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Data Quality Assessment Checklist

Identify the data sources

Determine the data gathering method

Perform initial data gathering

Review gathered data for initial issues

Resolve initial data discrepancies

Standardize data formatting

Discard irrelevant data

Normalize values across data set

Check data for completeness

Approval: Completeness Check

Scan data for anomalies

Investigate and correct anomalies

Validate data against predefined standards

Approval: Validation Check

Perform data consistency check

Carry out duplicate data check

Removal of Duplicate Data

Assess the quality of data

Approval: Data Quality Assessment

Document the data quality assessment results

Identify the data sources

This task involves identifying the various sources from which data is collected. It plays a crucial role in ensuring that all relevant data is included in the assessment. By identifying the data sources, you can determine the scope and accuracy of the assessment. The desired result is a comprehensive list of data sources. To complete this task, you will need to research internal databases, external websites, and any other relevant sources. You may encounter challenges such as incomplete or outdated information. To overcome these challenges, you can reach out to relevant stakeholders or conduct additional research.

List the data sources

Is the data source internal or external?

1

Internal
2

External

Select the relevant data sources

1

Database A
2

Database B
3

Website C
4

API D
5

File E

Determine the data gathering method

This task involves determining the method by which the data will be gathered. The chosen method will depend on factors such as the type of data and available resources. The impact of this task is ensuring that the data gathering process is efficient and effective. The desired result is a clearly defined data gathering method. To complete this task, you will need to consider options such as manual data entry, automated data extraction, or data import from external sources. You may encounter challenges such as data format compatibility or limited access to certain data sources. To overcome these challenges, you can explore data transformation tools or seek assistance from relevant experts.

Select the data gathering method

1

Manual data entry
2

Automated data extraction
3

Data import

What data formats are involved?

1

CSV
2

Excel
3

XML
4

JSON
5

API

Select the relevant data gathering tools

1

Data entry software
2

Automated extraction software
3

Data import tool
4

Transformation tool

Perform initial data gathering

This task involves gathering the initial set of data. It is a crucial step in the data quality assessment process as it provides the foundation for further analysis. The impact of this task is ensuring that the collected data is accurate and comprehensive. The desired result is a complete set of initial data. To complete this task, you will need to follow the chosen data gathering method and collect data from the identified sources. You may encounter challenges such as incomplete or inconsistent data. To address these challenges, you can validate the data against predefined standards or consult with subject matter experts.

Date of data gathering

Select the data sources to gather from

1

Database A
2

Database B
3

Website C
4

API D
5

File E

Review gathered data for initial issues

This task involves reviewing the gathered data for any initial issues. By reviewing the data, you can identify any inconsistencies or errors that may affect its quality. The impact of this task is ensuring that the data is clean and reliable. The desired result is a list of initial issues identified in the gathered data. To complete this task, you will need to analyze the collected data and compare it against predefined standards or guidelines. You may encounter challenges such as large data sets or complex data structures. To overcome these challenges, you can utilize data analysis tools or seek assistance from data experts.

List any initial issues identified in the gathered data

Select the types of issues identified

1

Data inconsistency
2

Data duplication
3

Missing data
4

Incorrect data format
5

Outliers

Resolve initial data discrepancies

This task involves resolving the initial data discrepancies identified in the previous task. By resolving the discrepancies, you can ensure that the data is accurate and consistent. The impact of this task is improving the quality of the gathered data. The desired result is clean and standardized data. To complete this task, you will need to analyze the identified discrepancies and apply appropriate corrective measures. You may encounter challenges such as complex data relationships or conflicting data sources. To address these challenges, you can consult with subject matter experts or utilize data transformation tools.

Describe the corrective measures taken to resolve the initial data discrepancies

Select the methods used to resolve discrepancies

1

Data cleaning
2

Data transformation
3

Data merging
4

Data splitting
5

Data filtering

Standardize data formatting

This task involves standardizing the formatting of the gathered data. By standardizing the data formatting, you can ensure consistency and compatibility across the data set. The impact of this task is improving the usability and accuracy of the data. The desired result is a uniformly formatted data set. To complete this task, you will need to define formatting rules and apply them to the collected data. You may encounter challenges such as different data formats or conflicting formatting standards. To overcome these challenges, you can utilize data transformation tools or consult with data formatting experts.

