Skip to main content
SpringerLink
Log in

Big data analytics capabilities: a systematic literature review and research agenda

  • Original Article
  • Published:
Information Systems and e-Business Management Aims and scope Submit manuscript

Abstract

With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited attention paid to the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies, and to have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains. The research framework is grounded on past empirical work on IT business value research, and builds on the resource-based view and dynamic capabilities view of the firm. By identifying the main areas of focus for BDA and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of competitive advantage. To this end, we identify gaps in the extant literature and propose six future research themes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Abbasi A, Sarker S, Chiang RH (2016) Big data research in information systems: toward an inclusive research agenda. J Assoc Inf Syst 17(2):1–32

    Google Scholar 

  • Agarwal R, Dhar V (2014) Editorial-big data, data science, and analytics: the opportunity and challenge for IS research. Inf Syst Res 25(3):443–448

    Article  Google Scholar 

  • Akter S, Wamba SF (2016) Big data analytics in E-commerce: a systematic review and agenda for future research. Electron Mark 26(2):173–194

    Google Scholar 

  • Akter S, Wamba SF, Gunasekaran A, Dubey R, Childe SJ (2016a) How to improve firm performance using big data analytics capability and business strategy alignment? Int J Prod Econ 182:113–131

    Google Scholar 

  • Akter S, Wamba SF, Gunasekaran A, Dubey R, Childe SJ (2016b) How to improve firm performance using big data analytics capability and business strategy alignment? Int J Prod Econ 182:113–131

    Google Scholar 

  • Amit R, Schoemaker PJ (1993) Strategic assets and organizational rent. Strateg Manag J 14(1):33–46

    Google Scholar 

  • Aral S, Weill P (2007) IT assets, organizational capabilities, and firm performance: how resource allocations and organizational differences explain performance variation. Organ Sci 18(5):763–780

    Google Scholar 

  • Bärenfänger R, Otto B, Österle H (2014) Business value of in-memory technology—multiple-case study insights. Ind Manag Data Syst 114(9):1396–1414

    Google Scholar 

  • Barney J (1991) Firm resources and sustained competitive advantage. J Manag 17(1):99–120

    Google Scholar 

  • Barney JB (2001) Resource-based theories of competitive advantage: a ten-year retrospective on the resource-based view. J Manag 27(6):643–650

    Google Scholar 

  • Barney JB, Ketchen DJ Jr, Wright M (2011) The future of resource-based theory: revitalization or decline? J Manag 37(5):1299–1315

    Google Scholar 

  • Barreto I (2010) Dynamic capabilities: a review of past research and an agenda for the future. J Manag 36(1):256–280

    Google Scholar 

  • Bekmamedova N, Shanks G (2014) Social media analytics and business value: a theoretical framework and case study. In: Proceedings of 2014 47th Hawaii international conference on system sciences (HICSS). IEEE

  • Besson P, Rowe F (2012) Strategizing information systems-enabled organizational transformation: a transdisciplinary review and new directions. J Strateg Inf Syst 21(2):103–124

    Google Scholar 

  • Beyer MA, Laney D (2012) The importance of ‘big data’: a definition. Gartner, Stamford, pp 2014–2018

    Google Scholar 

  • Bharadwaj AS (2000) A resource-based perspective on information technology capability and firm performance: an empirical investigation. MISQ 24(1):169–196

    Google Scholar 

  • Bharadwaj A, El Sawy OA, Pavlou PA, Venkatraman NV (2013) Digital business strategy: toward a next generation of insights. MISQ 37(2):471–482

    Google Scholar 

  • Bhatt GD, Grover V (2005) Types of information technology capabilities and their role in competitive advantage: an empirical study. J Manag Inf Syst 22(2):253–277

    Google Scholar 

  • Bowman C, Ambrosini V (2003) How the resource-based and the dynamic capability views of the firm inform corporate-level strategy. Br J Manag 14(4):289–303

    Google Scholar 

  • Boyd D, Crawford K (2012) Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Inf Commun Soc 15(5):662–679

    Google Scholar 

  • Brinkhues R, Maçada AC, Casalinho G (2014) Information management capabilities: antecedents and consequences. In: Proceedings of Americas conference on information systems (AMCIS)

