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Understanding Variation · Donald J. Wheeler · 1993
Wheeler values Process Behaviour Charts that illustrate data over time with control limits. This accounts for natural variation, offering a better understanding than single-point comparisons. This easy-to-understand book helps you understand what is signal and what is noise.
Key Highlights
Data are meaningless without their context. Presenting data without context effectively renders them useless. Monthly reports usually do not provide sufficient context.
Comparing data to specifications, goals, and targets doesn't provide a rational context for analysis and doesn't promote a consistent purpose. It encourages the perspective of either being "operating okay" or "in trouble," without considering the impact of variation on the data and treating every fluctuation as a signal.
Comparisons to average values are slightly better, but still don't account for data variation. Again, every fluctuation is treated as a signal.
Shewhart's control charts provide a superior data analysis approach, addressing these shortcomings by explicitly considering data variation. By distinguishing between predictable and unpredictable variations, the focus shifts from the results to the behavior of the system that produced them. This shift is a major step towards continual improvement.
If a system displays statistical control, it's already as consistent as possible. Searching for Special Causes is wasteful. Instead, efforts can be directed towards improvements and modifications.
If a system lacks statistical control, trying to improve or modify the process will be futile. The focus should be on identifying the Special Causes disrupting the system.
Failing to differentiate between these two actions is a significant source of confusion and wasted effort in businesses today.
All data contain noise, but some also contain signals. To detect a signal, the noise must first be filtered out. Among all the statistical techniques designed to distinguish signals from noise, Shewhart's control charts are the simplest. This simplicity contributes to their status as one of the most powerful analysis methods available today.
The goal of analysis is to gain insight. The best analysis is the simplest one that provides the required insight. By using control charts in combination with histograms, flow charts, cause and effect diagrams, Pareto charts, and running records, it is possible to extract insights from the data. These insights may remain hidden when using traditional analyses.
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Maps of Bounded Rationality · Daniel Kahneman · 2003
Herbert A. Simon (1955, 1979) had proposed much earlier that decision makers should be viewed as boundedly rational, and had offered a model in which utility maximization was replaced by satisficing. Our research attempted to obtain a map of bounded rationality, by exploring the systematic biases that separate the beliefs that people have and the choices they make from the optimal beliefs and choices assumed in rational-agent models. The rational-agent model was our starting point and the main source of our null hypotheses, but Tversky and I viewed our research primarily as a contribution to psychology, with a possible contribution to economics as a secondary benefit. We were drawn into the interdisciplinary conversation by economists who hoped that psychology could be a useful source of assumptions for economic theorizing, and indirectly a source of hypotheses for economic research.
Key Points:
Humans have two modes of thinking: "System 1" (fast, automatic, intuitive) and "System 2" (slower, effortful, controlled).
People often make judgments based on System 1, relying on heuristics and biases rather than careful reasoning.
Heuristics like representativeness, availability, and anchoring can lead to predictable errors and biases in judgment.
Framing effects powerfully shape decisions based on how options are presented.
People are loss averse, weighting potential losses more heavily than equivalent gains.
Decisions are reference dependent, evaluated as changes relative to a reference point rather than final states.
People poorly predict their future utility and preferences, exhibiting biases like duration neglect.
Prospect theory better describes actual decision making under risk than expected utility theory.
Economists should incorporate these psychological insights to improve models of behaviour and policy prescriptions.
Thoughts
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Book Highlights
People often start out with tests that are too complex, too long, or too product-centric. Giff Constable & Frank Rimalovski · Testing With Humans
The customer-driven movement has failed to produce the desired results because asking the customer what he wants solicits not only the wrong inputs, but inputs that inadvertently cause the failures that managers are fervently trying to avoid. What Customers Want · Anthony Ulwick
Even if ML can’t solve your problem, it might be possible to break your problem into smaller components, and use ML to solve some of them. For example, if you can’t build a chatbot to answer all your customers’ queries, it might be possible to build an ML model to predict whether a query matches one of the frequently asked questions. If yes, direct the customer to the answer. If not, direct them to customer service.Chip Huyen · Designing Machine Learning Systems
Connected companies are quicker companies. To grab a competitive advantage, both leaders and contributors need to link up horizontally, breaking through barriers. John Doerr · Measure What Matters
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Best of X
Good product requirements are testable or observable. This means avoiding vague adjectives and focusing on quantifiable elements such as numbers, mocks and flow diagrams. Irene Yu
5 Tests of an effective customer segmentation:
- Reachable: there is a reliable way to identify & reach potential customers within each segment (totally fine if some creative execution is necessary)
- Consistent needs: the core needs of customers in each specific segment are fairly consistent (with non-trivial variance across segments)
- Product-specific: the segmentation approach is specific to your product or product category (other products are unlikely use the very same approach)
- Prioritizable: you can reasonably set different priorities for segments, with clarity on what segments are your highest priority and not a priority
- Winnable: you can create a differentiated value proposition for your highest priority segments such that you can plausibly win (and winning is worthwhile)
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What I’m Listening to.
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