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The article looks at the factors that drive performance in data warehousing. Data loading into a data warehouse is considered as one of the longest procedures. Data loading refers to the process of extracting different data feeds, processing them through data quality and data profiling, and loading them with or without transformations to a final destination. Data availability service level agreements (SLAs) also have a huge impact on the demand for high-performance environment. The operational cost of managing and maintaining a data warehouse has a great impact in many organizations.

The article focuses on business intelligence (BI). It can be defined as the extraction of useful information from the data generated by the operational systems of an enterprise, which was possibly added by data from an outside source. BI applications, such as master data management, data warehouse and data mart projects, are technically sound but deliver serious deficiencies. Conceptualization or visualization is one of the issues about enterprise information architecture. Enterprise information architecture comprises of various dimensions and is deemed as complex.

This article proposes a comprehensive architecture for capturing data quality events and measuring and controlling data quality in the data warehouse. The proposed architecture may be added to existing data warehouse and data integration investments, and through the use of this comprehensive architecture, it is possible to progress toward a Six Sigma level of quality management. Several factors converging to place data quality concerns in the list of organization executives include the long-term cultural trend, organizations that are profoundly distributed, and increased demands for compliance.

The article discusses issues related to business intelligence (BI). With the use of data integration and technology, various sources are identified, the data is consolidated and aggregated into a data warehouse of data mart so it can be analyzed in the form of an online analytical processing (OLAP) cube and put into some sort of presentation format. According to the article, Microsoft has leveled the playing field for emerging and enterprise organizations with its structured query language (SQL) Server database engine.

The article presents an interview with Inmon Data Systems president Bill Inmon. When asked why businessmen need the Data Warehouse (DW) 2.0, he states that it is for the integrity of DW and to correct the wrong beliefs regarding data warehousing. He discusses the distinctions between DW 1.0 and DW 2.0. He stresses that business executives need to free DW from boundaries of any one given application.

The article discusses the concept behind the Corporate Information Factory (CIF). The principles behind CIF are enumerated in this paper. These include the program orientation of the business intelligence environment, the sourcing of data from the operational systems environment and external data stores and the storage of an enterprise view of the data in a data warehouse and operational data store. In terms of data consolidation, data is physically extracted, transformed, cleansed and integrated and loaded into a downstream data structure. Moreover, data propagation is supported mostly by enterprise application integration.

The article focuses on the success of British online banking company Egg PLC in customer data warehouse (CDW) management. Technologies from Sun Microsystems were selected by Egg to establish a Web infrastructure to support its more than 2.5 million transactions per day. The system comprised of Sun Fire V880 servers running on the Solaris Operating System (OS) and Sun Java Application Server software running the application layer of the Web site. The CDW runs on Solaris 9 OS and the database is Oracle9i.

This article offers background on information quality (IQ). One bank had to scrap a $29 million data warehouse project because their original design was faulty. They had not designed the data acquisition process to capture data at the original sources, nor had they addressed the quality of information to support the data warehouse knowledge-workers. Another bank lost more than $200 million in default loans when they approved credit based on faulty credit scores. Yet another bank lost $600 million as the result of misinterpretation of a risk code when the company they invested in failed. These examples of waste represent one of the primary objectives of IQ--that is to eliminate waste or muda. There are nine types of muda in information quality: Muda of overproduction; Muda of inventory; Muda of repair/rejects; Muda of motion; Muda of processing; Muda of transport; Muda of waiting; Muda of process failure caused by defective information; and Muda of wrong or sub-optimized decisions caused by defective information.

The article presents various recommendations for data warehouse project management. It is imperative that project managers allocate sufficient time to gain a complete and comprehensive understanding of the corporate vision including short- and long-term objectives, strategic plan to achieve those goals and key performance indicators used to measure performance objectives. Project managers must utilize a proven methodology for managing the design, development and deployment of a data warehousing initiative. An experience-based approach will identify potential conflicts and misunderstandings at an early stage of a project, but will also offer the opportunity to reduce project risk, increase project team productivity, control project costs and accelerate the project timeline. Data warehousing initiatives also require the coordination of numerous resources and tasks that must be integrated at the right time to ensure success. Project status monitoring and communication are proven techniques for achieving that objective, as well as for measuring performance, deploying a quality' solution and assessing stakeholder satisfaction.

