Informatica Data Quality Online Training

Informatica Data Quality and Profiling

Use the data quality capabilities in the Developer tool to analyze the content and structure of your data and enhance the data in ways that meet your business needs.

Use the Developer tool to design and run processes to complete the following tasks:

• Profile data. Profiling reveals the content and structure of data. Profiling is a key step in any data project as it can identify strengths and weaknesses in data and help you define a project plan.
• Create scorecards to review data quality. A scorecard is a graphical representation of the quality measurements in a profile.
• Standardize data values. Standardize data to remove errors and inconsistencies that you find when you run a profile. You can standardize variations in punctuation, formatting, and spelling. For example, you can ensure that the city, state, and ZIP code values are consistent.
• Parse data. Parsing reads a field composed of multiple values and creates a field for each value according to the type of information it contains. Parsing can also add information to records. For example, you can define a parsing operation to add units of measurement to product data.
• Validate postal addresses. Address validation evaluates and enhances the accuracy and deliverability of postal address data. Address validation corrects errors in addresses and completes partial addresses by comparing address records against address reference data from national postal carriers. Address validation can also add postal information that speeds mail delivery and reduces mail costs.
• Find duplicate records. Duplicate analysis calculates the degrees of similarity between records by comparing data from one or more fields in each record. You select the fields to be analyzed, and you select the comparison strategies to apply to the data. The Developer tool enables two types of duplicate analysis: field matching, which identifies similar or duplicate records, and identity matching, which identifies similar or duplicate identities in record data.
• Manage exceptions. An exception is a record that contains data quality issues that you correct by hand. You can run a mapping to capture any exception record that remains in a data set after you run other data quality processes. You review and edit exception records in the Analyst tool.
• Create reference data tables. Informatica provides reference data that can enhance several types of data quality process, including standardization and parsing. You can create reference tables using data from profile results.
• Create and run data quality rules. Informatica provides rules that you can run or edit to meet your project objectives. You can create mapplets and validate them as rules in the Developer tool.
• Collaborate with Informatica users. The Model repository stores reference data and rules, and this repository is available to users of the Developer tool and Analyst tool. Users can collaborate on projects, and different users can take ownership of objects at different stages of a project.
• Import mappings from PowerCenter. You can import and run mappings from PowerCenter to reuse the metadata for physical data integration.

Key Features

Deliver the right data quality capabilities at the right time and the right scale to the right users.

• Role-based capabilities -Empower business users and facilitate collaboration between IT and business stakeholders.
• Flexible deployment-Manage the quality of multi-cloud and on-premises data for all use cases and for all workloads.
• Exception management-Incorporates human tasks into the workflow, allowing business users to review, correct, and approve exceptions throughout the automated process.
• Enterprise discovery, search, and profiling-Profile data and perform iterative data analysis to uncover relationships and better detect problems.
• Intelligent data quality-Use AI-driven insights to automate the most critical tasks and streamline data discovery to increase productivity and effectiveness.
• Rule builder for business analysts-Enable business users to build and test logical business rules without relying on IT.
• Pre-built, reusable rules and accelerators-Accelerate projects with a comprehensive set of pre-built business rules and accelerators; reuse common data quality rules across any data from any source to save time and resources.
• Rich set of data quality transformations-Ensure delivery of high-quality information with data standardization, validation, enrichment, de-duplication, and consolidation capabilities.

Data Quality Benefits:

• Make better and more Immediate marketing decisions.
• Increase market share.
• Increase revenue and profitability.
• Improve customer support.
• One tool that acts as a single platform for data quality
• Identify, resolve and prevent data quality problems.Data becomes more trusted
• Effective data profiling and more effective ways to share the profiling rules and results with business;
• All you need to do is generate a scorecard for the profile and share the URL with business. This enhance trust in business.
• Enhance IT productivity with powerful business-IT collaboration tools and a common data quality project environment.
• Develop routines like address standardization, exception handling, data masking and integrate them with PowerCenter to utilize them as components / mapplets;
• Universal Connectivity to All Data Sources
• Centralized Data Quality Rules for All Applications
• All rules, reference data, and processes can be reused for all types of data integration projects, including data migration, data consolidation, and MDM.