Having more, and more current, information is fast becoming a requirement for business survival. The need permeates the enterprise because it enables proactive decision-making for problem avoidance, rather than reactive problem impact minimization. And that is the key. Thus the need for, and focus on, is real-time. Companies no longer have the long strategic time-frames in which to plan, design, and manage their business processes. Yearly revenue goals and measurements are fast becoming quarterly goals and measurements. Investors and share-holders are more demanding. They are more critical, and less forgiving, of missed performance goals. And these demands are coming at a time when the volume of data is growing, there is an increase in business mergers and acquisitions, the use of strategic outsourcing is growing, and there is an increasing requirement for faster and faster turnaround on information requests. This has put an enormous burden on the information technology (IT) organizations. And most of this change is centered around business intelligence, because that is the environment responsible for providing information for management decision-making. This IBM Redbook explores the techniques and capabilities for evolving to a real-time enterprise. It also demonstrates approaches for that evolution and provides examples to help guide you in developing your strategy and implementation methodology to become a real-time enterprise.

This IBM Redbook is primarily intended for use by IBM Clients and IBM Business Partners. The current direction in the Business Intelligence marketplace is towards data mart consolidation. Originally data marts were built for many different reasons, such as departmental or organizational control, faster query response times, easier and faster to design and build, and fast payback. However, data marts did not always provide the best solution when it came to viewing the business enterprise as a whole. They provide benefits to the department or organization to whom they belong, but typically do not give management the information they need to efficiently and effectively run the business. In many cases the data marts led to the creation of departmental or organizational data silos (non-integrated sources of data). That is, information was available to the particular department or organization, but was not integrated across all the department's or organizations. Worse yet, many data marts were built without concern for the others. This led to inconsistent definitions of the data, inconsistent collection of data, inconsistent collection times for the data, and so on. The result was an inconsistent picture of the business for management, and an inability for good business performance management. The solution is to consolidate those data silos to provide management the information they need.

In this IBM Redbook, we discuss business performance management (BPM) and its integration with business intelligence. BPM is all about taking a holistic approach for managing business performance and achieving the business goals. Businesses align strategic and operational objectives, and business activities, to fully manage performance through more informed and proactive decision making. This holistic approach enables the integration and use of business intelligence, process management, business service management, activity monitoring, and corporate performance management to achieve a single view of their enterprise. Businesses are evolving to an environment capable of supporting continuous data flow, which enables the support of business intelligence environments with more current data. This is referred to as real-time business intelligence, and represents a significant competitive advantage. In this redbook, we demonstrate how BPM can be enabled, using DB2 for data warehousing and WebSphere Business Integration for monitoring and managing the business processes. The result is proactive business performance management and problem avoidance, in addition to the more typical reactive problem impact minimization. We also discuss techniques, architectures, and approaches for implementing BPM as a proactive means of managing the attainment of business measurements and business goals.

SAP is a leading ERP vendor, with a large install base. A key element in their product set is the SAP Business Information Warehouse (BW). The primary objective of this IBM Redbook is to provide guidelines to help you implement your SAP Business Information Warehouse on DB2 UDB ESE. Two major considerations when building a business information warehouse are scalability and performance. In this book, we have demonstrated the wide range of scalability of BW when implemented on DB2, while maintaining the performance requirements that are so critical. The parallelism and data partitioning capabilities of DB2 Universal Database ESE, enables a robust, highly scalable, and high performance business information warehouse. For a common understanding, we first discuss the concepts of data warehousing and then describe the SAP architecture and robust component capabilities of the SAP business information warehouse. To help in your implementation, we provide guidelines for how to configure SAP BW when it is built on DB2. We also describe and discuss the key capabilities and parameters to help you get the best from DB2. Key topics such as sizing, partitioning, performance tuning, and systems administration, are discussed to assist in the implementation and maintenance of your system. This redbook will help enable you to more quickly and easily implement a robust SAP BW on DB2 UDB ESE.

In this IBM Redbook we discuss primary processes and various alternatives that prepare you in implementing a DB2 near-realtime business intelligence environment. We discuss architectural alternatives and include overviews of software products that you can use in an implementation. As a primary focus, we tested the capabilities for supporting continuous update of a DB2 data warehouse while running a continuous concurrent query workload against that data warehouse. We tested several implementation scenarios and the variables that impact them. The results of our testing and the issues we encountered are provided. We used an IBM p-Series multiprocessor, along with a number of software products, including WebSphere MQ, DB2 MQ Listener, DB2 UDB, and DB2 Information Integrator - Replication Edition. We discuss ETL processing in a near-realtime environment, with reference to DB2 Warehouse Manager and DataStage from Ascential Software. We used WebSphere Studio Application Developer to generate online query applications, along with Java, SQL, and C-based stored procedures. We discuss DB2 Query Patroller and DB2 Governor. We provide guidelines and recommended best practices, and this redbook is what you need to get prepared to implement near-realtime business intelligence in your environment.

