1 July 2015 Zementis for IBM z Systems
2 Page 1 Zementis for IBM z Systems An integrated predictive analytics deployment and scoring capability for organizations managing data and transactions with IBM z Systems Highlights Drives timely and relevant insights via in-line predictive analytics Maintains the integrity of supported business processes via in-transaction processing Scores thousands of data records per second, scaling with business needs to enable instant decisions Improves performance and cost efficiency by reducing or eliminating movement of data off-platform to conduct analysis Maximizes the value of existing IT infrastructure to drive capital efficiency Enhances governance and security, not just of the data, but also of the predictive analytics Broadens consistency and assures compatibility across data mining tools with PMML industry standard Overview Mainframe computers have been with us for a very long time, but they are hardly a thing of the past. In fact, most organizations today manage critical, real-time business operations and maintain supporting data on mainframe computers. Just how prevalent are mainframes? Worldwide, 92% of top banks, 84% of top insurers, 92% of top retailers, 90% of top airlines rely on the mainframe 71% of the Fortune Global 500 utilize mainframes The mainframe processes roughly 30 billion business transactions every day 55% of all enterprise applications need the mainframe to complete transactions The need to analyze data in the context of live business operations has made the role of mainframe computers even more critical, as these computing workhorses contain information that powers key operational processes. Analyzing that data is essential to driving business process efficiency, effectively managing risk, strengthening and maintaining competitive advantage and driving sustainable, profitable growth. Predictive analytics applies data science techniques to data, allowing organizations to uncover patterns and nuances that would have been difficult or impossible to discern without such techniques. Insights from predictive analytics on data allow organizations to develop informed, accurate perspectives on future outcomes and make insight-driven decisions with a high degree of confidence. Leading organizations have embraced big data technologies to store, access, manage and analyze their data. These organizations also widely utilize mainframes to process all data that is essential to their core operational processes. Yet in many organizations, big data analytics solutions, predictive analytics solutions and mainframe computing infrastructure remain disconnected or only loosely coupled. Anything that puts a distance between the data, the computation and the actions that define the business process creates inefficiency, risk and potential loss of value. Why would any business find that acceptable?
3 Page 2 This disjointed universe creates a series of critical challenges: Significant complexity: Separate data warehouses lead to duplication of effort to set up, integrate and manage Analytics latency: Transactional data is not readily available when critical business processes require analytical results Lack of synchronization: Data is not easily aggregated, leading to pools of stale data, different versions of the same data, and inaccurate analysis Data duplication: Multiple copies of the same data create process and cost inefficiencies, while perpetuating data siloes Excessive costs: Moving data from multiple locations to the point of analysis inflates IT process and labor costs, while reducing ROI on capital equipment investment Delayed time-to-insight: data warehouse complexity, analytics latency, unsynchronized data and data duplication delay time-to-insight for critical business decisions, less accurate decisions, missed opportunities and loss of value These challenges also represent a significant opportunity for data-driven organizations to change the paradigm to their advantage. How? Integrate predictive analytics with the business processes that they support at all stages of the data lifecycle, spanning both software solutions and the mainframe hardware on which missioncritical transactions are processed. This is the vision of IBM z Systems. Zementis and IBM have teamed up to integrate enterprise-grade predictive analytics into the IBM z Systems z/os data lifecycle ecosystem. Zementis for IBM z Systems 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 and WebSphere. Solution Overview Zementis for IBM z Systems embraces a common platform strategy: integrating predictive analytics with the data, the business process and the underlying hardware, with the option to execute predictive models at the point of greatest efficiency with respect to transaction performance or cost efficiency. This closes the distance between the data, the computation and the actions that define the business process, creating an optimal IT architecture to support agile business operations. Zementis for IBM z Systems: Is an integrated predictive analytics deployment and scoring capability for organizations managing data and transactions with z Systems Helps companies rapidly deploy and easily integrate scalable, standards-based predictive analytics directly into critical business processes and execute predictive analytics within those processes. Enables data scientists to retain full flexibility to develop predictive models in whichever commercial or open-source data mining tools they choose (e.g. IBM SPSS, KNIME, Python, R and SAS), based on the PMML industry standard Delivers certified execution engines for applications compatible with the Java environment for z/os (e.g. CICS, WebSphere ) Performs and scales extremely well, enabling integration into synchronous in-transaction, real-time scenarios such as fraud / risk detection and customer insight analytics
