Enabling Real Time Analytics On Ibm Z Systems Platform

Author : Lydia Parziale,Oliver Benke,Willie Favero,Ravi Kumar,Steven LaFalce,Cedrine Madera,Sebastian Muszytowski,IBM Redbooks
Publisher : IBM Redbooks
Page : 214 pages
File Size : 42,9 Mb
Release : 2016-08-08
Category : Computers
ISBN : 9780738441863

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Enabling Real-time Analytics on IBM z Systems Platform by Lydia Parziale,Oliver Benke,Willie Favero,Ravi Kumar,Steven LaFalce,Cedrine Madera,Sebastian Muszytowski,IBM Redbooks Pdf

Regarding online transaction processing (OLTP) workloads, IBM® z SystemsTM platform, with IBM DB2®, data sharing, Workload Manager (WLM), geoplex, and other high-end features, is the widely acknowledged leader. Most customers now integrate business analytics with OLTP by running, for example, scoring functions from transactional context for real-time analytics or by applying machine-learning algorithms on enterprise data that is kept on the mainframe. As a result, IBM adds investment so clients can keep the complete lifecycle for data analysis, modeling, and scoring on z Systems control in a cost-efficient way, keeping the qualities of services in availability, security, reliability that z Systems solutions offer. Because of the changed architecture and tighter integration, IBM has shown, in a customer proof-of-concept, that a particular client was able to achieve an orders-of-magnitude improvement in performance, allowing that client's data scientist to investigate the data in a more interactive process. Open technologies, such as Predictive Model Markup Language (PMML) can help customers update single components instead of being forced to replace everything at once. As a result, you have the possibility to combine your preferred tool for model generation (such as SAS Enterprise Miner or IBM SPSS® Modeler) with a different technology for model scoring (such as Zementis, a company focused on PMML scoring). IBM SPSS Modeler is a leading data mining workbench that can apply various algorithms in data preparation, cleansing, statistics, visualization, machine learning, and predictive analytics. It has over 20 years of experience and continued development, and is integrated with z Systems. With IBM DB2 Analytics Accelerator 5.1 and SPSS Modeler 17.1, the possibility exists to do the complete predictive model creation including data transformation within DB2 Analytics Accelerator. So, instead of moving the data to a distributed environment, algorithms can be pushed to the data, using cost-efficient DB2 Accelerator for the required resource-intensive operations. This IBM Redbooks® publication explains the overall z Systems architecture, how the components can be installed and customized, how the new IBM DB2 Analytics Accelerator loader can help efficient data loading for z Systems data and external data, how in-database transformation, in-database modeling, and in-transactional real-time scoring can be used, and what other related technologies are available. This book is intended for technical specialists and architects, and data scientists who want to use the technology on the z Systems platform. Most of the technologies described in this book require IBM DB2 for z/OS®. For acceleration of the data investigation, data transformation, and data modeling process, DB2 Analytics Accelerator is required. Most value can be achieved if most of the data already resides on z Systems platforms, although adding external data (like from social sources) poses no problem at all.

Author : Lydia Parziale
Publisher : Unknown
Page : 128 pages
File Size : 55,5 Mb
Release : 2016
Category : Commercial statistics
ISBN : OCLC:1078368551

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Enabling Real-time Analytics on IBM Z Systems Platform by Lydia Parziale Pdf

Author : Domenico D'Alterio,Michael Bonett,Eric Goodson,Matt Hunter,Keith Miller,Fabio Riva,Volkmar Burke Siegemund,John Strymecki,IBM Redbooks
Publisher : IBM Redbooks
Page : 44 pages
File Size : 45,7 Mb
Release : 2018-07-26
Category : Computers
ISBN : 9780738457062

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Introduction to IBM Common Data Provider for z Systems by Domenico D'Alterio,Michael Bonett,Eric Goodson,Matt Hunter,Keith Miller,Fabio Riva,Volkmar Burke Siegemund,John Strymecki,IBM Redbooks Pdf

IBM Common Data Provider for z Systems collects, filters, and formats IT operational data in near real-time and provides that data to target analytics solutions. IBM Common Data Provider for z Systems enables authorized IT operations teams using a single web-based interface to specify the IT operational data to be gathered and how it needs to be handled. This data is provided to both on- and off-platform analytic solutions, in a consistent, consumable format for analysis. This Redpaper discusses the value of IBM Common Data Provider for z Systems, provides a high-level reference architecture for IBM Common Data Provider for z Systems, and introduces key components of the architecture. It shows how IBM Common Data Provider for z Systems provides operational data to various analytic solutions. The publication provides high-level integration guidance, preferred practices, tips on planning for IBM Common Data Provider for z Systems, and example integration scenarios.

