05-Unit5 - Business Intelligence and Tools Unit 5 Unit 5...

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Unformatted text preview: Business Intelligence and Tools Unit 5 Unit 5 Implementing and Maintaining a Data Warehouse Environment Structure 5.1 5.2 5.3 5.4 5.5 Introduction Objectives Approaches in Implementing a Data Warehouse 5.2.1 Top Down Approach 5.2.2 Bottom Up Approach 5.2.3 A Combined Approach Self Assessment Question(s) (SAQs) Visual Warehouse 5.3.1 The Architecture of Visual Warehouse Self Assessment Question(s) (SAQs) Measuring the Data Warehouse Results DW Tools in Use 5.5.1 Data Modeling Tools 5.5.2 ETL and Metadata Creation Tools 5.5.3 Data Analysis and Report Generation Tools 5.5.4 Commercial Tools Self Assessment Question(s) (SAQs) 5.6 Summary 5.7 Terminal Questions (TQs) 5.8 Multiple Choice Questions (MCQs) 5.9 Answers to SAQs, TQs, and MCQs 5.9.1 Answers to Self Assessment Questions (SAQs) 5.9.2 Answers to Terminal Questions (TQs) 5.9.3 Answers to Multiple Choice Questions (MCQs) 5.1 Introduction After the selection of an appropriate architecture, the data warehouse has to be implemented and maintained effectively so as to meet the objectives set. These implementation methods include the top down approach and bottom Sikkim Manipal University Page No. 107 Business Intelligence and Tools Unit 5 up approach. The project managers of the BI projects are expected to monitor the performance of the data warehouse on a continuum basis to ensure that the data warehouse is successful in providing the best possible assistance to the decision-making processes of the organization. Also, the current unit makes an attempt to make the reader aware of the data warehousing tools available in the present market. Objectives: The objectives of the Unit are to make you understand: The approaches involved in implementing a data warehouse The architecture of a visual data warehouse Measuring the data warehouse results Critical success factors of BI systems Various data warehousing tools available in the market 5.2 Approaches in Implementing a Data Warehouse The approaches that are in practice in implementing a data warehouse include Top down approach Bottom up approach A combination of both A project manager can adopt any of these approaches as these implementation choices offer flexibility in determining the criteria that are important in any particular implementation. Here, the choice of an implementation approach is influenced by various factors such as the current infrastructure, availability and affordability of the resources, the type of architecture chosen, the scope of the implementation, and return-oninvestment requirements. 5.2.1 Top Down Approach The top-down approach views the data warehouse (refer Fig 5.1) as the mainstay of the entire analytic environment. The data warehouse holds Sikkim Manipal University Page No. 108 Business Intelligence and Tools Unit 5 atomic or transaction level data that has been extracted from the source systems and integrated within a normalized, enterprise data model. Later, the data is summarized, dimensionalized, and distributed to one or more “dependent” data marts. (here, the data marts are “dependent” because they derive the entire data from a centralized data warehouse). Also, organizations make use of staging area to collect and store source system data before it can be moved to the data warehouse. The advantage of a “top-down” approach is that it provides an integrated, flexible architecture to support the downstream analytic data structures. Here, the data warehouse is a single departure point of data for all data marts and thereby organizations can achieve a single version of the truth, maintain consistency and standardization. As the warehouse consists of the atomic data, it enables the organizations re-purpose that data in any number of ways to meet new and unexpected business needs. On the other hand, a top-down approach may take longer time and may cost more to deploy the data than any other approaches (especially in the initial increments). This is because organizations must create a reasonably detailed enterprise data model as well as the physical infrastructure to house the staging area, data warehouse, and the data marts before the deployment of their applications or reports. A top down implementation requires more planning and design work at the beginning of the project itself. In order to implement this approach, an organization needs to involve people from each of the workgroups, departments, or lines of business. Also, decisions concerning the data sources to be used, security, data structure, data quality, and data standards need to be completed before starting of an actual implementation. Though it can result in more consistent data definitions and the enforcement of business rules across the organization since its inception, the cost of the initial planning and design becomes significant. It is a time-consuming process and can delay the actual implementation, benefits, and return-onSikkim Manipal University Page No. 109 Business Intelligence and Tools Unit 5 investment. This approach can work well when there is a good centralized organization that is responsible for all hardware and other computer resources. In many organizations, the workgroups, departments, or lines of business may not have the resources to implement their own data marts. Data Mart 1 Data Source 1 Data Source 2 Data Staging Area Global Data Warehouse Data Source 3 Data Mart 2 Data Mart 3 Fig. 5.1: Top - Down Approach 5.2.2 Bottom Up Approach The objective of bottom-up approach (refer Fig 5.2) is to deliver business value by deploying the dimensional data marts at the earliest. Unlike the top-down approach, the data marts in this approach contain all the data (both atomic and summary) that users may want. The data is modeled in a star schema design to optimize usability and query performance. Also, each data mart builds on the next one, reusing dimensions and facts, so that users can query across data marts, if desired, to obtain a single version of the truth and both summary and atomic data. Also, the approach consciously attempts at minimizing the back-office operations, preferring to focus an organization’s effort on developing dimensional designs that meet end-user requirements. Here, the staging area is non-persistent, and may simply stream flat files from source systems Sikkim Manipal University Page No. 110 Business Intelligence and Tools Unit 5 to data marts using the file transfer protocol. In a majority of cases, the dimensional data marts are logically stored within a single database. Thus the approach minimizes data redundancy and makes it easier to extend existing dimensional models to accommodate new subject areas. The ‘bottom-up approach’ involves the planning and designing of data marts without waiting for a more global infrastructure to be put in place. This does not mean that a more global infrastructure will not be developed; but it will be built incrementally. This approach is more widely accepted today than the top-down approach because immediate results from the data marts can be realized. Along with the positive aspects, this approach has some considerations. For example, as more and more data marts are created, data redundancy and inconsistency between the data marts may occur. This needs to be minimized with careful planning, monitoring, and design guidelines. Multiple data marts may bring with them an increased load on operational systems because more data extract operations are required. Integration of the data marts into a more global environment can also be difficult unless some degree of planning has been done. Data Source 1 Data Source 2 Data Mart 1 Data Staging Area Data Source 3 Data Mart 2 Global Data Warehouse Data Mart 3 Fig. 5.2: Bottom - Up Approach Sikkim Manipal University Page No. 111 Business Intelligence and Tools Unit 5 5.2.3 A Combined Approach As we have already discussed, there are both positive and negative considerations while implementing any of the two approaches. In most of the cases, a right approach could be a combination of the two approaches. Of course, this is a difficult balancing act, but can be done with proper planning. One of the key issues in this scenario is to determine the degree of planning and design that is required for the global approach to support integration as the data marts are being built with the bottom up approach. Initially, you can develop a base level infrastructure definition for the global data warehouse at a business level. For example, you may simply identify the lines of business that will be participating and then a high level view of the business processes and data areas of interest to them will provide the elements for a plan for implementation of the data marts. As data marts are implemented, you can develop a plan as to how to handle the data elements that are needed by multiple data marts. This could be the start of a global data warehouse structure or simply a common data store accessible by all the data marts. In some cases, it may be appropriate to duplicate the data across multiple data marts as this is a trade-off decision between storage space, ease of access, and the impact of data redundancy along with the requirement to keep the data in the multiple data marts at the same level of consistency. Self Assessment Question(s) (SAQs) For Section 5.2 1. Differentiate the Top down and Bottom up data warehouse implantation approaches? Sikkim Manipal University Page No. 112 Business Intelligence and Tools Unit 5 5.3 Visual Warehouse Visual Warehouse is an integrated product for building and maintaining a data warehouse or data mart in a LAN environment. The visual warehouse integrates many of the business intelligence component functions into a single product and it can be used to automate the process of bringing the