Architecting Business Intelligence

Everything you read recently declares: “It seems like all roads lead to business intelligence. For organizations that have started business intelligence initiatives, they soon find that they have huge storage issues.

Typically, DW projects are initiated without a large concern for much more than the software required for actions such as extraction, transition & loading, modeling and report generation. Building and maintaining such systems require you the architect to either contract for or perform design work to ensure that there is focus on deploying user-driven data warehouses that are based on an enterprise architecture and are scalable, fast, available, and secure.

Integration is needed between operational and analytical systems to support the applications.

Six key business goals are needed to ensure that the business intelligence applications that bridge large packaged software applications and data warehouses:

  1. User-driven. Unless data warehouses are designed and architected to address specific business drivers, they will not support the strategic applications considered key to your organization.
  2. Architected. All business intelligence initiatives should be supported by a common enterprise architecture that standardizes processes, components, and tools. The architecture needs to provide a common systems and application infrastructure that makes it easy generate new data marts and modify and leverage existing applications.
  3. Scalable. When you architect a user-driven data warehouse, it typically grows fast, both in numbers of users and volume of data. Huge amounts of detailed data come from both packaged and custom applications that users may want to analyze inside a data warehouse. The correct servers must be included, as well as enterprise storage to manage terabytes of data.
  4. Fast. Users always want speedy responses to their queries, but this is always the expectation with any system. Massive systems require powerful servers designed for precisely the purpose of handling complex query workloads.
  5. Available. Data warehouses supporting analytic systems will be continuously updated. Users and applications will want real-time information from the previous day, previous hour, or even the previous minute. Loading and backup and restore capabilities must be planned in order to minimize the impact of any unforeseen outages.
  6. Secure. If you must open up a data warehouse to external parties and link them with multiple heterogeneous systems, close attention must be paid to security issues. Access control and encrypted data are also considerations that you need to make.

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