The Data-Driven Business Value Matrix - A Classification Scheme for Data-Driven Business Models

Increasing digitization is generating more and more data in all areas of business. Modern analytical methods open up these large amounts of data for business value creation. Expected business value ranges from process optimization such as reduction of maintenance work and strategic decision support to business model innovation. In the development of a data-driven business model, it is useful to conceptualise elements of data-driven business models in order to differentiate and compare between examples of a data-driven business model and to think of opportunities for using data to innovate an existing or design a new business model. The goal of this paper is to identify a conceptual tool that supports data-driven business model innovation in a similar manner: We applied three existing classification schemes to differentiate between data-driven business models based on 30 examples for data-driven business model innovations. Subsequently, we present the strength and weaknesses of every scheme to identify possible blind spots for gaining business value out of data-driven activities. Following this discussion, we outline a new classification scheme. The newly developed scheme combines all positive aspects from the three analysed classification models and resolves the identified weaknesses

The Data Product Canvas - A Visual Collaborative Tool for Designing Data-Driven Business Models

The availability of data sources and advances in analytics and artificial intelligence offers the opportunity for organizations to develop new data-driven products, services and business models. Though, this process is challenging for traditional organizations, as it requires knowledge and collaboration from several disciplines such as data science, domain experts, or business perspective. Furthermore, it is challenging to craft a meaningful value proposition based on data; whereas existing research can provide little guidance. To overcome those challenges, we conducted a Design Science Research project to derive requirements from literature and a case study, develop a collaborative visual tool and evaluate it through several workshops with traditional organizations. This paper presents the Data Product Canvas, a tool connecting data sources with the user challenges and wishes through several intermediate steps. Thus, this paper contributes to the scientific body of knowledge on developing data-driven business models, products and services

Show me the Money: How to monetize data in data-driven business models?

Increasing digitization and the associated tremendous usage of technology have led to data of unprecedented quantity, variety, and speed, which is generated, processed, and required in almost all areas of industry and life. The value creation and capturing from data presents companies with numerous challenges, as they must create or adapt appropriate structures and processes. As a link between corporate strategy and business processes, business models are a suitable instrument for meeting these challenges. However, few research has been conducted focusing on data-based monetization in the context of data-driven business models so far. Based on a systematic literature review the paper identifies five key components and 23 characteristics of data-driven business models having crucial influence on data-based value creation and value capturing and thus on monetization. The components represent key factors for achieving commercial benefits from data and serve as guidance for exploring and designing suitable data-driven business models

Designing business model taxonomies – synthesis and guidance from information systems research

Classification is an essential approach in business model research. Empirical classifications, termed taxonomies, are widespread in and beyond Information Systems (IS) and enjoy high popularity as both stand-alone artifacts and the foundation for further application. In this article, we focus on the study of empirical business model taxonomies for two reasons. Firstly, as these taxonomies serve as a tool to store empirical data about business models, we investigate their coverage of different industries and technologies. Secondly, as they are emerging artifacts in IS research, we aim to strengthen rigor in their design by illustrating essential design dimensions and characteristics. In doing this, we contribute to research and practice by synthesizing the diffusion of business model taxonomies that helps to draw on the available body of empirical knowledge and providing artifact-specific guidance for building taxonomies in the context of business models

What about Data-Driven Business Models? Mapping the Literature and Scoping Future Avenues

The paper aims to perform an assessment of the literature at the intersection of data and business models, examining the extent to which the data-driven business model (DDBM) is considered in the current literature and how it is characterised in terms of approaches, benefits and barriers. A systematic literature review (SRL) of the available body of knowledge on these topics was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. The SRL reveals limited but rapidly growing coverage of the cutting-edge phenomenon on the part of scientific studies. In problematising the extant literature, competitive, cultural and strategic approaches are proposed together with the relative enablers fostering the adoption of each approach. Benefits and barriers to the implementation of a DDBM are also discussed across technical, organisational and financial dimensions. The insights derived from a critical review of the DDBM literature point out gaps, which may itself inform future research and theory development in this area, as well as support practitioners’ decision-making on the datatisation of business models

Big data analytics e o desempenho das empresas em Portugal

Dissertação de mestrado em Economia Industrial e da EmpresaNuma sociedade cada vez mais globalizada, interconectada e automatizada, big data analytics surge como uma oportunidade única no tecido empresarial para, não só apoiar as empresas em processos de tomada de decisão, na melhoria das operações e na identificação de novas oportunidades, como também ajudá-las a tonarem-se mais eficientes, competitivas e resilientes. Desta forma, big data analytics é um processo que já começa a estar incorporado em vários setores de atividade, como o setor da saúde, financeiro, retalho, entre outros. Apesar do crescimento da literatura sobre o potencial de big data analytics nos negócios, certo é que apenas 13% das empresas europeias fazem big data analytics e escassos têm sido os estudos empíricos realizados sobre os efeitos que a realização de big data analytics possui nas empresas, tanto a nível mundial, como em Portugal. Assim, o principal objetivo deste trabalho é aumentar o conhecimento sobre big data analytics e o desempenho das empresas com a aplicação de um modelo quantitativo e inovador ao contexto empresarial português, dando resposta à seguinte questão de investigação: Qual é a relação e o efeito de big data analytics no desempenho das empresas em Portugal? Para responder ao objetivo e à questão formulada, foi então criado um modelo de regressão linear múltipla para análise de dados, de 2016, 2018 e 2020, fornecidos pelo Instituto Nacional de Estatística (INE) ao abrigo de um protocolo de colaboração com instituições de ensino. Os resultados obtidos mostram que big data analytics tem de facto um efeito positivo no desempenho das empresas em Portugal, a relação entre big data analytics e o ponderador do volume de negócios é estatisticamente significativa e o p-value registado é menor que o nível de significância de 5%. Os resultados corroboram a literatura sobre o tópico e, por isso, urge-se um movimento de adoção nas empresas que ainda não praticam big data analytics.In an increasingly globalised, interconnected, and automated society, big data analytics emerges as a unique opportunity in business to not only support companies in decision-making processes, improving operations and identifying new opportunities, but also to help them become more innovative, competitive and resilient. In this way, big data analytics is a process that is already starting to be incorporated in various sectors of activity, such as the health, financial and retail sectors, among others. Despite the growth of literature on the potential of big data analytics in business, it is true that only 13% of European companies perform big data analytics and there have been few empirical studies carried out on the effects that performing big data analytics has on companies, both at European level and in Portugal. Thus, the main objective of this work is to increase knowledge about big data analytics and company performance by applying a quantitative and innovative model to the Portuguese business context, answering the following research question: What is the relationship and the effect of big data analytics on company performance in Portugal? To answer the objective and the formulated question, a multiple linear regression model was then created to analyse data, from 2016, 2018 and 2020, provided by the National Statistics Institute (INE) under a collaboration protocol with educational institutions. The results obtained show that big data analytics has indeed a positive effect on the performance of companies in Portugal, the relationship between big data analytics and the turnover weighting is statistically significant and the p-value recorded is smaller than the 5% significance level. Results corroborate the literature on the topic and thus, an adoption movement is urged at companies that do not yet practice big data analytics