Data Warehousing and Reporting

Publishers often use operational transactions as their data source, for example-

• criminal incidents, vehicle crashes or fire incidents for a region,
• port shipments and receipts bills of lading,
• regional student enrollments,
• real estate transactions (listings/sales) for a metropolitan area.

The publisher’s objective is to summarize their vast quantity of data into a compelling multi-dimensional dataset, but first the large amounts of data must be rationalized. Rationalization usually involves a technology such as MS SQL Server. With such technologies and appropriate analysis, even massive transaction files can be organized in a way that is susceptible to summarization as a multi-dimensional dataset, commonly known as an OLAP cube. Scalable both in terms of the number of dimensions, and the number of transactions, OLAP technologies such as Microsoft's Analysis Services can easily address large volume publishing problems, and once automated, be made to operate virtually “hands-free”.

Relational/OLAP technologies are also appropriate when extremely large summary datasets overwhelm small scale publishing solutions. For example, deploying detailed European Union external trade data from 1990 to the present is better deployed as one or more OLAP cubes.

Employed by Beyond 20/20 in the delivery of high volume transaction or summary publishing solutions in the public sector, the company confidently assists publishers facing such challenges, blending Microsoft and Beyond 20/20 technologies in all deployments.