[02/Architecture/01]

<p>Queries against relational OLAP cubes</p> Signup and view all the answers

<p>Data Complexity</p> Signup and view all the answers

<p>Iterative development process</p> Signup and view all the answers

<p>They increase</p> Signup and view all the answers

<p>Increasing data volumes</p> Signup and view all the answers

<p>Relational data</p> Signup and view all the answers

<p>To maintain the functionality of the individual information marts</p> Signup and view all the answers

<p>The implementation effort for each information mart</p> Signup and view all the answers

<p>Velocity of data</p> Signup and view all the answers

<p>Metrics Vault</p> Signup and view all the answers

<p>To provide and present information</p> Signup and view all the answers

<p>Business Vault</p> Signup and view all the answers

<p>A staging area, a data warehouse layer, and an information delivery layer</p> Signup and view all the answers

<p>The category of the aircraft</p> Signup and view all the answers

<p>To indicate the category of the aircraft</p> Signup and view all the answers

<p>Remove the category completely</p> Signup and view all the answers

<p>In the satellite table</p> Signup and view all the answers

<p>Hard business rules</p> Signup and view all the answers

<p>When the data is loaded into the staging area</p> Signup and view all the answers

<p>Hard business rules only affect the enforcement of data types</p> Signup and view all the answers

<p>They can break the loading process if the data violates the rule</p> Signup and view all the answers

<p>Both historic and modern aircraft analysis</p> Signup and view all the answers

<p>No need to adapt ETL jobs</p> Signup and view all the answers

<p>They remain unchanged</p> Signup and view all the answers

<p>Information mart</p> Signup and view all the answers

<p>The new data is a modified copy of the 'ancient' ETL routine</p> Signup and view all the answers

<p>ETL jobs</p> Signup and view all the answers

<p>An iterative development process</p> Signup and view all the answers

<p>They increase</p> Signup and view all the answers

<p>The maintenance nightmare of implementing multiple information marts</p> Signup and view all the answers

<p>To ensure the new functionality does not break the existing functionality</p> Signup and view all the answers

<p>Metrics Vault</p> Signup and view all the answers

<p>To provide and present aggregated, summarized and consolidated data</p> Signup and view all the answers

<p>Behavioral analysis</p> Signup and view all the answers

<p>Data growth</p> Signup and view all the answers

<p>Remove the category completely except for historic aircrafts</p> Signup and view all the answers

<p>'Unknown category'</p> Signup and view all the answers

<p>No default value</p> Signup and view all the answers

<p>The structure of the satellite</p> Signup and view all the answers

<p>Veracity of data</p> Signup and view all the answers

<p>Volume of data</p> Signup and view all the answers

<p>Hard rules are enforceable and have strict compliance requirements, while soft rules are more flexible and can be violated under certain circumstances</p> Signup and view all the answers

<p>Sensor and machine generated data</p> Signup and view all the answers

<p>Data governance lineage traceability</p> Signup and view all the answers

<p>Click-stream analysis</p> Signup and view all the answers

<p>Parallelization of loads</p> Signup and view all the answers

<p>Availability of data sources</p> Signup and view all the answers

<p>The information mart needs to be changed</p> Signup and view all the answers

<p>The ETL jobs are unchanged</p> Signup and view all the answers

<p>Allows for easy adaptation to fit new categorizations</p> Signup and view all the answers

<p>Information mart</p> Signup and view all the answers

<p>Allows for easy adaptation of ETL jobs</p> Signup and view all the answers

<p>They remain unchanged</p> Signup and view all the answers

<p>Hard business rules modify the incoming data to fit the requirements of the business, while soft business rules enforce the business requirements stated by the business user.</p> Signup and view all the answers

<p>When loading the data into the staging area tables.</p> Signup and view all the answers

<p>Because if the data violates the rule and this case has not been accounted for, the ETL routine will stop and break the loading process.</p> Signup and view all the answers

<p>Hard business rules only change the data or the meaning of the data, while soft business rules align the data domains and enforce data type matching.</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>Variety of data = Refers to the different types of data that an enterprise captures, including relational or mainframe data, semi-structured data, unstructured data, sensor and machine generated data Volume of data = Refers to the rate at which companies generate and accumulate new data, leading to much larger data sets Velocity of data = Refers to the rate at which the data is created, which is increasing rapidly Workload = Refers to the amount of work that is put on typical data warehouse environments, which is increasing more and more</p> Signup and view all the answers

<p>Semi-structured data = Includes examples like emails, e-forms or HTML and XML files Unstructured data = Includes examples like document collections, social network data, images, video and sound files Relational or mainframe data = Traditional types of data that an enterprise captures Sensor and machine generated data = Data generated by sensors and machines, which might require specific handling</p> Signup and view all the answers

<p>Variety of data = Sensor and machine generated data Volume of data = Weblog data and machine generated data Velocity of data = Financial data from financial markets Workload = Rapidly increasing data volumes and application workloads</p> Signup and view all the answers

<p>Variety of data = Refers to the different types of data that an enterprise captures Volume of data = Refers to the rate at which companies generate and accumulate new data Velocity of data = Refers to the rate at which the data is created Workload = Refers to the amount of work that is put on typical data warehouse environments</p> Signup and view all the answers

<p>Data warehouse = A system used for reporting and data analysis Iterative development process = Functionality of the data warehouse is designed, developed, implemented and deployed in iterations Big-bang approach = All functionality is developed in one large process and finally deployed as a whole Information mart = A subset of the data warehouse that is oriented to a specific business line or team</p> Signup and view all the answers

<p>The development team has to maintain the existing solution and take care of existing dependencies when implementing the second information mart = Iterative development process All functionality is developed in one large process and finally deployed as a whole = Big-bang approach The effort to implement the first information mart is relatively low = Iterative development process The old functionality has to be retested to make sure it doesn't break when deploying the new functionality for the second information mart = Iterative development process</p> Signup and view all the answers

<p>Data sources = Systems or applications that provide data to be stored in a data warehouse Operational systems = Systems that are used to run the day-to-day operations of an organization Maintenance nightmare = The effort and costs tied to add another functionality usually increases because of existing dependencies Refactoring = Process of restructuring existing computer code without changing its external behavior</p> Signup and view all the answers

