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There are quite a few data quality myths that need to be dispelled in order to move forward and mitigate the data quality risks. Last year I’ve covered 4 myths about Data Quality everyone thinks are true that started an entire trend on LinkedIn and also sparked a series of YouTube videos. So, here is
Organizations have several lines of defense in addressing various risks, at the project, department, or enterprise level and it all start with the awareness and monitoring of these risks. Risks also occur within and from data and data management areas (data quality, data security ,data architecture, etc.) as well as data governance and so data
In this article I’ll provide an introduction to the TDWI data governance maturity model, but here are the other ones covered so far: Stanford’s Data Governance Maturity Model IBM’s Data Governance Maturity Model DataFlux’s Data Governance Maturity Model Gartner’s Data Governance Maturity Model Oracle’s Data Governance Maturity Model Open Universiteit Nederland Data Governance Maturity Model
Introduction Data lineage documents where data is coming from, where it is going, and what transformations are applied to it as it flows through multiple processes. It helps in understanding the data life cycle. It is one of the most critical pieces of information from a metadata management point of view. From data-quality and data-governance perspectives, it is essential
How do we move Data Accessibility and, therefore, Decision Intelligence closer to the business users? Clearly, there is great benefit in providing seamless and secure accessibility to the right data as and when it is needed…such a capability provides your business with the means to make the right choices at the right time at the
When it comes to data accessibility or data democratization, there are those who believe that nothing should change while the rest believe it is time for data democratization to become the default state. To understand both of these views, here are the opportunities and considerations of data democratization. Quick Navigation The opportunities of data democratization: 1.
Among the most important phases of DW/BI testing is front-end report testing. The DW/BI project team should ensure that everything tested in the back-end DW and ETLs is now processed and displayed correctly in end-user reports. BI reports are often the most visible product of any DW/BI system. Data quality defects that are not discovered
It is encouraging to see the level of emerging focus on the data-centric enterprise, putting data, at least, near the forefront on the business mindset. But how do we keep such a step change true to itself? If we continue with the same behaviours, will we not just keep getting the same results? The Data
Decisions in today’s organizations have become increasingly data-driven and real-time. Therefore, the business intelligence databases that support decision makers must be of exceptional quality. We sometimes confuse testing a data warehouse that produce business intelligence (BI) reports with backend or database testing or with testing the BI reports themselves. Data warehouse testing is much more
Do you want to be successful data steward? This question sounds like a sales pitch, it’s not. If you want to be a successful data steward, you need to have these 4 sets of skills. You need to have the business know-how and experience, technical skills, analysis skills and interpersonal skills. OK, so you’re basically
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