02-Unit2 - Business Intelligence and Tools Unit 2 Unit 2...

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Unformatted text preview: Business Intelligence and Tools Unit 2 Unit 2 Basics of a Data Warehouse Structure 2.1 Introduction Objectives 2.2 Fundamentals of a Data Warehouse 2.2.1 Evolution of Data Warehouse 2.2.2 Need of a Data Warehouse 2.2.3 Characteristics of a Data Warehouse 2.2.4 Types of Data Warehouses 2.2.5 Advantages and Limitations of a DW System Self Assessment Question(s) (SAQs) 2.3 Data Warehouse Concepts and Terminology 2.4 Architecture of a Data Warehouse 2.5 Data Warehouse Components 2.5.1 Data sources 2.5.2 Extraction/Propagation 2.5.3 Transformation/Cleansing 2.5.4 Data Loading 2.5.5 Data Refining 2.5.6 Presentation and Analysis Tools Self Assessment Question(s) (SAQs) 2.6 The Cost of a Data Warehouse 2.7 Summary 2.8 Terminal Questions (TQs) 2.9 Multiple Choice Questions (MCQs) 2.10 Answers to SAQs, TQs, and MCQs 2.10.1 Answers to Self Assessment Questions (SAQs) 2.10.2 Answers to Terminal Questions (TQs) 2.10.3 Answers to Multiple Choice Questions (MCQs) Sikkim Manipal University Page No. 25 Business Intelligence and Tools Unit 2 2.1 Introduction Today, businesses of all sizes in different industries are finding that they can realize significant benefits by maintaining a data warehouse. It is accepted that developing a data warehousing system provides an excellent approach for transforming the vast amounts of data available in the organizations into a useful and reliable information for getting answers to their queries and to support the decision making process. Objectives: The objectives of the Unit are: The purpose of a Data warehouse The goals and characteristics of a Data warehouse The architecture of a Data Warehouse The costs involved in developing a Data Warehouse 2.2 Fundamentals of a Data Warehouse In simple terms, a data warehouse is the repository of an organization's historical data (also termed as the corporate memory). For example, an organization would get the information that is stored in its data warehouse to find out what day of the week they sold the most number of gadgets in May 2002, or how employees were on sick leave for a specific week. A data warehouse is a database designed to support decision making in an organization. Here, the data from various production databases are copied to the data warehouse so that queries can be forwarded without disturbing the stability or performance of the production systems. So the main factor that leads to the use of a data warehouse is that complex queries and analysis can be obtained over the information without slowing down the operational systems. While operational systems are optimized for simplicity and speed of modification (online transaction processing, or OLTP), the data Sikkim Manipal University Page No. 26 Business Intelligence and Tools Unit 2 warehouse is optimized for reporting and analysis (online analytical processing, or OLAP). (The concepts of OLTP and OLAP are discussed in later Units). Apart from traditional query and reporting, a data warehouse provides the base for the powerful data analysis techniques such as data mining and multidimensional analysis (discussed in detail in later Units). Making use of these techniques will result in easier access to the information you need for informed decision making. 2.2.1 Evolution of Data Warehouse Data Warehouses became a distinct type of computer databases during the early 1990s. The intention to develop these databases is to meet the growing demand for management information and analysis as the operational systems were unable to meet this need for a range of reasons: The processing load of reporting reduced the response time of the operational systems The database designs of operational systems were not optimized to prove information analysis and reporting Generally, organizations had more than one operational system and so organizational-wide reporting was not possible from a single system Report generation through operational systems often required specific computer programs which was time-consuming and expensive As a result, separate computer databases began to be built that were specifically designed to support management information and analysis purposes. These data warehouses were able to bring in data from a range of different data sources (such as mainframe computers, minicomputers, personal computers, etc.) and integrate this information in a single place. This capacity, coupled with user-friendly reporting tools and freedom from operational impacts, has led to a growth of data warehouses. As technology Sikkim Manipal University Page No. 27 Business Intelligence and Tools Unit 2 improved (lower cost for more performance) and user requirements increased (faster data load cycle times and more features), data warehouses have evolved through several fundamental stages: Offline Operational Databases: During this stage, data warehouses are developed by simply copying the database of an operational system to an off-line server where the processing load of reporting does not impact on the operational system's performance. Offline Data Warehouse: Data warehouses in this stage are updated on a regular basis (usually daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting- oriented data structure. Real Time Data Warehouse: Data warehouses at this stage are updated on a transaction or event basis. The data is updated every time an operational system performs a transaction (e.g. an order or a delivery or a booking, etc.) Integrated Data Warehouse: Data warehouses at this stage are used to generate activity or transactions that are passed back into the operational systems for use in the daily activity of the organization. 