Success Factors for the Implementation of Big Data as a New Economic Resource

Purpose: it is to determine the most important factors that condition the ability of an enterprise to successfully implement the big data as a new economic resource.

Methods: the methodological foundation of this research is the analytical framework of the resource-based view, which is applied to highlight the most important factors of the organizational capacity for the implementation of big data into economic activity. These factors are classified by blocks of internal factors of the organizational capacity in two hierarchical levels (organizational and individual). The study is based on the primary information obtained through a survey in the form of semi-structured interviews of managers and experts of the companies pioneering in implementation of big data.

Results: a based on the analysis of scientific publications in accordance with positive and normative approaches to the understanding of big data, the concept of "big data" as an economic resource is developed. Its attributes are identified with emphasis on the heterogeneity of big data which allows filtering information about the subsystems of a complex economic system representing the modern enterprise. This information cannot be obtained from traditional sources of economic data. By systematizing the primary information on the projects of implementation of big data into economic activity by foreign companies by applying the analytical framework of the resource-based view, the key conceptual factors of the organizational capacity for the use of big data and relationship among significant factors have been identified. These key internal factors emerge as a result of the revolution in information technology and represent the necessary condition to ensure the transformation of the analytical procedures for decision making at the corporate level based on big data. The study reveals that sufficient condition represents a system of intangible resources and organizational capabilities, the most important of which is the capability to coordinate data processing and analysis. This capability, in a system with the other key organizational level capabilities, enables the integration of analytical and data processing technologies, on the one hand, and individual competencies of employees, on the other.

Conclusions and Relevance: the implications of this study are aimed at researchers studying the problems of the information and networked economy, and practitioners of the Russian companies that are implementing or consider the implementation of the big data into economic activity. In business perspective, the most important implication of this research is that effective implementation of big data is not a technical challenge but an organizational and economic one. The basis of the organizational capacity for the implementation of big data is information resources, human resources and corporate culture and systems (technologies) for data processing and analysis.

1. Sejahtera F., Wang W., Indulska M., Sadiq S. Enablers and Inhibitors of Effective Use of Big Data: Insights from a Case Study. In: Tanabu M., Senoo D. Yokohama. (eds.). Proceedings of PACIS 2018 – 22nd Pacific Asia Conference on Information Systems. 2018 (in Eng.). Available from: https://aisel.aisnet.org/pacis2018/27

2. Big Data Executive Survey 2016. An Update on the Adoption of Big Data in the Fortune 1000. Boston: New Vantage Partners LLC., 2016. 16 p. (in Eng.). Available from: https://newvantage.com/wp-content/uploads/2016/01/Big-Data-Executive-Survey-2016Findings-FINAL.pdf

3. Kleiner G.B. System paradigm and system management. Russian Journal of Management. 2008; 3(6):27–50 (in Russ.).

4. Bachenskaya M.V. The intellectual capital of an organization: methodological approaches to definition. St. Petersburg State University Herald. 2011; 12(3):280–285 (in Russ.).

5. Edvinsson L. Corporate Longitude: What you need to know to navigate the knowledge economy. Published July 15th 2002 by Financial Times Prentice Hall Hardcover, 256 p. (in Eng.)

6. Brooking A. Intellectual capital: Core asset for the third millennium. London: Thomson Learning Publ., 1996. 224 p. (Russ. ed.: Brooking, A. Intellektual'nyy capital. St. Petersburg: Piter Publ., 2001. 288 p.)

7. Roos G., Pike S., Fernstrom L. Managing Intellectual Capital in Practice. London: Routledge, 2006. 400 p. (in Eng.). https://doi.org/10.4324/9780080479118

8. Stuart T. Wealth from the mind: business bestseller. The wealth of the mind: business bestseller / D. Mikhailov (Ed.), V.A. Nozdrina (Trans. from Eng.). Minsk: Paradox, 1998. 352 p. ( in Russ.).

9. Cox M., Ellsworth D. Managing Big Data for scientific visualization. In: Proceedings of ACM Siggraph, Ames: NASA. 1997. pp. 21–38. (in Eng.). Available from: https://www.researchgate.net/publication/238704525_Managing_big_data_for_scientific_visualization

10. Laney D. Data Management: Controlling Data Volume, Velocity, and Variety. Stamford: META group Inc., 2001. (in Eng.). Available from: http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-DataManagement-Controlling-Data-Volume-Velocity-and-Variety.pdf

11. Blazquez D., Domenech J. Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change journal. 2018; (130):99–113 (in Eng.). Available from: https://www.sciencedirect.com/science/article/pii/S0040162517310946 (In Eng.)

