Informatica CDQ | Learn Data Quality Management Process Cycle

1. Day4-Informatica Cloud Data Quality(CDQ) Agenda Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com

2. Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com The Data Quality Management (DQM) Process Cycle refers to a set of systematic steps used to ensure that data is accurate, consistent, complete, and usable. This process cycle is critical for organizations to maintain high-quality data and to leverage it for business decisions, analytics, and operational effectiveness. The DQM cycle typically involves a continuous, iterative process that spans the entire data lifecycle, ensuring that data remains high quality from collection through to analysis. Key Steps in the Data Quality Management Process Cycle 1. Data Profiling Definition: Data profiling involves assessing the current state of data to identify anomalies, patterns, inconsistencies, and gaps in the data. This step helps establish a baseline for data quality. Activities: Examine the structure, completeness, and format of data. Identify missing, duplicated, or invalid data. Assess the distribution of values (e.g., range, frequency). Goal: To understand the quality of the data and identify areas that need improvement. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

3. Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com 2. Data Cleansing (or Data Cleaning) Definition: Data cleansing is the process of identifying and correcting errors or inconsistencies in the data. It involves removing duplicate records, correcting inaccurate values, and filling in missing data. Activities: Removing duplicates: Identifying and removing identical records. Correcting inaccuracies: Fixing incorrect data, such as misspelled names, invalid addresses, or incorrect phone numbers. Filling missing values: Replacing missing data with valid values, such as using default values or imputation methods. Goal: To improve the accuracy and consistency of the data. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

4. Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com 3. Data Standardization Definition: Data standardization involves converting data into a consistent format to ensure uniformity across different data sources and systems. Activities: Standardizing date formats (e.g., converting all dates to YYYY-MM-DD). Normalizing address formats (e.g., making sure that addresses include street, city, state, and postal code in the same order and format). Standardizing units of measurement (e.g., ensuring that all monetary values are in the same currency). Goal: To ensure that data follows standardized formats and rules across the organization, making it easier to process, compare, and integrate. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

5. Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com 4. Data Enrichment Definition: Data enrichment is the process of adding valuable, external data to the existing dataset to enhance its quality and value. Activities: Adding demographic, geographic, or firm graphic information to customer records. Integrating third-party data sources (e.g., social media data, credit scores). Goal: To improve the completeness and relevance of the data by incorporating additional external data that can provide context or insights. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

6. Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com 5. Data Validation Definition: Data validation is the process of ensuring that data complies with predefined rules and business logic before being used for analysis or integration. Activities: Ensuring that values match expected data types (e.g., numerical values in a price field, email format for email addresses). Validating data against predefined rules or business constraints (e.g., ensuring that a customer's age is within a valid range). Running consistency checks across different systems to ensure uniformity. Goal: To prevent invalid or non-conforming data from being used, which could lead to errors in processing or analysis. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

7. Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com 6. Data Monitoring Definition: Data monitoring involves continuously tracking data quality metrics to ensure that data quality remains high over time. This step includes real-time or periodic checks on data quality to spot issues before they escalate. Activities: Continuously monitoring data for errors, missing values, or inconsistencies. Setting up automated data quality reports and alerts. Tracking data quality trends over time (e.g., improvements or declines in data accuracy, completeness, etc.). Goal: To maintain data quality on an ongoing basis and quickly identify and address issues. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

8. Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com 7. Data Governance Definition: Data governance refers to the overall management of data, including policies, procedures, and standards that ensure data is properly handled, protected, and used responsibly. Activities: Defining and enforcing data quality standards and policies. Assigning data stewards or owners responsible for specific data sets. Implementing security measures to protect data integrity and privacy. Goal: To ensure that data quality management aligns with organizational goals and regulatory requirements, and that there are clear responsibilities for maintaining data quality. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

9. Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com 8. Data Integration Definition: Data integration is the process of combining data from different sources into a unified view while ensuring that the integrated data maintains its quality. Activities: Integrating data from various departments, databases, or external sources. Ensuring that the integrated data adheres to consistency, accuracy, and format rules. Merging datasets without creating duplicates or inconsistencies. Goal: To create a consolidated dataset that combines data from multiple sources while maintaining high data quality. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

10. Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com 9. Data Quality Reporting and Analytics Definition: Reporting and analytics involve generating reports on data quality, measuring key metrics, and analyzing trends in data quality over time. Activities: Generating dashboards or reports that show key data quality metrics (e.g., accuracy, completeness, consistency). Analyzing the causes of data quality issues and trends. Communicating data quality insights to stakeholders for further action. Goal: To track the effectiveness of the data quality program and identify areas for improvement. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

11. Data Quality Management Process Cycle InventModel Technology Solution +91-98219-31210 support@inventmodel.com Summary of the Data Quality Management Process Cycle Data Profiling: Assess current data to identify issues. Data Cleansing: Correct errors and remove duplicates. Data Standardization: Convert data to a uniform format. Data Enrichment: Add external data to enhance value. Data Validation: Ensure data meets business rules. Data Monitoring: Continuously track data quality over time. Data Governance: Implement policies and procedures for managing data quality. Data Integration: Combine data from multiple sources while maintaining quality. Data Quality Reporting and Analytics: Track data quality metrics and improve processes. This cycle helps ensure that data is of high quality throughout its lifecycle, supporting better business decisions, operational processes, and customer experiences. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

12. Thank You ! References – https://informatica.com InventModel Technology Solution +91-98219-31210 support@inventmodel.com