  • Cao G, Duan Y (2014a) A path model linking business analytics, data-driven culture, and competitive advantage. In: European conference on information systems (ECIS)

  • Cao G, Duan Y (2014b) Gaining competitive advantage from analytics through the mediation of decision-making effectiveness: an empirical study of UK manufacturing companies. In: Proceedings of the Pacific Asia conference on information systems (PACIS), p 377

  • Cao G, Duan Y, Li G (2015) Linking business analytics to decision making effectiveness: a Path model analysis. IEEE Trans Eng Manag 62(3):384–395

    Google Scholar 

  • Chae BK, Yang C, Olson D, Sheu C (2014) The impact of advanced analytics and data accuracy on operational performance: a contingent resource based theory (RBT) perspective. Decis Support Syst 59:119–126

    Google Scholar 

  • Chatfield AT, Shlemoon VN, Redublado W, Rahman F (2014) Data scientists as game changers in big data environments. In: Proceedings of the 25th Australasian conference on information systems (ACIS)

  • Chen CP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci 275:314–347

    Google Scholar 

  • Chen Y, Chen H, Gorkhali A, Lu Y, Ma Y, Li L (2016) Big data analytics and big data science: a survey. J Manag Anal 3(1):1–42

    Google Scholar 

  • Constantiou ID, Kallinikos J (2015) New games, new rules: big data and the changing context of strategy. J Inf Technol 30(1):44–57

    Google Scholar 

  • Cragg P, Caldeira M, Ward J (2011) Organizational information systems competences in small and medium-sized enterprises. Inf Manag 48(8):353–363

    Google Scholar 

  • Davenport TH (2006) Competing on analytics. Harv Bus Rev 84(1):98–107

    Google Scholar 

  • Davenport TH (2013) Analytics 3.0. Harv Bus Rev 91(12):64–72

    Google Scholar 

  • Davenport TH, Harris JG (2007) Competing on analytics: the new science of winning. Harv Bus Press, Boston

    Google Scholar 

  • Davenport TH, Patil DJ (2012) Data scientist. Harv Bus Rev 90(5):70–76

    Google Scholar 

  • Davenport TH, Harris JG, David W, Jacobson AL (2001) Data to knowledge to results: building an analytic capability. Calif Manag Rev 43(2):117–138

    Google Scholar 

  • Davis CK (2014) Beyond data and analysis. Commun ACM 57(6):39–41

    Google Scholar 

  • De Mauro A, Greco M, Grimaldi M (2015) What is big data? A consensual definition and a review of key research topics. In: AIP Conference Proceedings, vol 1644, no 1, pp 97–104

  • Demchenko Y, Grosso P, De Laat C, Membrey P (2013) Addressing big data issues in scientific data infrastructure. In: 2013 International conference on collaboration technologies and systems (CTS). IEEE, pp 48–55

  • Domingue J, d’Aquin M, Simperl E, Mikroyannidis A (2014) The web of data: bridging the skills gap. IEEE Intell Syst 1(29):70–74

    Google Scholar 

  • Dong XL, Srivastava D (2013) Big data integration. In: Proceedings of 2013 IEEE 29th international conference on data engineering (ICDE), pp 1245–1248)

  • Drnevich PL, Kriauciunas AP (2011) Clarifying the conditions and limits of the contributions of ordinary and dynamic capabilities to relative firm performance. Strateg Manag J 32(3):254–279

    Google Scholar 

  • Eisenhardt KM, Martin JA (2000) Dynamic capabilities: what are they? Strateg Manag J 21(10–11):1105–1121

    Google Scholar 

  • Elbashir MZ, Collier PA, Sutton SG, Davern MJ, Leech SA (2013) Enhancing the business value of business intelligence: the role of shared knowledge and assimilation. J Inf Syst 27(2):87–105

    Google Scholar 

  • Erevelles S, Fukawa N, Swayne L (2016) Big data consumer analytics and the transformation of marketing. J Bus Res 69(2):897–904

    Google Scholar 

  • Espinosa JA, Armour F (2016) The big data analytics gold rush: a research framework for coordination and governance. In: Proceedings of 2016 49th Hawaii international conference on system sciences (HICSS), pp 1112–1121