data together from heterogeneous sources into a central, integrated, information providing environment. It does not simply create a data warehouse or an information database; but provides the processes to define, build, manage, monitor and maintain an environment which provides information. The visual warehouse can be managed either centrally or from the workgroup environment. Therefore, business groups can meet their own information needs without burdening information systems resources, and can enjoy the autonomy of their own data mart without compromising overall data integrity and security in the enterprise. Following are some of the important features of visual warehouses: Visual Warehouse has the ability to extract and transform data from a wide range of heterogeneous data sources (both internal and external sources of an enterprise); such as the DB2 family, Microsoft SQL Server, Oracle, Sybase, Informix, and flat files (for example, from spreadsheets). On the basis of the metadata defined by the administrative component of visual warehouse, the data from any of these sources can be extracted and transformed. Also, the extraction process, which supports full refreshing of data, can run on demand or on an automated scheduled basis. The transformed data can be placed in a data warehouse built on any of the DB2 UDB platforms (including DB2 for Windows NT, DB2 for AIX, DB2 for HP-UX, DB2 for Sun Solaris, DB2 for SCO, DB2 for SINIX, DB2 for OS/2, DB2 for OS/400, and DB2 for OS/390) or on flat files. The visual warehouse provides the flexibility and scalability to populate any Sikkim Manipal University Page No. 113 Business Intelligence and Tools Unit 5 combination of the supported databases. Also, visual warehouse supports Oracle, Sybase, Informix, and Microsoft SQL Server using IBM DataJoiner. Once the data is in the target data warehouse, the data can be accessible by a variety of end user query tools. These tools can be from IBM, such as Lotus Application, or QMF for Windows, or from any other vendors whose products comply with the DB2 Client Application Enabler (CAE) or the Open Database Connectivity (ODBC) interface, such as Business Objects, and Cognos Impromptu. The data can also be browsed using any of the popular web browsers with additional webinfrastructure components. 5.3.1 The Architecture of Visual Warehouse The architecture of a visual warehouse (refer Fig 5.3) provides a fully distributed Client/Server system that allows users attain the benefits of network computing. The major components in the architecture are discussed below. 5.3.1.1 Visual Warehouse Server The visual warehouse server runs on a Windows NT workstation or server. It controls the interaction of the various data warehouse components and provides for automation of data warehousing processes through a powerful scheduling facility (either a calendar-based scheduling or event-based scheduling). The server component monitors and manages the data warehousing processes and also controls the activities performed by the visual warehouse agents. 5.3.1.2 Visual Warehouse Administrative Clients The Administrative Client also runs on Windows NT server and provides an interface for administrative functions, such as defining the business views, defining the target data warehouse databases, registering data resources, Sikkim Manipal University Page No. 114 Business Intelligence and Tools Unit 5 filtering source data. The warehouse can support an unlimited number of administrative clients and provides comprehensive security facilities to control and manage client access to the administrative functions as well. 5.3.1.3 Visual Warehouse Agents The architecture of visual warehouse agents is a key enabler for scalable business intelligence solutions. These agents run on Windows NT, OS/2, AS/400, AIX, and Sun Solaris and they handle access to the source data, filtering, transformation, sub-setting, and delivery of transformed data to the target warehouse as directed by the Visual Warehouse Server. Multiple agents of the warehouse populate a data warehouse and so there will be a significant impact on the throughput. Databases VW Agents VW Admin Clients Relational Source End User VW Server DB2 Target VW Agent NonRelational Source End User VW Agent Scheduling Modeling Operations Control Database OEM Target Flat File Fig. 5.3: Visual Warehouse 5.3.1.4 Visual Warehouse Control Database A control database is set up in DB2 to be used by visual warehouse to store control information used by the Server. The control database stores the Sikkim Manipal University Page No. 115 Business Intelligence and Tools Unit 5 metadata required to build and manage the warehouse. The information in the control database includes the mappings between the source and target data, the schedules for data refresh, the business views, and operational logs. When a request for service is made to the Server, the control information pertinent to the request is retrieved from the database and sent to the appropriate agent that actually provides the service. Also, different warehouses could use different control databases. 