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So far I’ve provided an overview on 6 different data governance maturity models which I will post links below in case you’d like to learn more about them. Maturity models can provide a road map to follow in order to advance from one maturity stage/ level to another. Today I’ll provide an introduction into the …read more
In the domain of agile and data warehouse (DW) project testing, a DW Master Test Plan (MTP) is often considered an out-dated practice, even unnecessary. With this viewpoint, project teams may overlook long-standing motives and rationale for a project-wide DW MTP. A DW “Master Test Plan” represents the plan of action and processes designed to accomplish …read more
Digital Transformation can mean a lot of different things to different people. But for everyone it means dealing with more data than ever before. Industry 4.0, Cloud, Big Data, Robotics, Artificial Intelligence, Machine Learning, blockchain, IoT (internet of things) and similar technological advancements are changing the very nature of commerce across every business sector in …read more
In the first evening of my 6-week course on Data Quality Improvement I try to communicate the high cost of poor data quality (PDQ) to my students. Their eyes are usually starting to close while I cover the course administrative details as for most of them it has already been a hard week of working …read more
An introduction to DW/ BI data quality risk assessmentsData warehouse and business intelligence (DW/ BI) projects are showered with risks – from data quality in the warehouse to analytic values in BI reports. If not addressed properly, data quality risks can bring entire projects to a halt, leaving planners scrambling for cover, sponsors looking for …read more
I’m not a fan of meetings in general and data governance requires quite a few and they are always packed. To be as efficient as possible, you need an agenda, for the attendees to come prepared and for the chair to keep the meeting on schedule and within its scope. There are also a lot …read more
The principles of data quality management are a set of fundamental beliefs, standards, rules and values that are accepted as true and can be used as a foundation for guiding an organization’s data quality management. They have been adapted from ISO 9000 principles of quality management. These principles are not listed in an order based …read more
Ad hoc means “for this” in Latin and refers to something done for a specific purpose. Ad hoc reporting, then, is the process of creating reports for a specific occasion (as opposed to for general use). In business intelligence, ad hoc reporting supplements canned reports by enabling end users to either duplicate and edit premade …read more
Throughout my career and experience within the data management and data governance industries, I always encounter interesting data roles and responsibilities. Is there a lot of overlap? Definitely! That’s why I think it is most important to focus on the meaning of the roles – their responsibilities. Let me also give you a tip: ” …read more
Data is here to stay. In fact, your current data has already outlived or will most likely outlive its current systems and processes. What you need to be mindful of is that every time data goes through a data integration process, there are chances of errors. These are the 5 data integration processes prone to …read more
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