Scanmar

Topline Modeler

In our architecture, your marketing data warehouse is a logical combination of 'datamarts'. These are "source" dedicated multidimensional OLAP datamarts, like:

• Sales & Marketing datamart (Shipments),
• Point of Sales datamart,
• Retail scanning datamart (Nielsen / IRI / GfK),
• Media Expenditure datamart,
• Consumer survey datamart (Brand Tracking, Product usage, Awareness),
• Claritas datamart,
• CBS, Euro stat datamart,
• Etc.

MarketingTracker is able to show you multiple datamart information combined in a dashboard and even map the information at dashboard level.
For particular analytical applications, you may need to have the data from different data sources in one combined datamart, to mirror an organization's marketing or business model. In most of these applications where you need to combine internal and external marketing information, it concerns so-called topline information at a more aggregated (top line) level, where (product, account, channel, etc) definitions are by nature more constant over time. For example: the major brands market shares combined with their promotional expenditures and awareness, over time.

A consolidated top line datamart can logically be put on top of the existing datamarts. Combining the information from various (heterogeneous) external data sources is (mostly) only meaningful at more aggregated levels. So why place a load on the overall system by storing it in a classic data warehouse instead of using a datamart solution. This approach overcomes a lot of unnecessary mapping problems.

With the TopLine Modeler, MarketingTracker power users can build their own topline data views. Data from different datamarts (or data sources) can be merged and integrated in an overall view. Performance indicators from different business area's (internal & external) can be combined and related. Executives can have their topline view and monitoring instrument on major changes in performance and their causes.

System features

• Template/menu driven.
• Create new (topline) data cubes, measures and dimensions.
• Map dimensions from different MT datamarts to a common (or more hierarchical) dimension in the  topline datamart, including new aggregations.
• Takes care of synchronization of different time dimensions.
• Full periodic and batch maintenance.
• Ends up with a complete MT datamart, which can be treated as any other MT datamart (reporting, custom measures, custom aggregates etc.).   

 

 

 

---