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Usage of analytical services in industry today and tomorrow

 
: Colangelo, Eduardo; Bauernhansl, Thomas

:
Fulltext urn:nbn:de:0011-n-4288969 (537 KByte PDF)
MD5 Fingerprint: e87e96b0902ce3b4670b4773b5a3ea47
(CC) by-nc-nd
Created on: 13.1.2017


Procedia CIRP 57 (2016), pp.276-280
ISSN: 2212-8271
Conference on Manufacturing Systems (CMS) <49, 2016, Stuttgart>
English
Journal Article, Conference Paper, Electronic Publication
Fraunhofer IPA ()
Small and Medium Sized Enterprises (SME); Analytik; Visualisierung; Datenanalyse; Big Data; Kleine und mittlere Unternehmen KMU; Analyse

Abstract
Data is everywhere. Both, machines and men leave a digital shadow behind, which, for some means the success or failure of their business. Enterprises strive to make the most of this scattered, diverse and ever growing data, in order to obtain information they can apply to the decision-making processes. But, apart from the known and researched technical issues of volume, variety and velocity; more essential issues have to be addressed. Namely, how does an enterprise find the analytical model it needs to obtain the information it desires? From simple regression analyses to artificial intelligence, the variety in which data can be analyzed is immense. Involving specialist and consultants is timeconsuming, needs effort and is usually too expensive, especially for SMEs.
This paper discusses the current options in the usage of analytics by enterprises as well as the existing challenges and elaborates recommendations for the future. Special focus is put on customer-oriented analytics by means of analytical services. In these, the building blocks of analytics are modularized in three layers: Data Interpretation & Cleansing Layer, Data Processing Layer, and Data Visualization Layer. This modularization allows building analytics in a standardized manner. Such services aim at reducing the gap between the holders of expert knowledge and the users of analytics. This is achieved by placing the attention on obtaining the desired information (choosing from a portfolio of analytics) instead of solving fundamental challenges, already addressed by the respective modules.

: http://publica.fraunhofer.de/documents/N-428896.html