Decision Intelligence: A Primer — Part 2

The Decision Intelligence NAVIGATOR: Implications for Executives

The Decision Intelligence Navigator has been developed (and is regularly improved) to help students and executives better navigating in uncertain and ambiguous environments. Its four elements serve as guiding elements to make better decisions.

In the following, I provide some practical advice about how to transform the conceptual logic of the Decision Intelligence Navigator into real-world activities:

  • #1 DECISION CONTEXT
    Understand the decisions to make and the questions to be answered.
  • #2 FRAMEWORK PROFICIENCY
    Select the most appropriate frameworks & models that help to answer the open questions
  • #3 INTELLIGENCE ACCESS
    For each dimension of any relevant framework & model you apply, define what kind of insights you need and which intelligence source is most suitable to fill each dimension with the necessary input.
  • #4 DECISION PROFICIENCY
    If you were successful to create a FIT (i.e. Decision Intelligence) between the questions to be answered (#1) and the frameworks (#2) & intelligence (#3) you selected and gathered, you have created INSIGHTS (i.e. decision-relevant knowledge). Now you need to transform these insights into actual decisions and actions without bias and personal opinions interfering with your mindset.
Decision Intelligence Navigator (Version 3.0). Source: Roger Moser

The Decision Context element of the Decision Intelligence Navigator requires from executives that they clearly define the circumstances of the specific decision-making challenge(s) that they face. Many scholars and consultants refer in this context to the VUCA acronym (Volatile, Uncertain, Complex, Ambiguous) to describe a challenging environment. However, in my experience, this structure is not very helpful to locate any specific decision-making challenge and draw conclusions for the next steps; e.g. in terms of framework selection and/or intelligence gathering.

That’s why I have developed an alternative structure for executives to better understand what kind of framework and intelligence gathering challenges they face.

From VUCA to the INSIGHT Level and CHANGE Level Matrix. Source: Roger Moser

To further support these two matrices (INSIGHT Level matrix & CHANGE Level matrix) I have conducted two (applied) research projects in the recent past. The results of the most recent research project jointly with Srinath Rengarajan and Dr. Gopalakrishnan Narayanamurthy we have published in early 2021 in Technological Forecasting & Social Change. A slightly older study already provides a good picture of the logic of the two matrices when trying to better understand the specific DECISION CONTEXT an executive is in, and especially which strategy frameworks & models to use. The exploratory study indicates that specific strategy (analysis/innovation) models fit well with selected DECISION CONTEXTS defined by the levels of uncertainty & ambiguity as well as the levels of industry dynamics & industry convergence.

Strategy (analysis/innovation) models show different levels of suitability for selected DECISION CONTEXTS. Source: Roger Moser

The key challenge for executives during the DECISION CONTEXT assessment is still the clear definition of the decision(s) that need to be made and the respective questions that need to be answered to make the decisions. There are plenty of books from all sorts of backgrounds and if executives ask me which book(s) I recommend, I refer to Albert Einstein and try to highlight that making time to think is much more important than reading yet another book about decision-making practices and rules.

Selection of books on decision-making practices — however, what really matters is time to think.

These books can offer some valuable but mostly very generic advice about what to do — or even just what kind of questions to ask yourself. Just like this checklist from the book called The Phoenix Checklist. It’s a good example of how its different checklists actually mirror the key elements of the Decision Intelligence Navigator — just like defining the decision-making challenge and understanding what you need to know as an essential part of the DECISION CONTEXT.

Checklists are great but are often generic but don’t replace your own thinking & creativity in a specific decision-making challenge.

I would like to close the DECISION CONTEXT section with a quote from one of the wisest management thinkers of all times — Peter F. Drucker:

“The most common source of mistakes in management decisions is the emphasis on finding the right answer rather than the right question.”

Before I continue with the second element of the Decision Intelligence Navigator — Framework Proficiency (#2) — I would like to provide a more detailed academic background and empirical support for my current definition of Decision Intelligence as the FIT between the questions to be answered (#1) and the most suitable frameworks & models (#2) and the required intelligence access (#3).

The theoretical underpinning of the Decision Intelligence Navigator is rooted in the Organizational Information Processing Theory (OIPT) which was primarily established in the early 1970s but is still regularly used in different research contexts. The core idea of the OIPT is that companies need to create a FIT between the ‘VUCA’ environment they face (i.e. their intelligence requirements; i.e. the ‘open questions’ to make specific decisions) and the intelligence gathering and processing capacities of the company and its employees (i.e. how a company and its employees are selecting their mental models, how they gather and analyze intelligence and make it available; i.e. how to optimize their ‘framework proficiency’, ‘intelligence access and ‘decision proficiency’). If companies achieve such a FIT, they achieve maximum effectiveness. In a study with my colleagues Christian Kuklinski and Mohit Srinastava published in the Journal of Business Research in 2017, we were able to empirically confirm that a FIT between the Intelligence Requirements of a company and its Intelligence Processing Capacities results in significantly improved strategic decision-making and that there is predictive validity of this improved strategic decision-making to increase company performance.

