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Data analytics in product development: Implications from expert interviews | IEEE Conference Publication | IEEE Xplore

Data analytics in product development: Implications from expert interviews


Abstract:

An increasing number of technical products are being equipped with connectivity components, which enables the collection of use phase data. Such data helps to better desi...Show More

Abstract:

An increasing number of technical products are being equipped with connectivity components, which enables the collection of use phase data. Such data helps to better design products or understand customer needs. Available studies only take a cross-industry perspective on data analytics. Due to longer development and product life cycles, engineering companies work under special circumstances. The authors therefore conducted expert interviews to better understand the needs and current practices in engineering companies. Experts highlighted the potential of data analytics for instance in requirements engineering. Experts also mentioned the various problems that occur when identifying and implementing use cases. Besides support for technical issues, experts raised the need for additional support during the initial planning phase.
Date of Conference: 10-13 December 2017
Date Added to IEEE Xplore: 12 February 2018
ISBN Information:
Electronic ISSN: 2157-362X
Conference Location: Singapore
References is not available for this document.

I. Introduction

Digitalization is progressing fast and changes products, companies, business models, and competition. Technical products no longer consist only of physical parts but also of sensors, microprocessors and additional parts for the connectivity [1]. Studies indicate that the number of connected products will vastly increase in the future [2]. The rise of connected products is one reason for the large amount of available data worldwide, often referred to as Big Data [3]. The term Big Data comprises not only data, but also analytical methods (data analytics), processes and technologies [4]. Five characteristics describe Big Data: volume, variety, veracity, velocity, and value [5].

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References

References is not available for this document.