2.2.2 Need of a Data Warehouse Typically, the data in an organization is available in different formats, exist on various platforms, and reside in many different file and database structures developed by different vendors. Thus organizations have had to develop perhaps hundreds of programs to extract, prepare, and consolidate data for use, analysis and reporting. Also, decision makers often want to dig deeper into the data once initial findings are made and this would require development of new programs or modification of the current programs. Thus the process is becoming costly, inefficient, and time-consuming. Also, the Sikkim Manipal University Page No. 28 Business Intelligence and Tools Unit 2 volume of data in an organization is normally very large, particularly when one considers the requirements for historical data analysis. Thus the data analysis programs are required to scan vast amounts of that data, which may result in a negative impact on operational applications. As operational applications are more performance sensitive, there is a need to separate the two environments to minimize conflicts and degradation of performance in the operational environment. In such a scenario, the data warehousing concept has evolved out of the need for easy access to a structured store and retrieval of quality data that can be effectively used for decision making. Data warehousing implements the process to access different types of data sources (called heterogeneous data sources), clean, filter, and transform the data; and store the data in a structure that is easy to access, understand, and use. The data is then used for query, reporting, and data analysis. As such, the access, use, technology, and performance requirements in a data warehousing environment are quite different from those in a transaction-oriented operational environment. 2.2.3 Characteristics of a Data Warehouse According to Bill Inmon, who is considered to be the Father of Data warehousing, the data in a Data Warehouse consists of the following characteristics: 2.2.3.1 Subject oriented The first feature of DW is its orientation toward the major subjects of the organization instead of applications. The subjects are categorized in such a way that the subject-wise collection of information helps in decision-making. For example, the data in the data warehouse of an insurance company can be organized as customer ID, customer name, premium, payment period, etc. rather auto insurance, life insurance, fire insurance, etc. Sikkim Manipal University Page No. 29 Business Intelligence and Tools Unit 2 2.2.3.2 Integrated The data contained within the boundaries of the warehouse are integrated. This means that all inconsistencies regarding naming convention and value representations need to be removed in a data warehouse. For example, one of the applications of an organization might code gender as ‘m’ and ‘f’ and the other application might code the same functionality as ‘0’ and ‘1’. When the data is moved from the operational environment to the data warehouse environment, this will result in conflict. 2.2.3.3 Time variant The data stored in a data warehouse is not the current data. The data is a time series data as the data warehouse is a place where the data is accumulated periodically. This is in contrast to the data in an operational system where the data in the databases are accurate as of the moment of access. 2.3.3.4 Non-volatility of the data The data in the data warehouse is non-volatile which means the data is stored in a read-only format and it does not change over a period of time. This is the reason the data in a data warehouse forms as a single source for all decision system support processing. Keeping the above characteristics in view, ‘data warehouse’ can be defined as a subject-oriented, integrated, non-volatile, time-variant collection of data designed to support the decision-making requirements of an organization. 2.2.4 Types of Data Warehouses The Data Warehouses can be divided into two types: Enterprise Data Warehouse Data Mart Sikkim Manipal University Page No. 30 Business Intelligence and Tools Unit 2 2.2.4.1 Enterprise Data Warehouse The Enterprise data warehouse consists of the data drawn from multiple operational systems of an organization. This data warehouse supports timeseries and trend analysis across different business areas of an organization and so can be used for strategic decision-making. Also, this data warehouse is used to populate various data marts. 2.2.4.2 Data Mart As data warehouses contain larger amounts of data, organizations often create ‘data marts’ that are precise, specific to a department or product line. Thus data mart is a physical and logical subset of an Enterprise data warehouse and is also termed as a department-specific data warehouse. Generally, data marts are organized around a single business process. There are two types of data marts; independent and dependant. The data is fed directly from the legacy systems in case of an independent data mart and the data is fed from the enterprise data warehouse in case of a dependent data mart. In the long run, the dependent data marts are much more stable architecturally than the independent data marts. 