<p>The functionality of the data warehouse, as requested by the business users, is designed, developed, implemented and deployed in iterations = Iterative development process All functionality is developed in one large process and finally deployed as a whole = Big-bang approach In each iteration, more functionality is added to the data warehouse = Iterative development process This is opposite to a “big-bang” approach where all functionality is developed in one large process = Iterative development process</p> Signup and view all the answers

<p>Staging area = Not storing historical information and not applying any changes to the data except ensuring the expected data type Data warehouse layer = Modeled after the Data Vault modeling technique Information mart layers = Depend on the data warehouse layer Metrics Vault = Used to capture and record runtime information</p> Signup and view all the answers

<p>Business Vault = Used to store information where the business rules have been applied Operational Vault = Stores data feed into the data warehouse from operational systems Metrics Vault = Used to capture and record runtime information Data Vault = Integrated into the data warehouse layer</p> Signup and view all the answers

<p>Data Vault 2.0 = A three-layer data warehouse architecture that addresses extensibility and dimensions of scalability Information mart = A term preferred over data mart in the BI community, used to provide and present aggregated, summarized, and consolidated data Staging area = A component that collects the raw data from the source systems NoSQL = A type of database system that can be integrated into the Data Vault 2.0 architecture</p> Signup and view all the answers

<p>Batch loading = A loading method supported by the Data Vault 2.0 architecture for source systems Real-time loading = A loading method supported by the Data Vault 2.0 architecture from the enterprise service bus or any other service-oriented architecture Platform independence = A characteristic of Data Vault 2.0 that allows for integration of NoSQL database systems Hybrid solution = An approach where a NoSQL database is integrated with the Data Vault layer via a hashed business key</p> Signup and view all the answers

<p>Prefix = Indicates the country where the aircraft is registered Second letter (before 1948) = Indicated the category of the aircraft Second letter (after 1948) = No longer has any meaning Second character (German registration) = Indicates that the plane is a single-engine aircraft</p> Signup and view all the answers

<p>German aircraft = D US aircraft = N Unknown category = 1-9</p> Signup and view all the answers

<p>Stage area = Where the data is loaded from before being transferred to the normalized data warehouse Normalized data warehouse = Where the category would most probably be a column in the aircraft table Information mart = Built on top of the data warehouse layer and can be modified after changing the ETL jobs Data mart ETL routines = Modified after changing the information mart</p> Signup and view all the answers

<p>How do we deal with historic data in the normalized data warehouse? = A business decision Where do we keep the historic data for later analysis? = If required by the business at a later time Will there be multiple dimensions in the same information mart or multiple information marts for ancient and modern aircraft? = Depends on the analysis requirements What is the default value for the ancient category in modern aircraft? = Unknown category</p> Signup and view all the answers

<p>Hard business rules = Technical rules that align the data domains, such as data type matching Soft business rules = Enforce the business requirements stated by the business user, and can change the data or the meaning of the data Staging area = Where hard business rules are enforced when the data is extracted from the source systems and loaded into Data Warehouse layer = Where soft business rules are applied early in the loading process to fit the data into the required structures</p> Signup and view all the answers

<p>Hard business rules = Affect only the enforcement of data types, but don't convert any values to fit the analytical requirements of the business Soft business rules = Change the data or the meaning of the data, for example by modifying the grain or interpretation Hard business rules enforcement = Occurs when the data is extracted from the source systems and loaded into the staging area Soft business rules implementation = Occurs early in the loading process of the data warehouse, in order to fit the data into the required structures</p> Signup and view all the answers

<p>Modifying the incoming data to fit the requirements of the business = Soft business rules Truncation of source strings that are longer than defined in the stage table = Hard business rules Consolidation of data from multiple sources = Soft business rules Normalization of hierarchical Cobol copybooks from mainframe systems or XML structures = Hard business rules</p> Signup and view all the answers

<p>Hard business rules = Technical rules that align the data domains, such as data type matching Soft business rules = Enforce the business requirements stated by the business user, and can change the data or the meaning of the data Data Vault 2.0 = An architecture that distinguishes between hard and soft business rules Staging area = Where hard business rules are enforced when the data is extracted from the source systems and loaded into</p> Signup and view all the answers

<p>Veracity = Refers to the trustworthiness of data Analytical Complexity = Refers to the complexity of analytical tasks due to the availability of large volumes of data with high velocity and variety Query Complexity = Refers to the complexity of queries against the data warehouse, which can become complex and take a long time to complete Availability = Refers to the responsibility of the data warehouse team for the availability of the whole data warehouse, including the data marts, reports, OLAP cubes, and any other front-end that is used by the business users</p> Signup and view all the answers

<p>Customer Segmentation and Purchase Behavior = Historical analysis and reporting of customer demographics and purchase transactions Cross-sell Opportunities = Analyzing market baskets that show products that can be sold together Online Customer Behavior = Click-stream analysis Social Network Data = Analyzing product reviews, ratings, likes and dislikes, comments, customer service interactions, and so on</p> Signup and view all the answers

<p>Microsoft SQL Server = Optimized for data warehouse applications, applies heuristic methods to identify star schema query patterns, and uses advanced filter techniques to improve query performance Relational Databases = Provide simple data structures and high-level, set-oriented languages that make them ideal for data warehouse applications Data Warehouse = System where queries can become complex and take a long time to complete RDBMS = Selected for the storage and management of warehouse data, and its SQL language processors map SQL statements into parallel low-level operations to achieve improved query performance</p> Signup and view all the answers

<p>Hard Business Rules = Affect only the enforcement of data types and do not convert any values to fit the analytical requirements of the business Soft Business Rules = Change the data or the meaning of the data, such as modifying the grain or interpretation Data Vault 2.0 = Architecture that separates hard and soft business rules ETL Jobs = Jobs that need to be adapted when thinking about the advantage of separating hard and soft rules</p> Signup and view all the answers

<p>Hard business rules = Can cause problems for the ETL routines Soft business rules = Can be easily adapted to fit the new categorization ETL jobs = Load the historic data and remain unchanged Information mart = Needs to be changed when loading new data to the second satellite</p> Signup and view all the answers

