What Does Data Analytics Offer for Extracting Knowledge from Middle-of-Life Product Data?

Companies are getting increasingly interested in learning how different customers use their products. Collecting data about the use of products provides useful insights and facilitates design enhancements. Effective data analytics needs dedicated tools. In this paper, we summarize the results of our literature research done with special attention to existing tools. We observed that everything is changing rapidly and getting more complex in terms of data and processing methods and tools. While remarkable attention has been paid to processing big data, much less is being devoted to effective semantic progressing of middle-of-life (MoL) data. One of our findings is that commercialized data analytics tools have not addressed extraction, aggregation, and handling genuine MoL data adequately. Another one is that the currently available tools are in the lack of the capability to adapt themselves to designers needs and to produce results that could be reused in multiple design tasks. Nowadays products are equipped with smart capabilities and this offers new opportunities for exploiting middle-of-life data. The knowledge aggregated in this study will be used in the development of a sophisticated toolbox. This will: (i) integrate various tools under a unified interface, (ii) implement various semantics orientated and smart reasoning-based functions, and (iii) facilitate data transformations by practicing designers in contexts.