31 October 2006

David Parmenter - Collected Quotes

"All good KPIs that I have come across, that have made a difference, had the CEO’s constant attention, with daily calls to the relevant staff. [...] A KPI should tell you about what action needs to take place. [...] A KPI is deep enough in the organization that it can be tied down to an individual. [...] A good KPI will affect most of the core CSFs and more than one BSC perspective. [...] A good KPI has a flow on effect." (David Parmenter, "Pareto’s 80/20 Rule for Corporate Accountants", 2007)

"If the KPIs you currently have are not creating change, throw them out because there is a good chance that they may be wrong. They are probably measures that were thrown together without the in-depth research and investigation KPIs truly deserve." (David Parmenter, "Pareto’s 80/20 Rule for Corporate Accountants", 2007)

"Many management reports are not a management tool; they are merely memorandums of information. As a management tool, management reports should encourage timely action in the right direction, by reporting on those activities the Board, management, and staff need to focus on. The old adage “what gets measured gets done” still holds true." (David Parmenter, "Pareto’s 80/20 Rule for Corporate Accountants", 2007)

"Reporting to the Board is a classic 'catch-22' situation. Boards complain about getting too much information too late, and management complains that up to 20% of their time is tied up in the Board reporting process. Boards obviously need to ascertain whether management is steering the ship correctly and the state of the crew and customers before they can relax and 'strategize' about future initiatives. The process of assessing the current status of the organization from the most recent Board report is where the principal problem lies. Board reporting needs to occur more efficiently and effectively for both the Board and management." (David Parmenter, "Pareto’s 80/20 Rule for Corporate Accountants", 2007)

"Financial measures are a quantification of an activity that has taken place; we have simply placed a value on the activity. Thus, behind every financial measure is an activity. I call financial measures result indicators, a summary measure. It is the activity that you will want more or less of. It is the activity that drives the dollars, pounds, or yen. Thus financial measures cannot possibly be KPIs." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"'Getting it right the first time' is a rare achievement, and ascertaining the organization’s winning KPIs and associated reports is no exception. The performance measure framework and associated reporting is just like a piece of sculpture: you can be criticized on taste and content, but you can’t be wrong. The senior management team and KPI project team need to ensure that the project has a just-do-it culture, not one in which every step and measure is debated as part of an intellectual exercise." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"In order to get measures to drive performance, a reporting framework needs to be developed at all levels within the organization." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"Key performance indicators (KPIs) are those indicators that focus on the aspects of organizational performance that are the most critical for the current and future success of the organization." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"Key Performance Indicators (KPIs) in many organizations are a broken tool. The KPIs are often a random collection prepared with little expertise, signifying nothing. [...] KPIs should be measures that link daily activities to the organization’s critical success factors (CSFs), thus supporting an alignment of effort within the organization in the intended direction." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"Most organizational measures are very much past indicators measuring events of the last month or quarter. These indicators cannot be and never were KPIs." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"The traditional balanced-scorecard (BSC) approach uses performance measures to monitor the implementation of the strategic initiatives, and measures are typically cascaded down from a top-level organizational measure such as return on capital employed. This cascading of measures from one another will often lead to chaos, with hundreds of measures being monitored by staff in some form of BSC reporting application." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"We need indicators of overall performance that need only be reviewed on a monthly or bimonthly basis. These measures need to tell the story about whether the organization is being steered in the right direction at the right speed, whether the customers and staff are happy, and whether we are acting in a responsible way by being environmentally friendly. These measures are called key result indicators (KRIs)." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"Every day spent producing reports is a day less spent on analysis and projects." (David Parmenter)

Danyel Fisher - Collected Quotes

"A dimension is an attribute that groups, separates, or filters data items. A measure is an attribute that addresses the question of interest and that the analyst expects to vary across the dimensions. Both the measures and the dimensions might be attributes directly found in the dataset or derived attributes calculated from the existing data." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Creating effective visualizations is hard. Not because a dataset requires an exotic and bespoke visual representation - for many problems, standard statistical charts will suffice. And not because creating a visualization requires coding expertise in an unfamiliar programming language [...]. Rather, creating effective visualizations is difficult because the problems that are best addressed by visualization are often complex and ill-formed. The task of figuring out what attributes of a dataset are important is often conflated with figuring out what type of visualization to use. Picking a chart type to represent specific attributes in a dataset is comparatively easy. Deciding on which data attributes will help answer a question, however, is a complex, poorly defined, and user-driven process that can require several rounds of visualization and exploration to resolve." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Designing effective visualizations presents a paradox. On the one hand, visualizations are intended to help users learn about parts of their data that they don’t know about. On the other hand, the more we know about the users’ needs and the context of their data, the better we can design a visualization to serve them." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

 "Dimensionality reduction is a way of reducing a large number of different measures into a smaller set of metrics. The intent is that the reduced metrics are a simpler description of the complex space that retains most of the meaning." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"The general concept of refining questions into tasks appears across all of the sciences. In many fields, the process is called operationalization, and refers to the process of reducing a complex set of factors to a single metric. The field of visualization takes on that goal more broadly: rather than attempting to identify a single metric, the analyst instead tries to look more holistically across the data to get a usable, actionable answer. Arriving at that answer might involve exploring multiple attributes, and using a number of views that allow the ideas to come together. Thus, operationalization in the context of visualization is the process of identifying tasks to be performed over the dataset that are a reasonable approximation of the high-level question of interest." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Visualizations provide a direct and tangible representation of data. They allow people to confirm hypotheses and gain insights. When incorporated into the data analysis process early and often, visualizations can even fundamentally alter the questions that someone is asking." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

Zachary Karabell - Collected Quotes

"Culture is fuzzy, easy to caricature, amenable to oversimplifications, and often used as a catchall when all other explanations fail." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"Defining an indicator as lagging, coincident, or leading is connected to another vital notion: the business cycle. Indicators are lagging or leading based on where economists believe we are in the business cycle: whether we are heading into a recession or emerging from one." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"[…] economics is a profession grounded in the belief that 'the economy' is a machine and a closed system. The more clearly that machine is understood, the more its variables are precisely measured, the more we will be able to manage and steer it as we choose, avoiding the frenetic expansions and sharp contractions. With better indicators would come better policy, and with better policy, states would be less likely to fall into depression and risk collapse." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"[…] humans make mistakes when they try to count large numbers in complicated systems. They make even greater errors when they attempt - as they always do - to reduce complicated systems to simple numbers." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"In the absence of clear information - in the absence of reliable statistics - people did what they had always done: filtered available information through the lens of their worldview." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"Most people do not relate to or retain columns of numbers, however much those numbers reflect something that they care about deeply. Statistics can be cold and dull." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"Our needs going forward will be best served by how we make use of not just this data but all data. We live in an era of Big Data. The world has seen an explosion of information in the past decades, so much so that people and institutions now struggle to keep pace. In fact, one of the reasons for the attachment to the simplicity of our indicators may be an inverse reaction to the sheer and bewildering volume of information most of us are bombarded by on a daily basis. […] The lesson for a world of Big Data is that in an environment with excessive information, people may gravitate toward answers that simplify reality rather than embrace the sheer complexity of it." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"Statistics are meaningless unless they exist in some context. One reason why the indicators have become more central and potent over time is that the longer they have been kept, the easier it is to find useful patterns and points of reference." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"Statistics are what humans do with the data they assemble; they are constructs meant to make sense of information. But the raw material is itself equally valuable, and rarely do we make sufficient use of it." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"Statistics represents the fusion of mathematics with the collection and analysis of data." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"The concept that an economy (1) is characterized by regular cycles that (2) follow familiar patterns (3) illuminated by a series of statistics that (4) determine where we are in that cycle has become part and parcel of how we view the world." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"The indicators - through no particular fault of anyone in particular - have not kept up with the changing world. As these numbers have become more deeply embedded in our culture as guides to how we are doing, we rely on a few big averages that can never be accurate pictures of complicated systems for the very reason that they are too simple and that they are averages. And we have neither the will nor the resources to invent or refine our current indicators enough to integrate all of these changes." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"The search for better numbers, like the quest for new technologies to improve our lives, is certainly worthwhile. But the belief that a few simple numbers, a few basic averages, can capture the multifaceted nature of national and global economic systems is a myth. Rather than seeking new simple numbers to replace our old simple numbers, we need to tap into both the power of our information age and our ability to construct our own maps of the world to answer the questions we need answering." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"We don’t need new indicators that replace old simple numbers with new simple numbers. We need instead bespoke indicators, tailored to the specific needs and specific questions of governments, businesses, communities, and individuals." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"When statisticians, trained in math and probability theory, try to assess likely outcomes, they demand a plethora of data points. Even then, they recognize that unless it’s a very simple and controlled action such as flipping a coin, unforeseen variables can exert significant influence." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"Yet our understanding of the world is still framed by our leading indicators. Those indicators define the economy, and what they say becomes the answer to the simple question 'Are we doing well?'" (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

24 October 2006

Field Cady - Collected Quotes

"A common misconception is that data scientists don’t need visualizations. This attitude is not only inaccurate: it is very dangerous. Most machine learning algorithms are not inherently visual, but it is very easy to misinterpret their outputs if you look only at the numbers; there is no substitute for the human eye when it comes to making intuitive sense of things." (Field Cady, "The Data Science Handbook", 2017)

"AI failed (at least relative to the hype it had generated), and it’s partly out of embarrassment on behalf of their discipline that the term 'artificial intelligence' is rarely used in computer science circles (although it’s coming back into favor, just without the over-hyping). We are as far away from mimicking human intelligence as we have ever been, partly because the human brain is fantastically more complicated than a mere logic engine." (Field Cady, "The Data Science Handbook", 2017)

"At very small time scales, the motion of a particle is more like a random walk, as it gets jostled about by discrete collisions with water molecules. But virtually any random movement on small time scales will give rise to Brownian motion on large time scales, just so long as the motion is unbiased. This is because of the Central Limit Theorem, which tells us that the aggregate of many small, independent motions will be normally distributed." (Field Cady, "The Data Science Handbook", 2017)

"By far the greatest headache in machine learning is the problem of overfitting. This means that your results look great for the data you trained them on, but they don’t generalize to other data in the future. [...] The solution is to train on some of your data and assess performance on other data." (Field Cady, "The Data Science Handbook", 2017) 

"Extracting good features is the most important thing for getting your analysis to work. It is much more important than good machine learning classifiers, fancy statistical techniques, or elegant code. Especially if your data doesn’t come with readily available features (as is the case with web pages, images, etc.), how you reduce it to numbers will make the difference between success and failure." (Field Cady, "The Data Science Handbook", 2017)

"Feature extraction is also the most creative part of data science and the one most closely tied to domain expertise. Typically, a really good feature will correspond to some real‐world phenomenon. Data scientists should work closely with domain experts and understand what these phenomena mean and how to distill them into numbers." (Field Cady, "The Data Science Handbook", 2017)

"Outliers make it very hard to give an intuitive interpretation of the mean, but in fact, the situation is even worse than that. For a real‐world distribution, there always is a mean (strictly speaking, you can define distributions with no mean, but they’re not realistic), and when we take the average of our data points, we are trying to estimate that mean. But when there are massive outliers, just a single data point is likely to dominate the value of the mean and standard deviation, so much more data is required to even estimate the mean, let alone make sense of it." (Field Cady, "The Data Science Handbook", 2017)

"The first step is always to frame the problem: understand the business use case and craft a well‐defined analytics problem (or problems) out of it. This is followed by an extensive stage of grappling with the data and the real‐world things that it describes, so that we can extract meaningful features. Finally, these features are plugged into analytical tools that give us hard numerical results." (Field Cady, "The Data Science Handbook", 2017)

"Theoretically, the normal distribution is most famous because many distributions converge to it, if you sample from them enough times and average the results. This applies to the binomial distribution, Poisson distribution and pretty much any other distribution you’re likely to encounter (technically, any one for which the mean and standard deviation are finite)." (Field Cady, "The Data Science Handbook", 2017)

"With time series though, there is absolutely no substitute for plotting. The pertinent pattern might end up being a sharp spike followed by a gentle taper down. Or, maybe there are weird plateaus. There could be noisy spikes that have to be filtered out. A good way to look at it is this: means and standard deviations are based on the naïve assumption that data follows pretty bell curves, but there is no corresponding 'default' assumption for time series data (at least, not one that works well with any frequency), so you always have to look at the data to get a sense of what’s normal. [...] Along the lines of figuring out what patterns to expect, when you are exploring time series data, it is immensely useful to be able to zoom in and out." (Field Cady, "The Data Science Handbook", 2017)

21 October 2006

Donald J Wheeler - Collected Quotes

"Averages, ranges, and histograms all obscure the time-order for the data. If the time-order for the data shows some sort of definite pattern, then the obscuring of this pattern by the use of averages, ranges, or histograms can mislead the user. Since all data occur in time, virtually all data will have a time-order. In some cases this time-order is the essential context which must be preserved in the presentation." (Donald J Wheeler," Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Before you can improve any system you must listen to the voice of the system (the Voice of the Process). Then you must understand how the inputs affect the outputs of the system. Finally, you must be able to change the inputs (and possibly the system) in order to achieve the desired results. This will require sustained effort, constancy of purpose, and an environment where continual improvement is the operating philosophy." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Data are collected as a basis for action. Yet before anyone can use data as a basis for action the data have to be interpreted. The proper interpretation of data will require that the data be presented in context, and that the analysis technique used will filter out the noise."  (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Data are generally collected as a basis for action. However, unless potential signals are separated from probable noise, the actions taken may be totally inconsistent with the data. Thus, the proper use of data requires that you have simple and effective methods of analysis which will properly separate potential signals from probable noise." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"No comparison between two values can be global. A simple comparison between the current figure and some previous value and convey the behavior of any time series. […] While it is simple and easy to compare one number with another number, such comparisons are limited and weak. They are limited because of the amount of data used, and they are weak because both of the numbers are subject to the variation that is inevitably present in weak world data. Since both the current value and the earlier value are subject to this variation, it will always be difficult to determine just how much of the difference between the values is due to variation in the numbers, and how much, if any, of the difference is due to real changes in the process." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"No matter what the data, and no matter how the values are arranged and presented, you must always use some method of analysis to come up with an interpretation of the data.
While every data set contains noise, some data sets may contain signals. Therefore, before you can detect a signal within any given data set, you must first filter out the noise." (Donald J Wheeler," Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"We analyze numbers in order to know when a change has occurred in our processes or systems. We want to know about such changes in a timely manner so that we can respond appropriately. While this sounds rather straightforward, there is a complication - the numbers can change even when our process does not. So, in our analysis of numbers, we need to have a way to distinguish those changes in the numbers that represent changes in our process from those that are essentially noise." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"When a system is predictable, it is already performing as consistently as possible. Looking for assignable causes is a waste of time and effort. Instead, you can meaningfully work on making improvements and modifications to the process. When a system is unpredictable, it will be futile to try and improve or modify the process. Instead you must seek to identify the assignable causes which affect the system. The failure to distinguish between these two different courses of action is a major source of confusion and wasted effort in business today." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"When a process displays unpredictable behavior, you can most easily improve the process and process outcomes by identifying the assignable causes of unpredictable variation and removing their effects from your process." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"While all data contain noise, some data contain signals. Before you can detect a signal, you must filter out the noise." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Without meaningful data there can be no meaningful analysis. The interpretation of any data set must be based upon the context of those data. Unfortunately, much of the data reported to executives today are aggregated and summed over so many different operating units and processes that they cannot be said to have any context except a historical one - they were all collected during the same time period. While this may be rational with monetary figures, it can be devastating to other types of data." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"[…] you simply cannot make sense of any number without a contextual basis. Yet the traditional attempts to provide this contextual basis are often flawed in their execution. [...] Data have no meaning apart from their context. Data presented without a context are effectively rendered meaningless." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Data analysis is not generally thought of as being simple or easy, but it can be. The first step is to understand that the purpose of data analysis is to separate any signals that may be contained within the data from the noise in the data. Once you have filtered out the noise, anything left over will be your potential signals. The rest is just details." (Donald J Wheeler," Myths About Data Analysis", International Lean & Six Sigma Conference, 2012)

