🏗️Software Engineering: Feedback (Just the Quotes)

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

"Optimism is an occupational hazard of programming: feedback is the treatment." (Kent Beck, "Extreme Programming Explained", 2000) 

"Extreme Programming is a discipline of software development with values of simplicity, communication, feedback and courage. We focus on the roles of customer, manager, and programmer and accord key rights and responsibilities to those in those roles." (Ron Jeffries, "Extreme Programming Installed", 2001)

"The values of XP are simplicity, communication, feedback, and courage. [...] Use simple design and programming practices, and simple methods of planning, tracking, and reporting. Test your program and your practices, using feedback to decide how to steer the project. Working together in this way gives the team courage."" (Ron Jeffries, "Extreme Programming Installed", 2001)

"XP isn't slash and burn programming, not code and fix, not at all. Extreme Programming is about careful and continuous design, rapid  feedback from extensive testing, and the maintenance of relentlessly clear and high-quality code." (Ron Jeffries, "Extreme Programming Installed, 2001)

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

"The deployment pipeline has its foundations in the process of continuous integration and is in essence the principle of continuous integration taken to its logical conclusion. The aim of the deployment pipeline is threefold. First, it makes every part of the process of building, deploying, testing, and releasing software visible to everybody involved, aiding collaboration. Second, it improves feedback so that problems are identified, and so resolved, as early in the process as possible. Finally, it enables teams to deploy and release any version of their software to any environment at will through a fully automated process." (David Farley & Jez Humble, "Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation", 2010)

"In essence, Continuous Integration is about reducing risk by providing faster feedback. First and foremost, it is designed to help identify and fix integration and regression issues faster, resulting in smoother, quicker delivery, and fewer bugs. By providing better visibility for both technical and non-technical team members on the state of the project, Continuous Integration can open and facilitate communication channels between team members and encourage collaborative problem solving and process improvement. And, by automating the deployment process, Continuous Integration helps you get your software into the hands of the testers and the end users faster, more reliably, and with less effort." (John F Smart, "Jenkins: The Definitive Guide", 2011)

"Agile development methods require a disciplined approach to ensure that customer feedback, continuous testing, and iterative development actually lead to frequent deliveries of working, valuable software." (Michael Hűttermann et al, "DevOps for Developers", 2013)

"DevOps is essentially about gaining fast feedback and decreasing the risk of releases through a holistic approach that is meaningful for both development and operations. One major step for achieving this approach is to improve the fl ow of features from their inception to availability. This process can be refined to the point that it becomes important to reduce batch size" (the size of one package of changes or the amount of work that is done before the new version is shipped) without changing capacity or demand." (Michael Hűttermann et al, "DevOps for Developers", 2013)

"A value stream is a series of activities required to deliver an outcome. The software development value stream may be described as: validate business case, analyze, design, build, test, deploy, learn from usage analytics and other feedback - rinse and repeat." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Areas of low complexity or that are unlikely to be invested in can be built without the need for perfect code quality; working software is good enough. Sometimes feedback and first-to-market are core to the success of a product; in this instance, it can make business sense to get working software up as soon as possible, whatever the architecture." (Scott Millett, "Patterns Principles and Practices of Domain Driven Design", 2015)

"Feedback is what makes it iterative; otherwise, it is just mini-waterfall. Merely splitting use cases into stories does not make for iterative development if we wait until all stories are developed before we seek feedback. The point of splitting is to get feedback faster so that it can be incorporated into ongoing development. However, seeking stakeholder/user feedback for small batches of functionality" (stories) is often not feasible with formal stage-gate processes. They were conceived with linear flows of large batches in mind." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"This is what the Agile Manifesto means when it says responding to change over following a plan. To maximize adaptability, it is essential to have good, fast feedback loops. This is why there is so much emphasis on iterative development." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

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

"No methodology can guarantee success. But a good methodology can provide a feedback loop for continual improvement and learning." (Ash Maurya, "Scaling Lean: Mastering the Key Metrics for Startup Growth", 2016)

📉Graphical Representation: Prototyping (Just the Quotes)

"An important rule in the drafting of curve charts is that the amount scale should begin at zero. In comparisons of size the omission of the zero base, unless clearly indicated, is likely to give a misleading impression of the relative values and trend." (Rufus R Lutz, "Graphic Presentation Simplified", 1949)

"Of course statistical graphics, just like statistical calculations, are only as good as what goes into them. An ill-specified or preposterous model or a puny data set cannot be rescued by a graphic" (or by calculation), no matter how clever or fancy. A silly theory means a silly graphic." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The information on a plot should be relevant to the goals of the analysis. This means that in choosing graphical methods we should match the capabilities of the methods to our needs in the context of each application. [...] Scatter plots, with the views carefully selected as in draftsman's displays, casement displays, and multiwindow plots, are likely to be more informative. We must be careful, however, not to confuse what is relevant with what we expect or want to find. Often wholly unexpected phenomena constitute our most important findings." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"A good way to evaluate a model is to look at a visual representation of it. After all, what is easier to understand - a table full of mathematical relationships or a graphic displaying a decision tree with all of its splits and branches?" (Seth Paul et al. "Preparing and Mining Data with Microsoft SQL Server 2000 and Analysis", 2002)

"There are two main reasons for using graphic displays of datasets: either to present or to explore data. Presenting data involves deciding what information you want to convey and drawing a display appropriate for the content and for the intended audience. [...] Exploring data is a much more individual matter, using graphics to find information and to generate ideas.Many displays may be drawn. They can be changed at will or discarded and new versions prepared, so generally no one plot is especially important, and they all have a short life span." (Antony Unwin, "Good Graphics?" [in "Handbook of Data Visualization"], 2008)

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

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

"In analytics, it’s more important for individuals to be able to formulate problems well, to prototype solutions quickly, to make reasonable assumptions in the face of ill-structured problems, to design experiments that represent good investments, and to analyze results." (Foster Provost & Tom Fawcett, "Data Science for Business", 2013)

"If I had to pick a single go-to graph for categorical data, it would be the horizontal bar chart, which flips the vertical version on its side. Why? Because it is extremely easy to read. The horizontal bar chart is especially useful if your category names are long, as the text is written from left to right, as most audiences read, making your graph legible for your audience." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"The intention behind prototypes is to explore the visualization design space, as opposed to the data space. A typical project usually entails a series of prototypes; each is a tool to gather feedback from stakeholders and help explore different ways to most effectively support the higher-level questions that they have. The repeated feedback also helps validate the operationalization along the way." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Rapid prototyping is a process of trying out many visualization ideas as quickly as possible and getting feedback from stakeholders on their efficacy. […] The design concept of 'failing fast' informs this: by exploring many different possible visual representations, it quickly becomes clear which tasks are supported by which techniques." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

✏️Gavin Ambrose - Collected Quotes

"Art direction is a process that guides the creation of the visual elements of a design. An art director harnesses and integrates the various elements within a design to produce a certain look and feel for the resulting artwork so that it contains the required expressions and conveys the appropriate messages." (Gavin Ambrose & Paul Harris, "The Fundamentals of Graphic Design", 2009)

"As an art, graphic design creates striking images and layouts to communicate ideas and information to different audiences. The discipline is at the forefront of creative thought, advancing theory on how to communicate effectively through visual media by using a wide range of intellectual tools to establish meaningful connections between different design elements." (Gavin Ambrose & Paul Harris, "The Fundamentals of Graphic Design", 2009)