  • Galbraith JR (2014) Organization design challenges resulting from big data. J Organ Des 3:2–13

    Google Scholar 

  • Gandomi A, Haider M (2015) Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manag 35(2):137–144

    Google Scholar 

  • Gantz J, Reinsel D (2012) The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. IDC iView IDC Anal Future 2012:1–16

    Google Scholar 

  • Garmaki M, Boughzala I, Wamba SF (2016) The effect of big data analytics capability on firm performance. In: Proceedings of 20th Pacific Asia conference on information systems (PACIS)

  • George G, Osinga CE, Lavie D, Scott B (2016) Big data and data science methods for management research. Acad Manag J 59(5):1493–1507

    Google Scholar 

  • Ghasemaghaei M, Hassanein K, Turel O (2015) Impacts of big data analytics on organizations: a resource fit perspective. In: Proceedings of 21st Americas conference on information systems (AMCIS)

  • Grant RM (1991) The resource-based theory of competitive advantage: implications for strategy formulation. Calif Manag Rev 33(3):114–135

    Google Scholar 

  • Grant RM (1996) Prospering in dynamically-competitive environments: organizational capability as knowledge integration. Organ Sci 7(4):375–387

    Google Scholar 

  • Größler A, Grübner A (2006) An empirical model of the relationships between manufacturing capabilities. Int J Oper Prod Manag 26(5):458–485

    Google Scholar 

  • Gupta M, George JF (2016) Toward the development of a big data analytics capability. Inf Manag 53(8):1049–1064

    Google Scholar 

  • Helfat CE, Peteraf MA (2003) The dynamic resource-based view: capability lifecycles. Strateg Manag J 24(10):997–1010

    Google Scholar 

  • Helfat CE, Peteraf MA (2009) Understanding dynamic capabilities: progress along a developmental path. Strateg Organ 7(1):91–102

    Google Scholar 

  • Henderson JC, Venkatraman H (1993) Strategic alignment: leveraging information technology for transforming organizations. IBM Syst J 32(1):472–484

    Google Scholar 

  • Higgins JP, Green S (eds) (2008) Cochrane handbook for systematic reviews of interventions, vol 5. Wiley-Blackwell, Chichester

    Google Scholar 

  • Hodgkinson GP, Healey MP (2011) Psychological foundations of dynamic capabilities: reflexion and reflection in strategic management. Strateg Manag J 32(13):1500–1516

    Google Scholar 

  • Hoopes DG, Madsen TL (2008) A capability-based view of competitive heterogeneity. Ind Corp Change 17(3):393–426

    Google Scholar 

  • Jacobi F, Jahn S, Krawatzeck R, Dinter B, Lorenz A (2014) Towards a design model for interdisciplinary information systems curriculum development, as exemplified by big data analytics education. In: Proceedings of European conference on information systems (ECIS)

  • Jelinek M, Bergey P (2013) Innovation as the strategic driver of sustainability: big data knowledge for profit and survival. IEEE Eng Manag Rev 41(2):14–22

    Google Scholar 

  • Kamioka T, Tapanainen T (2014) Organizational use of big data and competitive advantage—exploration of antecedents. In: Pacific Asia conference on information systems (PACIS), p 372

  • Kim G, Shin B, Kim KK, Lee HG (2011) IT capabilities, process-oriented dynamic capabilities, and firm financial performance. J Assoc Inf Syst 12(7):487–517

    Google Scholar 

  • Kiron D, Shockley R (2011) Creating business value with analytics. MIT Sloan Manag Rev 53(1):57–63

    Google Scholar 

  • Kiron D, Prentice PK, Ferguson RB (2014) The analytics mandate. MIT Sloan Manag Rev 55(4):1–25

    Google Scholar 

  • Kitchenham BA (2004) Procedures for performing systematic reviews. Joint technical report. Computer Science Department, Keele University (TR/SE-0401) and National ICT Australia Ltd (0400011T.1)

  • Kitchenham BA (2007) Guidelines for performing systematic literature reviews in software engineering version 2.3, Keele University and University of Durham, EBSE technical report

