There are scads of books, conferences and seminars on data warehousing. Magazines devote entire sections to data warehousing issues. Companies have appointed data warehousing managers and teams. And almost every organization is building a data warehouse or data mart of some kind. No doubt, data warehousing is big business. In fact, it's a multi-billion dollar business. But, really, it's only the tip of the iceberg. Many folks are beginning to recognize that a data warehouse is the first step toward building an information infrastructure that supports a complete range of analytical activities and applications.
So what do we call the whole iceberg? Business intelligence. Here's a good definition that has been put forth by The Data Warehousing Institute:
Business intelligence refers to the process of turning data into knowledge and knowledge into action for business gain. It is an end-user activity that is facilitated by various analytical and collaborative tools and applications as well as a data warehousing infrastructure. It encompasses all types of data hierarchical, relational, text, spatial, audio, video, etc.
Whereas a data warehouse focuses on structured data that can be stored neatly into relational tables, business intelligence encompasses the broad range of data and information that business people use to make decisions and take action. A data warehouse is just one source of information that companies need to make sound decisions and plans. (See Figure 1.)
Business intelligence also implies that information is presented to users in the context of the business processes in which they work. Users access any type of information through applications designed to support their work processes. Behind the scenes, the application supports a variety of data access engines that fetch the appropriate information and embed it transparently into the user's application.
In business intelligence, users are not forced to shift paradigms to access and analyze data. Query/reporting and analysis tools are embedded into applications which support core business processes. Access is intuitive, analysis is immediate and little training is required. Users can take action within the same context as their analysis. All analyses, actions and the effectiveness of past decisions are documented and shareable with co-workers, creating a continuous feedback loop.
Some people might claim that what we are really talking about here is knowledge management. And they might be right. There is considerable overlap between the two disciplines. The only difference is that business intelligence implies the existence of a data warehouse, whereas knowledge management does not. Also, knowledge management tries to extract, document and catalog unwritten business rules and contextual information based on experience that individuals "store" in their own minds.
From a high-level perspective, business intelligence and knowledge management start at different ends of the information analysis spectrum. Business intelligence starts with a data warehouse and query/reporting and analysis tools for the purpose of measuring historical activity. Over time, business intelligence activities will expand outward to embrace other kinds of data and business processes that currently fall within the domain of knowledge management.
For example, many companies and software developers are already integrating data mining tools to anticipate the future based on historical data or visualization tools to quickly scan large chunks of relevant information. Other companies are trying to integrate text and images with data warehouses, using collocated document management systems or object/relational databases. Many companies are also beginning to "push" relevant information to users in real-time based on predefined business rules or collaborative arrangements with co-workers.
Knowledge management initiatives, on the other hand, have focused on unstructured data, such as text, documents, personal notes, Web pages and experiential knowledge. Key knowledge management tools are text retrieval engines and linguistic analysis and artificial intelligence tools for searching large bodies of unstructured data for specific information or patterns.
Knowledge management applications tend to support previously ad hoc business processes, such as gathering competitive intelligence, creating personalized newspapers, managing skills inventories, documenting "lessons learned" from business projects and synthesizing "best practices" from previous decisions and programs.
Ultimately, the disciplines of business intelligence and knowledge management will converge. From a technology perspective, we will either store all critical business information in a relational knowledgebase, or we will integrate various engines to create a giant publish-and-subscribe architecture that can dish up information to users or applications based on predefined preferences or business processes. I suspect both types of architectures will gain momentum.
From a business perspective, companies will continually look for ways to leverage information for competitive advantage. In the current business climate, companies are recognizing that information and knowledge and the ability to act on them are their most valuable resources. Consequently, companies will continually realign processes and reshape organizational structures to facilitate the acquisition, utilization and application of information and knowledge.
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