Home » Archive for category "Matching"

Archive for the ‘Matching’ Category:


Click & Collect – How To Do It Successfully?

In the UK this Christmas, the most successful retailers have been those that sell online but allow collection by the shopper – in fact, these companies have represented a large proportion of the retailers that had a good festive season. One innovation has been the rise of online retailers paying convenience stores to take delivery

Read More…

Data Quality and Gender Bending

We have all heard the story about the man who was sent a mailing for an expectant mother. Obviously this exposed the organization sending it to a good deal of ridicule, but there are plenty of more subtle examples of incorrect targeting based on getting the gender wrong. Today I was amused to get another

Read More…

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,

Read More…

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: Accuracy – The correctness of values contained in each field of each database record. Completeness – Users must be aware of what data is the minimum required

Read More…

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

Read More…

 
© Copyright 2012
credit