Category Archives: Data Quality

data quality goal or enabler

Data quality: goal or enabler?

Recently I had a discussion on the question if the goal of data management would be to achieve good data quality or if data quality is an enabler for the most different business purposes. Data quality (DQ) is, in my personal opinion, an extremely interesting

5 crucial data warehouse testing checklists

5 crucial data warehouse testing checklists

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

best practices for optimal source data profiling

Best practices to achieve optimal source data profiling

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

how to implement data profiling for source data discovery

How to implement data profiling for successful source data discovery

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,

what you didn't know about fault tree analysis

What is fault tree analysis?

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

3 new ideas improving datawarehouse lifecycle quality process

3 new ideas on improving the data warehouse lifecycle quality process

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

3 key data integrity testing strategies for DW/ BI

3 key data integrity testing strategies for DW/ BI systems

Data warehousing and business intelligence users assume, and need, trustworthy data.In the Gartner Group’s Online IT Glossary, data integrity and data integrity testing are defined as follows:Data Integrity: the quality of the data residing in data repositories and database objects. The measurement which users consider