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
The value of data
It is time to consider ‘the value of data’ – a number of recent conversations (such as with Stuart Ravens and Martin Paver), an excellent recent article on Highways England’s data valuation and related events such as the emergence of a proposed
Symptoms of data quality problems
A recent conversation about a large organisation highlighted an interesting question – How do you quickly and easily know that you have got a data quality problem? Clearly, you may have a data manager/ data team who are stating that data quality
Start well….
There are two sayings that we will all have heard at some time: Start with the end in mind Start like you mean to go on A client of ours demonstrates why these two sayings are important from a data
Has lockdown exposed your data weaknesses?
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
Online training now available
To enable us to continue to support our clients during the changes arising from the COVID-19 pandemic, we are now offering online training at https://courses.dpadvantage.co.uk/. Our popular ‘Managing Data Quality‘ course has been set up as four individual courses within
ISO 8000-61 – the data quality management standard
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 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
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