"Branding is a process that allows a company to differentiate itself and its products from its competitors, while also establishing positive links to its customers in order to create and preserve loyalty. Branding is important in competitive markets as it provides a means for consumers to make buying decisions based on their perceptions of the brand; this may include quality, safety, luxury, value or other considerations that are important to the consumer at the moment of purchase." (Gavin Ambrose & Paul Harris, "The Fundamentals of Graphic Design", 2009)

"Colour is a crucial part of graphic design today, but it is something that consumers, clients and designers take for granted. Colour can bring a design to life, help to establish hierarchies, highlight key information and add pace and emotion to a design. However, it is a design aspect that is easy to get wrong and causes problems when a job prints incorrectly." (Gavin Ambrose & Paul Harris, "The Fundamentals of Graphic Design", 2009)

"Getting from a design idea to a realised project is not always straightforward and may require a considerable amount of prototyping to get things right. Once the design team has a consolidated idea of how to answer a brief, prototyping provides an opportunity for testing some of the proposed solutions without going to the expense of full development." (Gavin Ambrose & Paul Harris, "The Fundamentals of Graphic Design", 2009)

"Graphic design is subject to the evolving intellectual and aesthetic trends that influence the work of designers and reflect the attitudes of society at large." (Gavin Ambrose & Paul Harris, "The Fundamentals of Graphic Design", 2009)

"Graphic design takes ideas, concepts, text and images and presents them in a visually engaging form through print, electronic or other media. It imposes an order and structure to the content in order to facilitate and ease the communication process, while optimising the likelihood that the message will be received and understood by the target audience. A designer achieves this goal through the conscious manipulation of elements; a design may be philosophical, aesthetic, sensory, emotional or political in nature." (Gavin Ambrose & Paul Harris, "The Fundamentals of Graphic Design", 2009)

"Semiotics is the study of signs that offers an explanation of how people extract meaning from words, sounds and pictures. An understanding of semiotics helps a designer to instil work with references that enable them to communicate multiple layers of information to a reader." (Gavin Ambrose & Paul Harris, "The Fundamentals of Graphic Design", 2009)

"The grid is a template or guide used for positioning and organising the elements of a design in order to facilitate and ease decision-making. Grids are the bone structure of a layout and serve as a tool to help a designer achieve balance while presenting a potentially large degree of creative possibilities." (Gavin Ambrose & Paul Harris, "The Fundamentals of Graphic Design", 2009)

"Typography is the means by which a written idea is given a visual form. It is one of the most influential elements that establishes the character and emotional attributes of a design; the visual form it takes dramatically affects the accessibility of an idea and how a reader reacts towards it." (Gavin Ambrose & Paul Harris, "The Fundamentals of Graphic Design", 2009)

🪙Business Intelligence: Issues (Just the Quotes)

"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)

"Dashboards and visualization are cognitive tools that improve your 'span of control' over a lot of business data. These tools help people visually identify trends, patterns and anomalies, reason about what they see and help guide them toward effective decisions. As such, these tools need to leverage people's visual capabilities. With the prevalence of scorecards, dashboards and other visualization tools now widely available for business users to review their data, the issue of visual information design is more important than ever." (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)

"[…] dirt and stains are more noticeable on white or light-colored clothing. In the same way, dirty data and data quality issues have existed for a long time. But due to the inherent nature of operational data these issues have not been as visible or immense enough to affect the bottom line. Just as dark clothing hides spills and stains, dirty data have been hidden or ignored in operational data for decades." (Margaret Y Chu, "Blissful Data", 2004)

"Gauging the quality of the operational data becomes an important first step in predicting potential dirty data issues for an organization. But many organizations are reluctant to commit the time and expense to assess their data. Some organizations wait until dirty data issues blow up in their faces. The greater the pain being experienced, the bigger the commitment to improving data quality." (Margaret Y Chu, "Blissful Data", 2004)

"Visualization is an issue at the heart of good dashboard software. Good visualization can be the difference between information overload and information insight. Commonly used graphs (charts) are one example of visualization. However, present-day technology has raised the bar of visualization beyond commonplace charts and data widgets. The three key characteristics requiring evaluation within the area of visualization are: (1) Visual intelligence ( 2) Geographic mapping (3) Screen resolution." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)

"Data governance is about putting people in charge of fixing and preventing data issues and using technology to help aid the process. Any time data is synchronized, merged, and exchanged, there have to be ground rules guiding this. Data governance serves as the method to organize the people, processes, and technologies for data-driven programs like data quality; they are a necessary part of any data quality effort." (Neera Bhansali, "Data Governance: Creating Value from Information Assets", 2014)

"Although performance measurement is often linked to tools such as scorecards, dashboards, performance targets, indicators and information systems, it would be naïve to consider the measurement of performance as just a technical issue. Indeed, measurement is often used as a way of attempting to bring clarity to complex and confusing situations." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"Data governance presents a clear shift in approach, signals a dedicated focus on data management, distinctly identifies accountability for data, and improves communication through a known escalation path for data questions and issues. In fact, data governance is central to data management in that it touches on essentially every other data management function. In so doing, organizational change will be brought to a group is newly - and seriously - engaging in any aspect of data management." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)

"Data is owned by the enterprise, not by systems or individuals. The enterprise should recognize and formalize the responsibilities of roles, such as data stewards, with specific accountabilities for managing data. A data governance framework and guidelines must be developed to allow data stewards to coordinate with their peers and to communicate and escalate issues when needed. Data should be governed cooperatively to ensure that the interests of data stewards and users are represented and also that value to the enterprise is maximized." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)

"The desire to collect as much data as possible must be balanced with an approximation of which data sources are useful to address a business issue. It is worth mentioning that often the value of internal data is high. Most internal data has been cleansed and transformed to suit the mission. It should not be overlooked simply because of the excitement of so much other available data." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)

"Any change in the reporting requirement had to go through a long-winded process of data model changes, ETL code changes, and respective changes to the reporting system. Often, the ETL process was a specialized skill and became a bottleneck for reducing data to insight turnover time. The nature of analytics is unique. The more you see the output, the more you demand. Many EDW projects were deemed a failure. The failure was not from a technical perspective, but from a business perspective. Operationally, the design changes required to cater to these fast-evolving requirements were too difficult to handle." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"It is often said that data scientists and data analysts spend only 20% of their time doing data analysis work, with 80% consumed by data 'issues'. The bulk of their time is spent finding, evaluating, understanding, and preparing data before analysis can begin. A data catalog inverts this principle by enabling data analysts and data scientists to spend 20% of their time looking for data and 80% performing analysis." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"The main challenges include relentlessly chasing data issues that include schema and quality changes (data drift). Sometimes, fixing these issues can cause outages and delays to existing jobs. This is tied tightly to the underlying infrastructure, process, and technology and can be vulnerable to any changes there. For example, a temporary glitch in the cloud ecosystem will result in a failure of the data pipeline." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Consumability is a very important requirement because it will directly affect the experience domain consumers will have in a streaming data mesh. If other domains cannot easily consume streaming data products, then they may opt out of the streaming data mesh and decide to build their own integrations by hand, bypassing any issues they encounter with the data mesh. Some factors to consider when ingesting data derivatives that will affect the consumability of other domains are as follows: (*) Lack of scalability (*) Lack of interoperability" (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"Choosing the right data ingestion strategy is a significant business decision that partially determines how well your organization can leverage its data for business decision making and operations. The stakes are high; the wrong strategy can lead to poor data quality, performance issues, increased costs, and even regulatory compliance breaches." (James Serra, "Deciphering Data Architectures", 2024)