Author : Makenzie Manna,Erhan Mengusoglu,Artem Minin,Krishna Teja Rekapalli,Thomas Rüter,Pia Velazco,Markus Wolff,IBM Redbooks
Publisher : IBM Redbooks
Page : 128 pages
File Size : 53,9 Mb
Release : 2022-11-30
Category : Computers
ISBN : 9780738460925

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Optimized Inferencing and Integration with AI on IBM zSystems: Introduction, Methodology, and Use Cases by Makenzie Manna,Erhan Mengusoglu,Artem Minin,Krishna Teja Rekapalli,Thomas Rüter,Pia Velazco,Markus Wolff,IBM Redbooks Pdf

In today's fast-paced, ever-growing digital world, you face various new and complex business problems. To help resolve these problems, enterprises are embedding artificial intelligence (AI) into their mission-critical business processes and applications to help improve operations, optimize performance, personalize the user experience, and differentiate themselves from the competition. Furthermore, the use of AI on the IBM® zSystems platform, where your mission-critical transactions, data, and applications are installed, is a key aspect of modernizing business-critical applications while maintaining strict service-level agreements (SLAs) and security requirements. This colocation of data and AI empowers your enterprise to optimally and easily deploy and infuse AI capabilities into your enterprise workloads with the most recent and relevant data available in real time, which enables a more transparent, accurate, and dependable AI experience. This IBM Redpaper publication introduces and explains AI technologies and hardware optimizations, and demonstrates how to leverage certain capabilities and components to enable AI solutions in business-critical use cases, such as fraud detection and credit risk scoring, on the platform. Real-time inferencing with AI models, a capability that is critical to certain industries and use cases, now can be implemented with optimized performance thanks to innovations like IBM zSystems Integrated Accelerator for AI embedded in the Telum chip within IBM z16TM. This publication describes and demonstrates the implementation and integration of the two end-to-end solutions (fraud detection and credit risk), from developing and training the AI models to deploying the models in an IBM z/OS® V2R5 environment on IBM z16 hardware, and integrating AI functions into an application, for example an IBM z/OS Customer Information Control System (IBM CICS®) application. We describe performance optimization recommendations and considerations when leveraging AI technology on the IBM zSystems platform, including optimizations for micro-batching in IBM Watson® Machine Learning for z/OS. The benefits that are derived from the solutions also are described in detail, including how the open-source AI framework portability of the IBM zSystems platform enables model development and training to be done anywhere, including on IBM zSystems, and enables easy integration to deploy on IBM zSystems for optimal inferencing. Thus, allowing enterprises to uncover insights at the transaction-level while taking advantage of the speed, depth, and securability of the platform. This publication is intended for technical specialists, site reliability engineers, architects, system programmers, and systems engineers. Technologies that are covered include TensorFlow Serving, WMLz, IBM Cloud Pak® for Data (CP4D), IBM z/OS Container Extensions (zCX), IBM CICS, Open Neural Network Exchange (ONNX), and IBM Deep Learning Compiler (zDLC).

Author : Terry Purcell,IBM Redbooks
Publisher : IBM Redbooks
Page : 44 pages
File Size : 47,7 Mb
Release : 2017-06-28
Category : Computers
ISBN : 9780738456126

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DB2 12 for z Optimizer by Terry Purcell,IBM Redbooks Pdf

