09 August 2022

Business Intelligence: Power BI’s Learning Curve - Part I

A learning curve attempts depicting the (average) time it takes a person to learn how to use a method, tool, or technique, tracing the path from newbie to mastery. A common definition of the learning curve is based on the correlation between a learner’s performance on a task or activity and the number of attempts or amount of time required to complete it.

There are several diagrams in circulation which depict the correlation between the difficulty of Power BI concepts and probably their implementation as functionality. Even if they reflect to some degree the rate of learning, their simplicity and fuzziness can easily make one question their accuracy in reflecting the reality.

Researchers tend to categorize the curves associated with the learning process in simple idealized patterns like S-curve (aka sigmoid), exponential growth, exponential rise and fall to limit, or power law, however the learning process in IT-based endeavors is seldom characterized by a linear or exponential curve, given that the tasks seldom allow a steady path. The jumps of knowledge between tasks can be wide enough to appear insurmountable, and they can prove to be quite of a challenge without some help.

Like a baby’s first steps, we, as learners, must learn first to crawl, before making some unsteady steps, and it can take long time until visible progress is made. It’s a slow progress until we suddenly hit a (tipping) point from which everything seems easy, fact that increases our confidence in us. On the other side, when we find that we make no visible progress for a long period, it’s easy to arrive to the opposite, a critical zone, which in extremis could make one lose interest.

As beginners, after the first tipping point on the learning journey, it’s easy to arrive at a plateau in which there seem no need to learn new things, the current knowledge allowing to handle a range of tasks of small to average complexity. This can last for a long time, and then, a big thing comes our way – a hard problem to solve or a concept hard to understand. It’s the point where we stagnate, and the deeper we go, and the more such challenges are thrown in our way, the more difficult the learning seems to be. However, with new understanding, small steps are made, one step after the other, the pace makes us to evolve faster until we reach again a critical point from which the process increases smoothly until we seem to stagnate again. We meet again a hard limit to growth, which seems to be more solid than the previous one.

Power BI's learning curve

Both limits to growth can appear to be hard, however, considering that the knowledge in the field expands, more opportunity for growth appear, thus, the limits are apparent. Even if knowledge tends to increase ‘indefinitely’, the limits are there in terms of complexity, time available, knowledge quality (incl. availability) or any other dimension of the learning process. Moreover, these successions of tipping points, growth limits, plateaus, critical, steady and fast progress zones can occur in several iterations in the learning path. Thus, the path seems to resemble a snakelike curve with many ups and downs.

For the learner is important to be aware of this last aspect, there are always ups and downs, taking effort, patience and maybe an expert’s help to bridge the gaps in between. The chances are high that the gap between what we think we know and what we know is considerable, therefore a reality check is useful from time to time. A new problem to tackle will provide that occasion!

07 August 2022

Data Science: Decision Theory (Just the Quotes)

"Years ago a statistician might have claimed that statistics deals with the processing of data [...] today’s statistician will be more likely to say that statistics is concerned with decision making in the face of uncertainty." (Herman Chernoff & Lincoln E Moses, "Elementary Decision Theory", 1959)

"Another approach to management theory, undertaken by a growing and scholarly group, might be referred to as the decision theory school. This group concentrates on rational approach to decision-the selection from among possible alternatives of a course of action or of an idea. The approach of this school may be to deal with the decision itself, or to the persons or organizational group making the decision, or to an analysis of the decision process. Some limit themselves fairly much to the economic rationale of the decision, while others regard anything which happens in an enterprise the subject of their analysis, and still others expand decision theory to cover the psychological and sociological aspect and environment of decisions and decision-makers." (Harold Koontz, "The Management Theory Jungle," 1961)

"The term hypothesis testing arises because the choice as to which process is observed is based on hypothesized models. Thus hypothesis testing could also be called model testing. Hypothesis testing is sometimes called decision theory. The detection theory of communication theory is a special case." (Fred C Scweppe, "Uncertain dynamic systems", 1973)

"In decision theory, mathematical analysis shows that once the sampling distribution, loss function, and sample are specified, the only remaining basis for a choice among different admissible decisions lies in the prior probabilities. Therefore, the logical foundations of decision theory cannot be put in fully satisfactory form until the old problem of arbitrariness (sometimes called 'subjectiveness') in assigning prior probabilities is resolved." (Edwin T Jaynes, "Prior Probabilities", 1978)

"Decision theory, as it has grown up in recent years, is a formalization of the problems involved in making optimal choices. In a certain sense - a very abstract sense, to be sure - it incorporates among others operations research, theoretical economics, and wide areas of statistics, among others." (Kenneth Arrow, "The Economics of Information", 1984) 

"Cybernetics is concerned with scientific investigation of systemic processes of a highly varied nature, including such phenomena as regulation, information processing, information storage, adaptation, self-organization, self-reproduction, and strategic behavior. Within the general cybernetic approach, the following theoretical fields have developed: systems theory (system), communication theory, game theory, and decision theory." (Fritz B Simon et al, "Language of Family Therapy: A Systemic Vocabulary and Source Book", 1985)

"A field of study that includes a methodology for constructing computer simulation models to achieve better under-standing of social and corporate systems. It draws on organizational studies, behavioral decision theory, and engineering to provide a theoretical and empirical base for structuring the relationships in complex systems." (Virginia Anderson & Lauren Johnson, "Systems Thinking Basics: From Concepts to Casual Loops", 1997) 

"A decision theory that rests on the assumptions that human cognitive capabilities are limited and that these limitations are adaptive with respect to the decision environments humans frequently encounter. Decision are thought to be made usually without elaborate calculations, but instead by using fast and frugal heuristics. These heuristics certainly have the advantage of speed and simplicity, but if they are well matched to a decision environment, they can even outperform maximizing calculations with respect to accuracy. The reason for this is that many decision environments are characterized by incomplete information and noise. The information we do have is usually structured in a specific way that clever heuristics can exploit." (E Ebenhoh, "Agent-Based Modelnig with Boundedly Rational Agents", 2007)

18 July 2022

Performance Management: Excellence (Just the Quotes)

"Excellence is an art won by training and habituation. We do not act rightly because we have virtue or excellence, but we rather have those because we have acted rightly. We are what we repeatedly do. Excellence, then, is not an act but a habit." (Aristotle)

"With regard to excellence, it is not enough to know, but we must try to have and use it." (Aristotel, "Nochomachean Ethics", cca. 340 BC)

"It takes a long time to bring excellence to maturity." (Publilius Syrus, "Moral Sayings", cca. 1st century BC)

