Data warehousing services are business-driven, end-user-oriented, and adaptive to change. Our consultants work with your team to determine key business opportunities. We implement the data warehouse as the foundation for your business intelligence environment, supporting specific business process change or performance measurement initiatives.

The systems we deliver are capable of integrating the complex inter-relationships that exists between customers, suppliers and business partners. Specific applications include:

  • Customer Service Analytics
  • Financial Analysis, including Consolidation and Forecasting
  • Customer Relationship Management
  • Sales and Distribution Analysis
  • Database Marketing
  • Customer Acquisition
  • Inventory Management
  • Enterprise Performance Measurement (EPM)
Creation of a Data Warehouse would be useful for:

  • IT or business professionals, who spend a lot of time developing special reports for decision makers.
  • Assimilating data which is otherwise scattered on different systems, in different departments about which a user might be unaware.
  • Decision makers, who require timely meaningful information.
 
ETL
 

Extract, transform, and load ( ETL ) is a process in data warehousing that involves

 
  • extracting data from outside sources,
  • transforming it to fit business needs, and ultimately
  • loading it into the data warehouse.
 

Each system may also use a different data organization / format for their data. Common data source formats are relational databases and flat files. Extraction converts the data into a format for transformation processing.

The transform phase applies a series of rules or functions to the extracted data to derive the data to be loaded. Different transformation phrases includes

 
  • Deduping of data
  • Data Rengineering or Data Massaging
  • Data Cleansing
 

The load phase loads the data into the data warehouse. Depending on the requirements of the organization, this process ranges widely. Some data warehouses merely overwrite old information with new data. More complex systems can maintain a history and audit trail of all changes to the data.

 
 
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