4 Page 3 Delivers a lightweight Java implementation for predictive analytics that executes on IBM z Systems ziip specialty engines Solution Benefits Optimizes decisions, solving complex problems faster. Introducing predictive analytics directly into the flow of the business process provides business advantage by dramatically reducing time-to-insight for critical business decisions By integrating analytics and transaction processing, customer value increases with every interaction, allowing organizations to: Increase business agility, via accurate insights delivered at the right time and point of impact Improve data governance and security, via integrated software, hardware and business processes Reduce infrastructure cost and complexity, via streamlined architecture and business processes Business Value In-line analytics drives timely and relevant insights. By embedding predictive analytics directly into the transaction workflow, IBM and Zementis optimize the analytical environment to support business processes with the most recent operational data. Fresh data, rapid predictive analytics execution and in-line IT architecture deliver fresh insights directly to the targeted point of impact in the process, and do so with speed that drives competitive advantage. Performance without compromise. Zementis for IBM z Systems performs predictive analytics processing within the business transaction, with integrated functionality that does not degrade the performance of the business process. This design philosophy helps organizations impart powerful predictive analytics to business operations while upholding levels specified in an organization s SLAs. Predictive analytics at scale. IBM z Systems is optimized for high performance and scalability, to support business processes in both routine and highly variable situations. Whether data flows in steady streams, in anticipated cycles or in unanticipated bursts, z Systems scales to ingest data, analyze it, and deliver optimized decisions directly into the associated business transaction for the supported business process. With Zementis, z Systems incorporates integrated predictive analytics that scale to support dynamic analytical requirements. Zementis effortlessly scores thousands of data records per second, supporting use cases in the most rigorous business environments. Zementis for IBM z Systems is designed to scale with business needs and enable instant decisions. Efficiency through minimal data movement. Embedding predictive analytics directly within the flow of the business process minimizes data movement and meshes the technical aspects of analytics with the process aspects of decision making, whether the decisions are automated or require human involvement. This increases processing speed for performing predictive analytical functions and applying predictive insights to business processes, enhancing performance efficiency of the supported business process. Avoiding data movement minimizes system costs by eliminating the use of mainframe processing cycles for ETL (extract, transfer, load) processes, while substantially reducing strain on network, storage and distributed server capacity to receive that data. Moving the analytics to the data and keeping operational data in a single location while it is being analyzed utilizes capital equipment more efficiently and boosts ROI. Additionally, minimal data movement implies far less human intervention in the analytical process, reducing allocated labor costs. Zementis for
5 Page 4 IBM z Systems allows organizations to fully utilize all existing capacity, thereby reducing overall IT capital expenditure. Maximize the value of your IT infrastructure. Mainframes are everywhere, running critical business processes that rely on timely availability of accurate data. IBM z Systems embeds predictive analytics directly into the business process where it lives on the mainframe, and Zementis brings predictive analytics to z Systems right where it matters. Not only does this integration close the gap between data, analytics, business process software and supporting hardware, it makes more efficient use of all IT infrastructure. Zementis for IBM z Systems allows organizations to efficiently add enterprise-grade predictive analytics functionality to their IBM z Systems deployment. This improves organizations ability to fully utilize all existing capacity, reducing IT capital expenditure and boosting capital efficiency. Before an organization adds more infrastructure, let IBM and Zementis help you make greater use of what you already have. Reduce risk and enhance compliance. Data created by transactional systems will typically contribute to a number of critically important reporting, analysis, and decisioning functions in what IBM terms the data lifecycle. IBM z Systems has made available a portfolio of products and services that enable an integrated life cycle management ecosystem for data, joining the analytics with the transactional data environment. This ecosystem not only supports business processes, but is embedded directly into the IT architecture and business workflow of these processes. Users benefit from tightly embedded predictive capabilities within an integrated system, allowing the organization to reduce the data management and security risks of users working with data and analytical outputs outside the system (i.e. off-platform ) This