Knowledge Graph Support for Descriptive Business Analytics
Sprache des Vortragstitels:
Englisch
Original Kurzfassung:
Business analytics provides decision-makers with the fundamental for an informed, fact-driven choice of the best course of action for an organization. The analysis results, however, are often not self-explanatory, nor is the best course of action following the results always obvious. In order to interpret the analysis results correctly, decision-makers require a deeper understanding---knowledge---of the development and analysis process as well as the employed data. The required knowledge is often not properly documented or only possessed by certain individuals. Obtaining such tacit knowledge retrospectively can be challenging, and costly for the organization. In the worst case, obtaining tacit knowledge may even have become impossible if, for example, the employee with the required knowledge has already left the organization. Even in case tacit knowledge about the analysis is indeed documented, however, another challenge is to retrieve the information required to support a decision without having to search through large amounts of knowledge manually. Based on years of practical experience of an industry specialist in business intelligence and analytics, we propose a method for employing a knowledge graph to capture tacitly available knowledge that is generated during the execution of the business analytics process. A knowledge graph can be queried to provide information about provenance, preprocessing steps, and other characteristics of the data, e.g., providing hints regarding the interpretation, to support decision-makers with the best possible foundation to correctly interpret analysis results.
Forschungs-