This article describes two components of the business intelligence dashboard. These two components, spatial data and visualization, foster information such that insight can be easily recognized by users. Current systems handle the who, what and when, but the where is completely underexploited. Spatial data enhances the who by creating new informational content as well as directly enabling analysis for the where. Interrelationships that might otherwise be difficult to describe or explain are often readily understood when visually presented. The visualization helps the analyst interrogate the data, while also serving as an excellent means of explaining the information to a broader audience, including executives and customers. For centuries maps, graphs, charts and various diagrams have provided visual cues to show context and connections, allowing measurement and evaluation. Regardless of how abstract or complex, effective visualization exposes the information insight lying just beneath the obvious data. Put simply visualization is a means to present highly complex and abstract data without overwhelming the user. There are several differences between visualizations based on spatial data and those visuals created for traditional structured data or computer-aided drafting or graphic applications. Following are four critical distinctions: (1) utilizes explicit real-world relationships; (2) geo-coded to known locations around the globe; (3) exploits specific geographic features; and (4) thematically organized.

This article discusses database locking problems, as well as how to troubleshoot and circumvent them. Enterprise databases carry the freight in an organization, running almost every major enterprise information system. Yet as a successful organization begins to expand applications, implement Web-based commerce or deliver real-time services, growth and success can be too much of a good thing for a database management system. More users mean more contention in the database, leading to database locking that slows business processes and commerce. Database locking is a core mechanism for data integrity, concurrency control and transaction management. As enterprise applications are pushed to accommodate Web commerce and deliver real-time updates, database locking becomes a real, measurable obstacle to achieving corporate performance objectives. Update-intensive applications cause the most critical locking problems. Database locking is often misdiagnosed because it is poorly understood. To begin a discussion of locking, it must be assumed that existing database servers are properly installed and that the operating system is properly configured to maximize hardware capabilities. All databases have locking capabilities. At its most simple, locking is a mechanism that is designed to maintain database internal structures and enforce the database's rules of engagement--or how changes can be made by users or applications. These locks can be caused by explicit operations of an application's code. INSETS: About Bottlenecks;Do's and Dont's.

This article offers a look at the growing stature of business intelligence (BI) directors in the U.S. BI directors oversee data warehousing and business intelligence projects. The regularly manage multimillion dollar maintenance and capital budgets, oversee sizable teams of developers and contractors, and negotiate large deals with software vendors. More significantly, BI Directors manage what their top executives consider to be a strategic resource that supports fact-based decision making. One result of these responsibilities is that BI Directors currently earn close to the highest salaries among all information technology (IT) professionals. In 2004, BI Directors received an average salary of $117,866 and bonuses of $19,898 for a total compensation package of $137,764, according to a TDWI survey that was released as of March 2005. The increasingly strategic role played by BI in the U.S. also translates into bigger annual raises for BI Directors. In 2004, BI Directors boosted their salaries by 5 percent, a figure validated by both TDWI and Computerworld salary surveys. Enterprise Scale. A major reason why BI Directors are among the highest paid IT professionals is that they build and manage much larger systems than in the past. No longer do BI applications consist of an Online Analytical Processing tool running against a departmental data mart serving a number of power users.

This article focuses on the data warehousing solution used by Solectron Corporation, an electronics manufacturing services company. When Solectron found itself struggling to retrieve and coordinate business-critical information, it turned to Sun Microsystems Inc. and Sun iForce Partner Eakins Open Systems for guidance in developing an improved global data warehouse solution and infrastructure robust and scalable enough to cope with its enormous data inputs. The first challenge for the Solectron team was to find the right hardware platform on which to consolidate all existing databases and data management applications. The specific choice was the Sun Fire 12K server, running the Solaris 8 Operating System, because it is the most scalable and highly available hardware architecture, while remaining still an open system. The Sun Fire 12K Architecture brought another advantage for Solectron's data consolidation efforts. It can be easily partitioned into multiple domains, each essentially acting as an independent machine. The newly architected data warehouse, currently at approximately 2 terabytes, has already more than doubled in capacity, and it is expected that over the next two years it will double again and possibly even triple.

