The close links between data quality and business intelligence & data warehousing (BI/DW) have long been recognised. Their relationship is symbiotic. Robust data quality is a keystone for successful BI/DW; BI/DW can highlight data shortcomings and drive the need for better data quality. A key driver for the invention of data warehouses was that they would improve the integrity of the data they store and process.
Despite this close bond between these data disciplines, their marriage has not always been a successful one. Our industry is littered with failed BI/DW projects, with an inability to tackle and resolve underlying data quality issues often cited as a primary reason for failure. Today many analytics and data science projects are also failing to meet their goals for the same reason.
Why has the history of BI/DW been plagued with an inability to build and sustain the solid data quality foundation it needs? This presentation tackles these issues and suggests how BI/DW and data quality can and must support each other. The Ancient Greeks understood this. We must do the same.
This session will address:
- What is data quality and why is it the core of effective data management?
- What can happen when it goes wrong – business and BI/DW implications
- The synergies between data quality and BI/DW
- Traditional approaches to tackling data quality for DW / BI
- The shortcomings of these approaches in today’s BI/DW world
- New approaches for tackling today’s data quality challenges
- Several use cases of organisations who have successfully tackled data quality & the key lessons learned.