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Digitalization and enterprise architecture management: a perspective on benefits and challenges
(2023)
Many companies digitally transform their business models, processes, and services. They have also been using Enterprise Architecture Management approaches for a long time to synchronize corporate strategy and information technology. Such digitalization projects bring different challenges for Enterprise Architecture Management. Without understanding and addressing them, Enterprise Architecture Management projects will fail or not deliver the expected value. Since existing research has not yet addressed these challenges, they were investigated based on a qualitative expert study with leading industry experts from Europe. Furthermore, potential benefits of digitalization projects for Enterprise Architecture Management were researched. Our results provide a theoretical framework consisting of five identified challenges, triggers and a number of benefits. Furthermore, we discuss in what ways digitalization and EAM is a promising topic for future research.
Platforms feature increasingly complex architectures with regard to interconnecting with other digital platforms as well as with a variety of devices and services. This development also impacts the structure of digital platform ecosystems and forces providers of these services, devices, and services to incorporate this complexity in their decision-making. To contribute to the existing body of knowledge on measuring ecosystem complexity, the present research proposes two key artefacts based on ecosystem intelligence: On the one hand, complementarity graphs represent ecosystems with an ecosystem's functional modules as vertices and complementarities as edges. The nodes carry information about the category membership of the module. On the other hand, a process is suggested that can collect important information for ecosystem intelligence using proxies and web scraping. Our approach allows replacing data, which today is largely unavailable due to competitive reasons. We demonstrated the use of the artefacts in category-oriented complementarity maps that aggregate the information from complementarity graphs and support decision-making. They show which combination of module categories creates strong and weak complementarities. The paper evaluates complementarity maps and the data collection process by creating category-oriented complementarity graphs on the Alexa skill ecosystem and concludes with a call to pursue more research based on functional ecosystem intelligence.
Assistant platforms
(2023)
Many assistant systems have evolved toward assistant platforms. These platforms combine a range of resources from various actors via a declarative and generative interface. Among the examples are voice-oriented assistant platforms like Alexa and Siri, as well as text-oriented assistant platforms like ChatGPT and Bard. They have emerged as valuable tools for handling tasks without requiring deeper domain expertise and have received large attention with the present advances in generative artificial intelligence. In view of their growing popularity, this Fundamental outlines the key characteristics and capabilities that define assistant platforms. The former comprise a multi-platform architecture, a declarative interface, and a multi-platform ecosystem, while the latter include capabilities for composition, integration, prediction, and generativity. Based on this framework, a research agenda is proposed along the capabilities and affordances for assistant platforms.
Purpose
In recognising the key role of business intelligence and big data analytics in influencing companies’ decision-making processes, this paper aims to codify the main phases through which companies can approach, develop and manage big data analytics.
Design/methodology/approach
By adopting a research strategy based on case studies, this paper depicts the main phases and challenges that companies “live” through in approaching big data analytics as a way to support their decision-making processes. The analysis of case studies has been chosen as the main research method because it offers the possibility for different data sources to describe a phenomenon and subsequently to develop and test theories.
Findings
This paper provides a possible depiction of the main phases and challenges through which the approach(es) to big data analytics can emerge and evolve over time with reference to companies’ decision-making processes.
Research limitations/implications
This paper recalls the attention of researchers in defining clear patterns through which technology-based approaches should be developed. In its depiction of the main phases of the development of big data analytics in companies’ decision-making processes, this paper highlights the possible domains in which to define and renovate approaches to value. The proposed conceptual model derives from the adoption of an inductive approach. Despite its validity, it is discussed and questioned through multiple case studies. In addition, its generalisability requires further discussion and analysis in the light of alternative interpretative perspectives.
Practical implications
The reflections herein offer practitioners interested in company management the possibility to develop performance measurement tools that can evaluate how each phase can contribute to companies’ value creation processes.
