Imagine the scene, you are a newly appointed/ promoted Data Governance Manager or Data Manager with a remit of ‘sorting out the data mess‘. Colleagues are impatient for results and some are starting to blame you for the poor quality
I frequently use an analogy of comparing how your organisation ‘does data’ as a machine, but have recently started using a nautical analogy as an extension of this. Which of the following typifies how your organisation delivers data related activities?
How well do you understand your business processes? Each individual process may be well documented, implemented and assured and even delivering great business performance, however, can these same processes be reducing your data quality?
An organisation that works in isolation and does not share data with other organisations is probably rare. Most organisations will collaborate with other organisations to some degree and will be providing data as part of this collaboration. So, what factors
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