Metadata And Data Quality

Metadata is indeed stored in a repository. A repository is a centralized location where data and information are stored and managed. In the context of metadata, a repository is used to store and organize information about data, such as its structure, format, source, and other relevant details. This allows for easy access and retrieval of metadata, which is crucial for effective data management and analysis.

Data quality audit involves examining and evaluating the accuracy and reliability of data. One aspect of this process is to ensure traceability between the original and corrected values. This means that any changes or corrections made to the data can be traced back to their original values, allowing for transparency and accountability. Therefore, the statement that data quality audit provides traceability between original and corrected values is true.

During the source system analysis, the process involves gathering metadata from the source system. This metadata includes information about the structure, format, and content of the data in the source system. It helps in understanding the data sources, their relationships, and the quality of the data. Therefore, it is true that source system metadata is acquired during the source system analysis.

In a relational database management system (RDBMS), metadata refers to the information about the database structure, such as the names and types of tables, columns, indexes, and constraints. This metadata is commonly referred to as a catalog. Therefore, the statement that RDBMS metadata is referred to as a catalog is true.

Maintaining metadata history for changes even if the base source is deleted is important because it allows for traceability and accountability. Even if the original source is no longer available, having a record of the metadata history can help in understanding the context and rationale behind the changes made. This can be useful for auditing purposes, data governance, and ensuring data integrity. Additionally, it provides a historical reference that can be valuable for future analysis or decision-making.

When two sources are merged together, it is important to merge their metadata as well. Metadata provides information about the data, such as its source, format, and other relevant details. By merging the metadata, the combined dataset will have a comprehensive and accurate representation of the merged sources. This ensures that the merged data is properly organized, searchable, and can be effectively used for analysis or other purposes. Therefore, merging metadata is necessary to maintain data integrity and maximize the usefulness of the merged dataset.

Customer name matching is a process that involves comparing and matching customer names based on their similarity or closeness. This is typically done using fuzzy and intelligent logic algorithms that take into account various factors such as spelling variations, abbreviations, phonetic similarity, and other patterns. These algorithms are designed to intelligently identify and match customer names even if there are slight differences or variations in the way they are written or recorded. Therefore, the statement "Customer name matching is done by fuzzy and intelligent logic" is true.

Customer merging is the process of combining duplicate records by selecting the best attribute from each duplicate and merging it into the surviving record. This helps to eliminate duplicate data and ensure that the most accurate and complete information is retained in the system. Therefore, the given statement is true.

An extraction tool is used to extract metadata from textual sources. This tool is specifically designed to identify and extract relevant information from unstructured or semi-structured data. It helps in organizing and categorizing the extracted metadata, making it easier to analyze and utilize for various purposes such as data integration, data mining, or information retrieval.

A conversion tool is used to convert the format of metadata from one format to another. This tool allows for the seamless transformation of metadata, ensuring compatibility and consistency across different systems and platforms. It simplifies the process of migrating data from one system to another by automatically converting the metadata format, saving time and effort. This tool is essential in maintaining data integrity and ensuring that metadata can be effectively utilized and understood in different environments.

Data masking and mask pattern analysis are indeed used in substituting string patterns. Data masking is a technique used to protect sensitive data by replacing it with fictitious data or masking characters. This helps in preventing unauthorized access to sensitive information. Mask pattern analysis, on the other hand, involves the identification and analysis of specific patterns within the data that need to be masked. By using these techniques, organizations can ensure the privacy and security of their data while still being able to use it for various purposes.

When metadata is hierarchically structured, it means that the metadata is organized in a hierarchical manner, with different levels of information and relationships between them. This type of structure is commonly found in ontologies, which are formal representations of knowledge that capture concepts, relationships, and properties within a specific domain. Therefore, when metadata is hierarchically structured, it can be considered as an ontology.

The correct answer is Both XML and Binary. This means that metadata storage formats can be both in human-readable form (such as XML) and in non-human-readable form (such as binary). XML allows for easy interpretation and editing by humans, while binary formats are more efficient for storing large amounts of data and are not easily readable by humans.