5.3.1.5 Visual Warehouse Target Databases The target databases in a data warehouse contain the visual warehouse data stored in structures defined as Business Views (BVs). When visual warehouse populates a BV, the data is extracted from the source and is transformed according to the rules defined in the BV, which is then stored in the target database. Self Assessment Question(s) (SAQs) For Section 5.3 1. List out the important components involved in the visual warehouse architecture? 5.4 Measuring the Data Warehouse Results The only way to conclude the success of a data warehouse project is to monitor and measure the results of the project. These measurements can be both subjective and objective. Some measures are costly as they require latest technical tools and the expert manpower to use those tools. So the project manager needs to be selective in choosing an appropriate mix of metrics to measure the results of a data warehouse implementation. Some of the metrics that the project managers can consider in this context include: 1. Functional Quality: To understand the functional quality, you may ask yourself the following questions: Sikkim Manipal University Page No. 116 Business Intelligence and Tools Unit 5 Do the capabilities of the data warehouse satisfy the user requirements? Does the data warehouse provide the information necessary for the users to do their job? 2. Data Quality: There are two means of measuring quality. One is, you may ask the users whether their reports are accurate Second is, you may use a software tool to provide a scorecard on the quality of the data. 3. Computer Performance: There are three performance indicators you may consider and they are: Query response time Report response time Time to load/update/refresh the data warehouse Some organizations have established benchmark performance numbers for known queries and reports, and they exercise and measure these benchmarks periodically. 4. Network Performance: The ability of the network to handle the data traffic will directly impact response time. Network software measures line load, line traffic and indicate conditions where an activity was waiting for line availability. Besides the software, network administrators must be available to analyze the results and take appropriate action. 5. Number of Queries: Many of the query tools provide metrics on the number of queries executed by department and by individuals. 6. User Satisfaction: Users must be polled shortly after being given the data warehouse capability and then polled periodically to identify changes in their level of satisfaction. Sikkim Manipal University Page No. 117 Business Intelligence and Tools Unit 5 5.5 DW Tools in Use The data warehouse development tools can be broadly divided into two categories: Tools integrated with the generic software such as server OS, RDBMS, ERP packages, etc. For example, the tools supplied by Microsoft (along with Windows 2003 Server), Oracle (with Oracle Database), IBM with IBM servers and SAP (with the ERP package) Specialized commercial tools for developing data marts/warehouses that are supplied by vendors. These tools can be classified as: Data modeling tools ETL and Metadata creation tools Data analysis and Report generation tools 5.5.1 Data Modeling Tools The data modeling tools involve in the conversion of the existing E-R models to multidimensional models that are required for data warehousing. There are several data modeling tools available in the market, but many of the project managers prefer to do the modeling manually. Some of the data modeling tools include: Rational Rose of IBM Corporation Oracle Designer of Oracle Corporation ERwin of Computer Associates Power Designer of Sybase Corporation 5.5.2 ETL and Metadata Creation Tools These are the back-end tools used by developers and administrators. These tools provide the GUI to carry out the functions using a user-friendly interface. These tools extract the data from different data sources (such as flat files, legacy databases, RDBMS, ERP, CRM and SCM applications packages, etc.) Sikkim Manipal University Page No. 118 Business Intelligence and Tools Unit 5 Some of the important functions carried out by these tools include: To carry out the transformations such as joining of tables, sorting, applying filters, etc. To create the metadata, target data and the transformation logic To provide the GUI for carrying out the ETL operations To provide the necessary administration tools to manage multiple users to access the tool simultaneously 5.5.3 Data Analysis and Report Generation Tools These are the 'front-end' tools that provide the end-user applications. These tools are also referred to as On-Line Analytical Processing (OLAP) tools. Some of the important functions provided by these tools include: To analyze the data from multiple dimensions To generate standard reports for business intelligence To provide a good number of statistical analysis features To provide the capability to generate ad-hoc queries To present the data in graphical/tabular form and also 5.5.4 Commercial Tools Here, we discuss the important commercial tools being used in the data warehouse development process. 