2.2.5 Advantages and Limitations of a DW System Use of a data warehouse brings in the following advantages for an organization: End-users can access a wide variety of data. Management can obtain various kinds of trends and patterns of data. A warehouse provides competitive advantage to the company by providing the data and timely information. A warehouse acts as a significant enabler of commercial business applications viz., Customer Relationship Management (CRM) applications. Sikkim Manipal University Page No. 31 Business Intelligence and Tools Unit 2 However, following are the concerns that one has to keep in mind while using a data warehouse: The scope of a Data warehousing project is to be managed carefully to attain the defined content and value. The process of extracting, cleaning and loading the data and finally storing it into a data warehouse is a time-consuming process. The problems of compatibility with the existing systems need to be resolved before building a data warehouse. Security of the data may become a serious issue, especially if the warehouse is web accessible. Building and maintenance of the data warehouse can be handled only through skilled resources and requires huge investment. Self Assessment Questions For Section 2.2.3 1. Discuss the fundamental characteristics of a Data warehouse? For Section 2.2.5 1. Examine the advantages and limitations of building and using a data warehouse in an organization? 2.3 Data Warehouse Concepts and Terminology Various concepts and the key terms used in the study of data warehouse are provided below. Dashboard: This is a reporting tool that consolidates aggregates and arranges measurements, metrics (measurements compared to a goal) on a single screen so that information can be monitored at a glance. Sikkim Manipal University Page No. 32 Business Intelligence and Tools Unit 2 Data Management: This is the process of controlling, protecting, and facilitating access to data in order to provide the end users with timely access to the data they need. Data Mining (or Data Surfing): This is a technique geared for the typical user who does not know exactly what he is searching for, but is looking for particular patterns or trends. Data mining is the process of sifting through large amounts of data to produce data content relationships. It can predict future trends and behaviors, allowing businesses to make proactive, knowledgedriven decisions. The most valuable results from data mining include clustering, classifying, and estimating the things that occur together. There are many kinds of tools that play a role in data mining and they include neural networks, decision trees, visualization, general algorithms, fuzzy logic, etc. Data Modeling: A method used to define and analyze data requirements needed to support the business functions of an organization. Data Profiling: Data Profiling is a critical step in data migration that automates the identification of problematic data and metadata, and enables organizations to correct inconsistencies, redundancies and inaccuracies in their databases. Data Visualization: Data visualization involves examining the data represented by dynamic images rather than pure numbers. These are the techniques that turn the data into information by using the high capacity of the human brain to visually recognize patterns and trends. Sikkim Manipal University Page No. 33 Business Intelligence and Tools Unit 2 Decentralized Warehouse: A remote data source that users can query/access via a central gateway that provides a logical view of corporate data in terms that users can understand. The gateway parses and distributes queries in real time to remote data sources and returns result sets back to users. Drill-down: This is the capacity to browse the information through a hierarchical structure as shown below. Sales in the Country Sales in West Region Sales in East Region Sales in Central Region Sales in Area 1 Sales in Area 2 Sales in Area 3 Sales in City X External Data Source: This is the data that is not available in the OLTP systems, but is required to enhance the information quality in the data warehouse. The examples of this data include the data of the competitors, information of the regulatory and government bodies, research data of the professional bodies and universities. Metadata: Metadata is data about data. The examples of metadata include data element descriptions, data type descriptions, attribute descriptions, and process descriptions. Sikkim Manipal University Page No. 34 Business Intelligence and Tools Unit 2 On-Line Analytical Processing (OLAP): This is a category of software technology that enables the users gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the organization. This is implemented in a multi-user client/server mode and offers consistently rapid response to queries, regardless of database size and complexity. This software is also called Multidimensional Analysis Software. On-Line Transaction Processing (OLTP): This is the way the data is processed by an end user/a computer system. Here, the data is detail oriented, highly repetitive with larger amounts of updates and changes. The major task of these systems is to perform on-line transaction and query processing. These systems cover most of the day-to-day operations of the organization, such as purchasing, inventory, manufacturing, payroll, banking, accounting and registration. Operational Databases: These are detail oriented databases defined to meet the needs of complex processes of an organization. Here, the data is highly normalized to avoid data redundancy and double-maintenance. A large number of transactions take place every hour on these databases and are always "up to date" and represent a snapshot of the current situation. Contrast to these databases, there are Informational databases that are stable over a period of time to represent a situation at a specific point in time in the past. 2.4 Architecture of a Data Warehouse The architecture describes the overall system of a Data Warehouse from various perspectives such as data, process, and infrastructure to study the inter-relationships among various components. Sikkim Manipal University Page No. 35 Business Intelligence and Tools Unit 2 The data