Companies have invested heavily in institutional data warehouses, which have since become part of standard operations. However, there is increasing pressure to leverage this investment to grow advanced analytics technologies and practices. Using the framework of a green field build of a data warehouse, we will look at different near-real-time techniques to enable an ERP-centric data warehouse and better position it as a component of an advanced analytics architecture. We will then discuss new development steps that can and should be applied to a legacy data warehouse for advanced analytics.
You Will Learn
- Near-real-time data warehouse best practices
- Modifications that can be made today to position your current data warehouse for the future
- How an institutional data warehouse fits into an advanced analytics architecture
- When it is worth building a data warehouse
- BI and analytics architects designing and developing analytics systems; business leaders trying to guide teams through the changes happening in analytics; data analysts and data scientists who need to work with legacy BI environments; IT leaders investing in platforms, tools, and training for analytics and machine learning