a project, stakeholders of an enterprise-level organization, or data scientists working on machine learning models.
There are so many avenues to explore when it comes to data visualization. Powerful BI and analytics platforms have enabled the visualization and analysis of data en-masse, but open-source, scalable Python libraries such as Plotly, Seaborn, and Matplotlib are equipping data scientists and engineers with the flexibility they need to visualize data for many ends.