Adapting Quickly to Data Quality Challenges in the Real-World
Today’s diverse data landscape poses some real challenges for data professionals. How do you prepare yourself for these challenges? Let’s start by reviewing some of these challenges and the impact they have on the organization. Then, we will examine some of the approaches that can be taken to solve those challenges.
This presentation will highlight useful solutions for real-world problems.
- Data Quality Life Cycle: a best practice approach
- Data Discovery Methods (data profiling and metadata discovery)
- Data Quality Correction (data improvement)
- Data Quality Monitoring
- Collaboration (communication, data standards, glossary, workflow, data lineage, business rules)
In the SAS Global Technology Practice, Jeff Stander has responsibility for Data Management products. He listens to customers’ needs, and ensures alignment of SAS products and services to meet those needs. Stander has 31 years of experience at SAS, working with organizations in more than 50 countries. He has lived and worked for SAS in Germany, California, Florida, and North Carolina. He was awarded an MBA degree and a B.S. in Systems Science from the University of West Florida. He resides in Cary, North Carolina. During his time away from work, Jeff enjoys travel and photography.