  • Kitchenham BA, Brereton OP, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering—a systematic literature review. Inf Softw Technol 51(1):7–15

    Google Scholar 

  • Kohli R, Grover V (2008) Business value of IT: an essay on expanding research directions to keep up with the times. J Assoc Inf Syst 9(1):23–39

    Google Scholar 

  • Kowalczyk DWIM, Buxmann P (2014) Big data and information processing in organizational decision processes. Bus Inf Syst Eng 6(5):267–278

    Google Scholar 

  • Kung L, Kung HJ, Jones-Farmer A, Wang Y (2015) Managing big data for firm performance: a configurational approach. In: Americas conference on information systems (AMCIS)

  • Kwon O, Lee N, Shin B (2014) Data quality management, data usage experience and acquisition intention of big data analytics. Int J Inf Manag 34(3):387–394

    Google Scholar 

  • Lamba HS, Dubey SK (2015) Analysis of requirements for big data adoption to maximize IT business value in reliability. In: 2015 4th International conference on infocom technologies and optimization (ICRITO) (trends and future directions). IEEE, pp 1–6

  • LaValle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N (2011) Big data, analytics and the path from insights to value. MIT Sloan Manag Rev 52(2):21–32

    Google Scholar 

  • Lim EP, Chen H, Chen G (2013) Business intelligence and analytics: research directions. ACM Trans Manag Inf Syst (TMIS) 3(4):17–27

    Google Scholar 

  • Loebbecke C, Picot A (2015) Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda. J Strateg Inf Syst 24(3):149–157

    Google Scholar 

  • Makadok R (2001) Toward a synthesis of the resource-based and dynamic-capability views of rent creation. Strateg Manag J 22(5):387–401

    Google Scholar 

  • Markus ML (2015) New games, new rules, new scoreboards: the potential consequences of big data. J Inf Technol 30(1):58–59

    Google Scholar 

  • McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Barton D (2012) Big data: the Manag revolution. Harv Bus Rev 90(10):61–67

    Google Scholar 

  • Meredith R, Remington S, O’Donnell P, Sharma N (2012) Organisational transformation through business intelligence: theory, the vendor perspective and a research agenda. J Decis Syst 21(3):187–201

    Google Scholar 

  • Meyer-Waarden L (2016) Big data resources, marketing capabilities, and firm performance. In: Proceedings of the 37th international conference on information systems (ICIS)

  • Mikalef P, Pateli AG (2016) Developing and validating a measurement instrument of IT-enabled dynamic capabilities. In: Proceedings of the 24th European conference on information systems (ECIS)

  • Mikalef P, Pateli A (2017) Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: findings from PLS-SEM and fsQCA. J Bus Res 70:1–16

    Google Scholar 

  • Mikalef P, Pateli A, Batenburg RS, Wetering RVD (2015) Purchasing alignment under multiple contingencies: a configuration theory approach. Ind Manag Data Syst 115(4):625–645

    Google Scholar 

  • Mikalef P, Pateli A, van de Wetering R (2016a) IT flexibility and competitive performance: the mediating role of IT-enabled dynamic capabilities. In: Proceedings of the 24th European conference on information systems (ECIS)

  • Mikalef P, Pappas IO, Giannakos MN, Krogstie J, Lekakos G (2016b) Big data and strategy: a research framework. In: Proceedings of the 10th mediterranean conference on information systems (MCIS)

  • Mikalef P, Framnes V, Danielsen F, Krogstie J, Olsen DH (2017) Big data analytics capability: antecedents and business value. In: Proceedings of the 21st Pacific Asia conference on information systems (PACIS)

  • Mohanty S, Jagadeesh M, Srivatsa H (2013) Big data imperatives: enterprise “Big Data” warehouse, BI implementations and analytics. Apress, New York

    Google Scholar 

  • Müller O, Junglas I, vom Brocke J, Debortoli S (2016) Utilizing big data analytics for information systems research: challenges, promises and guidelines. Eur J Inf Syst 25(4):289–302

    Google Scholar 

  • Olszak CM (2014) Towards an understanding business intelligence a dynamic capability-based framework for business intelligence. In: 2014 Federated conference on computer science and information systems (FedCSIS). IEEE, pp 1103–1110

