Revisiting Ralph Sprague’s Framework for Developing Decision Support Systems

Ralph H. Sprague Jr. was a leader in the MIS field and helped develop the conceptual foundation for decision support systems (DSS). In this paper, I pay homage to Sprague and his DSS contributions. I take a personal perspective based on my years of working with Sprague. I explore the history of DSS and its evolution. I also present and discuss Sprague’s DSS development framework with its dialog, data, and models (DDM) paradigm and characteristics. At its core, the development framework remains valid in today’s world of business intelligence and big data analytics. I present and discuss a contemporary reference architecture for business intelligence and analytics (BI/A) in the context of Sprague’s DSS development framework. The practice of decision support continues to evolve and can be described by a maturity model with DSS, enterprise data warehousing, real-time data warehousing, big data analytics, and the emerging cognitive as successive generations. I use a DSS perspective to describe and provide examples of what the forthcoming cognitive generation will bring

Business Intelligence: Past, Present and Future

Business Intelligence (BI) is critical to the performance of many organizations. It isn’t a completely new phenomenon, however, and reflects 40 years of evolution in decision support. These roots are traced in the context of what is important in BI today. Contemporary BI is explored – concepts, applications, technology, and processes – including definitions, frameworks, architectures, OLAP based reporting, dashboards/scorecards, predictive analytics, operational BI, agile development, and more. Also described is the future of BI, including pervasive BI, data visualization, and what can be called the information based organization. Examples are drawn from a variety of companies. The tutorial closes with a discussion of resources for teaching BI

Tutorial: Mobile BI

Smartphones and tablets are ubiquitous for personal use and are increasingly employed for business purposes. When paired with business intelligence to create mobile BI, workers are able to access information wherever they are, at any time, and through a variety of devices. This use has the potential to increase employee productivity, enhance customer service and satisfaction, improve decision making, provide a competitive advantage, and improve the bottom line. Mobility can make BI pervasive throughout an organization, but it is most likely to be used by executives, mid-level and operational managers, sales representatives, and field and internal technicians. To be successful with mobile BI, we must address various issues and challenges such as creating a roadmap, getting started right, meeting user expectations, creating an appropriate technology infrastructure, designing for screen size, and providing for security. Case studies of U.S.Xpress, a leading trucking company, and GUESS, a leading retailer of clothing and accessories, provide many real-world examples of the concepts, options, and best practices associated with mobile BI. Following mainframe, client/server, and Web-based approaches, mobile BI is the fourth generation of how information is delivered

Reflections on Engaging the Business Community to Support Academic Research

In this historical perspective, I share my thoughts and experiences working with companies to engage and support academic research. I show the process from finding the right topic to deciding when it is time to move on to something new. As I go through my experiences, I will introduce 10 lessons learned to help in your research efforts. I also introduce three example professors who operate in different academic environments, have different academic and personal goals, and take different paths in working with the business community. I close by exploring the four evolutionary stages of academic IS research. The latest stage, big data/machine learning/artificial intelligence, offers new opportunities for engaging the business community, as well as impacting what academic IS research is and how it is conducted

Tutorial: Business Intelligence – Past, Present, and Future

Business intelligence (BI) is a broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better decisions. This tutorial discusses some of the early, landmark contributions to BI; describes a comprehensive, generic BI environment; and discusses four impor-tant BI trends: scalability, pervasive BI, operational BI, and the BI based organization. It also identifies BI resources that are available for faculty and students

Update Tutorial: Big Data Analytics: Concepts, Technology, and Applications

In 2014, I wrote a paper on big data analytics that the Communications of the Association for Information Systems published (volume 34). Since then, we have seen significant advances in the technologies, applications, and impacts of big data analytics. While the original paper’s content remains accurate and relevant, with this new paper, I update readers on important, recent developments in the area

Tutorial: Big Data Analytics: Concepts, Technologies, and Applications

We have entered the big data era. Organizations are capturing, storing, and analyzing data that has high volume, velocity, and variety and comes from a variety of new sources, including social media, machines, log files, video, text, image, RFID, and GPS. These sources have strained the capabilities of traditional relational database management systems and spawned a host of new technologies, approaches, and platforms. The potential value of big data analytics is great and is clearly established by a growing number of studies. The keys to success with big data analytics include a clear business need, strong committed sponsorship, alignment between the business and IT strategies, a fact-based decision-making culture, a strong data infrastructure, the right analytical tools, and people skilled in the use of analytics. Because of the paradigm shift in the kinds of data being analyzed and how this data is used, big data can be considered to be a new, fourth generation of decision support data management. Though the business value from big data is great, especially for online companies like Google and Facebook, how it is being used is raising significant privacy concerns

Recent Developments in Data Warehousing

Data warehousing is a strategic business and IT initiative in many organizations today. Data warehouses can be developed in two alternative ways -- the data mart and the enterprise-wide data warehouse strategies -- and each has advantages and disadvantages. To create a data warehouse, data must be extracted from source systems, transformed, and loaded to an appropriate data store. Depending on the business requirements, either relational or multidimensional database technology can be used for the data stores. To provide a multidimensional view of the data using a relational database, a star schema data model is used. Online analytical processing can be performed on both kinds of database technology. Metadata about the data in the warehouse is important for IT and end users. A variety of data access tools and applications can be used with a data warehouse - SQL queries, management reporting systems, managed query environments, DSS/EIS, enterprise intelligence portals, data mining, and customer relationship management. A data warehouse can be used to support a variety of users - executive, managers, analysts, operational personnel, customers, and suppliers. Data warehousing concepts are brought to life through a case study of Harrah\u27s Entertainment, a firm that became a leader in the gaming industry with its CRM business strategy supported by data warehousing

Addressing the Growing Need for Algorithmic Transparency

Today, many organizations use personal data and algorithms for ads, recommendations, and decisions. However, some have expressed concern that this use negatively impacts individual privacy and poses a risk to individuals and society. In response, many have called for greater algorithmic transparency; that is, for organizations to be more public and open about their use of personal data and algorithms. To better understand algorithmic transparency, we reviewed the literature and interviewed 10 experts. We identified the factors that influence algorithmic transparency, the Association for Computing Machinery’s principles for ensuring that one uses personal data and algorithms fairly, and recommendations for company best practices. We also speculate about how personal data and algorithms may be used in the future and suggest research opportunities