September 30, 2011 – 12:28 am
Informatica supports Agile Data Integration for Agile BI with best practices that encourage good data governance, facilitate business-IT collaboration, promote reuse & flexibility through data virtualization, and enable rapid prototyping and test-driven development. Organizations that want to successfully adopt Agile Data Integration should standardize on the following best practices and leverage Informatica 9.1 to streamline [...]
By John Haddad
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Posted in Data Governance, Data Integration, Data masking, Informatica 9.1, Master Data Management
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Also tagged Compliance, data profiling, data subsetting, Data Virtualization, ETL, Lean Integration, multi-domain MDM, Self Service, validation
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I have been talking with a lot of customers lately on the topic of data governance. Over and above the obvious question of “what is data governance”, two other common questions seem to come up, both related to helping make it a reality. Time and time again, I get questions regarding what the right approach [...]
By Clarke Patterson
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Posted in Business Impact / Benefits, Data Governance, Data Quality, Enterprise Data Management, Governance, Risk and Compliance, Master Data Management, Operational Efficiency, Pervasive Data Quality, Profiling, Scorecarding
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Also tagged Data Governance, data profiling, Data Quality, data scorecarding, Master Data Management, MDM
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Building a business case for data quality is a waste of time. Nobody really cares. Improving data quality for quality’s sake is a waste of money. Sounds funny coming from a data quality specialist, someone who has spent the last decade preaching data profiling and data quality. But the fact is people from the business [...]
October 5, 2009 – 4:59 pm
“Imagine a world in which the business and IT are collaborating together.” This line comes from Chris Boorman’s overview of “the biggest and most significant release in the history of Informatica “ – Informatica 9. As I read his post I found myself humming the song, “Imagine” and thinking about the potential implications for data [...]
