MaCuDE IS Task Force Phase II Report: Views of Industry Leaders on Big Data Analytics and AI

2023 | Communications of the Association for Information Systems | Citations: 2

Authors: Lyytinen, Kalle; Topi, Heikki; Tang, Jing

Abstract: This paper represents the Phase II report of the Management Curriculum for the ... Expand

Abstract: This paper represents the Phase II report of the Management Curriculum for the Digital Era (MaCuDE) disciplinary task force on information systems (IS). Aligned with the current work of the AIS (Association for Information Systems) and ACM (Association for Computing Machinery), we focus on the current and future industry driven educational needs and requirements posed by big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and related innovations. In this report, we probe and report on the views of industry leaders regarding BDA/AI education needs. We conducted 18 rich semi-structured interviews with a representative sample of industry leaders situated around key changes and issues related to workforce demands in digital transformation and associated educational needs. We performed a grounded theory based analysis of key themes in reported education needs. We note the shifting meaning of AI and BDA phenomena and identify three main organizational level needs for the digital eracapability improvement and transformation, decision-making strategies and tactics, and changes in operations or products -and connect them to three individual professional competencies necessary to deliver them: fundamental environmental competencies, data information and content, and system design competencies. Based on the analysis we outline several novel competency-based IS curriculum recommendations for the master's and undergraduate level IS education.Architecting for cloud: "And so the thing I noticed, a lot of these companies, when their data is growing so big and their database costs are running so high, they think about going towards Open Source Software, Open Source databases to store all the data and, hence, that creates a demand on the workforce to actually know how to use those. "Understanding data structures, architectures, and governance: "So that's his role, and the key requirement for that role again is Data Architecture, data Engineering and Data Management" Building and managing machine learning models: "Second, of course, is strong competency in the use of Machine Learning packages or models. This field is evolving aggressively." "For developing or programming our own AI solutions, our employees have no deep knowledge of mathematical models and how to build mature and value-creating AI applications." IS management and operations: "The talent sort of wars out there are really, really tough, and finding people who understand that, (how to) generate the data, use the data, think about the data, manipulate it are really, really important. I agree. It used to be you had to have people with Project Management. That was critical and that was everyone competed for that, and now it's that plus the data, the Machine Learning."Based on the data collected in this study, we cannot offer a clear answer to the questions of how BDA and AI affect areas that do not belong to the two main areas of the study. Given, however, how quickly BDA and AI have grown and how substantial related resource requirements are in organizations, it is clear that they already have an impact on several competency needs such as programming language selection, selection of BDA and AI tools based on cloud-based solutions, data architecture, data governance, service repertoires available on platform environments, and special characteristic of BDA and AI projects and how they affect education for project management needs and competencies. Summary of Findings Related to Individual CompetenciesGenerally, we observe a strong alignment and continuity between the recent information systems curriculum recommendations and the findings of this study. The data highlights the continued importance of IS core topics covered in all past curricula, such as data management, systems development, IT infrastructure and architecture (including security), and IS (project) management.The results also highlight also multiple areas that need more emphasis and careful design decisions when schools plan and implement their BDA and AI based programs: Integration of business and technology competencies in ways that allow seamless communication with both business and technology experts  A stronger emphasis on multiple life cycle models and concepts related to data management and data science than in the recent model curricula focused on system life cycles  Heightened focus on new dimensions of data analytics competencies (including communication-related elements such a storytelling and visualization)  The growing importance of ethics as a foundation for ensuring privacy and security of BDA and AI models  Need for more skills in statistics  Understanding the novel requirements that AI solutions set up for IT infrastructure  The importance of competencies in AI systems development and deployment. Collapse

Topics: artificial intelligence big data analytical information system data management decision making

Methods: qualitative interview machine learning meta learning field study personal interview

Robotic Process Flexibilization in the Term of Crisis: A Case Study of Robotic Process Automation in a Public Health Department

2022 | European Conference On Information Systems | Citations: 0

Authors: Koch, Julian; Vollenberg, Carolin; Matthies, Benjamin; Coners, André

Abstract: Due to the pandemic, institutions of the health sector, especially public healt ... Expand

Abstract: Due to the pandemic, institutions of the health sector, especially public health departments, are facing major challenges in managing their processes. In a constantly changing environment, new and existing processes have to be adopted or implemented in the shortest possible time, while the process volumes to be managed are constantly increasing. In our article, we use a case study to show how the concept of "flexibility by design" can be influenced by RPA in the sensitive environment of healthcare and how exactly flexibility in process execution can be achieved with it. As a result, we show that RPA can positively implement or enable three of the six realization options from the concept. In addition, the concept was supplemented by two aggregated theoretical dimensions, namely "Response" and "Range," which summarize the supporting conditions for a process flexibilization with RPA. In the article, we thereby show how exactly RPA can complement existing processes in a healthcare environment and thus, serve to subsequently make rigid process models more flexible. Collapse