5.5.4.1 Informatica This suite consists of five components and provides the complete business intelligence solutions. The five components in conjunction with Informatica Server and Informatica Repository server enable you to perform the entire extraction, transformation and loading processes. The five components are: Repository Server Administrator Console: This is used to connect to the Repository Server. Repository Manager: This is used to create/organize/manage the repository (related database managed by the Repository Server that Sikkim Manipal University Page No. 119 Business Intelligence and Tools Unit 5 stores information, or metadata, used by Informatica Server and Client tools). Designer: Designer is used to create mappings that contain transformation instructions for the Informatica Server. Before you can create mappings, you must add source and target definitions to the repository. It contains Source Analyzer, Warehouse Designer and Mapping Designer. Workflow Manager: This tool is used to create and run workflows and tasks. Workflow Monitor: This is used to monitor scheduled and running workflows for each Informatica Server. 5.5.4.2 Cognos Cognos is a rich set of tools for development of data mines, data marts and data warehouses and the main components available in this tool include: Cognos DecisionStream: This is used to carry out the ETL process and metadata creation. Cognos Impromptu: This is used to generate business intelligence reports. Cognos Scenario: This is used for data mining applications. Cognos Query: This is used for data navigation to process ad hoc queries. Cognos PowerPlay: This is used for multi-dimensional on-line analysis of data. 5.5.4.3 Business Objects This suite provides a set of business intelligence tools. The various tools of this suite are: Data Integration Tools: These tools extract, transform and load the data from the source databases to the target database. There are two Sikkim Manipal University Page No. 120 Business Intelligence and Tools Unit 5 categories; Data Integrator and Rapid Marts. Data Integrator is an ETL tool with a GUI. Rapid Marts is a packaged ETL with pre-built data models for reporting and query analysis that makes initial prototype development easy and fast for ERP applications. The important components of Data Integrator include; Graphical designer: This is a GUI used to build and test ETL jobs for data cleansing, validation and auditing. Data integration server: This integrates data from different source databases. Metadata repository: This repository keeps source and target metadata and the transformation rules. Administrator: This is a web-based tool that can be used to start, stop, schedule and monitor ETL jobs. BI Platform: This platform provides a set of common services to deploy, use and manage the tools and applications. These services include providing the security, broadcasting, collaboration, metadata and developer services. Reporting Tools and Query & Analysis Tools: These tools provide the facility for standard reports generation, ad hoc queries and data analysis. Performance Management Tools: These tools help in managing the performance of a business by analyzing and tracking key metrics and goals. 5.5.4.4 DataStage DataStage provides a set of powerful tools for developing a data warehouse. It has a number of client and server components. The DataStage client components are as follows: DataStage Manager: This provides the user interface to view contents of the data repository. Sikkim Manipal University Page No. 121 Business Intelligence and Tools Unit 5 DataStage Designer: This tool is used to create the DataStage jobs. Using this tool, you can specify the data sources, transformations required and the destination database. These jobs are compiled and executable files are then created. These executable files can be scheduled by the DataStage Director and run by the Server. DataStage Director: This tool provides the user interface to schedule, run and monitor the server jobs. DataStage Administrator: This is used to perform administration tasks such as administration of the users, creation of projects, etc. The DataStage server components are as follows: DataStage Repository: This repository contains all the required data to build a data warehouse. DataStage Server: This runs the server jobs DataStage Package installer: This provides the GUI to install packaged server jobs and plug-ins. Self Assessment Question(s) (SAQs) For Section 5.5 1. Discuss the types of commercial tools available for developing data marts/warehouses? 