"Descriptive statistics are built on the assumption that we can use a single value to characterize a single property for a single universe. […] Probability theory is focused on what happens to samples drawn from a known universe. If the data happen to come from different sources, then there are multiple universes with different probability models. If you cannot answer the homogeneity question, then you will not know if you have one probability model or many. [...] Statistical inference assumes that you have a sample that is known to have come from one universe." (Donald J Wheeler, "Myths About Data Analysis", International Lean & Six Sigma Conference, 2012)

"In order to be effective a descriptive statistic has to make sense - it has to distill some essential characteristic of the data into a value that is both appropriate and understandable. […] the justification for computing any given statistic must come from the nature of the data themselves - it cannot come from the arithmetic, nor can it come from the statistic. If the data are a meaningless collection of values, then the summary statistics will also be meaningless - no arithmetic operation can magically create meaning out of nonsense. Therefore, the meaning of any statistic has to come from the context for the data, while the appropriateness of any statistic will depend upon the use we intend to make of that statistic." (Donald J Wheeler, "Myths About Data Analysis", International Lean & Six Sigma Conference, 2012)

"The four questions of data analysis are the questions of description, probability, inference, and homogeneity. Any data analyst needs to know how to organize and use these four questions in order to obtain meaningful and correct results. [...] 
THE DESCRIPTION QUESTION: Given a collection of numbers, are there arithmetic values that will summarize the information contained in those numbers in some meaningful way?
THE PROBABILITY QUESTION: Given a known universe, what can we say about samples drawn from this universe? [...] 
THE INFERENCE QUESTION: Given an unknown universe, and given a sample that is known to have been drawn from that unknown universe, and given that we know everything about the sample, what can we say about the unknown universe? [...] 
THE HOMOGENEITY QUESTION: Given a collection of observations, is it reasonable to assume that they came from one universe, or do they show evidence of having come from multiple universes?" (Donald J Wheeler," Myths About Data Analysis", International Lean & Six Sigma Conference, 2012)

19 October 2006

Jesús Rogel-Salazar - Collected Quotes

"[...] a data scientist role goes beyond the collection and reporting on data; it must involve looking at a business The role of a data scientist goes beyond the collection and reporting on data. application or process from multiple vantage points and determining what the main questions and follow-ups are, as well as recommending the most appropriate ways to employ the data at hand." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017)

"High-bias models typically produce simpler models that do not overfit and in those cases the danger is that of underfitting. Models with low-bias are typically more complex and that complexity enables us to represent the training data in a more accurate way. The danger here is that the flexibility provided by higher complexity may end up representing not only a relationship in the data but also the noise. Another way of portraying the bias-variance trade-off is in terms of complexity v simplicity." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017) 

"In terms of characteristics, a data scientist has an inquisitive mind and is prepared to explore and ask questions, examine assumptions and analyse processes, test hypotheses and try out solutions and, based on evidence, communicate informed conclusions, recommendations and caveats to stakeholders and decision makers." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017)

"Munging, or wrangling data is actually the most time-consuming task in the data science workflow. [...] Data preparation is key to the extraction of valuable insight and although some may prefer to concentrate only on the much more fun modelling part, the fact that you get to know your dataset inside out while munging it implies that any new or follow-up questions can probably be attained with less effort." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017)

"The tension between bias and variance, simplicity and complexity, or underfitting and overfitting is an area in the data science and analytics process that can be closer to a craft than a fixed rule. The main challenge is that not only is each dataset different, but also there are data points that we have not yet seen at the moment of constructing the model. Instead, we are interested in building a strategy that enables us to tell something about data from the sample used in building the model." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017)

"One important thing to bear in mind about the outputs of data science and analytics is that in the vast majority of cases they do not uncover hidden patterns or relationships as if by magic, and in the case of predictive analytics they do not tell us exactly what will happen in the future. Instead, they enable us to forecast what may come. In other words, once we have carried out some modelling there is still a lot of work to do to make sense out of the results obtained, taking into account the constraints and assumptions in the model, as well as considering what an acceptable level of reliability is in each scenario." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017)

18 October 2006

Ian Sommerville - Collected Quotes

"A feasibility study is a short, focused study that should take place early in the RE process. It should answer three key questions: a) does the system contribute to the overall objectives of the organization? b) can the system be implemented within schedule and budget using current technology? and c) can the system be integrated with other systems that are used? If the answer to any of these questions is no, you should probably not go ahead with the project." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"A model is an abstraction of the system being studied rather than an alternative representation of that system. Ideally, a representation of a system should maintain all the information about the entity being represented. An abstraction deliberately simplifies and picks out the most salient characteristics." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Agile approaches to software development consider design and implementation to be the central activities in the software process. They incorporate other activities, such as requirements elicitation and testing, into design and implementation. By contrast, a plan-driven approach to software engineering identifies separate stages in the software process with outputs associated with each stage." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Agile methods universally rely on an incremental approach to software specification, development, and delivery. They are best suited to application development where the system requirements usually change rapidly during the development process. They are intended to deliver working software quickly to customers, who can then propose new and changed requirements to be included in later iterations of the system. They aim to cut down on process bureaucracy by avoiding work that has dubious long-term value and eliminating documentation that will probably never be used." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Computer science is concerned with the theories and methods that underlie computers and software systems, whereas software engineering is concerned with the practical problems of producing software. Some knowledge of computer science is essential for software engineers in the same way that some knowledge of physics is essential for electrical engineers. Computer science theory, however, is often most applicable to relatively small programs. Elegant theories of computer science cannot always be applied to large, complex problems that require a software solution." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Design patterns are high-level abstractions that document successful design solutions. They are fundamental to design reuse in object-oriented development." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"In general, software engineers adopt a systematic and organized approach to their work, as this is often the most effective way to produce high-quality software. However, engineering is all about selecting the most appropriate method for a set of circumstances so a more creative, less formal approach to development may be effective in some circumstances. Less formal development is particularly appropriate for the development of web-based systems, which requires a blend of software and graphical design skills." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Models are used during the requirements engineering process to help derive the requirements for a system, during the design process to describe the system to engineers implementing the system and after implementation to document the system’s structure and operation." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Most designers think of design patterns as a way of supporting object-oriented design. Patterns often rely on object characteristics such as inheritance and polymorphism to provide generality. However, the general principle of encapsulating experience in a pattern is one that is equally applicable to all software design approaches." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Programming is a personal activity and there is no general process that is usually followed. Some programmers start with components that they understand, develop these, and then move on to less-understood components. Others take the opposite approach, leaving familiar components till last because they know how to develop them. Some developers like to define data early in the process then use this to drive the program development; others leave data unspecified for as long as possible." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Software systems do not exist in isolation. They are used in a social and organizational context and software system requirements may be derived or constrained by that context. Satisfying these social and organizational requirements is often critical for the success of the system. One reason why many software systems are delivered but never used is that their requirements do not take proper account of how the social and organizational context affects the practical operation of the system."(Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"System engineering is concerned with all aspects of the development and evolution of complex systems where software plays a major role. System engineering is therefore concerned with hardware development, policy and process design and system deployment, as well as software engineering. System engineers are involved in specifying the system, defining its overall architecture, and then integrating the different parts to create the finished system. They are less concerned with the engineering of the system components (hardware, software, etc.)." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

