Text, the basis of language, can be considered to be unstructured by technicians. However, for others, text and language have a definite structure. It is the simplest of questions. Ask any technician if text – language – is structured or
A managed metadata environment (MME) is the optimal approach to metadata architecture for meeting business and technical requirements Introduction Almost every corporation and government agency has already built, is in the process of building, or is looking to build a
Project management requires the use of a solid framework and an experienced project manager to ensure the success of any project, any size, in any industry Introduction I was hooked on watching the Iron Chef – what got me interested
Reference and master data provide the contextual capabilities for transactional data. Managing reference and master data enables organizations to understand operational data and analyze disparately collected data effectively. Introduction Reference and Master Data is the collection of generally non-transactional data
Metrics and the measurements they create are essential to the success of every data governance program and every data stewardship effort. Introduction Organizations that implement any new initiative must be able to measure its success so the program’s leadership can
Metadata management is one of the foundational components of an enterprise data management initiative. Many organizations struggle with its incorporation into their data management processes. Metadata Management’s Relationship to Data Management Data Management is one of the hottest topics in
For many industries, their only tangible product is their data.
Standards are different from guidelines. Standards are firm and must be followed. Successful data warehouses use standards Introduction Many dog owners give their dogs what they consider to be commands. They are really more like guidelines. (“Boscoe come!… pause, pause,
Master data management continues to be plagued by poor data quality, costing many organizations time, money, lost customers, lost reputation and inefficiencies. Introduction In several surveys, taken by various research organizations about master data management and data quality, on average, 83%