  • Opresnik D, Taisch M (2015) The value of big data in servitization. Int J Prod Econ 165:174–184

    Google Scholar 

  • Oracle (2012) Big data for the enterprise. Oracle, Redwood Shores

    Google Scholar 

  • Pappas IO, Mikalef P, Giannakos MN, Krogstie J, Lekakos G (2016) Social media and analytics for competitive performance: a conceptual research framework. In: International conference on business information systems. Springer, Cham, pp 209–218

    Google Scholar 

  • Pavlou PA, El Sawy OA (2006) From IT leveraging competence to competitive advantage in turbulent environments: the case of new product development. Inf Syst Res 17(3):198–227

    Google Scholar 

  • Pavlou PA, El Sawy OA (2011) Understanding the elusive black box of dynamic capabilities. Decis Sci 42(1):239–273

    Google Scholar 

  • Peteraf MA (1993) The cornerstones of competitive advantage: a resource-based view. Strateg Manag J 14(3):179–191

    Google Scholar 

  • Peteraf MA, Barney JB (2003) Unraveling the resource-based tangle. Manag Decis Econ 24(4):309–323

    Google Scholar 

  • Phillips-Wren G, Iyer LS, Kulkarni U, Ariyachandra T (2015) Business analytics in the context of big data: a roadmap for research. Commun Assoc Inf Syst 37(1):448–472

    Google Scholar 

  • Posavec AB, Krajnović S (2016) Challenges in adopting big data strategies and plans in organizations. In: Proceedings of 39th international convention on information and communication technology, electronics, and microelectronics

  • Prescott EM (2014) Big data and competitive advantage at Nielsen. Manag Decis 52(3):573–601

    Google Scholar 

  • Priem RL, Butler JE (2001) Is the resource-based “view” a useful perspective for strategic manag research? Acad Manag Rev 26(1):22–40

    Google Scholar 

  • Provost F, Fawcett T (2013) Data science and its relationship to big data and data-driven decision making. Big Data 1(1):51–59

    Google Scholar 

  • Ransbotham S, Kiron D (2017) Analytics as a source of business innovation. MIT Sloan Management Review. Research report. http://sloanreview.mit.edu/projects/analytics-as-a-source-of-business-innovation/

  • Ravichandran T, Lertwongsatien C (2005) Effect of information systems resources and capabilities on firm performance: a resource-based perspective. J Manag Inf Syst 21(4):237–276

    Google Scholar 

  • Ren S, Wamba SF, Akter S, Dubey R, Childe SJ (2016) Modelling quality dynamics, business value and firm performance in a big data analytics environment Int. J Prod Res 55(17):5011–5026

    Google Scholar 

  • Russom P (2011) Big data analytics. TDWI Best Practices Report, Fourth Quarter 1–35

  • Sambamurthy V, Zmud RW (1999) Arrangements for information technology governance: a theory of multiple contingencies. MISQ 23(2):261–290

    Google Scholar 

  • Schilke O (2014) On the contingent value of dynamic capabilities for competitive advantage: the nonlinear moderating effect of environmental dynamism. Strateg Manag J 35(2):179–203

    Google Scholar 

  • Schroeck M, Shockley R, Smart J, Romero-Morales D, Tufano P (2012) Analytics: The real-world use of big data. IBM Global Business Services 1–20

  • Schryen G (2013) Revisiting IS business value research: what we already know, what we still need to know, and how we can get there. Eur J Inf Syst 22(2):139–169

    Google Scholar 

  • Seddon JJ, Currie WL (2017) A model for unpacking big data analytics in high-frequency trading. J Bus Res 70:300–307

    Google Scholar 

  • Sharda R, Delen D, Turban E (2013) Business intelligence: a managerial perspective on analytics. Prentice Hall Press, Prentice

    Google Scholar 

  • Shuradze G, Wagner HT (2016) Towards a conceptualization of data analytics capabilities. In: 2016 49th Hawaii international conference on system sciences (HICSS). IEEE, pp 5052–5064

