Know your users. First, understand who’s performing the analysis, the type, and how much data they require. If a power user wants to explore departmental data, then all they might need is an ad hoc query or OLAP tool and a data mart. If it’s an IT person creating a complex standard report with sophisticated metrics and functions, then it’s likely they can use a scalable BI tool running against an enterprise data warehouse. If a business analyst wants to run ad hoc queries or apply complex analytical functions against large volumes of detailed data without DBA assistance, then you probably need a specialized analytical database that supports analytical functions.
Performance and scalability. Second, understand your performance and scalability requirements. What query response times are required to make various types of analyses worth doing? If you have to wait days for a result set, then you either need to upgrade your existing data warehousing environment, offload these queries to a specialized analytical platform, or reduce the amount of data by aggregating data or reducing the time span of the analysis.
In-database analytics. Third, evaluate your need for in-database analytics. If complex models or analytics drive a critical portion of your business, then it’s likely you can benefit from creating and scoring these models in the DW rather than a secondary system.
Other. Finally, investigate whether the analytic database integrates with existing tools in your environment, such as ETL, scheduling, and BI tools. If you plan to use it as an enterprise data warehouse replacement, find out how well it supports mixed workloads, including tactical queries, strategic queries, and inserts, updates, and deletes.