Joel Katz - Collected Quotes

"By understanding why things don’t work, you can figure out how to design them so that they do." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012) 

"Color can modify - and possibly even contradict – our intuitive response to value, because of its own powerful connotations." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"If the user can’t understand it, the design and the designer have failed." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"Information design, when successful - whether in print, on the web, or in the environment - represents the functional balance of the meaning of the information, the skills and inclinations of the designer, and the perceptions, education, experience, and needs of the audience." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"Geographic maps have the advantage of being true to scale - great for walking. Diagrams have the advantage of being easily imaged and remembered, often true to a non-pedestrian experience, and the ability to open up congestion, reduce empty space, and use real estate efficiently. Hybrids 'mapograms' ? - often have the disadvantages of both map and diagram with none of the corresponding advantages." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012) 

"Many concepts and relationships are difficult to grasp because of enormous or microscopic sizes and distances or because of huge disparities of scale. Representing these things with objects with which we have all had experience is a way to start working on the problem." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"Notational complexity almost always results in informational inefficiency." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012) 

"Successful information design in movement systems gives the user the information he needs - and only the information he needs - at every decision point." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012) 

"The more dimensions used in quantitative comparisons, the larger are the disparities that can be accommodated. As irony would have it, however, the ease of comparison generally diminishes in direct proportion to the number of dimensions involved." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012) 

"The universal intelligibility of a pictogram is inversely proportional to its complexity and potential for interpretive ambiguity." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012) 

"Use the form that most clearly connotes the differentiations within the data to be visualized; avoid forms that intuitively contradict the data." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012) 

"Violating established and functional color conventions makes it more difficult for the audience to understand an information graphic or a map. Respecting them gives the user that much less on which to expend unnecessary energy." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012) 

Abraham Kaplan - Collected Quotes

"Every discipline develops standards of professional competence to which its workers are subject. [...] Every scientific community is a society in the small, so to speak, with its own agencies of social control." (Abraham Kaplan, "The Conduct of Inquiry: Methodology for Behavioral Science", 1964)

"Give a small boy a hammer, and he will find that everything he encounters needs pounding. It comes as no particular surprise to discover that a scientist formulates problems in a way which requires for their solution just those techniques in which he himself is especially skilled." (Abraham Kaplan, "The Conduct of Inquiry: Methodology for Behavioral Science", 1964)

"Measurement, we have seen, always has an element of error in it. The most exact description or prediction that a scientist can make is still only approximate." (Abraham Kaplan, "The Conduct of Inquiry: Methodology for Behavioral Science", 1964)

"The price of training is always a certain "trained incapacity": the more we know how to do something, the harder it is to learn to do it differently." (Abraham Kaplan, "The Conduct of Inquiry: Methodology for Behavioral Science", 1964)

"[…] statistical techniques are tools of thought, and not substitutes for thought." (Abraham Kaplan, "The Conduct of Inquiry: Methodology for Behavioral Science", 1964)

"We are caught up in a paradox, one which might be called the paradox of conceptualization. The proper concepts are needed to formulate a good theory, but we need a good theory to arrive at the proper concepts." (Abraham Kaplan, "The Conduct of Inquiry: Methodology for Behavioral Science", 1964)

Umesh R Hodeghatta - Collected Quotes

"A histogram represents the frequency distribution of the data. Histograms are similar to bar charts but group numbers into ranges. Also, a histogram lets you show the frequency distribution of continuous data. This helps in analyzing the distribution (for example, normal or Gaussian), any outliers present in the data, and skewness." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"Bias occurs normally when the model is underfitted and has failed to learn enough from the training data. It is the difference between the mean of the probability distribution and the actual correct value. Hence, the accuracy of the model is different for different data sets (test and training sets). To reduce the bias error, data scientists repeat the model-building process by resampling the data to obtain better prediction values." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"Clustering analysis is performed on data to identify hidden groups or to form different sectors. The objective of the clusters is to enable meaningful analysis in ways that help business. Clustering can uncover previously undetected relationships in a data set." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"Correlation explains the extent of change in one of the variables given the unit change in the value of another variable. Correlation assumes a very significant role in statistics and hence in the field of business analytics as any business cannot make any decision without understanding the relationship between various forces acting in favor of or against it." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"Graphs represent data visually and provide more details about the data, enabling you to identify outliers in the data, distribute data for each column variable, provide a statistical description of the data, and present the relationship between the two or more variables." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"If either bias or variance is high, the model can be very far off from reality. In general, there is a trade-off between bias and variance. The goal of any machine-learning algorithm is to achieve low bias and low variance such that it gives good prediction performance. In reality, because of so many other hidden parameters in the model, it is hard to calculate the real bias and variance error. Nevertheless, the bias and variance provide a measure to understand the behavior of the machine-learning algorithm so that the model model can be adjusted to provide good prediction performance." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"In machine learning, a model is defined as a function, and we describe the learning function from the training data as inductive learning. Generalization refers to how well the concepts are learned by the model by applying them to data not seen before. The goal of a good machine-learning model is to reduce generalization errors and thus make good predictions on data that the model has never seen." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"Machine learning is about making computers learn and perform tasks better based on past historical data. Learning is always based on observations from the data available. The emphasis is on making computers build mathematical models based on that learning and perform tasks automatically without the intervention of humans." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"Overfitting and underfitting are two important factors that could impact the performance of machine-learning models. Overfitting occurs when the model performs well with training data and poorly with test data. Underfitting occurs when the model is so simple that it performs poorly with both training and test data. [...]  When the model does not capture and fit the data, it results in poor performance. We call this underfitting. Underfitting is the result of a poor model that typically does not perform well for any data." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"Variance is a prediction error due to different sets of training samples. Ideally, the error should not vary from one training sample to another sample, and the model should be stable enough to handle hidden variations between input and output variables. Normally this occurs with the overfitted model." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

Chester I Barnard - Collected Quotes

"A formal and orderly conception of the whole is rarely present, perhaps even rarely possible, except to a few men of exceptional genius." (Chester I Barnard, "The Functions of the Executive", 1938)

"A low morality will not sustain leadership long, its influence quickly vanishes, it cannot produce its own succession." (Chester I Barnard, "The Functions of the Executive", 1938)

"A person can and will accept a communication as authoritative only when four conditions simultaneously obtain: (a) he can and does understand the communication; (b) at the time of his decision he believes that it is not inconsistent with the purpose of the organization; (c) at the time of his decision, he believes it to be compatible with his personal interest as a whole; and (d) he is able mentally and physically to comply with it." (Chester I Barnard, "The Functions of the Executive", 1938)

"Effectiveness relates to the accomplishment of the cooperative purpose which is social and non-personal in character. Efficiency relates to the satisfaction of individual motives and is personal in character." (Chester I Barnard, "The Functions of the Executive", 1938)

"Executive work is not that of the organization, but the specialized work of maintaining the organization." (Chester I Barnard, The Functions of the Executive, 1938)

"Organizations endure, however, in proportion to the breadth of the morality by which they are governed. Thus the endurance of organization depends upon the quality of leadership; and that quality derives from the breadth of the morality upon which it rests." (Chester I Barnard, "The Functions of the Executive", 1938)

"Planning is one of the many catchwords whose present popularity is roughly proportionate to the obscurity of its definition." (Chester I Barnard, "The Functions of the Executive", 1938)

