I have probably touched on this topic many times before: accessing the data that has been loaded into a master data environment. In recent weeks some client experiences are really highlighting something that is increasingly apparent (and should be obvious) for master data management: the need to demonstrate that it
There are multiple types of data models, and some companies choose to NOT data model purchased software applications. I view this a bit differently. I think that any purchased application is part of our enterprise, thus it is part of our enterprise data model (or that concept is part of the
When you examine where most data quality defects arise from, you soon realise that your source applications are a prime culprit. You can argue that the sales team always enter incomplete address details, or the surgeons can't remember the correct patient type codes but in my experience the majority of
Data. Our industry really loves that word, making it seem like the whole world revolves around it. We certainly enjoy revolving a lot of words around it. We put words like master, big, and meta before it, and words like management, quality, and governance after it. This spins out disciplines
Don't be shy! Interviewing people BEFORE or AFTER a facilitated session just takes a bit of confidence, and good preparation. Building your confidence gets easier and easier the more you participate in interviews. The objective is to prepare and not waste anyone’s valuable time. I like to prepare notes based on
Many managers still perceive data quality projects to be a technical endeavour. Data being the domain of IT and therefore an initiative that can be mapped out on a traditional project plan with well-defined exit criteria and a clear statement of requirements. I used to believe this myth too. Coming
Hadoop recently turned eight years old, but it was only 3-4 years ago that Hadoop really started gaining traction. It had many of us “older” BI/DW folks scratching our heads wondering what Hadoop was up to and if our tried-and-true enterprise data warehouse (EDW) ecosystems were in jeopardy. You didn't
Can a neat dataviz find the signal in the noise? Yes.
Data stewardship is one of the most important, and commonly misunderstood, aspects of data quality and data governance. Not only are the characteristics of a good data steward a rare combination of skills to find in an individual employee, the culture of most organizations does not nurture the development of data stewardship. It would not
Transaction systems that feed master data repositories ensure a degree of synchronization that ensures proper transaction execution. The transaction processing system generally only looks at a few records at one time, and updates are committed so that they do not interfere with other transactions. Transaction within each subsystem are isolated
