RESEARCH & RESOURCES

LESSON - Dynamic Warehousing: The Next Evolution of Data Warehousing

By Gary O’Connell, Senior Product Marketing Manager, IBM Information Platform & Solutions

Traditional data warehouses are increasingly being challenged by demands for real-time data access, analysis of structured and unstructured data, and the need to synchronize core customer and product information across operational systems to create a single view of the enterprise. These changes are the result of new business requirements to leverage enterprise information more effectively in order to:

  • Identify new opportunities and deliver new products to market faster
  • Optimize business processes through real-time information and analytics
  • Provide increased visibility to business performance
  • Meet industry compliance standards for reporting

Yet most long-standing data warehouses are designed to support a relatively small number of users who access information to support strategic decisions, financial planning, and the production of standard reports that track performance. Today, many more users need to access information in context and in-line so that critical functions are optimized to run efficiently. Information about customers— both structured and unstructured—must be analyzed and delivered wherever it is needed. Key performance indicators (KPIs) should also be available at all times to monitor performance. In short, business intelligence is becoming embedded in key business processes.

To create a true enterprise view of information that supports strategic and operational functions, enterprise data warehouses must be reinvented as a dynamic source of current and historical information. Dynamic warehousing is an approach that enables organizations to deliver more dynamic business insights by integrating, transforming, harvesting, and analyzing insights from structured and unstructured information. Capable of processing large amounts of information, a dynamic warehousing infrastructure can enable organizations to respond on demand to unscheduled analysis requests, and as events trigger the need for information throughout the day.

A key component of a dynamic warehouse environment is the data warehouse platform. To implement a dynamic warehouse, the platform should be able to:

  • Process transactions and analytical requests
  • Handle varying service level agreements (SLA)
  • Scale easily as the number of applications grows
  • Analyze structured as well as unstructured data
  • Provide real-time analytics that can be embedded in business processes
  • Support advanced analytics such as data mining within the data warehouse

Figure 1. A dynamic warehouse has an extended infrastructure that leverages metadata to extend thedata warehouse to operational systems as well as traditional business intelligence applications.


The requirements for dynamic warehousing go well beyond having the right data warehouse platform, however. A dynamic warehouse requires an extended infrastructure (see Figure 1) to:

  • Implement changes to the business model without affecting usage
  • Monitor and analyze data sources for structure and content to ensure the best data is being accessed for each application
  • Provide tools that enable business users and IT staff to collaborate on data requirements and definitions
  • Deliver impact analysis and data lineage reports to coordinate changes and provide visibility to critical data flows
  • Deliver data that has been cleansed and harmonized to the warehouse regardless of volume or latency requirements
  • Synchronize master data for key business entities across operational systems

Combining the right warehouse platform, a comprehensive industry data model with a unified platform for data integration that all share business concepts, transformation rules, and metadata will enable the deep collaboration between business analysts and IT that is required to deliver information on demand.

By creating a roadmap for a truly dynamic warehouse, organizations can meet their most pressing needs for business intelligence today, while ensuring a data warehouse environment that can help support rapid growth, significant change and increasing demand for real-time information.

This article originally appeared in the issue of .

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