5.6 Summary The implementation of a data warehouse include the following approaches; Top down approach, Bottom up approach, and/or a combination of both. In the top-down approach, the data warehouse holds transaction level data that has been extracted from the source systems and integrated within a normalized, enterprise data model. Later, the data is summarized into data marts. In the bottom up approach, the data is extracted to the data marts from the source system and the entire data is summarized in the data warehouse. In practice, a combination of the two approaches is adopted. Sikkim Manipal University Page No. 122 Business Intelligence and Tools Unit 5 Visual Warehouse is an integrated product for building and maintaining a data warehouse or data mart in a LAN environment. The major components in the architecture of a visual warehouse include visual warehouse server, visual warehouse administrative clients, visual warehouse agents, visual warehouse control database, and visual warehouse target databases. There are two types of data warehouse development tools; tools integrated with the generic software and specialized commercial tools for developing data marts/warehouses. The types of specialized tools include data modeling tools, ETL and metadata creation tools, and data analysis and report generation tools. Informatica, Cognos, Business Objects, and DataStage are the important commercial tools available in the market in developing the data warehouses. 5.7 Terminal Questions (TQs) 1. The data warehouse implementation approaches (Top down approach and Bottom up approach) are theoretical in nature. Comment. 2. You may not be sure if the data warehouse is a success, but we will always know when we have failed. Discuss various measures to assess failure. 3. Apart from the technical issues and challenges, what kind of steps are to be handled carefully by the project manager so as to complete the project successfully? 5.8 Multiple Choice Questions (MCQs) 1. Which of the following is the most practical approach of implementing a data warehouse? a. Top down approach b. Bottom up approach c. Any of the above d. A combination of the above Sikkim Manipal University Page No. 123 Business Intelligence and Tools Unit 5 2. Which of the following approaches involves extraction of the data from the source systems to data marts and then loading of the data into the data warehouse? a. Top down approach b. Bottom up approach c. Any of the above d. A combination of the above 3. Which of the following is an integrated product for building and maintaining a data warehouse or data mart in a LAN environment? a. Pseudo Warehouse b. Virtual Warehouse c. Visual Warehouse d. Local Warehouse 4. The target databases in a data warehouse contain the visual warehouse data stored in structures, called _____________. a. Data Visual Views b. Data Structure Views c. Business Views d. Management Views 5. Which of the following tools is involved in extracting the data from various source systems? a. Data modeling tools b. ETL and Metadata creation tools c. Data analysis and Report generation tools d. None of the above 6. Which of the following is not a Data modeling tool? a. Rational Rose b. Power Designer c. RDBMS d. Oracle Designer Sikkim Manipal University Page No. 124 Business Intelligence and Tools Unit 5 7. ERwin is an example of ________ type of tools. a. Data Modeling b. ETL Tool c. Data analysis tool d. Report generation tool 8. Which of the following tools of Cognos is useful multi-dimensional online analysis of data? a. Cognos Scenario b. Cognos Impromptu c. Cognos Query d. Cognos Powerplay 9. Workflow Manager is a part of ______ tool. a. DataStage b. Informatica c. Cognos d. Business Objects 10. Which of the following is not a client component of DataStage? a. DataStage Designer b. DataStage Developer c. DataStage Director d. DataStage Manager 5.9 Answers to SAQs, TQs, and MCQs 5.9.1 Answers to Self Assessment Questions (SAQs) Section 5.2 1. In the top down approach, the data warehouse holds atomic or transaction level data that has been extracted from the source systems and integrated within a normalized, enterprise data model. Later, the data is summarized, dimensionalized, and distributed to one or more Sikkim Manipal University Page No. 125 Business Intelligence and Tools Unit 5 “dependent” data marts. The characteristics of the top down approach are discussed in the section 5.2.1. In contrast to this approach, there is a bottom up approach, wherein the data is extracted to the data marts from the source system and the entire data is summarized in the data warehouse. The characteristics of the bottom up approach are discussed in the section 5.2.2. However, a combination of the two approaches is adopted in practice in order to ensure the benefits offered by both the approaches. Section 5.3 1. Visual Warehouse is an integrated product for building and maintaining a data