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Competitive Advantage for the Data-driven ENTerprise

Periodic Reporting for period 1 - CADENT (Competitive Advantage for the Data-driven ENTerprise)

Reporting period: 2016-07-01 to 2018-06-30

The purpose of the Competitive Advantage for the Data-driven ENTerprise (CADENT) project is to address the issue of how companies should optimally deploy and exploit big data as part of their competitive strategies. Following an interdisciplinary approach which bridges the domains of organizational science, strategic management, information science, marketing, and computer science, CADENT will explore the means by which big data are effectively leveraged in a range of contexts and industries (e.g. technology, retail, oil and gas, healthcare, telecommunications), and attempt to isolate key success factors. Therefore, the main focus of the project will be to (a) identify big data intelligence and analytics needs of companies (e.g. insight generation, opportunity/threat detection, market analysis and customer profiling, and real-time forecasting or else now-casting ), (b) understand how information sources, technological infrastructure, human skills and knowledge, organizational/team structures, and management practices coalesce to achieve desired ends, and (c) develop a set of clearly defined metrics to evaluate the business impact of big data initiatives. By empirically investigating these issues and developing a framework of best practices, the ultimate goal is to guide executives in their transition to the big-data era, and enable them to strengthen their companies’ competitive position through focused deployments.
The project followed the described implementation plan described in Part B of the grant agreement. In this section the work performed in each work package of the research plan and the results of each is presented.
The first objective of the project was the development of a theoretical framework to study the impacts of big data analytics in firm value. In this direction we constructed a theoretical model through which effects of firm-level big data analytics capabilities can be measured, and the corresponding mechanisms through which they deliver value can be outlined. Furthermore, we developed the notion of a big data analytics capability, which is defined as a firm’s capacity to orchestrate big data resources and talent in order to generate insight and drive decision-making. Adding to this, we also looked at the antecedents of the formation of such a capability, constructing the theoretical notion of big data analytics governance. The main objective was to understand how a firm’s big data analytics capability emerges and explain through what mechanisms value can be captured. Finally, we conducted an analysis of the necessary skills for the data scientist and performed an analysis of the current skill-gap and ways through which it can be reduced.

To consolidate the theoretically developed notions and causal associations, a series of quantitative studies were performed. These studies looked at a) the antecedents of forming a firm-wide big data analytics capability, b) the mechanisms through which value can be derived, c) the impact of big data analytics on different performance measures (e.g. financial and market performance, product/process/service innovation), and d) the conditions under which big data analytics capabilities are of increased relevance and value to firms. In doing so we gathered data from 202 Norwegian firms, and 175 Greek firms using custom-built questionnaires, with novel constructs. Responses were collected from top-level IT managers and were analysed through different methods. Our results indicate that firms that develop strong big data analytics capabilities are able to foster evolutionary fitness, which translates to stronger marketing and technical capabilities. In effect, big data analytics are shown to enable firms to make better marketing decisions and adjust their internal operations so as to increase efficiency and slice costs. Furthermore, we fine that such big data analytics capabilities have significant effects on overall firm performance measures. This finding is validated through subjective (self-reported) and objective (financial indicators) measures. In addition, we demonstrate that strong big data analytics capabilities have positive effects on incremental and radical innovations within firms. Our analyses uncover the conditions and core elements that contribute to developing such innovations. The results have been published in four conference proceeding articles and are to appear in three journal articles.
The outcomes of CADENT have moved knowledge beyond state of the art, resulting in several theoretical, managerial, and social implications. First, our work has helped develop the idea that big data analytics and business value are not a result of simply data and sophisticated analytics techniques, we have expanded knowledge on the core constituents of a big data analytics capability. This result has increased managerial relevance as managers engaging in the development of analytics initiatives have a roadmap of areas in which they know they should focus in order to derive value from their investments. Second, we showcase the process of big data analytics implementation explaining the inertial forces that emerge in the realization of business value. This part has been particularly novel as previous work has not adopted a process view or considered hindering forces during implementation and their implications. This body of work has major managerial implications as it allows practitioners to develop contingency plans and be able to foresee areas in which resistance and inertia will appear. Such inertial forces often have detrimental effects on viability of big data analytics projects and on overall business value. Third, by exploring the necessary skill-set of the data scientist and measuring the level and specific areas in which largest gaps appear, our work can be utilized towards the creation of new or adapted academic curricula. The requirement for data scientist jobs is one area in which the European Commission has placed great emphasis and our work contributes towards elucidating the specific skills that are needed by companies. In addition, our work described the different profiles that fall within the data scientist role, indicating that different study programs may be necessary to fulfil these diverse skills. Through academic publications and dissemination activities the work conducted as part of the CADENT project is expected to have significant positive effects on firm performance and efficiency in deploying big data analytics, and result in reduced costs associated with deployments and less failed or abandoned projects. This of course has spill-over effects with regard to society, as outcomes are also relevant for public organizations and non-governmental organizations. Lastly, by clarifying the role of the data scientist in the big data era and identifying skills that are particularly relevant for firms, our work contributes to more relevant study programs, and faster absorption of graduates in related jobs.
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