There has been a considerable focus on performance improvements as one of the main themes in recent IBM DB2® releases, and DB2 12 for IBM z/OS® is certainly no exception. With the high-value data retained on DB2 for z/OS and the z Systems platform, customers are increasingly attempting to extract value from that data for competitive advantage. Although customers have historically moved data off platform to gain insight, the landscape has changed significantly and allowed z Systems to again converge operational systems with analytics for real-time insight. Business-critical analytics is now requiring the same levels of service as expected for operational systems, and real-time or near real-time currency of data is expected. Hence the resurgence of z Systems. As a precursor to this shift, IDAA brought the data warehouse back to DB2 for z/OS and, with its tight integration with DB2, significantly reduces data latency as compared to the ETL processing that is involved with moving data to a stand-alone data warehouse environment. That change has opened up new opportunities for operational systems to extend the breadth of analytics processing without affecting the mission-critical system and integrating near real-time analytics within that system, all while maintaining the same z Systems qualities of service. Apache Spark on z/OS and Linux for System z also allow analytics in-place, in real-time or near real-time. Enabling Spark natively on z Systems reduces the security risk of multiple copies of the Enterprise data, while providing an application developer-friendly platform for faster insight in a simplified and more secure analytics framework. How is all of this relevant to DB2 for z/OS? Given that z Systems is proving again to be the core Enterprise Hybrid Transactional/Analytical Processing (HTAP) system, it is critical that DB2 for z/OS can handle its traditional transactional applications and address the requirements for analytics processing that might not be candidates for these rapidly evolving targeted analytics systems. And not only are there opportunities for DB2 for z/OS to play an increasing role in analytics, the complexity of the transactional systems is increasing. Analytics is being integrated within the scope of those transactions. DB2 12 for z/OS has targeted performance to increase the success of new application deployments and integration of analytics to ensure that we keep pace with the rapid evolution of IDAA and Spark as equal partners in HTAP systems. This paper describes the enhancements delivered specifically by the query processing engine of DB2. This engine is generally called the optimizer or the Relational Data Services (RDS) components, which encompasses the query transformation, access path selection, run time, and parallelism. DB2 12 for z/OS also delivers improvements targeted at OLTP applications, which are the realm of the Data Manager, Index Manager, and Buffer Manager components (to name a few), and are not identified here. Although the performance measurement focus is based on reducing CPU, improvement in elapsed time is likely to be similarly achieved as CPU is reduced and performance constraints alleviated. However, elapsed time improvements can be achieved with parallelism, and DB2 12 does increase the percentage offload for parallel child tasks, which can further reduce chargeable CPU for analytics workloads.

Author : Mike Ebbers,Dheeraj Reddy Chintala,Priya Ranjan,Lakshminarayanan Sreenivasan,IBM Redbooks
Publisher : IBM Redbooks
Page : 70 pages
File Size : 45,6 Mb
Release : 2013-04-11
Category : Computers
ISBN : 9780738437637

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Real-time Fraud Detection Analytics on IBM System z by Mike Ebbers,Dheeraj Reddy Chintala,Priya Ranjan,Lakshminarayanan Sreenivasan,IBM Redbooks Pdf

Payment fraud can be defined as an intentional deception or misrepresentation that is designed to result in an unauthorized benefit. Fraud schemes are becoming more complex and difficult to identify. It is estimated that industries lose nearly $1 trillion USD annually because of fraud. The ideal solution is where you avoid making fraudulent payments without slowing down legitimate payments. This solution requires that you adopt a comprehensive fraud business architecture that applies predictive analytics. This IBM® Redbooks® publication begins with the business process flows of several industries, such as banking, property/casualty insurance, and tax revenue, where payment fraud is a significant problem. This book then shows how to incorporate technological advancements that help you move from a post-payment to pre-payment fraud detection architecture. Subsequent chapters describe a solution that is specific to the banking industry that can be easily extrapolated to other industries. This book describes the benefits of doing fraud detection on IBM System z®. This book is intended for financial decisionmakers, consultants, and architects, in addition to IT administrators.

Author : Ute Baumbach,Patric Becker,Uwe Denneler,Eberhard Hechler,Wolfgang Hengstler,Steffen Knoll,Frank Neumann,Guenter Georg Schoellmann,Khadija Souissi,Timm Zimmermann,IBM Redbooks
Publisher : IBM Redbooks
Page : 216 pages
File Size : 43,9 Mb
Release : 2015-12-11
Category : Computers
ISBN : 9780738441191

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Accelerating Data Transformation with IBM DB2 Analytics Accelerator for z/OS by Ute Baumbach,Patric Becker,Uwe Denneler,Eberhard Hechler,Wolfgang Hengstler,Steffen Knoll,Frank Neumann,Guenter Georg Schoellmann,Khadija Souissi,Timm Zimmermann,IBM Redbooks Pdf

