Visual data is memorable. According to the New York Times-bestselling book Brain Rules by John Medina, a person can typically retain 65% of what they see in an image after three days, compared to only 10% for information they heard. The human brain is efficient at processing visual media. Plus, visual content such as infographics, videos and illustrations has thrived in the age of 24/7 online connectivity and busy social media feeds, driving much higher engagement than text-only articles on platforms such as Facebook.
Data scientists also use visual media to sustain user and stakeholder attention and drive action. Many of these professionals oversee projects involving the conversion of massive amounts of information into clean, concise and actionable data visualizations such as dashboards. These visualizations are sometimes described as “data analytics,” although that framing isn’t exactly right. Visualizations are the results of the analytics production process, through which potentially valuable insights are extracted from datasets and presented in a digestible format.
That could be a visualization, or it could be a report generated by a business intelligence (BI) solution, or any form of analysis that guides the development and refinement of the processes, products and services an organization relies on. The relationship between data visualizations and data analytics is sort of like that between a square and a rectangle: While all visualizations are analytics, not all analytics are visualizations.
The past, present and future of data visualizations
Informational visualizations have existed for thousands of years, in every form from graphs drawn on papyrus and paper to digitally generated pie charts, bar graphs and cartograms. The current wave of attention focused on them comes from their relationships with big data and BI — two important concerns for enterprise organizations — as well as with content marketing and social media management, both of which rely heavily on visual content.
Newer visualizations are more likely to be interactive. These types of visualizations are ubiquitous across the web. Some examples include:
- This DensityDesign page allowing the viewer to see where certain languages are spoken around the world.
- FiveThirtyEight’s numerous probability charts on sports outcomes, some of which allow the user to change variables such as whether a team wins its next game to see how that changes the expected result.
- The New York Times’ many top-notch visualizations providing insight into complex interactions such as how geography, income and life expectancy are all related.
For internal use, organizations rely on both static and interactive visualizations created by teams of data scientists and designers. A chart in a quarterly report is a visualization, as is a dashboard that employees can use to monitor key performance indicators (KPIs) across the company. The latter type of visualization in particular gained a lot of traction in the 2010s and will be a key focus area going forward.
Having a dashboard populated with the right KPIs allows for efficient tracking of the health of a whole organization or the progress of a specific initiative or project. The best dashboards feature up-to-date data, in an easily navigable interface/format, without any of the information overload that can easily happen with poor design. Interviews with stakeholders and end users, verification of data systems, and the use of specialized tools for data analysis and graphic design are all necessary steps toward effective visualizations.
Overview and outlook for data analytics
Data analytics constitute a more abstract subject than data visualizations. Most of the time, the term refers to a set of solutions for analyzing digital information, the insights they produce or both. Some common types of data analytics tools include:
- BI software: A BI solution can pull data from multiple sources and perform many functions, from formatting it for storage into a data warehouse to data mining that reveals key patterns in a data set.
- Online analytical processing (OLAP) utilities: OLAP is a set of database technologies optimized for fast querying and reporting. Users can navigate OLAP to find specific insights that are structured into “cubes,” which sit between the user and the data warehouse on the backend and contain dimensions such as time, geography and product line for different data types.
- Advanced analytics: Practices such as machine learning, which involves training computers to perform sophisticated tasks without supervision, and various applications of artificial intelligence fall into this category.
Regardless of the actual tools used, analytics help organizations answer questions and optimize their performance. For example, a data scientist might use an OLAP tool to find a sales report for a particular year, and then compare the information in it to another from a different year, as part of an attempt to better understand long-term trends.
What you can learn about data visualization and data analytics at UCR
The online engineering degree program at the University of California, Riverside (UCR) features a concentration in data science, in which students can explore advanced techniques in both data visualization and data analytics. The specialized coursework for the data science track includes the following classes:
- Foundations of Applied Machine Learning
- Application of Visualization in Data Science
- Data Mining Techniques
- Advanced Computer Vision
- Statistical Computing
- Statistical Mining Methods
- Machine Learning
- Information Retrieval & Web Search
- Data Visualization & Big Data Tools
No residency is required, and the degree may be completed in as few as 13 months. You may choose from one of three start dates. Graduates can go on to careers such as chief data officer, data scientist and social media analyst, all of which are in high demand and offer excellent compensation. The U.S. Bureau of Labor Statistics estimates 19% growth in employment of computer and information scientists from 2016 to 2026, which is much faster than the average for all professions. Median annual pay in this field was more than $118,000 in May 2017.
To learn more about the data science specialization at UCR, visit its concentration page.
What is the difference between a data scientist and data engineer?