The value of zero
Do null sets always spell bad news? @philsimon says no. Find out why.
Tag: data quality
The hindrance of hindsight bias
Data expert (and IDEAS alum) Jim Harris (@ocdqblog) discusses the hindrance of hindsight bias.
Data quality for Hadoop
In my previous two posts I introduced the need for data quality and big data and discussed the need for fit for purpose data quality and for stretching the limits of data quality for big data. In this final post on data quality and big data I’ll address the following:
Big data quality: Think outside the box
In my last post I set the stage for data quality considerations for big data. Today, I’ll cover the following big data and data quality considerations: Data quality efforts should be "fit for purpose" Extend data quality by thinking “outside the box” Data quality efforts should be "fit for purpose"
Big data quality - don't tell me, another buzzword!
Marketing is a big part of my job so I should be supportive of efforts to capitalize on the trend of the day. But given my background in R&D, I am dubious of marketing efforts that are not backed up by real product or solution capabilities. So, I’m a bit
The sixth law of data quality
In his latest blog post, Jim Harris (@OCDQBlog) explains the Sixth Law of Data Quality
Announcing SAS Data Management & Integration Technical Forum and Blog
Many of us in the SAS world are responsible for the data that feeds the various business intelligence, analytics and business solutions provided by SAS. We’ve been involved in data integration, data migration, data quality, ETL / ELT, data access projects that support SAS solutions or are used for other
The Fifth Law of Data Quality
What's the Fifth Law of Data Quality? Jim Harris explains.
The Fourth Law of Data Quality
@ocdqblog explains the fourth law of #DataQuality in today's CoE blog post
The Third Law of Data Quality
The First Law of Data Quality explained the importance of understanding your Data Usage, which is essential to the proper preparation required before launching your data quality initiative. The Second Law of Data Quality explained the need for maintaining your Data Quality Inertia, which means a successful data quality initiative requires a