<p>The FAA decided to stop using the second prefix in the US aircraft registration numbers after December 31, 1948. = True Slow response times from the data warehouse are acceptable for business analysts. = False The primary purpose of an enterprise data warehouse in the Data Vault 2.0 architecture is to provide and present information that is aggregated, summarized, and consolidated data put into context. = True Relational databases are not suitable for data warehouse applications. = False</p> Signup and view all the answers

<p>Enterprise data warehouse = A system that consists of small databases Data Vault 2.0 = An architecture that supports both batch loading and real-time loading Iteration = An approach where the effort and costs tied to adding another functionality usually decreases Staging area = A part of the architecture that stores historical information and applies changes to the data</p> Signup and view all the answers

<p>When the logic in the source system changes, the old satellite is closed and all new data is loaded into a new satellite with an updated structure. = True The effort (and the costs tied to it) to add another functionality usually increases when using an iterative development process for a data warehouse. = False The new data is loaded to another target and is therefore a modified copy of the 'ancient' ETL routine. = True The ETL jobs that load the historic data remain unchanged and are ready to load more historic data if required. = True</p> Signup and view all the answers

<p>Data volume = The main factor contributing to the increase in enterprise data warehouses Campaign accuracy and timeliness = A requirement for improving in retail marketing Query performance = Can become complex and take a long time to complete against a data warehouse Aircraft registration numbers = Can be updated with only a number between 1 and 9 on the second position</p> Signup and view all the answers

<p>The information mart and its loading routines need to be changed when loading new data to the second satellite. = False An iterative development process for a data warehouse means that all functionality is developed in one large process and finally deployed as a whole. = False The FAA stopped using the second prefix in aircraft registration numbers after December 31, 1948. = True The effort to implement the second information mart is relatively low compared to the first information mart. = True</p> Signup and view all the answers

<p>Hard business rules = Enforced when the data is extracted from the source systems and loaded into the staging area, affecting only the enforcement of data types but don't convert any values to fit the analytical requirements of the business Soft business rules = Enforce the business requirements that are stated by the business user, changing the data or the meaning of the data</p> Signup and view all the answers

<p>Hard business rules = Truncation of source strings that are longer than defined in the stage table Soft business rules = Aggregation of data, e.g. allocating the data into categories like income-band, age groups, customer segments, etc.</p> Signup and view all the answers

<p>Hard business rules = Align the data domains, so called data type matching, and affect only the way the data is stored Soft business rules = Change the data or the data meaning, and define how the data is aggregated or consolidated</p> Signup and view all the answers

<p>Hard business rules = Pose a risk to ETL routines because if the data violates the rule and this case has not been accounted for, the ETL routine will stop and break the loading process Soft business rules = Only change the data or the data meaning, and do not pose a risk to the loading process</p> Signup and view all the answers

<p>Customer Segmentation = Analyzing market baskets that show products that can be sold together Click-stream Analysis = Understanding the online behavior of customers Time-series Analysis = Analyzing the sheer size of the data warehouse and can take very long to complete Data Integration = Requires strong data governance lineage traceability and robust data integration</p> Signup and view all the answers

<p>SLA = Document that specifies the requirements of the business and is the basis for any availability planning Added Functionality = Can affect the availability of the data warehouse system Parallelization of Loads = One solution to ensure the availability of the system Computing Resources = Adding more of these can ensure the availability of the system</p> Signup and view all the answers

<p>Relational Databases = Provide simple data structures and high-level, set-oriented languages Microsoft SQL Server = Optimized for data warehouse applications, applies heuristic methods to improve query performance NoSQL = Can be integrated into the Data Vault 2.0 architecture, but not discussed in the book RDBMS = Natural choice for BI vendors for the storage and management of warehouse data</p> Signup and view all the answers

<p>Query Complexity = Some queries against the data warehouse can become complex and take very long to complete Performance = Slow response times from the data warehouse are not acceptable for business analysts SQL Optimizer = Uses heuristic methods to improve the query performance of data warehouse applications Equi-join Conditions on INNER Joins = Some features are only available when query statements follow these guidelines</p> Signup and view all the answers

<p>Variety = Refers to the different types of data that an organization captures, such as semi-structured, unstructured, and sensor data Volume = Refers to the amount of data that an organization generates and accumulates, which is increasing rapidly Velocity = Refers to the rate at which data is created, which is also increasing rapidly Workload = Refers to the amount of work that is put on typical data warehouse environments, which is increasing more and more</p> Signup and view all the answers

<p>Variety of data = Refers to the increasing amount of semi-structured, unstructured, and sensor data that organizations capture Volume of data = Refers to the increasing rate at which companies generate and accumulate new data Velocity of data = Refers to the rate at which the data is created, which is also increasing rapidly Workload of the system = Refers to the amount of work that is put on typical data warehouse environments, which is increasing more and more</p> Signup and view all the answers

<p>Semi-structured data = Emails, e-forms, HTML and XML files Unstructured data = Document collections, social network data, images, video and sound files Structured data = Relational or mainframe master or transactional data Machine generated data = Data generated by sensors or machines, which might require specific handling</p> Signup and view all the answers

<p>Data complexity = Refers to the increasing variety, volume, and velocity of data that organizations need to handle Workload = Refers to the amount of work that is put on typical data warehouse environments, which is increasing more and more Parallel hardware or parallel database software = Refers to the infrastructure required to handle the increasing workload Optimized design of the databases = Refers to the need to design the databases in a way that can handle the expected data volumes</p> Signup and view all the answers

<p>Staging area = A component that is not storing historical information and not applying any changes to the data except ensuring the expected data type Data Warehouse layer = Modeled after the Data Vault modeling technique Information mart layers = Depend on the data warehouse layer Optional Operational Vault = Stores data feed into the data warehouse from operational systems</p> Signup and view all the answers

<p>Data Vault 2.0 = A system of Business Intelligence that aims to assist in solving security, by providing direct integration points in the data model Metrics Vault = Used to capture and record runtime information Business Vault = Used to store information where the business rules have been applied Operational Vault = Stores data feed into the data warehouse from operational systems</p> Signup and view all the answers

