Year: 2012
Type: dissertation
Abstract: Business intelligence (BI) management is an ability of a business organization to gather and analyze systematically information about its business operations and the business environment it is having business in, and turn that information and analysis of the information as knowledge and insight for the business organization. One of the key topics in the business intelligence development and usage situation is to understand what is the data quality level needed to create new knowledge and understand how well those quality targets are met. To be able to provide business intelligence for reporting and analytics purposes, different kind of transactional data along with master and reference data needs to be modeled, managed and aggregated as business metrics. When business organization is having targets for its execution, it can use the calculated and shared information and metrics to enhance its operations and competitive advantage. Data quality targets and metrics have a close relationship to organizational performance and result indicators, especially those which are the most important for the organization as key performance/result indicators. This requires clearly defined concepts and semantics to be in place behind performance and result indicators so the metrics can be understood semantically consistently as well. In the business environment when concepts are explicitly defined, shared and agreed inside an organization, the first ground work of having a common capability for knowledge sharing has been reached and when creating a common ontology for the company. In this descriptive case study research, theoretical framework for business intelligence data quality topics are covered by going through the semantics of business intelligence and how it is related to data, information and knowledge sharing as well as different data categories and modeling techniques. End result of this study is, that when improving data quality for business intelligence, it is crucial to focus on information semantic and ontology and to improve overall organizational data quality and knowledge sharing culture. This can be achieved by taking actually very simple principles of ontology creation in operational use.
Source: Theseus (Ammattikorkeakoulujen)
Author: Ari Anturaniemi
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W1745960518