[01/Awash/21]

<p>True (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Capture all data from sources = Include good, bad, and ugly data Provide data quality reports = To data stewards or data quality apps Cleansing bad data = Performed by data stewards in the source applications Consume exceptions = To be used in data cleansing rules</p> Signup and view all the answers

<p>Capture all data from sources = Data management task Provide data quality reports = Data management task Cleansing bad data = Role of data stewards Consume exceptions = Role of data cleansing rules</p> Signup and view all the answers

<p>Capture all data from sources = Step that includes good, bad, and ugly data Provide data quality reports = Step that involves data stewards or data quality apps Cleansing bad data = Step performed by data stewards in the source applications Consume exceptions = Step that involves using data cleansing rules</p> Signup and view all the answers

<p>Identify the sources of bad data = First step to address bad data, includes data entry errors and outdated systems Clean and prepare your data = Involves correcting errors, filling in missing values, and removing outliers Implement data quality controls = Prevents bad data from entering the system, involves data validation and audits Educate your employees = Teach them about data quality and accurate data entry</p> Signup and view all the answers

<p>Data validation = Part of implementing data quality controls to ensure data accuracy Data reconciliation = Another aspect of implementing data quality controls to ensure data consistency Data visualization and analytics = Used to identify patterns and trends in data Establish a data quality culture = Making data quality a priority for everyone in the organization</p> Signup and view all the answers

<p>Identify the sources of bad data = First step in addressing bad data, involving errors, processes, and systems Clean and prepare your data = Process of correcting errors, filling missing values, and removing outliers Implement data quality controls = Prevent bad data from entering the system, involves validation, reconciliation, and audits Educate your employees = Teach them about data quality and accurate data entry</p> Signup and view all the answers

<p>Data quality = A priority for everyone in the organization Data entry errors = One of the sources of bad data Data validation = A control mechanism to prevent bad data from entering the system Data visualization and analytics = Used to identify areas where data may be inaccurate or incomplete</p> Signup and view all the answers

<p>Identify the sources of bad data = First step in dealing with bad data, involves recognizing errors, processes, and systems Clean and prepare your data = Process of making data accurate and complete, including error correction and outlier removal Implement data quality controls = Actions taken to prevent bad data from entering the system Educate your employees = Important to ensure accurate data entry and understanding of data quality</p> Signup and view all the answers

<p>Data validation = Process of checking data for accuracy and completeness Data reconciliation = Process of comparing data from different sources to ensure consistency Data visualization and analytics = Methods used to analyze and interpret data Establish a data quality culture = Creating an environment where data quality is a priority for everyone</p> Signup and view all the answers

<p>Identify the sources of bad data = First step in dealing with bad data, includes recognizing errors, processes, and systems Clean and prepare your data = Process of making data accurate and complete, includes error correction and outlier removal Implement data quality controls = Actions taken to prevent bad data from entering the system, such as validation and reconciliation Educate your employees = Important to ensure accurate data entry and understanding of data quality</p> Signup and view all the answers

<p>Identify the sources of bad data = First step in addressing bad data, involves recognizing errors, processes, and systems Clean and prepare your data = Process of making data accurate and complete, includes error correction and outlier removal Implement data quality controls = Actions taken to prevent bad data from entering the system, such as validation and reconciliation Educate your employees = Important to ensure accurate data entry and understanding of data quality</p> Signup and view all the answers

<p>Data validation = Process of checking data for accuracy and completeness Data reconciliation = Process of comparing data from different sources to ensure consistency Data visualization and analytics = Methods used to analyze and interpret data Establish a data quality culture = Creating an environment where data quality is a priority for everyone</p> Signup and view all the answers

<p>Raise awareness = Making everyone understand the importance of data quality and accurate data entry Use technology = Utilizing available technologies for data cleaning, preparation, and quality control Get help from experts = Seeking assistance from consultants and companies to improve data quality Follow tips = Implementing recommended practices to handle bad data and improve data quality</p> Signup and view all the answers

<p>Better decision-making = Improvement in data quality leads to more informed and accurate decision-making processes Wasted resources = Poor data quality can result in inefficient allocation of resources Customer satisfaction = High quality data can lead to better understanding of customer needs and preferences Improved efficiency = Effective handling of bad data can enhance overall operational efficiency</p> Signup and view all the answers

<p>Data stewards = Responsible for managing data quality and cleansing bad data in the source applications Consultants and companies = Provide expert guidance and assistance in improving data quality Employees = Should be aware of the importance of data quality and accurately enter data Technology = Used for data cleaning, preparation, and quality control</p> Signup and view all the answers

<p>Data cleaning = Process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in data Data preparation = Process of transforming raw data into a form suitable for analysis and modeling Data quality control = Process of ensuring that data meets specific quality requirements for its intended use Bad data = Data that contains errors or is otherwise inaccurate, incomplete, or unusable</p> Signup and view all the answers

<p>Data capture = Process of acquiring and collecting data from various sources Data cleansing = Process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in data Data transformation = Process of converting data from one format or structure into another Data integration = Process of combining data from different sources into a unified view</p> Signup and view all the answers

<p>Raise awareness = To ensure that everyone understands the importance of data quality Use technology = To improve the efficiency and effectiveness of data quality improvement processes Get help from experts = To seek external assistance in dealing with complex data quality issues Follow tips = To implement best practices and recommendations for data quality improvement</p> Signup and view all the answers

<p>Improved decision-making = Data of higher quality leads to more accurate and reliable decision-making processes Cost savings = Effective data quality management can result in reduced costs associated with poor data Competitive advantage = Superior data quality can provide an edge over competitors in decision-making and customer satisfaction Process efficiency = Efficient handling of data quality issues can streamline business processes</p> Signup and view all the answers

<p>Data stewardship = Process of managing the data asset of an organization to improve its quality and usability Data governance = Framework for managing data assets, including data quality and data stewardship Data profiling = Process of analyzing the content, structure, and quality of data Data lineage = Tracking the origin and history of data from its creation to its current state</p> Signup and view all the answers

<p>Data capture = To acquire and collect data from various sources Data cleansing = To identify and correct or remove errors, inconsistencies, and inaccuracies in data Data transformation = To convert data from one format or structure into another Data integration = To combine data from different sources into a unified view</p> Signup and view all the answers

<p>Data stewards = Responsible for managing and improving data quality Consultants and companies = Offer expertise and services to help organizations improve data quality Employees = Should be trained to understand the importance of data quality and accurate data entry Customers = Benefit from improved data quality through better products and services</p> Signup and view all the answers

<p>Wasted resources (C)</p> Signup and view all the answers

<p>Identifying the sources of bad data (D)</p> Signup and view all the answers

<p>Identifying patterns and trends in data (A)</p> Signup and view all the answers

<p>To prevent bad data from entering the system (B)</p> Signup and view all the answers

<p>Cleansing bad data in source applications (A)</p> Signup and view all the answers

<p>Data quality culture (D)</p> Signup and view all the answers

<p>Producing high-quality data (A)</p> Signup and view all the answers

<p>Correcting errors and filling in missing values (D)</p> Signup and view all the answers

<p>Preventing bad data from entering the system (A)</p> Signup and view all the answers

<p>Ignoring bad data (A)</p> Signup and view all the answers

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

<p>Seek help from experts (B)</p> Signup and view all the answers

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

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

<p>Cleansing bad data in source applications (A)</p> Signup and view all the answers

<p>Take immediate action to clean and improve the data (A)</p> Signup and view all the answers

<p>Provide recommendations for improving data quality (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>To improve the quality of data (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers