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You need top-level management involvement to improve the quality of your data. Fact! It’s not enough for the IT manager to support your initiatives in improving the quality of your data. Thinking otherwise, won’t get your data quality efforts too far. Even if you’re not part of IT, your department’s manager understands the need to …read more
Data governance is the foundation of all data management programs. It is an essential disciple that supports all other data management knowledge areas like Data Literacy, Data Warehousing, Business Analytics, Big Data, Master Data Management and many others. Data governance has 10 key components that exist to meet the enterprise’s data management requirements for each …read more
Table of Contents Assumption 1: Process and Data can be detached with minimal consequencesAssumption 2: Simplicity of theory guarantees correct adoptionAssumption 3: Data Quality and Integrity is guaranteed by optimal structural data designAssumption 4: Nomenclatures fully define semantic meaningAssumption 5: All entities convey singular truth, all attributes are atomic, all relationships are trueAssumption 6: Temporality, …read more
Table of Contents 1. Reduce Data Redundancy2. Minimizing Security Risks3. Improving Data QualityDon’t miss an opportunity… What’s the return on investment from data governance? As the result of Coronavirus, demand for data governance is increasing, and this is all at a time where resources are scarce, which means now, more than ever, stakeholders need to …read more
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 the ones we’ve covered so far:Stanford’s Data …read more
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: Table of Contents …read more
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? …read more
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 …read more
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 …read more
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, …read more
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