"The transformation phase represents the most resource-intensive stage of most data projects, often consuming 60-80% of total project time and effort. This significant investment stems from the inherent complexity of converting raw, inconsistent data into clean, structured, and enriched information ready for business use. Every data quality issue must be identified and resolved, every business rule must be correctly implemented, and every integration point must be properly validated. This meticulous work serves as the essential bridge between raw data ingestion and meaningful business insights." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

📉Graphical Representation: Regions (Just the Quotes)

"The most important reason for portraying standard deviations is that they give us a sense of the relative variability of the points in different regions of the plot." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"The logarithm is an extremely powerful and useful tool for graphical data presentation. One reason is that logarithms turn ratios into differences, and for many sets of data, it is natural to think in terms of ratios. […] Another reason for the power of logarithms is resolution. Data that are amounts or counts are often very skewed to the right; on graphs of such data, there are a few large values that take up most of the scale and the majority of the points are squashed into a small region of the scale with no resolution." (William S. Cleveland, "Graphical Methods for Data Presentation: Full Scale Breaks, Dot Charts, and Multibased Logging", The American Statistician Vol. 38" (4) 1984)

"Do not allow data labels in the data region to interfere with the quantitative data or to clutter the graph. […] Avoid putting notes, keys, and markers in the data region. Put keys and markers just outside the data region and put notes in the legend or in the text." (William S Cleveland, "The Elements of Graphing Data", 1985)

"Make the data stand out and avoid superfluity are two broad strategies that serve as an overall guide to the specific principles […] The data - the quantitative and qualitative information in the data region - are the reason for the existence of the graph. The data should stand out. […] We should eliminate superfluity in graphs. Unnecessary parts of a graph add to the clutter and increase the difficulty of making the necessary elements - the data - stand out." (William S Cleveland, "The Elements of Graphing Data", 1985)

"Binning has two basic limitations. First, binning sacrifices resolution. Sometimes plots of the raw data will reveal interesting fine structure that is hidden by binning. However, advantages from binning often outweigh the disadvantage from lost resolution. [...] Second, binning does not extend well to high dimensions. With reasonable univariate resolution, say 50 regions each covering 2% of the range of the variable, the number of cells for a mere 10 variables is exceedingly large. For uniformly distributed data, it would take a huge sample size to fill a respectable fraction of the cells. The message is not so much that binning is bad but that high dimensional space is big. The complement to the curse of dimensionality is the blessing of large samples. Even in two and three dimensions having lots of data can bc very helpful when the observations are noisy and the structure non-trivial." (Daniel B Carr, "Looking at Large Data Sets Using Binned Data Plots", [in "Computing and Graphics in Statistics"] 1991)

"Data that are skewed toward large values occur commonly. Any set of positive measurements is a candidate. Nature just works like that. In fact, if data consisting of positive numbers range over several powers of ten, it is almost a guarantee that they will be skewed. Skewness creates many problems. There are visualization problems. A large fraction of the data are squashed into small regions of graphs, and visual assessment of the data degrades. There are characterization problems. Skewed distributions tend to be more complicated than symmetric ones; for example, there is no unique notion of location and the median and mean measure different aspects of the distribution. There are problems in carrying out probabilistic methods. The distribution of skewed data is not well approximated by the normal, so the many probabilistic methods based on an assumption of a normal distribution cannot be applied." (William S Cleveland, "Visualizing Data", 1993)

"A Venn diagram is a simple representation of the sample space, that is often helpful in seeing 'what is going on'. Usually the sample space is represented by a rectangle, with individual regions within the rectangle representing events. It is often helpful to imagine that the actual areas of the various regions in a Venn diagram are in proportion to the corresponding probabilities. However, there is no need to spend a long time drawing these diagrams - their use is simply as a reminder of what is happening." (Graham Upton & Ian Cook, "Introducing Statistics", 2001)

"The notion of outcomes covering a space is a very useful mental image, as it ties in strongly with the use of Venn diagrams and tables for clarifying the nature of possible events resulting from a trial. There are two important aspects to this. First, when enumerating the various outcomes that comprise an event, the number of" (equally. likely) outcomes should correspond, visually, with the area of that part of the diagram represented by the event in question - the greater the probability, the larger the area. Secondly, where events overlap" (for example, when rolling a die, consider the two events 'getting an even score' and 'getting a score greater than 2' ), the various regions in the Venn diagram help to clarify the various combinations of events that might occur." (Alan Graham, "Developing Thinking in Statistics", 2006)

"There is no strict dividing line between a region, a view, and a glyph. A view is a contiguous region in which visually encoded data is shown on the display. Sometimes a view is a full-blown window controlled by the computer’s operating system, sometimes it is a subcomponent such as a panel or a pane, and sometimes it simply means a region of the display that is visually distinguishable from other regions through some kind of visible boundary. A spatial region showing data visually encoded by a specific idiom might be called either a glyph or a view depending on its screen size, the amount of additional information beyond the visual encoding alone that is shown, and whether it is nested within another region. Large, stand-alone, highly detailed regions are likely to be called views, and small, nested, schematic regions are likely to be called glyphs." (Tamara Munzner, "Visualization Analysis and Design", 2014)

"Decision trees are also discriminative models. Decision trees are induced by recursively partitioning the feature space into regions belonging to the different classes, and consequently they define a decision boundary by aggregating the neighboring regions belonging to the same class. Decision tree model ensembles based on bagging and boosting are also discriminative models." (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", 2015)

"When using indexes in a data set, using an average aggregation is appropriate as long as you only use it at the individual region, month, and visitor type level (i.e., the lowest granularity of the data). You cannot use an average of the average to represent the total."  (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Often, finding the spatial scale that best matches the task at hand is a trial-and-error procedure. It may even be necessary to create further spatial scales by subsuming or subdividing spatial units. Coarser scales can be derived from the original scale by means of a suitable aggregation strategy. This includes the application of aggregation functions such as average, sum, or count. For the creation of finer scales, a suitable distribution strategy is required to assign data values to the newly specified sub-regions. Usually, additional context information is necessary to arrive at semantically meaningful aggregations and distribution." (Christian Tominski & Heidrun Schumann, "Interactive Visual Data Analysis", 2019)

"Before even thinking about charts, it should be recognised that the table on its own is extremely useful. Its clear structure, with destination regions organised in columns and origins in rows, allows the reader to quickly look up any value - including totals - quickly and precisely. That’s what tables are good for. The deficiency of the table, however, is in identifying patterns within the data. Trying to understand the relationships between the numbers is difficult because, to compare the numbers with each other, the reader needs to store a lot of information in working memory, creating what psychologists refer to as a high 'cognitive load'." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

🔭Data Science: Prototyping (Just the Quotes)

"A model is a qualitative or quantitative representation of a process or endeavor that shows the effects of those factors which are significant for the purposes being considered. A model may be pictorial, descriptive, qualitative, or generally approximate in nature; or it may be mathematical and quantitative in nature and reasonably precise. It is important that effective means for modeling be understood such as analog, stochastic, procedural, scheduling, flow chart, schematic, and block diagrams." (Harold Chestnut, "Systems Engineering Tools", 1965)

"A model is an attempt to represent some segment of reality and explain, in a simplified manner, the way the segment operates." (E Frank Harrison,The managerial decision-making process", 1975)

