Practical advice

A summary of advice based on a selection of research articles is given below (with references and links to internal article reviews where available):

Organizational agility:
Organizations can use data analytics tools to improve their agility (i.e. ability to cope with rapid, relentless, and uncertain changes and thrive in a competitive environment of continually and unpredictably changing opportunities. A high fit between the data analytics tools and people, data, and tasks, is central. Organizations should therefore ensure that [19]:

• The analytical tools provide what they need to analyze their data properly.
• The capabilities of their employees allow them to take advantage of and effectively utilize the analytical tools.
• The analytical tools provide good support for carrying out the organization's tasks.

Innovation:
Lehrer et al. [20] show that big data and analytics (BDA) technologies enable service innovation. In order to innovate services, organizations should capitalize on the combined effects of BDA-technologies that are related to sourcing, storing, analyzing, and exploiting data. BDA facilitates proactive service provision, increases the speed of service provision, and increases service individualization and tailoring. Organizations seeking to achieve such service innovations can apply these categories as a framework for evaluating their technology needs and potential.

Organizations can improve their abilities to innovate by enhancing their capabilities to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments (i.e. dynamic capabilities). This can be achieved by improving their big data analytics capability (BDAC). Organizations should simultaneously [21]:

• strengthen their data and technology resources
• build a data-driven culture and facilitate organizational learning
• develop their managerial and technical skills.

Competitive performance:
Organizations can improve their capability to make critical business process changes by building business intelligence and analytics (BI&A) solutions that are characterized by high data quality and high system quality [22].

• high data quality: data that are accurate, comprehensive, correct, and consistent.
• high system quality: solutions that are flexible/adjustable to new demands and conditions, integrate data from systems servicing different functional areas, versatile in addressing data needs as they arise, and effectively integrate data from a variety of data sources within the organization.

Organisations should also emphasize management process and practices that encourage and promote the use of BI&A within the organization, as well as standardization of BI&A development processes and user satisfaction. Data quality, system quality and management practices influence organizations’ abilities to identify opportunities for improved efficiency and effectiveness, sense the need to enhance the way the business works and make organizational changes, sense/be aware of opportunities and threats, identify inefficiencies in existing business processes, and foresee a wide range of actionable options based on its surroundings [22]. 

Big data characteristics may enhance data-driven insight generation and innovation competency [23]. Organizations seeking to improve their exploitative and explorative competencies, should improve their data-driven insight generation (with emphasis on descriptive and predictive insights) by: 

• Reducing the time between data reception and data exploration- and analysis (i.e. emphasize data velocity).
• Using several different sources of data, and analyzing many types of data, to gain insight (i.e. emphasize data variety).
• Improving the quality of data by focusing on reliability, consistency, and precision (i.e. emphasize data veracity).
• Analyzing and exploring large amounts of data.

Data quality is also emphasized by Côrte-Real et al. [24], who find that big data analytics and Internet of Things capabilities can create significant value in business processes if supported by a good level of data quality, which will lead to increased competitive advantage.

References / sources

1. Management challenges in creating value from business analytics. 
   Vidgen, R., Shaw, S. & Grant, D.B. (2017). European Journal of Operational Research, 261(12), 626-639.
2. Big data and management. 
   George, G., Haas, M.R. & Pentland, A. (2014). Academy of Management Journal, 57(2), 321-326.
3. Business intelligence and analytics: from big data to big impact. 
   Chen, H., Chiang, R.H.L. & Storey, V.C. (2012). Management Information Systems Quarterly, 36(4), 1165-1188.
4. Defining business analytics: an empirical approach. 
   Power, D.J., Heavin, C. McDermott, J. & Daly, M. (2018). Journal of Business Analytics, 1(1), 40-53.
5. Defining analytics: a conceptual framework.
   Rose, R. (2016). OR/MS Today, 43(3).
6. Research challenges and opportunities in business analytics.
   Delen, D. & Ram, S. (2018). Journal of Business Analytics, 1(1), 2-12.
7. Transformational issues of big data and analytics in networked business.
   Baesens, B., Bapna, R., Marsden, J.R., Vanthienen, J. & Zhao, J.L. (2016). Management Information Systems Quarterly, 40(4), 807-818.
8. Creating strategic business value from big data analytics: A research framework.
   Grover, V., Chiang, R.H.L., Liang, T-P. & Zhang, D. (2018). Journal of Management Information Systems, 35(2), 388-423.
9. The perils and promises of big data research in information systems.
   Grover, V., Lindberg, A., Benbasat, I. & Lyytinen, K. (2020). Journal of the Association for Information Systems, 21(2), 268-291.
10. Special issue: Strategic value of big data and business analytics.
    Chiang, R.H.L., Grover, V., Liang, T-P. & Zhang, D. (2018). Journal of Management Information Systems, 35(2), 383-387.
11. How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study.
    Wamba, S.F., Akter, S., Edwards, A., Chopin, G. & Gnanzou, D. (2015). International Journal of Production Economics, 165, 234-246.
12. Beyond the hype: Big data concepts, methods, and analytics.
    Gandomi, A. & Haider, M. (2015). International Journal of Information Management, 35(2), 137-144.
13. The 2019 SIM IT issues and trends study.
    Kappelman, L., Johnson, V.L., Maurer, C., Guerra, K., McLean, E., Torres, R., Snyder, M. & Kim, K. (2020). MIS Quarterly Executive, 19(1).
14. Big data: The management revolution.
    McAfee, A. & Brynjolfsson, E. (2012). Harvard Business Review, October.
15. Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organizations.
    Sharma, R., Mithas, S. & Kankanhalli, A. (2014). European Journal of Information Systems, 23, 433-441.
16. How CEOs can lead a data-driven culture.
    Davenport, T.H. & Mittal, N. (2020). Harvard Business Review, 3.
17. Survey analysis: Big data investments begin tapering in 2016.
    Hare, J. & Heudecker, N. (2016). Gartner Research.
18. Big data and business analytics: A research agenda for realizing business value.
    Mikalef, P., Pappas, I.O., Krogstie, J. & Pavlou, P.A. (2020). Information & Management, 57(1).
19. Increasing firm agility through the use of data analytics: The role of fit. 
    Ghasemaghaei, M., Hassanein, K. & Turel, O. (2017). Decision Support Systems, 101, 95-105.
20. How big data analytics enables service innovation: Materiality, affordance, and the individualization of service.  
    Lehrer, C., Wieneke, A., vom Brocke, J., Jung, R. & Seidel, S. (2018). Journal of Management Information Systems, 35(2), 424-460.
21. Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment. 
    Mikalef, P., Boura, M., Lekakos, G. & Krogstie, J. (2019). British Journal of Management, 30, 272-298.
22. Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective. 
    Torres, R., Sidorova, A. & Jones, M.C. (2018). Information & Management, 55, 822-839.
23. Does big data enhance firm innovation competency? The mediating role of data-driven insight. 
    Ghasemaghaei, M. & Calic, G. (2019). Journal of Business Research, 104, 69-84.
24. Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?
    Côrte-Real, N., Ruivo, P. & Oliveira, T. (2020). Information & Management, 57(1).  

Other external resources for practitioners

How to build a data-driven company. Article by Sara Brown at MIT Sloan.

Professor Thomas Davenport: How to lead a data-driven culture.

Article reviews related to Data & Business analytics