"The fine art of executive decision consists in not deciding questions that are not now pertinent, in not deciding prematurely, in not making decision that cannot be made effective, and in not making decisions that others should make. Not to decide questions that are not pertinent at the time is uncommon good sense, though to raise them may be uncommon perspicacity. Not to decide questions prematurely is to refuse commitment of attitude or the development of prejudice. Not to make decisions that cannot be made effective is to refrain from destroying authority. Not to make decisions that others should make is to preserve morale, to develop competence, to fix responsibility, and to preserve authority.
From this it may be seen that decisions fall into two major classes, positive decisions - to do something, to direct action, to cease action, to prevent action; and negative decisions, which are decisions not to decide. Both are inescapable; but the negative decisions are often largely unconscious, relatively nonlogical, "instinctive," "good sense." It is because of the rejections that the selection is good." (Chester I Barnard, "The Functions of the Executive", 1938)

"The making of decisions, as everyone knows from personal experience, is a burdensome task. Offsetting the exhilaration that may result from correct and successful decision and the relief that follows the termination of a struggle to determine issues is the depression that comes from failure, or error of decision, and the frustration which ensues from uncertainty." (Chester I Barnard, "The Functions of the Executive", 1938)

"The executive is primarily concerned with decisions which facilitate or hinder other decisions." (Chester I Barnard, "Organization and Management: Selected Papers", 1948)

"When a condition of honesty and sincerity is recognized to exist, errors of judgment, defects of ability, are sympathetically endured. They are expected. Employees don't ascribe infallibility to leaders or management. What does disturb them is insincerity and the appearance of insincerity when the facts are not in their possession." (Chester I Barnard, "Organization and Management: Selected Papers", 1948)

17 October 2006

John Naisbitt - Collected Quotes

"The acceleration of technological progress has created an urgent need for a counter ballast - for high-touch experience." (John Naisbitt, "Megatrends: Ten New Directions Transforming Our Lives", 1982)

"We are drowning in information but starved for knowledge." (John Naisbitt, "Megatrends: Ten New Directions Transforming Our Lives", 1982)

"We created the hierarchical, pyramidal, managerial system because we needed to keep track of people and the things people did; with the computer to keep track, we can restructure our institutions horizontally." (John Naisbitt, "Megatrends: Ten New Directions Transforming Our Lives", 1982)

"We lose all intelligence by averaging." (John Naisbitt, "Megatrends: Ten New Directions Transforming Our Lives", 1982)

"In an information society, education is no mere amenity; it is the prime tool for growing people and profits." (John Naisbitt, "Re-Inventing the Corporation", 1985) 

"Intuition becomes increasingly valuable in the new information society precisely because there is so much data." (John Naisbitt, "Re-Inventing the Corporation", 1985) 

"The most important skill to acquire now is learning how to learn." (John Naisbitt, "Re-Inventing the Corporation", 1985) 

"It is in the nature of human beings to bend information in the direction of desired conclusions." (John Naisbitt, "Mind Set!: Reset Your Thinking and See the Future", 2006) 

Benjamin Bengfort - Collected Quotes

"Graphs can embed complex semantic representations in a compact form. As such, modeling data as networks of related entities is a powerful mechanism for analytics, both for visual analyses and machine learning. Part of this power comes from performance advantages of using a graph data structure, and the other part comes from an inherent human ability to intuitively interact with small networks." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"Language is unstructured data that has been produced by people to be understood by other people. By contrast, structured or semistructured data includes fields or markup that enable it to be easily parsed by a computer. However, while it does not feature an easily machine-readable structure, unstructured data is not random. On the contrary, it is governed by linguistic properties that make it very understandable to other people." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"Machine learning is often associated with the automation of decision making, but in practice, the process of constructing a predictive model generally requires a human in the loop. While computers are good at fast, accurate numerical computation, humans are instinctively and instantly able to identify patterns. The bridge between these two necessary skill sets lies in visualization - the precise and accurate rendering of data by a computer in visual terms and the immediate assignation of meaning to that data by humans." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"Many model families suffer from 'the curse of dimensionality'; as the feature space increases in dimensions, the data becomes more sparse and less informative to the underlying decision space." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"Neural networks refer to a family of models that are defined by an input layer (a vectorized representation of input data), a hidden layer that consists of neurons and synapses, and an output layer with the predicted values. Within the hidden layer, synapses transmit signals between neurons, which rely on an activation function to buffer incoming signals. The synapses apply weights to incoming values, and the activation function determines if the weighted inputs are sufficiently high to activate the neuron and pass the values on to the next layer of the network." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"The current trade-offs between traditional models and neural networks concern two factors: model complexity and speed. Because neural networks tend to take longer to train, they can impede rapid iteration [...] Neural networks are also typically more complex than traditional models, meaning that their hyperparameters are more difficult to tune and modeling errors are more challenging to diagnose. However, neural networks are not only increasingly practical, they also promise nontrivial performance gains over traditional models. This is because unlike traditional models, which face performance plateaus even as more data become available, neural models continue to improve." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"The premise of classification is simple: given a categorical target variable, learn patterns that exist between instances composed of independent variables and their relationship to the target. Because the target is given ahead of time, classification is said to be supervised machine learning because a model can be trained to minimize error between predicted and actual categories in the training data. Once a classification model is fit, it assigns categorical labels to new instances based on the patterns detected during training." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"The trick is to walk the line between underfitting and overfitting. An underfit model has low variance, generally making the same predictions every time, but with extremely high bias, because the model deviates from the correct answer by a significant amount. Underfitting is symptomatic of not having enough data points, or not training a complex enough model. An overfit model, on the other hand, has memorized the training data and is completely accurate on data it has seen before, but varies widely on unseen data. Neither an overfit nor underfit model is generalizable - that is, able to make meaningful predictions on unseen data." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"There is a trade-off between bias and variance [...]. Complexity increases with the number of features, parameters, depth, training epochs, etc. As complexity increases and the model overfits, the error on the training data decreases, but the error on test data increases, meaning that the model is less generalizable." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"Unfortunately, because the search space is large, automatic techniques for optimization are not sufficient. Instead, the process of selecting an optimal model is complex and iterative, involving repeated cycling through feature engineering, model selection, and hyperparameter tuning. Results are evaluated after each iteration in order to arrive at the best combination of features, model, and parameters that will solve the problem at hand." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"Unsupervised learning or clustering is a way of discovering hidden structures in unlabeled data. Clustering algorithms aim to discover latent patterns in unlabeled data using features to organize instances into meaningfully dissimilar groups." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

16 October 2006

Ernest Dale - Collected Quotes

"Every company has beloved projects on which if prices had held up, if the contractors had finished on time (or finished at all), if the plans hadn't been altered, if the thing had actually worked, the planned return would have been earned. But since some or all of these calamities [things that don't go as expected] usually happen, any manager who neglects to allow for them is not planning - merely thinking wishfully. Desire for the project has, as usual, overtaken desire for profit." (Ernest Dale, "Planning and developing the company organization structure", 1952)

"Organization planning is the process of defining and grouping the activities of the enterprise so that they may be most logically assigned and effectively executed. It is concerned with the establishment of relationships among the units so as to further the objectives of the enterprise." (Ernest Dale, "Planning and developing the company organization structure", 1952)

"Management policies and the quality of leadership have a lot to do with individual performance." (Ernest Dale, "The Great Organizers", 1960)

"An organization that is based on pure rationality ignores many facets of human nature." (Ernest Dale, "Management: Theory and practice", 1965)

"Centralized controls are designed to ensure that the chief executive can find out how well the delegated authority and responsibility are being exercised." (Ernest Dale, "Management: Theory and practice", 1965)

"One difficulty in developing a good [accounting] control system is that quantitative results will differ according to the accounting principles used, and accounting principles may change." (Ernest Dale, "Readings in Management", 1970)

Alistair Cockburn - Collected Quotes

"A methodology is the conventions that your group agrees to. 'The conventions your group agrees to' is a social construction." (Alistair Cockburn, "Agile Software Development", 2001)