This article provides information on the use of analytic applications or operations analytics by companies that are mature in their use of data warehousing and business intelligence (DW/BI). According to the dictionary analytics is the science of analysis. Generally, analytics refers to analysis of data using Pareto analysis, trending, seasonality, regression, correlation, control charts and other statistical techniques. Many DW/BI solutions provide analytic tools and techniques in their data marts. Some characteristics of business problems for which an analytic application enabled by DW/BI is a good solution include the optimal decision, which is based on quantitative data and requires sophisticated analysis of multiple interrelated variables. Another is multiple, independent decision-makers remake the same decision and the current decision-making process is intuitive or informal. The locus of decision making in operations is the front-line manager. While executives and/or headquarters staff set strategy, actual performance results from tactical decisions are made at the front line. This is particularly true for service operations, because the front line typically is the point of contact with the customer. Operations analytics can help the front-line manager in two ways. The first is by utilizing the common, integrated data provided by DW/BI and analytical techniques to make a better decision than the manager can make using informal methods or intuition. The second is by saving the manager time, thereby freeing him or her for customer service, people management or other activities.

This article features the DM Review magazine's top 100 business intelligence, data warehousing and data management vendors in the U.S. for 2004. IBM is expanding on delivering to the customer a complete view of their business information--automating the flow of information throughout the enterprise and placing relevant business information at the core of business strategies. With Oracle Business Intelligence, companies can exploit all enterprise data in order to accurately monitor measure and improve performance faster and at a fraction of the cost of traditional business intelligence solutions. In 2005, Microsoft customers can look forward to the release of SQL Server 2005 that will continue Microsoft's vision in delivering an affordable, scalable, enterprise-ready and comprehensive business intelligence solution to meet the needs of every part of an organization.

This article focuses on the emergence of the Linux clusters as the choice for providing enterprise-level scalability, performance and availability for data warehousing solutions. The requirements of a successful data warehouse infrastructure are performance, availability, scalability and cost. Lack of any one of these requirements can put the data warehousing project in jeopardy. The key requirement for scalability is that the data warehouse must be able to support larger volumes of data and more users by adding additional hardware--without degrading performance. Interesting new capabilities are being introduced to Linux clusters in the area of grid computing. A grid is essentially an enterprise-wide cluster supporting multiple applications. A key characteristic of a grid is provisioning, which allows add or remove central processing units (CPU) as needed--even while the application is running. Database vendors are starting to support such capabilities. Databases can recognize new CPU instantly and dynamically allocate workload to new modes based on business priorities.

This article focuses on the data warehousing maturity model developed by The Data Warehousing Institute. The maturity model consists of six stages: prenatal, infant, child, teenager, adult and sage. Business value increases as the data warehouse moves through each successive stage. The stages are defined by a number of characteristics, including scope, analytic structure, executive perceptions, types of analytics, stewardship, founding, technology platform, change management and administration. Most established organizations have management reporting systems that generate a standard set of static reports, which are printed and distributed to large numbers employees on a regular basis. Spreadmarts are spreadsheets or desktop databases that function as surrogate data marts. Each contains a unique set of data, metrics and rules that do not align with other spreadmarts, management reports or analytical systems. In the child stage, departments recognize the need to empower all their knowledge-workers with timely information and insight, not just the business analysts and executives who primarily benefit from spreadmarts. A data mart is a shared, analytic structure that generally supports a single application area, business process or department. In the adult stage, organizations make a firm commitment to achieve a single version of the truth. Executives view data as a corporate asset that is as valuable as people, equipment and cash. The model has matured once the data warehouse becomes a strategic enterprise resource that drives the business with an ever growing panoply of mission-critical applications. However there are additional opportunities to increase the strategic value of your EDW by driving the resource outward and downward.

