Projects / Programmes source: ARIS

Big Data Analytics: From Insights to Business Process Agility

Research activity

Code Science Field Subfield
5.02.02  Social sciences  Economics  Business sciences 

Code Science Field
S189  Social sciences  Organizational science 

Code Science Field
5.02  Social Sciences  Economics and Business 
big data, big data analytics, business intelligence, business process agility
Evaluation (rules)
source: COBISS
Researchers (14)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  51146  PhD Mauro Castelli  Economics  Researcher  2018  141 
2.  37928  Erna Emrić    Technical associate  2016 - 2018 
3.  28350  PhD Jure Erjavec  Economics  Researcher  2016 - 2018  155 
4.  03158  PhD Mirko Gradišar  Economics  Researcher  2016 - 2018  828 
5.  14990  PhD Aleš Groznik  Economics  Researcher  2016 - 2018  611 
6.  34324  PhD Tanja Grublješič  Economics  Researcher  2016 - 2017  44 
7.  15478  PhD Mojca Indihar Štemberger  Economics  Researcher  2016 - 2018  626 
8.  13682  PhD Jurij Jaklič  Economics  Researcher  2016 - 2018  628 
9.  30716  PhD Anton Manfreda  Economics  Researcher  2016 - 2018  136 
10.  24105  PhD Aleš Popovič  Economics  Head  2016 - 2018  255 
11.  08613  PhD Metka Tekavčič  Economics  Researcher  2016 - 2018  1,036 
12.  33180  PhD Luka Tomat  Economics  Researcher  2017 - 2018  141 
13.  23021  PhD Peter Trkman  Economics  Researcher  2016 - 2018  382 
14.  13147  PhD Tomaž Turk  Economics  Researcher  2016 - 2018  318 
Organisations (1)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0584  University of Ljubljana, School of Economics and Business (SEB)  Ljubljana  1626922  43,254 
As highly competitive environments force firms to move more rapidly and boldly, and to experiment, firms are increasingly seeking ways to quickly respond to accelerating competition. Literature, thus, increasingly advises firms to focus on the development of organizational agility as a strategic capability (Chakravarty, Grewal, & Sambamurthy, 2013; Tallon & Pinsonneault, 2011). A form of organizational agility that is of particular relevance to firms is business process agility (Y. Chen et al., 2014) where data-driven insights are regularly emphasized as drivers for innovation and agility (Davenport, Barth, & Bean, 2012). Accordingly, business intelligence and analytics, and the related field of big data analytics have become increasingly important in both the academic and the business communities (H. Chen, Chiang, & Storey, 2012). While prior research has suggested big data analytics and IT infrastructure flexibility are the two important sources of an organization’s agility (X. Chen & Siau, 2011), several challenges regarding big data analytical capabilities, as well as understanding the processes and factors enabling, facilitating, or impeding successful adoption and utilization of big data analytics remain unanswered. The wealth of possibilities enabled by big data analytics will not be fully exploited without a deeper understanding of the organizational environment, management issues, technological capabilities, and human behavior (Agarwal & Dhar, 2014; Popovič, Hackney, Coelho, & Jaklič, 2014). To advance the current state of the field, the proposed project is focused on investigating what must an organization do right in order to develop appropriate big data analytical capabilities so as to fully leverage the functionality of big data analytics in enabling the agility of its business processes. We identify four key themes that frame our research: (1) The business value of big data and big data analytics. The goal is to understand the potential impacts and value of big data and big data analytics in achieving and sustaining competitive advantage in various high-impact industries (e.g. energy, healthcare). (2) Embeddedness as key mechanism for utilizing big data analytics for designing agile business processes. We seek to identify the determinants of business intelligence and analytics embeddedness to facilitate the development of a framework for adopting big data analytics. (3) The role of organizational characteristics in improving business process agility through big data analytics. We aim to understand how various organizational characteristics influence (i.e., promote or hinder) big data analytical capabilities for changing an organization’s business processes as to make them more agile. (4) Developing big data analysis methods to fuel the increasing business process information needs. Our goal is to reach beyond current methods to analyze and acquire information from big data (descriptive and predictive analytics) to develop automated methods that enable prescriptive analytics. We believe that the proposed project will provoke researchers from various fields to step up their collaborative efforts and will provide a platform to lead the next generation of insights around the role of big data analytics in facilitating business process agility. The content area of business intelligence and analytics has been recognized within Horizon 2020 ICT work program as a very important, yet, still understudied area addressing the fundamental research problems related to the scalability and responsiveness of analytical capabilities for organizational performance. All references can be found at the end of section “Detailed description of the work program”.
Significance for science
By answering to the proposed overarching research question our work will contribute to the development of the scientific field in various ways. To begin with, it will try to elucidate the differences in BD/BDA value creation process as opposed to existing technological solutions. Next, it will inform researchers about how do various types of existing organizational cultures, structures, routines and decision-making processes influence the ability of decision makers to generate and use insights from BD. Furthermore, it will shed light on how do decision-making processes influence the success of BDA. Lastly, through our research we advance the current predictive analytics methods that can be readily used to improve an organization’s process agility capability. The planned research is therefore an important step forward in these areas, as it proposes new and fresh insights into this intriguing questions. The central contribution is a better understanding of where the business value of BD and BDA actually lies, how to better develop BDAC for achieving process agility in spite of organizational characteristics importantly affecting the benefits BDA bring to process agility, and propose new predictive techniques to improve BD analysis. We expect that our results will have a potential of being applied in other fields of science, such as operations management and marketing.
Significance for the country
BD and BDA are being used to understand and respond to important economic and development issues, such as healthcare, energy, transportations, water supply, and protection against natural hazards. Experts have emphasized the importance of assessing organizations’ ‘‘information supply chain” to identify and prioritize data management issues (Kshetri, 2014). Data in different contexts may come from different combinations of sources and the structure of the data may differ across industries. There is likely to be a wide variation across economic activities and industries in the level of the diffusion of BDA. Even within an industry, differences in the diffusion of BDA are likely to be significant. Our findings will be applicable to both medium and large firms, as well as public sector organizations. Project leader and other researchers are actively participating in various different research projects, therefore the majority of the research results will contribute to more effective and successful business practices and consequently to the improvement of Slovenian economy. Our research carries important policy implications as well, which emphasize the need for enrichment of the BD and BDA environment and to ensure that appropriate regulations aimed at encouraging organizations’ BDA adoption in activities with positive social and economic contributions and outcomes are in place. Firstly, the government is a key actor that can drive the BD environment. Its role is especially linked to the introduction of policies, procedures, and interventions to ensure the privacy and confidentiality of sensitive data. Secondly, data consumption and exchange are no less important than data production and analysis. BDAC hinges critically upon the availability of manpower with BD competency. It is thus important for governments to direct more efforts towards developing BD manpower. Finally, guidelines, interventions, supports, and incentives are needed to encourage sharing existing data. In this regard, much of the valuable data that is relevant for the development context is often with the private sector. Incentives for information sharing between private and public sphere might facilitate a first step in this direction.
Most important scientific results Interim report, final report
Most important socioeconomically and culturally relevant results Interim report, final report
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