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 migrations are never the most attractive of projects to sponsor. For those who have sponsored them previously, migrations can be seen as a poison chalice. As for the first-timers, data migration initiatives are often perceived as a fairly insignificant part in a far grander production. The challenge with data
If you’re involved in some way with data quality management then you will no doubt have had to deal with the completeness dimension. This is often one of the starting points for organisations tackling data quality because it is easily understood and (fairly) easy to assess. Conventional wisdom has teams
Whilst it’s nice to imagine a world of perfect data quality the reality is that most organisations will be dealing with data quality defects on a daily basis. I’ve noticed a wide variation in the way organisations manage the life cycle of defects and nowhere is that more apparent in
If we look at how most data quality initiatives start, they tend to follow a fairly common pattern: Data quality defects are observed by the business or technical community Business case for improvement is established Remedial improvements implemented Long-term monitoring and prevention recommended Move on to the next data landscape
A lot of data quality projects kick off in the quest for root-cause discovery. Sometimes they’ll get lucky and find a coding error or some data entry ‘finger flubs’ that are the culprit. Of course, data quality tools can help a great deal in speeding up this process by automating
Data profiling is essential. So why do so many data quality teams fail to get the most out of this crucial technique? In my short video, you’ll discover the answers to unlocking the full potential of your data profiling efforts. By broadening and deepening your knowledge of data profiling with
If you work in data quality long enough you’ll meet detractors of data quality software. The viewpoint in this camp is that poor quality data should be driven out at the time of design, not retrospectively detected and fixed. They perceive data quality tools as a costly overhead, something that
Why do so many data migration projects fall off the rails? I’ve been asked this question a lot and whilst there are lots of reasons, perhaps the most common is a bias towards finding the wrong kind of data quality gaps. Projects often tear off at breakneck speed, validating and cleansing
Over the course of the last eight years, I've interviewed countless data quality leaders and learned so much about the common mistakes and failures they've witnessed in past projects. In this post I wanted to highlight five of the common issues and give some practical ideas for resolving them: #1: Not connecting
Whether you’re embarking on a data quality mission for the first time or your presence is well known, it never hurts to have allies throughout your organization. By finding and gaining these supporters, you can gain influence and achieve your data quality goals. It may be difficult due to the
