The book Decisions over Decimals fulfills three goals, which are to enable the reader to:-
1. Effectively frame the problem for stakeholders
2. Synthesize intelligence from incomplete information
3. Deliver decisions that stick.
It’s a book which positions itself at the nexus of statistical analysis, business decision making and psychology. It does so by the getting into Goldilocks principle of having just right of both worlds of intuition and quantitative data. The premise of the book is going beyond big data without ignoring it.
The book introduced the theory and practices of QI (Quantitative Intuition).
What is Quantitative Intuition? The ability to make decisions with incomplete information via precision questioning, contextual analysis, and synthesis to see the situation as a whole.
IQ premise is in the balance used of information, human judgment, experience and intuition in decision making.
This increases our ability to make confident decisions under incomplete information, this is crucial as most real world problems normally comes with incomplete information.
The book breaks down the decision making process into 5 steps
- Defining the Problem
- Data discovery
- data analysis
- Insights or Delivery
- Implementation
1, 4, 5 are highly intuitive (I) in nature requiring leadership and management skills while 2& 3 are quantitative (Q) requiring analytical and data skills.
The book suggests that one does not only follow their gut or rely only on information, but using both helps to avoid bias and reach better decisions. Since both intuition and data analysis suffers from bias.
The authors go on to explain data situation bias which ignores data like heuristics, overconfidence, optimism bias, impacts intuitive decision makers, giving example of 700 juicer. Also availability bias have similar impact on those don’t consult data.
Working purely on data is impacted by selective attention, anchoring and confirmation biases.
Chapter 1: Asking powerful question
“The important thing is not to sport questing“ We are born with questing ability and at age 5 we do master all the 5W and H questions. Our questing ability starts to pivot at School and continues at work where the emphasize shits and we are expected to have answers.
The first pillar of Quantitative Intuition is precision questioning, of which goals is become a fierce interrogators.
Precision questioning and precision answering (PA) builds on a Socratic method which was developed 2400 years ago. It uses answer to a question to ask a follow-up question until the responder answers I don’t know.
There are four types of questions, factual, converged, divergent and evaluative.
To build an inquisitive culture:-
- Start with an open-ended question.
2. Respond, don’t react. Embrace silence.
3. Ask a stream of questions
The smartest person in the room is not the one with an answer, but the person asking the best questions.
■ Build an inquisitive team by encouraging asking a series of open- ended questions.
■ Respond rather than react to questions.
■ Incorporate the following questions to advance the conversation: “Can you help me understand?”, “Have you considered. . .?”, and “What surprised you?”
■ Develop comfort with the four types of questions factual, convergent, divergent, and evaluative. Practice asking them on a regular basis.
CHAPTER 2: FRAMING THE PROBLEM
Improving the quality of your decision is directly proportional to the effort invested in framing the problem.
■ Applying the IWIK process is a four-step approach—ask, brain- storm, capture, and deliberate.
■ Capturing a series of strategic questions quickly enables you to clarify priorities and identify knowledge gaps pointing to the critical information needed while exposing potential biases.
■ IWIKs leads to finding the essential questions that you wish to have answered, a shortlist of success or outcome metrics that matter, along with any unknowns that you are curious to explore related to the issue at hand.
■ Assessing IWIKs is easily done using the Knowledge Matrix to identify the salient questions
Chapter 3: Working backward to move forward
Don’t expect the data to provide both the questions and the answers. It is your responsibility to home in on the essential question and then combine data with intuition to attempt to identify the answers.
■ Start with thinking about the decision at hand and work back- ward to scope out the needed data and analyses.
■ When faced with a vague problem definition, use the five whys approach to identify the essential question or the impend- ing decision. ■ Understand that starting from a decision involves upfront time and risk, but it maximizes the chances for purposeful and effective data-driven journeys.
4. Learning to Become a Fierce Data Interrogator
Remember: Your data is only as good as the questions you ask of it.
■ Ask yourself whether you have the right data and the right metrics to make the decision: What IWIKs cannot be answered with the data collected?
■ Be aware of averages. Ask yourself what are the splits of the data that are relevant for your analyses.
■ Ask yourself about missing data: What data points are you not seeing? Are they similar to the data you are seeing?
■ Plot the data to get intuition: This should be your first step in data analysis. Did the plot reveal any issues? Evaluate data reliability by asking a series of questions: What data was collected? How are metrics calculated? When and where was the data collected? Are the comparisons reasonable?
