Feldman (2004) outlines ten principles of research that make meaningful contributions to the field and are grounded in theory in some way. Some of these principles are fairly obvious: the research question is not trivial, the author shows mastery over the research process, the writing is of a particular quality, and it goes beyond synthesizing research to provide new insights. Other principles go beyond what we might consider obvious needs. First, there must be a tight balance between including too much in a literature review and being sparse. That balance demonstrates an understanding of what is relevant to the problem. The writing must also demonstrate a purpose, introducing new perspectives that make a difference in the field. It has clear focus and a specific theoretical domain (or domains, if necessary). New constraints are identified and there is a clear relationship between independent and dependent variables. Feldman (2004) also outlines three key questions of a solid paper: (1) Is there anything surprising here? (2) What theories can be used to explain it? (3) Can the theoretical perspectives be integrated?
Much has been made over the impact of business intelligence and a data-driven culture in organizations. Where leadership is concerned, the discussion typically has revolved around how executives in the organization must buy in to a company-wide culture change and implement new frameworks to support analytics at all levels of the organization. This is primarily a top-down view of the relationship between business intelligence, data-driven culture, and organizational leadership. However, very little research exists on how data-driven culture affects organizational leadership—from the bottom up, in other words—beyond empowering better decision making with improved metrics. This is an area open to much more investigation.
That is not to say that the topic has not been approached. Marshall, Mueck, and Shockley (2015) argue that organizational leaders “embrace analytics and actionable insights” but also integrate “analytics and insights with innovation” (p. 33). This is clearly a call for a two-way relationship between leadership and the data-driven culture. Leaders must not only drive the organization’s embrace of data-driven culture but be willing to be affected by it.
More recently, McCarthy, Sammon, and Murphy (2017) directly question how data-driven cultures affect leadership styles. Although the article is focused on higher education institutions, it is applicable across companies. It provides equal discussion on both data-driven problem solving (in this case, student retention) and leadership studies. A thorough review of leadership styles is distilled into 3 broad groups: task-oriented, relations-oriented, and change-oriented. Task-oriented focuses on short-term planning and role clarification. Relationship-oriented focuses on developing, supporting, and empowering individuals within the organization. Change-oriented focuses on larger shifts in company thinking, taking risks, and external monitoring. The authors find significant evidence of data-driven cultures moving away from task- and relationship-oriented styles and towards change-oriented styles.
This introduces a symbiotic relationship between data-driven culture, business intelligence, and a specific change-oriented leadership style. Power (2016) discusses the relationship between business intelligence and decision making, reminding us that although data scientists “provide support for managerial decision-making” (p. 354), the business intelligence efforts are there for support, and organizational leadership must “continue to assume personal responsibility for their choices and organisational actions they initiate” (p. 350).
McCarthy et al. (2017) demonstrate the relationship between data-driven culture and change-oriented leadership. It stands to reason that an organization focused on fact-based decision-making (leveraging corporate resources in a more accountable way) would move towards a leadership style that favors change management over taskmaster management or relationship-driven management. That isn’t to say that these other styles are out of place; in fact, further research would be appropriate in different organization types and varied business domains. The discussion of particular case studies by Marshall et al. (2015) suggest an openness to other leadership types within data-driven culture.
It is apparent that insufficient research exists on the relationships between data-driven culture, analytics capabilities, and leadership styles. My research interests around data-driven culture and BI maturity comprise a sort of domino effect across multiple domains and issues. In this case, we may think of a data-driven culture running parallel to whatever leadership style is present in the organization; from there, a company’s information resource governance would be a product of the two.
References
Feldman, D. C. (2004). What are we talking about when we talk about theory? Journal of Management, 30(5), 565–567.
Marshall, A., Mueck, S., & Shockley, R. (2015). How leading organizations use big data and analytics to innovate. Strategy and Leadership, 43(5), 32-39.
McCarthy, J., Sammon, D., & Murphy, C. (2017). Leadership styles in a data driven culture. Paper presented at the European Conference on Management, Leadership & Governance, Kidmore End.
Power, D. J. (2016). Data science: Supporting decision-making. Journal of Decision Systems, 25(4), 345-356.