This article focuses on how to create a business intelligence (BI) strategy document. The core reason for establishing a BI vision in the form of a strategy document is to ensure that implementation of specific technology or a data structure is not done in a haphazard manner. There are at least five issues that can be addressed when defining an enterprise strategy within the conceptual view section, including architecture goals and constraints, conceptual view, use-case view, architecturally significant design components and standards. The data architecture provides designers a venue to convey what data structures are going to be implemented, how that data is stored in each and how the data will propagate throughout the warehouse environment. As with the overall strategy, the technical architecture section can contain many topics. Following are a few to consider: technical architecture goals and constraints; technical architecture; architecturally significant design component. Within the implementation view section, designers and project planners begin to establish the guidelines necessary for building and maintaining the purposed warehouse structures and related technologies. The implementation of core processes and the sequence of establishing data structures are detailed in this section. A BI plan should start with high-level diagrams, broad policy statements and general definitions. The Bl strategy document is the necessary road map to follow in implementing the environment, iteratively.

This article focuses on data/information integration as a critical enabler of government performance management and the U.S. President's Management Agenda, which is aimed at improving Federal government performance. Accordingly, the business analysis methods and data warehousing design and development methods that have been successfully employed in private industry to deploy business intelligence and data warehousing applications are extremely well-suited for government performance management and for achieving specific President's Management Agenda agenda items. That said, there are barriers to applying business intelligence/data warehousing in the Federal environment that must be overcome to truly realize the potential of business intelligence/data warehousing to meet the challenges of the President's Management Agenda and the mandates of key legislative and regulatory actions. There is a very strong fit between the capabilities of business intelligence and data warehousing technologies and the critical need for integrated performance management information to meet requirements of the President's Management Agenda. In essence, file performance management information required by legislative and regulatory mandates represents distinct but overlapping and complementary views of the same thing, government performance.

This article focuses on the use of business-centric methods in developing business intelligence (BI) initiatives. Business-centric methods go beyond traditional approaches by putting rigor into defining the business value capture mechanism for each BI project. This includes determining and specifying how business processes must change in order to leverage BI investments. This also includes using reengineering and process improvement techniques to ensure that BI projects deliver the intended ROI. When using business-centric methods, the BI team no longer throws the BI application after users have been trained, hoping that the business organization will understand what changes are needed and how to make them. Rather, BI team responsibilities are extended to include helping the business organization execute the changes to the business processes and decision processes that drive business results. Business-centric methods recognize that the missing link in many data warehousing efforts is the lack of clarity in the value proposition and the lack of business process change to capture the business value of BI. All of the traditional data warehouse and data mart architectures and design methods encompass structures, components and activities that seek to put the right information into the hands of the right people at the right time.

This article discusses the organizational and cultural issues involved in delivering a successful real-time environment. There are several critical factors involved in real-time business intelligence systems that have nothing to do with technology. For example, it is imperative that you understand how your businesspeople define real time and what they are willing to pay to reduce the latency of certain types of information. Your organization needs to restructure business processes to ensure real-time decision-makers are empowered to act on the real-time information, not just analyze it. Finally, users need to understand the limits of real-time data in terms of cleanliness and latency. Technically, real time means something that happens instantaneously. In the world of data warehousing, this is impossible. To succeed in the world of real-time decision-making, you need to understand how your businesspeople define real-time and what real-time data is worth to them. This cost-justification process helps identify needed requirements.

This article discusses the difficulties associated with international customer data warehouse. The simultaneous distribution and publishing of data to autonomous and far-reaching locations is usually wrought with formidable difficulties. International data warehouse requirements can be extremely diverse. For instance, it must be decided early whether the data warehouse will be primarily for high level decision support reporting or detailed historical data mining and exploration. Global repositories will all have to be built so that they reflect how information and data is used in the company. An effective international data warehouse will need to reflect and reinforce the core values of the organization itself. An eye must always be kept on data latency issues. Data is commonly created in one location and then synchronized or replicated to numerous locations throughout the world. The more geographically diverse the systems and resources, the more elaborate the complications. Quality and performance controls are a must when trying to keep data up to date and consistent across countless cities and countries. Although more persistent refresh and replication frequencies will shrink latency and waiting periods for data, greater network bandwidth will be used, requiring increased monitoring and performance-tuning tasks.

