In many companies and organizations, the quantity of data is growing at a persistent speed creating Big Data.
Big Data involves datasets that grow to such an enormous size that it becomes difficult to work with them by using traditional data management applications.

There is an increasing demand to analyze and an organization’s data and business processes, to recognize competitive market movements or to understand customer behavior and social trend.

The key problematic areas of Big Data include stream processing, analytics and visualization. Visualization of Big Data means rendering the information into graphical means for analysis that can be used for strategic planning, projections and improvement.

Big Data visualization is very complex and requires a multidisciplinary team which for example includes engineers, social scientist, cultural anthropologists, psychologists, marketing experts, epidemiologists, statisticians and many more.

The visualization tools used are critical to the success of Big Data analytics.
In general, two broad categories of visualization tools are described:

1. Exploratory visualization enables any decision-maker and any analyst to investigate diverse axes and directions of data for learning about data relationships.
2. Narrative visualizations help to explore a specific axis of the data in a specific approach. They allow a time series visualization for example of sales and can analyze and present it by geography, a group of users or language that is pre-defined the analyst. The data may be tracked in a month-by-month cycle for any geography, language and other defined user group. However, it is currently impossible to monitor and analyze every data action and to ask any non-predefined question regarding the data using this visualization approach.

Big Data visualizations tell a defined story about the data, which are designed in pre-computations or reports. The available tools on the market are either better for the exploratory model to get an initial impression about data or are better for the constrained narrative data analysis to receive detailed information over time.

There are many different decision-making requirements that often need to be customized for visualizing data most effectively for the desired analysis of the specific project. The initial setup will be time and cost intensive before providing the optimized solution for visualization of the stream processing, but the insights and analysis that result can be invaluable for business growth.