Business Intelligence & Big Data for Innovative and Sustainable Development of Organizations

The development of the Internet, social media, distributed databases, and various mobile devices has caused a considerable increase in data. Much of this diverse data in unstructured and structured forms has a high business value and, if properly utilized, can become an important organizational asset. It contains various information about customers, competition, labor market, and development trends for industries, products, services, and the public and political mood. For innovative and sustainable development, organizations need to utilize data. They need to increase sales, identify future opportunities and new markets, outperform the competition, enhance products and services, recruit talent, improve operations, perform forecasting, protect the brand, and identify areas for improvement, to name a few ways of utilizing data. However, many organizations make limited use of this valuable data available to them either because they lack the necessary tools or do not understand the value of this data. The main objective of this special issue (SI) is to provide organizations with a theoretical, conceptual, and applied grounded discussion of Business Intelligence and Big Data (BI & BD) to aid in innovative and sustainable development and effective decision-making. This SI of Information Systems Management constitutes eight papers, six of which appear in this issue. The remaining two articles will appear in volume 39 issue 1. All authors did a great job of developing and delivering diverse papers relevant to the topic. Since all papers became high caliber papers at the end of the review process, they are all included. The first article titled “Data Mining for Small Shops Empowering Brick-and-Mortar Stores through BI Functionalities of a Loyalty Program,” is written by Michael Reiner Kamm, Jan-Peter Kucklick, Johannes Schneider, & Jan vom Brocke. This paper presents a case of how small stores could benefit from the application of sophisticated BI solutions. The authors show the analysis of shopping data of 13 years, 19,000 customers, and 55 shops and discuss how the loyalty program empowered data-based decision support for these show owners. In the second article entitled “Shortening Delivery Times by Predicting Customers’ Online Purchases: a Case Study in the Fashion” Jennifer Weingarten and Stefan Spinler examine on online retailers’ critical problems, especially on their disadvantage regarding the delivery times compared to traditional brick and mortar stores. The authors develop a prediction model for anticipatory shipping that can easily be implemented and used to predict purchases. The third article titled “User Related Challenges of Self-Service Business Intelligence” by Christian Lennerholt, Joeri van Laere, and Eva Söderström focus on Self-service Business Intelligence (SSBI). The paper aims to improve how non-technical casual users could use BI in a self-reliant manner without technical power users’ support. This research draws on an empirical study and identifies a wide range of user-related SSBI challenges. The fourth article entitled “What are the Critical Success Factors for Agile Analytics Projects?” by D. Sandy Staples and Mikhail Tsoy examines factors that play a key role in managing agile analytics projects. Based on four case studies, the study identifies 43 attributes of potential critical success factors. Sabine Nagel, Carl Corea, and Patrick Delfmann contribute to the special issue with the fifth article, “Cognitive Effects of Visualization Techniques for Inconsistency Metrics on Monitoring Data-Intensive Processes.” This in-depth study analyzes the cognitive effects of different visualization techniques for inconsistency metrics in the scope of monitoring data-intensive processes. INFORMATION SYSTEMS MANAGEMENT 2021, VOL. 38, NO. 4, 268–269 https://doi.org/10.1080/10580530.2021.1971021