Business Intelligence: Data Modeling (Part III: From Data to Storytelling I)

Business Intelligence Series

Data is an amalgam of signs, words, numbers and other visual or auditory elements used together to memorize, interpret, communicate and do whatever operation may seem appropriate with them. However, the data we use is usually part of one or multiple stories - how something came into being, what it represents, how is used in the various mental and non-mental processes - respectively, the facts, concepts, ideas, contexts places or other physical and nonphysical elements that are brought in connection with.

When we are the active creators of a story, we can in theory easily look at how the story came into being, the data used and its role in the bigger picture, respective the transformative elements considered or left out, etc. However, as soon we deal with a set of data, facts, or any other elements of a story we are not familiar with, we need to extrapolate the hypothetical elements that seem to be connected to the story. We need to make sense of these elements and consider all that seems meaningful, what we considered or left out shaping the story differently. 

As children and maybe even later, all of us dealt with stories in one way or another, we all got fascinated by metaphors' wisdom and felt the energy that kept us awake, focused and even transformed by the words coming from narrator's voice, probably without thinking too much at the whole picture, but letting the words do their magic. Growing up, the stories grew in complexity, probably became richer in meaning and contexts, as we were able to decipher the metaphors and other elements, as we included more knowledge about the world around, about stories and storytelling.

In the professional context, storytelling became associated with our profession - data, information, knowledge and wisdom being created, assimilated and exchanged in more complex processes. From, this perspective, data storytelling is about putting data into a (business) context to seed cultural ground, to promote decision making and better understanding by building a narrative around the data, problems, challenges, opportunities, and further organizational context.

Further on, from a BI's perspective, all these cognitive processes impact on how data, information and knowledge are created, (pre)processed, used and communicated in organizations especially when considering data visualizations and their constituent elements (e.g. data, text, labels, metaphors, visual cues), the narratives that seem compelling and resonate with the auditorium. 

There's no wonder that data storytelling has become something not to neglect in many business contexts. Storytelling has proved that words, images and metaphors can transmit ideas and knowledge, be transformative, make people think, or even act without much thinking. Stories have the power to seed memes, ideas, or more complex constructs into our minds, they can be used (for noble purposes) or misused. 

A story's author usually takes compelling images, metaphors, and further elements, manipulates them to the degree they become interesting to himself/herself, to the auditorium, to the degree they are transformative and become an element of the business vocabulary, respectively culture, without the need to reiterate them when needed to bring more complex concepts, ideas or metaphors into being.  

A story can be seen as a replication of the constituting elements, while storytelling is a set of functions that operate on them and change the initial structure and content into something that might look or not like the initial story. Through retelling and reprocessing in any form, the story changes independently of its initial form and content. Sometimes, the auditorium makes connections not recognized or intended by the storyteller. Other times, the use and manipulation of language makes the story change as seems fit. 

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Business Intelligence: New Technologies, Old Challenges II (ETL vs. ELT)

 

Data lakes and similar cloud-based repositories drove the requirement of loading the raw data before performing any transformations on the data. At least that’s the approach the new wave of ELT (Extract, Load, Transform) technologies use to handle analytical and data integration workloads, which is probably recommendable for the mentioned cloud-based contexts. However, ELT technologies are especially relevant when is needed to handle data with high velocity, variance, validity or different value of truth (aka big data). This because they allow processing the workloads over architectures that can be scaled with workloads’ demands.

This is probably the most important aspect, even if there can be further advantages, like using built-in connectors to a wide range of sources or implementing complex data flow controls. The ETL (Extract, Transform, Load) tools have the same capabilities, maybe reduced to certain data sources, though their newer versions seem to bridge the gap.

One of the most stressed advantages of ELT is the possibility of having all the (business) data in the repository, though these are not technological advantages. The same can be obtained via ETL tools, even if this might involve upon case a bigger effort, effort depending on the functionality existing in each tool. It’s true that ETL solutions have a narrower scope by loading a subset of the available data, or that transformations are made before loading the data, though this depends on the scope considered while building the data warehouse or data mart, respectively the design of ETL packages, and both are a matter of choice, choices that can be traced back to business requirements or technical best practices.

Some of the advantages seen are context-dependent – the context in which the technologies are put, respectively the problems are solved. It is often imputed to ETL solutions that the available data are already prepared (aggregated, converted) and new requirements will drive additional effort. On the other side, in ELT-based solutions all the data are made available and eventually further transformed, but also here the level of transformations made depends on specific requirements. Independently of the approach used, the data are still available if needed, respectively involve certain effort for further processing.

Building usable and reliable data models is dependent on good design, and in the design process reside the most important challenges. In theory, some think that in ETL scenarios the design is done beforehand though that’s not necessarily true. One can pull the raw data from the source and build the data models in the target repositories.

Data conversion and cleaning is needed under both approaches. In some scenarios is ideal to do this upfront, minimizing the effect these processes have on data’s usage, while in other scenarios it’s helpful to address them later in the process, with the risk that each project will address them differently. This can become an issue and should be ideally addressed by design (e.g. by building an intermediate layer) or at least organizationally (e.g. enforcing best practices).

Advancing that ELT is better just because the data are true (being in raw form) can be taken only as a marketing slogan. The degree of truth data has depends on the way data reflects business’ processes and the way data are maintained, while their quality is judged entirely on their intended use. Even if raw data allow more flexibility in handling the various requests, the challenges involved in processing can be neglected only under the consequences that follow from this.

Looking at the analytics and data integration cloud-based technologies, they seem to allow both approaches, thus building optimal solutions relying on professionals’ wisdom of making appropriate choices.

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Business Intelligence: New Technologies, Old Challenges I (Introduction)

Each important technology has the potential of creating divides between the specialists from a given field. This aspect is more suggestive in the data-driven fields like BI/Analytics or Data Warehousing. The data professionals (engineers, scientists, analysts, developers) skilled only in the new wave of technologies tend to disregard the role played by the former technologies and their role in the data landscape. The argumentation for such behavior is rooted in the belief that a new technology is better and can solve any problem better than previous technologies did. It’s a kind of mirage professionals and customers can easily fall under.

Being bigger, faster, having new functionality, doesn’t make a tool the best choice by default. The choice must be rooted in the problem to be solved and the set of requirements it comes with. Just because a vibratory rammer is a new technology, is faster and has more power in applying pressure, this doesn’t mean that it will replace a hammer. Where a certain type of power is needed the vibratory rammer might be the best tool, while for situations in which a minimum of power and probably more precision is needed, like driving in a nail, then an adequately sized hammer will prove to be a better choice.

