Data Quality Assessment (Data Scoping)

Performing a data quality assessment in the data scoping phase of Pricefx data readiness is crucial to identify and address any data quality issues that may impact the accuracy and reliability of pricing analysis. Here's a step-by-step guide on how to perform a data quality assessment:

1. Define Data Quality Metrics: Start by defining the data quality metrics that are relevant to Pricefx implementation. These metrics may include completeness, accuracy, consistency, timeliness, validity, and integrity. Determine the specific criteria and thresholds for each metric based on your organization's requirements.

2. Identify Data Quality Dimensions: Identify the key dimensions of data quality that need to be assessed. Common dimensions include attribute completeness, attribute accuracy, consistency across data sources, uniqueness, and adherence to business rules. These dimensions provide a comprehensive view of the overall data quality.

3. Select Sample Data: Select a representative sample of data from the identified data sources for assessment. The sample should include a diverse range of data elements and cover various aspects of pricing analysis.

4. Assess Data Completeness: Evaluate the completeness of the selected data sample by comparing it against the expected data elements and attributes. Identify any missing or incomplete data points and quantify the extent of data completeness.

5. Evaluate Data Accuracy: Analyze the accuracy of the data by comparing it against reliable sources or reference data. Identify any discrepancies, errors, or outliers in the data and assess their impact on pricing analysis.

6. Check Consistency: Assess the consistency of the data across different sources or data sets. Look for discrepancies or contradictions in the data values, formats, or definitions. Identify any inconsistencies that may hinder accurate pricing analysis.

7. Examine Timeliness: Evaluate the timeliness of the data by assessing the currency of the selected sample. Consider the freshness of the data and its relevance to current pricing analysis requirements.

8. Validate Data Integrity: Verify the integrity of the data by examining relationships, dependencies, and constraints within the data sample. Look for data referential integrity violations, data duplication, or any other data integrity issues that may affect pricing analysis.

9. Document Data Quality Findings: Document the findings of the data quality assessment, including any identified data quality issues, their severity, and the impacted data elements. Provide recommendations for data cleansing, enrichment, or remediation actions to address the identified issues.

10. Establish Data Quality Improvement Plan: Based on the assessment findings, develop a data quality improvement plan that outlines the actions, resources, and timeline required to address the identified data quality issues. Collaborate with relevant stakeholders to ensure their involvement and support in executing the improvement plan.

11. Monitor and Iterate: Data quality is an ongoing effort. Establish monitoring mechanisms to continuously assess and improve data quality throughout the Pricefx implementation. Iterate on the data quality assessment process based on feedback, data updates, and changing requirements.

By following these steps, organizations can effectively perform a data quality assessment during the data scoping phase of Pricefx data readiness. This assessment helps identify and address data quality issues early in the process, ensuring that the integrated data in Pricefx is of high quality and supports accurate pricing analysis.