Data warehouses are a popular solution for providing analytic capabilities to users. Until recently, data warehouses were the only reasonable solution for building robust analytic capability. However, as applications become easily interoperable and as technologies such as in-memory databases and OLAP become cost-effective and simpler to use, building a data warehouse is not the only solution to meet analytic requirements.
Table below presents several architecture options for integrating external data with the prebuilt
analytic solution; a data warehouse is just one of the options. The columns represent architecture options, whereas the rows represent the benefits and cost implications of each option.
When most data is in Microsoft Dynamics AX (assuming that Microsoft Dynamics AX is the
predominant source of data in the organization), you have two options.
The data mash-up option is best suited to an environment where capable users author and publish analyses for the use of others. This option relies on client tools such as Excel PowerPivot. Microsoft Dynamics AX 2012 enables Microsoft Dynamics AX queries to be published to data mash-up tools through OData feeds, or as data exports to Excel.
You can bring external data into Microsoft Dynamics AX either through services (data services consumed by means of inbound ports) or as batch jobs that are executed periodically to import data into tables. With this approach, external data is represented as read-only data within Microsoft Dynamics AX. The benefit to this approach is that external data appears as native Microsoft Dynamics AX data to Microsoft Dynamics AX tools. You can create analytics, reports, and inquiry forms that use the combined data.
A more complex approach involves integrating external data directly into the prebuilt BI solution.
With this option, a BI developer adds another data source to the prebuilt BI solution by using Business Intelligence Development Studio. Additional data tables are brought into the DSV by using the new data connection. It is possible to create dimensions and measures by using the new tables in the DSV.
The traditional ETL-based data warehouse option is suited to scenarios that require complex
transformations or large volumes of data. Although this option is more flexible in terms of capabilities, it is also the most expensive to implement and manage.
You might want to build a data warehouse to implement the following scenarios:
■ Integrate external data sources with Microsoft Dynamics AX data In this approach, the Microsoft Dynamics AX implementation serves as one of many corporate applications.
Although Microsoft Dynamics AX contains some of the corporate data, other systems contain
a considerable portion of the data. To make decisions, data must be combined across systems, and the data warehouse serves that need.
■ Incorporate legacy data into Microsoft Dynamics AX analytics Most organizations
migrate recent data when implementing Microsoft Dynamics AX. Legacy data is still
maintained in read-only instances of legacy applications. Although legacy data is no longer
used for operational purposes, it is required for historical trend analysis. A data warehouse
serves as the repository where legacy data is combined with current data.
Although Microsoft Dynamics AX 2012 does not directly support the creation of a data warehouse schema, the following artifacts generated in Microsoft Dynamics AX 2012 can be used to build a data warehouse:
■ The DSV generated as part of the prebuilt analytic solution can be used within SQL Server
Integration Services when an ETL package is developed to extract data from Microsoft
■ Microsoft Dynamics AX document services can be consumed as data sources based on Simple Object Access Protocol (SOAP).
■ Microsoft Dynamics AX queries can be exposed as OData feeds.