The DAO of Decision Intelligence

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Decision Intelligence Across Space and Time

In Chinese philosophy, DAO (“the way”) is the universe’s natural order. Daoists believe they can avoid violence, suffering, and struggle by harmonizing with the natural order.

Decisions shape the course of individual lives and thus have always been important. However, the complexities of the modern world have heightened the importance of decisions and decision-making processes.

There is a consensus that the emerging field of decision intelligence can play a crucial role in helping us navigate the complex world across space and time. But do we agree on the definition of decision intelligence? The answer is no.

In the practice of decision intelligence, it’s common to encounter ambiguity due to professionals prioritizing different aspects based on their expertise and interests. The ultimate goal of decision intelligence is to enhance decision-making through a combination of quantitative and qualitative approaches. However, the decision intelligence system implementations can vary widely regarding objectives, methodologies, and frameworks. While this diversity is good, it can create significant confusion within the field.

Imagine if there were a DAO dedicated to Decision Intelligence, guiding and assisting with the various implementations. It would be a tremendous asset to the field and help alleviate the confusion caused by varying objectives, methodologies, and frameworks.

Multiple Viewpoints of Decision Intelligence

Before delving into the specifics of the DAO of Decision Intelligence, consider various viewpoints currently prevalent in the field.

Information Viewpoint: Extract Hidden Wisdom in Data

The information perspective of Decision Intelligence is the practice of transforming data into valuable insights. This perspective rests on the belief that analyzing past data can help us make more informed decisions in the future, as history tends to repeat itself.

Imagine having the power to predict the future. Well, with data science, you’re a step closer. From an array of complex algorithms to statistical models, data science helps understand trends, patterns, and relationships in data.

Process Viewpoint: Plan & Orchestrate Actions for Superior Decisions

The perspective of decision intelligence that focuses on the process sees decision-making as a series of actions or stages that lead to a final decision rather than a single event. Systematically structuring these events is crucial for making superior decisions.

This approach emphasizes the importance of identifying the following actions, allocating appropriate resources, and monitoring progress. Additionally, orchestrating these actions requires support for collaboration, negotiation, conflict resolution, transparency, user engagement improvement, and buy-in.

People Viewpoint: Focus on Human Factors

Even with advanced algorithms and data-driven insights, it is important to remember that human beings ultimately make the decisions. Neglecting the behavioral aspects of decision-makers, stakeholders, and implementors can lead to poor outcomes or catastrophic consequences.

To ensure realistic and successful decisions, finding the right balance between automation and human input and understanding how people think, interact, and make decisions, is critical.

Decision Viewpoint: Use Decision Models

This perspective prioritizes decision modeling over data analysis. Managerial science, also called decision science or operations research, emphasizes enhancing managerial decision-making through models and methods. This approach provides practical and systematic techniques for tackling intricate issues.

Within the realm of decision intelligence, managerial science offers tactics for maximizing decisions and workflows. It assists with mapping out the decision-making process, comprehending the consequences of various choices, and directing the decision-maker toward the most efficient results.

Learning Viewpoint: Continuously Learn and Adapt

From a learning perspective, the approach of decision intelligence acknowledges that the environment in which we make decisions is constantly changing. Accordingly, the focus is on improving the quality of decisions rather than striving for perfect one-shot decision-making. The process involves making iterative decisions that incorporate new information by design.

This approach enables decision intelligence systems to make better decisions by evaluating the outcomes of previous decisions and incorporating feedback to refine models, adjust strategies, and optimize future decisions.

The Magic of Decision Intelligence DAO

The multiple views on decision intelligence are all significant and interrelated. A decision intelligence system can only offer a unique, holistic approach to decision-making by combining these diverse perspectives.

Ultimately, decision intelligence should be about making the complex simple and the difficult manageable. Incorporating multiple viewpoints allows us to navigate the labyrinth of decision-making with more confidence and clarity.

Many existing decision intelligence systems tend to favor one perspective over others, often neglecting or providing minimal support for alternative viewpoints.

Bringing together all viewpoints in a decision intelligence system can be difficult, but it is certainly achievable. The purpose of the DAO of decision intelligence is to work towards this noble goal.

Lorien Pratt, in her book, “Link,” begins with the first critical insight:

“A decision — the thought process that leads to actions, which leads, in turn, to outcomes — is the right “building block” for solving many of the world’s most complex problems and integrating humans with technology.”

The idea behind DAO for Decision Intelligence emanates from the understanding that framing the decision-making problem as a choice, data mining or learning problem can be limiting. Instead, the DAO approach involves a three-step process:

1. Define aspirational outcomes (Outcome-Centricity).
2. Identify the actions that can lead to those outcomes (Action Orientation).
3. Make decisions to commit to those actions (Decision Models Driven).

The DAO acronym represents the Decision Action Outcome trio, but it’s essential to follow it in reverse order in the context of decision intelligence.

Outcome-Centricity

Outcome-centricity in decision intelligence focuses on achieving the best possible outcome in any given situation rather than simply following predetermined rules or procedures. It involves:

• Generating the space of possible outcomes without being unduly constrained by prior biases or limitations
• Understanding the actual utility of the outcome for all stakeholders
• Modeling the consequential risk associated with the outcomes
• Eliminating unrealistic or risky outcomes from further consideration

By prioritizing outcomes over processes, organizations can better adapt to changing circumstances and make decisions genuinely in their stakeholders’ best interest. This approach to decision-making is becoming increasingly important in today’s rapidly evolving business landscape, where agility and flexibility are vital to remaining competitive.

Action Orientation

Action orientation in decision intelligence focuses on taking action to achieve the desired outcome rather than simply analyzing data and considering possible options. It involves a willingness to take calculated risks and make decisions quickly to adapt to changing circumstances and achieve the best possible result. Action orientation includes:

• Understanding the causal relationships among actions and outcomes in the face of uncertainty
• Developing a policy to guide actions from an adaptive network of actions
• Orchestrating the planning, execution, and monitoring of actions for time and resource constraints.

Organizations can more effectively implement their decisions and achieve goals by prioritizing action over analysis. This approach is fundamental in today’s fast-paced business environment, where speed and agility are essential to staying ahead of the competition.

Decision Model Driven

In decision intelligence, being model-driven means using models and frameworks to guide decision-making processes and decision-implementation actions. Being decision model-driven includes:

• Developing an ontology of decision-making concepts for the domain in question
• Identifying the causal dependencies among the decision-action-outcome trio
• Modeling of learning approaches, value of information, risk analysis, uncertainty reduction, and analysis of decision alternatives.

By relying on various decision models, organizations can make more informed and effective decisions based on data and evidence rather than intuition or guesswork. This approach can lead to better outcomes and success in achieving organizational goals.

Conclusion: The Synergy of Views

Decision intelligence is an interdisciplinary field that unites elements from data science, management science, and social science. Its power emerges from this blend of disciplines, offering a comprehensive approach to the decision-making process. Achieving this unification is a sophisticated undertaking. The DAO of Decision Intelligence assists in comprehending and merging these diverse aspects, enabling us to exploit the full potential of decision intelligence.