Big Data Management Canvas: A Reference Model for Value Creation from Data

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.

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

“…Indeed, there are many survey articles to discuss various aspects of big data including not only state-of-theart technology and platforms, but also algorithms, applications and challenges [384]- [398]. More recently, Kaufmann [399] introduced a big data management concept to address the critical needs of digital twin applications.…”

Altmetrics for Digital Libraries. Concepts, Applications, Evaluation and Recommendations

“…However, according to literature, there are different definitions and interpretations of the term "big data" because big data is not only about the size of the data but also about the change that is present with the digital reality (Kaufmann, 2019). Big data, first, has been characterized based on the three Vs dimension model: "Volume", which depicts the size of the data; "Velocity", which considers the speed of the data; and "Variety", which includes various data types.…”

The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives

“…This leads to define the business intelligence systems of datadriven organisations as socio-technical knowledge systems where BD collected, stored and analysed by machines triggers interactions between data scientists and decision-makers; the latter in turn resort to their past experiences to verify, make sense of and codify the insights extracted, thus contributing to refining the rules and programs on which machine learning routines are based (Lugmayr et al, 2017). This circuit formed by observations and actions taking place continuously and in parallel can be described by the expression 'knowing through making' (Mäkelä, 2007, p. 159), which emphasises how the mutual influence of data analytics activities and human analysts' creative efforts favours the emergence of new valuable knowledge from BD, which bears a potential positive effect on the business in terms of product, service, decision and process optimisation (Kaufmann, 2019). Designing knowledge management infrastructures capable of transforming BD into strategic information is one of the main challenges faced by firms in recent years.…”