<p>Hard business rules = Affect the enforcement of data types and convert values to fit the analytical requirements of the business Soft business rules = Do not convert any values, but change the structure of the data Hybrid business rules = Involve both data type enforcement and value conversion Optional business rules = Can be implemented or not, depending on the specific requirements of the organization</p> Signup and view all the answers

<p>Extensibility = The ability to add new data sources and new data structures without significant changes to the existing system Scalability = The ability to handle increasing amounts of data, users, and transactions without impacting performance Performance = The ability to process large volumes of data and complex queries efficiently Security = The ability to protect sensitive data from unauthorized access or modification</p> Signup and view all the answers

<p>Data warehouse = A system that makes it easy for analysts to access integrated data Iterative development process = The best approach for developing a data warehouse, where functionality is designed, developed, implemented and deployed in iterations Big-bang approach = An approach where all functionality is developed in one large process and finally deployed as a whole Information mart = A part of the data warehouse that is designed for a specific business function or area</p> Signup and view all the answers

<p>Implementing the first information mart = The effort is relatively low Implementing the second information mart = The development team has to maintain the existing solution and take care of existing dependencies Adding new sources to the overall solution = The existing solution needs to be refactored to maintain the functionality of the individual information marts Deploying the new functionality for the second information mart = The old functionality has to be retested to ensure it doesn't break</p> Signup and view all the answers

<p>Iteration = A process where more functionality is added to the data warehouse in each cycle Dependency = A relationship between two elements in a data warehouse, where a change in one element may require changes in other elements Functionality = The features and capabilities of a data warehouse that are designed, developed, and implemented to meet the requirements of the business users Maintenance nightmare = A situation where the effort and costs to add another functionality to a data warehouse usually increases due to existing dependencies that have to be taken care of</p> Signup and view all the answers

<p>Data sources = Integrated for the first information mart or operational systems consuming information from existing tables Existing solution = Has to be maintained and refactored to maintain the functionality of the individual information marts when new sources are added to the overall solution New functionality = Has to be deployed for the second information mart, while ensuring that the previously built functionality doesn't break First information mart = The effort to implement is relatively low compared to the second information mart</p> Signup and view all the answers

<p>Prefix = Indicates the country where the aircraft is registered Second character = Indicates the type of aircraft (e.g., single-engine) Second prefix (until December 31, 1948) = Indicated the category of the aircraft Second letter (after 1948) = Always a number between 1 and 9 with no meaning</p> Signup and view all the answers

<p>Logic in the source system changes, in this case, the format of the N-Number = Old satellite is closed and all new data is loaded into a new satellite with an updated structure New aircraft with a category other than the unknown = Introduce a new category 'Unknown category' Today's aircrafts are categorized by the operation code, air worthiness class, and other categories at the same time = Replace the category by multiple new categories Aircraft registration number has only a number between 1 and 9 on the second position = Update the business rule to remove the category completely</p> Signup and view all the answers

<p>Satellite = Table that contains descriptive data and is used to load the categorization of an aircraft Hard business rules = Affect the enforcement of data types and the conversion of values to fit the analytical requirements of the business Soft business rules = Change the data or the meaning of the data Base entities = Explained in detail in CHAPTER 4 - DATA VAULT 2.0 MODELING</p> Signup and view all the answers

<p>Germany = Prefix 'D' United States = Prefix 'N' Unknown category = Prefix 'N' followed by a number between 1 and 9 Experimental = Prefix 'N-X'</p> Signup and view all the answers

<p>Hard business rules = Pose a risk to ETL routines because if the data violates the rule and this case has not been accounted for, the ETL routine will stop and break the loading process Soft business rules = Implemented early in the loading process of a data warehouse in order to transform the data to meet the business requirements of the user ETL jobs that load historic data = Remain unchanged and are ready to load more historic data if required ETL routine for new data = Nothing needs to be changed, except the information mart (and its loading routines)</p> Signup and view all the answers

<p>Enterprise data warehouse = Primary purpose is to integrate data from multiple sources and provide a single source of truth for business intelligence and analytics Staging area = Stores historical information and applies changes to the data NoSQL database systems = Can be integrated into the Data Vault 2.0 architecture for every data warehouse layer Iterative development process = Recommended approach for developing a data warehouse</p> Signup and view all the answers

<p>Enforcing data types = Hard business rules Conversion of values to fit analytical requirements = Soft business rules</p> Signup and view all the answers

<p>What happens to the effort and costs tied to adding another functionality in an iterative development process? = They increase What is the best approach for developing a data warehouse? = An iterative development process What is the purpose of an enterprise data warehouse in the Data Vault 2.0 architecture? = To integrate data from multiple sources and provide a single source of truth for business intelligence and analytics</p> Signup and view all the answers

<p>Hard business rules = Pose a risk to ETL routines because if the data violates the rule and this case has not been accounted for, the ETL routine will stop and break the loading process Soft business rules = Implemented early in the loading process of a data warehouse in order to transform the data to meet the business requirements of the user</p> Signup and view all the answers

<p>Iterative development process = The existing solution needs to be refactored to maintain the functionality of the individual information marts when new sources are added to the overall solution An iterative development process = All functionality is developed in one large process and finally deployed as a whole</p> Signup and view all the answers

<p>Metrics Vault = Used to capture and record runtime information, including the run history, process metrics, and technical metrics Business Vault = An intermediate layer between the Raw Data Vault and the information marts, and eases the creation of the end-user structures Operational Vault = An extension to the Data Vault that is directly accessed by operational systems Data Warehouse = Provides and presents information that is aggregated, summarized, and consolidated data put into context</p> Signup and view all the answers

<p>Metrics Vault = Performance metrics information Business Vault = Business-rule changed data Operational Vault = Real-time data from a service-oriented architecture or enterprise service bus Data Warehouse = Aggregated, summarized, and consolidated data put into context</p> Signup and view all the answers

