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Bundle: Data Quality Packs and Plans

Free download

Posted by: Informatica Marketplace

Mapplets to meet your Data Quality needs.


Use Informatica Data Quality to analyze the content and structure of your data and enhance the data in ways that meet your business needs. You can use Informatica Data Quality to design and run processes or plans 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.


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.


In addition to this, Data Quality also helps you Manage exceptions, create reference data tables, create data quality rules and much more. Given below is the list of examples that can help you start your journey in the world of Informatica Data Quality.



1.  Data Quality : IDQ Best Practices

Best practices to be followed while building any Informatica Data Quality Plan.


2.  Data Quality : Address Cleansing for Excel®

AddressDoctor for Microsoft® Excel® is your personal data cleansing tool  for postal addresses. The results are saved directly to an Excel® worksheet.


3.  Data Quality : Credit Card Validation

Data Quality Mapplet to validate credit card numbers using Luhn algorithm.


4.  Data Quality : Parsing Tokens

Mapplet to parse individual words from a space delimited string into a single field.


5.  Data Quality : Email Validation Rule

Informatica Data Quality Mapplet that helps you to validate email addresses.


6.  Data Quality : Product UOM Standardization

Informatica Data Quality Mapplet that standardizes product Unit of Measure (UOM) codes.


7.  Data Quality : Country Name from Email/Web address

Identify country of a customer from either email or website address.


8.  Data Quality 9.5 : Credit Card Domain Discovery

Domain Data Discovery Sample - Discover Credit card data using domain data rule.


9.  Data Quality 9.5 : Birthday Domain Data Discovery

Domain Data Discovery Sample - Discover Birthday and age data using domain data rule.


10. Data Quality 9.5 : IP Address Domain Data Discover

Domain Data Discovery Sample - Discover IP Address data using domain data rule.


  • Informatica PowerCenter 9.5.

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