Topics: information technology infrastructure pandemic extract transform load process business process management robotic process automation

Methods: case study qualitative interview qualitative coding personal interview business process modeling

A Technology-Specific Modeling Method for Data ETL Processes

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Deme, Andrea; Buchmann, Robert

Abstract: The goal of this paper is to establish the notion of "technology-specific model ... Expand

Abstract: The goal of this paper is to establish the notion of "technology-specific modeling method" instantiated by a showcase example that was realized as a Design Science artifact, within the Agile Modeling Method Engineering framework, considering a particular technological context and technology-dependent purpose -the management of ETL (extract-transform-load) processes and their interoperability with an in-house ETL platform running in an organization that deals with heavy data management and orchestration tasks. With the term "technology-specific", we refer to a level of specificity that does not stem from concepts pertaining to the business sector or goals, but from technological abstractions already available in the model-driven environment where models are intended to be used. Besides reporting on the engineering details of a showcase modeling language, the paper also draws theoretical reflections on the notion of "technology-specific modeling" and what distinguishes it from the traditional domainspecific class of modeling languages. Collapse

Topics: extract transform load process data management systems development service oriented architecture data warehouse

Methods: design science conceptual modelling modeling language metamodel design artifact

A stitch in time saves nine – A meta-model for real-time prediction of product returns in ERP systems

2021 | European Conference On Information Systems | Citations: 0

Authors: Fuchs, Kevin; Lutz, Oliver

Abstract: The increasing number of product returns and the associated costs force enterpr ... Expand

Abstract: The increasing number of product returns and the associated costs force enterprises to avoid returns before they are even made. Therefore, customers should only submit an order if the return probability is low. Consequently, we designed a generic meta-model for real-time prediction of product returns in ERP systems (RCS). According to the Design Science Research approach, we defined the requirements based on the literature. Our meta-model supports customers with real-time recommendations to adapt their cart based on interactions with the website and ERP data to reduce the return rate. A feedback system evaluates data and gives individual suggestions to influence customers' behaviour. According to the evaluation step, we showed the feasibility of a generic RCS. Next, we integrated the first module of the RCS into a cloud-based ERP system supplier. We conducted a simulation-based evaluation for website adaptions and feature extraction of customer interactions. Collapse

Topics: enterprise resource planning website database system extract transform load process cloud computing

Methods: design science metamodel design principle computational algorithm machine learning

Theories: lemon market theory

Semantic Web Analysis Graphs: Guided Multidimensional Analysis of Linked Open Data

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Hilal, Median; Schuetz, Christoph

Abstract: An increasingly large number of sets of linked open data (LOD), typically in RD ... Expand

Abstract: An increasingly large number of sets of linked open data (LOD), typically in RDF format, are being published on the Semantic Web. Those data represent a potentially valuable resource for data analysis, particularly online analytical processing (OLAP), which often employs multidimensional (MD) models for conducting MD data analysis. Conducting MD analysis over LOD, however, is not a straightforward task. Most analysts will lack the technical skills to query LOD sources using an unfamiliar query language over data in a format not traditionally associated with MD data analysis. In this paper, we introduce the concept of the semantic web analysis graph (SWAG), which allows experts familiar with the LOD source to plot interesting courses of analysis for other users. We present a proof-of-concept prototype. The results of a usability study show that SWAGs may serve to build intuitive user interfaces. Collapse

Topics: usability user interface web application semantic web online analytical processing

Methods: design artifact design science usability testing survey experiment

Managing Data Quality of the Data Warehouse: A Chance-Constrained Programming Approach

2021 | Information Systems Frontiers | Citations: 0

Authors: Liu, Qi; Feng, Gengzhong; Tayi, Giri Kumar; Tian, Jun

Abstract: To make informed decisions, managers establish data warehouses that integrate mu ... Expand

Abstract: To make informed decisions, managers establish data warehouses that integrate multiple data sources. However, the outcomes of the data warehouse-based decisions are not always satisfactory due to low data quality. Although many studies focused on data quality management, little effort has been made to explore effective data quality control strategies for the data warehouse. In this study, we propose a chance-constrained programming model that determines the optimal strategy for allocating the control resources to mitigate the data quality problems of the data warehouse. We develop a modified Artificial Bee Colony algorithm to solve the model. Our work contributes to the literature on evaluation of data quality problem propagation in data integration process and data quality control on the data sources that make up the data warehouse. We use a data warehouse in the healthcare organization to illustrate the model and the effectiveness of the algorithm. Collapse

Topics: data quality data warehouse data integration resource allocation quality management

Methods: computational algorithm optimization model sensitivity analysis experiment Monte Carlo simulation

Sustainability Design Principles for a Wildlife Management Analytics System: An Action Design Research

2021 | European Journal of Information Systems | Citations: 9

Authors: Pan, Shan L.; Li, Mingwei; Pee, L.G.; Sandeep, M.S.

