However, there are requirements when the OOTB cleanse functions are not enough and there is a need for comprehensive functions to achieve data cleansing and standardization, for e. This blog post describes the various options to integrate Informatica MDM and IDQ, explains the advantages and disadvantages of each approach to aid in deciding the optimal approach based on the requirements. Figure 1: Informatica Platform Staging Process Advantages Stage tables are immediately available to use in the Developer tool after synchronization eliminating the need to manually create physical data objects. Changes to the synchronized structures are reflected into the Developer tool automatically.
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One of the key success criteria for these programs is to maintain good quality data. Businesses also demand more value from the data that is maintained in the enterprise data repository.
This 2-part blog series will provide a glimpse into the features these tools offer. This tool offers an editor where objects can be built with a wide range of data quality transformations like Parser, standardizer, address validator, match-merge etc.
Once the DQ transformations are deployed as services, they can be used across the enterprise and platforms. So, it is essential to profile the data in order to understand the content and structure of data. Both tools offer the capability for Data profiling. These tools have a default profile option which shows the statistics in each column of data objects flat files, relational table, etc.
Reference tables can be easily created from the list of unique values of column profiles and edit the table to add or remove values from it.
Rule — Rules are defined to validate if the data meets a business condition. For example, a rule can be created to check if an email has a domain name in it. Rules can be used while profiling or in data transformations. Scorecard can be generated for a column to display a graphical representation of valid values. It also presents a trend over time so it can be used to measure data quality initiative progress. The following screenshot shows a column profile and a list of Invalid records based on a rule.
Up next in this series — Basic data quality transformations….
Informatica Data Quality – A Peek Inside – Part 1
Informatica Data Quality Tutorial
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