Logo des Repositoriums

Auflistung nach Schlagwort "Data Ecosystem"

1 - 4 von 4
  • Textdokument
    Analyzing Smart Services from a (Data-) Ecosystem Perspective: Utilizing Network Theory for a graph-based Software Tool in the Domain Smart Living
    (INFORMATIK 2022, 2022) Kortum,Henrik; Hagen,Simon; Hühn,Janis; Thomas,Oliver
    There has long been a trend away from monolithic solutions toward integrated service systems that combine technical services and product functionalities across different manufacturers. This is especially true for the Smart Living domain, where interconnected products and services are used in one of the most private areas. However, there is a lack of tools to manage these complex systems. This work represents the second iteration of an ongoing research project in which we are developing an analysis and management tool in response to specific requirements of various stakeholder groups of the Smart Living domain. We interpret the services and products of the data ecosystem as nodes of a network graph, which we analyze using established methods of network theory, e.g., to identify weak points and bottlenecks in the service systems. The metrics provided help domain experts and managers from the domain to perform concrete tasks and provide a business benefit.
  • Zeitschriftenartikel
    Data-based Customer-Retention-as-a-Service: Induktive Entwicklung eines datenbasierten Geschäftsmodells auf Basis einer Fallstudie der Automobilbranche
    (HMD Praxis der Wirtschaftsinformatik: Vol. 58, No. 3, 2021) Kortum, Henrik; Rebstadt, Jonas; Gravemeier, Laura Sophie; Thomas, Oliver
    Viele Unternehmen setzen Künstliche Intelligenz zur Verarbeitung großer Datenmengen bereits heute erfolgreich für die Kundenbindung ein. So schaffen große Unternehmen individuelle Kundenerlebnisse basierend auf der Auswertung großer kundenbezogener Datenmengen zur kurz- aber auch langfristigen Kundenbindung, z. B. durch intelligente Empfehlungen von Inhalten auf Videoplattformen. Bei Unternehmen mit traditioneller Wertschöpfung wird dieses Potenzial jedoch noch nicht ausreichend genutzt. Vor diesem Hintergrund wird im Rahmen einer Fallstudie exemplarisch ein datengetriebenes Kundenbindungsszenario in Kooperation mit einer Autowerkstatt umgesetzt. Im konkreten Fall wurde eine zeitlich optimierte Kundenansprache auf Basis von KI-basierten Prognosen der täglichen Fahrleistung von Kunden angestrebt. Grundlage dafür war die Analyse eines Kundendatensatzes einer Autowerkstatt und die anschließende Entwicklung einer Künstlichen Intelligenz. Aufbauend auf der Fallstudie wird ein datenbasiertes Geschäftsmodell konzipiert, dessen Werteangebot vor allem Unternehmen mit traditioneller Wertschöpfung und wenig Wissen im Bereich Künstlicher Intelligenz dazu befähigt, datenbasierte Technologien in der Kundenbindung einzusetzen. Das dem Geschäftsmodell zugrundeliegende Plattformkonzept wird dabei als Open-Innovation-Modell entwickelt und soll neben der Entwicklung eigener Services auch die Interaktion von Datenkonsumenten, Datenlieferanten und anderen Datenbefähigern, mit dem Ziel sich als Datenökosystem für Kundenbindung zu etablieren, unterstützen. Many companies are already successfully using artificial intelligence (AI) to process large volumes of data for the purpose of customer retention. Large companies create individualized customer experiences and analyze massive amounts of data to achieve customer loyalty through intelligent recommendations, for example. However, companies with traditional value creation, as of yet often fail to sufficiently address this topic. Therefore, this contribution tackles the implementation of an exemplary use case for data-driven customer retention in a car repair shop. In particular, the aim was to optimize the timing of customer communication based on forecasts of the customers’ daily driving behavior. The basis for this analysis was a data set provided by a car repair shop and the subsequent development of a machine learning model. Based on this case study, a business model is developed that enables companies with traditional value creation and little AI-know-how to use data-driven technologies in customer retention. The underlying platform concept is conceptualized as an open innovation model and supports the interaction of data consumers, data providers and data enablers. In this way, the target is not only to develop own services, but also to establish a data ecosystem for customer loyalty.
  • Textdokument
    Design Options for Data-Driven Business Models in Data-Ecosystems
    (INFORMATIK 2022, 2022) Schweihoff,Julia Christina; Jussen,Ilka; Stachon,Maleen; Möller,Frederik
    Data has fundamentally changed the way companies cooperate. Traditional strategies of companies require to be rethought, revisioned, and reimagined based on the new environment of the data economy. Data as a key resource opens up new opportunities that companies need to leverage in data-driven business models (DDBM). Instead of acting in a vacuum, working together and creating value conjointly in an ecosystem based on data is paramount to success. Although DDBMs and ecosystems are not new, per se, there has been a lack of an overarching view of how many DDBMs interact in the context of ecosystems. The paper starts at this point as it develops a taxonomy of these business models in ecosystems. Thus, it contributes to helping researchers and practitioners understand the specifics of data-driven business models working in ecosystems.
  • Textdokument
    A Platform Framework for the Adoption and Operation of ML-based Smart Services in the Data Ecosystem of Smart Living
    (INFORMATIK 2022, 2022) Kortum,Henrik; Kohl,Tobias; Hubertus,Dominik; Hinz,Oliver; Thomas,Oliver
    Smart services utilizing machine learning (ML) take a more and more important position in our daily lives. As a result, the need for a large smart living data ecosystem has emerged that links the most diverse areas of life with each other. This ecosystem is characterized by a multitude of different actors, a heterogeneous system, product and service landscape as well as high data protection requirements. To provide real added value and holistic solutions in this tension field, the orchestration of different subservices is necessary, bundling the functionality of individual smart devices and models. For this goal to be achieved, a framework that considers the complex challenges of the ecosystem focusing on the adoption and operation of smart services is required. Here our paper makes a key contribution. Based on requirements from the literature and concrete smart living use cases, we derive a platform framework for this data ecosystem.