"A well-functioning team of adequate people will complete a project almost regardless of the process or technology they are asked to use (although the process and technology may help or hinder them along the way)." (Alistair Cockburn, "Agile Software Development", 2001)

"The thing that makes software design difficult is that we must express thoughts about a problem and a solution we typically do not understand fully, using a language that does not contain many of our accustomed features of expression, to a system that is unforgiving of mistakes." (Alistair Cockburn)

"Software development is a (resource-limited) cooperative game of invention and communication. The primary goal of the game is to deliver useful, working software. The secondary goal, the residue of the game, is to set up for the next game. The next game may be to alter or replace the system or to create a neighboring system." (Alistair Cockburn, "Agile Software Development: The Cooperative Game" 2nd Ed., 2006)

"[…] Software development is a cooperative game, in which the participants help each other in reaching the end of the game - the delivery of software […]" (Alistair Cockburn, "Agile Software Development: The Cooperative Game" 2nd Ed., 2006)

"Heart of Agile is a meme. Heart of Agile is four words stripped down to nothing. It contains only four words – collaborate, deliver, reflect, improve." (Alistair Cockburn, [interview] 2017)


12 October 2006

Warren G Bennis - Collected Quotes

"If we view organizations as adaptive, problem-solving structures, then inferences about effectiveness have to be made, not from static measures of output, but on the basis of the processes through which the organization approaches problems. In other words, no single measurement of organizational efficiency or satisfaction - no single time-slice of organizational performance can provide valid indicators of organizational health." (Warren G Bennis, "General Systems Yearbook", 1962)

"Leaders do not avoid, repress, or deny conflict, but rather see it as an opportunity" (Warren G Bennis, "Why Leaders Can't Lead: The Unconscious Conspiracy Continues", 1976)

"We have more information now than we can use, and less knowledge and understanding than we need. Indeed, we seem to collect information because we have the ability to do so, but we are so busy collecting it that we haven't devised a means of using it. The true measure of any society is not what it knows but what it does with what it knows." (Warren G Bennis, "Why leaders can't lead: the unconscious conspiracy continues", 1976)

"Leaders value learning and mastery, and so do people who work for leaders. Leaders make it clear that there is no failure, only mistakes that give us feedback and tell us what to do next." (Warren G Bennis, Training and Development Journal, 1984)

"Excellence is a better teacher than is mediocrity. The lessons of the ordinary are everywhere. Truly profound and original insights are to be found only in studying the exemplary." (Warren G Bennis, "Organizing Genius: The Secrets of Creative Collaboration", 1997)

"Great things are accomplished by talented people who believe they will accomplish them." (Warren G Bennis, "Organizing Genius: The Secrets of Creative Collaboration", 1997)

"The ability to plan for what has not yet happened, for a future that has only been imagined, is one of the hallmarks of leadership." (Warren G Bennis, "Organizing Genius: The Secrets of Creative Collaboration", 1997)

"Too many companies believe people are interchangeable. Truly gifted people never are. They have unique talents. Such people cannot be forced into roles they are not suited for, nor should they be. Effective leaders allow great people to do the work they were born to do." (Warren Bennis, "Organizing Genius: The Secrets of Creative Collaboration", 1997)

"Failing organizations are usually overmanaged and under-led." (Warren G Bennis, 1988)

"Leadership is the capacity to translate vision into reality." (Warren G Bennis, 1988)

"There is a profound difference between information and meaning." (Warren G Bennis, 1988)

"Leaders wonder about everything, want to learn as much as they can, are willing to take risks, experiment, try new things. They do not worry about failure but embrace errors, knowing they will learn from them." (Warren G Bennis, "On Becoming a Leader", 1989)

"Manage the dream: Create a compelling vision, one that takes people to a new place, and then translate that vision into a reality." (Warren G Bennis, "On Becoming a Leader", 1989) 

"Taking charge of your own learning is a part of taking charge of your life, which is the sine qua non in becoming an integrated person." (Warren G Bennis, "On Becoming a Leader", 1989)

"With a vision, the executive provides the all-important bridge from the present to the future of the organization." (Warren G Bennis, "Beyond Leadership: Balancing Economics, Ethics, and Ecology", 1994)

"Good leaders make people feel that they're at the very heart of things, not at the periphery. Everyone feels that he or she makes a difference to the success of the organization. When that happens, people feel centered and that gives their work meaning." (Warren G Bennis, "Managing People Is Like Herding Cat", 1999) 

"The basis of leadership is the capacity of the leader to change the mindset, the framework of the other person." (Warren Bennis, "Managing People Is Like Herding Cat", 1999) 

Diego Rasskin-Gutman - Colected Quotes

"A chess hypothesis is basically the equivalent to drawing up a strategic plan. Experimentation in chess is equivalent to the moves that are found to carry out each plan. Throughout the history of chess, both the plans (the hypotheses) as well as the moves (the experiments) have been evolving (thanks to results from the practice of the game and from analyses), and this knowledge is the patrimony of professional players." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"An algorithm refers to a successive and finite procedure by which it is possible to solve a certain problem. Algorithms are the operational base for most computer programs. They consist of a series of instructions that, thanks to programmers’ prior knowledge about the essential characteristics of a problem that must be solved, allow a step-by-step path to the solution." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Any scientific hypothesis springs from knowledge that was previously generated by observations of facts in the real world. In addition, hypotheses produce predictions that need to be tested. For some, scientific definitions are limited to natural phenomena (although this definition would require mathematics to stop being a science since it deals with ideal objects)." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"The brain and its cognitive mental processes are the biological foundation for creating metaphors about the world and oneself. Artificial intelligence, human beings’ attempt to transcend their biology, tries to enter into these scenarios to learn how they function. But there is another metaphor of the world that has its own particular landscapes, inhabitants, and laws. The brain provides the organic structure that is necessary for generating the mind, which in turn is considered a process that results from brain activity." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"The problem of identifying the subset of good moves is much more complicated than simply counting the total number of possibilities and falls completely into the domain of strategy and tactics of chess as a game." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Generally, these programs fall within the techniques of reinforcement learning and the majority use an algorithm of temporal difference learning. In essence, this computer learning paradigm approximates the future state of the system as a function of the present state. To reach that future state, it uses a neural network that changes the weight of its parameters as it learns." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"The simplest basic architecture of an artificial neural network is composed of three layers of neurons - input, output, and intermediary (historically called perceptron). When the input layer is stimulated, each node responds in a particular way by sending information to the intermediary level nodes, which in turn distribute it to the output layer nodes and thereby generate a response. The key to artificial neural networks is in the ways that the nodes are connected and how each node reacts to the stimuli coming from the nodes it is connected to. Just as with the architecture of the brain, the nodes allow information to pass only if a specific stimulus threshold is passed. This threshold is governed by a mathematical equation that can take different forms. The response depends on the sum of the stimuli coming from the input node connections and is 'all or nothing'." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Vision is a capacity to understand a position and to generate solid strategic plans." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

Tim Brown - Collected Quotes

"A culture that believes that it is better to ask forgiveness afterward rather than permission before, that rewards people for success but gives them permission to fail, has removed one of the main obstacles to the formation of new ideas." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009) 

