TDWI Checklist Report Series

May 4, 2009

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: Big Data Analytics

TDWI Checklist Report: Big Data Analytics

There are two major trends causing organizations to rethink the way they approach doing analytics.

Big data. First, data volumes are exploding. More than a decade ago, the Data Warehouse Terabyte Club highlighted the few leading-edge organizations whose data warehouses had reached or exceeded a terabyte in size. Today, the notion of a terabyte club seems quaint, as many organizations have blasted through that threshold. In fact, it is now time to start a petabyte club, since a handful of companies, including Internet businesses, banks, and telecommunications companies, have publicly announced that their data warehouses will soon exceed a petabyte of data.

Deep analytics. Second, organizations want to perform “deep analytics” on these massive data warehouses. Deep analytics ranges from statistics—such as moving averages, correlations, and regressions—to complex functions such as graph analysis, market basket analysis, and tokenization. In addition, many organizations are embracing predictive analytics by using advanced machine learning algorithms, such as neural networks and decision trees, to anticipate behavior and events. Whereas in the past, organizations may have applied these types of analytics to a subset of data, today they want to analyze every transaction. The reason: profits.

This TDWI Checklist Report is designed to provide a basic set of guidelines for implementing big data analytics. The analytical techniques and data management structures of the past no longer work in this new era of big data. This report will help you take the first steps toward achieving a lasting competitive edge with analytics.

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TDWI Checklist Report: Product Data Quality

TDWI Checklist Report: Product Data Quality

Now is a good time to revisit the tools and techniques of data quality software solutions as they relate to product data. Why? Improving the quality of product data has tangible benefits. It helps control procurement costs, manage supplier risk, raise the quality of goods and their services, optimize supply chains, and manage warranty programs.

Recent changes are pushing product data to the forefront. Manufacturing and retail industries are catching up with others in terms of IT automation. Furthermore, as the third world industrializes, product-oriented industries are expanding operations and penetrating markets globally. Finally, in response to real-world demand, vendor tools—long focused on customer and financial data—have beefed up their support for the unique needs of the product data domain.

This TDWI Checklist Report on Product Data Quality dives into all these issues and more by pointing out the most pressing technology requirements for improving the quality of product data, as well as the business benefits of these improvements.

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TDWI Checklist Report: Top Ten Best Practices for Data Integration

TDWI Checklist Report Top Ten Best Practices for Data Integration

The data management discipline known as data integration (DI) has undergone an impressive expansion over the last decade. It’s not surprising that people in the field might not be up to speed on the current incarnation of DI. Even DI specialists and the colleagues who depend on them sometimes forget the new techniques, diversity, independence, collaboration, and governance typical of modern DI practices. Many suffer misconceptions and out-of-date mindsets that need adjustment.

The 10 practices described in this TDWI Checklist Report paint a modern landscape of current DI practices and bust common DI myths. The report will redefine DI for you and your peers, helping you set higher goals and aspirations for DI work and its outcome. The practices listed here can be the guidelines that help you achieve more modern, high-value, diverse, independent, well-designed, far-reaching, green, collaborative, and well-governed uses of DI tools and techniques.

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TDWI Checklist Report: Operational Data Quality

TDWI Checklist Report Operational Data Quality

Failing to ensure high-quality operational data may put at risk many worthwhile business goals for operational excellence. For example, a Zero Defect or Six Sigma program in manufacturing needs clean, complete, consistent, and current operational data that quantifies supplies, suppliers, and products (both on the product floor and in the field). As another example, sales and marketing initiatives need cleansed, standardized, and enhanced operational data drawn from customer-facing operational applications and third-party providers if they want to increase conversion rates via better-targeted campaigns.

There are many other areas within the average enterprise where high-quality operational data contributes to operational excellence. In all these examples, people, processes, organizational structures, data, software, and other resources are already in place and operating. Most definitions of operational excellence prescribe incremental enhancements and optimizations for individual, pre-existing resources; the assumption is that localized improvements have a global impact on broad processes. In that spirit, this TDWI Checklist Report provides recommendations for improving the quality of operational data, which in turn contributes to an organization’s drive toward operational excellence.

Report Sponsor

TDWI Checklist Report: Data Federation

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.

