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We are approaching winter, the days are shorter but IT’s always there, running everywhere in your mind, including at night between two dreams: Master Data Management (MDM)! For some time now, your company is working to be data-driven and your evolution in this field has made you understand that you have to go this way: …read more
Today’s Data Warehoues (DW) source data often originates from a large variety of data formats as illustrated in Figure 1. Original sources may consist of external data, reference data, business transactions, production raw data and more. The multitude of data sources each has its own methods, systems, and architectures for storing data. When working with …read more
When you’re setting up a data stewardship program, you need to start thinking of not only who to assign as data stewards and the operational framework, but also who the data stewards should report to, from a data governance organization perspective. The role of the data steward is usually not a new position that is …read more
Effective Data Warehoues (DW) data source profiling is often an overlooked step in data warehouse data preparation. DW project teams need to understand all quality aspects of source data before preparation for downstream consumption. Beyond simple visual examination, you need to profile, visualize, detect outliers, and find null values and other junk data in your …read more
This quick guide provides an overview of the basic concepts in fault tree analysis technique, as it applies to data quality. For some more well-known and useful root cause analysis techniques, please check out the: 5 whys analysis Fishbone diagram Barrier analysis Pareto analysis Definition The fault tree analysis is a top-down, deductive failure analysis …read more
In the competitive world today, clearly defining and understanding business terms is the first step to guaranteed success. Incorrect interpretation is causing misunderstandings and increase the risk of having bad information and bad decisions. These, in turn, lead to expensive operational, marketing, sales, procurement mistakes and so on. When employees misinterpret business terms, like when …read more
As most of us already know, the data governance sponsor plays a critical role in the success of a data governance program. Without sponsorship at an executive level, data governance programs often lack the funding and resources required to get off the ground. They simply remain at the initiative level or in a best case …read more
If I could give some advice for all colleagues who are about to start a data governance project, I would say: Speak up! In my professional career I have seen countless projects fail because leaders didn’t know how to manage communication and expectations. My objective here is to explain how communication practices can save (or …read more
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 often injected during the multiple lifecycle stages of the warehouse. …read more
Project Managers have a very structured way of conducting activities. This skill is very helpful when managing data governance projects and your intention is to add more value to the expected deliverables, keeping everything else on track. When I was a hired by xMatters I was given a very specific mission: ISO 27001 certification by …read more
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