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.
One of the most common questions I get asked by our members on Data Quality Pro is, “Can you do more articles on data quality dimensions?” Part of the reason for this request is when people first start getting involved with data quality, they invariably buy data quality books and
Jim Harris recently penned an interesting article describing what happens to data quality at the top of the bell curve. The central theme of the article explains how, as we strive for greater levels of quality, we hit diminishing returns. For example, the cost of sending an engineer down a
Getting buy-in for data quality can be a real challenge, and a big part of the problem is the language barrier. If you’re on the data quality side, the financial and strategic terminology used by management may seem alien to you. Similarly, there is probably nothing more confusing to a
Over the holidays I had a chance to settle down to the usual array of Christmas movies and, as ever, Star Trek made an appearance. Now, I’m no Trekkie. But anyone with even a casual interest in Star Trek over the ages will be aware of the prime directive. This
One of the benefits of running an online data quality and data governance community is that over the course of many interviews, you start to see common threads and patterns emerging in the way practitioners create success in their data-driven programs. Data governance is a relatively new discipline, so it’s
I’m continuing my series on practical techniques that anyone can use to leverage data quality for bottom-line gains. Last week I talked about the link between data quality and service consistency, giving you some tips on how to improve both. This week I want to talk about a simple technique
I want to create a new series on The Data Roundtable that focuses on providing practical tips for improving data quality. Consistency is a key dimension often referred to in data quality practitioner circles. This refers to a rule where data must be consistent between two locations. For example, if
We’re in the process of trying to find a builder to complete an extension on our home. My "project" is five years overdue, behind budget and struggling with supplier issues. What is interesting is that every builder we interview for the new build brings his own frame of reference to the
If you’re reading this, there’s a strong chance your organisation is on the road to data quality management maturity. One of the challenges you’ll obviously face is how to deal with all the defects discovered. Many data quality problems can be "cleansed" instantly using appropriate technology, but for a lot
I was speaking to one of our UK members last week who is responsible for data quality in a large public sector organisation. When we got talking about the most useful techniques she has drawn on to mature data quality in her organisation, I got one of the most common