The correct answer is "External Storage & Internal Storage" because metadata storage can be done in both internal and external storage. Internal storage refers to the storage space within the device itself, such as the device's memory or hard drive. External storage, on the other hand, refers to storage devices that are connected to the device externally, such as USB drives or SD cards. Therefore, metadata can be stored in either the internal storage or external storage depending on the specific requirements and preferences of the system or application.

Metadata can be classified based on various factors, including mutability, logical function, and content. Mutability refers to whether the metadata can be modified or not. Logical function refers to the purpose or role of the metadata within a system. Content classification involves categorizing metadata based on the type of information it represents. Therefore, all the given options are valid factors for classifying metadata.

The rules for cleansing are embedded in the parameter file. This file contains the specific instructions and guidelines for cleaning and transforming the data. It defines the actions to be taken, such as removing duplicates, correcting errors, and standardizing formats. The parameter file serves as a reference for the data cleansing process, ensuring consistency and accuracy in the resulting cleaned data.

The success factor of Crosswalk depends on all the options provided, which include similarity between metadata, granularity of elements, and compatibility of the contents. This means that for Crosswalk to be successful, it is important for the metadata to be similar, the elements to have the appropriate level of granularity, and for the contents to be compatible. All of these factors contribute to the effectiveness and efficiency of Crosswalk, ensuring that it can accurately and seamlessly connect different systems or platforms.

When data is processed, it undergoes a series of operations such as sorting, organizing, and analyzing, which transforms it into meaningful and useful information. Raw data on its own may not hold any significance or convey any message, but once processed, it becomes valuable and provides insights that can be used for decision-making and understanding various phenomena. Therefore, the statement "Data when processed becomes information" is true.

Data quality refers to the accuracy, consistency, integrity, and uniqueness of data. Volume, on the other hand, refers to the amount of data that is being stored or processed. While volume is an important aspect of data management, it is not directly related to data quality. Therefore, volume does not fall under the category of factors that data quality refers to.

Data cleansing and standardization involve the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in data. This is typically done to ensure data accuracy, completeness, and consistency. Data Quality Tools are specifically designed to handle these tasks by providing functionalities such as data profiling, data cleansing, and data standardization. These tools can automatically identify and fix data issues, validate data against predefined rules, and enforce data quality standards. Therefore, it is logical to conclude that Data Quality Tools will be responsible for data cleansing and standardization.

Both ANSI X3.285 and ISO 11179 are standards that define the requirements and guidelines for a metadata repository. A metadata repository is a centralized system that stores and manages metadata, which provides information about data and its attributes. These standards ensure that the metadata repository follows a consistent structure, format, and naming conventions, making it easier for organizations to share, exchange, and understand metadata across different systems and platforms. By adhering to these standards, organizations can improve data quality, data integration, and data governance processes.

A markup tool is used to generate XML, SGML, and DTD. Markup refers to the process of adding tags or annotations to a document to define its structure and format. XML (Extensible Markup Language), SGML (Standard Generalized Markup Language), and DTD (Document Type Definition) are all markup languages used to define the structure and format of documents. Therefore, a markup tool is the correct tool to use for generating XML, SGML, and DTD.

The correct answer is Master Data Store. In MDM, master data holds the components that are stored in the Master Data Store. This store is responsible for managing and storing the master data, which includes important information about key entities such as customers, products, employees, and suppliers. The Master Data Store acts as a central repository for all the master data, ensuring consistency and accuracy across different systems and applications within an organization. It allows for efficient data management, access, and sharing, enabling better decision-making and improving overall business processes.

OLAP metadata depends on both Data Warehouses (DW) and Relational Database Management Systems (RDBMS). Data Warehouses are specifically designed to store and manage large amounts of data for OLAP purposes. They provide a structured and optimized environment for storing and querying data. On the other hand, RDBMS is responsible for managing the structured data within the Data Warehouse and providing efficient data retrieval and manipulation capabilities. Therefore, both DW and RDBMS play crucial roles in supporting and managing the metadata required for OLAP operations.

The layers of metadata are the symbolic layer and the logical layer. The symbolic layer refers to the metadata that is represented through symbols or codes, such as file names or identifiers. This layer helps in identifying and locating specific data or resources. On the other hand, the logical layer refers to the metadata that provides a logical structure or organization to the data, such as data relationships, data definitions, or data models. These layers work together to ensure efficient data management and retrieval.

