One of my favorite software tools is the spell checker, due to its entertainment value. Colloquially known as the spill chucker due to the fact that if you mistype spell checker as spill chucker, the spell checker identifies that both “spill” and “chucker” are valid words, the spell checker has no concept of context. I was reminded of…
Before any data quality project, it is critical to go beyond the immediate issues of duplicate records or bad addresses and understand the fundamental business needs of the organization and how cleaner day will enable you to make better business decisions.
Every day we work with customers to begin the process of evaluating helpIT data quality software (along with other vendors they are looking at). That process can be daunting for a variety of reasons from identifying the right vendors to settling on an implementation strategy, but one of the big hurdles that occurs early on in the process is running an initial set of data through the application.
If you’ve found yourself reading this blog then you’re no doubt already aware of the importance of maintaining data quality through processes such as data verification, suppression screening, and duplicate detection.
One of the more interesting aspects of working for a data quality company is the challenge associated with solving real world business issues through effective data management. In order to provide an end-to-end solution, several moving parts must be taken into consideration, data quality software being just one of them.
A lot has been written about “phonetic algorithms” since Soundex was created for the US Census in (I think) 1880, but the world seemed to stand fairly still until computer software started to implement name matching in the 70’s. The strange thing is that Soundex seems to have remained the de facto standard until well into the 90’s,…
If you take a good look around the master data management (MDM) industry, data quality is the buzz word of the day. Blog posts, surveys, analyst briefings, white papers and testimonials are filled with commentary on the importance of good data quality. What is the importance?