Dylan Jones is the founder of Data Quality Pro and Data Migration Pro, popular online communities that provide a range of practical resources and support to their respective professions. Dylan has an extensive information management background and is a prolific publisher of expert articles and tutorials on all manner of data related initiatives.
Data profiling is a core technique of data quality management and often the starting point for so many projects these days. Because it’s such a relatively simple technique to apply, it’s easy to overlook some of the more advanced techniques that can take your data profiling to the next level.
One of the growing trends I’m witnessing when talking to Data Quality Pro’s guest interviewees is the use of federated data quality tactics. The idea is a simple but compelling one. Rather than having a large team that manages data quality across the organisation, you create satellite teams that adopt
Many people don’t know where to start with data quality. They get bogged down with questions on dimensions, ownerships, rules and tools. The problem can seem too vast to even begin making sense of their data landscape, let alone transforming it into a well-governed and high-quality asset. A lot of
Ask any battle-hardened data quality practitioner and they will tell you that one of the leading causes of data quality defects stems from an inability to design quality into information systems. I am going to take a specific example of bad system design to explain how data defects quickly become
For most organisations, issue management is seen as an administrative chore. Scattered across the organisation, data workers diligently resolve issues often via their own local issue management process. With silos of data comes silos of maintenance, and this is a real shame because the data these systems possess is a
Some of the most common reasons for defective data come down to a simple lack of coordination between the various groups involved with preserving the integrity of data logic, content, structure and assurance. By improving this process of coordination between the different silos, you can dramatically improve the quality and governance
One of the big problems with data migration projects is that, to the outside sponsor, they appear very much like a black box. You may be told that lots of activities and hustle are taking place, but there isn’t a great deal to show for it until the final make-or-break
It is easy to consider data migration as a movement problem. After all, we need to get our data from A to B with as little effort and cost as possible. With this viewpoint, many practitioners commence mapping and linking the source and target systems together to form an elaborate
Dylan Jones says one way to improve data accuracy is to increase the frequency and quality of reality checks.
If you work for a bank or utilities organisation, then gaining a foothold for data quality is largely focused on proving you can increase the profits of an organisation. In the public sector of course there are different priorities so how can you demonstrate the value of your data quality