■ Put the data in the context by looking at it in absolute, over time, and relative to competitors and comparables.
■ Pressure test the analysis by diving only into figures that are both uncertain and can have an impact on the decision. Quickly evaluate them by looking at 0, 1, and infinity. Does this reveal any issue with the analysis or the model?
5. Developing Intuition for Numbers
■Begin by determining the level of accuracy needed for the decision at hand. For many decisions, rough approximations are all you need.
■ When evaluating a number presented to you, before jumping into complicated analysis, start by getting a rough estimate for whether the figure is in the ballpark.
■ Use back-of-the-envelope calculations to save time and under- stand the problem.
Follow Fermi’s approach to obtain rough estimates:
■ Break down the problem into small subproblems with factors that are easier to estimate.
■ Start with facts that you may know.
■ For figures you don’t know, remember the Goldilocks rule: too small, too big, about right.
■ Assess possible ranges or comparables.
■ Don’t worry about small details; you’re looking for rough estimates.
■ Use worst-case scenario bounds but be honest with your estimates.
6.From Analysis to Synthesis
■ Synthesize, do not summarize.
■ Make your bottom line the top line. Use the Pyramid Principle, synthesizing bottom up from insights to synthesis and communicating top down starting from the synthesis and sup- porting it with the necessary analyses.
■ To encourage a synthesis, create a safe environment that encourages making judgments and taking risks.
■ Create a gallery walk or post the analysis prior to the meeting to encourage a synthesis.
■ Make every meeting actionable by limiting the time allocated to discussing what is in the data and analysis and moving toward what it means and what we are going to do about it.
7.Decision Moment
■Begin with agreement from stakeholders on the objective, time- line, and who must participate in a decision.
■ Understand the elements of time, risk, and trust for both the decision to be made and for the organizational impact.
■ Consider whether the decision is reversible or not, and if it is not reversible, can it become reversible.
■ If dealing with high levels of ambiguity and uncertainty, docu ment the plan to close those gaps via investigation of adjacent domains to find corollary insights.
■ Focus on being vaguely right rather than precisely wrong. Get the best data you can even if it is not perfect. With or without perfect data a decision (and a good one) needs to be made.
■ Involve large teams with heterogeneous expertise
8. Delivering the Decision
Success in driving a decision can be improved with focus on the stakeholder audience and focus on the essential problem to be solved. An effective method is to build a presentation brief for planning purposes:
■ Know your audience. Identify project stakeholders, their expectations, and their relationship to each other. Recognize the difference between the true stakeholders and strong influencers.
■ Consider how your analysis and insights will be received. Ensure the right level of validated information and memorable details are included to highlight the value and impact of the recommendation.
■ Understand immediate and long-term issues and sensitivities. What is the sentiment of the stakeholders and has that changed? Why is this a priority now?
■ Deliver a coherent, compelling story with synthesized information that brings forward new insights rather than simply summarizing data. Use data wisely and edit ruthlessly. Don’t bury the lead.
■ Make your recommendation actionable and clear in an explicit ask that leads to a specific decision with supported outcomes.
9.Chasing the Decision
■ Remember, Questions are king.
■ Invest time framing the problem and defining success.
■ Saying “yes” to one choice, means saying “no” to something else
■ Receiving fast “no” is better than a slow “yes.”
■ Realize no decision is a decision.
10. Creating a Quantitative Intuition™ Culture
■The current gap in the workforce is less about people with deep analytical skills, and more about leaders who can lead them and make better decisions with analytics.
■ In hiring individuals, look for the QI skills along QI’s three pillars: precision questioning, contextualizing data, and synthesizing.
■ In interviews, focus on people’s ability to move from the “what?” to the “so what?” and “now what?”
■ Build a team composed of the four roles: data scientists, data engineers, data translators, and data artists.
■ Invest time and energy in visualization. Hire data artists.
■ Data translators should make up the majority of your team.
11. The Future of Data-Driven Decision-Making
With machine automation individuals can find more time to discuss what matters more at a human level. The business leader’s main role will be to balance deep quantitative understanding with an intuitive mindset.
The rapid pace of innovation may increase with the removal of constraints given that system
can address the fundamentals of decision processes. The vast effort, resources, and creativity
applied today to simply manage untamed, siloed, and uncertain information flows could
be redirected to rapidly grownew business, evolve operating models, and launch new innovative products. Smart people grounded in QI with more time to think strategically
may be the path to solve the next wave of great challenges and put decisions over decimals.
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