Business Intelligence and KM (Aug. 2)

There is a clear and growing relationship between business intelligence (BI) and knowledge management (KM). In Herschel and Yermish (2009) the view is that BI focuses on identifying trends or patterns in (typically large) explicit data warehouses. The aim is to aid in decision-making or in planning of corrective actions when the trend deviates from organizational goals. BI usually employs explicit quantitative data, which may imply an (excessive) emphasis in the technological tools and methods employed to analyze data, such as data mining and online analytical processing - OLAP). Accordingly, Herschel and Yermish argue that BI by itself is insufficient for generating value. Since we have already discussed the value creation emphasis of KM, then the idea is that BI be viewed as a subset of KM in order to make the technological capabilities useful and value-adding. For instance, an organization might place their BI activities and support tools in the context of a KM strategy led by learning objectives, using Nonaka and Takeuchi's SECI model for example.Also, since KM encompasses both explicit and tacit knowledge, a KM process should make knowledge explicit prior to employing BI techniques. One way in which this can be achieved is employing so-called knowledge exchange protocols. These offer a template and a structure for sharing knowledge, thereby stimulating and facilitating entry of new knowledge (from individuals into the system) as well as consumption of existing knowledge (from system to individual). An example protocol is presented by Herschel and Yermish: the SOAP protocol (not to be confused with service-oriented jargon) includes Subjective, Objective, Assessment and Plan categories. This template is applicable to different domains and enables an explicit categorization of tacit knowledge via text.

A different take on the relationship between KM and BI is offered by Cody et al. (2002). Out of their work at IBM research, the authors contend that the main difference between BI and KM is that the first is centered on (quantitative) explicit data analysis, while the latter is focused on tacit textual information. As such, the point is to move towards a single BIKM system in which it is possible to associate text with data. Often, the presence of meta-data will be enough to componentize text and link it to data, but in other cases there will be no meta-data or the link will not be explicit beforehand, requiring more preparation steps. In the end a single model with shared dimensions will integrate both text and data in order to improve the quality of business decisions (enriching the analyses offered by only using data).