perspective includes the source and target data structures and so it aids the user in understanding what data assets are available in a data warehouse and how they are related. The process perspective is primarily concerned with communicating the process and flow of data from the originating source system through the process of loading the data warehouse and extracting data from the warehouse. The infrastructure or technology perspective details the various hardware and software products used to implement the distinct components of the overall system. Depending upon the specifics of an organizational situation, the following types of Data Warehouse architectures are provided below: Basic architecture of a Data warehouse Architecture of a Data warehouse with Staging area Architecture of a Data warehouse with Staging area and Data marts Fig 2.1 shows a simple architecture of a data warehouse wherein the end users directly access the data derived from several source systems through the data warehouse. Analysis Operation al System Meta data Reporting Operation al System Summary Data Raw data Mining Users Flat Data Warehouse Data Sources Fig 2.1: Architecture of a Data Warehouse (Basic) Sikkim Manipal University Page No. 36 Business Intelligence and Tools Unit 2 Fig. 2.2 depicts the architecture of a data warehouse with the staging area, a place where the data is processed before entering the warehouse. This staging area is used to clean and process the operational data before putting it into the warehouse. Data Data Warehouse Users Analysis Operation al System Meta data Data Staging Area Operation al System Reporting Summary Data Raw data Mining Flat files Fig. 2.2: Architecture of a Data Warehouse with Staging area Fig. 2.3 represents the data warehouse with a staging area and data marts. This is useful when you want to customize your warehouse's architecture for different groups within your organization. The data marts are designed for a specific line of business, say, purchasing, sales, and inventories. So, a financial analyst might want to analyze historical data for purchases and sales. Sikkim Manipal University Page No. 37 Business Intelligence and Tools Data Unit 2 Data Marts Data Purchasing Operational System Data Staging Area User Analy sis Meta data Reporting Sales Operational System Summary Data Raw data Mining Inventory Flat files Fig 2.3: Data Warehouse Architecture with Staging 2.5 Data Warehouse Components The components in a data warehouse are detailed below. Extraction Refining Refining Data Source 1 External Data Source Transformation/ Cleansing Staging Area Data Source 2 Loading Data Warehouse Data Mart 1 Data Mart 2 Reports Data Mart 3 Fig. 2.4: Components of a Data Warehouse Sikkim Manipal University Page No. 38 Business Intelligence and Tools Unit 2 2.5.1 Data sources Data sources can be operational databases, historical data (usually archived on tapes), external data (for example, data sources from market research companies, the Internet), or information from the already existing data in the data warehouse environment. The data sources can be relational databases from the line of business applications. They also can reside on many different platforms and can contain structured information, such as tables or spreadsheets, or unstructured information, such as plain text files or pictures and other multimedia information. 2.5.2 Extraction/Propagation Data extraction/data propagation is the process of collecting data from various sources to move it into the data warehouse. This is a selective process to import decision-relevant information into the data warehouse. Depending on the technique, this process can be either pulling (extraction) or pushing (propagation). 2.5.3 Transformation/Cleansing Transformation of data usually involves code resolution with mapping tables (for example, changing ‘0’ to female and ‘1’ to male in the gender field in the entire code) and the resolution of hidden business rules in data fields. In the early stages of the process, the transformations are used to consolidate the data from different sources, whereas, the data is transformed to suit a specific analysis problem and/or tool in the later stages. The cleansing ensures that the data warehouse will have valid, useful, and meaningful information and so can also be described as standardization of data. 2.5.4 Data Loading Data loading is the process wherein the transformed data in the staging area is loaded into the data warehouse. Thus this is considered as a Sikkim Manipal University Page No. 39 Business Intelligence and Tools Unit 2 process of populating a data warehouse. This process is accomplished by utilities, user-written programs, or specialized software from independent vendors. 2.5.5 Data Refining Data refining is the process of creating subsets of the enterprise data warehouse, which have either a multidimensional or a relational organization format for optimized OLAP performance. This layer in the data warehouse architecture increases the query performance and minimizes the amount of data that is transmitted over the network to the end user query or analysis tool. 2.5.6 Presentation and Analysis Tools The presentation layer is the most important component in the data warehouse architecture from the end user’s perspective. Here, different user-types need different front-end tools and this is done based on their information requirements. Self Assessment Questions For Section 2.5 1. What process does the data in an operational data source (ODS) undergo before it finally gets stored in a data warehouse? 