1. Profiling the data by “Informatica Analyst”
Need to identify the problems in data before we can begin to cleanse and standardize the data. Data profiling is the process of identifying the data problems with respect to completeness (What data is missing), Conformity (What data is held in a Non-standard format) and Consistency(Data in one field being consistent throughout the enterprise).After profiling, a scorecard can be created to obtain a high-level view of completeness, conformity, consistency and accuracy in the data set.
2. Standardize the data using “Informatica Analyst and Informatica Developer”
After the data profiling, the analyst can identify the inconsistencies/irregularities and decide on the standardization needs to be performed. The requirements for standardization of data can be documented within the profile which enables the Informatica Analysts and Informatica DQ Developers to collaborate on Data quality projects. Data Analysts and Data quality developers can easily collaborate and transfer the project knowledge using these tools (Informatica Analyst and Informatica Developer).
3. Address validation by using Informatica Developer
The Next would be to ensure the addresses are accurate by validating and enhancing them using the reference data from postal agencies.Informatica data quality has a rich feature which helps to standardize your address information management, validate your critical address information and format your address information for marketing and billing operations.
4. Matching by using the Informatica Developer
Once the data is cleansed, standardized, duplicate records can be identified using the different matching techniques.
5. Consolidation using “Informatica Analyst and Informatica Developer”
Using the business rules defined by the data analyst, the Informatica data quality developer can build mappings to automatically consolidate the matched records.
The IDQ develop can build mappings to consolidate matched records using the rules defined by the analyst. They can take the best value from each field across a record and use these to create a new master record.
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