"The best way to come to grips with one’s own business knowledge is to look at the things the business has done well, and the things it apparently does poorly. […] Knowledge is a perishable commodity. It has to be reaffirmed, relearned, repracticed all the time. One has to work constantly at regaining one’s specific excellence. […] The right knowledge is the knowledge needed to exploit the market opportunities." (Peter F Drucker, "Managing for Results: Economic Tasks and Risk-taking Decisions", 1964)

"Because ease of use is the purpose, this ratio of function to conceptual complexity is the ultimate test of system design. Neither function alone nor simplicity alone defines a good design. [...] Function, and not simplicity, has always been the measure of excellence for its designers." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"The achievement of excellence can occur only if the organization promotes a culture of creative dissatisfaction." (Lawrence M Miller, "American Spirit", 1984)

"Managers jeopardize product quality by setting unreachable deadlines. They don’​​​​​​t think about their action in such terms; they think rather that what they’​​​​​​re doing is throwing down an interesting challenge to their workers, something to help them strive for excellence." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"When a team outgrows individual performance and learns team confidence, excellence becomes a reality." (Joe Paterno, American Heritage, 1988)

"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)

"The desire for excellence is an essential feature for doing great work. Without such a goal you will tend to wander like a drunken sailor. The sailor takes one step in one direction and the next in some independent direction. As a result the steps tend to cancel each other, and the expected distance from the starting point is proportional to the square root of the number of steps taken. With a vision of excellence, and with the goal of doing significant work, there is tendency for the steps to go in the same direction and thus go a distance proportional to the number of steps taken, which in a lifetime is a large number indeed." (Richard Hamming, "The Art of Doing Science and Engineering: Learning to Learn", 1997)

"If a team cannot perform with excellence at a moment's notice, they probably will fail in the long run." (Mike Krzyzewski, "Leading with the Heart: Coach K's Successful Strategies for Basketball, Business, and Life", 2010)

"In the pursuit of excellence, there is no finish line." (Robert H Farman)

09 July 2022

Strategic Management: Customers (Just the Quotes)

"A business process is a collection of activities that takes one or more kinds of input and creates an output that is of value to the customer. A business process has a goal and is affected by events occurring in the external world or in other processes." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"Reengineering posits a radical new principle: that the design of work must be based not on hierarchical management and the specialization of labor but on end-to-end processes and the creation of value for the customer." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"This is what systems thinking is all about: the idea of building an organization in which each piece, and partial solution of the organization has the fit, alignment, and integrity with your overall organization as a system, and its outcome of serving the customer." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"In business, as in game theory and chess, all great strategies start with a vision of the future. In one sense, the recipe is simple: it should include a sense of where the organization should go, what customers are likely to pay for, and how the organization can offer a unique product or service that customers will buy. The devil, of course, lies in the details." (David B Yoffie & Michael A Cusumano, "Strategy Rules", 2015)

"Thinking strategically is the fun part of business. Great strategists think big thoughts about the purpose of their enterprises, the long-run visions for their firms, the big bets they plan to make, and the products, platforms, and ecosystems they hope to build. But it is not enough to think big thoughts. To become a great strategist, you must turn your vision and high-level ideas into tactics, actions, and organizations that reach the customer and fend off the competition." (David B Yoffie & Michael A Cusumano, "Strategy Rules", 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)

"A clear, thoughtful mission statement, developed collaboratively with and shared with managers, employees, and often customers, provides a shared sense of purpose, direction, and opportunity." (Philip Kotler & Kevin L Keller, "Marketing Management" 15th Ed., 2016) 

"Evidence is freely available which demonstrates a gap between what the company thinks is important to customers and what customers actually deem to be the most important when it comes to making their choices. The failure to understand what is really important leads to customers receiving a sub-optimal experience and the company sub-optimising its commercial position." (Alan Pennington, "The Customer Experience Book", 2016)

"Data from the customer interactions is the lifeblood for any organisation to view, understand and optimise the customer experience both remotely and on the front line! In the same way that customer experience experts understand that it’s the little things that count, it’s the small data that can make all the difference." (Alan Pennington, "The Customer Experience Book", 2016)

"[…] deliver a customer experience where the customer sees real value from how you use the data that they share with you and they will keep interacting/sharing that data and their consent for you to use it!" (Alan Pennington, "The Customer Experience Book", 2016)

"Somebody once told me, 'Manage the top line, and the bottom line will follow.' What's the top line? It's things like, why are we doing this in the first place? What's our strategy? What are customers saying? How responsive are we? Do we have the best products and the best people? Those are the kind of questions you have to focus on." (Steve Jobs, "Motivating Thoughts of Steve Jobs", 2016)

"The bad news is that companies tend to focus on three out of the four elements of the balanced scorecard and emphasis is skewed away from the customer component, which is the least understood and believed by many to be the least quantifiable." (Alan Pennington, "The Customer Experience Book", 2016)

03 July 2022

Data Science: Myths (Just the Quotes)

"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)

"An oft-repeated rule of thumb in any sort of statistical model fitting is 'you can't fit a model with more parameters than data points'. This idea appears to be as wide-spread as it is incorrect. On the contrary, if you construct your models carefully, you can fit models with more parameters than datapoints [...]. A model with more parameters than datapoints is known as an under-determined system, and it's a common misperception that such a model cannot be solved in any circumstance. [...] this misconception, which I like to call the 'model complexity myth' [...] is not true in general, it is true in the specific case of simple linear models, which perhaps explains why the myth is so pervasive." (Jake Vanderplas, "The Model Complexity Myth", 2015) [source]

"One of the biggest truths about the real–time analytics is that nothing is actually real–time; it's a myth. In reality, it's close to real–time. Depending upon the performance and ability of a solution and the reduction of operational latencies, the analytics could be close to real–time, but, while day-by-day we are bridging the gap between real–time and near–real–time, it's practically impossible to eliminate the gap due to computational, operational, and network latencies." (Shilpi Saxena & Saurabh Gupta, "Practical Real-time Data Processing and Analytics", 2017)

"The field of big-data analytics is still littered with a few myths and evidence-free lore. The reasons for these myths are simple: the emerging nature of technologies, the lack of common definitions, and the non-availability of validated best practices. Whatever the reasons, these myths must be debunked, as allowing them to persist usually has a negative impact on success factors and Return on Investment (RoI). On a positive note, debunking the myths allows us to set the right expectations, allocate appropriate resources, redefine business processes, and achieve individual/organizational buy-in." (Prashant Natarajan et al, "Demystifying Big Data and Machine Learning for Healthcare", 2017) 

