Software AG Extends High-Impact Predictive Analytics for the Mainframe
Software AG has announced the evolution and integration of its Zementis Predictive Analytics product with mainframe computing platforms—namely, IBM z Systems and Adabas & Natural applications and databases.
As a core capability of Software AG’s Digital Business Platform, Zementis Predictive Analytics dramatically reduces the complexity, expense and time required for enterprises to execute artificial intelligence (AI) and machine learning models in batch or real-time transactions, which in turn delivers operational AI for fast-moving, big data applications. Software AG’s Zementis product line integrates enterprise-grade predictive analytics into the IBM z Systems z/OS® data lifecycle ecosystem. The joint offering represents a family of certified products that enable exceptionally fast operational deployment and embedded scoring functionality of predictive models in key transaction environments, including but not limited to CICS®, Apache SparkTM and WebSphere® for z/OS.
Extending Zementis for IBM z Systems to Adabas & Natural enables the integration of transactional data from Adabas mainframe databases to powerful, real-time analytics and predictive insight deriving greater business value. Enterprise data can be replicated, accessed and analyzed with minimal overhead and data movement, thus further protecting sensitive data and improving efficiency.
As part of Software AG’s Adabas & Natural 2050+ initiative, this Digital Business Platform integration will enable customers to take advantage of differentiated business logic and high-value data for new innovative solutions in a digital-driven business.
Dr. Wolfram Jost, Chief Technology Officer, Software AG noted: “There are many large businesses and government organizations that have significant investments in mainframe systems. By extending and evolving these mainframe systems with powerful analytics capabilities during transaction processing, these enterprises can gain significant insights affecting a variety of outcomes, including real-time fraud detection, risk avoidance, and monitoring of sensitive big data for improper use or access.”
Data scientists adopt Zementis because they remain flexible to choose their preferred data mining algorithms and model development tools. IT teams value that Zementis leverages the vendor-neutral Predictive Model Markup Language (PMML) industry standard, which eliminates the need for custom code or proprietary deployment solutions.