A technology is to be used in certain (business/technological) contexts, and even if contexts often overlap, the further details (aka requirements) should lead to the proper use of tools. It’s in a professional’s duties to be able to differentiate between contexts, requirements and the capabilities of the tools appropriate for each context. In this resides partially a professional’s mastery over its field of work and of providing adequate solutions for customers’ needs. Especially in IT, it’s not enough to master the new tools but also have an understanding about preceding tools, usage contexts, capabilities and challenges.

From an historical perspective each tool appeared to fill a demand, and even if maybe it didn’t manage to fill it adequately, the experience obtained can prove to be valuable in one way or another. Otherwise, one risks reinventing the wheel, or more dangerously, repeating the failures of the past. Each new technology seems to provide a deja-vu from this perspective.

Moreover, a new technology provides new opportunities and requires maybe to change our way of thinking in respect to how the technology is used and the processes or techniques associated with it. Knowledge of the past technologies help identifying such opportunities easier. How a tool is used is also a matter of skills, while its appropriate use and adoption implies an inherent learning curve. Having previous experience with similar tools tends to reduce the learning curve considerably, though hands-on learning is still necessary, and appropriate learning materials or tutoring is upon case needed for a smoother transition.

In what concerns the implementation of mature technologies, most of the challenges were seldom the technologies themselves but of non-technical nature, ranging from the poor understanding/knowledge about the tools, their role and the implications they have for an organization, to an organization’s maturity in leading projects. Even the most-advanced technology can fail in the hands of non-experts. Experience can’t be judged based only on the years spent in the field or the number of projects one worked on, but on the understanding acquired about implementation and usage’s challenges. These latter aspects seem to be widely ignored, even if it can make the difference between success and failure in a technology’s implementation.

Ultimately, each technology is appropriate in certain contexts and a new technology doesn’t necessarily make another obsolete, at least not until the old contexts become obsolete.

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Knowledge Management: Definitions II (What's in a Name)

Browsing through the various books on databases and programming appeared over the past 20-30 years, it’s probably hard not to notice the differences between the definitions given even for straightforward and basic concepts like the ones of view, stored procedure or function. Quite often the definitions lack precision and rigor, are circular and barely differentiate the defined term (aka concept) from other terms. In addition, probably in the attempt of making the definitions concise, important definitory characteristics are omitted.

Unfortunately, the same can be said about other non-scientific books, where the lack of appropriate definitions make the understanding of the content and presented concepts more difficult. Even if the reader can arrive in time to an approximate understanding of what is meant, one might have the feeling that builds castles in the air as long there is no solid basis to build upon – and that should be the purpose of a definition – to offer the foundation on which the reader can build upon. Especially for the readers coming from the scientific areas this lack of appropriateness and moreover, the lack of definitions, feels maybe more important than for the professional who already mastered the respective areas.

In general, a definition of a term is a well-defined descriptive statement which serves to differentiate it from related concepts. A well-defined definition should be meaningful, explicit, concise, precise, non-circular, distinct, context-dependent, relevant, rigorous, and rooted in common sense. In addition, each definition needs to be consistent through all the content and when possible, consistent with the other definitions provided. Ideally the definitions should cover as much of possible from the needed foundation and provide a unitary consistent multilayered non-circular and hierarchical structure that facilitates the reading and understanding of the given material.

Thus, one can consider the following requirements for a definition:

Meaningful: the description should be worthwhile and convey the required meaning for understanding the concept.

Explicit: the description must state clearly and provide enough information/detail so it can leave no room for confusion or doubt.

Context-dependent: the description should provide upon case the context in which the term is defined.

Concise: the description should be as succinct as possible – obtaining the maximum of understanding from a minimum of words.

Precise: the description should be made using unambiguous words that provide the appropriate meaning individually and as a whole.

Intrinsic non-circularity: requires that the term defined should not be used as basis for definitions, leading thus to trivial definitions like “A is A”.

Distinct: the description should provide enough detail to differentiate the term from other similar others.

Relevant: the description should be closely connected or appropriate to what is being discussed or presented.

Rigorous: the descriptions should be the result of a thorough and careful thought process in which the multiple usages and forms are considered.  

Extrinsic non-circularity: requires that the definitions of two distinct terms should not be circular (e.g. term A’s definition is based on B, while B’s definition is based on A), situation usually met occasionally in dictionaries.

Rooted in common sense: the description should not deviate from the common-sense acceptance of the terms used, typically resulted from socially constructed or dictionary-based definitions.

Unitary consistent multilayered hierarchical structure: the definitions should be given in an evolutive structure that facilitates learning, typically in the order in which the concepts need to be introduced without requiring big jumps in understanding. Even if concepts have in general a networked structure, hierarchies can be determined, especially based on the way concepts use other concepts in their definitions. In addition, the definitions must be consistent – hold together – respectively be unitary – form a whole.

Project Management: The Butterflies of Project Management

Expressed metaphorically as "the flap of a butterfly’s wings in Brazil set off a tornado in Texas”, in Chaos Theory the “butterfly effect” is a hypothesis rooted in Edward N Lorenz’s work on weather forecasting and used to depict the sensitive dependence on initial conditions in nonlinear processes, systems in which the change in input is not proportional to the change in output.  

Even if overstated, the flapping of wings advances the idea that a small change (the flap of wings) in the initial conditions of a system cascades to a large-scale chain of events leading to large-scale phenomena (the tornado) . The chain of events is known as the domino effect and represents the cumulative effect produced when one event sets off a chain of similar events. If the butterfly metaphor doesn’t catch up maybe it’s easier to visualize the impact as a big surfing wave – it starts small and increases in size to the degree that it can bring a boat to the shore or make an armada drown under its force. 

Projects start as narrow activities however the longer they take and the broader they become tend to accumulate force and behave like a wave, having the force to push or drawn an organization in the flood that comes with it. A project is not only a system but a complex ecosystem - aggregations of living organisms and nonliving components with complex interactions forming a unified whole with emergent behavior deriving from the structure rather than its components - groups of people tend to  self-organize, to swarm in one direction or another, much like birds do, while knowledge seems to converge from unrelated sources (aka consilience). 

