Best Data Management Software for SAS Visual Analytics

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

JMP, data analysis software Mac and Windows, combines powerful statistics with interactive visualization. It is simple to import and process data. Drag-and-drop interface, dynamically linked graphics, libraries of advanced analytics functionality, scripting language, and ways to share findings with others allow users to dig deeper into their data with greater ease. JMP was originally developed in 1980 to capture the new value of GUI for personal computers. JMP continues to add cutting-edge statistical methods to the software's functionality with every release. John Sall, the organization's founder, is still Chief Architect.

SAS® Viya® offers a robust and scalable analytics platform that is both efficient and easy to implement, allowing organizations to address a variety of business challenges. The insights generated automatically help in pinpointing the most frequently used variables across all models, highlighting key variables selected along with evaluation outcomes for each model. With the integration of natural language generation, project summaries are produced in straightforward language, which simplifies the interpretation of reports for users. Moreover, members of the analytics team can enhance the insights report with project notes, promoting better communication and teamwork. SAS further enables the integration of open source code within analyses, allowing users to utilize open source algorithms effortlessly in its platform. This flexibility encourages collaboration throughout your organization, as users are free to program in their preferred language. Additionally, you can leverage SAS Deep Learning with Python (DLPy), an open-source package available on GitHub, to expand your analytical capabilities even further. By using these tools, businesses can significantly enhance their data-driven decision-making processes.