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Table of Contents Business driversResourcesFrameworkIndustryConclusion Is this you? You understand the benefits of using a Data Governance Maturity Model, you want to use one, but you don’t know which one to go for. Understandably so, because there are plenty out there. Out of the more notable ones, here are
Unfortunately a lot of data governance programs fail and there are many reasons why. The silver lining is that there are great lessons from these failures that we can learn from and make sure that we will avoid them in our data governance program. Here are the 9 keys to data governance success:__CONFIG_colors_palette__{“active_palette”:0,”config”:{“colors”:{“40f3f”:{“name”:”Main
I like to think of the data steward as the unsung hero of data. Truth be told is that without them, data scientists wouldn’t be able to understand and trust the data that they are using, AI/ML wouldn’t output correct results, and a company wouldn’t be able to become data-driven. So who is this unsung hero?
What is a Business Glossary? At times I feel that the answer to this is obvious and that it does not merit its own article. But then I read a blog post, a LinkedIn post, a whitepaper, or even watch a webinar that talks about the Business Glossary, that talks about it incorrectly. That’s when
Data governance maturity models are a favorite topic and a highly sought after online course here on LightsOnData. Why is that? The short answer is that a data governance maturity model is a sought after tool as it brings quite a few benefits which could be placed in the following 5 buckets: 1. Regulation Sometimes
The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills. Data integrations are at the core of data warehousing,
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
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