encapsulated workflow preserves the integrity of the data, enhances visibility into the data and analyses, allows policy-based access to the data and analytical outputs and provides an audit trail. These features reduce business risk from data breaches or unintentional compromises, while also simplifying audit compliance and forensic review. For z/os customers, Zementis for IBM z Systems supports the Analyze and Transact stages of the data life cycle for z Systems, embedding predictive analytics directly into the secure, integrated workflow from start to finish. Technology Benefits Analyze: Faster, more accurate modeling Build efficiently: write model once in SPSS, R, Python, SAS Deploy fast: eliminate manual process & errors via the PMML industry standard Zementis utilizes the PMML (Predictive Model Markup Language) industry standard and its own proprietary technology to streamline and accelerate model development and deployment. Data scientists can write a predictive model once, in whichever commercial or open-source data mining tools they choose (such as IBM SPSS, KNIME, Python, R and SAS), and then deploy the model within minutes instead of months. This flexibility and accelerated deployment cycle time get predictive analytics into the hands of business decision makers quickly and enable scalable predictive capabilities, doing so at a fraction of the cost and resources typically required. This efficient deployment process also eliminates cumbersome manual processes that lead to transcription errors and rework. The Zementis solution supports many types of statistical models and machine learning algorithms, covering a wide variety of predictive requirements. Supported modeling techniques range from simple regression models to the most complex ensemble models. It also handles missing and invalid values and manages outliers, ensuring operational data consistency. Additionally, it addresses myriad other functions for data pre- and post-processing, including: value mapping, discretization, normalization, scaling, logical
6 Page 5 and arithmetic operators, conditional logic, built-in functions, and business decisions and thresholds. The solution incorporates a PMML converter, which extends its functionality to all versions of PMML. When an older version of PMML is presented, the solution automatically converts it into the latest version, PMML 4.1. The predictive model deployment efficiency of Zementis for IBM z Systems complements other IBM technology capabilities, including IBM SPSS data mining tools. Transact: Deliver timely predictive insights directly to the point of impact Generate insights quickly: enable rapid time-to-insight from predictive analytics, integrated directly into business processes Drive economic value: scalable, OpEx- & CapEx-efficient, high ROI IBM z Systems offers a unique approach to driving timely, accurate decisions for businesses based on data analytics, and the cornerstone of this approach is in-line, integrated analytics functionality. Integrating analytics and transaction processing increases customer value with every interaction, by directly embedding analytics functionality within the core activities that define the business process. Zementis for IBM z Systems enhances the relevance, accuracy and timeliness of these activities segmentation, decisions, and actions by introducing predictive analytics as an organic component of the operational process. Model portability, guaranteed through the PMML industry standard, enables the scoring of models developed using IBM data mining solutions, as well as third-party statistical platforms. It also fuses predictive decisions and transaction processing into a scalable, high-performance, massively parallel platform that easily processes data at petascale volumes. The solution is certified using the IBM 64-bit SDK for z/os, Java Technology Edition, Version 7, Release 1 SR1. Applications compatible with the Java environment for z/os (e.g. CICS, WebSphere) can integrate Zementis predictive scoring. Zementis for IBM z Systems takes full advantage of IBM s high-performance CICS Transaction Server functionality for rapid execution. Alternatively, Zementis leverages the IBM WebSphere Application Server on z/os or Apache Spark for standards-based model execution, allowing maximum flexibility in the target execution framework depending on the actual processing requirements. As an integrated component of IBM z Systems, the Zementis technology enables near real-time, ondemand scoring of analytic models for operational systems and also allows organizations to conduct massively parallel, batch-oriented scoring of historical data. Zementis for IBM z Systems simplifies the integration of operational analytics into business processes so that the actual analysis can proceed sooner, delivering results faster. Zementis for IBM z Systems IBM conceived its z Systems analytics ecosystem on the premise that analyzing historical data was no longer sufficient in a real-time world. Streaming data, interactive queries and opportunities to apply data in the moment to intervene in a business process and alter the outcome in a favorable way (both for customer and commercial enterprise) have shifted the data analytics lens from backward-looking to forward-looking. While historical data still plays a critical role in shaping an analytics-driven picture of a situation, predictive analytics has emerged as the lens on the future. The IBM z Systems ecosystem incorporates both of these dimensions, and Zementis for IBM z Systems contributes powerful technology that enables enterprise-grade predictive analytics at scale.
