Towards integrated Data Analysis Quality: Criteria for the application of Industrial Data Science

Abstract

The application of Industrial Data Science in context of connected Smart Products requires modeling and structuring data for its design, development and use. Especially for Smart Products, a comprehensive handling of data quality is mandatory, because of their interdisciplinary character and broad range of heterogeneous stakeholders covering the entire product lifecycle. The overall goal of data preparation is to provide high-quality data for application and evaluation by users. Established process models for industrial data analysis often treat the specification and assurance of data quality as a single-point activity with a defined conclusion. Providing end-to-end data quality has received little attention in the field of industrial data analytics. In this paper, we will (1) structure four distinct phases for ensuring end-to-end data quality along data analytics activities, (2) define a set of criteria and measures for meeting and quantifying data quality requirements based on established criteria, and (3) provide a step-by-step model for establishing and maintaining high Data Quality for Industrial Data Science applications. The quality criteria aim to identify pointwise and continuous actions during the data analysis process. Such criteria target a shared responsibility for maintaining data quality during analyses between analyst and user. The developed model provides an actionable approach for assessing and ensuring the requirements of Data Analysis Quality.

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