In smaller organizations, iMIS Analytics might be the only data warehouse providing you with business intelligence. In larger organizations with several major databases besides your iMIS database, the data warehousing experts in your organization can implement iMIS Analytics as a data mart in the bus architecture of a larger enterprise data warehouse solution (per the Kimball model). Describing this type of data mart implementation is beyond the scope of this document, because such implementations are highly customized solutions. A data mart implementation is possible, however, because the iMIS Analytics data warehouse uses dimensional models and a star schema, and because the iMIS Analytics OLAP cubes have been designed around conformed dimensions and conformed measures (facts). Your data warehousing experts can rely on documentation for Microsoft Analysis Services or any related third-party documentation to be applicable to the design of the iMIS Analytics data warehouse and analysis database.
Whether iMIS Analytics is implemented as your only data warehouse or as a data mart, there are major decisions that you must make when implementing iMIS Analytics:
Server configurations
iMIS Analytics server components can be implemented on a single tier with your iMIS database, or spread across two hosts in a two-tier configuration:
The two-tier configuration is slightly more difficult to install than a single-tier configuration, because on the iMIS Analytics host you must create a linked server definition for the iMIS database on the iMIS host to enable iMIS Analytics to connect to your iMIS database. See Creating a linked server definition for two-tier configurations. The single-tier configuration is the easiest to install and use, and generally won't affect performance of your iMIS database unless you have many people constantly using the analysis database.
The following information about the three databases involved in an iMIS Analytics implementation can also help you determine whether a two-tier or single-tier configuration is best for your organization:
current data stored in relational tables
Your iMIS database contains some historical records of things like donations, purchases, etc. Much of the information in your iMIS database, however, is always current data. For example, if a product description or customer's address changes, the old product description or the old customer address is not saved in the iMIS database.
historical data stored in dimensional tables using a star schema
The iMIS Analytics data warehouse is a special database that contains a copy of all your iMIS data. The data warehouse does not use relational tables, but instead structures your data in a dimensional model using a star schema. Because much of the copied data comprises slowly changing dimensions that preserve history (Type 2 SCDs), the data warehouse will grow over time, so space requirements for growth are a factor in determining whether to implement a two-tier or single-tier configuration. However, since the data warehouse is used only during the extraction, transform, and load (ETL) process during off-peak hours, the performance impact on the iMIS host in a single-tier configuration is negligible.
OLAP cubes derived from data warehouse contents, using conformed dimensions and conformed measures (facts)
The iMIS Analytics analysis database contains a copy of the information from the data warehouse, transformed into cubes in an OLAP database. As with the data warehouse, the analysis database will grow over time, so space requirements for growth are a factor in determining whether to implement a two-tier or single-tier configuration. Unlike the data warehouse, this database might be used heavily during peak hours of the business day. However, OLAP viewers such as ProClarity Professional do not generate heavy SQL activity, so the performance impact on the iMIS host in a single-tier configuration is minimal.
Custom iMIS 10 demographics
The Customer module of iMIS has a feature called the iMIS Customizer, which enables you to create custom tables for use in custom window tabs that capture your own demographic information about each customer. With some limitations, you can configure your implementation of iMIS Analytics to include your custom demographic information about each customer. The following limitations apply to custom iMIS 10 demographics:
Examining these limitations in more detail can help you plan how to configure iMIS Analytics to track custom iMIS 10 demographics.
A multi-instance custom table is one for which the Multiple Instances Allowed checkbox was selected when creating the custom table with the iMIS Customizer. In a multi-instance custom table, you can have multiple records for the same customer ID. For example, you might want to send out annual surveys to each of your customers and track the survey results in a custom table that is viewable on a custom Surveys tab in the Manage Customers window. In this scenario, the Surveys tab for a specific customer would show one row for each year's survey results.
In a single-instance custom table, by contrast, there would never be multiple records for the same customer ID. For example, you might have created a custom table that tracks general demographic information for customers, such as their spouse's name and the school they attended. In this scenario, there would never be more than one row needed per customer ID to store the demographic information.
Because multi-instance custom tables can have more than one record per customer ID, iMIS Analytics cannot determine which of those multiple records to display as attributes for a specific customer when you are examining the detail information for that customer in an OLAP viewer such as ProClarity Professional. Therefore, only fields from single-instance custom tables can be included in your implementation of iMIS Analytics.
The other limitation of custom tables is that the fields from these tables are essentially extended attributes of a specific customer ID, instead of being a category of business information that could be considered a dimension member in its own right. Therefore, the custom fields that you add to iMIS Analytics are not treated as members (levels) in a dimension that you can directly graph on a chart in OLAP viewers such as ProClarity Professional. Instead, you can view the custom fields only as attributes of a specific customer, once you have drilled-down far enough into a view to display specific customers.
Slowly changing dimensions
The most difficult decision to make when configuring iMIS Analytics to track your custom demographics is which type of slowly changing dimension (SCD) to use for each demographic. In a data warehouse, the measures (facts) change very rapidly, but by comparison, the data in your dimensions change slowly. For example, a custom demographic such as staff size is likely to change over time for a given customer.
iMIS Analytics supports two types of slowly changing dimensions, as well as giving you the option to not use a slowly changing dimension for demographics that never change over time. There are two questions you should consider for each demographic that you want to track in iMIS Analytics:
The following information about slowly changing dimensions can also help you determine which SCD type to use for each custom field:
Preserves history
When changes occur to a Type 2 SCD, a new record is added with the new attribute information, preserving records of the old attribute information. Type 2 SCDs track history very efficiently because they create new records only when the attribute information changes. However, because Type 2 changes add new records, they can significantly increase the size of your data warehouse.
Overwrites history
When changes occur to a Type 1 SCD, no new records are created. Instead, the change overwrites the existing attribute with new information. The old information is lost forever.
Use no SCD
A Type 0 SCD is a special value used only in iMIS Analytics to indicate that the custom demographic is not a slowly changing dimension. The demographic is a value that will never change for a given customer. The advantage of using a Type 0 SCD for custom demographics that never change is that the data warehouse requires less overhead to maintain that demographic.
10.6 Production Release. Updated 9/28/2005 3:57:51 PM
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