"A mathematical model is any complete and consistent set of mathematical equations which are designed to correspond to some other entity, its prototype. The prototype may be a physical, biological, social, psychological or conceptual entity, perhaps even another mathematical model." (Rutherford Aris, :Mathematical Modelling", 1978)

"Modeling in its broadest sense is the cost-effective use of something in place of something else for some [cognitive] purpose. It allows us to use something that is simpler, safer, or cheaper than reality instead of reality for some purpose. A model represents reality for the given purpose; the model is an abstraction of reality in the sense that it cannot represent all aspects of reality. This allows us to deal with the world in a simplified manner, avoiding the complexity, danger and irreversibility of reality." (Jeff Rothenberg,The Nature of Modeling. In: Artificial Intelligence, Simulation, and Modeling", 1989)

"Model building is the art of selecting those aspects of a process that are relevant to the question being asked. As with any art, this selection is guided by taste, elegance, and metaphor; it is a matter of induction, rather than deduction. High science depends on this art." (John H Holland, "Hidden Order: How Adaptation Builds Complexity", 1995)

"A model is a deliberately simplified representation of a much more complicated situation. […] The idea is to focus on one or two causal or conditioning factors, exclude everything else, and hope to understand how just these aspects of reality work and interact." (Robert M Solow,How Did Economics Get That Way and What Way Did It Get?", Daedalus, Vol. 126, No. 1, 1997)

"More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills - skills that are also necessary for understanding biases in the data, and for debugging logging output from code. Once she gets the data into shape, a crucial part is exploratory data analysis, which combines visualization and data sense. She’ll find patterns, build models, and algorithms - some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications." (Rachel Schutt,Doing Data Science: Straight Talk from the Frontline", 2013)

"A mockup shows what we should expect to take away from a project. In contrast, an argument sketch tells us roughly what we need to do to be convincing at all. It is a loose outline of the statements that will make our work relevant and correct. While they are both collections of sentences, mockups and argument sketches serve very different purposes. Mockups give a flavor of the finished product, while argument sketches give us a sense of the logic behind the solution." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)

"Keep in mind that a mockup is not the actual answer we expect to arrive at. Instead, a mockup is an example of the kind of result we would expect, an illustration of the form that results might take. Whether we are designing a tool or pulling data together, concrete knowledge of what we are aiming at is incredibly valuable. Without a mockup, it’s easy to get lost in abstraction, or to be unsure what we are actually aiming toward. We risk missing our goals completely while the ground slowly shifts beneath our feet. Mockups also make it much easier to focus in on what is important, because mockups are shareable. We can pass our few sentences, idealized graphs, or user interface sketches off to other people to solicit their opinion in a way that diving straight into source code and spreadsheets can never do." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)

"Creating mockups to communicate is not intrinsically a bad idea. But, as we are subject to confirmation bias, there’s always a risk that we will stop at our first design attempt and become reluctant to ask if there are better ways to achieve the same goals. Making these first ideas very detailed; putting them into a document; and especially blessing that document with the label 'requirements' are all moves which make further revision less likely, and put us more at risk from confirmation bias." (Laurent Bossavit, "The Leprechauns of Software Engineering", 2015)

🪙Business Intelligence: Mistakes (Just the Quotes)

"Using data quality rules brings comprehensive data quality assessment from fantasy world to reality. However, it is by no means simple, and it takes a skillful skipper to navigate through the powerful currents and maelstroms along the way. Considering the volume and structural complexity of a typical database, designing a comprehensive set of data quality rules is a daunting task. The number of rules will often reach hundreds or even thousands. When some rules are missing, the results of the data quality assessment can be completely jeopardized, Thus the first challenge is to design all rules and make sure that they indeed identify all or most errors." (Arkady Maydanchik, "Data Quality Assessment", 2007)

"A common misconception about BI standardization is the assumption that all users must use the same tool. It would be a mistake to pursue this strategy. Instead, successful BI companies use the right tool for the right user. For a senior executive, the right tool might be a dashboard. For a power user, it might be a business query tool. For a call center agent, it might be a custom application or a BI gadget embedded in an operational application."(Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)

"Business intelligence tools can only present the facts. Removing biases and other errors in decision making are dynamics of company culture that affect how well business intelligence is used." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)

"The problem is when biases and inaccurate data also get filtered into the gut. In this case, the gut-feel decision making should be supported with objective data, or errors in decision making may occur." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008

"Tests are sometimes mistaken with quality assurance. These two notions are not identical: 1) quality assurance ensures that the organization's processes are implemented and applied correctly; 2) testing identifies defects and failures, and provides information on the software and the risks associated with their release to the market." (Bernard Homes, "Fundamentals of Software Testing", 2012)

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

"Most discussions of decision making assume that only senior executives make decisions or that only senior executives' decisions matter. This is a dangerous mistake. Decisions are made at every level of the organization, beginning with individual professional contributors and frontline supervisors. These apparently low-level decisions are extremely important in a knowledge-based organization." (Zach Gemignani et al, "Data Fluency", 2014)

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

"A common mistake when implementing a data catalog is to focus only on technical metadata. This limits its use and the potential value. It also excludes business users who have valuable related input or need to use the catalog. A catalog should in fact function as a two-way translation layer between technical and business users." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Since data engineering is such a crucial field, you may be wondering who the main players are and what skill sets they possess. Building a data product involves several folks, all of whom need to come together with seamless handoffs to ensure a successful end product or service is created. It would be a mistake to create silos and increase both the number and complexity of integration points as each additional integration is a potential failure point." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"The problem of bad data has existed for a very long time. Data copies diverge as their original source changes. Copies get stale. Errors detected in one data set are not fixed in duplicate ones. Domain knowledge related to interpreting and understanding data remains incomplete, as does support from the owners of the original data." (Adam Bellemare, "Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)

"Domain autonomy should not be mistaken for a lack of governance or accountability. Autonomy, in this context, implies a higher level of responsibility. Domains are free to act and accountable for their actions, especially regarding how well their data strategies align with domain-specific and broader organizational objectives." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

🖍️Alessandro Negro - Collected Quotes

"A graph is a simple and quite old mathematical concept: a data structure consisting of a set of vertices (or nodes/points) and edges (or relationships/lines) that can be used to model relationships among a collection of objects." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"A widely adopted technique for solving the data sparsity issue and the cold-start problem is based on graph representation, navigation, and processing. Graph navigation methods (like the pathfinding example [...]) and graph algorithms (such as PageRank) are applied to fill some gaps and create a denser representation of [a] dataset."

