This blog post gives an overview of integrating Informatica MDM and IDQ, explains the advantages and disadvantages of each approach to aid. Informatica Data Quality(IDQ) helps in creating a true data-driven environment that supports better business decision making regardless of the amount of data. One of the first steps in solving a data quality problem is to perform data profiling. As seen in Jason Hover’s article, Data Profiling: What, Why.
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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 infromatica maintained in the enterprise data repository.
This 2-part blog series will provide a glimpse into the features these tools offer. IDQ has 2 variants:.
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. Data Profiling — BI programs involve data in disparate systems. 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. A typical column infkrmatica will show:.
Informatica Data Quality – A Peek Inside – Part 1 – Perficient Blogs
Reference tables — IDQ enables users to maintain reference tables where they can define a set of allowed values. 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.
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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 iddq on a rule.
Up next in this series — Basic data quality transformations…. This site uses Akismet to reduce spam. Learn how your comment data is processed. Automotive Communications Consumer Markets.
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High Tech Life Sciences Manufacturing. IDQ has 2 variants: A typical column profiling will show: Up next in this series — Basic data quality transformations… Albert Qian. Data profiling data quality IDQ. Leave a Reply Cancel reply.