Business Intelligence and OLAP systems are no longer limited to the privileged few business analysts: they are being democratized by being shared with the rank and file employee demanding a Relational Database Management System (RDBMS) that is more OLAP-aware. DB2 Cube Views and its cube model provide DB2 the ability to address multidimensional analysis and become an actor in the OLAP world. This IBM Redbook focuses on the innovative technical functionalities of IBM DB2 Cube Views V8.1 to store multidimensional metadata in DB2 catalog; to build automatically model-based summary tables to speed up query performance; and to provide an advanced API to allow other Business Intelligence partners' tools to benefit from both metadata exchange and improved query performance. This book positions the new functionalities and their benefits, so you can understand and evaluate their applicability in your own Business Intelligence and OLAP system environment. It provides information and examples to help you to get started planning and implementing the new functionalities.

Data warehouses in the 1990s were for the privileged few business analysts. Business Intelligence is now being democratized by being shared with the rank and file employee demanding higher levels of RDBMS scalability and ease of use, being delivered through Web portals. To support this emerging e-Business Intelligence world, the challenges that face the enterprises for their centralized data warehouse RDBMS technology are scalability, performance, availability and smart manageability. This IBM Redbook focuses on the innovative technical functionalities of DB2 UDB ESE V8.1 and discusses: - Guidelines on building the large database and determining the number of partitions - Bulk load using the new multipartition load - Performance enhancements using MultiDimensional Clustering and Materialized Query Tables. - Availability through the new online utilities - Self Managing And Resource Tuning features - Migration scenarios This book positions the new functionalities, so you can understand and evaluate their applicability in your own enterprise data warehouse environment, and get started prioritizing and implementing them.

This IBM Redbook deals with exploiting DB2 UDB's materialized views (also known as ASTs/MQTs), statistics, analytic, and OLAP functions in e-business applications to achieve superior performance and scalability. This redbook is aimed at a target audience of DB2 UDB application developers, database administrators (DBAs), and independent software vendors (ISVs). We provide an overview of DB2 UDB's materialized views implementation, as well as guidelines for creating and tuning them for optimal performance. We introduce key statistics, analytic, and OLAP functions, and describe their corresponding implementation in DB2 UDB with usage examples. Finally, we describe typical business level queries that can be answered using DB2 UDB's statistics, analytic, and OLAP functions. These business queries are categorized by industry, and describe the steps involved in resolving the query, with sample SQL and visualization of results.

Using IBM DB2 Universal Database and DB2 Warehouse Manager provides a central point of control for managing data sources, including processes for building and populating data warehouses (or datamarts). The objective of this IBM Redbook is to deliver guidelines for building a High Availability DB2 warehouse environment and to assist with problem determination when using DB2 Warehouse Manager. This book will help you to understand the DB2 Warehouse Manager architecture and components, and to cope with system failures and their consequences. It also provides a set of documentation on the different High Availability techniques you can use when facing database, server, or disk failures. The book discusses a problem determination methodology that takes a customer through various unplanned outages. We explain how to proceed when a problem occurs, how to do problem determination and problem source identification, and how to fix the problem. Included are many helpful hints and tips on warehouse application and performance issues.

Many companies across a host of industries are looking at cross-functional business processes to provide better customer-centric services. Companies want to understand and react faster to the immediate concerns of a customer by improving on activities such as customer service, campaign management, and real-time fraud detection. These services require the ability to access and update an integrated, consolidated, current view of detailed operational data. In this IBM Redbook we describe how an Operational Data Store (ODS) can be used to address all of these business issues. We describe how an ODS fits within the business intelligence architecture, and we explain the differences between the ODS and the data warehouse. The capabilities of IBM Data Replication, WebSphere MQ Family, and DB2 Warehouse Manager are explored to show how they can be used to integrate heterogeneous legacy systems and solve many of the ODS issues. Various data integration and transformation methods are described. We show how DB2 Warehouse Manager can be used to manage and monitor the population subsystem. We also take an in-depth look at how DB2 UDB EEE for UNIX and DB2 UDB for z/OS can be used as target ODS databases.