The article examines the idea that many exploratory efforts into corporate performance management (CPM) will turn into failures unless what lies beneath the CPM solution is a consistent and integrated data architecture. CPM in the application of fundamentally sound business management practices, enhanced by timely and accurate information, in order to effectively communicate, comprehend and control (C3) the performance of an organization. C3 is performed through the arrangement of people, equipment, facilities and operational processes employed by management to plan, direct, coordinate and control the enterprise in the achievement of its objectives. There are two major components of CPM road map development which are the assessment and planning cycle and the deployment and execution cycle. A company should begin with an assessment and plan for CPM, followed by deployment and execution of measurement and monitoring processes. The optimal data architecture that lies beneath CPM solution and provides the maximum benefit is a single version of corporate truth. In order for the CPM solution to provide the necessary visibility into a business, and to ensure that a single version of the truth lies beneath, the data warehouse infrastructure components must be reliable and predictable. The benefits provided by such an infrastructure are enumerated in many business intelligence and data warehousing best practices which include the ability to apply a consistent set of business rules for the extraction, transformation and integration of source system data and access to consistent and accurate information in an environment that promotes a single version of corporate truth.

The article elaborates how a business-time information architecture (BTIA) foundation enables organizations to solve their information and analysis problems. The concept of business time recognizes that users' needs are not covered by access to the most current data. The BTIA employs technologies such as enterprise information integration (EII) tools and business intelligence (BI) engines that enable organizations to integrate content from multiple sources such as data warehouses, extensible markup language documents, enterprise resource planning systems, legacy systems, operational data stores and content repositories. With its focus on information integration, the BTIA facilitates business process management, enterprise performance management, enterprise analytics and business activity monitoring. These activities are critical to the continued competitiveness and success of any organization. The foundational component of the BTIA is a best-of-breed EII solution. EII tools differ from vendor to vendor in how they integrate organizational information. It does not take the place of the organization's operational data stores, data warehouses or data marts. The next essential component of the BTIA is a best-in-class BI engine to make sense of all the data that is made available through the EII solution. The EII and BI technologies weave together all the other components of the BTIA including business activity monitoring, business process management and performance management and monitoring.

Focuses on the creation of an effective workforce in business organizations. Analysis of the capability of human capital investments; Use of workforce analytics to assess human capital ratio; Approach to data warehouse design.

Considers the implications of small-world property for the business intelligence and data warehousing fields. Details of the Associative Link Analysis methodology; Translation of the warehouse schema into an associative graph; Example of a recursive schema; Details on a V-shaped many-to-many relationship between entities.

Suggests a new approach to analyzing enterprise systems using the small-world property of complex systems. Basis for the function of the property; Outline of a methodology called Associative Link Analysis; Approach usually adopted to the analysis of enterprise data.

The article offers information on the result of a survey on the adoption of data governance by business enterprises. According to the study, 13 percent have plans to implement data governance and 8 percent had already implemented data governance throughout the organization. Fifty-two percent have made no attempt to estimate the costs of poor data while almost two thirds do not measure or monitor the quality of data. The factors that drive focus on embracing data governance are ensuring the ability to use timely, reliable, trusted information, improving the quality of business decision-making and ensuring consistent use of information. The five levels of maturity of data governance are basic, tactical, awareness, strategic and visionary.

The article discusses the role of structured data discovery in the search revolution, particularly in business enterprises' transactional systems and data warehouses. Through structured data search or data discovery, data that is stored in databases and used as a complement to business intelligence applications are optimized. Structured data discovery fills the information gap between traditional business intelligence and reporting. Among the solutions provided by structured data discovery are modern search paradigms, proper data security, intelligent leveraging of metadata and relationships, and high performance federated query. The three ways by which enterprises adopt data discovery solutions are user-driven, information technology-driven, or embedded-solution adoption.

An interview with John Thompson, chief executive officer of Kognitio in the U.S., about data warehousing in emergent delivery models and hardware designs is presented. Thompson clarifies the legacy of Kognitio with WhiteCross and different product strategies in the U.S. and Great Britain. He describes the trade-off in the appliance model for cost and performance. He talks about the pilots being run on desk tops as a service or DaaS and about the trend in the in-house and outsourced data model.