Based on this understanding of FIT, I believe that Decision Intelligence can (as one option) be understood in such a way because it requires executives and other decision-makers to clearly define their DECISION CONTEXT (i.e. intelligence requirements) and try to achieve an optimal match with the best available frameworks & models from any kind of science (FRAMEWORK PROFICIENCY) and the best possible access to accurate data, information, knowledge or insights (INTELLIGENCE ACCESS). Moreover, the results from this FIT (i.e. the insights) need to be transferred into decisions and actions avoiding the interference of biases and (too much) personal opinions (DECISION PROFICIENCY).

As we work on the FIT between the general decision-making situation through the Insight & Change Matrices and the open questions identified to make a specific decision as part of the DECISION CONTEXT (#1), the next step is to identify the most suitable frameworks & models to optimize one’s FRAMEWORK PROFICIENCY.

So, how do you improve your FRAMEWORK PROFICIENCY?

Well, almost everything new that you learn (i.e. build up expertise) or experience can be valuable but the most promising approach might be to look at people who have really excelled at this — people like Charlie Munger. Charlie who?

If you haven’t heard of Charlie Munger then you certainly have not yet reached Framework Proficiency. What’s important about Charlie Munger and other great thinkers such as Alain de Botton is what they say about mental models or how the news distract us from seeing and understanding what is really important (i.e. patterns, mental models). My preferred source to learn more about mental models is Farnam Street — a Blog, Podcast, Article and even Online Training Provider focused on mental models and better decision-making in general.

Mental model definition by Farnam Street & key message of Alain de Botton’s The New: A User’s Manual book

A quick introduction to the logic and importance of mental models is provided in the following video by Farnam Street:

Well invested 90 seconds to learn more about mental models: Source: Farnam Street

Another infographic that I have recently found and which I have used to list my suggestions on how to improve your FRAMEWORK PROFICIENCY is shown below:

How to improve your Framework Proficiency? Source: Roger Moser

As a scholar, board member & investor and executive coach I have also created my own collection of (management) frameworks (not broad mental models) that I regularly use to assess the business model of potentially interesting companies (i.e. start-ups) or for my executive clients. I call it my “Strategic Analysis, Strategy Creation & Realization Canvas Collection”. It contains not only the most prominent management frameworks but is inspired by other science areas such as psychology or selected engineering sciences (i.e. technologies) to identify mechanisms that help companies to create competitive advantages. One of the best collections of “business model patterns” comes from my colleague Prof. Dr. Oliver Gassmann from the University of St.Gallen, Switzerland.

At the end of the day, improving your FRAMEWORK PROFICIENCY means that you look way beyond your usual horizon but don’t get distracted by news or the latest trends pushed by consultants and/or academics but you look for more stable patterns or mechanisms that might help you better understand or even overcome decision-making challenges in your (daily) line of work.

Once you have identified the most suitable mental models and the respective frameworks you basically already know what kind of intelligence (in the form of data, information or knowledge) you need because each dimension of the selected frameworks needs now to be filled with input. Whether this input is small data, big data, right data, deep data etc. is, at the conceptual level that we apply here, of secondary importance. This also applies to whether you need machine learning, deep learning or any other sort of artificial intelligence in the form of algorithms or whether single experts or the ‘wisdom of the crowd’ needs to analyze the gathered intelligence and draw conclusions.

Visual representation of all the data and analysis types and combinations that you need to have access to.

What is more important and also significantly differentiates Decision Intelligence from concepts such as Business Intelligence, Competitive Intelligence and Market Intelligence or Contextual Intelligence is the focus of Decision Intelligence on the DECISION while the aforementioned concepts focus on how to gather and use INTELLIGENCE (i.e. data, information, knowledge) from or about a company’s business operations, competitive and market environment or the ability to understand to which degree you can transfer specific (management) frameworks and intelligence from one (business) context to another.

This is why the INTELLIGENCE ACCESS element of the Decision Intelligence Navigator is as much about data & analytics as it is about the decision-making processes and structures of an organization. As a result, executives who apply our Decision Intelligence understanding have to spend a lot of time reflecting on how and where their organizations make decisions and how well the decision-makers have access to the necessary intelligence.

