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
It has been stated that the most common failure of a software project is caused by not capturing or understanding what the system is required to achieve. This is equally true of your information needs; have you truly captured your
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
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
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
The world is changing, people are changing, organisations are changing, and this is no different for data requirements. An organisation needs to accept this and makes sure that change requirements are captured, impact assessed, acted upon and communicated. During my
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
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
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).
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?