TDWI Checklist Report Series
05/04/09
TDWI Checklist Reports provide an overview of success factors for a specific project in business intelligence, data warehousing, or a related data management discipline. Companies may use this overview to get organized before beginning a project or to identify goals and areas of improvement for current projects. Most Checklist Reports list the technology requirements of a particular best practice or project type, but the series may also cover anything that makes a good list, including user best practices, staff members, skill sets, tool types, user constituencies, types of applications, or use cases.
TDWI Checklist Report: Data Federation
Data federation is an important tool in today's data integration portfolio. Data and application architects use the middleware to query and join data from multiple sources on the fly and deliver the results to data-hungry decision makers. It makes a lot of sense to use data federation tools when it takes too long or costs too much to create a persistent store of consolidated data, such as a data warehouse or data mart.
While data federation is not a new technique, data federation tools have recently broadened their capabilities and appeal. They go by many labels, including data virtualization, data services, and distributed query; they are used in a variety of situations, including data warehousing, reporting, dashboards, mashups, portals, master data management, data services in a service-oriented architecture (SOA), post-acquisition systems integration, and cloud computing.
This Checklist Report will help you understand when and how to use data federation tools to deliver optimal solutions.
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TDWI Checklist Report: Enterprise Data Management
In most organizations today, data and other information aremanaged in isolated silos by independent teams using assorteddata management tools for data quality, integration, governance,meta- and master data management (MDM), content management,and so on. From a technology viewpoint, the lack of coordinationamong data management disciplines leads to redundant teamstaffing and limited developer productivity. Even worse, competingdata management solutions can inhibit data’s quality, consistency,standards, scalability, architecture, and so on. From a businessviewpoint, data-driven business initiatives suffer (including BI,CRM, and business operations) as a result of poor data quality andincomplete information, inconsistent data definitions, noncompliantdata, and uncontrolled data usage.
Forward-looking organizations are solving these technology and business problems by adopting enterprise data management (EDM). Download this TDWI Checklist Report to learn more about this best practice for unifying diverse data management disciplines.
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TDWI Checklist Report: Mainframe Modernization
There is no question that IBM System z mainframes continue to serve a wide range of organizations by providing a secure, high-performance, and scalable computing platform that’s hard to match on other systems. The issue comes when you attempt to extend mainframe data or applications to participate in new business applications on so-called open systems (servers running Linux, UNIX, or Windows) and Web environments (whether Internet, intranet, or extranet).
Mainframe modernization takes many forms. For many organizations,it’s about providing a more streamlined method for using mainframeinformation on other platforms. For others, it’s about extendingthe mainframe to the Web. Some forward-looking organizationsare making the mainframe an active participant in service-basedcomposite applications, utilizing Web services standards to supporta service-oriented architecture (SOA). Organizations seeking thegreatest value from the mainframe must consider all of thesefactors. This Checklist Report touches on all aspects of mainframemodernization, but focuses primarily on data integration issues.
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TDWI Checklist Report: Data Requirements for Advanced Analytics
According to a survey from TDWI Research, 38% oforganizations surveyed are practicing advanced analytics today,whereas 85% say they’ll be practicing it within three years. These organizations will face challenges as they move into advancedanalytics. Many don’t understand that reporting and analytics aredifferent practices, often with different data requirements. Most of theseorganizations are experienced in data integration, data quality, datamodeling, and so on; yet, they don’t know how to adjust these datamanagement practices to fit the needs of advanced analytics.
This TDWI Checklist Report seeks to clear the confusion by listing andexplaining data requirements that are unique to advanced analytics.The assumption is that it’s hard for organizations to succeed withanalytics when they haven’t given it the right data in the rightcondition. Hence, this report focuses on the data requirements ofadvanced analytics so organizations may become better equipped topopulate a data warehouse or analytic database with data and datamodels that ensure the success of advanced analytic applications.
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TDWI Checklist Report: Self-Service BI
Self-service is the holy grail of BI—a mantra repeated incessantly byoverworked BI managers who find it difficult to stay ahead of userrequests for new reports and applications. With self-service BI, userscreate their own reports without having to rely on the IT department.Users get exactly the reports they want, when they want them, andthe BI team no longer serves as an intermediary between users andthe data. Users no longer have to wait days, weeks, or months fora report, only to discover that it is missing key functionality or is nolonger relevant, and the BI team eliminates the backlog of reportsthat prevents it from focusing on more valuable activities.
If everybody wins with self-service BI, why isn’t it more pervasive?To make self-service BI a reality requires discipline and foresight.This report outlines several techniques that can help yourorganization successfully implement self-service BI.
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TDWI Checklist Report: Data Synchronization
The amount of operational and transactional data being integratedand synchronized across enterprise applications continues to grow.As a consequence, tools and techniques for data synchronizationare used today at an unprecedented level. The practice of datasynchronization—or simply "data sync"—is driven up by leadingtrends in data-driven business activities.
As the economy becomes ever more global, mission-criticalapplications must operate 24/7. Data sync is a tried-and-truestrategy for database high availability, and it can handle thebidirectional active-active configurations that are becoming thestandard architecture for database high availability.
These trends and use cases demonstrate that data synchronizationis an amazingly versatile technology and practice that has manyvaluable applications across an enterprise. This TDWI ChecklistReport celebrates the versatility of data synchronization byshowcasing many of its valuable capabilities and popular use cases.
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