 "The first myth is that prediction is always based on time-series extrapolation into the future (also known as forecasting). This is not the case: predictive analytics can be applied to generate any type of unknown data, including past and present. In addition, prediction can be applied to non-temporal (time-based) use cases such as disease progression modeling, human relationship modeling, and sentiment analysis for medication adherence, etc. The second myth is that predictive analytics is a guarantor of what will happen in the future. This also is not the case: predictive analytics, due to the nature of the insights they create, are probabilistic and not deterministic. As a result, predictive analytics will not be able to ensure certainty of outcomes." (Prashant Natarajan et al, "Demystifying Big Data and Machine Learning for Healthcare", 2017) 

"One of the biggest myths is the belief that data science is an autonomous process that we can let loose on our data to find the answers to our problems. In reality, data science requires skilled human oversight throughout the different stages of the process. [...] The second big myth of data science is that every data science project needs big data and needs to use deep learning. In general, having more data helps, but having the right data is the more important requirement. [...] A third data science myth is that modern data science software is easy to use, and so data science is easy to do. [...] The last myth about data science [...] is the belief that data science pays for itself quickly. The truth of this belief depends on the context of the organization. Adopting data science can require significant investment in terms of developing data infrastructure and hiring staff with data science expertise. Furthermore, data science will not give positive results on every project." (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"[...] the focus on Big Data AI seems to be an excuse to put forth a number of vague and hand-waving theories, where the actual details and the ultimate success of neuroscience is handed over to quasi- mythological claims about the powers of large datasets and inductive computation. Where humans fail to illuminate a complicated domain with testable theory, machine learning and big data supposedly can step in and render traditional concerns about finding robust theories. This seems to be the logic of Data Brain efforts today. (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

02 July 2022

Strategic Management: Integration (Just the Quotes)

"By integration we mean the process of achieving unity of effort among the various subsystems in the accomplishment of the organization's tasks." (Paul R Lawrence, "Organization and environment: Managing differentiation and integration", 1967)

"No matter how difficult or unprecedented the problem, a breakthrough to the best possible solution can come only from a combination of rational analysis, based on the real nature of things, and imaginative reintegration of all the different items into a new pattern, using nonlinear brainpower. This is always the most effective approach to devising strategies for dealing successfully with challenges and opportunities, in the market arena as on the battlefield." (Kenichi Ohmae, "The Mind Of The Strategist", 1982)

"Culture [is] a pattern of basic assumptions invented, discovered, or developed by a given group as it learns to cope with its problems of external adaptation and internal integration that has worked well enough to be considered valid and, therefore, to be taught to new members as the correct way to perceive, think, and feel in relation to those problems." (Edgar H Schein, "Organizational Culture and Leadership", 1985)

"To keep the business from disintegrating, the concept of information systems architecture is becoming less of an option and more of a necessity." (John Zachman, "A Framework for Information Systems Architecture", 1987)

"Conventional process structures are fragmented and piecemeal, and they lack the integration necessary to maintain quality and service. They are breeding grounds for tunnel vision, as people tend to substitute the narrow goals of their particular department for the larger goals of the process as a whole. When work is handed off from person to person and unit to unit, delays and errors are inevitable. Accountability blurs, and critical issues fall between the cracks." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990) [source]

"But the net effect of increasing scale, centralization of capital, vertical integration and diversification within the corporate form of enterprise has been to replace the 'invisible hand' of the market by the 'visible hand' of the managers." (David Harvey, "The Limits To Capital", 2006)

Software Engineering: Integration (Just the Quotes)

"With increasing size and complexity of the implementations of information systems, it is necessary to use some logical construct (or architecture) for defining and controlling the interfaces and the integration of all of the components of the system." (John Zachman, "A Framework for Information Systems Architecture", 1987)

"The longer we wait between integrations and acceptance tests, the worse things get. Wait twice as long and we'll have four or more times the hassle. The reason is that one bug written just yesterday is pretty easy to find, while ten or a hundred written weeks ago can become almost impossible." (Ron Jeffries, "Extreme Programming Installed", 2001)

"The main activity of programming is not the origination of new independent programs, but in the integration, modification, and explanation of existing ones." (Terry Winograd, "Beyond Programming Languages", 1991)

"As the size of software systems increases, the algorithms and data structures of the computation no longer constitute the major design problems. When systems are constructed from many components, the organization of the overall system - the software architecture - presents a new set of design problems. This level of design has been addressed in a number of ways including informal diagrams and descriptive terms, module interconnection languages, templates and frameworks for systems that serve the needs of specific domains, and formal models of component integration mechanisms." (David Garlan & Mary Shaw, "An introduction to software architecture", Advances in software engineering and knowledge engineering Vol 1, 1993)

"Enterprise architecture is the organizing logic for business processes and IT infrastructure reflecting the integration and standardization requirements of a company's operation model. […] The key to effective enterprise architecture is to identify the processes, data, technology, and customer interfaces that take the operating model from vision to reality." (Jeanne W Ross et al, "Enterprise architecture as strategy: creating a foundation for business", 2006)

"Enterprise-architecture is the integration of everything the enterprise is and does. Even the term ‘architecture’ is perhaps a little misleading. It’s on a much larger scale, the scale of the whole rather than of single subsystems: more akin to city-planning than to the architecture of a single building. In something this large, there are no simple states of ‘as-is’ versus ‘to-be’, because its world is dynamic, not static. And it has to find some way to manage the messy confusion of what is, rather than the ideal that we might like it to be." (Tom Graves, "Real Enterprise-Architecture : Beyond IT to the whole enterprise", 2007)

"In many applications, integration or functional tests are used by default as the standard way to test almost all aspects of the system. However integration and functional tests are not the best way to detect and identify bugs. Because of the large number of components involved in a typical end-to-end test, it can be very hard to know where something has gone wrong. In addition, with so many moving parts, it is extremely difficult, if not completely unfeasible, to cover all of the possible paths through the application." (John F Smart, "Jenkins: The Definitive Guide", 2011)

Software Engineering: Components (Just the Quotes)

"With increasing size and complexity of the implementations of information systems, it is necessary to use some logical construct (or architecture) for defining and controlling the interfaces and the integration of all of the components of the system." (John Zachman, "A Framework for Information Systems Architecture", 1987)