2.6 The Cost of a Data Warehouse Before going for building and deploying a Data warehouse, you need to understand what costs the organization incurs because of this activity. Primarily, the costs that the organization incurs in this regard can be categorized into initial costs (these can be further divided into hardware and software costs) Sikkim Manipal University Page No. 40 Business Intelligence and Tools Unit 2 ongoing costs In addition, the costs can be categorized into two areas; Capital costs (associated with acquisition of a data warehouse), and Operational costs (associated with running and maintaining the data warehouse). Initial costs Hardware Disk DBMS CPU Middleware Network Log utility Capital costs Terminal analysis Meta data Ongoing costs Software Infrastructure Hardware maintenance Software maintenance Terminal analysis Middleware Integration/transformation processing specification Ongoing refreshment Meta data infrastructure population Operatio nal costs Data model definition, Database design definition Integration transformation Data model maintenance Data aging within the Data System of record definition Data dictionary language definition Network transfer definition CASE/Repository interface Initial data warehouse population Archival of data Record identification maintenance Meta data infrastructure maintenance Table 2.1: Costs involved in building a Data warehouse Table 2.1 provides the details of the costs that an organization would incur if it opts for setting up of a data warehouse environment. But the right way to address the cost justification is to say that the data warehouse dramatically reduces the cost of obtaining the key information into the hands of the endusers in time. Sikkim Manipal University Page No. 41 Business Intelligence and Tools Unit 2 2.7 Summary A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. Typically, a data warehouse contains historical data derived from transaction data including the data from various other data sources of an organization. The data in a data warehouse has the following characteristics; subject-orientation, integration, nonvolatility, and time-variance in order to support the decision-making requirements of an organization. In addition to a relational database, a data warehouse environment includes an extraction, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, and other applications to manage the process of gathering and delivering the required information to the users. The architecture describes the overall system of a Data Warehouse from various perspectives such as data, process, and infrastructure to study the inter-relationships among various components. However, you need to understand what costs the organization would incur before going for a Data warehouse. 2.8 Terminal Questions (TQs) 1. What according to you, is the capacity a data warehouse should have to give the best possible performance? 2. Discuss the characteristics of a Data warehouse and analyze how the development of a Data warehouse helps you in managing various functions in your organization? 3. Define a ‘Data warehouse’ and also an ‘Operational Data Store’? Discuss how a data warehouse is different from an Operational data store? Sikkim Manipal University Page No. 42 Business Intelligence and Tools Unit 2 2.9 Multiple Choice Questions (MCQs) 1. Which of the following is not a characteristic of a Data Warehouse? a. The data in a warehouse is non-volatile. b. The data in a warehouse is segregated according to subjects c. The data stored in a warehouse is the current data d. The data in a warehouse does not have any inconsistencies regarding naming convention, value representations. 2. Which of the following systems are in use by various organizations to perform online transactions? a. OLAP systems b. OLTP systems c. Data warehouse systems d. All the above 3. Which of the following describes the data about the data? a. Informational database b. Operational database c. Data Mart d. Meta Data 4. Which of the following techniques are in use for establishing the business relationships in a data? a. Data staging b. Data profiling c. Data modeling d. Data mining Sikkim Manipal University Page No. 43 Business Intelligence and Tools Unit 2 5. Which of the following is not true with regard to an External Data Source? a. the data in the external data sources is also to be considered while building a data warehouse b. the data in the external data sources is also available in the OLTP systems c. the examples of this data in the external data sources include the data of the competitors, information of the regulatory and government bodies d. None of the above 6. The study of the architecture of a Data warehouse enables its reader to gain__________. a. Data perspective b. Infrastructure perspective c. Technology perspective d. All the above 7. Which of the following statements is false about data warehousing? a. A data warehouse provides high quality data to its end-users b. A data warehouse is a source of code of all applications being used in an organization c. Operational systems does not get impacted by data warehouse queries d. The data in a data warehouse forms as a single source for all decision system support processing. 