"Deep learning approaches the problem of representation learning by introducing representations that are expressed in terms of other, simpler representations. In deep learning, the machine builds multiple levels of increasing complexity over the underlying simpler concepts." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"GNNs are capable of generating representations of nodes that depend on the structure of the graph as well as on any feature information we have. These features could be nodes’ properties, relationship types, and relationship properties. That’s why GNNs could drive the final tasks to better results. These embeddings represent the input for tasks such as node classification, link prediction, and graph classification." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"[...] graphs are extremely useful for encoding information, and data in graph format is increasingly plentiful. In many areas of machine learning - including natural language processing, computer vision, and recommendations - graphs are used to model local relationships between isolated data items (users, items, events, and so on) and to construct global structures from local information. Representing data as graphs is often a necessary step (and at other times only a desirable one) in dealing with problems arising from applications in machine learning or data mining." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs are powerful structures useful not only for representing connected information, but also for supporting multiple types of analysis. Their simple data model, consisting of two basic concepts such as nodes and relationships, is flexible enough to store complex information. If you also store properties in nodes and relationships, it is possible to represent practically everything of any size." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs are useful for representing how things are either physically or logically linked in simple or complex structures. A graph in which we assign names and meanings to the edges and vertices becomes what is known as a network. In these cases, a graph is the mathematical model for describing a network, whereas a network is a set of relations between objects, which could include people, organizations, nations, items found in a Google search, brain cells, or electrical transformers." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs can be used to model and analyze the relationships between entities as well as their properties. This aspect brings an additional dimension of information that graph-powered machine learning can harness for prediction and categorization. The schema flexibility provided by graphs also allows different models to coexist in the same dataset." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs can support machine learning by doing what they do best: representing data in a way that is easily understandable and easily accessible. Graphs make all the necessary processes faster, more accurate, and much more effective. Moreover, graph algorithms are powerful tools for machine learning practitioners. Graph community detection algorithms can help identify groups of people, page rank can reveal the most relevant keywords in a text, and so on." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs, with multiple node types and different types of relationships, are far from being a Euclidean space. This task is where graph neural networks (GNNs) comes in. GNNs are deep learning-based methods that operate on a graph domain to perform complex tasks such as node classification (the bot example), link prediction (the disease example), and so on. Due to its convincing performance, GNN has become a widely applied graph analysis method." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"In many areas of machine learning, graphs are used to model local relationships between data elements and to build global structures from local information. Building graphs is sometimes necessary for dealing with problems arising from applications in machine learning or data mining, and at other times, it's helpful for managing data. It's important to note that the transformation from the original data to a graph data representation can always be performed in a lossless manner. The opposite is not always true." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"One of the main goals of machine learning is to make sense of data and deliver some sort of predictive capability to the end user ([...] data analysis in general aims at extracting knowledge, insights, and finally wisdom from raw data sources, and prediction represents a small portion of possible uses). In this learning path, data visualization plays a key role because it allows us to access and analyze data from a different perspectiv." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"The performance of machine learning algorithms, both in terms of accuracy and speed, is affected almost directly from the way in which we represent our training data and store our prediction model. The quality of algorithm prediction is as good as the quality of the training dataset. Data cleansing and feature selection, among other tasks, are mandatory if we would like to achieve a reasonable level of trust in the prediction. The speed at which the system provides prediction affects the usability of the entire product." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"To compute the similarity between items, we must define a similarity measure. Cosine similarity is the standard metric in item-based recommendation approaches: it determines the similarity between two vectors by calculating the cosine of the angle between them In machine learning applications, this measure is often used to compare two text documents, which are represented as vectors of terms [...] prediction represents a small portion of possible uses). In this learning path, data visualization plays a key role because it allows us to access and analyze data from a different perspective." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

🖍️Aldo Marzullo - Collected Quotes

"[...] a graph is a mathematical model that is used for describing relationships between entities. However, each complex network presents intrinsic properties. Such properties can be measured by particular metrics, and each measure may characterize one or several local and global aspects of the graph." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"A point of caution when dealing with projections: be aware of the dimension of the projected graph.  In certain cases, such as the one we are considering here, projection may create extremely large numbers of edges, which makes the graph hard to be analyzed." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"A small-world network is characterized by a high clustering coefficient and a short average path length, meaning that most nodes can be reached from any other node through a small number of intermediate connections. This structure often mirrors real-world social networks, where individuals are typically connected through a few mutual acquaintances, allowing for rapid information dissemination." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Although creating simple subgraphs and merging them is a way to generate new graphs of increasing complexity, networks may also be generated by means of probabilistic models   and/or generative models that let a graph grow by itself. Such graphs usually share   interesting properties with real networks and have long been used to create benchmarks and synthetic graphs, especially in times when the amount of data available was not as overwhelming as today." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"As with many other deep learning-based approaches, another major challenge is in interpretability. While knowledge graphs provide a structured and transparent way to store relationships, LLMs operate as a black box, making it difficult to understand how specific outputs are generated. [...] Data alignment is also a key issue, as structured knowledge graphs and unstructured text data must be carefully preprocessed to ensure consistency.  Differences in data formats, ontology mismatches, and information redundancy can create inefficiencies when integrating these two paradigms. Developing robust pipelines that seamlessly connect graph-based insights with LLM-generated text remains an open challenge." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Assortativity is used to quantify the tendency of nodes being connected to similar nodes, which can impact the network’s ability to withstand failures or 'attacks'. High assortativity indicates that nodes of similar degrees are more likely to be connected, leading to a resilient structure where the failure of some nodes does not significantly disrupt overall connectivity. Conversely, networks with low assortativity tend to have nodes connecting with dissimilar degrees, making them more vulnerable to targeted attacks on high-degree nodes [...]" (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Besides allowing us to compress a sparse representation into a denser vector, autoencoders are also widely used to process a signal in order to filter out noise and extract only a relevant (characteristic) signal. This can be very useful in many applications, especially when identifying anomalies and outliers." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Despite their impressive capabilities, LLMs are not without limitations. One of the most significant challenges is the problem of hallucination, where an LLM generates factually incorrect or misleading information that appears plausible. This is particularly problematic in domains requiring high factual accuracy, such as healthcare, finance, and legal applications. To mitigate hallucinations and enhance the reliability of LLM outputs,  Retrieval-Augmented Generation (RAG) has emerged as a powerful technique. RAG works by dynamically retrieving relevant information from an external knowledge source (such as a knowledge graph) at inference time, rather than just relying on pre-trained knowledge. This approach ensures that the model has access to up-to-date and accurate data, grounding answers in verified information rather than generating content purely from its internal representations." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Generally speaking, all the unsupervised embedding algorithms based on matrix factorization use the same principle. They all factorize an input graph expressed as a matrix in different components. The main difference between each method lies in the loss function used during the optimization process and the constraints/formulations posed for the V and H matrices. Indeed, different loss functions allow the creation of an embedding space that emphasizes specific properties of the input graph." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Graph analytics is generally very effective in clustering users, merchants, and communities to provide an effective implementation of behavior analytics. On the other hand, second-party fraud can be identified with the implementation of monitoring employee behavior, as well as compliance checks. Graph analytics can indeed be useful for these use cases. Similar to the first-party models, employee behavior can also be analyzed using graph machine learning, although the dataset may need to encode a number of other sources of information besides transactional data. From a compliance standpoint, process mining techniques that still rely on a graph representation of the various procedural steps/pathways can be effective in identifying fraudulent behavior or non-compliant processes. Finally, third-party fraud, especially in the form of phishing attacks, can also be addressed using graph machine learning. In this context, understanding the network from which the phishing attack comes as well as the URLs being used (which can also benefit from a graph representation) can be critical for building an effective phishing classifier." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Graphs are mathematical structures that are used for describing relationships between entities, and they are used almost everywhere." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"In a nutshell, training LLMs involves optimizing a large number of parameters on very large datasets. This process, known as pretraining, typically employs unsupervised learning objectives, such as predicting missing words in a sentence (masked language modeling) or forecasting subsequent words (causal language modeling). As a side effect, the pre-training phase lets the model learn and 'understand' a language, resulting in a remarkable ability to generalize across various tasks, often achieving state-of-the-art performance. LLMs have demonstrated proficiency in a diverse array of applications, reflecting their versatility and depth of language understanding. Key areas include text generation, language translation, question answering, and summarization, among many others." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Machine learning is a subset of artificial intelligence that aims to provide systems with the ability to learn and improve from data. It has achieved impressive results in many different applications, especially where it is difficult or unfeasible to explicitly define rules to solve a specific task." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Most of the complexity indeed arises from the presence of entities that appear only once or very few times, but still generate cliques within the graph. Such entities are not very informative to capture patterns and provide insights. Besides, they are possibly strongly affected by statistical variability. On the other hand, we should focus on strong correlations that are supported by larger occurrences and provide more reliable statistical results." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Overfitting is one of the main problems that affect machine learning practitioners. It can occur due to several reasons. Some of the reasons can be as follows: The dataset can be ill-defined or not sufficiently representative of the task. In this case, adding more data could help to mitigate the problem. The mathematical model used for addressing the problem is too powerful for the task. In this case, proper constraints can be added to the loss function in order to reduce the model’s 'power'. Such constraints are called regularization terms.(Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"RAG is a framework that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs. In this setup, an LLM is augmented with a retrieval component that fetches relevant information from external data sources, such as knowledge bases or databases, to produce more accurate and contextually relevant responses. This method enhances the LLM’s output by grounding it in authoritative, up-to-date information." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Reinforcement learning is used for training machine learning agents to make a sequence of decisions. The artificial intelligence algorithm faces a game-like situation, where the agent gets penalties or rewards based on the actions performed The goal of the agent is to understand how to act in order to maximize rewards and minimize penalties". (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"The concept of temporal graphs is useful in all the real-world problems that can be represented as a graph, where the nodes and edges of the graph may change over time. For example, temporal graphs are extensively applied in modeling social networks. By capturing the evolving relationships between individuals, temporal graphs enable a more accurate representation of social dynamics. This is particularly useful for predicting changes in friendships, community structures, and the information diffusion over time." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"The label spreading algorithm is another semi-supervised shallow embedding algorithm.  It was built in order to overcome one big limitation of the label propagation method: the   initial labeling. Indeed, according to the label propagation algorithm, the initial labels cannot be modified in the training process and, in each iteration, they are forced to be equal to their original value. This constraint could generate incorrect results when the initial labeling is affected by errors or noise. As a consequence, the error will be propagated in all nodes of the input graph." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"The main difference between unsupervised and supervised embedding methods essentially lies in the task they attempt to solve. Indeed, if unsupervised shallow embedding algorithms try to learn a good graph, node, or edge representation in order to understand the underlying structure, the supervised algorithms try to find the best solution for a prediction task such as node classification, label prediction, or graph classification." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

