Data warehousing for business intelligence and “big data initiatives” continues to gain significance as organizations become more aware of the benefits of decision oriented data warehouses. However, a key issue, with the rapid development and implementation of data warehouses, is that data quality defects are
Fixing data quality issues is only part of the solution. In order for these not to reoccur, one needs to identify the root cause and prevent from further creating poor data quality. As we know, for every effect there is a cause, and that’s the
Data warehousing and business intelligence users assume, and need, trustworthy data.In the Gartner Group’s Online IT Glossary, data integrity and data integrity testing are defined as follows:Data Integrity: the quality of the data residing in data repositories and database objects. The measurement which users consider
There are different takes on what veracity refers to. The overall consensus is that data veracity reflects the truthfulness of a data set and your level of confidence or trust in it. I’ll take this a step further and say that data veracity is your
In the 6 weeks of my course, Data Quality Improvement at BCIT, I try to impart to them an understanding of how to ‘discover’ the instances of poor data quality in the data sets they are responsible for so that they can begin to help
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
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
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
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
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