From Data to Decisions: My Journey into Decision Intelligence
My journey with Decision Intelligence started with my time at IBM. At the beginning of my industry career, I was struggling to find how as a data scientist I could convince key stakeholders to invest in machine learning and data science projects that were more than just exploratory data analysis. A core memory of an early time of mine at IBM was a time when an IT Operations machine learning proposal that I made was shut down because “we don’t want to reinvent the wheel”, where I would be told to instead look at dashboarding solutions that didn’t involve machine learning. The key thing is finding business problems that ultimately would drive the business forward, but sometimes the path to what the business needs is not as straightforward.
Enter the book Link by Dr. Lorien Pratt (who I just got to meet recently!) which helped change my world on this, and in more ways than one. Dr. Lorien Pratt, the inventor of the discipline of Decision Intelligence, is also the inventor of the methodology of Transfer Learning, for which without, we would not have Transformer architectures or Generative AI today.
The core part of the book “Link” is a diagram called a causal decision diagram (or CDD for short), which is a form of a causal graph where the nodes of the graph are labeled as a kind of decision element. These elements inform a decision maker on aspects of a decision. These decision elements are:
Lever: Captures a set of choices that a decision-maker can control.
Outcome: An element by which the success of the decision can be measured.
External: A factor that influences outcomes, but the decision maker has no control over them.
Intermediate: An element that is a link in the chain between Levers and Outcomes.
A Causal Decision Diagram.
A workshop can be done to create these diagrams. The key thing about these diagrams from the perspective of machine learning is that the links between elements can be any arbitrary function - and this is where machine learning can be involved. Thus, since a machine learning algorithm is involved somewhere in the link between the lever and the outcome, we have a clear relationship between a machine learning algorithm and the outcomes associated with it.
Lorien Pratt followed up with the book Decision Intelligence Handbook which formalizes the methods in Link into a series of formalized processes that can be easily packaged into services consultants like myself can provide.
The DI processes are:
Decision Objective Statement: A statement that is a trigger to create a DI initiative, which serves as an anchor for later decision-making and keeps the team focused on the original request from the decision customer.
Decision Framing: Documents the frame of the decision: Validate that this decision is appropriate for DI, and frame any constraints, boundaries, and/or requirements that come from outside of the decision team.
Decision Design: Elicit information from the decision team and stakeholders in one or more joint and/or offline exercises to create a first-draft Causal Decision Diagram (CDD) - this is the workshop of the previous book “Link”.
Decision Asset Investigation: Identify and document existing and missing data, information, human knowledge, and other technology that inform decision elements on the CDD, in preparation for integrating these assets into a computerized decision model.
Decision Simulation: Plan and build a software system to help the decision team understand the cause-and-effect behavioral dynamics of the CDD, determine how actions and externals lead to outcomes, and select the best action(s) to take.
Decision Assessment: Assess a decision diagram and/or simulation to decide what to do next. Is it time to implement the recommended actions, or do we need to do more decision modeling?
Decision Monitoring: Monitor and modify the decision as it plays out over time. The amount of time depends on how long it takes to complete the decision action(s) and measure the outcome(s). This can be anywhere from a few days to a few years.
Decision Artifacts Retention: Store each decision artifact in the appropriate repository.
Decision Retrospective: After a decision has been made and its outcomes have played out, assess the decision processes and artifacts and improve them for future use. Capture your findings in a Decision Quality Report.
Not all decisions need all of these processes, of course (That is part of the point of the Decision Framing process - to validate this) and the value of using DI methodology is relative to the opportunity cost of not making an informed decision - more complicated, high stakes, or operational decisions can use more of the methodology. But much like CRISP-DM in Data Science, this methodology allows the leveraging of causal AI to inform better decision-making.
If you're intrigued by the transformative power of Decision Intelligence and curious about how these processes can be tailored to your unique business challenges, I invite you to reach out. Together, we can explore the potential of Decision Intelligence through real use cases, aligning cutting-edge methodologies with your strategic goals. Stay tuned for future posts where I'll share case study examples that highlight the impact and possibilities. Let's embark on this journey to smarter decision-making together. Contact me to start the conversation.
