[01/Awash/21]

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Questions and Answers

Which of the following is NOT a step in Total Quality Management for data?

  • Providing data quality reports
  • Cleaning bad data in the source applications (correct)
  • Consuming exceptions for data cleansing rules
  • Capturing all data from sources

What is the purpose of data quality reports in Total Quality Management?

  • To clean bad data in the source applications
  • To mark exceptions in the data
  • To capture all data from sources
  • To provide information to data stewards (correct)

What can data stewards do in Total Quality Management?

  • Capture all data from sources
  • Cleanse bad data in the source applications
  • Consume exceptions for data cleansing rules
  • Comment data and mark exceptions (correct)

True or false: Total Quality Management captures all data from sources, including good, bad, and ugly data?

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

True or false: Data stewards are responsible for cleansing bad data in the source applications in Total Quality Management?

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

True or false: Exceptions in data are consumed to be used in data cleansing rules in Total Quality Management?

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

Match the following actions in Total Quality Management with their descriptions:

<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

Match the following data management tasks with their roles in Total Quality Management:

<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

Match the following Total Quality Management steps with their descriptions:

<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

Match the following steps in dealing with bad data in Total Quality Management (TQM) with their descriptions:

<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

Match the following actions in Total Quality Management (TQM) with their descriptions:

<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

Match the following tips for dealing with bad data in Total Quality Management (TQM) with their descriptions:

<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

Match the following aspects of Total Quality Management (TQM) with their definitions:

<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

Match the following Total Quality Management (TQM) steps with their descriptions:

<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

Match the following Total Quality Management (TQM) actions with their descriptions:

<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

Match the following Total Quality Management (TQM) tips with their descriptions:

<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

Match the following steps in dealing with bad data in Total Quality Management (TQM) with their descriptions:

<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

Match the following actions in Total Quality Management (TQM) with their descriptions:

<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

Match the following strategies for dealing with bad data in Total Quality Management with their descriptions:

<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

Match the following potential outcomes of dealing with bad data in Total Quality Management with their descriptions:

<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

Match the following roles in Total Quality Management with their responsibilities:

<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

Match the following Total Quality Management concepts with their definitions:

<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

Match the following Total Quality Management steps with their descriptions:

<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

Match the following Total Quality Management strategies with their purposes:

<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

Match the following Total Quality Management benefits with their descriptions:

<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

Match the following Total Quality Management concepts with their explanations:

<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

Match the following Total Quality Management actions with their purposes:

<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

Match the following Total Quality Management stakeholders with their roles:

<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

Which of the following is a potential consequence of bad data in Total Quality Management?

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

What is the first step in dealing with bad data in Total Quality Management?

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

What can data visualization and analytics tools be used for in Total Quality Management?

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

What is the purpose of implementing data quality controls in Total Quality Management?

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

What is the role of data stewards in Total Quality Management?

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

What should be a priority for everyone in the organization in Total Quality Management?

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

What is the purpose of educating employees in Total Quality Management?

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

What is the purpose of cleaning and preparing data in Total Quality Management?

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

What is the role of data quality controls in Total Quality Management?

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

Which of the following is NOT a recommended method for improving data quality in Total Quality Management (TQM)?

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

What can technology help with in Total Quality Management (TQM)?

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

If someone is struggling to deal with bad data in Total Quality Management (TQM), what is a recommended course of action?

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

What are the potential benefits of dealing with bad data in Total Quality Management (TQM)?

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

What is the purpose of data quality reports in Total Quality Management (TQM)?

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

What role do data stewards play in Total Quality Management (TQM)?

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

What is the recommended approach for dealing with bad data in Total Quality Management (TQM)?

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

What can consultants and companies do to help improve data quality in Total Quality Management (TQM)?

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

True or false: Total Quality Management (TQM) captures only good data from sources?

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

What is the main goal of Total Quality Management (TQM) in relation to data quality?

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

Total Quality Management (TQM) emphasizes the importance of data quality and accurate data entry.

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

Using technology can help improve the quality of data more efficiently and effectively.

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

Getting help from experts is not recommended when dealing with bad data in TQM.

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

Improving data quality in TQM does not lead to better decision-making and customer satisfaction.

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

Data cleaning, data preparation, and data quality control are not supported by available technologies.

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

Data stewards play a crucial role in Total Quality Management (TQM).

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

Cleaning and preparing data is not an important step in TQM.

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

Data quality reports do not serve any purpose in Total Quality Management.

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

Data visualization and analytics tools are not useful in Total Quality Management.

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

Total Quality Management captures all data from sources, including good, bad, and ugly data.

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

True or false: Bad data can hinder continuous improvement in Total Quality Management (TQM).

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

True or false: Data visualization and analytics tools are not useful in identifying patterns and trends in data for TQM.

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

True or false: Educating employees about data quality is not important in TQM.

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

True or false: Implementing data quality controls is not necessary to prevent bad data in TQM.

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

True or false: Cleaning and preparing data is not required to address bad data in TQM.

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

True or false: Establishing a data quality culture is not crucial in TQM.

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

True or false: Data stewards are responsible for cleansing bad data in TQM.

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

True or false: Standardizing data formats is not necessary to deal with bad data in TQM.

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

True or false: Identifying the sources of bad data is not the first step in dealing with it in TQM.

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

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