The concept of depicting data visually has been around for centuries; from using maps and graphs in the seventeenth century to inventing the pie chart in the eighteenth century. Today, computers process substantial amounts of data rapidly to create the visuals people need to analyze data. This is data visualization (DV), a modern approach to depicting data.
DV presents large data sets and metrics as visuals such as charts and graphs. Because of how the human brain processes information, this makes it easier for decision-makers or business intelligence users to:
- Understand difficult concepts
- Find and share real-time trends
- Find new informational patterns in the data
The following applications use DV to collect and aggregate multi-source data”
- Enterprise resource planning (ERP)
- Customer relationship management (CRM)
- Sales force automation (SFA)
Visualizing data offers organizations and their users fast data access, data integration, data cleaning, and analytics tools. DV helps proven technologies such as cloud, big data, and advanced analytics platforms to work together to produce superior data management solutions that traditional data warehouses could not achieve.
You should be aware data visualization and data virtualization are not the same concepts. Although they sound similar, recall that DV refers to the display of data to end-users as charts, graphs, maps, reports, etc. Data virtualization is middleware that supplies data services to other data visualization tools and applications. Although it has some data visualization for users and developers, that is not the main use of data virtualization.
Benefits that organizations see when they move toward DV include:
- Increased access speed for real-time data Reduced data storage requirements Reduced cost and data replication
- Reduced risk of data loss or inconsistencies Reduced system workload
- Enhanced data governance through DV policies
However, disadvantages of moving toward DV do exist. These include complexity in change management and the initial risk of impacting system response time.
If your organization wants to implement data visualization, the organization needs to do the following analytical activities:
- Understand the data you want to visualize. This includes understanding the size of datasets and the uniqueness of data values in their columns. Keep in mind that big data is often generated faster than it can be managed and analyzed.
- Decide what you want to visualize and what kind of information you want to communicate through the visuals.
- Know your audience and understand how the audience processes visual information. Use a visual that conveys the information in the best and simplest form for your audience.
Ghosh, P. (2021, Sept 2). Fundamentals of data visualization. https://www.dataversity.net/fundamentals-of- data-virtualization/
IBM. (n.d.). What is data visualization? https://www.ibm.com/analytics/data-visualization
Miller, L. C. (2019). Data virtualization for dummies. Chichester, West Sussex, UK: John Wiley & Sons.
SAS. (2021). Data visualization: What it is and why it matters. https://www.sas.com/en_us/insights/big- data/data-visualization.html