Data Visualization vs Data Analytics

While people sometimes use the terms interchangeably, data analytics and data visualization are very different areas that require distinctive skill sets. However, successful organizations need people with expertise in both.

That’s because one does not work well without the other. To be effective, data analytics and data visualization must work in tandem.

Data analytics involves using specialized software to collect and analyze large data sets with the goal of finding trends and gleaning insights. Data visualization involves presentation of the findings of data analytics in a way that even non-technical people will understand.

Both data analytics and data visualization are important parts of the same process. That process involves finding insights and presenting them in a way that supports data clarity and data-driven decision making.

What is Data Analytics?

Data analytics involves analyzing data sets to extract useful information that organizational leaders can use to make better decisions. It’s a process used in every area of business. Examples include understanding consumer behavior, improving marketing campaigns and personalizing content. In whatever area data analytics are applied, the goal is to increase effectiveness and efficiency, leading to a better bottom line.

As pointed out by Forbes, data analysis is exploratory and starts with specific questions, adding: “It requires curiosity, the desire to find answers and a good level of tenacity, because those answers aren’t always easy to come by.”

What is Data Visualization?

Data visualization involves presenting data through the use of visual representations. Forms of visualization include charts, graphs, maps, tables and comprehensive dashboards. The tools used in data visualization focus on taking insights from data analytics and making them accessible, allowing decision makers to understand the trends and patterns in the data. 

The goal of data visualization, according to Forbes, is to make presentations that “significantly reduce the amount of time it takes for your audience to process information and access valuable insights.”

Data Analytics and Data Visualization Uses and Benefits

The benefits of having experts in both data analytics and data visualization is clear. Without data visualization, insights derived from analysis are not well communicated to decision makers. And without in-depth analysis of data, the findings presented with data visualization are shallow and potentially obsolete.  

People in both positions have key roles to play in making data useful to organizations.

Uses of Data Visualization

Javier Leon, a project manager with Amazon Produce Network, said data visualization encompasses three areas: business analysis, business analytics and business intelligence. He added, “If you are really into it, it can also include data mining.”

Leon, who teaches Villanova University’s Essentials of Data Visualization course, has overseen the implementation of data visualization, databases and forecasting for Amazon. For his class, he creates data visualizations that show the flexibility and usefulness of data visualization in any area.

For example, he created data visualizations that show the cost of a night on the town at different locations around the world and where medication comes from for different conditions that include arthritis, pain, headaches and cholesterol.

While useful in almost any area, Leon said people who work in data visualization will do well to remember the advice of painter Bob Ross, who said no painting is ever perfect. “I believe that’s the same case when you are creating data visualizations,” Leon said.

Uses of Data Analytics

Virtually every business now has the ability to collect data. The job of data analytics is to analyze that information and extract insights that make the data useful for business leaders who then use it to make better strategic decisions. 

Data analytics provide the inputs needed to create data visualizations that are not obsolete or erroneous. Data analysts and data scientists need to understand how to work with statistics, create experiments and work with hypotheses, as taught in the Essentials of Data Visualization class. They also must have the ability to work with machine learning or artificial intelligence, advanced mathematics, and with at least one or two programming languages.

IBM reports that by using data analytics, “You can ultimately fuel better and faster decision-making, modelling and predicting future outcomes and enhanced business intelligence.”

The emergence of data visualization has also made it easier to get important information in front of decision makers.

Ellie Fields, senior vice president of product development at Tableau, which makes data visualization software, told U.S. News that data visualization software gives decision-makers more access to data insights. Before data visualization, “We definitely saw people who specialized in business intelligence or data or analytics, and that was their job, and everyone else was expected to send a request to those people and wait for reports back,” she said.

For those interested in data visualization, Leon offered this advice: keep reading.

“I believe that in order for you to keep growing, you need to keep reading, keep yourself always learning,“ he said. “One thing that I really like is for you to go see what other people are doing on public websites such as Tableau and Qlik’s portals, where you can see what other people are doing regarding different topics.”