"Architecture is defined as a clear representation of a conceptual framework of components and their relationships at a point in time […] a discussion of architecture must take into account different levels of architecture. These levels can be illustrated by a pyramid, with the business unit at the top and the delivery system at the base. An enterprise is composed of one or more Business Units that are responsible for a specific business area. The five levels of architecture are Business Unit, Information, Information System, Data and Delivery System. The levels are separate yet interrelated. [...] The idea if an enterprise architecture reflects an awareness that the levels are logically connected and that a depiction at one level assumes or dictates that architectures at the higher level." (W Bradford Rigdon, "Architectures and Standards", 1989)

"Programmers are responsible for software quality - quality in their own work, quality in the products that incorporate their work, and quality at the interfaces between components. Quality has never been and will never be tested in. The responsibility is both moral and professional." (Boris Beizer, "Software Testing Techniques", 1990)

"As the size of software systems increases, the algorithms and data structures of the computation no longer constitute the major design problems. When systems are constructed from many components, the organization of the overall system - the software architecture - presents a new set of design problems. This level of design has been addressed in a number of ways including informal diagrams and descriptive terms, module interconnection languages, templates and frameworks for systems that serve the needs of specific domains, and formal models of component integration mechanisms." (David Garlan & Mary Shaw, "An introduction to software architecture", Advances in software engineering and knowledge engineering Vol 1, 1993)

"Although the concept of an enterprise architecture (EA) has not been well defined and agreed upon, EAs are being developed to support information system development and enterprise reengineering. Most EAs differ in content and nature, and most are incomplete because they represent only data and process aspects of the enterprise. […] An EA is a conceptual framework that describes how an enterprise is constructed by defining its primary components and the relationships among these components." (M A Roos, "Enterprise architecture: definition, content, and utility", Enabling Technologies: Infrastructure for Collaborative Enterprises, 1994)

"An enterprise architecture is an abstract summary of some organizational component's design. The organizational strategy is the basis for deciding where the organization wants to be in three to five years. When matched to the organizational strategy, the architectures provide the foundation for deciding priorities for implementing the strategy." (Sue A Conger, "The new software engineering", 1994)

"Software architecture involves the description of elements from which systems are built, interactions among those elements, patterns that guide their composition, and constraints on these patterns. In general, a particular system is defined in terms of a collection of components and interactions among those components. Such a system may in turn be used as a (composite) element in a larger system design." (Mary Shaw & David Garlan,"Characteristics of Higher-Level Languages for Software Architecture", 1994)

"When the behavior of the system depends on the behavior of the parts, the complexity of the whole must involve a description of the parts, thus it is large. The smaller the parts that must be described to describe the behavior of the whole, the larger the complexity of the entire system. […] A complex system is a system formed out of many components whose behavior is emergent, that is, the behavior of the system cannot be simply inferred from the behavior of its components." (Yaneer Bar-Yamm, "Dynamics of Complexity", 1997)

"Complexity is that property of a model which makes it difficult to formulate its overall behaviour in a given language, even when given reasonably complete information about its atomic components and their inter-relations." (Bruce Edmonds, "Syntactic Measures of Complexity", 1999)

"Enterprise architecture is a family of related architecture components. This include information architecture, organization and business process architecture, and information technology architecture. Each consists of architectural representations, definitions of architecture entities, their relationships, and specification of function and purpose. Enterprise architecture guides the construction and development of business organizations and business processes, and the construction and development of supporting information systems." (Gordon B Davis, "The Blackwell encyclopedic dictionary of management information systems"‎, 1999)

"Enterprise architecture is a holistic representation of all the components of the enterprise and the use of graphics and schemes are used to emphasize all parts of the enterprise, and how they are interrelated. [...] Enterprise architectures are used to deal with intra-organizational processes, interorganizational cooperation and coordination, and their shared use of information and information technologies. Business developments, such as outsourcing, partnership, alliances and Electronic Data Interchange, extend the need for architecture across company boundaries." (Gordon B Davis," The Blackwell encyclopedic dictionary of management information systems"‎, 1999)

"Generically, an architecture is the description of the set of components and the relationships between them. […] A software architecture describes the layout of the software modules and the connections and relationships among them. A hardware architecture can describe how the hardware components are organized. However, both these definitions can apply to a single computer, a single information system, or a family of information systems. Thus 'architecture' can have a range of meanings, goals, and abstraction levels, depending on who’s speaking." (Frank J Armour et al, "A big-picture look at enterprise architectures", IT professional Vol 1 (1), 1999)

"Like a physical model, a conceptual model is an artificial system. It is however, made up of conceptual, and not physical components." (Ibrahim A Halloun, "Modeling Theory in Science Education", 2007) 

"Standards make it easier to reuse ideas and components, recruit people with relevant experience, encapsulate good ideas, and wire components together. However, the process of creating standards can sometimes take too long for industry to wait, and some standards lose touch with the real needs of the adopters they are intended to serve." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Taking a systems approach means paying close attention to results, the reasons we build a system. Architecture must be grounded in the client’s/user’s/customer’s purpose. Architecture is not just about the structure of components. One of the essential distinguishing features of architectural design versus other sorts of engineering design is the degree to which architectural design embraces results from the perspective of the client/user/customer. The architect does not assume some particular problem formulation, as “requirements” is fixed. The architect engages in joint exploration, ideally directly with the client/user/customer, of what system attributes will yield results worth paying for."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"In many applications, integration or functional tests are used by default as the standard way to test almost all aspects of the system. However integration and functional tests are not the best way to detect and identify bugs. Because of the large number of components involved in a typical end-to-end test, it can be very hard to know where something has gone wrong. In addition, with so many moving parts, it is extremely difficult, if not completely unfeasible, to cover all of the possible paths through the application." (John F Smart, "Jenkins: The Definitive Guide", 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)

Strategic Management: Standards (Just the 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)

"Leadership is lifting a person's vision to higher sights, the raising of a person's performance to a higher standard, the building of a personality beyond its normal limitations." (Peter Drucker, "Management: Tasks, Responsibilities, Challenges", 1973)

"Autonomation [..] performs a dual role. It eliminates overproduction, an important waste in manufacturing, and prevents the production of defective products. To accomplish this, standard work procedures, corresponding to each player's ability, must be adhered to at all times." (Taiichi Ohno, "Toyota Production System: Beyond Large-Scale Production", 1978)

"Five coordinating mechanisms seem to explain the fundamental ways in which organizations coordinate their work: mutual adjustment, direct supervision, standardization of work processes, standardization of work outputs, and standardization of worker skills." (Henry Mintzberg, "The Structuring of Organizations", 1979)

"There is no question that having standards and believing in them and staffing an administrative unit objectively using forecasted workloads will help you to maintain and enhance productivity." (Andrew S Grove, "High Output Management", 1983)