Report Sponsor

TDWI Checklist Report: Enterprise Data Management

Enterprise Data Management

In most organizations today, data and other information are managed in isolated silos by independent teams using assorted data 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 coordination among data management disciplines leads to redundant team staffing and limited developer productivity. Even worse, competing data management solutions can inhibit data’s quality, consistency, standards, scalability, architecture, and so on. From a business viewpoint, data-driven business initiatives suffer (including BI,CRM, and business operations) as a result of poor data quality and incomplete information, inconsistent data definitions, noncompliant data, 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.

Report Sponsor

TDWI Checklist Report: Mainframe Modernization

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 mainframe information on other platforms. For others, it’s about extending the mainframe to the Web. Some forward-looking organizations are making the mainframe an active participant in service-based composite applications, utilizing Web services standards to support a service-oriented architecture (SOA). Organizations seeking thegreatest value from the mainframe must consider all of these factors. This Checklist Report touches on all aspects of mainframe modernization, but focuses primarily on data integration issues.

Report Sponsor

TDWI Checklist Report: Data Requirements for Advanced Analytics

Data Requirements for Advanced Analytics

According to a survey from TDWI Research, 38% of organizations 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 advanced analytics. Many don’t understand that reporting and analytics are different practices, often with different data requirements. Most of these organizations are experienced in data integration, data quality, data modeling, and so on; yet, they don’t know how to adjust these data management practices to fit the needs of advanced analytics.

This TDWI Checklist Report seeks to clear the confusion by listing and explaining data requirements that are unique to advanced analytics. The assumption is that it’s hard for organizations to succeed with analytics when they haven’t given it the right data in the right condition. Hence, this report focuses on the data requirements of advanced analytics so organizations may become better equipped to populate a data warehouse or analytic database with data and data models that ensure the success of advanced analytic applications.

Report Sponsors

TDWI Checklist Report: Self-Service BI

Self-Service BI

Self-service is the holy grail of BI—a mantra repeated incessantly by overworked BI managers who find it difficult to stay ahead of user requests for new reports and applications. With self-service BI, users create their own reports without having to rely on the IT department. Users get exactly the reports they want, when they want them, andt he BI team no longer serves as an intermediary between users and the data. Users no longer have to wait days, weeks, or months for a report, only to discover that it is missing key functionality or is no longer relevant, and the BI team eliminates the backlog of reports that 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 your organization successfully implement self-service BI.

Report Sponsor

TDWI Checklist Report: Data Synchronization

Data Synchronization

The amount of operational and transactional data being integrated and synchronized across enterprise applications continues to grow. As a consequence, tools and techniques for data synchronization are used today at an unprecedented level. The practice of data synchronization—or simply "data sync"—is driven up by leading trends in data-driven business activities.

As the economy becomes ever more global, mission-critical applications must operate 24/7. Data sync is a tried-and-true strategy for database high availability, and it can handle the bidirectional active-active configurations that are becoming the standard architecture for database high availability.

These trends and use cases demonstrate that data synchronization is an amazingly versatile technology and practice that has many valuable applications across an enterprise. This TDWI Checklist Report celebrates the versatility of data synchronization by showcasing many of its valuable capabilities and popular use cases.

Report Sponsor


TDWI Checklist Report: Top Ten Best Practices for Data Integration

TDWI Checklist Report Top Ten Best Practices for Data Integration

The data management discipline known as data integration (DI) has undergone an impressive expansion over the last decade. It’s not surprising that people in the field might not be up to speed on the current incarnation of DI. Even DI specialists and the colleagues who depend on them sometimes forget the new techniques, diversity, independence, collaboration, and governance typical of modern DI practices. Many suffer misconceptions and out-of-date mindsets that need adjustment.

The 10 practices described in this TDWI Checklist Report paint a modern landscape of current DI practices and bust common DI myths. The report will redefine DI for you and your peers, helping you set higher goals and aspirations for DI work and its outcome. The practices listed here can be the guidelines that help you achieve more modern, high-value, diverse, independent, well-designed, far-reaching, green, collaborative, and well-governed uses of DI tools and techniques.

