Data Warehouse is a repository of data, whereby the application of that data is 'limited only' by the detail and the way data is stored. Query and analysis, business modeling and data mining are the buzzword use of data warehouses. However, there are more fundamental and bigger usages possible for DW. For example- with enterprise reporting, which can provide you capability to report on Summary level as well as transaction level data, one can drive operational management benefits. You not only can use data warehouse to find the sales trends, but also to generate the list of all 5000 sales officers, along with the list of every sales order , which they have booked in last six months.

When you are creating the business case for your Data Warehouse, you can include the following benefits, with only some of them belonging to the traditional analytics:

• Enterprise Reporting
• Offline Operational Data Store (refer ODS types ask a question)
• Data Analytics
• Data Mining
• Business Modeling
• Operational BI
  

As you design and scope your Data Warehouse should account for potential use, which goes beyond data analytics. This is how your Data Warehouse design will be influenced, with more broad-based applications:

• More granular data- Data at lowest level of detail is needed, if you want to utilize DW for enterprise reporting, operational BI and root-cause analysis, Data Mining and business modeling. Apart from Data Analytics, most of other applications will demand transaction level data as users get more adapt on DW.
• More Descriptive Attributes- If you are using DW for enterprise reporting and operational data store application, you would like to include the non-analytics attributes like address, description etc.
• More Robust and scalable platforms- As you store data at more granular level, you would need to have platforms which can handle large volumes of data and also can handle issues like data explosion. Though Data explosion is a challenge, even if you are using DW for data analytics only, it will be accentuated with more granular data.
• Load and Job schedule Management- If you are using your DW platform for enterprise reporting, it will have a bearing on how you plan your end of the day jobs. As you do enterprise reporting, it may lock large tables, which can impact your online querying and analysis.
• Your Dimensional Model: You may think of creating star-schema at detailed level and also at an aggregate level to fulfill different applications of Data Warehouse.
• Your OLAP strategy: If you are having a combination of detailed as well as summary data, you may go for OLAP architecture, which allows you to handle different level of details in the data. For example you may like to choose HOLAP or ROLAP instead of MOLAP

It all depends on the granularity of the data, which is stored in the data warehouse. Refer Why Data warehouse for greater perspective on this field tip.