Data Quality (DRM Workbook)

Overview

In the Pricefx data readiness methodology, the Data Quality Workbook is a tool used to assess and improve the quality of data that will be integrated or utilized within the Pricefx platform. It helps identify data quality issues, define data quality requirements, and outline actions to address those issues.

Using Data Quality Workbook

Here's a general overview of how to use the Data Quality Workbook:

1. Identify Data Quality Dimensions: Start by identifying the key dimensions of data quality that are important for your organization and the Pricefx implementation. These dimensions may include accuracy, completeness, consistency, validity, timeliness, integrity, and usability. Document these dimensions in the workbook.

2. Define Data Quality Metrics: For each data quality dimension, define specific metrics that will be used to measure the quality of the data. For example, accuracy can be measured by the percentage of data records with no errors or inconsistencies. Document these metrics in the workbook.

3. Assess Data Quality: Evaluate the current state of data quality by collecting relevant data samples or conducting data profiling activities. Use the defined data quality metrics to assess the quality of the data. Document the findings and observations in the workbook, highlighting any data quality issues or challenges.

4. Identify Data Quality Requirements: Based on the assessment and your organization's specific needs, define data quality requirements that should be met for the Pricefx implementation. These requirements may specify the acceptable thresholds for each data quality metric or establish targets for improvement. Document these requirements in the workbook.

5. Plan Data Cleansing Activities: Determine the data cleansing activities required to address the identified data quality issues. This may involve data standardization, deduplication, error correction, or other data cleansing techniques. Define the specific tasks, responsibilities, and timelines for these activities in the workbook.

6. Establish Data Quality Monitoring: Define a data quality monitoring plan to ensure ongoing assessment and maintenance of data quality. This may involve regular data profiling, data validation checks, or data quality audits. Document the monitoring plan and associated activities in the workbook.

7. Define Data Quality Improvement Actions: Based on the assessment findings and data quality requirements, outline the actions or initiatives to improve data quality. This may include process improvements, system enhancements, data governance measures, or data stewardship activities. Document these improvement actions in the workbook, along with responsible stakeholders and timelines.

8. Track Progress and Performance: Regularly update the workbook to track the progress of data quality improvement efforts. Document any changes, updates, or insights gained throughout the process. This will help monitor the effectiveness of the data quality initiatives and ensure ongoing data quality management.

9. Collaborate and Communicate: Engage with stakeholders, data owners, and data users throughout the data quality assessment and improvement process. Collaborate with them to gather insights, validate findings, and gain their commitment to data quality improvement efforts. Use the workbook as a communication tool to share information and facilitate discussions.

The Data Quality Workbook provides a structured approach to assess, define, and improve the quality of data in the context of the Pricefx implementation. By following these steps and leveraging the workbook, you can systematically address data quality issues, establish data quality requirements, and implement measures to ensure high-quality data for optimal utilization within the Pricefx platform.