"Although it can at times seem forbiddingly abstract, design thinking is embodied thinking - embodied in teams and projects, to be sure, but embodied in the physical spaces of innovation as well." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Although it might seem as though frittering away valuable time on sketches and models and simulations will slow work down, prototyping generates results faster." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Anything tangible that lets us explore an idea, evaluate it, and push it forward is a prototype." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Design has the power to enrich our lives by engaging our emotions through image, form, texture, color, sound, and smell. The intrinsically human-centered nature of design thinking points to the next step: we can use our empathy and understanding of people to design experiences that create opportunities for active engagement and participation." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Design thinking taps into capacities we all have but that are overlooked by more conventional problem-solving practices. It is not only human-centered; it is deeply human in and of itself. Design thinking relies on our ability to be intuitive, to recognize patterns, to construct ideas that have emotional meaning as well as functionality, to express ourselves in media other than words or symbols." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Just as it can accelerate the pace of a project, prototyping allows the exploration of many ideas in parallel. Early prototypes should be fast, rough, and cheap. The greater the investment in an idea, the more committed one becomes to it. Overinvestment in a refined prototype has two undesirable consequences: First, a mediocre idea may go too far toward realization - or even, in the worst case, all the way. Second, the prototyping process itself creates the opportunity to discover new and better ideas at minimal cost." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Mostly we rely on stories to put our ideas into context and give them meaning. It should be no surprise, then, that the human capacity for storytelling plays an important role in the intrinsically human-centered approach to problem solving, design thinking." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Prototypes should command only as much time, effort, and investment as is necessary to generate useful feedback and drive an idea forward. The greater the complexity and expense, the more 'finished' it is likely to seem and the less likely its creators will be to profit from constructive feedback - or even to listen to it. The goal of prototyping is not to create a working model. It is to give form to an idea to learn about its strengths and weaknesses and to identify new directions for the next generation of more detailed, more refined prototypes. A prototype’s scope should be limited. The purpose of early prototypes might be to understand whether an idea has functional value." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Prototyping at work is giving form to an idea, allowing us to learn from it, evaluate it against others, and improve upon it." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Prototyping is always inspirational - not in the sense of a perfected artwork but just the opposite: because it inspires new ideas. Prototyping should start early in the life of a project, and we expect them to be numerous, quickly executed, and pretty ugly." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Since openness to experimentation is the lifeblood of any creative organization, prototyping - the willingness to go ahead and try something by building it - is the best evidence of experimentation." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"The project is the vehicle that carries an idea from concept to reality." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Traditionally, one of the problems with architectural design is that full-scale prototyping is virtually impossible because it is just too expensive." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"To be sure, prototyping new organizational structures is difficult. By their nature, they are suspended in webs of interconnectedness. No unit can be tinkered with without affecting other parts of the organization. Prototyping with peoples’ lives is also a delicate proposition because there is, rightly, less tolerance for error. But despite this complexity, some institutions have taken a designer’s approach to organizational change." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

11 October 2006

Jake Knapp - Collected Quotes

"Because of the short timeline, it’s tempting to jump into prototyping as soon as you’ve selected your winning ideas. But if you start prototyping without a plan, you’ll get bogged down by small, unanswered questions. Pieces won’t fit together, and your prototype could fall apart." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"But perhaps the biggest problem is that the longer you spend working on something - whether it’s a prototype or a real product - the more attached you’ll become, and the less likely you’ll be to take negative test results to heart. After one day, you’re receptive to feedback. After three months, you’re committed." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"No problem is too large for a sprint. Yes, this statement sounds absurd, but there are two big reasons why it’s true. First, the sprint forces your team to focus on the most pressing questions. Second, the sprint allows you to learn from just the surface of a finished product." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"Sometimes, the best way to broaden your search is to look inside your own organization. Great solutions often come along at the wrong time, and the sprint can be a perfect opportunity to rejuvenate them. Also look for ideas that are in progress but unfinished - and even old ideas that have been abandoned." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"Sometimes when people work together in groups, they start to worry about consensus and try to make decisions that everybody will approve - mostly out of good nature and a desire for group cohesion, and perhaps in part because democracy feels good. Well, democracy is a fine system for governing nations, but it has no place in your sprint." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"Sometimes you can’t fit everything in. Remember that the sprint is great for testing risky solutions that might have a huge payoff. So you’ll have to reverse the way you would normally prioritize. If a small fix is so good and low-risk that you’re already planning to build it next week, then seeing it in a prototype won’t teach you much. Skip those easy wins in favor of big, bold bets." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"The prototype is meant to answer questions, so keep it focused. You don’t need a fully functional product - you just need a real-looking façade to which customers can react." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"There are only six working hours in the typical sprint day. Longer hours don’t equal better results. By getting the right people together, structuring the activities, and eliminating distraction, we’ve found that it’s possible to make rapid progress while working a reasonable schedule." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"When a big problem comes along, like the challenge you selected for your sprint, it’s natural to want to solve it right away. The clock is ticking, the team is amped up, and solutions start popping into everyone’s mind. But if you don’t first slow down, share what you know, and prioritize, you could end up wasting time and effort on the wrong part of the problem." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

"You can prototype anything. Prototypes are disposable. Build just enough to learn, but not more. The prototype must appear real." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

10 October 2006

Steven S Skiena - Collected Quotes

"Bias is error from incorrect assumptions built into the model, such as restricting an interpolating function to be linear instead of a higher-order curve. [...] Errors of bias produce underfit models. They do not fit the training data as tightly as possible, were they allowed the freedom to do so. In popular discourse, I associate the word 'bias' with prejudice, and the correspondence is fairly apt: an apriori assumption that one group is inferior to another will result in less accurate predictions than an unbiased one. Models that perform lousy on both training and testing data are underfit." (Steven S Skiena, "The Data Science Design Manual", 2017)

"Exploratory data analysis is the search for patterns and trends in a given data set. Visualization techniques play an important part in this quest. Looking carefully at your data is important for several reasons, including identifying mistakes in collection/processing, finding violations of statistical assumptions, and suggesting interesting hypotheses." (Steven S Skiena, "The Data Science Design Manual", 2017)

"Repeated observations of the same phenomenon do not always produce the same results, due to random noise or error. Sampling errors result when our observations capture unrepresentative circumstances, like measuring rush hour traffic on weekends as well as during the work week. Measurement errors reflect the limits of precision inherent in any sensing device. The notion of signal to noise ratio captures the degree to which a series of observations reflects a quantity of interest as opposed to data variance. As data scientists, we care about changes in the signal instead of the noise, and such variance often makes this problem surprisingly difficult." (Steven S Skiena, "The Data Science Design Manual", 2017)

"The advent of massive data sets is changing in the way science is done. The traditional scientific method is hypothesis driven. The researcher formulates a theory of how the world works, and then seeks to support or reject this hypothesis based on data. By contrast, data-driven science starts by assembling a substantial data set, and then hunts for patterns that ideally will play the role of hypotheses for future analysis." (Steven S Skiena, "The Data Science Design Manual", 2017)

"The danger of overfitting is particularly severe when the training data is not a perfect gold standard. Human class annotations are often subjective and inconsistent, leading boosting to amplify the noise at the expense of the signal. The best boosting algorithms will deal with overfitting though regularization. The goal will be to minimize the number of non-zero coefficients, and avoid large coefficients that place too much faith in any one classifier in the ensemble." (Steven S Skiena, "The Data Science Design Manual", 2017)

"Using noise (the uncorrelated variables) to fit noise (the residual left from a simple model on the genuinely correlated variables) is asking for trouble." (Steven S Skiena, "The Data Science Design Manual", 2017)

"Variables which follow symmetric, bell-shaped distributions tend to be nice as features in models. They show substantial variation, so they can be used to discriminate between things, but not over such a wide range that outliers are overwhelming." (Steven S Skiena, "The Data Science Design Manual", 2017)

"Variance is error from sensitivity to fluctuations in the training set. If our training set contains sampling or measurement error, this noise introduces variance into the resulting model. [...] Errors of variance result in overfit models: their quest for accuracy causes them to mistake noise for signal, and they adjust so well to the training data that noise leads them astray. Models that do much better on testing data than training data are overfit." (Steven S Skiena, "The Data Science Design Manual", 2017)

Andrew S Grove - Collected Quotes

"Automation is certainly one way to improve the leverage of all types of work. Having machines to help them, human beings can create more output." (Andrew S. Grove, "High Output Management", 1983)

"Detect and fix any problem in a production process at the lowest-value stage possible." (Andrew S Grove, "High Output Management", 1983)