warehouse or data mart in a LAN environment. It integrates the business intelligence component functions into a single product and it can be used to automate the process of bringing the data together from heterogeneous sources into a central, integrated, informational environment. The architecture of the visual warehouse includes the components; visual warehouse server, visual warehouse administrative clients, visual warehouse agents, visual warehouse control database, and visual warehouse target databases. Section 5.5 1. The commercial tools can be categorized into Data modeling tools, ETL and Metadata creation tools, and Data analysis and Report generation tools. The data modeling tools involve in the conversion of the existing E-R models to multidimensional models that are required for data warehousing. The examples of data modeling tools are listed in section 5.5.1. The ETL and metadata creation tools provide the GUI to carry out the functions using a user-friendly interface. These details are discussed in Section 5.5.2. The data analysis and report generation Tools involve in Sikkim Manipal University Page No. 126 Business Intelligence and Tools Unit 5 the preparation of various business reports. These tools are discussed in Section 5.5.3. 5.9.2 Answers to Terminal Questions (TQs) 1. Both Top down approach and Bottom up approach have their own advantages and disadvantages. The advantages of the Top down approach include: Provides an enterprise view of the data Inherently architected (not a union of data marts) Centralized control and rules Single, central storage of data about the content The disadvantages of this approach include: Takes a longer time for the implementation Needs high level of cross-functional skills High exposure/risk to failure The advantages of the Bottom up approach include: Faster and easier implementation of manageable pieces Less risk of failure Favorable return on investment Inherently incremental (can schedule important data marts first) The disadvantages of this approach include: Each data mart will have its own narrow view of data Permeates redundant data in every data mart Allows inconsistent and irreconcilable data Proliferates unmanageable interfaces Although each of these approaches has their own advantages and limitations, a practical approach that can accommodate the advantages of both the approaches is implemented. Therefore, both Top down Sikkim Manipal University Page No. 127 Business Intelligence and Tools Unit 5 approach and Bottom up approach remain to be theoretical and a combination of these approaches is implemented in practice. 2. We may not be sure if the data warehouse is a success, but we will always know when we have failed. Some of the indications of failure are: 1. Funding has dried up. 2. Users are unhappy with the quality of the data. 3. Users are unhappy with the query tools. 4. Only a small percentage of users take advantage of the data warehouse. 5. Poor performance in terms of query response, report response, time to load/update/refresh the data warehouse 6. The data warehouse has the inability to expand (not scalable) and the components are not integrated. 3. The project manager has to undergo the following project management related activities to implement the project successfully: Scope the project to be able to deliver within the planned duration. Select a specific business subject area (but not try to solve all business requirements within one project) Find a sponsor from the upper management of the business side of the company. Involve the sponsor throughout the project. Establish a sound information and communication structure that includes business and technical staff inside and outside the project. Define the contents and type of the deliverables of the project as early and in as much detail as possible. Together with the end users, validate the results of the analysis phase (the initial dimensional models) against the deliverables definition. Sikkim Manipal University Page No. 128 Business Intelligence and Tools Unit 5 Deploy the solution quickly to a limited audience and iterate development. Establish commonly agreed on business definitions for all items within the scope of the project. Validate the quality and correctness of the information before making it available to the end user community. Keep the end users involved and informed throughout the project. Be prepared for political and cultural obstacles between business departments or between business and IT departments. 5.9.3 Answers to Multiple Choice Questions (MCQs) 1. Ans: d 2. Ans: b 3. Ans: c 4. Ans: c 5. Ans: b 6. Ans: c 7. Ans: a 8. Ans: d 9. Ans: b 10. Ans: b . Sikkim Manipal University Page No. 129 ...
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This note was uploaded on 04/15/2010 for the course MBA mba taught by Professor Smu during the Spring '10 term at Manipal University.

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