Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.
Common general types of data visualization:
More specific examples of methods to visualize data:
- Area Chart
- Bar Chart
- Box-and-whisker Plots
- Bubble Cloud
- Bullet Graph
- Circle View
- Dot Distribution Map
- Gantt Chart
- Heat Map
- Highlight Table
- Polar Area
- Radial Tree
- Scatter Plot (2D or 3D)
- Text Tables
With data now being a critical source of competitive advantage, enterprises are cutting across size and geographies seeking newer methods to identify and analyze the data they generate. Most enterprise decision makers are now familiar with intuitive graphs, pie-charts, and other forms of visualizations that try to make sense of sales, revenue, and other aspects of company operations. However, the usefulness of such data visualizations depends on the effectiveness of the data, or how the data is used to come up with conclusions. A balanced approach in data visualization and analytics is thus pivotal in formulating an effective data strategy.
Many enterprises confuse data analytics with data visualization. Both allow users to make sense of data and obtain the relevant metrics that helps in better decision making. In today’s age of information overload, where data generated is multiplying every 3 years, interpreting them turns out to be the need of the hour. On the other side, we have these forecasts and projections hinting at an exponential growth in revenue for the big data software market in the coming years. The confusion, however, stems from the fact that both data visualization and analytics represent data in visual interfaces.
While there is considerable overlap between the two, data analytics deals with data at a much deeper level, compared to visualization. An end-to-end business intelligence solution consists not just of the front end dashboard, which transforms data into a visual context, but also tools and algorithms at the backend.
Difference between Data Visualization and Data Analytics
Data visualization represents data in a visual context by making explicit the trends and patterns inherent in the data. Such pattern and trends may not be explicit in text-based data. Most tools allow the application of filters to manipulate the data as per user requirements. The traditional forms of visualization, in the form of charts, tables, line graphs, column charts, and many other forms, have of late been supplanted by highly insightful 3D visualizations.
Data analytics go a step deeper, identifying or discovering the trends and patterns inherent in the data. Data visualizations, while allowing users to make sense of the data, need not give the complete picture. Visualizations are only as effective as the data used to prepare the visualization in the first place. Feeding visualization engine with incomplete data will render half-baked, obsolete, or erroneous visualization.
Both data visualization and analytics deal with data. Visualization tools generate a beautiful and easy to comprehend report, but only robust backend capability, which handles the messy data and processes the data by applying advanced algorithms, gives an accurate report. Data analytics offers the complete picture, while visualization summarizes the available data in the best possible way. The best solutions co-opt both.
Data analytics has proven its worth time and again by helping businesses examine structured and unstructured datasets and extract useful information so key stakeholders can make more-informed, more effective decisions. Analytics can be prescriptive, predictive, diagnostic, and/or descriptive to produce insights, observe trends, compare metrics, and more.
But it can only do so much. Endless columns and rows of alphanumeric data can be difficult to digest at scale. Depending upon the level of detail that stakeholders need to draw actionable conclusions, as well as the need to interact with or drill-down into the data, traditional data analytics might not be sufficient for businesses to excel in today’s competitive marketplace. Additional tools are needed to help extract more timely, more nuanced, and more interactive insights than data analysis alone can provide.
Those tools are data visualization tools.
The reason data analytics is limited might be simple enough. Data analytics helps businesses understand the data they have collected. More precisely, it helps them become cognizant of the performance metrics within the collected data that are most impactful to the business. And it can provide a clearer picture of the business conditions that are of greatest concern to decision-makers.
7 Reasons To Employ Data Visualization Tools:
There are seven primary reasons to use data visualization tools to communicate the results of data analytics:
Absorb large amounts of data at scale
Compare and contrast metrics
Make explicit business trends, patterns, and insights
Monitor trends and patterns
Reveal questions that would otherwise be missed
Experiment with different scenarios