Ensuring that business intelligence data is clean, accurate and consistent is crucial to the success of BI efforts; analyzing bad data will send BI users off track -- quickly. And as data volumes grow and the number of data sources that organizations are looking to analyze increases, maintaining high levels of information quality is becoming a bigger challenge for BI and IT teams.
In this handbook, readers will find practical advice on how to effectively manage data integration and data quality processes as part of BI and data warehousing programs, including a checklist of action items for identifying and resolving BI data quality problems. Access >>>
Table of contents
- Effective data quality strategy begets BI success
- Data errors, internal miscues block BI integration
- Big data, real-time BI add to integration hurdles
Premium Content for Free.
More Premium Content Accessible For Free
Technological buzz, like that surrounding Hadoop, can easily blur the lines between software benefits and drawbacks. Hadoop clusters make it easier ...
With adoption of cloud BI services hovering at 33% for the past few years, this report examines the market trends for implementing cloud business ...
Location intelligence goes beyond finding a good restaurant. In business, location intelligence technologies can boost profits and decrease costs. ...