A few years ago we did a series of blog posts on ISO 8000-150 which have been perennially popular. Well, since those posts were created ISO 8000-61 has been published which provides a richer and more comprehensive approach to data
Data quality is free
You may be wondering why you should bother improving your data quality or what the benefits of this activity may be. You may be wondering how to secure suitable resources and funding to deliver improvements to data quality. Read on
Human data errors
Data quality problems all, at their root, involve some form of human error. Whilst this is easy to say, it is perhaps harder to identify and resolve the causes of these human errors. In this blog post, I will explore
Continuity – the new data quality dimension
When talking about data quality, it is usual to consider different aspects or ‘dimensions’ of data quality – validity, completeness, uniqueness, consistency, timeliness and accuracy. These six dimensions were agreed as the most relevant and representative of data quality as
Garbage In, Gospel Out
Most people should be familiar with the old adage “Garbage In, Garbage Out” intended to remind people that if your input data is poor, then any outputs will also be poor. There is a variant of this that cropped up
Does your data trust you?
A colleague of mine recently posted a very interesting blog titled Do you trust your data? This led me to think about the issues from the perspective of the data i.e. if I am a data set can I trust
Do you trust your data?
Those days when you need to make an important decision can be trying at the best of times. The old saying “Garbage In, Garbage Out” is never more relevant (particularly if an AI tool is making automated decisions).
Does your organisation have a Victorian attitude to data quality?
In the Victorian era, accident rates were far higher than today yet at the time were considered regrettable, but just the way things are. How about modern attitudes to data?
Do you need a data MOT?
How you manage your data quality can have quite an impact on your organisation’s results. If you think about taking your car for an MOT*, you’re taking it to be assessed against the criteria set out by the government to
Why is data quality like making apple crumble?
Is it possible to obtain the same great result if you use poor quality ingredients? Being a keen cook, it struck me that data quality is the same as a recipe. If you use inferior quality ingredients, you won’t