William Scott Spangler

The integration of business intelligence and knowledge management

Enterprise executives understand that timely, accurate knowledge can mean improved business performance. Two technologies have been central in improving the quantitative and qualitative value of the knowledge available to decision makers: business intelligence and knowledge management. Business intelligence has applied the functionality, scalability, and reliability of modern database management systems to build ever-larger data warehouses, and to utilize data mining techniques to extract business advantage from the vast amount of available enterprise data. Knowledge management technologies, while less mature than business intelligence technologies, are now capable of combining todays content management systems and the Web with vastly improved searching and text mining capabilities to derive more value from the explosion of textual information. We believe that these systems will blend over time, borrowing techniques from each other and inspiring new approaches that can analyze data and text together, seamlessly. We call this blended technology BIKM. In this paper, we describe some of the current business problems that require analysis of both text and data, and some of the technical challenges posed by these problems. We describe a particular approach based on an OLAP (on-line analytical processing) model enhanced with text analysis, and describe two tools that we have developed to explore this approach--eClassifier performs text analysis, and Sapient integrates data and text through an OLAP-style interaction model. Finally, we discuss some new research that we are pursuing to enhance this approach.

A smarter process for sensing the information space

As a result of the growth of the Internet, the amount of available information is exponentially increasing. However, increasing the amount of information does not imply increasing usefulness. Furthermore, as the complexity of business relationships increases, there is a natural tendency toward less structured interaction between entities. This highlights the growing relevance of unstructured information in documenting the interactions of organizations and individuals. Analyzing and making sense of this unstructured information space requires more than text-mining algorithms; it requires a strategic approach. We propose a unified approach that addresses a variety of information space analytics problems. Our method for making sense of unstructured data is described by six steps that are analogous to the algebraic order of operations PEMDAS (parenthesis, exponent, multiplication, division, addition, and subtraction). These basic text-mining operations can be combined in many interesting ways to handle a diverse set of problems, and just as in algebra, it is critical that these operations be performed in the correct order to guarantee a meaningful result. In this paper, we describe how PEMDAS has been implemented within organizations to enable decisions that produced measurable business value.