Data and Analytics Driven Digital Transformation in Organizations and Society
Permanent URI for this collectionhttps://hdl.handle.net/10125/107554
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Item type: Item , Data Quality Tools: Towards a Software Reference Architecture(2024-01-03) Altendeitering, Marcel; Guggenberger, Tobias MoritzOrganizations crave to succeed in the ongoing digital transformation, and central to this is the quality of data as a major source for business innovation. Data quality tools promise to increase the quality of data by managing and automating the different tasks of data quality management. However, established tools often lack support for the fundamental changes accompanying an ongoing digital transformation, such as data mesh architectures. In this paper, we propose a software reference architecture for data quality tools that guides organizations in creating state-of-the-art solutions. Our reference architecture is based on the knowledge captured from ten data quality tools described in the scientific literature. For evaluation, we conducted two qualitative focus group discussions using the adapted architecture tradeoff analysis method as a basis. Our findings reveal that the proposed reference architecture is well-suited for creating successful data quality tools and can help organizations assess offerings in the market.Item type: Item , Leveraging Corporate Data Platforms: An Architectural Perspective(2024-01-03) Hackl, Tobias; Vetterling, Dennis; Winter, RobertThis paper investigates the leverages of digital platforms in corporate data management from an architectural perspective. We propose the idea of corporate data platforms to address pressing challenges in data management architecture’s supply side: inflexibility and inefficiency. Based on a systematic literature review of 14 papers, we investigate (i) to which extent data platforms in general – and corporate data platforms in particular – can be regarded as subtypes of the general concept of digital platforms and (ii) which architectural components of corporate data platforms can drive architectural leverages. We find evidence that modularity is vital to exploit two architectural leverages. Implications for research are to extend the data platforms discourse by systematically linking it to general digital platform concepts and findings. For practice, this study provides guidance for corporate data platform design from the perspective of ‘beneficial’ architectural components and more clarity about means and ends that underlie data platform adaption.Item type: Item , Toward a Curriculum for Data Literacy in Enterprises(2024-01-03) Lefebvre, Hippolyte; Legner, ChristineTo create business value from data, firms need a data literate workforce capable of reading, working, analyzing, and arguing with data. Prior studies on data literacy have mostly focused on educational settings and identified data-related skills. However, the suggested generic skill catalogs do not account for the highly situated nature of data practices. In this paper, we delve into five data literacy programs at multinational companies and examine their unique scope and characteristics. We leverage curriculum theory to analyze the different curriculum components and how they foster workplace data practices. As a contribution to data literacy research, we propose a theory-inspired and situated curriculum for data literacy in enterprises built upon five learning blocks, namely generic skills, disciplinary content, disciplinary skills, workplace awareness, and workplace experience. We also disclose each block's target audience, scope, and delivery mode and thereby inform practitioners on how to build their own curricula.Item type: Item , Introduction to the Minitrack on Data and Analytics Driven Digital Transformation in Organizations and Society(2024-01-03) Marjanovic, Olivera; Dinter, Barbara; Ariyachandra, Thilini