8. In general, the data pertaining to a specific department or a product line is stored in a ___. a. Data Mart b. Meta Data c. Staging area d. None of the above Sikkim Manipal University Page No. 44 Business Intelligence and Tools Unit 2 9. Which of the following statements is false with regard to a data warehouse? a. the data in a data warehouse is dynamic b. a data warehouse is a copy of the transaction data c. the data warehouse is set up to be the central repository of the entire organizational data that is useful for exploration and reporting d. linking data from within and outside the data warehouse mainly leads to the biggest gains in terms of advantages 10. Dashboard is a type of _____________? a. data modeling tool b. data profiling tool c. reporting tool d. data mining tool 2.10 Answers to SAQs, TQs, and MCQs 2.10.1 Answers to Self Assessment Questions (SAQs) Section 2.2.3 1. The fundamental characteristics of a data warehouse are: Subject oriented: The data is categorized according to subjects so that the subject-wise collection of the information helps in decisionmaking. Integrated: The data needs to be integrated which means that all inconsistencies regarding naming convention and value representations are removed. Time variant: The data stored in a data warehouse is not the current data. The data is a time series data as the data warehouse is a place where the data is accumulated periodically. Sikkim Manipal University Page No. 45 Business Intelligence and Tools Unit 2 Non-volatility of the data: The data is non-volatile which means the data is stored in a read-only format and it does not change over a period of time. Section 2.2.5 1. Following are the advantages of using a data warehouse: wide variety of data access to the end-users. enables the managers to observe the trends and patterns of data. competitive advantage to the company through providing the timely information. acts as a significant enabler of commercial business applications like CRM applications. The limitations of a data warehouse are as follows: The process of extracting, cleaning and loading the data is a timeconsuming process. The problems of compatibility within the existing systems need to be resolved before building a warehouse. Building and maintenance can be handled only by skilled resources. Section 2.5 1. Before getting stored in a data warehouse the data in an operational data stores undergoes the following processes: Extraction/Propagation: This is the process of collecting data from various sources to move it into the data warehouse. This is a selective process to import decision-relevant information into the data warehouse. Depending on the technique, this process can be either pulling (extraction) or pushing (propagation). Transformation/Cleansing: This is the process wherein the data in cleansed in the staging area of the data warehouse system. This process involves code resolution with mapping tables. The process Sikkim Manipal University Page No. 46 Business Intelligence and Tools Unit 2 consolidates the data from different sources and transforms it to suit to a specific analysis problem. Data Loading: This is the process wherein the transformed data in the staging area is loaded into the data warehouse. This is a process of populating a data warehouse and is accomplished by utilities, user-written programs, or specialized software. 2.10.2 Answers to Terminal Questions (TQs) 1. Following are the important capabilities a data warehouse should have to give the best possible performance: Speedy access and retrieval of the data from warehouse Maintenance of consistency in the data Ability of the users to slice and dice the data and publish accordingly Use of easy to operate browsing tools to browse the data warehouse High-level quality data to enable business reengineering 2. Building of a Data warehouse helps managers in making timely decisions in various functional areas as provided below. Marketing: To determine real-time product sales in order to make strategic pricing and distribution decisions To analyze the history of products to conclude the success or a failure of a product’s attributes To determine the successful products and evaluate the key success factors To understand the revenue impact for a specific decision item To identify the right customer segments based on the past records To understand the performance of an individual sales person Sikkim Manipal University Page No. 47 Business Intelligence and Tools Unit 2 Finance: To compare the budget allocations and actual cost for a specific area on weekly, monthly, or annual basis To prepare the future estimates of profitability To review the past cash flow trends and forecast the same for future periods To monitor a set of key financial indicators and ratios. Human Resources: To evaluate the trends in a specific employee benefit program To monitor the performance of an individual or a set of individuals To calculate the Return on Investment for specific resources To review the compliance levels for regulated activities 3. A data warehouse is the repository of an organization's historical data that is used for decision making. Whereas, the operational data store is a place where in a large number of transactions take place every hour to provide the up-to-date snapshot. The distinguishable characteristics between a data warehouse and an operational data store are detailed below. Characteristic Data warehouse Operational data store Nature of building Multiple subjects at a time One subject area at a time Focus of design Higher flexibility with reasonable performance Limited flexibility, but high performance Area of support For managerial activities and to formulate strategies For day-to-day business operations Volume of data Very large to a typical day transaction volume A little larger to a typical day transaction volume Updation of the data At regular intervals of time Up-to-the second and Real time Retrieval of data Scans large data sets to retrieve results for single or multiple queries Small number of records are retrieved via a single query Sikkim Manipal University Page No. 48 Business Intelligence and Tools Unit 2 2.10.3 Answers to Multiple Choice Questions (MCQs) 1. Ans: c 2. Ans: b 3. Ans: d 4. Ans: d 5. Ans: b 6. Ans: d 7. Ans: b 8. Ans: a 9. Ans: a 10. Ans: c Sikkim Manipal University Page No. 49 ...
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