7 Page 6 IBM defines the data lifecycle ecosystem in terms of six sequential components: 1. Analyze: with faster, more accurate modeling 2. Act: with streamlined decisioning processes 3. Transact: with faster, more accurate scoring and transactions that incorporate business insight 4. Transform: transforming data via faster preparation and cleansing 5. Integrate: simplifying integration of source data into analyses and outputs into data stores and other applications 6. Report: render analytical outputs rapidly and accurately, to enable faster reporting and utilization of insights Zementis for IBM z Systems enhances IBM s vision and capabilities for the data lifecycle by integrating predictive analytics directly into the data lifecycle ecosystem in the following areas: Analyze: via faster, more accurate modeling Transact: by delivering timely predictive insights directly to the point of impact Zementis offers two core solutions: ADAPA (Adaptive Decision and Predictive Analytics): an extremely fast, standards-based deployment platform and scoring engine for predictive analytics UPPI (Universal PMML Plug-in): an extremely fast, standards-based deployment platform and scoring engine for predictive analytics, packaged as a plug-in tool for industry-leading analytics and data warehouse platforms Zementis offers the following deployment solution for predictive analytics on IBM z Systems: UPPI for Java on z/os
8 Page 7 Zementis and IBM: Revolutionizing Analytics In addition to supporting IBM z Systems, Zementis delivers enterprise-grade predictive analytics deployment and operational capabilities for big data on multiple other IBM solutions. These solutions include: UPPI for IBM PureData System for Analytics UPPI for IBM InfoSphere BigInsights (Hive, Spark, Storm) ADAPA running on IBM WebSphere (AIX /Linux ) Functional Accelerator for IBM SPSS Predictive Customer Intelligence (PCI) Extensions for IBM SPSS Modeler and SPSS Statistics The following diagram depicts Zementis-enabled IBM solutions and platforms:
9 Page 8 Zementis Zementis, Inc. provides software solutions for predictive analytics. The company was founded on the principle that data science teams and IT departments can collaborate seamlessly and efficiently, allowing predictive models to rapidly move from development to deployment, so that businesses and other datacentric organizations can easily incorporate predictive analytics into their routine operations. Agile deployment and operational enablement of predictive solutions is the cornerstone of the Zementis philosophy. Zementis partners with leading analytics and data warehouse solution providers such as IBM to enrich and extend customer capabilities. For more information, visit IBM IBM is a globally integrated technology and consulting company headquartered in Armonk, New York. With operations in more than 170 countries, IBM attracts and retains some of the world's most talented people to help solve problems and provide an edge for businesses, governments and non-profits. Innovation is at the core of IBM's strategy. The company develops and sells software and systems hardware and a broad range of infrastructure, cloud and consulting services. Today, IBM is focused on five growth initiatives - Cloud, Big Data and Analytics, Mobile, Social Business and Security. IBMers are working with customers around the world to apply the company's business consulting, technology and R&D expertise to enable systems of engagement that deliver dynamic insights for businesses and governments worldwide. For more information, visit
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