 Quite often ignored, the context in which a project starts is very important, especially because these initial factors or conditions can have a considerable impact reflected in people’s perception regarding the state or outcomes of the project, perception reflected eventually also in the decisions made during the later phases of the project. The positive or negative auspices can be easily reinforced by similar events. Given the complex correlations and implications, aspects not always correct perceived and understood can have a domino effect. 

The preparations for the project start – the Business Case, setting up the project structure, communicating project’s expectation and addressing stakeholders’ expectations, the kick-off meeting, the approval of the needed resources, the knowledge available in the team, all these have a certain influence on the project. A bad start can haunt a project long time after its start, even if the project is on the right track and makes a positive impact. In reverse, a good start can shade away some mishaps on the way, however there’s also the danger that the mishaps are ignored and have greater negative impact on the project. It may look as common sense however the first image often counts and is kept in people’s memory for a long time. 

As people are higher perceptive to negative as to positive events, there are higher the chances that a multitude of negative aspects will have bigger impact on the project. It’s again something that one can address as the project progresses. It’s not necessarily about control but about being receptive to the messages around and of allowing people to give (constructive) feedback early in the project. It’s about using the positive force of a wave and turning negative flow into a positive one. 

Being aware of the importance of the initial context is just a first step toward harnessing waves or winds’ power, it takes action and leadership to pull the project in the right direction.

Data Science: Context (Just the Quotes)

"Some of the common ways of producing a false statistical argument are to quote figures without their context, omitting the cautions as to their incompleteness, or to apply them to a group of phenomena quite different to that to which they in reality relate; to take these estimates referring to only part of a group as complete; to enumerate the events favorable to an argument, omitting the other side; and to argue hastily from effect to cause, this last error being the one most often fathered on to statistics. For all these elementary mistakes in logic, statistics is held responsible." (Sir Arthur L Bowley, "Elements of Statistics", 1901)

"When evaluating the reliability and generality of data, it is often important to know the aims of the experimenter. When evaluating the importance of experimental results, however, science has a trick of disregarding the experimenter's rationale and finding a more appropriate context for the data than the one he proposed." (Murray Sidman, "Tactics of Scientific Research", 1960)

"Data in isolation are meaningless, a collection of numbers. Only in context of a theory do they assume significance […]" (George Greenstein, "Frozen Star" , 1983)

"Graphics must not quote data out of context." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The problem solver needs to stand back and examine problem contexts in the light of different 'Ws' (Weltanschauungen). Perhaps he can then decide which 'W' seems to capture the essence of the particular problem context he is faced with. This whole process needs formalizing if it is to be carried out successfully. The problem solver needs to be aware of different paradigms in the social sciences, and he must be prepared to view the problem context through each of these paradigms." (Michael C Jackson, "Towards a System of Systems Methodologies", 1984)

"It is commonly said that a pattern, however it is written, has four essential parts: a statement of the context where the pattern is useful, the problem that the pattern addresses, the forces that play in forming a solution, and the solution that resolves those forces. [...] it supports the definition of a pattern as 'a solution to a problem in a context', a definition that [unfortunately] fixes the bounds of the pattern to a single problem-solution pair." (Martin Fowler, "Analysis Patterns: Reusable Object Models", 1997)

"We do not learn much from looking at a model - we learn more from building the model and manipulating it. Just as one needs to use or observe the use of a hammer in order to really understand its function, similarly, models have to be used before they will give up their secrets. In this sense, they have the quality of a technology - the power of the model only becomes apparent in the context of its use." (Margaret Morrison & Mary S Morgan, "Models as mediating instruments", 1999)

"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)

"[…] 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)

"All scientific theories, even those in the physical sciences, are developed in a particular cultural context. Although the context may help to explain the persistence of a theory in the face of apparently falsifying evidence, the fact that a theory arises from a particular context is not sufficient to condemn it. Theories and paradigms must be accepted, modified or rejected on the basis of evidence." (Richard P Bentall,  "Madness Explained: Psychosis and Human Nature", 2003)

"Mathematical modeling is as much ‘art’ as ‘science’: it requires the practitioner to (i) identify a so-called ‘real world’ problem (whatever the context may be); (ii) formulate it in mathematical terms (the ‘word problem’ so beloved of undergraduates); (iii) solve the problem thus formulated (if possible; perhaps approximate solutions will suffice, especially if the complete problem is intractable); and (iv) interpret the solution in the context of the original problem." (John A Adam, "Mathematics in Nature", 2003)

"Context is not as simple as being in a different space [...] context includes elements like our emotions, recent experiences, beliefs, and the surrounding environment - each element possesses attributes, that when considered in a certain light, informs what is possible in the discussion." (George Siemens, "Knowing Knowledge", 2006)

"Statistics can certainly pronounce a fact, but they cannot explain it without an underlying context, or theory. Numbers have an unfortunate tendency to supersede other types of knowing. […] Numbers give the illusion of presenting more truth and precision than they are capable of providing." (Ronald J Baker, "Measure what Matters to Customers: Using Key Predictive Indicators", 2006)

"A valid digit is not necessarily a significant digit. The significance of numbers is a result of its scientific context." (Manfred Drosg, "Dealing with Uncertainties: A Guide to Error Analysis", 2007)

"[… ] statistics is about understanding the role that variability plays in drawing conclusions based on data. […] Statistics is not about numbers; it is about data - numbers in context. It is the context that makes a problem meaningful and something worth considering." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"Context (information that lends to better understanding the who, what, when, where, and why of your data) can make the data clearer for readers and point them in the right direction. At the least, it can remind you what a graph is about when you come back to it a few months later. […] Context helps readers relate to and understand the data in a visualization better. It provides a sense of scale and strengthens the connection between abstract geometry and colors to the real world." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Readability in visualization helps people interpret data and make conclusions about what the data has to say. Embed charts in reports or surround them with text, and you can explain results in detail. However, take a visualization out of a report or disconnect it from text that provides context (as is common when people share graphics online), and the data might lose its meaning; or worse, others might misinterpret what you tried to show." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"The data is a simplification - an abstraction - of the real world. So when you visualize data, you visualize an abstraction of the world, or at least some tiny facet of it. Visualization is an abstraction of data, so in the end, you end up with an abstraction of an abstraction, which creates an interesting challenge. […] Just like what it represents, data can be complex with variability and uncertainty, but consider it all in the right context, and it starts to make sense." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Without context, data is useless, and any visualization you create with it will also be useless. Using data without knowing anything about it, other than the values themselves, is like hearing an abridged quote secondhand and then citing it as a main discussion point in an essay. It might be okay, but you risk finding out later that the speaker meant the opposite of what you thought." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"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)

