Data integration flows for Business Intelligence

Abstract

Business Intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable better decision making. Today's BI architecture typically consists of a data warehouse (or one or more data marts), which consolidates data from several operational databases, and serves a variety of front-end querying, reporting, and analytic tools. The back-end of the architecture is a data integration pipeline for populating the data warehouse by extracting data from distributed and usually heterogeneous operational sources; cleansing, integrating and transforming the data; and loading it into the data warehouse. Since BI systems have been used primarily for off-line, strategic decision making, the traditional data integration pipeline is a oneway, batch process, usually implemented by extract-transform-load (ETL) tools. The design and implementation of the ETL pipeline is largely a labor-intensive activity, and typically consumes a large fraction of the effort in data warehousing projects. Increasingly, as enterprises become more automated, data-driven, and real-time, the BI architecture is evolving to support operational decision making. This imposes additional requirements and tradeoffs, resulting in even more complexity in the design of data integration flows. These include reducing the latency so that near real-time data can be delivered to the data warehouse, extracting information from a wider variety of data sources, extending the rigidly serial ETL pipeline to more general data flows, and considering alternative physical implementations. We describe the requirements for data integration flows in this next generation of operational BI system, the limitations of current technologies, the research challenges in meeting these requirements, and a framework for addressing these challenges. The goal is to facilitate the design and implementation of optimal flows to meet business requirements. Copyright 2009 ACM.