Consolidating data warehouses

Technological advances, mergers and acquisitions, consolidation, and regulatory compliances have led to significant increases in data sources and volume and a more complex and dynamic business environement than ever before.

To successfully implement and manage an enterprise data warehouse, organizations need to develop a strategy to rapidly adapt to these changes.

Issues with combining heterogeneous data sources, often referred to as information silos, under a single query interface have existed for some time.

Data warehouses have a lower total cost of ownership than data marts. It means multiple business projects can use the data without having to build separate robust data layers.

That's understood, but what is data warehousing in this business/financial context? Allowing concurrent use of data at the data warehouse layer or creating a mart off the data warehouse is a lot less work, reduces risk, and lowers overall costs than does building from uncultivated, original source data.

Our proven methodology for data cleansing and analysis includes identifying the right data sources for integration.

We ensure that the data profiling and quality of the data in the warehouse is accurate, consistent and standardized.

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We understand how to design conceptual, logical and physical data models for traditional OLTP systems and dimensional modeling for data warehousing projects.

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