Making the Case for Just-in-Time Data and Analytics
Does all data need to be treated the same? Maybe not. From the beginning, BI implementers thought that any data analyzed had to be fully integrated, cleaned up as much as possible, and loaded into its own repository called a data warehouse. The “single version of the truth” mantra mandated this, but is that really necessary? In reality, some analytics and reports may not need this stringent level of integration and quality. In these cases, the native data can provide sufficient accuracy for specific business decisions without the time and costs of the traditional data cleansing.
Join analyst Claudia Imhoff as she explores an alternative approach to analytics in which data can be used in its raw original format, mitigating the need for full ETL and data quality processes on all data.
What you will learn:
- Use cases demonstrating when it makes sense for this "just-in-time" scenario
- The best practices to clarify when this approach is appropriate and when it should be avoided
- Getting started with “just-in-time” data
Claudia Imhoff, Ph.D.