Transforming data from operational data models to purpose-oriented data structures has been commonplace for the last decades. Data transformations are heavily used in all types of industries to provide information to various users at different levels. Depending on individual needs, the transformed data is stored in various different systems. Sending operational data to other systems for further processing is then required, and introduces much complexity to an existing information technology (IT) infrastructure. Although maintenance of additional hardware and software is one component, potential inconsistencies and individually managed refresh cycles are others. For decades, there was no simple and efficient way to perform data transformations on the source system of operational data. With IBM® DB2® Analytics Accelerator, DB2 for z/OS is now in a unique position to complete these transformations in an efficient and well-performing way. DB2 for z/OS completes these while connecting to the same platform as for operational transactions, helping you to minimize your efforts to manage existing IT infrastructure. Real-time analytics on incoming operational transactions is another demand. Creating a comprehensive scoring model to detect specific patterns inside your data can easily require multiple iterations and multiple hours to complete. By enabling a first set of analytical functionality in DB2 Analytics Accelerator, those dedicated mining algorithms can now be run on an accelerator to efficiently perform these modeling tasks. Given the speed of query processing on an accelerator, these modeling tasks can now be performed much quicker compared to traditional relational database management systems. This speed enables you to keep your scoring algorithms more up-to-date, and ultimately adapt more quickly to constantly changing customer behaviors. This IBM Redbooks® publication describes the new table type that is introduced with DB2 Analytics Accelerator V4.1 PTF5 that enables more efficient data transformations. These tables are called accelerator-only tables, and can exist on an accelerator only. The tables benefit from the accelerator performance characteristics, while maintaining access through existing DB2 for z/OS application programming interfaces (APIs). Additionally, we describe the newly introduced analytical capabilities with DB2 Analytics Accelerator V5.1, putting you in the position to efficiently perform data modeling for online analytical requirements in your DB2 for z/OS environment. This book is intended for technical decision-makers who want to get a broad understanding about the analytical capabilities and accelerator-only tables of DB2 Analytics Accelerator. In addition, you learn about how these capabilities can be used to accelerate in-database transformations and in-database analytics in various environments and scenarios, including the following scenarios: Multi-step processing and reporting in IBM DB2 Query Management FacilityTM, IBM Campaign, or Microstrategy environments In-database transformations using IBM InfoSphere® DataStage® Ad hoc data analysis for data scientists In-database analytics using IBM SPSS® Modeler

Author : Makenzie Manna,Ravinder Akula,Matthew Cousens,Pabitra Mukhopadhyay,Anand Shukla,IBM Redbooks
Publisher : IBM Redbooks
Page : 90 pages
File Size : 43,8 Mb
Release : 2021-03-15
Category : Computers
ISBN : 9780738459530

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Getting Started: Journey to Modernization with IBM Z by Makenzie Manna,Ravinder Akula,Matthew Cousens,Pabitra Mukhopadhyay,Anand Shukla,IBM Redbooks Pdf

Modernization of enterprise IT applications and infrastructure is key to the survival of organizations. It is no longer a matter of choice. The cost of missing out on business opportunities in an intensely competitive market can be enormous. To aid in their success, organizations are facing increased encouragement to embrace change. They are pushed to think of new and innovative ways to counter, or offer, a response to threats that are posed by competitors who are equally as aggressive in adopting newer methods and technologies. The term modernization often varies in meaning based on perspective. This IBM® Redbooks® publication focuses on the technological advancements that unlock computing environments that are hosted on IBM Z® to enable secure processing at the core of hybrid. This publication is intended for IT executives, IT managers, IT architects, System Programmers, and Application Developer professionals.