"Quality is a matter of faith. You set your standards, and you have to stick by them no matter what. That's easy when you've got plenty of product on hand, but it's another thing when the freezer is empty and you've got a truck at the door waiting for the next shipment to come off the production line. That's when you really earn your reputation for quality." (Ben Cohen, Inc. Magazine, 1987)

"Without a standard there is no logical basis for making a decision or taking action." (Joseph M Juran, "Managerial Breakthrough: The Classic Book on Improving Management Performance", 1995)

"A standard which is not revised after six months of its establishment, indicates that it is not in use." (Kaoru Ishikawa)

"If you do not conduct sufficient analysis and if you do not have firm technical knowledge, you cannot carry out improvement or standardization, nor can you perform good control or prepare control charts useful for effective control." (Kaoru Ishikawa)

"Standardization can progress and management can be conducted only when management policy is defined." (Kaoru Ishikawa)

"Standardization enables delegation of authority, allowing the top management and executives to have time to think about future plans and policy, which is their most important duty." (Kaoru Ishikawa)

"Standardize technology so that you may accumulate technology organically in your company." (Kaoru Ishikawa)

"Standardization is not only for quality control. It involves establishing standards for managing the business well as well as for all employees to enjoy their work with comfort." (Kaoru Ishikawa)

"Standardization without needs or clear objectives tends to become ritual." (Kaoru Ishikawa)

"The fact that standards are not revised demonstrates that your technology has stopped progressing." (Kaoru Ishikawa)

"The key is to standardize every technically definable area, and leave what cannot be standardized to the skills." (Kaoru Ishikawa)

"Top management is responsible for demonstrating methods for evaluating quality as well as standards." (Kaoru Ishikawa)

Software Engineering: Standards (Just the Quotes)

"Autonomation [..] performs a dual role. It eliminates overproduction, an important waste in manufacturing, and prevents the production of defective products. To accomplish this, standard work procedures, corresponding to each player's ability, must be adhered to at all times." (Taiichi Ohno, "Toyota Production System: Beyond Large-Scale Production", 1978)

"Recognition of the idea that a programming language should have a precise mathematical meaning or semantics dates from the early 1960s. The mathematics provides a secure, unambiguous, precise and stable specification of the language to serve as an agreed interface between its users and its implementors. Furthermore, it gives the only reliable grounds for a claim that different implementations are implementations of the same language. So mathematical semantics are as essential to the objective of language standardisation as measurement and counting are to the standardisation of nuts and bolts." (C Anthony R Hoare, "Communicating Sequential Processes", 1985)

"The [software] builders’​​​​​​ view of quality, on the other hand, is very different. Since their self-esteem is strongly tied to the quality of the product, they tend to impose quality standards of their own. The minimum that will satisfy them is more or less the best quality they have achieved in the past. This is invariably a higher standard than what the market requires and is willing to pay for." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"A pattern is a fully realized form original, or model accepted or proposed for imitation. With patterns, small piecework is standardized into a larger chunk or unit. Patterns become the building blocks for design and construction. Finding and applying patterns indicates progress in a field of human endeavor." (Peter Coad, "Object-oriented patterns", 1992)

"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)

"With each pattern, small piecework is standardized into a larger chunk or unit. Patterns become the building blocks for design and construction. Finding and applying patterns indicates progress in a field of human endeavor." (Peter Coad, "Object-oriented patterns", 1992)

"Standards make it easier to reuse ideas and components, recruit people with relevant experience, encapsulate good ideas, and wire components together. However, the process of creating standards can sometimes take too long for industry to wait, and some standards lose touch with the real needs of the adopters they are intended to serve." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Coding standards are rules, sometimes relatively arbitrary, that define the coding styles and conventions that are considered acceptable within a team or organization. In many cases, agreeing on a set of standards, and applying them, is more important than the standards themselves." (John F Smart, "Jenkins: The Definitive Guide", 2011)

"In many applications, integration or functional tests are used by default as the standard way to test almost all aspects of the system. However integration and functional tests are not the best way to detect and identify bugs. Because of the large number of components involved in a typical end-to-end test, it can be very hard to know where something has gone wrong. In addition, with so many moving parts, it is extremely difficult, if not completely unfeasible, to cover all of the possible paths through the application." (John F Smart, "Jenkins: The Definitive Guide", 2011)

Data Science: Nonlinearity (Just the Quotes)

"The term chaos is used in a specific sense where it is an inherently random pattern of behaviour generated by fixed inputs into deterministic (that is fixed) rules (relationships). The rules take the form of non-linear feedback loops. Although the specific path followed by the behaviour so generated is random and hence unpredictable in the long-term, it always has an underlying pattern to it, a 'hidden' pattern, a global pattern or rhythm. That pattern is self-similarity, that is a constant degree of variation, consistent variability, regular irregularity, or more precisely, a constant fractal dimension. Chaos is therefore order (a pattern) within disorder (random behaviour)." (Ralph D Stacey, "The Chaos Frontier: Creative Strategic Control for Business", 1991)

"In nonlinear systems - and the economy is most certainly nonlinear - chaos theory tells you that the slightest uncertainty in your knowledge of the initial conditions will often grow inexorably. After a while, your predictions are nonsense." (M Mitchell Waldrop, "Complexity: The Emerging Science at the Edge of Order and Chaos", 1992)

"In addition to dimensionality requirements, chaos can occur only in nonlinear situations. In multidimensional settings, this means that at least one term in one equation must be nonlinear while also involving several of the variables. With all linear models, solutions can be expressed as combinations of regular and linear periodic processes, but nonlinearities in a model allow for instabilities in such periodic solutions within certain value ranges for some of the parameters." (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"The dimensionality and nonlinearity requirements of chaos do not guarantee its appearance. At best, these conditions allow it to occur, and even then under limited conditions relating to particular parameter values. But this does not imply that chaos is rare in the real world. Indeed, discoveries are being made constantly of either the clearly identifiable or arguably persuasive appearance of chaos. Most of these discoveries are being made with regard to physical systems, but the lack of similar discoveries involving human behavior is almost certainly due to the still developing nature of nonlinear analyses in the social sciences rather than the absence of chaos in the human setting."  (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"So we pour in data from the past to fuel the decision-making mechanisms created by our models, be they linear or nonlinear. But therein lies the logician's trap: past data from real life constitute a sequence of events rather than a set of independent observations, which is what the laws of probability demand. [...] It is in those outliers and imperfections that the wildness lurks." (Peter L Bernstein, "Against the Gods: The Remarkable Story of Risk", 1996)

