How to keep your SQL Server data clean – efficiently!
Working with very large datasets (for example when identifying duplicate records using matching software) frequently can throw up performance problems if you are running queries returning large volumes of data. However there are some tips and tricks that you can use to ensure your SQL code works as efficiently as possible. In this blog post, I’m going to…
Where Is Your Bad Data Coming From?
As Kimball documents in The Data Warehouse Lifecycle Toolkit (available in all good book stores), there are five concepts that together, can be considered to define data quality:
Data Quality and the Spill Chucker
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…
Assessing Your Data Quality Needs
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.
helpIT Systems is Driving Data Quality
For most of us around the US, the Department of Motor Vehicles is a dreaded place, bringing with it a reputation of long lines, mountains of paperwork, and drawn-out processes.
Creating Your Ideal Test Data
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.
Golden Records Need Golden Data: 7 Questions to Ask
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.
The Retail Single Customer View
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.