"Individual contributors who gather and disseminate know-how and information should also be seen as middle managers, because they exert great power within the organization." (Andrew S Grove, "High Output Management", 1983)

"Information gathering is the basis of all other managerial work, which is why I choose to spend so much of my day doing it." (Andrew S Grove, "High Output Management", 1983)

"Stressing output is the key to improving productivity, while looking to increase activity can result in just the opposite." (Andrew S Grove, "High Output Management", 1983)

"There is an especially efficient way to get information, much neglected by most managers. That is to visit a particular place in the company and observe what's going on there." (Andrew S Grove, "High Output Management", 1983)

"A strong corporate culture is the invisible hand that guides how things are done in an organization. The phrase, 'You just can't do that here', is extremely powerful, more so than any written rules or policy manuals." (Andrew S Grove, "One-On-One With Andy Grove", 1987)

"Setting and communicating the right expectations is the most important tool a manager has for imparting that elusive drive to the people he supervises." (Andrew S. Grove, "One-On-One With Andy Grove", 1987)

09 October 2006

Jakob Nielsen - Collected Quotes

"Even better than good error messages is a careful design which prevents a problem from occurring in the first place. Either eliminate error-prone conditions or check for them and present users with a confirmation option before they commit to the action." (Jakob Nielsen, "Usability Engineering", 1993)

"Even though it is better if the system can be used without documentation, it may be necessary to provide help and documentation. Any such information should be easy to search, focused on the user's task, list concrete steps to be carried out, and not be too large." (Jakob Nielsen, "Usability Engineering", 1993)

"A basic reason for the existence of usability engineering is that it is impossible to design an optimal user interface just by giving it your best try. Users have infinite potential for making unexpected misinterpretations of interface elements and for performing their job in a different way than you imagine." (Jakob Nielsen, "Usability Engineering", 1993)

"A problem with this 'waterfall' approach is that there will then be no user interface to test with real users until this last possible moment, since the "intermediate work products" do not explicitly separate out the user interface in a prototype with which users can interact. Experience also shows that it is not possible to involve the users in the design process by showing them abstract specifications documents, since they will not understand them nearly as well as concrete prototypes." (Jakob Nielsen, "Usability Engineering", 1993)

"Guidelines list well-known principles for user interface design which should be followed in the development project. In any given project, several different levels of guidelines should be used: general guidelines applicable to all user interfaces, category-specific guidelines for the kind of system being developed […] and product-specific guidelines for the individual product." (Jakob Nielsen, "Usability Engineering", 1993)

"If users' needs are not known, considerable development efforts may be wasted on such features in the mistaken belief that some users may want them. Users rarely complain that a system can do too much (they just don’t use the superfluous features), so such over-design normally does not become sufficiently visible to make the potential development savings explicitly known. They are there nevertheless." (Jakob Nielsen, "Usability Engineering", 1993)

"It is always better if users can operate the system without having to refer to a help system. Usability is not a quality that can be spread out to cover a poor design like a thick layer of peanut butter, so a user-hostile interface does not get user-friendly even by the addition of a brilliant help system." (Jakob Nielsen, "Usability Engineering", 1993)

"One should not start full-scale implementation efforts based on early user interface designs. Instead, early usability evaluation can be based on prototypes of the final systems that can be developed much faster and much more cheaply, and which can thus be changed many times until a better understanding of the user interface design has been achieved." (Jakob Nielsen, "Usability Engineering", 1993)

"Scenarios are an especially cheap kind of prototype. […] Scenarios are the ultimate reduction of both the level of functionality and of the number of features: They can only simulate the user interface as long as a test user follows a previously planned path. […] Scenarios are the ultimate minimalist prototype in that they describe a single interaction session without any flexibility for the user. As such, they combine the limitations of both horizontal prototypes (users cannot interact with real data) and vertical prototypes (users cannot move freely through the system)." (Jakob Nielsen, "Usability Engineering", 1993)

"The concept of 'user' should be defined to include everybody whose work is affected by the product in some way, including the users of the system's end product or output even if they never see a single screen." (Jakob Nielsen, "Usability Engineering", 1993)

"The difference between standards and guidelines is that a standard specifies how the interface should appear to the user, whereas a set of guidelines provides advice about the usability characteristics of the interface." (Jakob Nielsen, "Usability Engineering", 1993)

"The entire idea behind prototyping is to cut down on the complexity of implementation by eliminating parts of the full system. Horizontal prototypes reduce the level of functionality and result in a user interface surface layer, while vertical prototypes reduce the number of features and implement the full functionality of those chosen (i.e., we get a part of the system to play with)." (Jakob Nielsen, "Usability Engineering", 1993)

"The entire idea behind prototyping is to save on the time and cost to develop something that can be tested with real users. These savings can only be achieved by somehow reducing the prototype compared with the full system: either cutting down on the number of features in the prototype or reducing the level of functionality of the features such that they seem to work but do not actually do anything." (Jakob Nielsen, "Usability Engineering", 1993)

"The most basic advice with respect to interface evaluation is simply to do it , and especially to conduct some user testing. The benefits of employing some reasonable usability engineering methods to evaluate a user interface rather than releasing it without evaluation are much larger than the incremental benefits of using exactly the right methods for a given project." (Jakob Nielsen, "Usability Engineering", 1993)

"The system should always keep users informed about what is going on, through appropriate feedback within reasonable time." (Jakob Nielsen, "Usability Engineering", 1993)

"Usability engineering is not a one-shot affair where the user interface is fixed up before the release of a product. Rather, usability engineering is a set of activities that ideally take place throughout the lifecycle of the product, with significant activities happening at the early stages before the user interface has even been designed." (Jakob Nielsen, "Usability Engineering", 1993)

"User interfaces should be simplified as much as possible, since every additional feature or item of information on a screen is one more thing to learn, one more thing to possibly misunderstand, and one more thing to search through when looking for the thing you want. Furthermore, interfaces should match the users' task in as natural a way as possible, such that the mapping between computer concepts and user concepts becomes as simple as possible and the users' navigation through the interface is minimized." (Jakob Nielsen, "Usability Engineering", 1993)

"Users often do not know what is good for them. […] Users have a very hard time predicting how they will interact with potential future systems with which they have no experience. […] Furthermore, users will often have divergent opinions when asked about details of user interface design." (Jakob Nielsen, "Usability Engineering", 1993)

"Users often raise questions that the development team has not even dreamed of asking. This is especially true with respect to potential mismatches between the users' actual task and the developers' model of the task. Therefore, users should be involved in the design process through regular meetings between designers and users. Users participating in a system design process are sometimes referred to as subject matter experts, or SMEs." (Jakob Nielsen, "Usability Engineering", 1993)

"Users are not designers, so it is not reasonable to expect them to come up with design ideas from scratch. However, they are very good at reacting to concrete designs they do not like or that will not work in practice. To get full benefits from user involvement, it is necessary to present these suggested system designs in a form the users can understand." (Jakob Nielsen, "Usability Engineering", 1993)

"A general principle for all user interface design is to go through all of your design elements and remove them one at a time." (Jakob Nielsen, "Designing Web Usability", 1999)

"The web is the ultimate customer-empowering environment. He or she who clicks the mouse gets to decide everything. It is so easy to go elsewhere; all the competitors in the world are but a mouseclick away." (Jakob Nielsen, "Designing Web Usability", 1999)

"Ultimately, users visit your website for its content. Everything else is just the backdrop." (Jakob Nielsen, "Designing Web Usability", 1999)

"Developing fewer features allows you to conserve development resources and spend more time refining those features that users really need. Fewer features mean fewer things to confuse users, less risk of user errors, less description and documentation, and therefore simpler Help content. Removing any one feature automatically increases the usability of the remaining ones." (Jakob Nielsen, "Prioritizing Web Usability", 2006)

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