What is data visualization? | Benefits, types and best practices

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At the heart of digital transformation lies data democratization, the practice of making data accessible by all departments of a business, not just the C-suite and IT team.

Data sets are growing rapidly. Forget data analysis – business leaders only struggle to visualize and map out data to begin with. Data visualization works to fix this.

See also: Top Data Visualization Tools

What is data visualization?

Data visualization is the graphical representation of data sets and information. Data visualization is an umbrella term for visualizing all types of data through charts, graphs and charts.

The ultimate goal is to visually represent your data in an accessible and easily understandable way. Visualizing data is a fundamental step in understanding trends, discovering patterns, and detecting outliers.

Experts predict that the data visualization market will expand greatly within the next five years, with compound annual growth rates of more than 10%, growing to a market valuation of $19.2 billion by 2027. Although the rise of data democratization and digital transformation causes of this rise is , much of this growth is due to growing interest in business intelligence.

What are the benefits of data visualization?

There are three key benefits to data visualization:

1. Make Big Data digestible

There is no doubt that datasets are growing tenfold. Combine this with the growing advancement of increasingly advanced IT systems, and you have a colossal pain point for businesses.

The fact that data is becoming so overwhelming for organizations has spurred the rise of AIOs. AIOps helps businesses with various use cases such as:

Predictive alerting Cause analysis Prioritization of events Predictive outages Service desk tickets

How does this relate to data visualization? Data visualization tools are an efficient way to map out data in an automated manner – and this automation is at the heart of AIOps. This hands-on approach makes approaching large datasets fairly easy to collect, and then in turn digest using data visualization techniques.

2. Greater accessibility

We mentioned how large data sets are now accessible to a greater number of users. This demonstrates that data visualization is a key factor in data democratization.

Data visualization tools help simplify complex robust data points and present them in highly digestible ways. This growing accessibility can help upgrade employees and make businesses more efficient.

3. Greater efficiency and understanding

Traditional ways of sifting through data were painstaking and time-consuming. Data visualization helps businesses discover insights at a much faster rate than before the advent of visualization tools.

Speed ​​is central here. This growing scalability means that business leaders have more room to be granular in their analysis. If data is mapped faster, IT teams and data scientists have more time to extract more complex insights from their well-organized databases.

Before data democratization, gaps in communication were all too common for enterprises and businesses. Boiling down and explaining advanced insights can be difficult without a common understanding of what the datasets behind these insights mean. With modern data visualization software, such as Tableau and Microsoft Power BI, data analysis is extended to virtually any department within your organization.

See also: Best Business Intelligence Tools

What are the different types of data visualization?

Here are examples of various forms of data visualization and use cases:

Bar charts: These types of charts are best used to compare aspects of different groups or to track those aspects over time. Bar charts are best used when changes are quite large.

Line Charts: One of the most popular and fundamental forms of data visualization are line charts, which are used to track changes over short and long periods of time. Line charts are especially useful for highlighting smaller changes.

Graphics are, for the most part, fairly modular. Above is an example of a line graph tracking bounce rates; this is in contrast to a bar graph that represents page load times.

Pie Charts: Another fundamental form of data visualization, pie charts are effective for comparing parts of a whole. Because they are not placed on an XY plot, tracking data over time is not possible with a pie chart.

These charts are very basic examples of data visualization. Many modern tools are designed to open up complex methods to the everyday user. For example, here is one way a management trainee in the finance industry used data visualization software for their needs:

“Power BI is a software widely used in our organization where we deal with a large amount of raw data and process it to obtain actionable insights. It helps us visualize scattered and unfiltered information in an efficient and easy-to-understand way… Overall, I would say that this is a must-have software for any business that directly sees a lot of data being collected to strategize and plan actions. formulate. Financial Industry Management Apprentice Review of Microsoft Power BI at Gartner Peer Insights.

Scatterplots: A slightly more advanced data visualization method is scatterplot. Scatter plots are an effective way to explore the relationship between two variables and multiple sets of data. Below is an example of a scatter plot depicting profitability in various US cities. Notice how cities with greater profitability have larger circles.

As you can see above, data visualization tools can display different graphs on the same page to provide holistic insights more clearly. Advanced software solutions will let users build interactive graphs that help stakeholders understand insights even more deeply.

See also: Top Data Mining Techniques

Best practices for data visualization

Keep in mind that the main purpose behind data visualization is to present data in a more interesting, relevant and accessible way to diverse audiences. Each best practice is specifically designed for this outcome.

Best practices include:

Choose the optimal graph and chart: Be sure to consider your needs before choosing the right way to visualize your data. Consider whether you want to track data over time or how many variables you intend to analyze. These questions can help your decision-making process.

Use templates: Many data visualization tools offer templates that businesses can plug and play with. You want your data to be interesting, but don’t stray too far from the norm. Templates are an effective way to boost well-presented data and information. Don’t overthink it.

Leverage Color: Color is a subtle, yet highly effective way to map data. For example: you can choose to make an anomalous point in your data set red to instantly distinguish it from other points. For example, use red to indicate heat and blue to indicate cold when you track again.

Don’t distort data: If users go overboard with their designs and data visualization charts, they can end up accidentally distorting insights. For example, pie charts should always be 2D. 3D pie charts may look more interesting and even attract more attention, but the unfortunate result is that each proportion of the graph is harder to decipher. An effective pie chart, or any chart for that matter, should not be a puzzle.

See also: Top Data Analytics Tools