Author : Samantha Buhler,Guanjun Cai,John Goodyear,Edrian Irizarry,Nora Kissari,Zhuo Ling,Nicholas Marion,Aleksandr Petrov,Junfei Shen,Wanting Wang,He Sheng Yang,Dai Yi,Xavier Yuen,Hao Zhang,IBM Redbooks
Publisher : IBM Redbooks
Page : 180 pages
File Size : 47,8 Mb
Release : 2018-09-11
Category : Computers
ISBN : 9780738457130

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Turning Data into Insight with IBM Machine Learning for z/OS by Samantha Buhler,Guanjun Cai,John Goodyear,Edrian Irizarry,Nora Kissari,Zhuo Ling,Nicholas Marion,Aleksandr Petrov,Junfei Shen,Wanting Wang,He Sheng Yang,Dai Yi,Xavier Yuen,Hao Zhang,IBM Redbooks Pdf

The exponential growth in data over the last decade coupled with a drastic drop in cost of storage has enabled organizations to amass a large amount of data. This vast data becomes the new natural resource that these organizations must tap in to innovate and stay ahead of the competition, and they must do so in a secure environment that protects the data throughout its lifecyle and data access in real time at any time. When it comes to security, nothing can rival IBM® Z, the multi-workload transactional platform that powers the core business processes of the majority of the Fortune 500 enterprises with unmatched security, availability, reliability, and scalability. With core transactions and data originating on IBM Z, it simply makes sense for analytics to exist and run on the same platform. For years, some businesses chose to move their sensitive data off IBM Z to platforms that include data lakes, Hadoop, and warehouses for analytics processing. However, the massive growth of digital data, the punishing cost of security exposures as well as the unprecedented demand for instant actionable intelligence from data in real time have convinced them to rethink that decision and, instead, embrace the strategy of data gravity for analytics. At the core of data gravity is the conviction that analytics must exist and run where the data resides. An IBM client eloquently compares this change in analytics strategy to a shift from "moving the ocean to the boat to moving the boat to the ocean," where the boat is the analytics and the ocean is the data. IBM respects and invests heavily on data gravity because it recognizes the tremendous benefits that data gravity can deliver to you, including reduced cost and minimized security risks. IBM Machine Learning for z/OS® is one of the offerings that decidedly move analytics to Z where your mission-critical data resides. In the inherently secure Z environment, your machine learning scoring services can co-exist with your transactional applications and data, supporting high throughput and minimizing response time while delivering consistent service level agreements (SLAs). This book introduces Machine Learning for z/OS version 1.1.0 and describes its unique value proposition. It provides step-by-step guidance for you to get started with the program, including best practices for capacity planning, installation and configuration, administration and operation. Through a retail example, the book shows how you can use the versatile and intuitive web user interface to quickly train, build, evaluate, and deploy a model. Most importantly, it examines use cases across industries to illustrate how you can easily turn your massive data into valuable insights with Machine Learning for z/OS.

Author : Deepak Kohli,IBM Redbooks
Publisher : IBM Redbooks
Page : 18 pages
File Size : 48,9 Mb
Release : 2016-04-17
Category : Computers
ISBN : 9780738455228

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New Approach to Analytics for IBM IMS Data by Deepak Kohli,IBM Redbooks Pdf

IBM® Information Management System (IMSTM) applications and data are the core of critical online transaction processing (OLTP) workloads for many of the world's major organizations. This operational data, when analyzed properly, forms the basis for making better decisions by organizations running IMS. With IBM DB2® Analytics Accelerator for z/OS®, you can exploit your IBM z SystemsTM platform's IMS data where it originates so that delivering new insights to improve efficiency and drive smart outcomes is possible. Critical business insights that are gained by performing analytics on IMS operational data is a valuable corporate asset and must be delivered efficiently across an organization, with high quality and proper governance, which is possible with this solution. This IBM Redbooks® Solution Guide describes DB2 Analytics Accelerator for z/OS and how it enables you to exploit the IMS data. It explains the business value of the solution, provides an overview and high-level solution architecture and includes usage scenarios.

Author : Blanca Borden,Calvin Fudge,Jen Nelson,Jim Porell,IBM Redbooks
Publisher : IBM Redbooks
Page : 38 pages
File Size : 44,9 Mb
Release : 2021-04-13
Category : Computers
ISBN : 9780738457291

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Accelerating Digital Transformation on Z Using Data Virtualization by Blanca Borden,Calvin Fudge,Jen Nelson,Jim Porell,IBM Redbooks Pdf