<p>Metrics Vault = Used to capture and record runtime information Business Vault = Sparsely modeled data warehouse based on Data Vault design principles Operational Vault = Directly accessed by operational systems Data Warehouse = Provides and presents information that is aggregated, summarized, and consolidated data put into context</p> Signup and view all the answers

<p>Metrics Vault = Used to capture and record runtime information, including the run history, process metrics, and technical metrics Business Vault = An intermediate layer between the Raw Data Vault and the information marts, and eases the creation of the end-user structures Operational Vault = An extension that is directly accessed by operational systems Data Warehouse = Provides and presents information that is aggregated, summarized, and consolidated data put into context</p> Signup and view all the answers

<p>Metrics Vault = Captures and records runtime information Business Vault = An intermediate layer that eases the creation of the end-user structures Operational Vault = Directly accessed by operational systems Data Warehouse = Provides and presents information that is aggregated, summarized, and consolidated data put into context</p> Signup and view all the answers

<p>Metrics Vault = Captures and records runtime information Business Vault = An intermediate layer between the Raw Data Vault and the information marts Operational Vault = An extension that is directly accessed by operational systems Data Warehouse = Provides and presents information that is aggregated, summarized, and consolidated data put into context</p> Signup and view all the answers

<p>Staging Area Layer = Used to load batch data into the data warehouse, does not contain historical data, and allows the execution of SQL statements against the source data Operational Systems = Source systems from which data is extracted into the staging area Data Warehouse Layer = Contains the historical data and is the primary purpose of the enterprise data warehouse Source System = The system from which data is extracted into the staging area, can be flat files or databases</p> Signup and view all the answers

<p>Extract data from source system = The primary purpose of the staging area is to extract the source data as fast as possible from the source system in order to reduce the workload on the operational systems Execute SQL statements against the source data = The staging area allows the execution of SQL statements against the source data which might not be the case with direct access to flat files No historical data = The staging area does not contain historical data, instead only the batch that has to be loaded next into the data warehouse layer is present Avoid dealing with changing data structures = The primary purpose of having no history in the staging area is to not having to deal with changing data structures</p> Signup and view all the answers

<p>No error in data loading = If there are no errors in the data loading process, there will be only one batch in the staging area Error on the weekend = If an error happened on the weekend and the data from the last couple of days has to be loaded into the data warehouse, there might be multiple batches in the staging area Change in source table over time = If the staging area would keep historic data, there would have to be logic in place for defining the loading procedures into the data warehouse, and this logic would become more and more complex over time Quick adaption to changes = The goal of the Data Vault 2.0 architecture is to move complex business rules towards the end-user in order to ensure quick adaption to changes, and the staging area is part of this approach</p> Signup and view all the answers

<p>Tables = The staging area consists of tables that duplicate the structures of the source system Columns = The staging area includes all the tables and columns of the source, including the primary keys Source System Data = The staging area is used to extract the source data as fast as possible from the source system in order to reduce the workload on the operational systems Batch to be loaded next = Instead of historical data, only the batch that has to be loaded next into the data warehouse layer is present in the staging area</p> Signup and view all the answers

<p>The staging area is used to load real-time data into the data warehouse = False The staging area does not contain historical data = True The staging area allows the execution of SQL statements against the source data = True The staging area consists of tables that duplicate the structures of the source system = True</p> Signup and view all the answers

<p>Historical Data = Data that is stored in the data warehouse layer and contains the complete history of the business Batch Data = Data that is present in the staging area and is the only data that does not contain historical information Real-time Data = Data that is not directly loaded into the data warehouse, but can be transformed and loaded into the staging area</p> Signup and view all the answers

<p>Staging area = Where the raw data from the source system, including bad data, is loaded Data Warehouse layer = Holds all historical, time-variant data, where the data is stored in the granularity as provided by the source systems Information Mart layer = Not directly accessed by end-users, provides the data in a way that the end-user feels most comfortable with Raw Data Vault layer = Often called the Raw Data Vault layer as it holds raw data, modeled using the Data Vault 2.0 model</p> Signup and view all the answers

<p>Hard business rules = Applied to the incoming data, they do not change the data or the meaning of the data Soft business rules = Change the data or the meaning of the data, such as modifying the grain or interpretation Business keys = Used to integrate data from multiple source systems, but also within a source system Hash key = Used for identification purposes in the data warehouse</p> Signup and view all the answers

<p>Staging area = Data is fed from here in batch loading, or in real-time loading, the data is fed directly from the enterprise service bus (ESB) into the data warehouse Data Warehouse layer = Holds raw data, not modified by any business rule other than hard business rules, and every change in the source system is tracked by the Data Vault structure Information Mart layer = Provides the data in a way that the end-user feels most comfortable with, and the information in it is subject oriented and can be in aggregated form, flat or wide, prepared for reporting Raw Data Vault layer = Holds raw data, modeled using the Data Vault 2.0 model</p> Signup and view all the answers

<p>Data Warehouse layer = It is not directly accessed by end-users, typically the end-user accesses only the information mart Hard business rules = They are the only business rules that are applied to the incoming data Soft business rules = They change the data or the meaning of the data, such as modifying the grain or interpretation Information Mart layer = The goal of the enterprise data warehouse is to provide valuable information to its end-users, so we use the term information instead of data for this layer</p> Signup and view all the answers

<p>Record source = Indicates the source system where the data record originates from Timestamp = The date and time when the record arrives in the data warehouse Sequence number = Identifies the order of the data in the source system Hash key = Used for identification purposes in the data warehouse</p> Signup and view all the answers

<p>Staging area = Includes meta-data information that is required for loading the data into the next layer, the Data Warehouse layer Data Warehouse layer = Purpose is to hold all historical, time-variant data, and the data is non-volatile and every change in the source system is tracked by the Data Vault structure Information Mart layer = Provides the data in a way that the end-user feels most comfortable with, and the information in it is subject oriented Raw Data Vault layer = Often called the Raw Data Vault layer as it holds raw data, modeled using the Data Vault 2.0 model</p> Signup and view all the answers