The article examines the case with the newer concept of data governance and established principles and methods of business process management (BPM). BPM is a method of efficiently aligning an organization with the needs of its customers, often using technology to help create flexible, integrated and continuously improving processes. The author illustrates the basic challenges that have dominated his experience with data governance projects and how it can be addressed conceptually with data governance program stakeholders as the business case is built. It stresses that time and changing interactions with needed stakeholders are the enemy of any fledgling data governance initiative.

The article outlines the phases to support the data-centric service-oriented architecture (SOA). SOA refers to an environment in which discoverable, accessible, interoperable and trusted services are business applications aligned with business goals. The purpose of a data-centric SOA transition is to allow organizations to better leverage new and existing information technology (IT) investments to support business requirements. The focus of the architect and plan phase is on architectural alignment, gap analysis and transition planning. The next phase centers on decoupling data from systems to provide the basis for an information warehouse.

The article discusses two personality types managers should exude to be successful in managing the Integrated Enterprise Data Warehouse. The dimension manager defines the content and structure of a conformed dimension and delivers that conformed dimension to downstream clients or fact providers. One of the tasks of the dimension manager is defining the content of the customer dimension. Meanwhile the fact provider responds to late-arriving dimension updates. The fact provider is also responsible for synchronizing dimension releases with other fact providers.

The article discusses the advanced levels of master data management (MDM). Central hub processing, which implies a common, purpose-built platform for MDM, removes the complexity of peer-to-peer connectivity. It eliminates the need to support system location and navigation logic. The MDM hub centrally handles any distributed details associated with the system. At this level, target data value representation is established and data are taken through the necessary steps to deliver an accurate master value. Other MDM levels include enterprise data convergence and integration of business rule and policy support.

The article discusses the development of an integrated enterprise data warehouse (EDW). The mission statement for integrated EDW is to provide the platform needed to apply business analysis consistently across the enterprise. Consistency entails detailed textual description, similar interpretation of aggregated groups such as types, categories, and zone as defined within entities, and proper presentation of numeric facts. An EDW must also provide a mechanism for delivering integrated reports and analyses from business intelligence tools to several remote databases. The use of bus matrix to communicate with executives is also discussed.

The article offers an outlook for the data management sector in 2008. Acquisitions will provide opportunities for technology innovation based on the interaction and integration of architectural layers. Furthermore, consolidation will bring closer to realization the potential of data warehousing made simple. The convergence of business intelligence (BI) and business process management (BPM) will progress as well as reduce uncertainty and risk related to the future of BI platforms. Meanwhile, social networking will continue to look for a revenue model.

The article provides an overview of the evolution of relational database management systems (RDBMSs) and how the gap between data warehouse requirements and data warehouse database capabilities has come about. Relational databases emerged in the nineteen seventies as a way to improve business data processing. The idea was that storing data in row-oriented tables that group related information was better than using hierarchical data models. The row-oriented architectures are optimized for write-intensive online transaction processing (OLTP), which enables simple data storage and retrieval. However, RDBMS were not designed to handle read-intensive analytical workloads. Seven database innovations that will revolutionize data warehousing are explained.

An interview with Cognizant Technology Solutions executive Veera Narayanaswamy is presented. He mentions that the company has observed customer organizations building a central data warehousing organization then creating a business intelligence (BI) factory. Narayanaswamy also stated that BI companies only pay for projects that have return of return (ROI) in seven to eight months. He added that the BI industry should focus on enhancing the strategic use of data available because it is important for their clients.

An interview with Ben Barnes, vice president and general manager of HP, is presented. He explained the concept underlying Neoview, a high-end data warehousing and business intelligence software from HP. He mentioned the role of the acquisition of Knightsbridge by HP in the launching of Neoview. He also discussed his views on the company's intellectual assets.