Select the formatting rules applied to the data

1

Date format (e.g. MM/DD/YYYY)
2

Numeric format (e.g. ####.##)
3

Text format (e.g. Title case)
4

Address format (e.g. Street, City, Country)
5

Phone number format (e.g. ###-###-####)

Select the data fields to be formatted

1

Date field
2

Numeric field
3

Text field
4

Address field
5

Phone number field

Discard irrelevant data

This task involves discarding any irrelevant data from the collected data set. By discarding irrelevant data, you can focus on the relevant and useful information. The impact of this task is improving the efficiency and accuracy of the data assessment. The desired result is a streamlined data set without irrelevant data. To complete this task, you will need to identify and exclude any data that is not relevant to the assessment objectives. You may encounter challenges such as limited data relevance criteria or subjective judgment. To overcome these challenges, you can consult with relevant stakeholders or utilize data relevance guidelines.

List any data fields or records identified as irrelevant

Select the criteria used to identify irrelevant data

1

Data relevance criteria
2

Subjective judgment
3

Stakeholder feedback
4

Data quality guidelines
5

Industry standards

Normalize values across data set

This task involves normalizing the values across the data set. By normalizing the values, you can ensure consistency and comparability of the data. The impact of this task is improving the accuracy and reliability of the data assessment. The desired result is a standardized data set with normalized values. To complete this task, you will need to identify any inconsistent or non-standardized values and apply transformation techniques to normalize them. You may encounter challenges such as complex data relationships or conflicting value representations. To address these challenges, you can consult with subject matter experts or utilize data transformation tools.

Describe the transformation techniques used to normalize the values

Select the data fields to be normalized

1

Numeric field
2

Text field
3

Date field
4

Address field
5

Categorical field

Check data for completeness

This task involves checking the gathered data for completeness. By ensuring the data is complete, you can avoid gaps or missing information that may affect the assessment results. The impact of this task is improving the reliability and validity of the data assessment. The desired result is a complete and comprehensive data set. To complete this task, you will need to review the data and check for any missing or incomplete values. You may encounter challenges such as limited data availability or ambiguous data definitions. To overcome these challenges, you can consult with data experts or utilize data validation techniques.

Select the methods used to check data completeness

1

Data field validation
2

Data record validation
3

Data reconciliation
4

Data aggregation
5

Data sampling

Were any missing or incomplete values found?

1

Yes
2

No

Approval: Completeness Check

Check data for completeness
Will be submitted

Scan data for anomalies

This task involves scanning the gathered data for any anomalies or outliers. By identifying anomalies, you can ensure the accuracy and reliability of the data. The impact of this task is improving the quality and credibility of the data assessment. The desired result is a list of identified anomalies. To complete this task, you will need to analyze the data and compare it against predefined rules or statistical models. You may encounter challenges such as complex data patterns or rare occurrences. To address these challenges, you can utilize data visualization tools or consult with data anomaly experts.

List any anomalies or outliers identified in the data

Select the anomaly detection methods used

1

Statistical analysis
2

Data profiling
3

Machine learning algorithms
4

Rule-based approach
5

Visual inspection

Investigate and correct anomalies

This task involves investigating and correcting the anomalies identified in the previous task. By investigating and correcting anomalies, you can ensure the accuracy and reliability of the data. The impact of this task is improving the quality and credibility of the data assessment. The desired result is clean and accurate data without anomalies. To complete this task, you will need to analyze the identified anomalies and apply appropriate corrective measures. You may encounter challenges such as complex data relationships or conflicting information sources. To address these challenges, you can consult with data experts or utilize anomaly detection tools.