"The term data, unlike the related terms facts and evidence, does not connote truth. Data is descriptive, but data can be erroneous. We tend to distinguish data from information. Data is a primitive or atomic state (as in ‘raw data’). It becomes information only when it is presented in context, in a way that informs. This progression from data to information is not the only direction in which the relationship flows, however; information can also be broken down into pieces, stripped of context, and stored as data. This is the case with most of the data that’s stored in computer systems. Data that’s collected and stored directly by machines, such as sensors, becomes information only when it’s reconnected to its context." (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)

"Infographics combine art and science to produce something that is not unlike a dashboard. The main difference from a dashboard is the subjective data and the narrative or story, which enhances the data-driven visual and engages the audience quickly through highlighting the required context." (Travis Murphy, "Infographics Powered by SAS®: Data Visualization Techniques for Business Reporting", 2018)

"For numbers to be transparent, they must be placed in an appropriate context. Numbers must presented in a way that allows for fair comparisons." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Without knowing the source and context, a particular statistic is worth little. Yet numbers and statistics appear rigorous and reliable simply by virtue of being quantitative, and have a tendency to spread." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

More quotes on "Context" at the-web-of-knowledge.blogspot.com. 

Data Science: Data Storytelling (Definitions)

"A narrative way of describing a scenario, product idea, or strategy intended to provide a real-world context to promote decision making and better understanding." (Steven Haines, "The Product Manager's Desk Reference", 2008)

[storytelling:] "A method of communicating and sharing ideas, experiences and knowledge in a specific context." (Darren Dalcher, "Making Sense of IS Failures", Encyclopedia of Information Science and Technology 2nd Ed., 2009)

"A method of explaining a series of events through narrative." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"using a combination of data facts and a qualitative 'story' that provides effective communication of a business message." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Data storytelling can be defined as a structured approach for communicating data insights using narrative elements and explanatory visuals." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

[storytelling:] "The social and cultural activity of sharing stories, with great application to journalism." (Georgios Vassis et al, "Review and Evaluation of Systems Supporting Data Journalism", 2021)

"Data storytelling forms a compelling narrative by putting data in context to show the challenges, insights and solutions of a specific business problem. It normally highlights a series of changes or trends over time through linked visualizations that combine to tell a story." (Sisense) [source]

"Data storytelling is a method of visually presenting data to make it more understandable and easy to digest. Visualizations such as charts and graphs guide users toward a conclusion about their data and empower them to make a decision based on that conclusion." (Logi Analytics) [source]

"Data storytelling is a methodology for communicating information, tailored to a specific audience, with a compelling narrative. It is the last ten feet of your data analysis and arguably the most important aspect." (Nugit) [source]

"Data storytelling is the practice of building a narrative around a set of data and its accompanying visualizations to help convey the meaning of that data in a powerful and compelling fashion." (TDWI)

Data Science: Noise (Just the Quotes)

"Information that is only partially structured (and therefore contains some 'noise' is fuzzy, inconsistent, and indistinct. Such imperfect information may be regarded as having merit only if it represents an intermediate step in structuring the information into a final meaningful form. If the partially Structured information remains in fuzzy form, it will create a state of dissatisfaction in the mind of the originator and certainly in the mind of the recipient. The natural desire is to continue structuring until clarity, simplicity, precision, and definitiveness are obtained." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"To understand the need for structuring information, we should examine its opposite - nonstructured information. Nonstructured information may be thought of as exists and can be heard (or sensed with audio devices), but the mind attaches no rational meaning to the sound. In another sense, noise can be equated to writing a group of letters, numbers, and other symbols on a page without any design or key to their meaning. In such a situation, there is nothing the mind can grasp. Nonstructured information can be classified as useless, unless meaning exists somewhere in the jumble and a key can be found to unlock its hidden significance." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Neither noise nor information is predictable." (Ray Kurzweil, "The Age of Spiritual Machines: When Computers Exceed Human Intelligence", 1999)

"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 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)

"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)

"The acquisition of information is a flow from noise to order - a process converting entropy to redundancy. During this process, the amount of information decreases but is compensated by constant re-coding. In the recoding the amount of information per unit increases by means of a new symbol which represents the total amount of the old. The maturing thus implies information condensation. Simultaneously, the redundance decreases, which render the information more difficult to interpret." (Lars Skyttner, "General Systems Theory: Ideas and Applications", 2001)

"In fact, an information theory that leaves out the issue of noise turns out to have no content." (Hans Christian von Baeyer, "Information, The New Language of Science", 2003)

"This phenomenon, common to chaos theory, is also known as sensitive dependence on initial conditions. Just a small change in the initial conditions can drastically change the long-term behavior of a system. Such a small amount of difference in a measurement might be considered experimental noise, background noise, or an inaccuracy of the equipment." (Greg Rae, Chaos Theory: A Brief Introduction, 2006)

"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)

"Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)

"Economists should study financial markets as they actually operate, not as they assume them to operate - observing the way in which information is actually processed, observing the serial correlations, bonanzas, and sudden stops, not assuming these away as noise around the edges of efficient and rational markets." (Adair Turner, "Economics after the Crisis: Objectives and means", 2012)

"Finding patterns is easy in any kind of data-rich environment; that's what mediocre gamblers do. The key is in determining whether the patterns represent signal or noise." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)

"The signal is the truth. The noise is what distracts us from the truth." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)

"Typically, most outlier detection algorithms use some quantified measure of the outlierness of a data point, such as the sparsity of the underlying region, nearest neighbor based distance, or the fit to the underlying data distribution. Every data point lies on a continuous spectrum from normal data to noise, and finally to anomalies [...] The separation of the different regions of this spectrum is often not precisely defined, and is chosen on an ad-hoc basis according to application-specific criteria. Furthermore, the separation between noise and anomalies is not pure, and many data points created by a noisy generative process may be deviant enough to be interpreted as anomalies on the basis of the outlier score. Thus, anomalies will typically have a much higher outlier score than noise, but this is not a distinguishing factor between the two as a matter of definition. Rather, it is the interest of the analyst, which regulates the distinction between noise and an anomaly." (Charu C Aggarwal, "Outlier Analysis", 2013)

"A complete data analysis will involve the following steps: (i) Finding a good model to fit the signal based on the data. (ii) Finding a good model to fit the noise, based on the residuals from the model. (iii) Adjusting variances, test statistics, confidence intervals, and predictions, based on the model for the noise." (DeWayne R Derryberry, "Basic data analysis for time series with R", 2014)

 "The random element in most data analysis is assumed to be white noise - normal errors independent of each other. In a time series, the errors are often linked so that independence cannot be assumed (the last examples). Modeling the nature of this dependence is the key to time series." (DeWayne R Derryberry, "Basic data analysis for time series with R", 2014)

"A signal is a useful message that resides in data. Data that isn’t useful is noise. […] When data is expressed visually, noise can exist not only as data that doesn’t inform but also as meaningless non-data elements of the display (e.g. irrelevant attributes, such as a third dimension of depth in bars, color variation that has no significance, and artificial light and shadow effects)." (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)

"Data contain descriptions. Some are true, some are not. Some are useful, most are not. Skillful use of data requires that we learn to pick out the pieces that are true and useful. [...] To find signals in data, we must learn to reduce the noise - not just the noise that resides in the data, but also the noise that resides in us. It is nearly impossible for noisy minds to perceive anything but noise in data." (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)

"When we find data quality issues due to valid data during data exploration, we should note these issues in a data quality plan for potential handling later in the project. The most common issues in this regard are missing values and outliers, which are both examples of noise in the data." (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, worked examples, and case studies", 2015)

"Information theory leads to the quantification of the information content of the source, as denoted by entropy, the characterization of the information-bearing capacity of the communication channel, as related to its noise characteristics, and consequently the establishment of the relationship between the information content of the source and the capacity of the channel. In short, information theory provides a quantitative measure of the information contained in message signals and help determine the capacity of a communication system to transfer this information from source to sink over a noisy channel in a reliable fashion." (Ali Grami, "Information Theory", 2016)

"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)

"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)

"The high generalization error in a neural network may be caused by several reasons. First, the data itself might have a lot of noise, in which case there is little one can do in order to improve accuracy. Second, neural networks are hard to train, and the large error might be caused by the poor convergence behavior of the algorithm. The error might also be caused by high bias, which is referred to as underfitting. Finally, overfitting (i.e., high variance) may cause a large part of the generalization error. In most cases, the error is a combination of more than one of these different factors." (Charu C Aggarwal, "Neural Networks and Deep Learning: A Textbook", 2018)

"[...] in the statistical world, what we see and measure around us can be considered as the sum of a systematic mathematical idealized form plus some random contribution that cannot yet be explained. This is the classic idea of the signal and the noise." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"Visualizations can remove the background noise from enormous sets of data so that only the most important points stand out to the intended audience. This is particularly important in the era of big data. The more data there is, the more chance for noise and outliers to interfere with the core concepts of the data set." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

Knowledge Management: Knowledge (Just the Quotes)

"There are two modes of acquiring knowledge, namely, by reasoning and experience. Reasoning draws a conclusion and makes us grant the conclusion, but does not make the conclusion certain, nor does it remove doubt so that the mind may rest on the intuition of truth unless the mind discovers it by the path of experience." (Roger Bacon, "Opus Majus", 1267)

"Knowledge being to be had only of visible and certain truth, error is not a fault of our knowledge, but a mistake of our judgment, giving assent to that which is not true." (John Locke, "An Essay Concerning Human Understanding", 1689)

"[…] the highest probability amounts not to certainty, without which there can be no true knowledge." (John Locke, "An Essay Concerning Human Understanding", 1689)

"It is your opinion, the ideas we perceive by our senses are not real things, but images, or copies of them. Our knowledge therefore is no farther real, than as our ideas are the true representations of those originals. But as these supposed originals are in themselves unknown, it is impossible to know how far our ideas resemble them; or whether they resemble them at all. We cannot therefore be sure we have any real knowledge." (George Berkeley, "Three Dialogues", 1713)

"Our knowledge springs from two fundamental sources of the mind; the first is the capacity of receiving representations (receptivity for impressions), the second is the power of knowing an object through these representations (spontaneity [in the production] of concepts)." (Immanuel Kant, "Critique of Pure Reason", 1781)

"Knowledge is only real and can only be set forth fully in the form of science, in the form of system." (G W Friedrich Hegel, "The Phenomenology of Mind", 1807)

"One may even say, strictly speaking, that almost all our knowledge is only probable; and in the small number of things that we are able to know with certainty, in the mathematical sciences themselves, the principal means of arriving at the truth - induction and analogy - are based on probabilities, so that the whole system of human knowledge is tied up with the theory set out in this essay." (Pierre-Simon Laplace, "Philosophical Essay on Probabilities", 1814) 

"We [...] are profiting not only by the knowledge, but also by the ignorance, not only by the discoveries, but also by the errors of our forefathers; for the march of science, like that of time, has been progressing in the darkness, no less than in the light." (Charles C Colton, "Lacon", 1820)

"Our knowledge of circumstances has increased, but our uncertainty, instead of having diminished, has only increased. The reason of this is, that we do not gain all our experience at once, but by degrees; so our determinations continue to be assailed incessantly by fresh experience; and the mind, if we may use the expression, must always be under arms." (Carl von Clausewitz, "On War", 1832)

"All knowledge is profitable; profitable in its ennobling effect on the character, in the pleasure it imparts in its acquisition, as well as in the power it gives over the operations of mind and of matter. All knowledge is useful; every part of this complex system of nature is connected with every other. Nothing is isolated. The discovery of to-day, which appears unconnected with any useful process, may, in the course of a few years, become the fruitful source of a thousand inventions." (Joseph Henry, "Report of the Secretary" [Sixth Annual Report of the Board of Regents of the Smithsonian Institution for 1851], 1852)

"Isolated facts and experiments have in themselves no value, however great their number may be. They only become valuable in a theoretical or practical point of view when they make us acquainted with the law of a series of uniformly recurring phenomena, or, it may be, only give a negative result showing an incompleteness in our knowledge of such a law, till then held to be perfect." (Hermann von Helmholtz, "The Aim and Progress of Physical Science", 1869)

"Simplification of modes of proof is not merely an indication of advance in our knowledge of a subject, but is also the surest guarantee of readiness for farther progress." (William T Kelvin, "Elements of Natural Philosophy", 1873)

"The whole value of science consists in the power which it confers upon us of applying to one object the knowledge acquired from like objects; and it is only so far, therefore, as we can discover and register resemblances that we can turn our observations to account." (William S Jevons, "The Principles of Science: A Treatise on Logic and Scientific Method", 1874)

"[…] when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely in your thoughts advanced to the state of science." (William T Kelvin, "Electrical Units of Measurement", 1883)

"The smallest group of facts, if properly classified and logically dealt with, will form a stone which has its proper place in the great building of knowledge, wholly independent of the individual workman who has shaped it." (Karl Pearson, "The Grammar of Science", 1892)

"Without a theory all our knowledge of nature would be reduced to a mere inventory of the results of observation. Every scientific theory must be regarded as an effort of the human mind to grasp the truth, and as long as it is consistent with the facts, it forms a chain by which they are linked together and woven into harmony." (Thomas Preston, "The Theory of Heat", 1894)

"Knowledge is the distilled essence of our intuitions, corroborated by experience." (Elbert Hubbard, "A Thousand & One Epigrams, 1911)

"It is experience which has given us our first real knowledge of Nature and her laws. It is experience, in the shape of observation and experiment, which has given us the raw material out of which hypothesis and inference have slowly elaborated that richer conception of the material world which constitutes perhaps the chief, and certainly the most characteristic, glory of the modern mind." (Arthur J Balfour, "The Foundations of Belief", 1912)

"We have discovered that it is actually an aid in the search for knowledge to understand the nature of the knowledge we seek." (Arthur S Eddington, "The Philosophy of Physical Science", 1938)

"Science usually advances by a succession of small steps, through a fog in which even the most keen-sighted explorer can seldom see more than a few paces ahead. Occasionally the fog lifts, an eminence is gained, and a wider stretch of territory can be surveyed - sometimes with startling results. A whole science may then seem to undergo a kaleidoscopic ‘rearrangement’, fragments of knowledge being found to fit together in a hitherto unsuspected manner. Sometimes the shock of readjustment may spread to other sciences; sometimes it may divert the whole current of human thought." (James H Jeans, "Physics and Philosophy" 3rd Ed., 1943)

"Every bit of knowledge we gain and every conclusion we draw about the universe or about any part or feature of it depends finally upon some observation or measurement. Mankind has had again and again the humiliating experience of trusting to intuitive, apparently logical conclusions without observations, and has seen Nature sail by in her radiant chariot of gold in an entirely different direction." (Oliver J Lee, "Measuring Our Universe: From the Inner Atom to Outer Space", 1950)

"The essence of knowledge is generalization. That fire can be produced by rubbing wood in a certain way is a knowledge derived by generalization from individual experiences; the statement means that rubbing wood in this way will always produce fire. The art of discovery is therefore the art of correct generalization." (Hans Reichenbach, "The Rise of Scientific Philosophy", 1951)

"Knowledge rests on knowledge; what is new is meaningful because it departs slightly from what was known before; this is a world of frontiers, where even the liveliest of actors or observers will be absent most of the time from most of them." (J Robert Oppenheimer, "Science and the Common Understanding", 1954)

"Knowledge is not something which exists and grows in the abstract. It is a function of human organisms and of social organization. Knowledge, that is to say, is always what somebody knows: the most perfect transcript of knowledge in writing is not knowledge if nobody knows it. Knowledge however grows by the receipt of meaningful information - that is, by the intake of messages by a knower which are capable of reorganising his knowledge." (Kenneth E Boulding, "General Systems Theory - The Skeleton of Science", Management Science Vol. 2 (3), 1956)

"Incomplete knowledge must be considered as perfectly normal in probability theory; we might even say that, if we knew all the circumstances of a phenomenon, there would be no place for probability, and we would know the outcome with certainty." (Félix E Borel, Probability and Certainty", 1963)

"Knowing reality means constructing systems of transformations that correspond, more or less adequately, to reality. They are more or less isomorphic to transformations of reality. The transformational structures of which knowledge consists are not copies of the transformations in reality; they are simply possible isomorphic models among which experience can enable us to choose. Knowledge, then, is a system of transformations that become progressively adequate." (Jean Piaget, "Genetic Epistemology", 1968)

"Scientific knowledge is not created solely by the piecemeal mining of discrete facts by uniformly accurate and reliable individual scientific investigations. The process of criticism and evaluation, of analysis and synthesis, are essential to the whole system. It is impossible for each one of us to be continually aware of all that is going on around us, so that we can immediately decide the significance of every new paper that is published. The job of making such judgments must therefore be delegated to the best and wisest among us, who speak, not with their own personal voices, but on behalf of the whole community of Science. […] It is impossible for the consensus - public knowledge - to be voiced at all, unless it is channeled through the minds of selected persons, and restated in their words for all to hear." (John M Ziman, "Public Knowledge: An Essay Concerning the Social Dimension of Science", 1968)

"Models constitute a framework or a skeleton and the flesh and blood will have to be added by a lot of common sense and knowledge of details."(Jan Tinbergen, "The Use of Models: Experience," 1969)

"Human knowledge is personal and responsible, an unending adventure at the edge of uncertainty." (Jacob Bronowski, "The Ascent of Man", 1973)

"Knowledge is not a series of self-consistent theories that converges toward an ideal view; it is rather an ever increasing ocean of mutually incompatible (and perhaps even incommensurable) alternatives, each single theory, each fairy tale, each myth that is part of the collection forcing the others into greater articulation and all of them contributing, via this process of competition, to the development of our consciousness." (Paul K Feyerabend, "Against Method: Outline of an Anarchistic Theory of Knowledge", 1975)

"Knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone 'memorizes' information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge." (Russell L Ackoff, "Towards a Systems Theory of Organization", 1985)

"There is no coherent knowledge, i.e. no uniform comprehensive account of the world and the events in it. There is no comprehensive truth that goes beyond an enumeration of details, but there are many pieces of information, obtained in different ways from different sources and collected for the benefit of the curious. The best way of presenting such knowledge is the list - and the oldest scientific works were indeed lists of facts, parts, coincidences, problems in several specialized domains." (Paul K Feyerabend, "Farewell to Reason", 1987)

"We admit knowledge whenever we observe an effective (or adequate) behavior in a given context, i.e., in a realm or domain which we define by a question (explicit or implicit)." (Humberto Maturana & Francisco J Varela, "The Tree of Knowledge", 1987)

"We live on an island surrounded by a sea of ignorance. As our island of knowledge grows, so does the shore of our ignorance." (John A Wheeler, Scientific American Vol. 267, 1992)

"Knowledge is theory. We should be thankful if action of management is based on theory. Knowledge has temporal spread. Information is not knowledge. The world is drowning in information but is slow in acquisition of knowledge. There is no substitute for knowledge." (William E Deming, "The New Economics for Industry, Government, Education", 1993) 

"Discourses are ways of referring to or constructing knowledge about a particular topic of practice: a cluster (or formation) of ideas, images and practices, which provide ways of talking about, forms of knowledge and conduct associated with, a particular topic, social activity or institutional site in society. These discursive formations, as they are known, define what is and is not appropriate in our formulation of, and our practices in relation to, a particular subject or site of social activity." (Stuart Hall, "Representation: Cultural Representations and Signifying Practices", 1997)

"An individual understands a concept, skill, theory, or domain of knowledge to the extent that he or she can apply it appropriately in a new situation." (Howard Gardner, "The Disciplined Mind", 1999)

"Knowledge is factual when evidence supports it and we have great confidence in its accuracy. What we call 'hard fact' is information supported by  strong, convincing evidence; this means evidence that, so far as we know, we cannot deny, however we examine or test it. Facts always can be questioned, but they hold up under questioning. How did people come by this information? How did they interpret it? Are other interpretations possible? The more satisfactory the answers to such questions, the 'harder' the facts." (Joel Best, Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists, 2001)

"Knowledge is in some ways the most important (though intangible) capital of a software engineering organization, and sharing of that knowledge is crucial for making an organization resilient and redundant in the face of change. A culture that promotes open and honest knowledge sharing distributes that knowledge efficiently across the organization and allows that organization to scale over time. In most cases, investments into easier knowledge sharing reap manyfold dividends over the life of a company." (Titus Winters, "Software Engineering at Google: Lessons Learned from Programming Over Time", 2020)

More quotes on "Knowledge" at the-web-of-knowledge.blogspot.com.

Graphical Representation: Context (Just the Quotes)

"The title for any chart presenting data in the graphic form should be so clear and so complete that the chart and its title could be removed from the context and yet give all the information necessary for a complete interpretation of the data. Charts which present new or especially interesting facts are very frequently copied by many magazines. A chart with its title should be considered a unit, so that anyone wishing to make an abstract of the article in which the chart appears could safely transfer the chart and its title for use elsewhere." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919) 

"Charts and graphs are a method of organizing information for a unique purpose. The purpose may be to inform, to persuade, to obtain a clear understanding of certain facts, or to focus information and attention on a particular problem. The information contained in charts and graphs must, obviously, be relevant to the purpose. For decision-making purposes. information must be focused clearly on the issue or issues requiring attention. The need is not simply for 'information', but for structured information, clearly presented and narrowed to fit a distinctive decision-making context. An advantage of having a 'formula' or 'model' appropriate to a given situation is that the formula indicates what kind of information is needed to obtain a solution or answer to a specific problem." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Generally speaking, a good display is one in which the visual impact of its components is matched to their importance in the context of the analysis. Consider the issue of overplotting." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"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)

"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)

"The content and context of the numerical data determines the most appropriate mode of presentation. A few numbers can be listed, many numbers require a table. Relationships among numbers can be displayed by statistics. However, statistics, of necessity, are summary quantities so they cannot fully display the relationships, so a graph can be used to demonstrate them visually. The attractiveness of the form of the presentation is determined by word layout, data structure, and design." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"Numbers are often useful in stories because they record a recent change in some amount, or because they are being compared with other numbers. Percentages, ratios and proportions are often better than raw numbers in establishing a context." (Charles Livingston & Paul Voakes, "Working with Numbers and Statistics: A handbook for journalists", 2005)

"The percentage is one of the best (mathematical) friends a journalist can have, because it quickly puts numbers into context. And it's a context that the vast majority of readers and viewers can comprehend immediately." (Charles Livingston & Paul Voakes, "Working with Numbers and Statistics: A handbook for journalists", 2005)

"By showing recent change in relation to many past changes, sparklines provide a context for nuanced analysis - and, one hopes, better decisions. [...] Sparklines efficiently display and narrate binary data (presence/absence, occurrence/non-occurrence, win/loss). [...] Sparklines can simultaneously accommodate several variables. [...] Sparklines can narrate on-going results detail for any process producing sequential binary outcomes." (Edward R Tufte, "Beautiful Evidence", 2006)

"Statistics can certainly pronounce a fact, but they cannot explain it without an underlying context, or theory. Numbers have an unfortunate tendency to supersede other types of knowing. […] Numbers give the illusion of presenting more truth and precision than they are capable of providing." (Ronald J Baker, "Measure what Matters to Customers: Using Key Predictive Indicators", 2006)

"The biggest difference between line graphs and sparklines is that a sparkline is compact with no grid lines. It isnʼt meant to give precise values; rather, it should be considered just like any other word in the sentence. Its general shape acts as another term and lends additional meaning in its context. The driving forces behind these compact sparklines are speed and convenience." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"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)

"Context (information that lends to better understanding the who, what, when, where, and why of your data) can make the data clearer for readers and point them in the right direction. At the least, it can remind you what a graph is about when you come back to it a few months later. […] Context helps readers relate to and understand the data in a visualization better. It provides a sense of scale and strengthens the connection between abstract geometry and colors to the real world." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Readability in visualization helps people interpret data and make conclusions about what the data has to say. Embed charts in reports or surround them with text, and you can explain results in detail. However, take a visualization out of a report or disconnect it from text that provides context (as is common when people share graphics online), and the data might lose its meaning; or worse, others might misinterpret what you tried to show." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"There is a story in your data. But your tools don’t know what that story is. That’s where it takes you - the analyst or communicator of the information - to bring that story visually and contextually to life." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Infographics combine art and science to produce something that is not unlike a dashboard. The main difference from a dashboard is the subjective data and the narrative or story, which enhances the data-driven visual and engages the audience quickly through highlighting the required context." (Travis Murphy, "Infographics Powered by SAS®: Data Visualization Techniques for Business Reporting", 2018)

"The second rule of communication is to know what you want to achieve. Hopefully the aim is to encourage open debate, and informed decision-making. But there seems no harm in repeating yet again that numbers do not speak for themselves; the context, language and graphic design all contribute to the way the communication is received. We have to acknowledge we are telling a story, and it is inevitable that people will make comparisons and judgements, no matter how much we only want to inform and not persuade. All we can do is try to pre-empt inappropriate gut reactions by design or warning." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"There is often no one 'best' visualization, because it depends on context, what your audience already knows, how numerate or scientifically trained they are, what formats and conventions are regarded as standard in the particular field you’re working in, the medium you can use, and so on. It’s also partly scientific and partly artistic, so you get to express your own design style in it, which is what makes it so fascinating." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"When narrative is coupled with data, it helps to explain to your audience what’s happening in the data and why a particular insight is important. Ample context and commentary are often needed to fully appreciate an analysis finding. The narrative element adds structure to the data and helps to guide the audience through the meaning of what’s being shared." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Without knowing the source and context, a particular statistic is worth little. Yet numbers and statistics appear rigorous and reliable simply by virtue of being quantitative, and have a tendency to spread." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"A semantic approach to visualization focuses on the interplay between charts, not just the selection of charts themselves. The approach unites the structural content of charts with the context and knowledge of those interacting with the composition. It avoids undue and excessive repetition by instead using referential devices, such as filtering or providing detail-on-demand. A cohesive analytical conversation also builds guardrails to keep users from derailing from the conversation or finding themselves lost without context. Functional aesthetics around color, sequence, style, use of space, alignment, framing, and other visual encodings can affect how users follow the script." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"A well-designed dashboard needs to provide a similar experience; information cannot be placed just anywhere on the dashboard. Charts that relate to one another are usually positioned close to one another. Important charts often appear larger and more visually prominent than less important ones. In other words, there are natural sizes for how a dashboard comprises charts based on the task and context." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Coloring needs to be semantically relevant and is also defined by the context." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"For a chart to be truly insightful, context is crucial because it provides us with the visual answer to an important question - 'compared with what'? No number on its own is inherently big or small – we need context to make that judgement. Common contextual comparisons in charts are provided by time ('compared with last year...') and place ('compared with the north...'). With ranking, context is provided by relative performance ('compared with our rivals...')." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

"Our visual perception is context-dependent; we are not good at seeing things in isolation." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

"Understanding language goes hand in hand with the ability to integrate complex contextual information into an effective visualization and being able to converse with the data interactively, a term we call analytical conversation. It also helps us think about ways to create artifacts that support and manage how we converse with machines as we see and understand data."(Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Understanding the context and the domain of the data is important to help disambiguate concepts. While reasonable defaults can be used to create a visualization, there should be no dead ends. Provide affordances for a user to understand, repair, and refine." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"When the colors are dull and neutral, they can communicate a sense of uniformity and an aura of calmness. Grays do a great job of mapping out the context of your story so that the more sharp colors highlight what you’re trying to explain. The power of gray comes in handy for all of our supporting details such as the axis, gridlines, and nonessential data that is included for comparative purposes. By using gray as the primary color in a visualization, we automatically draw our viewers’ eyes to whatever isn’t gray. That way, if we are interested in telling a story about one data point, we can do so quite easily."  (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

Jesús Barrasa - Collected Quotes

"A taxonomy is a classification scheme that organizes categories in a broader-narrower hierarchy. Items that share similar qualities are grouped into the same category, and the taxonomy provides a global organization by relating categories to one another." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"AI is intended to create systems for making probabilistic decisions, similar to the way humans make decisions. […] Today’s AI is not very able to generalize. Instead, it is effective for specific, well-defined tasks. It struggles with ambiguity and mostly lacks transfer learning that humans take for granted. For AI to make humanlike decisions that are more situationally appropriate, it needs to incorporate context." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Data architects often turn to graphs because they are flexible enough to accommodate multiple heterogeneous representations of the same entities as described by each of the source systems. With a graph, it is possible to associate underlying records incrementally as data is discovered. There is no need for big, up-front design, which serves only to hamper business agility. This is important because data fabric integration is not a one-off effort and a graph model remains flexible over the lifetime of the data domains." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Data fabrics are general-purpose, organization-wide data access interfaces that offer a connected view of the integrated domains by combining data stored in a local graph with data retrieved on demand from third-party systems. Their job is to provide a sophisticated index and integration points so that they can curate data across silos, offering consistent capabilities regardless of the underlying store (which might or might not be graph based) […]." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Despite their predictive power, most analytics and data science practices ignore relationships because it has been historically challenging to process them at scale." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Graph data models are uniquely able to represent complex, indirect relationships in a way that is both human readable, and machine friendly. Data structures like graphs might seem computerish and off-putting, but in reality they are created from very simple primitives and patterns. The combination of a humane data model and ease of algorithmic processing to discover otherwise hidden patterns and characteristics is what has made graphs so popular." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"In an era of machine learning, where data is likely to be used to train AI, getting quality and governance under control is a business imperative. Failing to govern data surfaces problems late, often at the point closest to users (for example, by giving harmful guidance), and hinders explainability (garbage data in, machine-learned garbage out)." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Knowledge graphs are a specific type of graph with an emphasis on contextual understanding. Knowledge graphs are interlinked sets of facts that describe real-world entities, events, or things and their interrelations in a human- and machine-understandable format." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"[…] knowledge graphs are useful because they provide contextualized understanding of data. They achieve this by adding a layer of metadata that imposes rules for structure and interpretation." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Knowledge graphs use an organizing principle so that a user (or a computer system) can reason about the underlying data. The organizing principle gives us an additional layer of organizing data (metadata) that adds connected context to support reasoning and knowledge discovery. […] Importantly, some processing can be done without knowledge of the domain, just by leveraging the features of the property graph model (the organizing principle)." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Many AI systems employ heuristic decision making, which uses a strategy to find the most likely correct decision to avoid the high cost (time) of processing lots of information. We can think of those heuristics as shortcuts or rules of thumb that we would use to make fast decisions." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Understanding the entire data ecosystem, from the production of a data point to its consumption in a dashboard or a visualization, provides the ability to invoke action, which is more valuable than the mere sum of its parts." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"We think of context as the network surrounding a data point of interest that is relevant to a specific AI system. […] AI benefits greatly from context to enable probabilistic decision making for real-time answers, handle adjacent scenarios for broader applicability, and be maximally relevant to a given situation. But all systems, including AI, are only as good as their inputs." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)