Shift left testing is an approach to Data Warehouse (DW) / BI development where testing is performed early in the lifecycle. Shifting left refers not only to dynamic data and BI application testing- it also refers to static testing, conducting reviews and inspections, and even
What better way to start this new century than to go over the pros and cons of the 4th Industrial Revolution. The 4th industrial revolution is a term coined by Professor Klaus Schwab. He is the founder and Executive chairman of the World Economic Forum,
I think that sometimes people still struggle to understand what data governance is. That’s why, when I’m talking about data governance I like to first make a reference to HR and Finance. This is usually when I’m asked what I do for work, what do
You probably use checklists to record and efficiently execute a wide range of daily work tasks. But if you don’t use data warehouse testing checklists for developing and monitoring your data warehouse quality assurance (QA), you’re missing an enormous boost in productivity and proficiency. Procedural
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
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
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
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,
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
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