The article provides practical recommendations for testing extract, transform and load (ETL) data warehouse applications. Some of the goals of testing an ETL application are data completeness, data transformation, data quality, performance and scalability as well as regression testing. According to the author, a simple but efficient strategy to retest basic functionality is to save source data sets and outcomes from successful runs of the code and compare new test results with previous runs. The author adds that test cases must be prioritized by risk in order to help identify which need to be rerun for each new release.

The article discusses business analytics (BA), which describes a superset software category including tools and applications for tracking, storing, analyzing, modeling and delivering data in support of automating decision-making and reporting processes. The modern era of business intelligence (BI) characterized by query, reporting and Online Analytical Processing (OLAP) technology migrated from client/server to Web-based architecture. BA projects include a range of software across a technology stack such as data integration, data warehouse management and the analytic applications and BI tools used by end users.

The article presents an interview with Scott Newman, chief executive officer (CEO) of Conversion Services International (CSI). Newman said that CSI started with data warehousing and later moved into data marts for more manageable projects. CSI promotes performance and process management requirements to clients as a way of bridging information technology and business. Newman stressed that CSI would like to start with strategic engagement and lay out a multiyear plan, strategy and vision.

The article discusses data governance and its importance in an increasingly integrated business world. The article compares families moving in together in one house as source systems coming together on one data platform such as an enterprise resource planning application or data warehouse. Data governance comprise the rules that ensure the people representing various business processes, data and technology are involved in the decisions affecting them. Guiding philosophies and principles are discussed.

The article presents a data warehouse architecture framework for data flow from the source systems along with manner of presentation and framework components. Data warehouse architecture appropriation is necessary to prevent the failure of data warehousing projects. The 5x5 architectural framework for an enterprise data warehouse in a medium to large organization is presented along with the five stages of data movement and project factors. These considered factors include size and regionalization of the organization, source systems and distribution of operational data among others.

The article presents an interview with president of consultancy Business Intelligence (BI) Research Colin White. When asked about his job and BI and data warehousing, he says he is heavily involved in portal, content management and collaboration systems. White stresses that people in the field could get political very quickly as BI programs are not only for sharing expertise, but also removing political barriers. He says that his job of being a market researcher and a consultant has no inherent conflict and that both jobs go together.

The article discusses the key factors toward the successful implementation of an enterprise data warehouse. The potential benefits of an enterprise data warehouse include the consolidation of data for a business platform creation. Oftentimes, the design and implementation of a logical data model (LDM) has become the problem of most companies. LDM may affect design and data movement techniques and standards, reference data, metadata and packaged applications. These logical data models are the foundation for data quality.

The article discusses the data quality problems in businesses according to a survey conducted by the Data Warehousing Institute. Data quality problems across industries are getting worse and merit corrective attention. According to the survey, customer data and product data are especially susceptible to quality problems. Origins for data quality problems are also identified, including inconsistency in data definition and data entry by employees and customers. The respondents reported several benefits of high-quality data, such as greater confidence in analytic systems and reduction of time spent on reconciling data. A large percentage of respondents also believed that by investing in a data quality initiative, a positive return on investment can be achieved.

The article presents an interview with Stephen Brobst, chief technology officer at California-based data management firm Teradata and Mike Koehler, senior vice president and general manager, about the company's strategy. The growth area in data warehousing is around operational intelligence and enabling the real-time enterprise with enterprise-wide business intelligence. Analytic applications at Teradata include customer relationship management, finance and performance management, profitability analytics, demand chain management and supply chain analytics.

This article presents an interview with Ralph Kimball, data warehousing expert and professor at Kimball University. He discusses how perception have changed in terms of the ways people use data. He cites his opinion on the question of whether security be the data warehouse team's problem. He outlines the impact of business intelligence tools and platforms on the role of data specialists. He responds to a forecast that data should be automated and available like the dial tone on telephone. He describes a viable strategy in the specialized, outsourced world.

This article offers tips on data warehouse project management. There are two paths to project managing a data warehouse. The first is to approach the project from a project management perspective by managing the project scope and timeline. The second is to follow through with the traditional responsibilities of project management. A technical project manager is not the same as a data warehouse project manager because a data warehouse does not function like any other system. A data warehouse project manager needs a team, solid requirements and a roadmap to deliver product on time and within budget.