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SAS – a familiar name in data analytics – has recorded positive revenue growth rates year after year throughout the last decade. The not-so-secret reason behind its performance is the depth of functionality it now provides across its products portfolio. Much of SAS’s success lies in its Visual Analytics and Visual Statistics products, which offer visualization-based exploratory analytics for faster and more accurate predictive analysis.

Data visualization and exploration is a “hot market” right now

It’s happy hour in the data visualization market and SAS has put its stake in the ground with its product Visual Analytics. Over the last two to three years, vendor interest has continued to grow in the data-visualization market, driven by the realization that there is a strong unmet demand for easy-to-use analytics solutions and a considerable untapped budget sitting within lines of businesses. Most exploratory analytics vendors offer interactive visualization (i.e. familiar charts and dashboards) to make their products more accessible to the everyday user. However, as the focus of analysis shifts from reporting and charting to complex predictive analytics, new-age exploratory analytics tools struggle to keep up – not due to technical limitations, but due to an increasingly diverse set of customers.

Visualizations will not lose relevance in the world of advanced data analytics. In fact, visualizations continue to be used in innovative ways by data scientists to understand patterns in complex data sets, such as those found in genomic and weather data. However, visualizations that work wonders in reporting do not always work well for data analysts and scientists who mine data. Also, visualizations vary significantly based on the industry and function mining the data because the structure of the data can take different forms.

Visual Analytics and Visual Statistics cater as much to the analytics enthusiast as to the data scientist or professional data analyst. Visual Statistics stands out for its cluster matrices to help investigate the distribution of different variables and understand relationships among them. The product offers decision trees to model “what if” scenarios and help optimize outcomes. Capabilities for write-back (which means that users can change independent variables to see the changes in dependent variables or vice versa) stand out on both products. The vendor is starting to include visualizations in other targeted SAS products such as SAS Visual Investigator.

This is by no means an end in itself, as I see further integration of features and functionality between the two products down the line. There is also an ever increasing list of things that SAS will need to do to ensure the products don’t lose relevance. But it’s a great place to start.


Further reading

How to Justify the Business Case for EPM, IT014-002906 (March 2014)

Fundamentals: Enterprise Performance Management, IT014-002874 (January 2014)

A Practitioner's Guide to Self-Service BI and Analytics, IT0014-002967 (December 2014)


Surya Mukherjee, Senior Analyst, IT – Information Management

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