  • Sirmon DG, Hitt MA, Ireland RD, Gilbert BA (2011) Resource orchestration to create competitive advantage: breadth, depth, and life cycle effects. J Manag 37(5):1390–1412

    Google Scholar 

  • Sun EW, Chen YT, Yu MT (2015) Generalized optimal wavelet decomposing algorithm for big financial data. Int J Prod Econ 165:194–214

    Google Scholar 

  • Tallon PP, Ramirez RV, Short JE (2013) The information artifact in IT governance: toward a theory of information governance. J Manag Inf Syst 30(3):141–178

    Google Scholar 

  • Tambe P (2014) Big data investment, skills, and firm value. Manag Sci 60(6):1452–1469

    Google Scholar 

  • Teece DJ (2007) Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strateg Manag J 28(13):1319–1350

    Google Scholar 

  • Teece D, Pisano G (1994) The dynamic capabilities of firms: an introduction. Ind Corp Change 3(3):537–556

    Google Scholar 

  • Teece DJ, Pisano G, Shuen A (1997) Dynamic capabilities and strategic management. Strateg Manag J 18(7):509–533

    Google Scholar 

  • Vidgen R, Shaw S, Grant DB (2017) Management challenges in creating value from business analytics. Eur J Oper Res 261(2):626–639

    Google Scholar 

  • Wade M, Hulland J (2004) Review: the resource-based view and information systems research: Review, extension, and suggestions for future research. MISQ 28(1):107–142

    Google Scholar 

  • Wamba SF, Akter S, Edwards A, Chopin G, Gnanzou D (2015) How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int J Prod Econ 165:234–246

    Google Scholar 

  • Wamba SF, Gunasekaran A, Akter S, Ren SJF, Dubey R, Childe SJ (2017) Big data analytics and firm performance: effects of dynamic capabilities. J Bus Res 70:356–365

    Google Scholar 

  • Wang N, Liang H, Zhong W, Xue Y, Xiao J (2012) Resource structuring or capability building? An empirical study of the business value of information technology. J Manag Inf Syst 29(2):325–367

    Google Scholar 

  • Wernerfelt B (1984) A resource-based view of the firm. Strateg Manag J 5(2):171–180

    Google Scholar 

  • White C (2011) Using big data for smarter decision making IBM. Yorktown Heights, New York

    Google Scholar 

  • Winter SG (2003) Understanding dynamic capabilities. Strateg Manag J 24(10):991–995

    Google Scholar 

  • Wixom BH, Watson HJ, Werner T (2011) Developing an enterprise business intelligence capability: the Norfolk southern journey. MISQ Exec 10(2):61–71

    Google Scholar 

  • Wu LY (2007) Entrepreneurial resources, dynamic capabilities and start-up performance of Taiwan’s high-tech firms. J Bus Res 60(5):549–555

    Google Scholar 

  • Wu SJ, Melnyk SA, Flynn BB (2010) Operational capabilities: the secret ingredient. Decis Sci 41(4):721–754

    Google Scholar 

  • Xu P, Kim J (2014) Achieving dynamic capabilities with business intelligence. In: Pacific Asia conference on information systems PACIS, p 330

  • Xu Z, Frankwick GL, Ramirez E (2016) Effects of big data analytics and traditional marketing analytics on new product success: a knowledge fusion perspective. J Bus Res 69(5):1562–1566

    Google Scholar 

  • Zikopoulos P, Eaton C (2011) Understanding big data: analytics for enterprise class Hadoop and streaming data. McGraw-Hill Osborne Media, New York City

    Google Scholar 

  • Zollo M, Winter SG (2002) Deliberate learning and the evolution of dynamic capabilities. Organ Sci 13(3):339–351

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Patrick Mikalef, Ilias O. Pappas, John Krogstie & Michail Giannakos

Authors
  1. Patrick Mikalef
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilias O. Pappas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Krogstie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michail Giannakos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patrick Mikalef.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mikalef, P., Pappas, I.O., Krogstie, J. et al. Big data analytics capabilities: a systematic literature review and research agenda. Inf Syst E-Bus Manage 16, 547–578 (2018). https://doi.org/10.1007/s10257-017-0362-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10257-017-0362-y

Keywords

Search

Navigation