<p>Raw Data Vault = A layer in the Data Vault 2.0 architecture that stores the data in its original form Business Vault = A layer in the Data Vault 2.0 architecture that implements some of the most important business rules Power User = A user in the Data Vault 2.0 architecture who has direct access to both the Raw Data Vault and the Business Vault Data Warehouse = A component of the Data Vault 2.0 architecture that provides and presents information that is aggregated, summarized, and consolidated data put into context</p> Signup and view all the answers

<p>Integrating data by its business key = Data Integration Rule Consolidating and quality checking data = Data Consolidation Rule Joining consolidated data with raw data = Data Joining Rule Implementing important business rules = Business Rule Implementation</p> Signup and view all the answers

<p>Raw Data = Data in its original form, before any processing or transformation Consolidated Data = Data that has been combined from various sources and is ready for analysis Cleaned Data = Data that has been processed to remove errors or inconsistencies Integrated Data = Data that has been combined from different systems or sources</p> Signup and view all the answers

<p>Raw Data Vault = The stage where the data is loaded in its original form, including integration using the business keys Business Vault = The stage where some of the most important business rules are implemented Data Warehouse = The final stage where the data is aggregated, summarized, and consolidated Iteration = A stage where new data is sourced and integrated into the Raw Data Vault to provide it to the power user for a managed self-service BI task</p> Signup and view all the answers

<p>Satellite = A table in the Data Vault 2.0 architecture that contains the descriptive attributes of a business key Hub = A table in the Data Vault 2.0 architecture that contains the unique business keys Link = A table in the Data Vault 2.0 architecture that connects two or more Hubs Staging Area = A component of the Data Vault 2.0 architecture that stores historical information and applies changes to the data</p> Signup and view all the answers

<p>Data Integration Rule = A rule that governs the integration of data by its business key Data Consolidation Rule = A rule that governs the process of consolidating and quality checking data Data Joining Rule = A rule that allows the business user to join consolidated data with raw data from specific source systems Business Rule Implementation = A rule that is implemented in the Business Vault and is considered one of the most important</p> Signup and view all the answers

<p>Operational Vault = Extension to the Data Vault where interfacing applications read directly from existing Data Vault structures Managed Self-Service BI = Approach that allows end-users to completely circumvent IT due to unresponsiveness, but with many associated problems Data Vault 2.0 = Standard that allows experienced or advanced business users to perform their own data analysis tasks on the raw data of the data warehouse Business Vault = Structure created by IT to provide a consolidated view on parts of the model or pre-calculate KPIs to ensure consistency among such calculations</p> Signup and view all the answers

<p>Limited team resources in IT = Not all business requests can be met, leading to low responsiveness and discomfort among business users End-users sourcing data from operational systems = Potential exposure of raw data with privacy concerns and circumvention of security access Unintegrated raw data from multiple source systems = Tedious and error-prone task for business users if performed manually Non-standardized business rules = End-users have to implement all business rules that transform the raw data into meaningful information, with potential inconsistencies</p> Signup and view all the answers

<p>Raw Data Vault = IT sources the raw data from operational systems or other data sources and integrates it using the business key Local Information Marts = Business user creates these using specialized tools to transform the data into meaningful information Business Key = Used by IT to integrate the raw data in the Raw Data Vault Managed Self-Service BI = Part of the Data Vault 2.0 standard that allows power users to obtain the data they need quickly, in a usable quality</p> Signup and view all the answers

<p>Unintegrated raw data can become a tedious and error-prone task if performed manually = Correct In a self-service BI approach, business users are left on their own with the whole process of sourcing the data from operational systems = Correct In the Data Vault 2.0 architecture, the business user uses the raw data and the business data to create local information marts = Correct IT just cannot deliver the requested functionality in the given time frame = Correct</p> Signup and view all the answers

<p>Operational Vault = Extension to the Data Vault in which interfacing applications read directly from existing Data Vault structures Managed Self-Service BI = Approach that allows end-users to completely bypass IT due to its unresponsiveness Data Vault 2.0 = Standard that allows experienced or advanced business users to perform their own data analysis tasks on the raw data of the data warehouse Business Vault = Structure created by IT to provide a consolidated view on parts of the model or pre-calculate KPIs to ensure consistency among such calculations</p> Signup and view all the answers

<p>Operational Vault = An extension to the Data Vault where interfacing applications read directly from existing Data Vault structures Managed Self-Service BI = An approach that allows end-users to completely circumvent IT due to unresponsiveness Data Vault 2.0 = A standard that allows experienced or advanced business users to perform their own data analysis tasks on the raw data of the data warehouse Business Vault = Structures created by IT to provide a consolidated view on parts of the model or pre-calculate KPIs to ensure consistency among such calculations</p> Signup and view all the answers

<p>Staging Area = Used to load batch data into the data warehouse, does not contain historical data Operational Systems = The source system from which the staging layer extracts data Data Warehouse Layer = Contains the data that has been loaded from the staging area Source System = The system from which the staging layer extracts data, such as flat files or Excel sheets</p> Signup and view all the answers

<p>The staging area contains historical data = False The staging area is used to reduce the workload on the operational systems = True The staging area allows the execution of SQL statements against the source data = True The staging area consists of tables that duplicate the structures of the source system = True</p> Signup and view all the answers

<p>Batch Data = Data that is loaded into the data warehouse through the staging area Historical Data = Data that is not present in the staging area of the Data Vault 2.0 architecture Source Data = Data that is extracted as fast as possible from the source system Changing Data = Data that the staging area avoids dealing with by not keeping historic data</p> Signup and view all the answers

<p>Primary Purpose = To extract the source data as fast as possible and reduce the workload on the operational systems Data Structures = The staging area avoids dealing with changing data structures by not keeping historic data Data Content = Only the batch that has to be loaded next into the data warehouse layer is present in the staging area Exception = If there are multiple batches to be loaded, there might be multiple batches in the staging area</p> Signup and view all the answers

<p>Data Warehouse Layer = The layer that contains the data that has been loaded from the staging area Staging Area = The layer used to load batch data into the data warehouse Operational Systems = The source system from which the staging layer extracts data Source System = The system from which the staging layer extracts data, such as flat files or Excel sheets</p> Signup and view all the answers