Describe the corrective measures taken to resolve the anomalies

Select the methods used to correct anomalies

1

Data cleaning
2

Data transformation
3

Data merging
4

Data filtering
5

Data imputation

Validate data against predefined standards

This task involves validating the gathered data against predefined standards or guidelines. By validating the data, you can ensure its compliance with quality requirements. The impact of this task is improving the reliability and validity of the data assessment. The desired result is a data set that meets the predefined standards. To complete this task, you will need to define the validation criteria and compare the data against them. You may encounter challenges such as subjective criteria or limited availability of standards. To overcome these challenges, you can consult with relevant stakeholders or utilize data validation tools.

Describe the predefined standards or guidelines used for data validation

Select the data fields to be validated

1

Numeric field
2

Text field
3

Date field
4

Address field
5

Categorical field

Approval: Validation Check

Validate data against predefined standards
Will be submitted

Perform data consistency check

This task involves performing a data consistency check to ensure that the data is consistent across different sources or data fields. By checking data consistency, you can identify any discrepancies or conflicts that may affect the data assessment. The impact of this task is improving the accuracy and reliability of the data. The desired result is a consistent and coherent data set. To complete this task, you will need to compare the data across different sources or fields and identify any inconsistencies. You may encounter challenges such as data complexity or limited data compatibility. To address these challenges, you can utilize data comparison tools or seek assistance from data experts.

Describe any inconsistencies or conflicts identified in the data

Select the data fields or sources compared for consistency check

1

Data source A vs Data source B
2

Data field X vs Data field Y
3

Data field Z vs Data field W

Carry out duplicate data check

This task involves carrying out a duplicate data check to identify and eliminate any duplicate records or entries in the data set. By checking for duplicate data, you can ensure data integrity and avoid redundancy. The impact of this task is improving the accuracy and efficiency of the data assessment. The desired result is a data set without duplicate records. To complete this task, you will need to compare the data and identify any duplicate values or patterns. You may encounter challenges such as large data sets or complex data structures. To overcome these challenges, you can utilize data deduplication techniques or consult with data experts.

Describe the duplicate data identified in the data set

Select the methods used for duplicate data identification

1

Exact match comparison
2

Fuzzy matching
3

Rule-based matching
4

Record linkage
5

Data profiling

Removal of Duplicate Data

This task involves removing the duplicate data identified in the previous task. By removing duplicate data, you can ensure data integrity and avoid duplication. The impact of this task is improving the accuracy and efficiency of the data assessment. The desired result is a data set without duplicate records. To complete this task, you will need to apply appropriate deduplication techniques to eliminate duplicate values or patterns. You may encounter challenges such as data complexity or conflicting information sources. To address these challenges, you can consult with data experts or utilize data deduplication tools.

Describe the deduplication techniques used to remove duplicate data

Select the methods used for duplicate data removal

1

Exact match removal
2

Fuzzy matching removal
3

Rule-based removal
4

Record linkage removal
5

Data profiling removal

Assess the quality of data

This task involves assessing the quality of the data based on predefined quality criteria or metrics. By assessing the data quality, you can determine its accuracy, completeness, consistency, and other relevant factors. The impact of this task is evaluating the suitability of the data for further analysis or decision-making. The desired result is a data quality assessment report. To complete this task, you will need to define the quality criteria or metrics and apply them to the data. You may encounter challenges such as subjective assessment or limited availability of benchmarks. To overcome these challenges, you can consult with domain experts or utilize data quality assessment tools.

Describe the quality criteria or metrics used for data assessment

What is the overall data quality rating?

1

Excellent
2

Good
3

Fair
4

Poor
5

Unusable

Approval: Data Quality Assessment

Assess the quality of data
Will be submitted

Document the data quality assessment results

This task involves documenting the results of the data quality assessment. By documenting the results, you can communicate the findings and recommendations to relevant stakeholders. The impact of this task is ensuring transparency and accountability in the data assessment process. The desired result is a comprehensive data quality assessment report. To complete this task, you will need to organize and present the assessment results in a clear and concise manner. You may encounter challenges such as complex data visualizations or data interpretation. To address these challenges, you can utilize data reporting tools or seek assistance from data communication experts.

Select the data quality issues identified