Every organization utilizes relational databases that are transactional in nature to support the business applications.  The data in these databases are normalized to minimize the redundancy of data , maintain referential integrity and increase efficiency. These databases are not suited for querying large datasets.  Data Warehouse is a paradigm shift where the data is de-normalized to allow for retrieval of large datasets as well as slicing and dicing the data.  This approach is called star schema where there is a central fact table where the data is maintained in a less granular form and there are several tables called dimensions that relate to the fact table. For example, these dimensions could be time, product etc. The datawarehouse is generally fed by the transactional databases where the data is extracted, transformed and loaded (ETL) at regularly scheduled intervals.

Organizations seeking to build data warehouses need to understand that properly organized data can provide valuable insights into the interactions and relationships within the business, as well as about customers and partners. Organizing data into well defined and disciplined structures allows for examination of an organization's activities from new perspectives. But, it requires sophisticated planning, modeling and technical integration between applications and data storage systems.

A Data Warehouse project must start with clear and concise goals in mind. For this reason we believe a thorough process of gathering the business requirements must be undertaken before design and development can take place.  After requirements are gathered and assessment is needed to validate that the information that is dictated by the requirements is in fact available within the business technology infrastructure.

Organizations often start from the standpoint that all data is good, which implies that the design and build of any information asset becomes a difficult and imprecise process of elimination. Because we believe that data is meaningful only in its proper context, we have developed an approach that starts with clearly articulating business priorities and, through a process of targeted analysis and continuous alignment, hones in on data to support key information priorities. We work with our clients to manage the technical complexities and compromises required to acquire, clean, transform, store, and present the data in its most meaningful context so that business users can extract full value.

Various toolsets are available to assist in the construction of a data warehouse. These tools, when combined with an understanding of the business and flow of data within an organization, can be used as an important catalyst in unlocking the value of corporate data. We have worked with a wide variety of toolsets listed below. We are also well versed in the raw capabilities of the database platforms offer via stored procedures and database utilities for extracting and loading data.

  • Microsoft BI suite (SSIS, SSAS, SSRS)
  • IBM Cognos
  • SAP Business Objects
  • MicroStrategy
  • Pentaho BI Suite (open source)
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