"There is a new science of complexity which says that the link between cause and effect is increasingly difficult to trace; that change (planned or otherwise) unfolds in non-linear ways; that paradoxes and contradictions abound; and that creative solutions arise out of diversity, uncertainty and chaos." (Andy P Hargreaves & Michael Fullan, "What’s Worth Fighting for Out There?", 1998)

"A system may be called complex here if its dimension (order) is too high and its model (if available) is nonlinear, interconnected, and information on the system is uncertain such that classical techniques can not easily handle the problem." (M Jamshidi, "Autonomous Control on Complex Systems: Robotic Applications", Current Advances in Mechanical Design and Production VII, 2000)

"Most physical systems, particularly those complex ones, are extremely difficult to model by an accurate and precise mathematical formula or equation due to the complexity of the system structure, nonlinearity, uncertainty, randomness, etc. Therefore, approximate modeling is often necessary and practical in real-world applications. Intuitively, approximate modeling is always possible. However, the key questions are what kind of approximation is good, where the sense of 'goodness' has to be first defined, of course, and how to formulate such a good approximation in modeling a system such that it is mathematically rigorous and can produce satisfactory results in both theory and applications." (Guanrong Chen & Trung Tat Pham, "Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems", 2001)

"Swarm intelligence can be effective when applied to highly complicated problems with many nonlinear factors, although it is often less effective than the genetic algorithm approach discussed later in this chapter. Swarm intelligence is related to swarm optimization […]. As with swarm intelligence, there is some evidence that at least some of the time swarm optimization can produce solutions that are more robust than genetic algorithms. Robustness here is defined as a solution’s resistance to performance degradation when the underlying variables are changed." (Michael J North & Charles M Macal, "Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation", 2007)

"Thus, nonlinearity can be understood as the effect of a causal loop, where effects or outputs are fed back into the causes or inputs of the process. Complex systems are characterized by networks of such causal loops. In a complex, the interdependencies are such that a component A will affect a component B, but B will in general also affect A, directly or indirectly.  A single feedback loop can be positive or negative. A positive feedback will amplify any variation in A, making it grow exponentially. The result is that the tiniest, microscopic difference between initial states can grow into macroscopically observable distinctions." (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007)

"All forms of complex causation, and especially nonlinear transformations, admittedly stack the deck against prediction. Linear describes an outcome produced by one or more variables where the effect is additive. Any other interaction is nonlinear. This would include outcomes that involve step functions or phase transitions. The hard sciences routinely describe nonlinear phenomena. Making predictions about them becomes increasingly problematic when multiple variables are involved that have complex interactions. Some simple nonlinear systems can quickly become unpredictable when small variations in their inputs are introduced." (Richard N Lebow, "Forbidden Fruit: Counterfactuals and International Relations", 2010)

"Given the important role that correlation plays in structural equation modeling, we need to understand the factors that affect establishing relationships among multivariable data points. The key factors are the level of measurement, restriction of range in data values (variability, skewness, kurtosis), missing data, nonlinearity, outliers, correction for attenuation, and issues related to sampling variation, confidence intervals, effect size, significance, sample size, and power." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

"Complexity is a relative term. It depends on the number and the nature of interactions among the variables involved. Open loop systems with linear, independent variables are considered simpler than interdependent variables forming nonlinear closed loops with a delayed response." (Jamshid Gharajedaghi, "Systems Thinking: Managing Chaos and Complexity A Platform for Designing Business Architecture" 3rd Ed., 2011)

"We have minds that are equipped for certainty, linearity and short-term decisions, that must instead make long-term decisions in a non-linear, probabilistic world." (Paul Gibbons, "The Science of Successful Organizational Change", 2015)

"Random forests are essentially an ensemble of trees. They use many short trees, fitted to multiple samples of the data, and the predictions are averaged for each observation. This helps to get around a problem that trees, and many other machine learning techniques, are not guaranteed to find optimal models, in the way that linear regression is. They do a very challenging job of fitting non-linear predictions over many variables, even sometimes when there are more variables than there are observations. To do that, they have to employ 'greedy algorithms', which find a reasonably good model but not necessarily the very best model possible." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Exponentially growing systems are prevalent in nature, spanning all scales from biochemical reaction networks in single cells to food webs of ecosystems. How exponential growth emerges in nonlinear systems is mathematically unclear. […] The emergence of exponential growth from a multivariable nonlinear network is not mathematically intuitive. This indicates that the network structure and the flux functions of the modeled system must be subjected to constraints to result in long-term exponential dynamics." (Wei-Hsiang Lin et al, "Origin of exponential growth in nonlinear reaction networks", PNAS 117 (45), 2020)

Data Science: Linearity (Just the Quotes)

"In addition to dimensionality requirements, chaos can occur only in nonlinear situations. In multidimensional settings, this means that at least one term in one equation must be nonlinear while also involving several of the variables. With all linear models, solutions can be expressed as combinations of regular and linear periodic processes, but nonlinearities in a model allow for instabilities in such periodic solutions within certain value ranges for some of the parameters." (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"So we pour in data from the past to fuel the decision-making mechanisms created by our models, be they linear or nonlinear. But therein lies the logician's trap: past data from real life constitute a sequence of events rather than a set of independent observations, which is what the laws of probability demand. [...] It is in those outliers and imperfections that the wildness lurks." (Peter L Bernstein, "Against the Gods: The Remarkable Story of Risk", 1996)

"All forms of complex causation, and especially nonlinear transformations, admittedly stack the deck against prediction. Linear describes an outcome produced by one or more variables where the effect is additive. Any other interaction is nonlinear. This would include outcomes that involve step functions or phase transitions. The hard sciences routinely describe nonlinear phenomena. Making predictions about them becomes increasingly problematic when multiple variables are involved that have complex interactions. Some simple nonlinear systems can quickly become unpredictable when small variations in their inputs are introduced." (Richard N Lebow, "Forbidden Fruit: Counterfactuals and International Relations", 2010)

"There are several key issues in the field of statistics that impact our analyses once data have been imported into a software program. These data issues are commonly referred to as the measurement scale of variables, restriction in the range of data, missing data values, outliers, linearity, and nonnormality." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

"Complexity is a relative term. It depends on the number and the nature of interactions among the variables involved. Open loop systems with linear, independent variables are considered simpler than interdependent variables forming nonlinear closed loops with a delayed response." (Jamshid Gharajedaghi, "Systems Thinking: Managing Chaos and Complexity A Platform for Designing Business Architecture" 3rd Ed., 2011)

"Without precise predictability, control is impotent and almost meaningless. In other words, the lesser the predictability, the harder the entity or system is to control, and vice versa. If our universe actually operated on linear causality, with no surprises, uncertainty, or abrupt changes, all future events would be absolutely predictable in a sort of waveless orderliness." (Lawrence K Samuels, "Defense of Chaos", 2013)

"An oft-repeated rule of thumb in any sort of statistical model fitting is 'you can't fit a model with more parameters than data points'. This idea appears to be as wide-spread as it is incorrect. On the contrary, if you construct your models carefully, you can fit models with more parameters than datapoints [...]. A model with more parameters than datapoints is known as an under-determined system, and it's a common misperception that such a model cannot be solved in any circumstance. [...] this misconception, which I like to call the 'model complexity myth' [...] is not true in general, it is true in the specific case of simple linear models, which perhaps explains why the myth is so pervasive." (Jake Vanderplas, "The Model Complexity Myth", 2015) [source]

"Random forests are essentially an ensemble of trees. They use many short trees, fitted to multiple samples of the data, and the predictions are averaged for each observation. This helps to get around a problem that trees, and many other machine learning techniques, are not guaranteed to find optimal models, in the way that linear regression is. They do a very challenging job of fitting non-linear predictions over many variables, even sometimes when there are more variables than there are observations. To do that, they have to employ 'greedy algorithms', which find a reasonably good model but not necessarily the very best model possible." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

Data Science: Analytics (Just the Quotes)

"Data are essential, but performance improvements and competitive advantage arise from analytics models that allow managers to predict and optimize outcomes. More important, the most effective approach to building a model rarely starts with the data; instead it originates with identifying the business opportunity and determining how the model can improve performance." (Dominic Barton & David Court, "Making Advanced Analytics Work for You", 2012) 

"Even with simple and usable models, most organizations will need to upgrade their analytical skills and literacy. Managers must come to view analytics as central to solving problems and identifying opportunities - to make it part of the fabric of daily operations." (Dominic Barton & David Court, "Making Advanced Analytics Work for You", 2012)

"There is another important distinction pertaining to mining data: the difference between (1) mining the data to find patterns and build models, and (2) using the results of data mining. Students often confuse these two processes when studying data science, and managers sometimes confuse them when discussing business analytics. The use of data mining results should influence and inform the data mining process itself, but the two should be kept distinct." (Foster Provost & Tom Fawcett, "Data Science for Business", 2013)

"It is important to remember that predictive data analytics models built using machine learning techniques are tools that we can use to help make better decisions within an organization and are not an end in themselves. It is paramount that, when tasked with creating a predictive model, we fully understand the business problem that this model is being constructed to address and ensure that it does address it." (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, worked examples, and case studies", 2015)

"Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more. Each of these is used by different communities and has different associations. Some have a long half-life, some less so." (Pedro Domingos, "The Master Algorithm", 2015)

"The human side of analytics is the biggest challenge to implementing big data." (Paul Gibbons, "The Science of Successful Organizational Change", 2015)

"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)

"One of the biggest truths about the real–time analytics is that nothing is actually real–time; it's a myth. In reality, it's close to real–time. Depending upon the performance and ability of a solution and the reduction of operational latencies, the analytics could be close to real–time, but, while day-by-day we are bridging the gap between real–time and near–real–time, it's practically impossible to eliminate the gap due to computational, operational, and network latencies." (Shilpi Saxena & Saurabh Gupta, "Practical Real-time Data Processing and Analytics", 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) 

"Big data is revolutionizing the world around us, and it is easy to feel alienated by tales of computers handing down decisions made in ways we don’t understand. I think we’re right to be concerned. Modern data analytics can produce some miraculous results, but big data is often less trustworthy than small data. Small data can typically be scrutinized; big data tends to be locked away in the vaults of Silicon Valley. The simple statistical tools used to analyze small datasets are usually easy to check; pattern-recognizing algorithms can all too easily be mysterious and commercially sensitive black boxes." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

27 June 2022

Data Science: Problems (Just the Quotes)

"The most important maxim for data analysis to heed, and one which many statisticians seem to have shunned is this: ‘Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.’ Data analysis must progress by approximate answers, at best, since its knowledge of what the problem really is will at best be approximate." (John W Tukey, "The Future of Data Analysis", Annals of Mathematical Statistics, Vol. 33, No. 1, 1962)

"The validation of a model is not that it is 'true' but that it generates good testable hypotheses relevant to important problems.” (Richard Levins, "The Strategy of Model Building in Population Biology”, 1966)

"Statistical methods are tools of scientific investigation. Scientific investigation is a controlled learning process in which various aspects of a problem are illuminated as the study proceeds. It can be thought of as a major iteration within which secondary iterations occur. The major iteration is that in which a tentative conjecture suggests an experiment, appropriate analysis of the data so generated leads to a modified conjecture, and this in turn leads to a new experiment, and so on." (George E P Box & George C Tjao, "Bayesian Inference in Statistical Analysis", 1973)

"The fact must be expressed as data, but there is a problem in that the correct data is difficult to catch. So that I always say 'When you see the data, doubt it!' 'When you see the measurement instrument, doubt it!' [...]For example, if the methods such as sampling, measurement, testing and chemical analysis methods were incorrect, data. […] to measure true characteristics and in an unavoidable case, using statistical sensory test and express them as data." (Kaoru Ishikawa, Annual Quality Congress Transactions, 1981)

"Doing data analysis without explicitly defining your problem or goal is like heading out on a road trip without having decided on a destination." (Michael Milton, "Head First Data Analysis", 2009)

"Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: 'there’s a lot of data, what can you make from it?'" (Mike Loukides, "What Is Data Science?", 2011)

"Smart data scientists don’t just solve big, hard problems; they also have an instinct for making big problems small." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"We have let ourselves become enchanted by big data only because we exoticize technology. We’re impressed with small feats accomplished by computers alone, but we ignore big achievements from complementarity because the human contribution makes them less uncanny. Watson, Deep Blue, and ever-better machine learning algorithms are cool. But the most valuable companies in the future won’t ask what problems can be solved with computers alone. Instead, they’ll ask: how can computers help humans solve hard problems?" (Peter Thiel & Blake Masters, "Zero to One: Notes on Startups, or How to Build the Future", 2014)

"Machine learning is a science and requires an objective approach to problems. Just like the scientific method, test-driven development can aid in solving a problem. The reason that TDD and the scientific method are so similar is because of these three shared characteristics: Both propose that the solution is logical and valid. Both share results through documentation and work over time. Both work in feedback loops." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"While Big Data, when managed wisely, can provide important insights, many of them will be disruptive. After all, it aims to find patterns that are invisible to human eyes. The challenge for data scientists is to understand the ecosystems they are wading into and to present not just the problems but also their possible solutions." (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

