Informatica CDQ | What is Data Quality & Its Key Dimensions

1. Day4-Informatica Cloud Data Quality(CDQ) Agenda What is Data Quality? Dimensions of Data Quality InventModel Technology Solution +91-98219-31210 support@inventmodel.com

2. What is Data Quality? InventModel Technology Solution +91-98219-31210 support@inventmodel.com Data Quality refers to the condition or fitness of data for its intended use, ensuring that the data is accurate, consistent, reliable, and relevant to meet the needs of business processes, decision-making, and analytics. High-quality data is essential for organizations to make informed decisions, maintain regulatory compliance, and enhance operational efficiency. Poor data quality can lead to incorrect analysis, lost opportunities, increased costs, and operational inefficiencies. Example of Data Quality in Practice: Imagine a retail company that collects customer data to send personalized marketing emails. If the customer records contain outdated or incorrect information (such as incorrect email addresses or missing names), the company might send marketing emails to the wrong recipients or miss potential customers. By ensuring high data quality—through cleaning up the data, ensuring proper formats, and filling in missing values—the company can improve the effectiveness of its marketing campaigns and customer relationships. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

3. Dimensions of Data Quality InventModel Technology Solution +91-98219-31210 support@inventmodel.com The Dimensions of Data Quality are the characteristics or attributes used to assess and measure the quality of data. These dimensions help organizations evaluate how well their data meets the required standards for business decision-making, operational processes, and compliance. Understanding and managing these dimensions is crucial to maintaining high-quality, reliable data. Here are the key dimensions of data quality: 1. Accuracy Definition: Data is considered accurate if it correctly reflects the real-world entity or event it represents. Example: If a customer's address is recorded, it should match their actual physical address, not contain errors like misspellings or incorrect information. Importance: Inaccurate data can lead to faulty business decisions and misunderstandings. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

4. Dimensions of Data Quality InventModel Technology Solution +91-98219-31210 support@inventmodel.com 2. Consistency Definition: Data is consistent when it does not conflict with other data sources. The same data element should have the same value across multiple datasets or systems. Example: A customer’s email address in the CRM system should match the one in the billing system. Importance: Consistency is vital for accurate reporting and integration between different data sources or systems. 3. Completeness Definition: Data is complete when all necessary information is present, without missing values or incomplete fields. Example: A customer record should have a full name, address, email, and phone number. Missing these details would be considered incomplete. Importance: Incomplete data can lead to missed opportunities, especially in analytics and decision-making. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

5. Dimensions of Data Quality InventModel Technology Solution +91-98219-31210 support@inventmodel.com 4. Timeliness Definition: Data is timely when it is up to date and available when needed. Outdated data may lead to poor decision-making or missed opportunities. Example: A product inventory database should reflect the real-time availability of products, not outdated stock levels. Importance: Timeliness is crucial for systems that rely on real-time or near-real-time data, such as financial transactions or inventory management. 5. Validity Definition: Valid data conforms to defined formats, rules, and constraints. It is data that adheres to business rules and expected values. Example: A date field should contain a valid date, and a phone number should follow a standard format (e.g., country code, area code, etc.). Importance: Invalid data can cause errors in data processing or analysis and may require extra effort to clean and standardize. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

6. Dimensions of Data Quality InventModel Technology Solution +91-98219-31210 support@inventmodel.com 6. Uniqueness Definition: Data is unique when there are no duplicates in the dataset, ensuring that each data point appears only once. Example: A customer should have only one record in the database, not multiple records with different variations of their name or contact details. Importance: Duplicate records can lead to inefficiencies, reporting errors, and operational confusion. 7. Relevance Definition: Data is relevant when it is appropriate for the context in which it is used. Irrelevant data can clutter systems and distract from meaningful analysis. Example: Collecting demographic data (like age and gender) may be relevant for a marketing campaign but not for inventory management. Importance: Relevant data ensures that business operations, decision-making, and analytics are based on useful and purposeful information. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

7. Dimensions of Data Quality InventModel Technology Solution +91-98219-31210 support@inventmodel.com 8. Integrity Definition: Data integrity refers to the accuracy and consistency of data over its lifecycle. It also includes the concept of maintaining relationships between different data points and ensuring data integrity across systems. Example: In a database, foreign key relationships between tables should be maintained, and data updates should not break these relationships. Importance: Data integrity is crucial for ensuring that data remains trustworthy, consistent, and usable across different systems and throughout its lifecycle. 9. Auditability Definition: Data is auditable when its history is traceable, and changes to the data can be tracked, including who made changes and when they were made. Example: If a customer's contact information is updated, the system should log the change with details about who made the update and when. Importance: Auditability is important for ensuring transparency and accountability in the data management process, especially for regulatory compliance. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

8. Dimensions of Data Quality InventModel Technology Solution +91-98219-31210 support@inventmodel.com 10. Accessibility Definition: Data is accessible when it can be easily retrieved, processed, and used by authorized users without unnecessary barriers. Example: Data stored in a centralized data warehouse should be easily accessible to users with the appropriate permissions and roles. Importance: Data accessibility ensures that teams can leverage data effectively for decision-making and operational tasks. 11. Conformity Definition: Conformity ensures that data adheres to established standards or formats across the organization. Example: If the address format is standardized across the organization (e.g., street, city, state, zip code), all data should follow this format. Importance: Conforming to standards ensures consistency and makes it easier to integrate data from different sources. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

9. Dimensions of Data Quality InventModel Technology Solution +91-98219-31210 support@inventmodel.com Why are These Dimensions Important? These dimensions are essential because they represent the criteria that determine whether the data will support accurate analysis, operational tasks, and strategic decision-making. Ensuring high data quality across these dimensions will: Increase Trust: Organizations can trust data for decision-making and compliance if it meets high-quality standards. Reduce Costs: Managing poor-quality data (e.g., errors, duplicates) is expensive. High-quality data reduces the need for rework and minimizes the costs of resolving data issues. Enhance Business Efficiency: Good-quality data supports efficient business processes by ensuring accurate insights, reducing errors, and streamlining workflows. InventModel Technology Solution +91-98219-31210 support@inventmodel.com

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