Abstract
Many big data projects are technology-driven and thus, expensive and inefficient. It is often unclear how to exploit existing data resources and map data, systems and analytics results to actual use cases. Existing big data reference models are mostly either technological or business-oriented in nature, but do not consequently align both aspects. To address this issue, a reference model for big data management is proposed that operationalizes value creation from big data by linking business targets with technical implementation. The purpose of this model is to provide a goal- and value-oriented framework to effectively map and plan purposeful big data systems aligned with a clear value proposition. Based on an epistemic model that conceptualizes big data management as a cognitive system, the solution space of data value creation is divided into five layers: preparation, analysis, interaction, effectuation, and intelligence. To operationalize the model, each of these layers is subdivided into corresponding business and IT aspects to create a link from use cases to technological implementation. The resulting reference model, the big data management canvas, can be applied to classify and extend existing big data applications and to derive and plan new big data solutions, visions, and strategies for future projects. To validate the model in the context of existing information systems, the paper describes three cases of big data management in existing companies.
Highlights
The global amount of data is exploding
The fourth section provides an epistemological theory of emergent knowledge and cognitive systems that can be applied for knowledge-based big data management
This theory is instantiated in the field of business intelligence, interpreting big data management as a cognitive system whose success depends on effectuation and intelligence
Summary
In the sense of “more is different,” big data is a class of data whose quantity brings forth new qualities. This framework focuses on interatction, it does not answer the questions about effectuation and intelligence, or how exactly this information is applied and how the knowledge in this context is managed Based on this literature survey, this study proposes a reference model for big data management that closes the above-mentioned gaps. It incorporates the big data definition of the 5V model; the value-oriented approach of the OECD model; and the focus on action, visualization, and access of the NIST model, and it adds the market focus of Davenport. The issues of data interaction and data effectuation and especially the focus on knowledge management in the big data life cycle (data intelligence) provides an extension to these existing approaches
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