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
A good way to understand the impact of poor data practices on your organisation is to imagine the approach to data gathering, storage and exploitation as a machine – a data machine. What does your data machine look like? How
In my previous blogs I introduced the Data Management Maturity Staircase (above) and then the various maturity steps required to climb the staircase. In this blog we shall be looking at ways to make the changes.
If you are responsible for managing a portfolio of assets, then your team need AD4s to help them. What does the acronym AD4 mean? Well, it stands for Asset Data Dictionary Definition Document – quite a mouthful, hence the shorter
In my last blog I introduced the Data Management Maturity Staircase (above). In this blog I shall look at the steps that make up the staircase.
In this series of Blog posts I shall be introducing the ‘data management staircase’, what it means and the necessary journey over time to climb the steps to becoming ‘Excellent’.
Increasingly, organisations are recognising the importance of good data management and how it can improve organisational performance. Managing data across large (and small) enterprises can be challenging, there are a range of standards and approaches that provide guidance applicable to
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