<p>The staging area consists of tables that duplicate the structures of the source system = True The staging area allows the execution of SQL statements against the source data = True The staging area contains historical data = False The primary purpose of the staging area is to not have to deal with changing data structures = True</p> Signup and view all the answers

<p>Metrics Vault = Captures and records runtime information, including process metrics and technical metrics Business Vault = Sparsely modeled data warehouse based on Data Vault design principles, but houses business-rule changed data Operational Vault = Extension to the Data Vault that is directly accessed by operational systems Information Marts = Used by end-users to analyze errors in the loading process or other problems in the data warehouse</p> Signup and view all the answers

<p>Staging Area = Used to store data temporarily during the ETL process Data Warehouse Layer = Stores the Business Vault, Metrics Vault, and Operational Vault Metrics Vault = Modeled after the Data Vault 2.0 technique and contains raw format, system or process driven, and non-auditable data Business Vault = Intermediate layer between the Raw Data Vault and the information marts</p> Signup and view all the answers

<p>Metrics Vault = Provides the performance metrics information to the user Business Vault = Not stored in a separate layer, but as an extension to the Data Vault model within the data warehouse layer Operational Vault = Directly accessed by operational systems, such as master data management systems Information Marts = Loaded after the Business Vault, which eases their loading processes</p> Signup and view all the answers

<p>Metrics Vault = Used to capture and record runtime information, including run history and technical metrics Business Vault = Provides a consolidated view of the data in the Raw Data Vault to the developers who populate the information marts Operational Vault = Directly accessed by operational systems that need to either retrieve data from the enterprise data warehouse or write data back to it Information Marts = Used by end-users, such as administrators, to analyze errors in the loading process or other problems in the data warehouse</p> Signup and view all the answers

<p>Staging Area = Temporary storage area for data during the ETL process Data Warehouse Layer = Stores the Business Vault, Metrics Vault, and Operational Vault Metrics Vault = Modeled after the Data Vault 2.0 technique and contains runtime information Business Vault = An intermediate layer that eases the creation of the end-user structures</p> Signup and view all the answers

<p>Metrics Vault = Captures and records runtime information, such as CPU loads and network throughput Business Vault = Sparsely modeled data warehouse that is an intermediate layer between the Raw Data Vault and the information marts Operational Vault = An extension to the Data Vault that is directly accessed by operational systems Information Marts = Used by end-users to analyze errors in the loading process or other problems in the data warehouse</p> Signup and view all the answers

<p>Raw Data Vault = Holds the raw, unprocessed data from the source systems Business Vault = Implements some of the most important business rules and provides the power user with both raw and consolidated data Soft Business Rules = Change the data or the meaning of the data, such as modifying the grain or interpretation Data Warehouse Team = Responsible for ensuring availability, stability, and performance of the data warehouse</p> Signup and view all the answers

<p>Real-time (RT) and near-real-time (NRT) environments = Additional capabilities offered by the Data Vault 2.0 architecture Unstructured data and NoSQL environments = Additional capabilities offered by the Data Vault 2.0 architecture Integration using the business keys = Demonstrated to be very easy in loading the raw data into the Raw Data Vault Managed self-service BI task = Possible when users ask for more data that is not in the data warehouse</p> Signup and view all the answers

<p>Power user = Has direct access to both the Raw Data Vault and the Business Vault Raw Data Vault = Can be easily loaded, including integration using the business keys Business Vault = Implements some of the most important business rules Data integration = Occurs by its business key in the Data Vault 2.0 architecture</p> Signup and view all the answers

<p>Raw Data Vault = Holds the raw, unprocessed data from the source systems Business Vault = Implements some of the most important business rules and provides the power user with both raw and consolidated data Soft Business Rules = Change the data or the meaning of the data, such as modifying the grain or interpretation Data Warehouse Team = Responsible for ensuring availability, stability, and performance of the data warehouse</p> Signup and view all the answers

<p>Raw Data Vault = Data is loaded into it, including integration using the business keys Business Vault = Implements some of the most important business rules and provides the power user with both raw and consolidated data Soft Business Rules = Change the data or the meaning of the data, such as modifying the grain or interpretation Data Warehouse Team = Responsible for ensuring availability, stability, and performance of the data warehouse</p> Signup and view all the answers

<p>Power user = Has direct access to both the Raw Data Vault and the Business Vault Raw Data Vault = Can be easily loaded, including integration using the business keys Business Vault = Implements some of the most important business rules Data integration = Occurs by its business key in the Data Vault 2.0 architecture</p> Signup and view all the answers

<p>Operational Vault = An extension to the Data Vault that interfacing applications read directly from Managed Self-Service BI = An approach that allows end-users to completely circumvent IT due to unresponsiveness Data Vault Structures = Become Operational Vault structures when interfacing applications read directly from them Self-Service BI = An approach where business users are left on their own with the whole process of sourcing the data, integration, and consolidation</p> Signup and view all the answers

<p>Direct access to source systems = End-users should not directly access the data from source systems, as it might circumvent security access Unintegrated raw data = When sourcing data from multiple source systems, business users are left alone with raw data integration Low data quality = Data from source systems often have issues regarding the data quality, requiring clean up before analysis Non-standardized business rules = End-users have to implement all business rules that transform the raw data into meaningful information</p> Signup and view all the answers

<p>Source the raw data = IT retrieves the raw data from operational systems or other data sources Integrate the raw data = IT integrates the raw data using the business key for the Raw Data Vault Create Business Vault structures = IT may create these to provide a consolidated view on parts of the model or pre-calculate KPIs Deliver the requested functionality = IT just cannot do this in the given time frame, which leads to managed self-service BI</p> Signup and view all the answers

<p>Data retrieval = Business users retrieve the data from the enterprise data warehouse Business rules implementation = End-users have to implement all business rules that transform the raw data into meaningful information Local information marts creation = Business users create these using specialized tools User-defined business rules = Because end-users are dealing with only the raw data, they have to implement these</p> Signup and view all the answers

<p>Direct access to source systems = This exposes potentially private raw data and might circumvent security access Unintegrated raw data = This can become a tedious and error-prone task if performed manually Low data quality = This can become a burden to the end-user without the right tools Non-standardized business rules = End-users have to implement these, but there is no guarantee of consistency with the rest of the organization</p> Signup and view all the answers

