Abstract
Tight and competitive market situations pose a serious challenge to enterprises in the manufacturing industry domain. Competing in the use of data analytics to enhance products and processes requires additional resources to deal with the complexity. On the contrary, the possibilities afforded by digitization and data analysis-based approaches make for a valuable asset. In this paper we suggest a guideline to a systematic course of action for the data-based creation of holistic insight. Building an overlaying corpus of knowledge accelerates the learning curve within specific projects as well as across projects by exceeding the project-specific view towards an integrated approach.
Highlights
Demand and supply for insights derived from all kinds of accessible data sources in enterprises are higher than ever before as the pressure to keep up with global competitors meets the ever-growing possibilities of data acquisition and exploitation
Our research is driven by the following research question (RQ): RQ: How can a reference model be provided for complex tasks in the industrial domain which provides methodological support for the data-driven construction and utilization of an overlaying corpus of knowledge?
Two more aspects are vital to exploit the full potential of data analytics in the industrial domain: to take into account the dimorphic system character of the target system consisting of analogous and digital components and to focus on context-sensitive engineering of conclusive features as this step constitutes the heart of the project and is complemented by the choice and application of fitting tools and methods, only rendered possible by the utilization of aforementioned concepts providing the necessary context. [29]
Summary
Demand and supply for insights derived from all kinds of accessible data sources in enterprises are higher than ever before as the pressure to keep up with global competitors meets the ever-growing possibilities of data acquisition and exploitation. The reference model aims to inspire rigorous and holistic investigation, to provide the means for communication, project management and documentation and to build the foundation for future software applications to support this holistic project-exceeding data mining approach paving the way for an analysis and optimization of the activities undertaken within data mining projects themselves.
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