Analytics Canvas – A Framework for the Design and Specification of Data Analytics Projects

Kühn, Arno; Joppen, Robert; Reinhart, Felix; Röltgen, Daniel; Enzberg, Sebastian von; Dumitrescu, Roman

doi:10.1016/j.procir.2018.02.031

Cited by 44 publications

(20 citation statements)

References 1 publication

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“…Examples of adaptations made for this purpose include: (1) integration of CRISP-DM with the Balanced Scorecard framework used for strategic performance management in organizations (Yun, Weihua & Yang, 2014); (2) integration with a strategic decisionmaking framework for revenue management Segarra et al (2016); (3) integration with a strategic analytics methodology Van Rooyen & Simoff (2008), and (4) integration with a so-called 'Analytics Canvas' for management of portfolios of data analytics projects Kühn et al (2018). Finally, Ahangama & Poo (2015) explored methodological attributes important for adoption of data mining methodology by novice users.…”

Section: Integrationmentioning

confidence: 99%

Adaptations of data mining methodologies: a systematic literature review

Plotnikova¹,

Dumas²,

Milani³

2020

PeerJ Computer Science

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The use of end-to-end data mining methodologies such as CRISP-DM, KDD process, and SEMMA has grown substantially over the past decade. However, little is known as to how these methodologies are used in practice. In particular, the question of whether data mining methodologies are used ‘as-is’ or adapted for specific purposes, has not been thoroughly investigated. This article addresses this gap via a systematic literature review focused on the context in which data mining methodologies are used and the adaptations they undergo. The literature review covers 207 peer-reviewed and ‘grey’ publications. We find that data mining methodologies are primarily applied ‘as-is’. At the same time, we also identify various adaptations of data mining methodologies and we note that their number is growing rapidly. The dominant adaptations pattern is related to methodology adjustments at a granular level (modifications) followed by extensions of existing methodologies with additional elements. Further, we identify two recurrent purposes for adaptation: (1) adaptations to handle Big Data technologies, tools and environments (technological adaptations); and (2) adaptations for context-awareness and for integrating data mining solutions into business processes and IT systems (organizational adaptations). The study suggests that standard data mining methodologies do not pay sufficient attention to deployment issues, which play a prominent role when turning data mining models into software products that are integrated into the IT architectures and business processes of organizations. We conclude that refinements of existing methodologies aimed at combining data, technological, and organizational aspects, could help to mitigate these gaps.

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Section: Integrationmentioning

confidence: 99%

Adaptations of data mining methodologies: a systematic literature review

Plotnikova¹,

Dumas²,

Milani³

2020

PeerJ Computer Science

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“…More explicitly, the authors in [47,43,48] highlight the absence of clear business objectives, insufficient ROI or business cases, and an improper scope of the project. For [54] there is a biased emphasis on the technical issues, which has limited the ability of organizations to unleash the full potential of data analytics.…”

Section: Building Data Analytics Teamsmentioning

confidence: 99%

Data Science Methodologies: Current Challenges and Future Approaches

2021

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Data science has employed great research efforts in developing advanced analytics, improving data models and cultivating new algorithms. However, not many authors have come across the organizational and socio-technical challenges that arise when executing a data science project: lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects and the ambiguity of roles in data science are among these challenges. Few methodologies have been proposed on the literature that tackle these type of challenges, some of them date back to the mid-1990, and consequently they are not updated to the current paradigm and the latest developments in big data and machine learning technologies. In addition, fewer methodologies offer a complete guideline across team, project and data & information management. In this article we would like to explore the necessity of developing a more holistic approach for carrying out data science projects. We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information management. Finally, we propose a conceptual framework containing general characteristics that a methodology for managing data science projects with a holistic point of view should have. This framework can be used by other researchers as a roadmap for the design of new data science methodologies or the updating of existing ones.

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“…The Canvas business model, as the answer to previous results, ranks among the widely used, clear-cut and transparent business models. The authors also emphasise the economic and social benefits of the business model [25]. B.M.…”

Section: Business Model Canvasmentioning

confidence: 99%

Canvas B.M. in the global revolutionary era of digitisation in the industry 4.0: A review

Domanižová

Janíčková

2021

SHS Web of Conf.

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Research background: Industry 4.0, also known as the Fourth Industrial Revolution, describes the digitisation of the manufacturing industry. The transition to 4.0 is crucial for manufacturing companies to maintain a competitive advantage and take advantage of new opportunities. This article contributes to research with a framework for the description, analysis, and classification of Canvas B.M. for 4.0. Purpose of the article: The following report provides a review of the literature to expand our knowledge of how Industry 4.0 affects business models. This article aims to specify and map the level of knowledge about the Canvas business model and its application in the new revolutionary era of the digital world in the manufacturing industry 4.0. The findings deepen the understanding of how 4.0 affects the manufacturing industry, B.M., and the behaviour of the current economic environment. Methods: A literature review. Then the comparison of the author’s views on the use of the business model, innovation, and adaptation under the influence of digitisation. The literature survey was conducted in articles indexed in the Web of Science and Scopus to carry out a critical analysis of collected data and the exposition of some conclusions and future research opportunities. Findings & value added: A lot of research in the field of industry 4.0 using Canvas B.M., but very often, the view of the Czech business environment is neglected. Focusing on this area may lead to future research. This literary review focuses on the latest literature.

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Analytics Canvas – A Framework for the Design and Specification of Data Analytics Projects

Cited by 44 publications

References 1 publication

Adaptations of data mining methodologies: a systematic literature review

Adaptations of data mining methodologies: a systematic literature review

Data Science Methodologies: Current Challenges and Future Approaches

Canvas B.M. in the global revolutionary era of digitisation in the industry 4.0: A review

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