Data Visualization

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.

The Uses of Data Visualization
Data visualization has many uses. Each type of data visualization can be used in different ways. We’ll get into the different types in a moment, but for now, here are some of the most common ways data visualization is used.

• Changes over time: This is perhaps the most basic and common use of data visualization, but that doesn’t mean it’s not valuable. The reason it is the most common is because most data has an element of time involved. Therefore, the first step in a lot of data analyses is to see how the data trends over time.
• Determining frequency: Frequency is also a fairly basic use of data visualization because it also applies to data that involves time. If time is involved, it is logical that you should determine how often the relevant events happen over time.
• Determining relationships (correlations): Identifying correlations is an extremely valuable use of data visualization. It is extremely difficult to determine the relationship between two variables without a visualization, yet it is important to be aware of relationships in data. This is a great example of the value of data visualization in data analysis.
• Examining a network: An example of examining a network with data visualization can be seen in market research. Marketing professionals need to know which audiences to target with their message, so they analyze the entire market to identify audience clusters, bridges between the clusters, influencers within clusters, and outliers.
• Scheduling: When planning out a schedule or timeline for a complex project, things can get confusing. A Gantt chart solves that issue by clearly illustrating each task within the project and how long it will take to complete.
• Analyzing value and risk: Determining complex metrics such as value and risk requires many different variables to be factored in, making it almost impossible to see accurately with a plain spreadsheet. Data visualization can be as simple as color-coding a formula to show which opportunities are valuable and which are risky.

Data Visualization and Big Data
The increased popularity of big data and data analysis projects have made visualization more important than ever. Companies are increasingly using machine learning to gather massive amounts of data that can be difficult and slow to sort through, comprehend and explain. Visualization offers a means to speed this up and present information to business owners and stakeholders in ways they can understand.

Big data visualization often goes beyond the typical techniques used in normal visualization, such as pie charts, histograms and corporate graphs. It instead uses more complex representations, such as heat maps and fever charts. Big data visualization requires powerful computer systems to collect raw data, process it and turn it into graphical representations that humans can use to quickly draw insights.

While big data visualization can be beneficial, it can pose several disadvantages to organizations. They are as follows:

• To get the most out of big data visualization tools, a visualization specialist must be hired. This specialist must be able to identify the best data sets and visualization styles to guarantee organizations are optimizing the use of their data.
• Big data visualization projects often require involvement from IT, as well as management, since the visualization of big data requires powerful computer hardware, efficient storage systems and even a move to the cloud.
• The insights provided by big data visualization will only be as accurate as the information being visualized. Therefore, it is essential to have people and processes in place to govern and control the quality of corporate data, metadata and data sources.