This IBM® RedpaperTM publication introduces a new data virtualization capability that enables IBM z/OS® data to be combined with other enterprise data sources in real-time, which allows applications to access any live enterprise data anytime and use the power and efficiencies of the IBM Z® platform. Modern businesses need actionable and timely insight from current data. They cannot afford the time that is necessary to copy and transform data. They also cannot afford to secure and protect each copy of personally identifiable information and corporate intellectual property. Data virtualization enables direct connections to be established between multiple data sources and the applications that process the data. Transformations can be applied, in line, to enable real-time access to data, which opens up many new ways to gain business insight with less IT infrastructure necessary to achieve those goals. Data virtualization can become the backbone for advanced analytics and modern applications. The IBM Data Virtualization Manager for z/OS (DVM) can be used as a stand-alone product or as a utility that is used by other products. Its goal is to enable access to live mainframe transaction data and make it usable by any application. This enables customers to use the strengths of mainframe processing with new agile applications. Additionally, its modern development environment and code-generating capabilities enable any developer to update, access, and combine mainframe data easily by using modern APIs and languages. If data is the foundation for building new insights, IBM DVM is a key tool for providing easy, cost-efficient access to that foundation.

Author : Domenico D'Alterio,Michael Bonett,Eric Goodson,Matt Hunter,Keith Miller,Fabio Riva,Volkmar Siegemund,John Strymecki
Publisher : Unknown
Page : 44 pages
File Size : 46,5 Mb
Release : 2018
Category : Electronic
ISBN : OCLC:1125078620

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Introduction to IBM Common Data Provider for Z Systems by Domenico D'Alterio,Michael Bonett,Eric Goodson,Matt Hunter,Keith Miller,Fabio Riva,Volkmar Siegemund,John Strymecki Pdf

IBM Common Data Provider for z Systems collects, filters, and formats IT operational data in near real-time and provides that data to target analytics solutions. IBM Common Data Provider for z Systems enables authorized IT operations teams using a single web-based interface to specify the IT operational data to be gathered and how it needs to be handled. This data is provided to both on- and off-platform analytic solutions, in a consistent, consumable format for analysis. This Redpaper discusses the value of IBM Common Data Provider for z Systems, provides a high-level reference architecture for IBM Common Data Provider for z Systems, and introduces key components of the architecture. It shows how IBM Common Data Provider for z Systems provides operational data to various analytic solutions. The publication provides high-level integration guidance, preferred practices, tips on planning for IBM Common Data Provider for z Systems, and example integration scenarios.

Author : Jeffrey Bisti,Frank De Gilio,Randy Frerking,Rich Jackson,Charlie Lawrence,Kellie Mathis,IBM Redbooks
Publisher : IBM Redbooks
Page : 86 pages
File Size : 54,8 Mb
Release : 2016-02-08
Category : Computers
ISBN : 9780738441535

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Creating IBM z/OS Cloud Services by Jeffrey Bisti,Frank De Gilio,Randy Frerking,Rich Jackson,Charlie Lawrence,Kellie Mathis,IBM Redbooks Pdf

This IBM® Redbooks® publication discusses the real world experience of an enterprise that developed and implemented IBM z/OS® cloud services. This book shares the experience of a team at Walmart Technology, Walmart Stores, Inc.® and some of the decisions they made to create business critical cloud services. These experiences and approaches relate to the z/OS platform, and might not apply to other hybrid cloud approaches. This book highlights the strengths and characteristics of z/OS that led the Walmart infrastructure and software engineers to use this platform as they transitioned from a traditional IT deployment to a cloud model. Embarking on a cloud strategy can be overwhelming. No shortage of approaches to cloud computing exists. This book focuses on a pragmatic approach for enterprises that are struggling to take advantage of their business assets in the cloud. This book introduces the basic cloud concepts as defined by the National Institute of Standards and Technology (NIST). Each chapter explains the importance of a particular NIST characteristic, the z/OS role in accomplishing the characteristic, and how it was implemented by the Walmart Technology team. This book is intended for IT professionals who are considering extending their IBM z SystemsTM environment to a hybrid cloud by unleashing the power of cloud services on z/OS. For information about creating cloud services that are hosted in IBM CICS®, see How Walmart Became a Cloud Services Provider with IBM CICS, SG24-8347.

