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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.

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.

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.

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.

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.

Companies are moving from a cottage industry to a factory approach to analytics, especially in regard to machine learning (ML) models. This change is motivating companies to adopt ML operations (MLOps) as a methodology for the timely development, deployment, and maintenance of ML models in order to positively impact business outcomes. The adoption of MLOps requires changes in processes, technology, and people, and these changes are informed by previous work on decision support systems (DSS), development operations (DevOps), and data operations (DataOps). The processes, technologies, and people needed for MLOps are discussed and illustrated using a customer purchase recommendation example. Current and future directions for MLOps practice driven by artificial intelligence (AI) are explored. Suggestions for further academic research are provided.

The IT implementation literature suggests that various implementation factors play critical roles in the success of an information system; however, there is little empirical research about the implementation of data warehousing projects. Data warehousing has unique characteristics that may impact the importance of factors that apply to it. In this study, a cross-sectional survey investigated a model of data warehousing success. Data warehousing managers and data suppliers from 111 organizations completed paired mail questionnaires on implementation factors and the success of the warehouse. The results from a Partial Least Squares analysis of the data identified significant relationships between the system quality and data quality factors and perceived net benefits. It was found that management support and resources help to address organizational issues that arise during warehouse implementations; resources, user participation, and highly-skilled project team members increase the likelihood that warehousing projects will finish on-time, on-budget, with the right functionality; and diverse, unstandardized source systems and poor development technology will increase the technical issues that project teams must overcome. The implementation's success with organizational and project issues, in turn, influence the system quality of the data warehouse; however, data quality is best explained by factors not included in the research model.

Background Arboviruses such as Dengue virus (DENV), Chikungunya virus (CHIKV), Zika virus (ZIKV), and Mayaro virus (MAYV) are major causes of acute febrile illness (AFI) in Peru. However, their overlapping clinical symptoms complicate differential diagnosis. Identifying immunological markers like cytokine profiles could facilitate differential diagnosis and improve understanding of their immunopathogenesis. This study aimed to describe the cytokine responses (IL-2, IL-6, IL-10, TNF-α, IFN-γ) associated with each infection. Methods A cross-sectional study was conducted from June 2020 to April 2022 in Cajamarca, Peru, including 20 patients each with DENV, CHIKV, ZIKV, or MAYV infection, and 20 healthy controls. Infections were confirmed by RT-PCR and virus-specific IgM ELISAs. Serum cytokine levels (IL-2, IL-6, IL-10, TNF-α, IFN-γ) were measured by ELISA. Cytokine profiles were compared across the four infections and controls, and cytokine kinetics were analyzed by stratifying patients by days post-symptom onset. Results All four infections presented with overlapping acute symptoms (e.g., fever, headache, myalgia, arthralgia). Despite these clinical similarities, each infection presented a different cytokine profile. IL-2 levels were comparable among the arbovirus groups (though higher in DENV and patients with ZIKV infection than in controls). IL-6 was elevated predominantly in CHIKV and ZIKV; IL-10 was highest in DENV; and TNF-α and IFN-γ were significantly elevated in DENV and ZIKV (with DENV levels exceeding those in MAYV). Analysis of cytokine dynamics showed that levels of all five cytokines generally peaked early (days 1–4 post-symptom onset) and declined thereafter, with the timing and magnitude of peak responses varying by virus. Conclusion Each arboviral infection analyzed presented a distinct cytokine profile, with elevated IL-6 levels in CHIKV and ZIKV. IL-2, IL-10, TNF-α, and IFN-γ levels were elevated among the flaviviruses studied, with a delayed increase in IL-2 observed in ZIKV patients. Clinical trial number Not applicable.

ObjectiveSatavaptan, a vasopressin V2 receptor antagonist, has been shown to improve the control of ascites in cirrhosis in short-term phase II studies. The aim of this study was to evaluate the efficacy and safety of satavaptan in three different populations of patients with cirrhosis and ascites.Methods1200 patients were included in three randomised double-blind studies comparing satavaptan with placebo in uncomplicated ascites (study 1: n=463 patients) and difficult-to-treat ascites, with and without concomitant diuretic treatment (studies 2 and 3: n=497 and n=240 patients, respectively).ResultsSatavaptan was not more effective than placebo in the control of ascites in any of the populations studied as estimated by the primary efficacy endpoints: worsening of ascites (study 1) and the cumulative number of large-volume paracenteses during 12 weeks (studies 2 and 3). Nevertheless, some of the secondary efficacy endpoints related to the treatment of ascites were met in the three studies, suggesting a slight advantage of satavaptan over placebo in delaying ascites formation. Moreover, satavaptan was more effective than placebo in improving the serum sodium concentration in patients with hyponatraemia. The incidence of major complications of cirrhosis during follow-up did not differ significantly between the satavaptan and placebo groups in the three studies. Overall, the rate of any treatment-related adverse events, serious treatment-related events and treatment-related events leading to permanent discontinuation of treatment did not differ significantly between the treatment groups. However, in study 2 mortality was higher in patients treated with satavaptan compared with placebo (HR 1.47; 95% CI 1.01 to 2.15); no significant differences in mortality between the two groups were observed in the other two studies. No specific cause for the increased mortality was identified. Most deaths were associated with known complications of liver cirrhosis.ConclusionSatavaptan, alone or in combination with diuretics, is not clinically beneficial in the long-term management of ascites in cirrhosis.

Previous studies have not been able to determine whether non-selective beta-blockers (NSBB) reduce the risk of sepsis in cirrhosis. We aimed to examine this question with data from 1198 patients with cirrhosis and ascites included in clinical studies of satavaptan, a vasopressin receptor antagonist with no effect on infection risk. Risk of sepsis was estimated for NSBB users vs nonusers. Patients were examined every four weeks, or in relation to hospitalization, for the one-year duration of the trials. We computed the cumulative risk of sepsis for patients who did vs did not use NSBB at baseline. We used Cox regression to compare hazard rates of sepsis between current users and nonusers, accounting for changes in NSBB use over time. We adjusted for patient sex and age, MELD-Na score, albumin, use of antibiotics, use of proton pump inhibitors, cirrhosis etiology, history of variceal bleeding or SBP, severity of ascites and HE, HCC, other cancers, and diabetes, while stratifying on geographical region. Of the 1198 patients, 54% used NSBB at some time. There were 56 sepsis episodes. The 1-year risk of sepsis was reduced to 5.7% (95% confidence interval [CI] 2.8-8.6) in baseline NSBB users vs 11.6% (95% CI 7.0-15.9) in baseline nonusers. The hazard ratio of sepsis for current NSBB users vs current nonusers was reduced to 0.5 (95% CI 0.3-0.8) and after adjustment to 0.7 (95% CI 0.4-1.3). NSBB use may reduce the risk of sepsis in patients with cirrhosis and ascites, but the precision of the estimate was limited by the number of episodes of sepsis.