Most organisations have problems with their data – not enough, too much, not good enough, missing, duplicated, inconsistent etc. Many managers and employees just want someone to ‘sort it all out’, but who is responsible? Often there is a view
Data quality assessments can provide large amounts of useful information. However, to gain a complete perspective on organisational data quality, it is essential to consider three key perspectives: The data itself – entries in databases and spreadsheets The requirements for
Staff can often be very resourceful in making poor systems and processes work and to overcome data problems within an organisation. If people are based in a single office then they can call out questions like: “Do you remember who
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
You may wonder why you should bother improving your data quality or what the benefits of this activity may be. You may wonder how to secure suitable resources and funding to deliver improvements to data quality. Read on to discover
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
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).