Business intelligence and datawarehousing professionals: Your comfort zone is shrinking. Dramatic changes lie ahead. Conventional applications are giving way to standards-based Web services. The lines between operational and analytic systems are blurring. And as organizations try to make sense out of what s contained in their myriad data stores, standard data integration and presentation techniques are proving costly and difficult.
The bottom line is that current practices aren t adequate. Business agility depends on the ability to assemble, disassemble and rearrange application components. These actions require a comprehensive understanding of not only data representation syntax-, but also data s meaning and its relationships to other data and information ? that is, the semantics. Over the past 15 years, we ve seen a sequence of integration technologies and methodologies emerge, flourish and hang on. First, extract, transform and load ETL- delivered data integration and movement for datawarehouses. Then enterprise application integration EAI-, along with message-oriented middleware, opened the door to business-to-business Web commerce. Now there s a surge of interest in enterprise information integration EII- among those looking to do real-time operational reporting and other time-sensitive activities. By supporting the delivery of queries to the data sources rather than waiting on ETL and data movement steps to get the data into the warehouse, EII addresses a weakness of conventional datawarehousing when it comes to real-time objectives. While each approach has its attributes, this collection of technologies can t be amalgamated to provide the level of information integration most organizations need. Analysts and vendors frequently suggest that ETL, data warehouse, EAI, EII and other integration tools are complementary. In other words, even if you apply them separately, you ll ultimately arrive at a complete solution. They re mistaken. Each technology crevice between the different tools requires a separate modeling and mapping effort, leaving organizations with multiple models. ETL demands a target database schema. EAI requires agreement on a canonical form among the applications. And with the less mature EII, technical demands vary from vendor to vendor ? from a simple set of views to a full model developed in Unified Modeling Language UML-. From a management perspective, each integration solution tends to operate from within a different technology stack and, therefore, carries unique design constraints, tuning characteristics and vendor upgrade cycles. Finally, integration tools generally don t expose their metamodels or use other means of communicating. Certainly, none integrates data beyond the syntactic level. The tools manage semantics ? information that focuses on conveying the meaning of data and information ? in ad hoc fashion, if at all. Because each tool manifests itself differently to those who use the information, it s unlikely that a knowledge worker could see a single view that combined the fruits of ETL, EAI and EII. Rather, each tool would more likely be an element of distinctly separate application or information architectures. Source and full article: www.intelligententerprise.coma>