- Enhanced offering provides citizen data scientists with streamlined data prep and analytic workflows for greater ease of use and increased efficiency
- New release features edge scoring capability to address nearly all IoT analytics use cases
- Latest version extends in-database analytics to multiple platforms including Apache Hive, MySQL, Oracle, and Teradata, while adding network analytics for easier fraud detection
Dell announced a major new release of its award-winning Statistica advanced analytics platform, Dell Statistica version 13.1. This latest version delivers a host of capabilities designed to empower citizen data scientists, help organizations better address growing IoT analytics requirements, and better leverage increasingly heterogeneous data environments. New features include functionality to help citizen data scientists easily prepare structured and unstructured data, the ability to deploy analytics on devices and gateways anywhere in the world for edge scoring, and the extension of in-database analytics capabilities to platforms such as Apache Hive, MySQL, Oracle, and Teradata.
Advanced analytics continue to gain traction as a primary means through which organizations understand and predict customer behavior, optimize and validate critical business and manufacturing processes, and drive innovation to gain competitive advantage. According to Gartner, “by 2018, more than half of large organizations globally will compete using advanced analytics and proprietary algorithms, causing the disruption of entire industries.” Gartner also predicts that by 2020, “predictive and prescriptive analytics will attract 40 percent of enterprises’ net new investment in business intelligence and analytics.”
With the latest enhancements to Dell Statistica 13.1, companies can better cope with the worldwide shortage of traditional data scientists, manage the complexities of modern IoT environments, and address the proliferation of new data sources and data types.
Streamlined workflows for citizen data scientists
The latest release of Statistica offers a set of new capabilities designed to meet the specific needs of the modern “citizen” data scientist. As the global need for traditional data scientists far outpaces the available supply, these citizen data scientists – everyday, non-technical users who are embedded in the line of business – will increasingly become the driving force behind analytics initiatives. Dell Statistica 13.1 addresses this emergent need with new data preparation functionality built specifically for the citizen data scientist that simplifies the preparation of structured and unstructured data.
In tandem with Statistica’s Reusable Process Templates, it’s now easier than ever for users to share and distribute analytic workflows with non-technical users. With Statistica, traditional data scientists can build analytic models and workflows once, and non-technical business analysts can reuse those workflow templates repeatedly within the organization. This eliminates redundancy and allows business users to efficiently use analytics to solve real business problems without the technical expertise traditionally required.
Edge scoring for IoT analytics
When used in combination with Dell Boomi, Statistica now gives users the ability to deploy “analytic atoms” on any edge device or gateway, including the Dell Edge Gateway 5000 Series, anywhere in the world. This edge scoring capability enables organizations to address nearly any IoT analytics use case by running analytic workflows directly at the edge of the network where data is created. This not only eliminates the effort and expense required to stream massive amounts of IoT data to a central analytic repository, but it allows for immediate action to be taken at the point of impact in response to data insights.
Expanded in-database analytics for complex data environments
The new release also extends the Statistica Native Distributed Analytics Architecture (NDAA) capability to deliver in-database analytics to an even wider range of databases. In addition to Microsoft SQL Server, Statistica users can now perform in-database analytics on Apache Hive (on Spark), MySQL, Oracle, and Teradata. The use of in-database analytics improves analytic and network performance by allowing users to carry out intensive computations directly within the source systems. This enables organizations to leverage the compute power of Hadoop clusters, database appliances and other high-performance platforms, while reducing network traffic by eliminating the time-consuming process of moving massive amounts of data across the network.
Network analytics for enhanced fraud detection
Statistica now also features new network analytics capabilities that enable users to combine the power of predictive analytics with human expertise to better detect fraud and understand relationships within complex networks. Network analytics allows users to look at problems in new ways by visualizing entity relationships and viewing graphical association maps.
Additional features of Dell Statistica 13.1 that simplify and enhance the delivery of advanced analytics include:
- Improved visualization dashboards, enabling users to easily visualize the results of any analytic node and tie process-specific visualizations to the top level of MAS dashboards
- An upgraded Web UI that allows users to distribute analytic outputs with a modern look and feel in any web browser
- Enhanced validated data entry, ensuring that individuals relying on manual data entry can build data quality into the point of collection so it can be trusted for robust analytics
- Dell Statistica 13.1 is available now worldwide
John K. Thompson, general manager, Dell Statistica
“Dell believes the two greatest challenges organizations face with respect to analytics are the explosive growth of IoT infrastructures and the dangerous worldwide shortage of data scientists. With the latest release of Dell Statistica, we’ve moved aggressively to help customers address both of those challenges. We’ve rapidly evolved Statistica from a solution that meets the needs of traditional data scientists into one that now also meets the needs of everyday citizen data scientists. In addition, we’ve taken the lead on enabling IoT analytics by empowering organizations to flip the traditional analytic model on its head and deliver predictive algorithms and edge scoring directly to the source of data.”
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