Author : Oliver Draese,Eberhard Hechler,Hong Min,Catherine Moxey,Pallavi Priyadarshini,Mark Simmonds,Mythili Venkatakrishnan,George Wang,IBM Redbooks
Publisher : IBM Redbooks
Page : 56 pages
File Size : 42,7 Mb
Release : 2016-02-09
Category : Computers
ISBN : 9780738455044

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Apache Spark for the Enterprise: Setting the Business Free by Oliver Draese,Eberhard Hechler,Hong Min,Catherine Moxey,Pallavi Priyadarshini,Mark Simmonds,Mythili Venkatakrishnan,George Wang,IBM Redbooks Pdf

Analytics is increasingly an integral part of day-to-day operations at today's leading businesses, and transformation is also occurring through huge growth in mobile and digital channels. Enterprise organizations are attempting to leverage analytics in new ways and transition existing analytics capabilities to respond with more flexibility while making the most efficient use of highly valuable data science skills. The recent growth and adoption of Apache Spark as an analytics framework and platform is very timely and helps meet these challenging demands. The Apache Spark environment on IBM z/OS® and Linux on IBM z SystemsTM platforms allows this analytics framework to run on the same enterprise platform as the originating sources of data and transactions that feed it. If most of the data that will be used for Apache Spark analytics, or the most sensitive or quickly changing data is originating on z/OS, then an Apache Spark z/OS based environment will be the optimal choice for performance, security, and governance. This IBM® RedpaperTM publication explores the enterprise analytics market, use of Apache Spark on IBM z SystemsTM platforms, integration between Apache Spark and other enterprise data sources, and case studies and examples of what can be achieved with Apache Spark in enterprise environments. It is of interest to data scientists, data engineers, enterprise architects, or anybody looking to better understand how to combine an analytics framework and platform on enterprise systems.

Author : Whei-Jen Chen,Rajeev Kamath,Alexander Kelly,Hector H. Diaz Lopez,Matthew Roberts,Yee Pin Yheng,IBM Redbooks
Publisher : IBM Redbooks
Page : 258 pages
File Size : 41,8 Mb
Release : 2015-12-03
Category : Computers
ISBN : 9780738441184

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Systems of Insight for Digital Transformation: Using IBM Operational Decision Manager Advanced and Predictive Analytics by Whei-Jen Chen,Rajeev Kamath,Alexander Kelly,Hector H. Diaz Lopez,Matthew Roberts,Yee Pin Yheng,IBM Redbooks Pdf

Systems of record (SORs) are engines that generates value for your business. Systems of engagement (SOE) are always evolving and generating new customer-centric experiences and new opportunities to capitalize on the value in the systems of record. The highest value is gained when systems of record and systems of engagement are brought together to deliver insight. Systems of insight (SOI) monitor and analyze what is going on with various behaviors in the systems of engagement and information being stored or transacted in the systems of record. SOIs seek new opportunities, risks, and operational behavior that needs to be reported or have action taken to optimize business outcomes. Systems of insight are at the core of the Digital Experience, which tries to derive insights from the enormous amount of data generated by automated processes and customer interactions. Systems of Insight can also provide the ability to apply analytics and rules to real-time data as it flows within, throughout, and beyond the enterprise (applications, databases, mobile, social, Internet of Things) to gain the wanted insight. Deriving this insight is a key step toward being able to make the best decisions and take the most appropriate actions. Examples of such actions are to improve the number of satisfied clients, identify clients at risk of leaving and incentivize them to stay loyal, identify patterns of risk or fraudulent behavior and take action to minimize it as early as possible, and detect patterns of behavior in operational systems and transportation that lead to failures, delays, and maintenance and take early action to minimize risks and costs. IBM® Operational Decision Manager is a decision management platform that provides capabilities that support both event-driven insight patterns, and business-rule-driven scenarios. It also can easily be used in combination with other IBM Analytics solutions, as the detailed examples will show. IBM Operational Decision Manager Advanced, along with complementary IBM software offerings that also provide capability for systems of insight, provides a way to deliver the greatest value to your customers and your business. IBM Operational Decision Manager Advanced brings together data from different sources to recognize meaningful trends and patterns. It empowers business users to define, manage, and automate repeatable operational decisions. As a result, organizations can create and shape customer-centric business moments. This IBM Redbooks® publication explains the key concepts of systems of insight and how to implement a system of insight solution with examples. It is intended for IT architects and professionals who are responsible for implementing a systems of insights solution requiring event-based context pattern detection and deterministic decision services to enhance other analytics solution components with IBM Operational Decision Manager Advanced.