CAPABILITIES FOR DATA ANALYTICS IN INDUSTRIAL INTERNET OF THINGS (IIOT)

2023 | European Conference On Information Systems | Citations: 0

Authors: Bagheri, Samaneh; Dijkstra, Jesse

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Topics: analytical information system internet of things mobile system organizational value cloud computing

Methods: qualitative interview machine learning literature study personal interview computational algorithm

Towards a domain-driven distributed reference architecture for big data systems

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Ataei, Pouya; Litchfield, Alan

Abstract: The proliferation of digital devices, rapid development of software and the inf ... Expand

Abstract: The proliferation of digital devices, rapid development of software and the infrastructure of today, have augmented user's capability to produce data at an unprecedented rate. The accelerated growth of data could be called the era of big data and forced a paradigm shift in data engineering because the variety, velocity and volume of data overwhelmed existing systems. While companies attempt to extract benefit from big data, success rates are still low. Challenges such as rapid changes in technology, organizational culture, complexity in data engineering, impediments to system development, and a lack of effective big data architectures mean that only an estimated 20% of companies achieved their goals. To this end, this study explores a domain-driven distributed big data reference architecture that addresses issues in data architecture, data engineering, and system development. This reference architecture is empirically grounded and evaluated through deployment in a real-world scenario as an instantiated prototype, solving a problem in practice. The results of the evaluation demonstrate utility and applicability but with architectural trade-offs and challenges. Collapse

Topics: software as a service cloud computing data lake systems development big data

Methods: design artifact simulation data modeling design science structured literature research

Conceptualization of Digital Twins in an Education Services Environment: A Straw Man Proposal

2023 | HICSS | Citations: 0

Authors: Sylvester, Allan; Mines, R.; David, Rodreck; Campbell-Meier, Jennifer

Abstract: Digital twins have been used in manufacturing to describe, predict, and prescrib ... Expand

Abstract: Digital twins have been used in manufacturing to describe, predict, and prescribe responses to complex problems. The digital twin is a constellation of technologies that mirror physical objects in the virtual world, including what has happened, is happening and could or should happen in the future for the mirrored object. What is common in previous conceptualizations of digital twins is that there is a physical boundary to the extent that digital twins can mirror real objects (sometimes including the objects’ environments). We propose a blended approach, using McKinsey’s straw man and Parmar et al.’s. (2020) framework, to offer a more rigorously structured process for arriving at a refined conceptualization of digital twins in the educational service environment. Collapse

Topics: digital twin production management decision making data lake use case modeling

Methods: business process modeling experiment case study expert panel qualitative content analysis

Deploying machine learning based data quality controls – Design principles and insights from the field

2022 | International Conference on Business Informatics | Citations: 0

Authors: Walter, Valerianne; Gyoery, Andreas; Legner, Christine

Abstract: Machine Learning (ML) has become one of the most promising technological advance ... Expand

Abstract: Machine Learning (ML) has become one of the most promising technological advances for enterprises to improve manual, highly resource- and time-consuming processes. Developing and deploying these ML based systems in an organizational setting, however, is linked to a range of processual and technical requirements and implications that researchers and enterprises have only started to comprehend. Based on an Action Design Research approach, this study develops a ML based solution for data quality (DQ) controls, an essential instrument in Data Quality Management. We synthesize our findings through a set of design principles for ML based DQ controls that describe key components in the three phases from proof-of-concept to deployment and business process integration. Our findings lay groundwork for future research in the field of ML based systems for DQ and contribute to the broader IS discourse on how to embed learning-based systems in real-world organizational contexts. Collapse

Topics: business process management digital dashboard data quality organizational context data lake

Methods: design principle design artifact action design research support vector machine design science

An Approach to Extracting Topic-guided Views from the Sources of a Data Lake

2021 | Information Systems Frontiers | Citations: 0

Authors: Diamantini, Claudia; Lo Giudice, Paolo; Potena, Domenico; Storti, Emanuele; Ursino, Domenico

Abstract: In the last years, data lakes are emerging as an effective and an efficient supp ... Expand

Abstract: In the last years, data lakes are emerging as an effective and an efficient support for information and knowledge extraction from a huge amount of highly heterogeneous and quickly changing data sources. Data lake management requires the definition of new techniques, very different from the ones adopted for data warehouses in the past. In this scenario, one of the most challenging issues to address consists in the extraction of topic-guided (i.e., thematic) views from the (very heterogeneous and often unstructured) sources of a data lake. In this paper, we propose a new network-based model to uniformly represent structured, semi-structured and unstructured sources of a data lake. Then, we present a new approach to, at least partially, “structuring” unstructured data. Finally, we define a technique to extract topic-guided views from the sources of a data lake, based on similarity and other semantic relationships among source metadata. Collapse

Topics: data lake knowledge representation knowledge graph Wikipedia database system

Methods: computational algorithm experiment DBpedia cluster analysis machine learning

Untangling the Open Data Value Paradox: How Organizations Benefit from Revealing Data

2021 | International Conference on Business Informatics | Citations: 0

Authors: Enders, Tobias; Benz, Carina; Satzger, Gerhard

Abstract: Inspired by governmental institutions publishing data for more than a decade, al ... Expand