Interestingly, many more recent organizational innovations demonstrate a (paradigm) shift from a mechanistic or even advanced organic view of organizational structures and coordination mechanisms to a much more self-organized or autonomous way to coordinate activities which is, for example in the case of the viable systems model, rooted in mental models of biology and mathematics (among others). According to my analysis, you find a lot of commonalities between the viable systems model (from the 1970s) and very recent approaches such as Holacracy, Haier’s Rendanheyi or Alibaba’s Self-Tuning Enterprise which try to provide models to allow organizations and their members to decide and act in a more self-organizing, autonomous manner in order to better deal with an increasingly dynamic and complex environment.

While there is no obvious solution to this challenge, new intelligence gathering and analysis technologies from IoT to deep learning allow for the realization of concepts in reality that where not possible decades ago. Executives with Decision Intelligence, therefore, focus as much on organizational innovations as they do on data and analytics innovations.

If you have now created a FIT between the open QUESTIONS to be answered to make a decision and the most suitable FRAMEWORKS and ACCESS to INTELLIGENCE you have provided yourself or somebody else with INSIGHTS; the knowledge you or others need to have to make the best possible decision. However, turning INSIGHTS into decisions and actions is still a tricky thing and you DECISION PROFICIENCY to not let you lead astray (e.g. in the form of biases).

If you follow the path from Data to Action as I have written in an earlier article on LinkedIn, the FIT between the open questions you have and the right frameworks and access to intelligence results in INSIGHTS; i.e. decision-relevant knowledge.

However, in order to get from INSIGHTS to actions you need to consider and combine different insights without creating ‘blind spots’ or the biased weighting of selected aspects. That’s what we call DECISION PROFICIENCY in the Decision Intelligence Navigator.

From data to action: An overview of different ‘filters’ and ‘outputs’ along the way.

You might see some similarity in terms of wording with FRAMEWORK PROFICIENCY and wonder why? In fact, there is a reason why our Decision Intelligence Navigator contains two ‘Proficiency’ elements. I follow Charlie Munger’s simple structure of mental models:

“Mental Models fall into a few categories, two of which are: (1) ones that help us simulate time (and predict the future) and better understand how the world works (e.g. understanding a useful idea autocatalysis), and (2) ones that help us better understand how our mental processes lead us astray (e.g., availability bias).”

Framework Proficiency relates to the first kind of mental models that help us to better understand how the world works and Decision Proficiency relates to the second category of mental models that help us to better understand how our mental processes lead us astray; i.e. avoiding any sort of biases and integrating all kinds of legitimate objectives.

If you want to learn more about different kinds of biases, I recommend the following overview of cognitive biases in decision-making:

Great overview of 180+ cognitive biases on the path from data to insights and then to decisions and actions.

When I explain cognitive biases to executives and students, I normally use the example of Survivorship Bias:

Survivorship Bias: Drawing conclusions from an incomplete set of data, because that data has ‘survived’ some selection criteria.

The (shortened) story goes as follows: Survivorship Bias played an important role during World War II, when Abraham Wald from Columbia University made use of the survivorship bias concept [you could also call it a mental model] when supporting the US military who tried to analyze how to minimize bomber losses due to enemy fire. When looking at the bombers that had returned (see picture above) the US military wanted to add additional armour to the most-hit areas indicated by the red dots. However, Wald argued that the US military had only looked at those aircraft that had actually returned home and had survived — meaning that the bombers could actually take a lot of damage in the red-dotted areas but most likely not in the remaining white ones. Finally, the US military got convinced by Wald and reinforced those areas of the bombers which were still white based on the later confirmed assumption that bombers that got hit in those areas would most likely crash.

The essence of this short Survivorship Bias story is that we often don’t see the failure cases. In business, in life, and in war. And in many cases, it’s the failures that hold the most important lessons learned. That’s also why I am very sceptical when we only look at the success cases in business such as Apple, Amazon or Facebook because we then most likely only look at those ‘aircraft’ with the many red dots and a few white spots — but we don’t really know (or even think about) whether the white or the red spots really matter for survival.

In conclusion, the Decision Intelligence Navigator is a real NAVIGATOR — not a tool or framework that you can directly apply to improve your decision-making. That’s why the Decision Intelligence Navigator can help you to improve your decision-making skills along its four major elements:

  • Defining the DECISION CONTEXT and identifying the QUESTIONS you need to get answered.
  • Matching the open questions with the most suitable FRAMEWORKS based on an in-depth understanding of mental models across disciplines.
  • Understanding that INTELLIGENCE ACCESS does not only mean focusing on big data and advanced analytics but all sorts of intelligence and especially how you structure your organization and manage processes to optimize effective decision-making.
  • Avoiding cognitive biases and increasing cognitive diversity to transform INSIGHTS into decisions and action.

I am looking forward to presenting you soon our Decision Intelligence Navigator 4.0!

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Faculty, Board Member, Investor — Entrepreneurial Scholar

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Roger Moser

Roger Moser

Faculty, Board Member, Investor — Entrepreneurial Scholar

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