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1 - The Dangers of Dirty Data

Published online by Cambridge University Press:  09 November 2021

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Summary

What is dirty data?

Let's start on some common ground. What exactly is dirty data?

Well, the truth is that it can mean different things to different people working with different types of data. For the purposes of this book, it will be based on information used in a business context.

At its most basic level, dirty data is anything incorrect. It could be things such as:

Misspelt names

This happens more than you think. If it's supplier names, it could be a simple switch of letters from ABC Printing to ABC Printign, a missing letter such as T Shoesmit instead of T Shoesmith, or something much more subtle like AT Jones, instead of TA Jones, which may not be easily picked up.

If you’re dealing with personal information, it's doubly important to get the name right because of data protection regulations, such as the General Data Protection Regulation (GDPR). Very recently, I received a piece of mail for my new limited company ‘The Classification Guru Ltd’. The address was correct and my first and middle names were correct, but I had someone else's surname and a business name that wasn't mine.

When I checked on Companies House, I could see that the surname and the business name were related to one person – everything else was my information. What I suspect happened in this instance is one of several things: firstly that the list of names for mailings was in Excel and someone possibly hadn't filtered all columns and the information was therefore mixed up. Secondly, it could have been that some lines of data were removed, which caused some of the information in certain columns to shift up or down and misalign. It could have been something as simple as a cut and paste error that caused the problem. This could have easily been rectified by applying some spot checks to the data before it was used as a mailing list. I’ll cover this further later.

Incorrect or misleading descriptions

In the work I do, I see this a lot in invoice or Purchase Order (PO) descriptions. It could be something as simple as ‘services’ in the description and the person's name as the supplier.

Type
Chapter
Information
Between the Spreadsheets
Classifying and Fixing Dirty Data
, pp. 1 - 36
Publisher: Facet
Print publication year: 2021

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