Implementing An Infosphere Optim Data Growth Solution

Today, organizations face tremendous challenges with data explosion and information governance. InfoSphereTM OptimTM solutions solve the data growth problem at the source by managing the enterprise application data. The Optim Data Growth solutions are consistent, scalable solutions that include comprehensive capabilities for managing enterprise application data across applications, databases, operating systems, and hardware platforms. You can align the management of your enterprise application data with your business objectives to improve application service levels, lower costs, and mitigate risk. In this IBM® Redbooks® publication, we describe the IBM InfoSphere Optim Data Growth solutions and a methodology that provides implementation guidance from requirements analysis through deployment and administration planning. We also discuss various implementation topics including system architecture design, sizing, scalability, security, performance, and automation. This book is intended to provide various systems development professionals, Data Solution Architects, Data Administrators, Modelers, Data Analysts, Data Integrators, or anyone who has to analyze or integrate data structures, a broad understanding about IBM InfoSphere Optim Data Growth solutions. By being used in conjunction with the product manuals and online help, this book provides guidance about implementing an optimal solution for managing your enterprise application data.

Managing information within the enterprise has always been a vital and important task to support the day-to-day business operations and to enable analysis of that data for decision making to better manage and grow the business for improved profitability. To do all that, clearly the data must be accurate and organized so it is accessible and understandable to all who need it. That task has grown in importance as the volume of enterprise data has been growing significantly (analyst estimates of 40 - 50% growth per year are not uncommon) over the years. However, most of that data has been what we call "structured" data, which is the type that can fit neatly into rows and columns and be more easily analyzed. Now we are in the era of "big data." This significantly increases the volume of data available, but it is in a form called "unstructured" data. That is, data from sources that are not as easily organized, such as data from emails, spreadsheets, sensors, video, audio, and social media sites. There is valuable information in all that data but it calls for new processes to enable it to be analyzed. All this has brought with it a renewed and critical need to manage and organize that data with clarity of meaning, understandability, and interoperability. That is, you must be able to integrate this data when it is from within an enterprise but also importantly when it is from many different external sources. What is described here has been and is being done to varying extents. It is called "information governance." Governing this information however has proven to be challenging. But without governance, much of the data can be less useful and perhaps even used incorrectly, significantly impacting enterprise decision making. So we must also respect the needs for information security, consistency, and validity or else suffer the potential economic and legal consequences. Implementing sound governance practices needs to be an integral part of the information control in our organizations. This IBM® Redbooks® publication focuses on the building blocks of a solid governance program. It examines some familiar governance initiative scenarios, identifying how they underpin key governance initiatives, such as Master Data Management, Quality Management, Security and Privacy, and Information Lifecycle Management. IBM Information Management and Governance solutions provide a comprehensive suite to help organizations better understand and build their governance solutions. The book also identifies new and innovative approaches that are developed by IBM practice leaders that can help as you implement the foundation capabilities in your organizations.

This IBM® Redbooks® publication describes how the IBM Big Data Platform provides the integrated capabilities that are required for the adoption of Information Governance in the big data landscape. As organizations embark on new use cases, such as Big Data Exploration, an enhanced 360 view of customers, or Data Warehouse modernization, and absorb ever growing volumes and variety of data with accelerating velocity, the principles and practices of Information Governance become ever more critical to ensure trust in data and help organizations overcome the inherent risks and achieve the wanted value. The introduction of big data changes the information landscape. Data arrives faster than humans can react to it, and issues can quickly escalate into significant events. The variety of data now poses new privacy and security risks. The high volume of information in all places makes it harder to find where these issues, risks, and even useful information to drive new value and revenue are. Information Governance provides an organization with a framework that can align their wanted outcomes with their strategic management principles, the people who can implement those principles, and the architecture and platform that are needed to support the big data use cases. The IBM Big Data Platform, coupled with a framework for Information Governance, provides an approach to build, manage, and gain significant value from the big data landscape.

