Management Information Systems: Managing The Digital Firm -- Student Value Edition (16th Edition)
Management Information Systems: Managing The Digital Firm -- Student Value Edition (16th Edition)
16th Edition
ISBN: 9780135191927
Author: Kenneth Laudon, Jane Laudon
Publisher: PEARSON
Expert Solution & Answer
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Chapter 3, Problem 1IQ

Explanation of Solution

Data quality:

  • Data quality is refers to a measure of the condition of data based on factors like accuracy, completeness, consistency, reliability and whether the data is up to date.
  • The quality depends on the context in which it is used.
  • Good data quality exists when data is suitable for the use case at hand.

Data management:

  • Data management includes all disciplines related to managing the data as a valuable resource.
  • It is an administrative process that incorporates acquiring, validating, storing, protecting and processing the data.
  • Data management ensures the accessibility, reliability, and timeliness of the data to the users.

Explanation of Solution

Experience in data quality and data management:

Yes”, one can have an experience in data quality and management.

Reasons:

  • The use of data management leads to high-quality of data.
  • Improved data quality results in decision-making across an organization.
  • Good data decreases risk and it results in consistent improvement.

Explanation of Solution

Data quality problem:

Yes”, one might encountered the data quality problem.

Reasons:

  • It may due to manual data entry errors. Humans can make errors and even small data set manually entered by humans likely to contain errors.
  • Lack of complete information leads to data quality problem.
  • In some cases, one can find data ambiguity, which leads to data quality problems.

Explanation of Solution

Solving data quality problem:

  • The common data quality problem issues it that missing information.
  • To solve this issues, one might understand the reason behind why the values are missing.
  • The next step is to track the data lineage of the data source to identify the systemic issues during data capture.
  • Knowing the source of missing value will often reduce to solve the problem.

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