The Current State of Business Intelligence

By Hugh J. Watson, Barbara H. Wixom. Computer, 2007

698 citations recorded by the source corpus.

Business intelligence (BI) is now widely used, especially in the world of practice, to describe analytic applications. BI is currently the top-most priority of many chief information officers. BI has become a strategic initiative and is now recognized by CIOs and business leaders as instrumental in driving business effectiveness and innovation. BI is a process that includes two primary activities: getting data in and getting data out. Getting data in, traditionally referred to as data warehousing, involves moving data from a set of source systems into an integrated data warehouse. Getting data in delivers limited value to an enterprise; only when users and applications access the data and use it to make decisions does the organization realize the full value from its data warehouse. Thus, getting data out receives most attention from organizations. This second activity, which is commonly referred to as BI, consists of business users and applications accessing data from the data warehouse to perform enterprise reporting, OLAP, querying, and predictive analytics.

Subfields: Management Information Systems, Management Science and Operations Research, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/0d72079450fba5b6c10fdf1238a11859c9e74555

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Citations in the corpus

Design of fusion technique-based mining engine for smart business (2015, background): “Smart business, by definition, indicates the ability to achieve goals which are set according to the development tendency of business [1].”
LifeLogging: Personal Big Data (2014, background): “Data quality is an important topic in areas as diverse as business informatics , Watson and Wixom (2007), and environmental sensing, Ganeriwal et al. (2008); O'Connor et al. (2009).”
Impact of business analytics and enterprise systems on managerial accounting (2017, background): “The impact of poor quality data has been identi ﬁ ed in numerous academic studies (Wu et al., 2014; Yeoh and Koronis, 2010; Watson and Wixom, 2007) and these concerns are magni ﬁ ed with big data.”
A unified foundation for business analytics (2014, background): “On the academic front, the literature shows that analytics,1 when narrowly viewed as an application of mathematical and statistical techniques, has long been studied in business schools under such titles as operations research/management science, simulation analysis, econometrics, and financial…”
MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud (2012, background): “Recent developments in business intelligence (BI) technologies have elevated the potential for discovering patterns associated with complex problem domains (Watson and Wixom 2007), such as fraud (Bolton and Hand 2002).”