Abstract: Inspired by governmental institutions publishing data for more than a decade, also private sector organizations have started engaging in open data initiatives in recent years. While monetary expenses to engage in open data are tangible, its benefits remain vague, thus fueling the open data paradox. We conduct a set of expert interviews to untangle this paradox to elicit potential benefits that may originate from engaging in open data in the private sector. Our preliminary results show three distinct groups of benefits: internal improvements, innovation driver, and external visibility. With this paper, we lay the foundation for a comprehensive model on exploring open data benefits. For practitioners, we showcase a novel path to extract value from data and to monetize it. Collapse

Topics: open data innovation management data lake organizational change business model

Methods: qualitative interview qualitative coding case study structured literature research personal interview

Toward big data and analytics governance: redefining structural governance mechanisms

2021 | HICSS | Citations: 3

Authors: Fadler, Martin; Legner, Christine

Abstract: Big Data and Analytics (BDA) enable innovative business models and, simultaneous ... Expand

Abstract: Big Data and Analytics (BDA) enable innovative business models and, simultaneously, increase existing business processes’ efficiency and effectiveness. Although BDA’s potential is widely recognized, companies face a variety of challenges when adopting BDA and endeavoring to generate business value. Researchers and practitioners emphasize the need for effective governance to delineate data and analytics’ roles and responsibilities. Existing studies focus either on data or on analytics governance, even though both approaches are closely interlinked and depend on each other. Our study aims to integrate these two distinct research perspectives into a unified view on structural mechanisms for BDA. Using design science research, we iteratively develop data and analytics roles, clarify their responsibilities and provide guidelines for their organizational assignment. Our study contributes to advancing research on data and analytics governance and supports practitioners managing BDA. Collapse

Topics: data lake supply chain management analytical information system data quality digital dashboard

Methods: focus group design science reference modelling survey literature study

Sysco’s Big Data Lake

2021 | International Conference on Information Systems | Citations: 0

Authors: O’Leary, Daniel

Abstract: There has been much written about big data lakes and the use of data and analyt ... Expand

Abstract: There has been much written about big data lakes and the use of data and analytics, but there are few real-world cases that allow analysis of this issue. The purpose of this paper is to discuss emerging issues in the cloud-based data lakes and the use of data analytic tools in those lakes. Accordingly, this paper develops a case designed to trace Sysco's development of a cloud-based data lake as part of their ecosystem for enterprise data. This case allows the analysis of several issues including data warehouses in contrast to data lakes and an analysis of the overall architecture to address these issues. The case also investigates, several emerging issues, including the nature of the material put in the data lake, the potential competitive advantage of a cloud-based lake, the changing nature of BI in the cloud, analytics in the cloud and other issues. Collapse

Topics: data lake cloud computing data warehouse digital ecosystem business intelligence

Methods: cross sectional research

Actualizing big data analytics affordances: A revelatory case study

2020 | Information & Management | Citations: 82

Authors: Dremel, Christian; Herterich, Matthias M.; Wulf, Jochen; vom Brocke, Jan

Abstract: Drawing on a revelatory case study, we identify four big data analytics (BDA) ac ... Expand

Abstract: Drawing on a revelatory case study, we identify four big data analytics (BDA) actualization mechanisms: (1) enhancing, (2) constructing, (3) coordinating, and (4) integrating, which manifest in actions on three socio-technical system levels, i.e., the structure, actor, and technology levels. We investigate the actualization of four BDA affordances at an automotive manufacturing company, i.e., establishing customer-centric marketing, provisioning vehicle-data-driven services, data-driven vehicle developing, and optimizing production processes. This study introduces a theoretical perspective to BDA research that explains how organizational actions contribute to actualizing BDA affordances. We further provide practical implications that can help guide practitioners in BDA adoption. Collapse

Topics: affordance user experience organization learning usability research and development

Methods: qualitative interview case study triangulation exploratory data analysis qualitative coding

Theories: theory of affordance sociotechnical systems theory information systems theory socio technical theory

Building Business Intelligence & Analytics Capabilities - A Work System Perspective

2020 | International Conference on Information Systems | Citations: 1

Authors: Fadler, Martin; Legner, Christine

Abstract: Although enterprises believe that they can achieve a competitive advantage with ... Expand

Abstract: Although enterprises believe that they can achieve a competitive advantage with big data and AI, their analytics initiatives’ success rate still lags behind expectations. Existing research reveals that value creation with business intelligence and analytics (BI&A) is a complex process with multiple stages between the initial investments in BI&A resources and ultimately obtaining value. While prior research mostly focused on value generation mechanisms, we still lack a thorough understanding of how enterprises actually build BI&A capabilities. We explain the process in our research using work system theory (WST). Based on case studies and focus groups, we identify four prevalent BI&A capabilities: reporting, data exploration, analytics experimentation, and analytics production. For each identified BI&A capability, we derive patterns for BI&A resource orchestration, using the WST lens. Our findings complement the BI&A value creation research stream by providing insights into capability building. Collapse

Topics: analytical information system big data value creation organization structure digital platform

Methods: case study focus group mixed method company material qualitative interview

Theories: work systems theory