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Visualizing Big Data with SAS Visual Analytics

A picture is worth a thousand words – especially when you are trying to understand and gain insights from data. It is particularly relevant when you are trying to find relationships among hundreds, or even thousands, of variables to determine their relative importance.

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Organizations of all types and sizes generate data each minute, hour and day. Everyone – from executives and departmental decision makers to call center workers and employees on production lines – hopes to learn things from collected data that can help them make better decisions, take smarter actions and operate more efficiently.

Regardless of how much data you have, one of the best ways to discern important relationships is through advanced analysis and high-performance data visualization. If sophisticated analyses can be performed quickly, even immediately, and results presented in ways that showcase patterns and allow querying and exploration, people across all levels in your organization can make faster, more effective decisions.

Big data brings new challenges to visualization because of the speed, size and diversity of data that must be taken into account. The cardinality of the columns you are trying to visualize should also be considered. One of the most common definitions of big data is data that is of such volume, variety and velocity that an organization must move beyond its comfort zone technologically to derive intelligence for effective decisions.

  • Volume refers to the size of the data.
  • Variety describes whether the data is structured, semi structured or unstructured.
  • Velocity is the speed at which data pours in and how frequently it changes.

Building upon basic graphing and visualization techniques, SAS Visual Analytics has taken an innovative approach to addressing the challenges associated with visualizing big data. Using innovative, in-memory capabilities combined with SAS Analytics and data discovery, SAS provides new techniques based on core fundamentals of data analysis and the presentation of results.

Handling Large Data Volumes

One challenge when working with big data is how to display results of data exploration and analysis in a way that is meaningful and not overwhelming. You may need a new way to look at the data that collapses and condenses the results in an intuitive fashion but still displays graphs and charts that decision makers are accustomed to seeing. You may also need to make the results available quickly via mobile devices, and provide users with the ability to easily explore data on their own in real time.

When working with massive amounts of data, it can be difficult to immediately grasp what visual might be the best to use. The autocharting capability in SAS Visual Analytics takes a look at the data you wish to examine and then, based on the amount of data and the type of data, it presents the most appropriate visualization. This intelligent autocharting helps business analysts and nontechnical users easily visualize their data. They can build hierarchies on the fly, interactively explore data and display the data in different ways to answer specific questions or solve new problems without having to rely on constant assistance from IT to provide changing views of information.

The autocharting capability in SAS® Visual Analytics takes a look at all of the data you wish to examine and then, based on the amount of data and the type of data, it presents the most appropriate visualization.

In addition, “what does it mean” explanations in SAS Visual Analytics display information about the analysis that has been performed, and identify and explain the relationships between the variables that are displayed. This makes analytics and the creation of data visualizations easy, even those with nontechnical or limited analytic backgrounds.

Data volume can become an issue because traditional architectures and software may not be able to process huge amounts of data in a timely manner, thus requiring you to make compromises and aggregate the details you want to visualize. Even the most common descriptive statistics calculations can become complicated when you are dealing with big data and don’t want to be restricted by column limits, storage constraints and limited support for different data types. The SAS in-memory engine solves these issues by speeding up the task of data exploration, and a visual interface displays the results in an easy-to-understand visualization.

Visualizing your data can be both fun and challenging. It is much easier to understand information in a visual compared to a large table with lots of rows and columns.

 

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