Planning for changes in the size of databases due to business growth, projected implementation of new applications and product build-out. DataVision will review the results of such efforts with Client on a regular basis for Client's comment and approval.

Maintaining, operating, and upgrading, as necessary, automated monitoring tools to monitor database performance. DataVision will monitor the databases on a frequent and regular basis to check for integrity, space utilization or performance problems.

• List Management

• Data Warehousing

Data Warehousing is the process of consolidating data from multiple sources into one or more query databases. The availability of clean data in data warehouses makes data mining easier, but if you want results now, consider data mining as a data exploration and cleaning prelude to the design, care and feeding of the data warehouse.

• Data Mining

Data Mining enables the identification of trends and relationships for new product, cross-selling, and up-selling opportunities. The ability to pinpoint potential customer issues and opportunities enables you to proactively provide customers with faster, more accurate responses and significantly increases customer satisfaction.

Stage 1 Develop Problem Statement. The problem statement is important. The focus is on immediate tactical business problems with a high potential value for the amount of data mining effort required. In each data mining exploration, the goal is clearly identified.

Stages 2-4 Data Preparation. 50% to 90% of the time is spent preparing the data. Data selection involves identification of internal and external data, such as adding demographic data to customer data. Data cleansing involves identification of metadata: the true definition of each data element, and resolution of inconsistencies, missing values, and data currency issues. Additional data preparation includes activities such as sampling, preprocessing, coding of discrete values, and the like.

Stages 5-7 Data Mining Discovery. Data Mining Discovery may use a variety of techniques, such as traditional statistical analysis, decision trees, neural networks, and visualization techniques. In this stage, we allocate data to testing as well as training datasets, and modeling and testing is iterative. The Data Mining Purpose is to model reality, thus, if the model works, we use it.

Stage 8 Deploy Models. When significant results have been found, the models are incorporated into decision support systems or OLAPs, or even into existing production systems.