Poor data quality can have a significant impact on the performance of the ETL (Extract, Transform, Load) process. When the data being extracted is of poor quality, it may contain errors, inconsistencies, or missing values, which can lead to issues during the transformation and loading stages. This can result in delays, errors, and inefficiencies in the ETL process, ultimately affecting its overall performance.

The correct answer includes four different data cleansing operations. The first operation is removing invalid characters from the data. This is important because invalid characters can cause issues in data processing and analysis. The second operation is correcting data format, which involves ensuring that the data is in the correct format and structure for analysis. The third operation is identifying and removing duplicate records, as duplicates can skew the results and cause inaccuracies. Finally, building data quality and reprocessing feedback with the source system is crucial for maintaining and improving the overall data quality and integrity.

RDBMS metadata exists as a part of the database and is referred to as a catalog. It provides information about foreign keys and primary keys, as well as information about views and indexes.

The correct answer includes four different data cleansing operations. The first operation is removing invalid characters from the data. This is important because invalid characters can cause errors or inconsistencies in the data. The second operation is correcting the data format. This involves ensuring that the data is in the correct format, such as converting dates to a standardized format. The third operation is identifying and removing duplicate records. Duplicate records can skew analysis and cause inaccuracies in the data. The fourth operation is building data quality and reprocessing feedback with the source system. This involves continuously improving the data quality and providing feedback to the source system for further improvement.

Trillium is a data quality tool. Data quality tools are used to ensure the accuracy, completeness, and consistency of data. They help identify and correct errors, validate data against predefined rules, and standardize data formats. Trillium specifically focuses on improving data quality by providing features such as data profiling, data cleansing, and data enrichment. It helps organizations maintain high-quality data, which is essential for making informed business decisions and improving overall operational efficiency.

The correct answer is "All the options". This means that all of the mentioned options - providing feedback to the source, de-duplication, and setting stringent rules in the validation or ETL process - can be used to control the poor quality of data. Each option tackles a different aspect of data quality control, allowing for a comprehensive approach to ensure data accuracy and reliability.

The rule repository can contain either a database or a flat file. This means that the rules can be stored and accessed from either a traditional database system or a flat file system. This allows for flexibility in choosing the storage method based on the specific requirements and preferences of the organization.

The correct answer includes four options: templates, mark-up tool, extraction, and conversion. These are all tools used in metadata creation. Templates provide a pre-designed structure for organizing metadata. A mark-up tool is used to add tags or labels to specific elements of a document or data. Extraction involves extracting relevant information from a source and converting it into metadata. Conversion refers to the process of transforming data into a different format or structure. These tools are essential for creating and organizing metadata effectively.

Different groups working in the same project must follow agreed standards of manipulating the same standards in order to ensure consistency and interoperability. This means that all groups should use the same set of rules and procedures when manipulating the project's standards, ensuring that there is a unified approach throughout the project. Additionally, they must also use compatible methods of collecting metadata, which refers to the process of gathering and organizing information about the project. This ensures that the metadata collected by different groups can be easily shared and integrated, further promoting collaboration and efficiency in the project.

The answer lists various vendors of metadata repositories, including Oracle Enterprise Metadata manager (EMM), SAS Metadata Repositories, Masai technologies M:Scan and M:Grid, InfoLibrarian Metadata Integration Framework, and Data Foundation Metadata Registry. These vendors offer different solutions for managing metadata, which is essential for organizing and understanding data within an organization. These repositories provide a centralized location for storing and accessing metadata, allowing users to easily search, analyze, and govern their data assets. By using these repositories, organizations can improve data quality, enhance data governance, and enable better decision-making processes.

Master Data Management (MDM) is implemented to track the duplicate value of records. MDM helps in identifying and eliminating duplicate data entries in a database, ensuring data accuracy and consistency. By tracking duplicate records, MDM enables organizations to maintain a single, reliable version of data across different systems and applications. This helps in improving data quality, reducing errors, and enhancing overall operational efficiency.