Report Sponsor

TDWI Checklist Report: Operational Data Quality

TDWI Checklist Report Operational Data Quality

Failing to ensure high-quality operational data may put at risk many worthwhile business goals for operational excellence. For example, a Zero Defect or Six Sigma program in manufacturing needs clean, complete, consistent, and current operational data that quantifies supplies, suppliers, and products (both on the product floor and in the field). As another example, sales and marketing initiatives need cleansed, standardized, and enhanced operational data drawn from customer-facing operational applications and third-party providers if they want to increase conversion rates via better-targeted campaigns.

There are many other areas within the average enterprise where high-quality operational data contributes to operational excellence. In all these examples, people, processes, organizational structures, data, software, and other resources are already in place and operating. Most definitions of operational excellence prescribe incremental enhancements and optimizations for individual, pre-existing resources; the assumption is that localized improvements have a global impact on broad processes. In that spirit, this TDWI Checklist Report provides recommendations for improving the quality of operational data, which in turn contributes to an organization’s drive toward operational excellence.

Report Sponsor

TDWI Checklist Report: Data Federation

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.

Report Sponsor

TDWI Checklist Report: Enterprise Data Management

Enterprise Data Management

In most organizations today, data and other information are managed in isolated silos by independent teams using assorted data 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 coordination among data management disciplines leads to redundant team staffing and limited developer productivity. Even worse, competing data management solutions can inhibit data’s quality, consistency, standards, scalability, architecture, and so on. From a business viewpoint, data-driven business initiatives suffer (including BI,CRM, and business operations) as a result of poor data quality and incomplete information, inconsistent data definitions, noncompliant data, 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.

Report Sponsor

TDWI Checklist Report: Mainframe Modernization

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 mainframe information on other platforms. For others, it’s about extending the mainframe to the Web. Some forward-looking organizations are making the mainframe an active participant in service-based composite applications, utilizing Web services standards to support a service-oriented architecture (SOA). Organizations seeking thegreatest value from the mainframe must consider all of these factors. This Checklist Report touches on all aspects of mainframe modernization, but focuses primarily on data integration issues.

Report Sponsor

TDWI Checklist Report: Data Requirements for Advanced Analytics

Data Requirements for Advanced Analytics

According to a survey from TDWI Research, 38% of organizations 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 advanced analytics. Many don’t understand that reporting and analytics are different practices, often with different data requirements. Most of these organizations are experienced in data integration, data quality, data modeling, and so on; yet, they don’t know how to adjust these data management practices to fit the needs of advanced analytics.

This TDWI Checklist Report seeks to clear the confusion by listing and explaining data requirements that are unique to advanced analytics. The assumption is that it’s hard for organizations to succeed with analytics when they haven’t given it the right data in the right condition. Hence, this report focuses on the data requirements of advanced analytics so organizations may become better equipped to populate a data warehouse or analytic database with data and data models that ensure the success of advanced analytic applications.

Report Sponsors

TDWI Checklist Report: Self-Service BI

Self-Service BI

Self-service is the holy grail of BI—a mantra repeated incessantly by overworked BI managers who find it difficult to stay ahead of user requests for new reports and applications. With self-service BI, users create their own reports without having to rely on the IT department. Users get exactly the reports they want, when they want them, andt he BI team no longer serves as an intermediary between users and the data. Users no longer have to wait days, weeks, or months for a report, only to discover that it is missing key functionality or is no longer relevant, and the BI team eliminates the backlog of reports that 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 your organization successfully implement self-service BI.

Report Sponsor

TDWI Checklist Report: Data Synchronization

Data Synchronization

The amount of operational and transactional data being integrated and synchronized across enterprise applications continues to grow. As a consequence, tools and techniques for data synchronization are used today at an unprecedented level. The practice of data synchronization—or simply "data sync"—is driven up by leading trends in data-driven business activities.

As the economy becomes ever more global, mission-critical applications must operate 24/7. Data sync is a tried-and-true strategy for database high availability, and it can handle the bidirectional active-active configurations that are becoming the standard architecture for database high availability.

These trends and use cases demonstrate that data synchronization is an amazingly versatile technology and practice that has many valuable applications across an enterprise. This TDWI Checklist Report celebrates the versatility of data synchronization by showcasing many of its valuable capabilities and popular use cases.

Report Sponsor