12. Ghoshal A., Larson E.C., Subramanyam R., Shaw M.J. The impact of business analytics strategy on social, mobile, and cloud computing adoption. In: Proceedings of the Thirty Fifth International International Conference on Information Systems, Auckland, New Zealand, December 14–17, 2014 (in Eng.). Available from: https://aisel.aisnet.org/icis2014/proceedings/ISStrategy/30/

13. Günther W.A., Rezazade Mehrizi M.H., Huysman M., Feldberg F. Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems (online). 2017; 3(26):191–209 (in Eng.). Available from: https://linkinghub.elsevier.com/retrieve/pii/S0963868717302615

14. Kouanou A.T., Tchiotsop D., Kengne R., Tansaa Z.D., Adele N.M., Tchinda R. An optimal big data workflow for biomedical image analysis. Technological Forecasting and Social Change. 2018; (130):99–113 (in Eng.). Available from: https://www.sciencedirect.com/science/article/pii/S0040162517310946?via%3Dihub

15. Manyika C.J., Miremadi M. Where Machines Could Replace Humans – and Where They Can’t (yet). N-Y.: McKinsey Quarterly, 2018 (in Eng.). Available from: https://www.mckinsey.com/business-functions/digitalmckinsey/our-insights/where-machines-could-replacehumans-and-where-they-cant-yet

16. Big Data Executive Survey 2017. An Update on the Adoption of Big Data in the Fortune 1000. Boston: New Vantage Partners LLC., 2017. 16 p. (in Eng.). Available from: https://newvantage.com/wp-content/uploads/2017/01/Big-Data-Executive-Survey-2017Executive-Summary.pdf

17. Tallon P. Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost. Computer. 2013; 6(46):32–39 (in Eng.). Available from: https://ieeexplore.ieee.org/document/6519236

18. Kogdenko V.G., Melnik M.V. Current trends in business analysis: a study of a company's ecosystem, an analysis of the information component of a business model, an assessment of growth opportunities. Economic analysis: theory and practice. 2017; 10(16):1878–1897 (in Russ.). https://doi.org/10.24891/ea.16.10.187819

19. Malinovskaya N.V. The concept of capital multiplicity in integrated reporting. International Accounting. 2018; 6(21):700–713 (in Russ.). https://doi.org/10.24891/ia.21.6.70020

20. Lukanina A.V. Analysis of the basic categories of IFRS in the framework of the principle of priority of content over form. International Accounting. 2016; 2(19):19–33 (in Russ.)

21. Ackoff R.L. From data to wisdom. Journal of Applied Systems Analysis. 1989; 16(1):3–9 (in Eng.)

22. Rowley J. The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science. 2007; 33(2):163–180 (in Eng.). Available from: https://doi.org/10.1177/0165551506070706

23. Boisot M. Knowledge assets: Securing competitive advantage in the information economy. New York: Oxford University Press, 1998. 312 p. (in Eng.)

24. LaValle S., Lesser E., Shockley R., Hopkins M. S., Kruschwitz N. Big Data, Analytics and the Path from Insights to Value. MIT sloan management review. 2013; 2(21):20–31 (in Eng.). Available from: http://foresight.ifmo.ru/ict/shared/files/201309/1_9.pdf

25. McIver D., Lengnick-Hall C. The causal ambiguity paradox: Deliberate actions under causal ambiguity. Strategic Organization. 2017; 16(3):304–322 (in Eng.). https://doi.org/10.1177/1476127017740081

26. Teece D., Pisano G., Shuen A. Dynamic Capabilities and Strategic Management. Strategic Management Journal. 1997; 7(18):509–533 (in Eng.). Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%2910970266%28199708%2918%3A7%3C509%3A%3AAIDSMJ882%3E3.0.CO%3B2-Z

27. Gao J., Koronios A., Selle S. Towards a Process View on Critical Success Factors in Big Data Analytics Projects. In: Proceedings of Twenty-first Americas Conference on Information Systems, Puerto Rico. 2015. pp. 1–14 (in Eng.). Available from: https://pdfs.semanticscholar.org/247b/fe6fa3365d74bd98c2c460785d62c3d7561d.pdf

28. Edvinsson L. Developing intellectual capital at Skandia. Long Range Planning Journal. 1997; 3(30):366– 373 (in Eng.). https://doi.org/10.1016/S00246301(97)90248-X

29. Smith G.T. On Construct Validity: Issues of Method and Measurement. Psychological Assessment. 2005; 4(17):296–408 (in Eng.). Available from: https://psycnet.apa.org/doiLanding?doi=10.1037%2F1040-3590.17.4.396

30. Given L.M. The Sage encyclopedia of qualitative research methods. Los Angeles: Sage Publications, 2008. 1014 p. (in Eng.)