<p>Data retrieval = Business users obtain the data they need quickly, in a usable quality Business rules implementation = End-users have to implement all business rules that transform the raw data into meaningful information Local information marts creation = Business users create these using specialized tools Raw data and business data usage = Business users use these to create local information marts</p> Signup and view all the answers

<p>Staging Area = The area where the raw data from the source system, including bad data, is loaded Data Warehouse Layer = The second layer in the Data Vault 2.0 architecture, which holds all historical, time-variant data Information Mart Layer = The layer that is directly accessed by end-users and provides the data in a way that the end-user feels most comfortable with Raw Data Vault Layer = The layer in the data warehouse that holds raw data, modeled using the Data Vault 2.0 model</p> Signup and view all the answers

<p>Hard Business Rules = The only business rules that are applied to the incoming data in Data Vault 2.0 Data Vault 2.0 Model = The modeling technique used for the data warehouse layer in the Data Vault 2.0 architecture Record Source = Indicates the source system where the data record originates from in the stage area Sequence Number = Identifies the order of the data in the source system in the stage area</p> Signup and view all the answers

<p>Raw Data = The type of data stored in the data warehouse layer of the Data Vault 2.0 architecture Subject-Oriented Data = The type of data stored in the information mart layer of the Data Vault 2.0 architecture Function-Oriented Data = The type of data stored in the Data Vault layer of the Data Vault 2.0 architecture Normalized Data = The type of data that can be provided if the end-user requires a normalized data warehouse in third-normal form</p> Signup and view all the answers

<p>Hard Business Rules = The only type of business rules applied to the incoming data Soft Business Rules = Not applied to the incoming data in Data Vault 2.0 Data Type Enforcement = An example of a hard business rule in Data Vault 2.0 Value Conversion = Not a characteristic of hard business rules in Data Vault 2.0</p> Signup and view all the answers

<p>Staging Area = Columns are nullable in this area to allow loading of raw data from the source system Data Warehouse Layer = Holds all historical, time-variant data and is not directly accessed by end-users Information Mart Layer = Provides the data in a way that the end-user feels most comfortable with Raw Data Vault Layer = Holds raw data, not modified by any business rule other than hard business rules</p> Signup and view all the answers

<p>Record Source = Indicates the source system where the data record originates from in the stage area Sequence Number = Identifies the order of the data in the source system in the stage area Timestamp = The date and time when the record arrives in the data warehouse Hash Key = Used for identification purposes in the stage area</p> Signup and view all the answers

<p>Metrics Vault</p> Signup and view all the answers

<p>Business Vault</p> Signup and view all the answers

<p>Operational Vault</p> Signup and view all the answers

<p>Business Vault</p> Signup and view all the answers

<p>Business Vault</p> Signup and view all the answers

<p>Business Vault</p> Signup and view all the answers

<p>Direct access to source systems</p> Signup and view all the answers

<p>Managed self-service BI</p> Signup and view all the answers

<p>All of the above</p> Signup and view all the answers

<p>All of the above</p> Signup and view all the answers

<p>Managed self-service BI</p> Signup and view all the answers

<p>Single database structure</p> Signup and view all the answers

<p>All of the above</p> Signup and view all the answers

<p>To consolidate and quality check the data</p> Signup and view all the answers

<p>Business Vault</p> Signup and view all the answers

<p>Both raw data and consolidated data</p> Signup and view all the answers

<p>By offering additional capabilities</p> Signup and view all the answers

<p>Data Vault modeling and project methodology</p> Signup and view all the answers

<p>Staging layer</p> Signup and view all the answers

<p>To extract source data as fast as possible</p> Signup and view all the answers

<p>No</p> Signup and view all the answers

<p>When there are multiple batches to be loaded</p> Signup and view all the answers

<p>To avoid dealing with changing data structures</p> Signup and view all the answers

<p>Tables that duplicate the structures of the source system</p> Signup and view all the answers

<p>Raw Data Vault</p> Signup and view all the answers

<p>To hold all historical, time-variant data</p> Signup and view all the answers

<p>Information mart</p> Signup and view all the answers

<p>Data in the data warehouse layer is stored in the granularity provided by the source systems, while data in the information mart layer is cleansed and redundant</p> Signup and view all the answers

<p>Hash key computations</p> Signup and view all the answers

<p>To identify the order of the data in the source system</p> Signup and view all the answers

<p>Staging Area Layer</p> Signup and view all the answers

<p>To reduce the workload on operational systems</p> Signup and view all the answers

<p>When there are multiple batches to be loaded</p> Signup and view all the answers

<p>To reduce the complexity of loading procedures</p> Signup and view all the answers

<p>Tables that duplicate the structures of the source system</p> Signup and view all the answers

<p>To move complex business rules towards the end-user</p> Signup and view all the answers

<p>Metrics Vault</p> Signup and view all the answers

<p>To provide a consolidated view of the data</p> Signup and view all the answers

<p>Business Vault</p> Signup and view all the answers

<p>Business Vault</p> Signup and view all the answers

<p>Business Vault</p> Signup and view all the answers

<p>Metrics Vault</p> Signup and view all the answers

<p>Data warehouse layer</p> Signup and view all the answers

<p>To load raw data from the source system</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>Cleaned data</p> Signup and view all the answers

<p>To implement important business rules</p> Signup and view all the answers

<p>Out of scope for this book</p> Signup and view all the answers

<p>To load the raw data</p> Signup and view all the answers

<p>The project methodology and Data Vault modeling</p> Signup and view all the answers

<p>A balance between IT agility and data management</p> Signup and view all the answers

<p>Direct access to source systems</p> Signup and view all the answers

<p>Managed self-service BI</p> Signup and view all the answers

<p>Source raw data from operational systems</p> Signup and view all the answers

<p>Managed self-service BI</p> Signup and view all the answers

<p>Direct access to source systems, Unintegrated raw data, Low data quality, Unconsolidated raw data, Non-standardized business rules</p> Signup and view all the answers

<p>Transform the data into meaningful information</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers