Solution Deployment (Analysis Services - Data Mining)
The last step in the data mining process, as highlighted in the following diagram, is to deploy to a production environment the models that performed the best.
.gif "Data mining sixth step: deploying mining models")
After the mining models have been moved to a production environment, you can perform many tasks, depending on your needs. The following are some common tasks that you can perform:
Use the models to create predictions, which you can then use to make business decisions. SQL Server provides the DMX language that you can use to create prediction queries, and Prediction Query Builder to help you build the queries.
Embed data mining functionality directly into an application. You can include Analysis Management Objects (AMO) or an assembly that contains a set of objects that your application can use to create, alter, process, and delete mining structures and mining models. Alternatively, you can send XML for Analysis (XMLA) messages directly to an instance of Analysis Services.
Use Integration Services to create a package in which a mining model is used to intelligently separate incoming data into multiple tables. For example, if a database is continually updated with potential customers, you could use a mining model together with Integration Services to split the incoming data into customers who are likely to purchase a product and customers who are likely to not purchase a product.
Create a report that lets users directly query against an existing mining model.
Updating the model is part of the deployment strategy. As more data comes into the organization, you must reprocess the models, thereby improving their effectiveness.