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.

Export mappings to PowerCenter. You can export and run mappings in PowerCenter. You can export mappings to PowerCenter to reuse the metadata for physical data integration or to create web services.