The #1 Guide to Excellence in Technical Communication—Fully Updated for Embedded Assistance, Mobile, Search, Multimedia, and More Direct from IBM’s own content design experts, this guide shows you how to design product interfaces and technical information that always place users front and center. This edition has been fully revised to help you consistently deliver the right content at the right time. You’ll master today’s best practices to apply nine essential characteristics of high-quality technical information: accuracy, clarity, completeness, concreteness, organization, retrievability, style, task orientation, and visual effectiveness. Coverage Includes Advocating for users throughout the entire product development process Delivering information in an ordered manner by following progressive disclosure techniques Optimizing content so that users can find it from anywhere Streamlining information for mobile delivery Helping users right where they are Whether you’re a writer, editor, information architect, user experience professional, or reviewer, this book shows you how to create great technical information, from the product design to the user interface, topics, and other media. Thoroughly revised and updated Extensive new coverage of self-documenting interfaces and embedded assistance Updated practical guidelines and checklists Hundreds of new examples

IBM® InfoSphere® Warehouse is the IBM flagship data warehouse platform for departmental data marts and enterprise data warehouses. It offers leading architecture, performance, backup, and recovery tools that help improve efficiency and reduce time to market through increased understanding of current data assets, while simplifying the daily operations of managing complex warehouse deployments. InfoSphere Warehouse Advanced Enterprise Edition delivers an enhanced set of database performance, management, and design tools. These tools assist companies in maintaining and increasing value from their warehouses, while helping to reduce the total cost of maintaining these complex environments. In this IBM Redbooks® publication we explain how you can build a business intelligence system with InfoSphere Warehouse Advanced Enterprise to manage and support daily business operations for an enterprise, to generate more income with lower cost. We describe the foundation of the business analytics, the Data Warehouse features and functions, and the solutions that can deliver immediate analytics solutions and help you drive better business outcomes. We show you how to use the advanced analytics of InfoSphere Warehouse Advanced Enterprise Edition and integrated tools for data modeling, mining, text analytics, and identifying and meeting the data latency requirements. We describe how the performance and storage optimization features can make building and managing a large data warehouse more affordable, and how they can help significantly reduce the cost of ownership. We also cover data lifecycle management and the key features of IBM Cognos® Business Intelligence. This book is intended for data warehouse professionals who are interested in gaining in-depth knowledge about the operational business intelligence solution for a data warehouse that the IBM InfoSphere Warehouse Advanced Enterprise Edition offers.

In this IBM® Redbooks® publication we describe how to build an advanced business application from end to end. We use a fictional scenario to define the application, document the deployment methodology, and confirm the roles needed to support its development and deployment. Through step-by-step instructions you learn how to: - Define the project lifecycle using IBM Solution for Collaborative Lifecycle Management - Build a logical and physical data model in IBM InfoSphere® Data Architect - Confirm business rules and business events using IBM WebSphere® Operational Decision Management - Map a business process and mediation using IBM Business Process Manager - Use IBM Cognos® Business Intelligence to develop business insight In addition, we articulate a testing strategy using IBM Rational® Quality Manager and deployment options using IBM Workload Deployer. Taken together, this book provides comprehensive guidance for building and testing a solution using core IBM Rational, Information Management, WebSphere, Cognos and Business Process Management software. It seeks to demystify the notion that developing and deploying advanced solutions is taxing. This book will appeal to IT architects and specialists who seek straightforward guidance on how to build comprehensive solutions. They will be able to adapt these materials to kick-start their own end-to-end projects.

IBM® InfoSphere® Master Data Management Reference Data Management Hub (InfoSphere MDM Ref DM Hub) is designed as a ready-to-run application that provides the governance, process, security, and audit control for managing reference data as an enterprise standard, resulting in fewer errors, reduced business risk and cost savings. This IBM Redbooks® publication describes where InfoSphere MDM Ref DM Hub fits into information management reference architecture. It explains the end-to-end process of an InfoSphere MDM Ref DM Hub implementation including the considerations of planning a reference data management project, requirements gathering and analysis, model design in detail, and integration considerations and scenarios. It then shows implementation examples and the ongoing administration tasks. This publication can help IT professionals who are interested or have a need to manage reference data efficiently and implement an InfoSphere MDM Ref DM Hub solution with ease.

This IBM® Redbooks® publication describes the architecture and components of IBM InfoSphere® OptimTM Performance Manager Extended Edition. Intended for DBAs and those involved in systems performance, it provides information for installation, configuration, and deployment. InfoSphere Optim Performance Manager delivers a new paradigm used to monitor and manage database and database application performance issues. It describes product dashboards and reports and provides scenarios for how they can be used to identify, diagnose, prevent, and resolve database performance problems. IBM InfoSphere Optim Query Workload Tuner facilitates query and query workload analysis and provides expert recommendations for improving query and query workload performance. Use InfoSphere Optim Performance Manager to identify slow running queries, top CPU consumers, or query workloads needing performance improvements and seamlessly transfer them to InfoSphere Optim Query Workload Tuner for analysis and recommendations. This is done using query formatting annotated with relevant statistics, access plan graphical or hierarchical views, and access plan analysis. It further provides recommendations for improving query structure, statistics collection, and indexes including generated command syntax and rationale for the recommendations.