SAS: Big Data Analytics Redefined

Jim Goodnight, Co-founder & CEO
Over the years, Hadoop has lucratively dealt with big data, which has made it a preferred framework for storing, processing, and analyzing large multi-structured datasets. However, Hadoop’s steep learning curve and lack of skilled talent to implement open distributed frameworks is posing a great challenge to various industries. Today, enterprises are looking for unique ways to centralize and streamline big data, and decision makers and data scientists are looking for an intuitive solution to leverage big data capabilities to enhance operational efficiency. To this end, SAS offers innovative Hadoop big data solutions that span the entire analytic life cycle—from data management to data exploration, model development, and deployment.

With the aim of helping customers to get more insights out of their data and to simplify data visualization, SAS offers Visual Analytics platform. SAS Visual Analytics platform is designed for those who want to leverage and derive insights from data—ranging from decision makers to data analysts and scientists. The Visual Analytics platform enables client to identify patterns and relationships in data through the capability of data visualization. Further, by integrating with Microsoft Office suites and mobile devices, SAS Visual Analytics platform enables users to uncover hidden insights from the data and turn them into actions, helping organizations to improve overall operational efficiency. In addition, web-based exploratory analysis and other user-friendly features allow users without analytical proficiency to use predictive analytics to reap precise insights.

In one instance, one of SAS’s most prominent clients, Cosmo Bank—an industry leader in Taiwan’s cash-card market, implemented SAS Visual Analytics to reap insights from big data for real-time customer intelligence and risk management. Through this solution, risk managers and board members were able to make informed decisions at the right time to help the bank stay ahead of the curve. In addition, with the help of SAS’s discovery capabilities, Cosmo Bank was able to recover an enormous amount of customer and transactional data accumulated in real time.


The Visual Analytics platform enables clients to identify patterns and relationships in data through the capability of data visualization


SAS has evidently made Cosmo’s life much simpler, enabling them to analyze data instantly and gain value from it.

SAS also offers data management solutions to manage big data without writing a line of code. The solution enables stakeholders to quickly access and store data from relational data sources as well as SAS datasets to improve the overall performance. It also lets users to profile, transform, and cleanse data on Hadoop, using an intuitive user interface. Further, through SAS Data Loader, data scientists and SAS developers can manage the data without writing scripts, simplify IT operations, and improve scalability and performance.

Furthermore, SAS In-Memory Statistics—an all-in-one interactive environment—will help organizations to enhance the pace of the analytic life cycle by allowing multiple users to simultaneously and interactively manage and explore data, build and compare models, and reap insights from the data to turn them into valuable actions. Along with this, SAS help customers to gain competitive edge and obtain maximum benefits from data by providing appropriate training, support, and necessary resources surrounding Hadoop.

Serving in the big data industry for more than two decades, SAS has offered its big data analytics solution to more than 83,000 customers across various industries. Forging ahead, SAS will continue to deliver proven solutions that drive innovation and improve performance across various industries globally, with respect to data science.

Company
SAS

Headquarters
Cary, NC

Management
Jim Goodnight, Co-founder & CEO

Description
Provides a path-breaking analytics solutions and services that span the entire analytic life cycle–from data management to data exploration, model development, and deployment