Originality/value
This paper contributes to the ongoing debate about the role of digital technologies in influencing managerial and social models. This paper provides a conceptual model that is able to support both researchers and practitioners in understanding through which phases big data analytics can be approached and managed to enhance value processes.
Unternehmen sind derzeit dabei, ihre Strategie, ihre Prozesse und ihre Informationssysteme zu verändern, um ihren Digitalisierungsgrad zu erhöhen. Das Potenzial des Internets und verwandter digitaler Technologien wie Internet der Dinge, Services Computing, Cloud Computing, künstliche Intelligenz, Big Data mit Analysen, mobile Systeme, Kollaborationsnetzwerke und cyber-physikalische Systeme treibt neue Geschäftsmodelle an und ermöglicht sie. Die Digitalisierung führt zu einer tiefgreifenden Umwälzung bestehender Unternehmen, Technologien und Volkswirtschaften und fördert die Architektur digitaler Umgebungen mit vielen eher kleinen und verteilten Strukturen. Dies hat starke Auswirkungen auf neue Wertschöpfungsmöglichkeiten und die Gestaltung digitaler Dienste und Produkte, die durch die Nutzung einer service-dominanten Logik gesteuert werden. Das Hauptergebnis des Buchkapitels erweitert Methoden für integrale digitale Strategien um wertorientierte Modelle für digitale Produkte und Dienstleistungen, die im Rahmen eines multiperspektivischen digitalen Unternehmensarchitektur-Referenzmodells definiert werden.
In diesem Kapitel wird eine Einführung in die sich abzeichnenden Trends bei der Gestaltung der digitalen Transformation gegeben, wobei der Schwerpunkt auf digitalen Produkten, intelligenten Diensten und damit verbundenen Systemen sowie auf Methoden, Modellen und Architekturen liegt. Das primäre Ziel dieses Buches ist es, einige der neuesten Forschungsergebnisse auf diesem Gebiet hervorzuheben. Wir stellen eine Reihe von Kurzbeschreibungen der im Buch enthaltenen Kapitel zur Verfügung.
Digital assistants like Alexa, Google Assistant or Siri have seen a large adoption over the past years. Using artificial intelligence (AI) technologies, they provide a vocal interface to physical devices as well as to digital services and have spurred an entire new ecosystem. This comprises the big tech companies themselves, but also a strongly growing community of developers that make these functionalities available via digital platforms. At present, only few research is available to understand the structure and the value creation logic of these AI-based assistant platforms and their ecosystem. This research adopts ecosystem intelligence to shed light on their structure and dynamics. It combines existing data collection methods with an automated approach that proves useful in deriving a network-based conceptual model of Amazon’s Alexa assistant platform and ecosystem. It shows that skills are a key unit of modularity in this ecosystem, which is linked to other elements such as service, data, and money flows. It also suggests that the topology of the Alexa ecosystem may be described using the criteria reflexivity, symmetry, variance, strength, and centrality of the skill coactivations. Finally, it identifies three ways to create and capture value on AI-based assistant platforms. Surprisingly only a few skills use a transactional business model by selling services and goods but many skills are complementary and provide information, configuration, and control services for other skill provider products and services. These findings provide new insights into the highly relevant ecosystems of AI-based assistant platforms, which might serve enterprises in developing their strategies in these ecosystems. They might also pave the way to a faster, data-driven approach for ecosystem intelligence.
Data analysis is becoming increasingly important to pursue organizational goals, especially in the context of Industry 4.0, where a wide variety of data is available. Here numerous challenges arise, especially when using unstructured data. However, this subject has not been focused by research so far. This research paper addresses this gap, which is interesting for science and practice as well. In a study three major challenges of using unstructured data has been identified: analytical know-how, data issues, variety. Additionally, measures how to improve the analysis of unstructured data in the industry 4.0 context are described. Therefore, the paper provides empirical insights about challenges and potential measures when analyzing unstructured data. The findings are presented in a framework, too. Hence, next steps of the research project and future research points become apparent.