🪙Business Intelligence: Data Sharing (Just the Quotes)

"Having multiple data lakes replicates the same problems that were created with multiple data warehouses - disparate data siloes and data fiefdoms that don't facilitate sharing of the corporate data assets across the organization. Organizations need to have a single data lake from which they can source the data for their BI/data warehousing and analytic needs. The data lake may never become the 'single version of the truth' for the organization, but then again, neither will the data warehouse. Instead, the data lake becomes the 'single or central repository for all the organization's data' from which all the organization's reporting and analytic needs are sourced." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"Data governance policies must not enforce constraints on data - Data governance intends to control the level of democracy within the data lake. Its sole purpose of existence is to maintain the quality level through audits, compliance, and timely checks. Data flow, either by its size or quality, must not be constrained through governance norms. [...] Effective data governance elevates confidence in data lake quality and stability, which is a critical factor to data lake success story. Data compliance, data sharing, risk and privacy evaluation, access management, and data security are all factors that impact regulation." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"A data product encapsulates more than just the data. It needs to contain all the structural components needed to manifest its baseline usability characteristics - discoverable, understandable, addressable, etc. - in an autonomous fashion, while continuing to share data in a compliant and secure manner." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data Mesh is a sociotechnical approach to share, access and manage analytical data in complex and large-scale environments - within or across organizations." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"In the case of data mesh, a data product is an architectural quantum. It is the smallest unit of architecture that can be independently deployed and managed. It has high functional cohesion, i.e., performing a specific analytical transformation and securely sharing the result as domain-oriented analytical data. It has all the structural components that it requires to do its function: the transformation code, the data, the metadata, the policies that govern the data, and its dependencies to infrastructure." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"A data sharing data service shares data, in any format and any size, from multiple sources within an organization or other organizations. This type of service provides the required control to share data and allows data-sharing policies to be created. It also enables data sharing in a structured manner and offers complete visibility into how the data is shared and how it is used. A data-sharing system uses APIs for data sharing." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"An API is an interface that allows applications to interact with an external service using a simple set of commands. Data can also be served as part of API interaction. As the data is exposed to multiple external services, API-based methods can scale to share data securely with external services. Data through an API is served in JSON format, therefore the technology used to serve the data using APIs should be able to support JSON formats. For example, a NoSQL database can store such data." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"Each domain data lakehouse may opt to have its data catalog. However, the critical component in this architecture is the data mesh catalog. The data mesh catalog is the master catalog used to discover the data elements available in different nodes. Each domain-oriented node will donate its metadata to the data mesh catalog. This donation of metadata determines the effectiveness of the data mesh architecture. Once the metadata is contributed, other nodes can browse through the data mesh catalog. They can select the data of interest and mutually share data between the nodes through a governed data sharing process. The critical point to note here is that, unlike the hub-spoke architecture, the data mesh architecture enables data sharing between the 'spoke nodes'. There is no hub node in a data mesh architecture." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"A data strategy must align with the business goals and overall framework of how data will be used and managed within an organization. It needs to include standards for how data will be discovered, integrated, accessed, shared, and protected. It needs to address how data will meet regulatory compliance policies, Master Data Management, and data democratization. There needs to be an assurance that both data and metadata have a quality control framework in place to achieve data trust. A data strategy needs to have a clear path on how an organization will accomplish data monetization." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

"Data lineage is the path the data took from its source origin, the stops it made along the way, and its destination. This includes information on all the systems it passed through, how it was cleansed, what it was enriched with, and how it was secured. Capturing all that metadata is difficult because many of those systems and applications don’t share information. It’s up to you to assemble the data’s path by pulling metadata from all those systems/applications and assembling them in hopes that you find the path your data took from its current location (destination) to its source system. Lineage is probably the hardest piece of metadata to acquire for either streaming or batching data pipelines." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"Manage data as a strategic asset that evolves into a data product. The premise here is to stop managing data as a byproduct and create an ecosystem that manages data as a valuable strategic asset that can evolve into a data product. Data producers are accountable for managing the life cycle of data from creation to end of life and ensuring it creates business value along the way for data consumers. This requires data that is governed, trusted, protected, secure, and easily accessible. Move data from technical data assets to Data Products by operationalizing data for high scale sharing." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

"Since domains are used to create data products, and sharing data products across many domains ultimately builds a mesh of data, we need to ensure that the data being served follows some guidelines. Data governance involves creating and adhering to a set of global rules, standards, and policies applied to all data products and their interfaces to ensure a collaborative and interoperable data mesh community. These guidelines must be agreed upon among the participating data mesh domains." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"Federation is about providing autonomy to each data product owner to make their own decisions about the storage, computing, and sharing of data. However, this autonomy cannot come at a risk to the security and compliance standards of the company." (Aniruddha Deswandikar, "Engineering Data Mesh in Azure Cloud", 2024)

