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.
If your organization is large enough, it probably has multiple data-related initiatives going on at any given time. Perhaps a new data warehouse is planned, an ERP upgrade is imminent or a data quality project is underway. Whatever the initiative, it may raise questions around data governance – closely followed by discussions about the
As consumers, the quality of our day is all too often governed by the outcome of computed events. My recent online shopping experience was a great example of how computed events can transpire to make (or break) a relaxing event. We had ordered grocery delivery with a new service provider. Our existing provider
One area that often gets overlooked when building out a new data analytics solution is the importance of ensuring accurate and robust data definitions. This is one of those issues that is difficult to detect because unlike a data quality defect, there are no alarms or reports to indicate a
The adoption of data analytics in organisations is widespread these days. Due to the lower costs of ownership and increased ease of deployment, there are realistically no barriers for any organisation wishing to exploit more from their data. This of course presents a challenge because the rate of data analytics adoption
One thing that always puzzled me when starting out with data quality management was just how difficult it was to obtain management buy-in. I've spoken before on this blog of the times I've witnessed considerable financial losses attributed to poor quality met with a shrug of management shoulders in terms
One of the common traps I see data quality analysts falling into is measuring data quality in a uniform way across the entire data landscape. For example, you may have a transactional dataset that has hundreds of records with missing values or badly entered formats. In contrast, you may have
Regulatory compliance is a principal driver for data quality and data governance initiatives in many organisations right now, particularly in the banking sector. It is interesting to observe how many financial institutions immediately demand longer timeframes to help get their 'house in order' in preparation for each directive. To the
One of the significant problems data quality leaders face is changing people's perception of data quality. For example, one common misconception is that data quality represents just another data processing activity. If you have a data warehouse, you will almost certainly have some form of data processing in the form
It’s common at the start of a new year to create a long list of resolutions that we hope to achieve. The reality, of course, is by February those resolutions will likely be a distant memory. The key to making any resolution stick is to start small. Create one small
We've explored data provenance and the importance of data lineage before on the Data Roundtable (see here). If you are working in a regulated sector such as banking, insurance or healthcare, it is especially important right now and one of the essential elements of data quality that they look for
