A Novel Use of Big Data Analytics for Service Innovation Harvesting

A. Ghose, Evan D. Morrison, Yingzhi Gou
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引用次数: 9

Abstract

Service innovation has assumed considerable significance with the growth of the services sectors of economies globally, yet progress has been slow in devising carefully formulated, systematic techniques to under pin service innovation. This paper argues that a novel approach to big data analytics offers interesting solutions in this space. The paper argues that the use of big data analytics for generating enterprise service insights is often ignored (while the extraction of insights about customers, the market and the enterprise context has received considerable attention). The paper offers a set of techniques (collectively referred to as innovation harvesting) which leverage big data in various forms, including object state sensor data, behaviour logs as well large-scale sources of open data such as the web to mine service innovation insights. The paper also outlines how systematic search might help overcome the limitations of big data analytics in this space.
大数据分析在服务创新收获中的新应用
随着全球经济服务部门的增长,服务创新具有相当大的意义,但在制定精心制定的系统技术来指导服务创新方面进展缓慢。本文认为,一种新颖的大数据分析方法为这一领域提供了有趣的解决方案。本文认为,利用大数据分析来产生企业服务洞察往往被忽视(而对客户、市场和企业环境的洞察的提取却受到了相当大的关注)。本文提供了一套技术(统称为创新收获),利用各种形式的大数据,包括对象状态传感器数据、行为日志以及大型开放数据源(如web)来挖掘服务创新见解。论文还概述了系统搜索如何帮助克服大数据分析在这一领域的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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