Abstract: Wildlife management is becoming increasingly critical to improving the sustainab ... Expand

Abstract: Wildlife management is becoming increasingly critical to improving the sustainability of biodiversity and the welfare of human beings. This paper uses affordance as a lens to explore the design of information systems that can assist in managing wildlife in protected areas. Through an action design research (ADR) study with a forest department, we develop and test design principles for a class of wildlife management analytics system (WMAS). We identify the initial design principles, including elements of the action potential, materiality, and boundary condition, and iteratively refine them based on an instantiation of WMAS through two iterations of design and implementation cycles. Through our work, we contribute to design knowledge by abstracting the artefacts, design principles in particular, and the ADR approach by generalising two new activities and corresponding principles when designing analytical models. Our findings can also be used to address a class of similar problems and systems in practice. Collapse

Topics: affordance user experience usability geographic information system extract transform load process

Methods: design principle design artifact action design research computational algorithm design science

Theories: theory of affordance

On Modelling Digital Healthcare Ecosystems and their Knowledge Management

2019 | Americas Conference on Information Systems | Citations: 0

Authors: Nimmagadda, Shastri; Reiners, Torsten; Wood, Lincoln; Chitti, Sashi K.

Abstract: The Human Anatomy, Physiology and Psychology Integrated Egghead Relationships ( ... Expand

Abstract: The Human Anatomy, Physiology and Psychology Integrated Egghead Relationships (HAPPIER) are embedded domains of a human ecosystem. In the digital ecosystem perspective, the idiom "Integrated-Egghead-Relationships" describes composite domains with encapsulation of multiple data attribute dimensions. The increase in physiological and psychological disorders among the mass population in periodic and geographic dimensions has reported an outburst of volumes and varieties of data in healthcare ecosystems. The data heterogeneity and multidimensionality are other challenges constraining the data integration process, at times disconnecting patients and medical practitioners. Knowledge-based data models are needed to manage unstructured cognitive healthcare Big Data. The purpose of the research is to build a framework and integrate multilayered models in multidimensional warehouse repository. The integrated framework can engage multiple scopes of HAPPIER and collaborate with human cognitive anatomy and physiological data events in large-scale depositories. The repositories can bring values in new knowledge domains, making healthcare projects operationally successful. Collapse

Topics: big data knowledge representation embedded system health information system knowledge management

Methods: ontological modelling data modeling multidimensional modeling blood pressure literature study

Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon’s Public Cloud Infrastructure

2019 | HICSS | Citations: 0

Authors: Nwanganga, Frederick; Chawla, Nitesh V; Madey, Gregory

Abstract: Workload modeling in public cloud environments is challenging due to reasons suc ... Expand

Abstract: Workload modeling in public cloud environments is challenging due to reasons such as infrastructure abstraction, workload heterogeneity and a lack of defined metrics for performance modeling. This paper presents an approach that applies statistical methods for distribution analysis, parameter estimation and Goodness-of-Fit (GoF) tests to develop theoretical (estimated) models of heterogeneous workloads on Amazon’s public cloud infrastructure using compute, memory and IO resource utilization data. Collapse

Topics: cloud computing Linux resource allocation Tencent QQ Python

Methods: simulation descriptive statistic integer linear programming literature study data modeling

CHAMALEON: Framework to improve Data Wrangling with Complex Data

2019 | International Conference on Information Systems | Citations: 0

Authors: Valencia-Parra, Álvaro; Gómez-López, María Teresa; Varela-Vaca, Ángel Jesús; Ceravolo, Paolo

Abstract: Data transformation and schema conciliation are relevant topics in Industry due ... Expand

Abstract: Data transformation and schema conciliation are relevant topics in Industry due to the incorporation of data-intensive business processes in organizations. As the amount of data sources increases, the complexity of such data increases as well, leading to complex and nested data schemata. Nowadays, novel approaches are being employed in academia and Industry to assist non-expert users in transforming, integrating, and improving the quality of datasets (i.e., data wrangling). However, there is a lack of support for transforming semi-structured complex data. This article makes a state-ofthe-art by identifying and analyzing the most relevant solutions that can be found in academia and Industry to transform this type of data. In addition, we propose a DomainSpecific Language (DSL) to support the transformation of complex data as a first approach to enhance data wrangling processes. We also develop a framework to implement the DSL and evaluate it in a real-world case study. Collapse

Topics: database languages domain-specific language Apache Spark Google+ extract transform load process

Methods: case study literature study data transformation design science data modeling