James Brenza and Maureen Metcalf recently published a paper Evaluating Big Data Projects – Success and Failure Using an Integral Lens in the Integral Leadership Review, a bridging publication that links authors and readers across cultures around the world. It serves leaders, professionals and academics engaged in the practice, development and theory of leadership. It bridges multiple perspectives by drawing on integral, transdisciplinary, complexity and developmental frameworks.
Excerpt from the paper: Big data projects are becoming more common in our technology based world and our ability to implement them effectively will provide organizations a competitive advantage. If they are done poorly, organizations lose valuable resources and in many cases lose credibility among their workforce and possibly within their markets. The stakes are high to get it right and these models provide insight to increase your probability of success.
Have you ever been part of a complex technology initiative that just can’t seem to get completed? Even worse, have you ever seen a complex technology initiative that can’t seem to even get started? If you answered “yes” to either question, chances are very high that you’re not alone.
With the increasing focus on information analytics and “big data,” the risks of lagging or failing projects are rising due to the complexity of the initiatives and the lack of available skilled resources. A recent blog post summarized the broad mix of skills and focus many enterprises expect of their analytic leaders (frequently called Data Scientists):
- Analytic skill set (mathematics, domain knowledge, technology)
- Commercial acumen
- Willingness to spend lots of time justifying your existence
Even though the last one is a bit farcical, it actually highlights part of the problem organizations encounter. A domino effect is that without these skills, initiatives are very likely to fail causing vital resources to focus on self-preservation rather than information-driven transformation. As you review the list, you’ll also discover we expect these resources to be “renaissance leaders” (i.e., resources so broadly skilled that they can fulfill all roles). A direct conclusion is the expectation that a single person carry so many roles may be a leading cause of failing initiatives or constrained progress. Many organizations have realized this and are sharing these roles across many resources. While that mitigates the individual risk, that transference assumes the organization has the processes and capabilities in place to effectively integrate the contributors. With the mix of required skills and team members, transformational initiatives will benefit from a formal structure that decomposes the initiative to phases and to specific projects. These challenging initiatives require holistic leadership that we will refer to in this paper as Innovative) Leadership to drive both the analytic and transformational outcome. An Innovative Leader is a leader who influences by equally engaging personal intentions, personal actions, culture, and systems. For this discussion, we will focus on the combination of Innovative Leadership and Data Scientist.
We believe that Innovative Leadership is actually necessary because it uses this entire range of skills to transform organizations. Our article gives two examples of transformations, one successful and one unsuccessful. We’ll use the integral model as the basis for evaluation since it offers an effective assessment framework to improve the leader’s effectiveness and the initiative outcome. The integral model, created by Ken Wilber, looks at the intersection of four key elements, that when aligned, promote successful transformation—and when not aligned contribute to transformation failure.
The key elements of the integral model are shown in the image above and include:
- Individual self is the leader’s values, goals, and beliefs. The leader needs traits such as curiosity, proactivity, and a belief that collaboration is important to success. These reflect some of the Innovative Leader traits in the list above.
- Action is where the Innovative Leader acts using the skills referenced above: Analytic skill set (mathematics, domain knowledge, technology), Communication skills, Collaboration, Commercial acumen, Customer-centric, Problem-solving and Strategic skills.
- Culture reflects the organization’s culture and the leader’s understanding of it to create alignment between himself and the culture. This understanding becomes crucial particularly when making changes that are not fully aligned with the existing culture.
- Systems include the organizational and technical systems and processes that dictate how the organization accomplishes its work. The leader needs to understand the current systems, especially those that reward and punish employees and leaders, and ensure these are updated to reflect the new actions required to be successful.
When implementing change, the leader must attend to each of the four areas and ensure they are changing in ways that are well aligned and support one another. To further illustrate this point, the image above reflects key areas of alignment, and all actions in the system have the potential to impact all elements of the system. It is this interconnected nature of leadership and systems that makes this model so important when implementing change. Leaders must take a more comprehensive view of the environment to ensure successful change. It’s no longer sufficient to manage change only from the systems view while ignoring the other three quadrants. To help demonstrate the model’s applicability, let’s review two initiatives.
The first initiative is an example of a great opportunity that never “left the launch pad.” Despite a very strong financial business case to save money and environmental resources, the available resources could not rally enough energy to reach critical mass (or escape velocity). The second initiative is an example of a very complex, very long business transformation to increase revenue and decrease cost. It required more resources for implementation and delivered incredibly positive results. The resources available to both initiatives were very similar, but the outcomes were startlingly different. We will explore both projects through the integral lens, considering how they performed against the four categories in the integral framework. After reviewing the two initiatives, we’ll explore the key differences and provide some insight on how the integral model can improve successful outcomes.
Click to read the full paper: Evaluating Big Data Projects – Success and Failure Using an Integral Lens