The answer states that the reasons for data quality issues are inaccurate entry of data, lack of proper validation, and lack of MDM (Master Data Management) strategy. This means that the data quality issues can occur when data is entered incorrectly or with errors, when there is insufficient validation to ensure the accuracy and integrity of the data, and when there is no proper strategy in place to manage and maintain the master data. These factors can lead to inconsistencies, errors, and unreliable data, affecting the overall quality of the data.

The correct answer is "Some countries have detailed address level." This means that not all countries have a detailed address level, but there are some countries that do. This suggests that the level of detail in addresses varies from country to country, with some having more detailed information than others.

The correct answer is "customer belongs to same family or same house." This means that household matching refers to identifying customers who are part of the same family or live in the same house. This can be useful for various purposes, such as identifying potential duplicate records or analyzing customer behavior within a household.

The correct answer is "Before building fully flagged data warehouse" because evaluating data quality before building a fully flagged data warehouse is important to ensure that the data being used is accurate, complete, and reliable. This evaluation helps identify any inconsistencies or errors in the data, allowing for necessary corrections and improvements to be made before the data warehouse is built. By doing so, the data warehouse can be built on a solid foundation, resulting in better decision-making and analysis in the future.

The Trillium server requires two input files in DDL format and one parameter file in .PAR format. The input files in DDL format contain the data that needs to be processed by the Trillium server. The parameter file in .PAR format contains the configuration settings and instructions for the Trillium server on how to process the input files. These files are necessary for the Trillium server to successfully perform its data processing tasks.

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Data profiling is the process of analyzing and understanding the content and structure of data. It involves examining the data to identify patterns, inconsistencies, and anomalies. By doing so, the frequency of data count can be obtained, which helps in gaining insights into the data and ensuring its quality. Data cleansing, on the other hand, focuses on removing errors and inconsistencies from the data. Data management involves activities related to organizing, storing, and maintaining the data. Therefore, the most appropriate option for obtaining the frequency of data count is data profiling.

Metadata must be adapted if the base resource has changed because metadata provides information about the resource, and if the resource has changed, the metadata needs to reflect those changes. Metadata should be merged if two sources are merged together because when multiple sources are combined, their respective metadata should also be merged to provide a comprehensive view. Metadata creator should be trained because creating accurate and relevant metadata requires knowledge and expertise. It is not necessary to merge metadata if sources are merged is incorrect because merging sources often requires merging their corresponding metadata as well.

The Analysis stage is not an IBM product. This can be inferred from the question which asks for the product that is not an IBM product. The other options, Meta Stage, Quality Stage, and Profile Stage are not mentioned in the question and their status as IBM products or not is not specified. Therefore, the only option that can be confirmed as not an IBM product is the Analysis stage.

The correct answer is "Identifies customer's duplicate record." This means that the individual matching process is used to identify if a customer's record already exists in the system, indicating a potential duplicate entry. It does not necessarily indicate if the customer belongs to the same family or house, as mentioned in the other option.

DataStage is a data integration tool, not a data quality tool. It is used for extracting, transforming, and loading data from various sources into a data warehouse or a data mart. While DataStage can help in improving data quality by performing data cleansing and transformation processes, it is not specifically designed as a data quality tool like Quality stage and Trillium. dfPower is not a known data quality tool, so it cannot be considered as the correct answer.

During the de-duplication process, the original values are kept in trail tables. This means that instead of deleting the original values or replacing them with new values in new tables, the original values are preserved in separate tables. This allows for a record of the original values to be maintained while still removing any duplicate entries from the main tables. Keeping the original values in trail tables can be useful for auditing purposes or for reference in case the new values need to be reverted back to the original ones.

The best practice in data quality is to fix data quality issues in the source system. This means addressing and resolving any inaccuracies, inconsistencies, or errors at the point of data entry or creation. By fixing data quality in the source system, it ensures that the data being captured is accurate, reliable, and consistent from the beginning. This approach helps to prevent downstream issues and ensures that the data stored in the data warehouse or other systems is of high quality.

Frontroom metadata is used for labeling, screening, and reporting purposes. It helps in organizing and categorizing data in the data warehouse (DW). This metadata allows users to easily identify and understand the data present in the DW. Additionally, frontroom metadata enables users to take actions and make decisions based on the data available in the DW. It is also used in the Extract, Transform, Load (ETL) process to bring operational data from OLTP systems into the DW.