"When data is considered a product, it creates opportunities for collaboration across different domains. This collaboration involves working with other teams to create, share, and use data products that span multiple areas of expertise, interest, or value. Data Mesh promotes cross-domain collaboration by focusing on the consumers rather than the producers. Data products are made available through standardized interfaces and protocols that support various modes of consumption and are governed by domain experts who understand the context and nuances of their data." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"A lakehouse is a data storage space that hosts and manages all types of data in one place (structured, semi-struc-tured, and unstructured), allowing different tools to normalize and examine this data according to organizational requirements and/or individual choices. A lakehouse thus combines the best aspects of a data lake and a data warehouse by eliminating data duplication and friction related to ingestion, transformation, and sharing of data within the organization, all in the open format, Delta Lake." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Fabric integrates the various technologies needed for an end-to-end data project (namely, ingestion, preparation, storage, processing, enrichment, analysis, visualization, and data sharing) within a single platform accessible as Software as a Service (SaaS), meaning via a simple connection on a web browser. This reduces complexity, costs, and delays related to using multiple tools and technologies, and eliminates all the operational maintenance of infrastructure serving data analytics needs." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"This transition to OneDrive highlights the importance of governance adapted to new methods of collaborative work and data sharing. The idea of OneLake is, therefore, based on this same concept: rather than subscribing to a data lake technology that must be maintained, why not simply subscribe to a storage service that offers a layer of abstraction over the complexities of these data storage infrastructures? As a result, the data lake becomes a controlled or governed environment, but still accessible to users who can view it as a simple and intuitive way to securely share data with their colleagues and IT teams."(Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

🎯Jean-Georges Perrin - Collected Quotes

"Accessibility is another key principle. It’s not enough for data to be findable; In fact, once found, a data product must also be easily accessible. Accessibility includes providing comprehensive documentation that explains how to use the data, as well as ensuring that the data can be easily integrated into various applications and workflows. A good data product should be as straightforward to use as a well-designed software application, with clear instructions and support."  (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Consider data silos. Data silos hinder data accessibility and collaboration, making it difficult to gain a holistic view and leverage the full potential of the available data. They present a real, present, and formidable challenge that almost all data practitioners experience in modern enterprises. Data silos, much like isolated islands in an immense ocean, are repositories of data that are confined within specific departments or systems, disconnected from the broader organizational data landscape. This segregation results in a fragmented data ecosystem, where valuable insights remain untapped, and the collective intelligence of the enterprise is underutilized." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Data Mesh addresses data governance challenges by advocating for a federated governance model, which positions accountability for governance with the data owners who are most knowledgeable about the data. In this model, governance is decentralized, with each domain team responsible for the governance of its data products. This approach ensures that governance decisions are made by those who have the deepest understanding of the data’s context, use, and risks. It leads to more relevant, efficient, and effective governance practices that are closely aligned with the specific needs of each domain." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Data Mesh advocates for domain-driven ownership of data, enabling individual teams to manage and share their data effectively while aligning with the overall organizational objectives. By embracing this paradigm, enterprises can gradually dismantle the barriers of data silos, paving the way for a more integrated, agile, and data-centric organizational culture." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"[...] Data Mesh introduces agility into the data landscape, emphasizing decentralized ownership, responsive data management, and collaborative cross-functional teams. Just as Agile promotes self-organizing teams, Data Mesh advocates for domain-oriented decentralized ownership, putting the power of data in the hands of individual domain teams. In an Agile context, customer collaboration involves continuous engagement with stakeholders to understand their evolving needs. Likewise, Data Mesh encourages domain teams to engage with data consumers within their organization, gathering feedback, and iterating on their data products to meet their specific requirements." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"[...] Data Mesh is, then, at its foundation, a conceptual framework in the realm of data architecture, which emphasizes decentralized data ownership and architecture. It recognizes that in large organizations, data is vast and varied, where each business domain has autonomy over its own data. By decentralizing control, it empowers individual domains to manage and make decisions about their data while maintaining a cohesive overall structure. And presumably, with this autonomy comes better, more localized, and faster decisions, which in-turn, leads to speed and agility." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Federated computational governance is essential for maintaining consistency and compatibility across the Data Mesh. It ensures that despite the decentralized nature of data ownership, there is a unified framework governing how data is managed, used, and shared. This unified approach is crucial in preventing data silos, ensuring data interoperability, and maintaining the overall integrity of the data ecosystem." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Implementing federated computational governance requires a delicate balance. It involves creating governance structures that are robust enough to ensure consistency and compliance, yet flexible enough to accommodate the unique needs and contexts of different data products. This balance is key to fostering an environment where innovation can thrive without compromising the standards and protocols essential for a cohesive data ecosystem." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"In the context of a Data Mesh, a data product is a package of data that is self-contained, self-descriptive, and oriented towards a specific business purpose or function. But, it is not just a mere collection of data, rather it is a coherent unit that provides value to its consumers, akin to a product in any other industry." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Observability extends the concept of reliability. It’s about having the ability to monitor the health and performance of the data product. By using tools to track various metrics like response times and error rates, organizations can proactively manage the data product’s health. This proactive management plays a crucial role in maintaining the product’s reliability, as it allows for the early identification and resolution of potential issues before they escalate." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"[...] scalability and maintainability are key aspects of a valuable data product. It should be capable of handling increasing volumes of data or user demands without necessitating significant redesign or rework. Alongside scalability, maintainability - the ease with which a data product can be updated, modified, or repaired - is critical for its long-term utility. This also includes the product’s ability to evolve based on user feedback and changing business needs, ensuring that it remains relevant and valuable over time." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Self-serve capability in a Data Mesh not only empowers users but also fosters a culture of innovation and agility. It enables individuals to leverage data for their specific needs, encouraging experimentation and personalized analysis. This capability reduces bottlenecks typically associated with centralized data systems, where requests for data access and analysis can slow down decision-making processes." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"The fourth principle, Reuse, focuses on the ability to apply data in multiple contexts. This principle is particularly important in maximizing the value of data. By designing data products to be modular and reusable, they can be used across different projects and applications. For instance, a data product containing customer demographic information can be used by marketing teams for campaign planning, by sales teams for sales strategy development, and by product development teams for market analysis."  (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"The principle of a clear boundary in a Data Mesh ensures that every data product is a well-defined entity within the larger ecosystem. This clarity prevents overlap and confusion, establishing a clear understanding of the data product’s purpose and scope. It aids in managing expectations and directs efforts and resources appropriately, ensuring that each data product can effectively fulfill its intended role." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"The ramifications of data silos extend beyond mere inefficiencies; they actively hinder collaboration and innovation within an organization. When data is trapped in silos, it becomes difficult for teams to access the information they need to collaborate effectively. This lack of accessibility and visibility leads to duplicated efforts, inconsistent data practices, and a general sense of organizational disjointedness." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"The role of an empowered owner is multifaceted. They are tasked with ensuring that the data product aligns with both specific business requirements and the overarching governance framework. This alignment is crucial for maintaining the integrity and usefulness of the data product, ensuring it remains a valuable asset within the organization’s data landscape." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"The structure of a data product is self-contained, meaning that it includes everything necessary for its effective utilization. It adheres to strict standards of quality and governance, thereby ensuring reliability, security, and compliance with relevant regulations. This comprehensive approach makes data products a trusted and dependable resource within the organization. They are designed with user accessibility in mind, offering interfaces and documentation that are easily navigable by a wide range of users, from data experts to those with minimal technical expertise." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Usability is a key determinant of a data product’s value - if it is complex or unintuitive, its potential utility diminishes irrespective of the underlying data quality. Therefore, the design and interface of a data product should facilitate ease of use to ensure that it can be effectively employed by its target users. Somewhat related to this is interoperability - in other words it is also usable from an operations perspective. A valuable data product should not only function in isolation but also integrate seamlessly with other data products. This interoperability is vital for comprehensive analytics and insight generation, as it allows for the combination and analysis of data across various domains. Additionally, compliance with regulatory requirements and security standards is non-negotiable."  (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

