Data Quality Issues Flashcards
refers to the presence of identical or nearly identical records or data entries within a dataset. It can lead to redundancy, inefficiency, and confusion in data management and analysis.
Duplicate Data
In a customer database, multiple entries exist for the same customer due to data entry errors, system glitches, or merging duplicate datasets. For instance, a customer may have two separate records with slightly different spellings of their name or variations in their contact information.
duplicate Data
refers to data that contains errors, mistakes, or inconsistencies, leading to incorrect or unreliable information. It can result from data entry errors, outdated information, or flaws in data collection processes.
Inaccurate data
In a product inventory database, the recorded stock levels for certain items do not match the actual physical inventory count due to data entry mistakes or discrepancies in tracking inventory movements. As a result, the database inaccurately reflects the availability of products for sale.
Inaccurate data
refers to data that is unclear, vague, or open to interpretation, making it difficult to understand or use effectively. It can result from poorly defined data definitions, inconsistent formatting, or lack of context.
Ambiguous data
A customer survey collects responses to open-ended questions without providing clear instructions or categories for the responses. As a result, the data includes comments or feedback that are difficult to categorize or analyze effectively.
Ambiguous data
refers to data that is not easily accessible or visible to users, either intentionally or unintentionally. It can include data stored in obscure locations, outdated files, or unindexed databases.
Hidden data
An organization’s website contains outdated web pages that are no longer linked from the main navigation menu but are still accessible through direct URLs. As a result, users may encounter information or content that is not readily visible or updated.
Hidden data
refers to data that does not follow uniform standards, formats, or conventions, leading to discrepancies or contradictions within a dataset. It can result from disparate sources, manual data entry, or lack of data governance.
Inconsistent data
A sales database records customer addresses using different formats (e.g., street name abbreviations, inconsistent capitalization), making it difficult to generate accurate mailing lists or perform geographic analysis.
Inconsistent Data
also known as data overload, refers to an overwhelming volume of data that exceeds the capacity or resources available for effective management, processing, or analysis. It can lead to information overload, decreased productivity, and difficulty in identifying relevant insights.
Too much data
An e-commerce website collects vast amounts of user browsing and purchasing data, including clickstream data, transaction histories, and demographic information. Analyzing and interpreting this massive dataset to extract meaningful patterns or trends becomes challenging and resource-intensive.
Too much data
refers to periods of time during which data or data systems are unavailable or inaccessible, resulting in disruption or loss of access to critical information. It can be caused by system failures, maintenance activities, or network outages.
Data downtime
A cloud-based storage service experiences unexpected outage due to server maintenance, preventing users from accessing their files or data stored on the platform. As a result, businesses relying on this service may experience disruptions in their operations and workflow.
Data Downtime
