Purpose – Big Data constitute both a recent technological paradigm and an emerging business opportunity through which the adopters of Big Data solutions can implement and experiment the benefits of the open innovation strategy. In such scenario, the ICT companies have the opportunity to leverage the Big Data paradigm to stimulate their customers to undertake open innovation processes. However, the firms operating in the ICT industry have not often any standard way for implementing Big Data projects, and usually resort to agile approaches or ad-hoc development roadmaps that may generate critical issues related to the delivery strategy, software engineering techniques, and team capability. Drawing inspiration from the Open Innovation paradigm and exploiting the results coming from the analysis of the implementation of several Big Data projects, this paper proposes a roadmap for implementing Big Data initiatives. The roadmap proposed allows to solve the above mentioned critical points, and generate value from four key dimensions such as time efficiency in the solution delivery, cost effectiveness in the resources usage, functional efficacy in the business process execution, and full exploitation of the value embedded into the data sources. Design/methodology/approach – Considering the exploratory nature of the research, the complexity and novelty of the topic, the research methodology adopted is based on the grounded theory. The authors analysed ten Big Data initiatives related to several industrial domains, implemented by a multinational company operating in the ICT industry. For each initiative, the authors have examined the technical documentation and made semi-structured interviews to both project managers and technical leaders involved. Originality/value – This paper proposes a roadmap specifically designed for implementing Big Data projects, which draws inspiration from the Open Innovation paradigm. This roadmap allows for overcoming the critical issues characterizing the agile approaches or the ad-hoc development roadmaps currently adopted in Big Data projects, such as delivery strategy, software engineering techniques, and team capability. At the same time, the proposed roadmap may contribute to generate value by ensuring time efficiency, cost effectiveness, functional efficacy and data value exploitation. In such a way, the proposed roadmap is a first methodological contribution that ICT companies can adopt to enhance the success rate of Big Data projects. Practical implications – From a research perspective, the proposed roadmap aims at contributing to the debate on how Open Innovation strategy can fertilise Big Data paradigm. From a practitioner perspective, the results presented represents a first attempt to formalize a structured roadmap for supporting the implementation of Big Data initiatives.

An Open Innovation Roadmap for implementing Big Data initiatives

Gianluca Elia;Gianluca Solazzo;Ylenia Maruccia;
2017-01-01

Abstract

Purpose – Big Data constitute both a recent technological paradigm and an emerging business opportunity through which the adopters of Big Data solutions can implement and experiment the benefits of the open innovation strategy. In such scenario, the ICT companies have the opportunity to leverage the Big Data paradigm to stimulate their customers to undertake open innovation processes. However, the firms operating in the ICT industry have not often any standard way for implementing Big Data projects, and usually resort to agile approaches or ad-hoc development roadmaps that may generate critical issues related to the delivery strategy, software engineering techniques, and team capability. Drawing inspiration from the Open Innovation paradigm and exploiting the results coming from the analysis of the implementation of several Big Data projects, this paper proposes a roadmap for implementing Big Data initiatives. The roadmap proposed allows to solve the above mentioned critical points, and generate value from four key dimensions such as time efficiency in the solution delivery, cost effectiveness in the resources usage, functional efficacy in the business process execution, and full exploitation of the value embedded into the data sources. Design/methodology/approach – Considering the exploratory nature of the research, the complexity and novelty of the topic, the research methodology adopted is based on the grounded theory. The authors analysed ten Big Data initiatives related to several industrial domains, implemented by a multinational company operating in the ICT industry. For each initiative, the authors have examined the technical documentation and made semi-structured interviews to both project managers and technical leaders involved. Originality/value – This paper proposes a roadmap specifically designed for implementing Big Data projects, which draws inspiration from the Open Innovation paradigm. This roadmap allows for overcoming the critical issues characterizing the agile approaches or the ad-hoc development roadmaps currently adopted in Big Data projects, such as delivery strategy, software engineering techniques, and team capability. At the same time, the proposed roadmap may contribute to generate value by ensuring time efficiency, cost effectiveness, functional efficacy and data value exploitation. In such a way, the proposed roadmap is a first methodological contribution that ICT companies can adopt to enhance the success rate of Big Data projects. Practical implications – From a research perspective, the proposed roadmap aims at contributing to the debate on how Open Innovation strategy can fertilise Big Data paradigm. From a practitioner perspective, the results presented represents a first attempt to formalize a structured roadmap for supporting the implementation of Big Data initiatives.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/439523
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact