How to Combine Cognitive Diversity With Data Tools for Effective Organizational Decision-Making
Introduction
This post is the first in a series of “working papers” where I will explore how cognitive diversity, knowledge management, artificial intelligence, and data democratization can work together to support effective organizational decision-making. These are exciting times for practitioners of data across the entire spectrum of professions. Our tools are expanding at a rapid clip and becoming more powerful by leaps and bounds. However, the race to chase the latest breakthroughs can leave organizational frameworks struggling to keep pace. If these frameworks break down, we practitioners will be unable to unlock the full potential of our tools. Data-driven syndromes like collecting data for data’s sake, analysis paralysis, solving the wrong problems, answering the wrong questions, and delivering overly complex messages could actually worsen as data technologies overwhelm the consumers of insights who need to make (business) decisions.
I see good news in expanding the concept of the Insights Supply Chain to incorporate this ever more complex landscape of data tools and methodologies. There is a large process at work whose success relies on a functional Insights Supply Chain. This large process starts with knowledge management, is enabled and scaled by artificial intelligence, is driven by cognitive diversity, produces data democratization, and, at the end, creates effective organizational decision-making. Let’s start with a refresher on the Insights Supply Chain.
The Insights Supply Chain
The Insights Supply Chain explains how companies organize to transform data into actionable insights across a network of contributors. The embedded process includes the following tenets:
- Use data as a tool for managing knowledge in service of value-creation.
- Specialize knowledge management to create value from a strategic core competence in data.
- Consider centralizing knowledge management to maximize data leverage and create consistent value from standards of data excellence.
- Tune the amount of specialization and centralization according to the degree of dependence across business units. Use knowledge management as a binding organizational imperative.
I developed this framework over years of living it in pieces and components. The Insights Supply Chain makes explicit a number of practices and principles which can go unsaid and unrecognized in the world of data practitioners. With the language of the the Insights Supply Chain in place, I was able to help my data teams recognize the holistic job entrusted to us. This language also helped me communicate to executives the importance of thinking about data as an organizational challenge and not just one of technology. Those challenges are expanding along with an ever increasing diversity in the data landscape.
Unlock Cognitive Diversity
In Psychology Today, Gary Klein, PhD defines cognitive diversity (CD) as “differences in how team members think about important tasks and activities.” Yet, Klein also acknowledges that the literature on cognitive diversity is itself diverse in opinion and perspective: “Researchers have studied cognitive diversity by measuring differences in a wide variety of psychological factors, including personality, values, preferences, thinking styles, education, experience, and problem-solving strategies.” I step into this scientific minefield with my own perspective based on leading, observing, and evaluating my data teams over the years. CD produces alternative and creative ways of interpreting and using data. This richness can be lost without an explicit approach to supporting this diversity with knowledge management and without sanctioned methods for applying and testing these perspectives in the decision-making process. I treat cognitive diversity as a key catalyst for a healthy data ecosystem.
In “Teams Solve Problems Faster When They’re More Cognitively Diverse“, Harvard Business Review authors Alison Reynolds and David Lewis caution that “we cannot easily detect cognitive diversity from the outside. It cannot be predicted or easily orchestrated. The very fact that it is an internal difference requires us to work hard to surface it and harness the benefits.” When applied to data ecosystems, these observations imply that a powerful confirmation of cognitive diversity may be in the data products delivered by data practitioners. The internal differences get expressed in the external data products.
However, note that cognitive diversity may exist in an organization and yet be suppressed by organizational culture or design. Reynolds and Lewis find evidence suggesting that “we need to encourage people to reveal and deploy their different modes of thinking. We need to make it safe to try things multiple ways.” In other words, if an organization does not see cognitive diversity, especially after trying hard to hire it into existence, then there could be something dysfunctional in the Insights Supply Chain that obstructs the ability to generate value from cognitive diversity.
The Framework
The above diagram explains my conceptual approach and positioning for data ecosystems. By integrating cognitive diversity with data tools, organizations can craft more effective decision-making. Specifically…
- Knowledge Management acts as an engine driving this process, ensuring the selection, curation, and accessibility of the most relevant and insightful inputs. Knowledge management includes organizing, sharing, and analyzing information and data within the organization to support decision-making.
- Artificial Intelligence (AI) is the “hyperscaler” of this framework. AI analyzes vast amounts of data, identifies patterns, and suggests innovative alternatives based on the prompts of data practitioner in service of decision-making. Tasks like data extraction, data cleaning, feature engineering, and data modeling become faster, more powerful, and more extensible. AI can even translate across functions and domains and thus contribute to organizational cohesion.
- Data Democratization is many ways both an input and an outcome. Democratization ensures that data and insights are accessible to everyone within the organization, not just to those in specialized roles. This process encourages collaboration and facilitates the contributions of cognitive diversity into decision-making. The extensibility of AI supports data democratization. Knowledge management sustains data democratization.
The Future
In some ways, this framework is an over-simplification. Each component represents a confluence of deep and reach disciplines. The good news is that this tapestry means a lot of people have a lot to offer in improving the use of data in organizational decision-making. This framework also provides fertile ground for me to explore how the Insights Supply Chain can further evolve to harness a comprehensive spectrum of human thought processes and intellect. The rapidly expanding and improving data tools of today will unlock even greater potential by integrating diverse cognitive approaches, allowing organizations to leverage the full breadth of human creativity and analytical power in their decision-making processes. This on-going evolution promises not only more effective outcomes but also a more inclusive and dynamic environment where both learning and implementation are stretched to their fullest potentials.
In future posts, I will take a deeper look at the role of knowledge management, artificial intelligence, and data democratization in this framework as well as explore the meaning and significance of cognitive diversity in a data ecosystem.
