Accurate data is imperative for an organization to conduct cost effective decision making. Like everything else, change is constant for your data. There is a need to cleanse and validate data when received and on a regular basis. Unfortunately, cleansing and validating data is difficult with the native SQL Server toolset. How do we leverage the SQL Server tool set to achieve these goals?
2020-01-03
Problems with data quality are easier and cheaper to prevent than to solve later. In this article, Joe Celko talks about data quality and where the issues tend to happen.
2018-12-20
2,693 reads
Producing a crib sheet for data quality drives a shared understanding of the data used to power the business.
2016-07-28
1,255 reads
In an OLTP DB, other than an autogenerated Identity , there should be something else on the table which makes the non-identity data unique, as in, unique if you had to drop the ID column.
2016-02-03 (first published: 2016-01-22)
467 reads
The other day at a conference, the subject of data degradation/corrosion arose. The speaker at the conference said that data in a database degraded or corroded over time. The statement was made as if degradation over time applied to all databases. I found this blanket statement to be misguided. Indeed, I think that data does degrade in some databases, but not all.
2009-05-14
2,180 reads
Got a new database you know nothing about? Here's my basic proposal on how to find interesting tables in it.
2009-01-09
10,321 reads
Enhanced model performance comes from extracting as much information content as possible… relative to the specific performance metrics you are using to measure success.
2008-02-12
2,257 reads
This article outlines 15 principles of ROI development for data quality (DQ) projects that can be leveraged across any industry.
2008-02-05
1,747 reads
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