"The term [Big Data] simply refers to sets of data so immense that they require new methods of mathematical analysis, and numerous servers. Big Data - and, more accurately, the capacity to collect it - has changed the way companies conduct business and governments look at problems, since the belief wildly trumpeted in the media is that this vast repository of information will yield deep insights that were previously out of reach." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"There are other problems with Big Data. In any large data set, there are bound to be inconsistencies, misclassifications, missing data - in other words, errors, blunders, and possibly lies. These problems with individual items occur in any data set, but they are often hidden in a large mass of numbers even when these numbers are generated out of computer interactions." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

"Your machine-learning algorithm should answer a very specific question that tells you something you need to know and that can be answered appropriately by the data you have access to. The best first question is something you already know the answer to, so that you have a reference and some intuition to compare your results with. Remember: you are solving a business problem, not a math problem."(Prashant Natarajan et al, "Demystifying Big Data and Machine Learning for Healthcare", 2017)

"Data scientists should have some domain expertise. Most data science projects begin with a real-world, domain-specific problem and the need to design a data-driven solution to this problem. As a result, it is important for a data scientist to have enough domain expertise that they understand the problem, why it is important, an dhow a data science solution to the problem might fit into an organization’s processes. This domain expertise guides the data scientist as she works toward identifying an optimized solution." (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"One of the biggest myths is the belief that data science is an autonomous process that we can let loose on our data to find the answers to our problems. In reality, data science requires skilled human oversight throughout the different stages of the process. [...] The second big myth of data science is that every data science project needs big data and needs to use deep learning. In general, having more data helps, but having the right data is the more important requirement. [...] A third data science myth is that modern data science software is easy to use, and so data science is easy to do. [...] The last myth about data science [...] is the belief that data science pays for itself quickly. The truth of this belief depends on the context of the organization. Adopting data science can require significant investment in terms of developing data infrastructure and hiring staff with data science expertise. Furthermore, data science will not give positive results on every project." (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"The goal of data science is to improve decision making by basing decisions on insights extracted from large data sets. As a field of activity, data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting nonobvious and useful patterns from large data sets. It is closely related to the fields of data mining and machine learning, but it is broader in scope." (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"Many people have strong intuitions about whether they would rather have a vital decision about them made by algorithms or humans. Some people are touchingly impressed by the capabilities of the algorithms; others have far too much faith in human judgment. The truth is that sometimes the algorithms will do better than the humans, and sometimes they won’t. If we want to avoid the problems and unlock the promise of big data, we’re going to need to assess the performance of the algorithms on a case-by-case basis. All too often, this is much harder than it should be. […] So the problem is not the algorithms, or the big datasets. The problem is a lack of scrutiny, transparency, and debate." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

Strategic Management: Output (Just the 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)

"The definition of a problem and the action taken to solve it largely depend on the view which the individuals or groups that discovered the problem have of the system to which it refers. A problem may thus find itself defined as a badly interpreted output, or as a faulty output of a faulty output device, or as a faulty output due to a malfunction in an otherwise faultless system, or as a correct but undesired output from a faultless and thus undesirable system. All definitions but the last suggest corrective action; only the last definition suggests change, and so presents an unsolvable problem to anyone opposed to change." (Herbert Brün, "Technology and the Composer", 1971)

"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. But in both widget manufacturing and administrative work, something else can also increase the productivity of the black box. This is called work simplification. To get leverage this way, you first need to create a flow chart of the production process as it exists. Every single step must be shown on it; no step should be omitted in order to pretty things up on paper. Second, count the number of steps in the flow chart so that you know how many you started with. Third, set a rough target for reduction of the number of steps." (Andrew S Grove, "High Output Management", 1983)

"In other words, the output of the planning process is the decisions made and the actions taken as a result of the process." (Andrew S Grove, "High Output Management", 1983)

"Five coordinating mechanisms seem to explain the fundamental ways in which organizations coordinate their work: mutual adjustment, direct supervision, standardization of work processes, standardization of work outputs, and standardization of worker skills." (Henry Mintzberg, "The Structuring of Organizations", 1979)

"[...] in the work of the soft professions, it becomes very difficult to distinguish between output and activity. And as noted, 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)

"[...] leverage, which is the output generated by a specific type of work activity. An activity with high leverage will generate a high level of output; an activity with low leverage, a low level of output." (Andrew S Grove, "High Output Management", 1983)

"Managerial productivity - that is, the output of a manager per unit of time worked - can be increased in three ways: 1.  Increasing the rate with which a manager performs his activities, speeding up his work. 2.  Increasing the leverage associated with the various managerial activities. 3.  Shifting the mix of a manager’s activities from those with lower to those with higher leverage." (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)

"[...] the output of a manager is a result achieved by a group either under her supervision or under her influence. While the manager’s own work is clearly very important, that in itself does not create output. Her organization does." (Andrew S Grove, "High Output Management", 1983)

"The single most important task of a manager is to elicit peak performance from his subordinates. So if two things limit high output, a manager has two ways to tackle the issue: through training and motivation." (Andrew S Grove, "High Output Management", 1983)

"A business process is a collection of activities that takes one or more kinds of input and creates an output that is of value to the customer. A business process has a goal and is affected by events occurring in the external world or in other processes." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"The aim of leadership should be to improve the performance of man and machine, to improve quality, to increase output, and simultaneously to bring pride of workmanship to people. Put in a negative way, the aim of leadership is not merely to find and record failures of men, but to remove the causes of failure: to help people to do a better job with less effort." (W Edwards Deming, "Out of the Crisis", 2000)

"Efficiency refers to the amount of resources used to achieve the organization’s goals. It is based on the quantity of raw materials, money, and employees necessary to produce a given level of output. Effectiveness is a broader term, meaning the degree to which an organization achieves its goals." (Richard L Daft, "Organization Theory and Design", 3rd Ed., 2010)

"Key results are the levers you pull, the marks you hit to achieve the goal. If an objective is well framed, three to five KRs will usually be adequate to reach it. Too many can dilute focus and obscure progress. Besides, each key result should be a challenge in its own right. If you’re certain you’re going to nail it, you’re probably not pushing hard enough. [...] Key results should be succinct, specific, and measurable. A mix of outputs and inputs is helpful. Finally, completion of all key results must result in attainment of the objective. If not, it’s not an OKR." (John Doerr, "Measure what Matters", 2018)

Related Posts Plugin for WordPress, Blogger...