📉Graphical Representation: Features (Just the Quotes)

"Graphic methods convey to the mind a more comprehensive grasp of essential features than do written reports, because one can naturally gather interesting details from a picture in far less time than from a written description. Further than this, the examination of a picture allows one to make deductions of his own, while in the case of a written description the reader must, to a great degree, accept the conclusions of the author." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"An important rule in the drafting of curve charts is that the amount scale should begin at zero. In comparisons of size the omission of the zero base, unless clearly indicated, is likely to give a misleading impression of the relative values and trend." (Rufus R Lutz, "Graphic Presentation Simplified", 1949)

"The quantile plot is a good general display since it is fairly easy to construct and does a good job of portraying many aspects of a distribution. Three convenient features of the plot are the following: First, in constructing it, we do not make any arbitrary choices of parameter values or cell boundaries [...] and no models for the data are fitted or assumed. Second, like a table, it is not a summary but a display of all the data. Third, on the quantile plot every point is plotted at a distinct location, even if there are duplicates in the data. The number of points that can be portrayed without overlap is limited only by the resolution of the plotting device. For a high resolution device several hundred points distinguished." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"Maps containing marks that indicate a variety of features at specific locations are easy to produce and often revealing for the reader. You can use dots, numbers, and shapes, with or without keys. The basic map must always be simple and devoid of unnecessary detail. There should be no ambiguity about what happens where." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Maps used as charts do not need fine cartographic detail. Their purpose is to express ideas, explain relationships, or store data for consultation. Keep your maps simple. Edit out irrelevant detail. Without distortion, try to present the facts as the main feature of your map, which should serve only as a springboard for the idea you're trying to put across." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Scatter charts show the relationships between information, plotted as points on a grid. These groupings can portray general features of the source data, and are useful for showing where correlationships occur frequently. Some scatter charts connect points of equal value to produce areas within the grid which consist of similar features." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Boxplots provide information at a glance about center (median), spread (interquartile range), symmetry, and outliers. With practice they are easy to read and are especially useful for quick comparisons of two or more distributions. Sometimes unexpected features such as outliers, skew, or differences in spread are made obvious by boxplots but might otherwise go unnoticed." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"A well-constructed graph can show several features of the data at once. Some graphs contain as much information as the original data, and so (unlike numerical summaries) do not actually simplify the data; rather, they express it in visual form. Unexpected or unusual features, which are not obvious within numerical tables, often jump to our attention once we draw a graph. Because the strengths and weaknesses of graphical methods are opposite those of numerical summary methods, the two work best in combination." (Lawrence C Hamilton, "Data Analysis for Social Scientists: A first course in applied statistics", 1995)

"When analyzing data it is many times advantageous to generate a variety of graphs using the same data. This is true whether there is little or lots of data. Reasons for this are: (1) Frequently, all aspects of a group of data can not be displayed on a single graph. (2) Multiple graphs generally result in a more in-depth understanding of the information. (3) Different aspects of the same data often become apparent. (4) Some types of graphs cause certain features of the data to stand out better (5) Some people relate better to one type of graph than another." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"[…] a chart is a picture of relationships, and only the picture counts. Everything else - titles, labels, scale values - merely identifies and explains. The most important feature of the picture is the impression you receive. Scaling has an important controlling effect on that impression." (Gene Zelazny. "Say It with Charts: The executive’s guide to visual communication" 4th Ed., 2001)

"Anyone who has seen, and especially used, a highly responsive interactive visualization tool will be struck by two features. First, that a mere rearrangement of how the data is displayed can lead to a surprising degree of additional insight into that data. Second, that the very property of interactivity can considerably enhance that tool's effectiveness, especially if the computer's response follows a user's action virtually immediately, say within a fraction of a second." (Robert Spence, "Information Visualization", 2001)

"A feature shared by both the range and the interquartile range is that they are each calculated on the basis of just two values - the range uses the maximum and the minimum values, while the IQR uses the two quartiles. The standard deviation, on the other hand, has the distinction of using, directly, every value in the set as part of its calculation. In terms of representativeness, this is a great strength. But the chief drawback of the standard deviation is that, conceptually, it is harder to grasp than other more intuitive measures of spread." (Alan Graham, "Developing Thinking in Statistics", 2006)

"A useful feature of a stem plot is that the values maintain their natural order, while at the same time they are laid out in a way that emphasises the overall distribution of where the values are concentrated (that is, where the longer branches are). This enables you easily to pick out key values such as the median and quartiles." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Old code rarely offers trendy graphics or flavor-of-the-month features, but it has one considerable ad - vantage: It tends to work. A program that has been well used is like an old garden that has been well tended or a vintage guitar that has been well played: Its rough edges have been filed away, its bugs have been found and fixed, and its performance is a known and valuable quantity." (Scott Rosenberg, "Dreaming in Code", 2007)

"Multivariate techniques often summarize or classify many variables to only a few groups or factors (e.g., cluster analysis or multi-dimensional scaling). Parallel coordinate plots can help to investigate the influence of a single variable or a group of variables on the result of a multivariate procedure. Plotting the input variables in a parallel coordinate plot and selecting the features of interest of the multivariate procedure will show the influence of different input variables." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"Parallel coordinate plots are often overrated concerning their ability to depict multivariate features. Scatterplots are clearly superior in investigating the relationship between two continuous variables and multivariate outliers do not necessarily stick out in a parallel coordinate plot. Nonetheless, parallel coordinate plots can help to find and understand features such as groups/clusters, outliers and multivariate structures in their multivariate context. The key feature is the ability to select and highlight individual cases or groups in the data, and compare them to other groups or the rest of the data." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009) 

"Histograms are often mistaken for bar charts but there are important differences. Histograms show distribution through the frequency of quantitative values (y axis) against defined intervals of quantitative values(x axis). By contrast, bar charts facilitate comparison of categorical values. One of the distinguishing features of a histogram is the lack of gaps between the bars [...]" (Andy Kirk, "Data Visualization: A successful design process", 2012)

"When various types of data are layered directly on top of one another, the viewer is able to spatially correlate multiple features. This is immediately intuitive in the case of spatial relationships […]" (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"When you decide how to depict your data, you decide on the abstraction. Will you present a graph? A cartoon? An accurate molecular model? And which features will you include in these representations? Your preferred abstraction should include all necessary information, exclude unnecessary information, and make use of your reader’s preexisting knowledge without being confined by it." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"Three high-level targets are very broadly relevant, for all kinds of data: trends, outliers, and features. A trend is a high-level characterization of a pattern in the data. Simple examples of trends include increases, decreases, peaks, troughs, and plateaus. Almost inevitably, some data doesn’t fit well with that backdrop; those elements are the outliers. The exact definition of features is task dependent, meaning any particular structures of interest." (Tamara Munzner, "Visualization Analysis and Design", 2014)

"A predictive model overfits the training set when at least some of the predictions it returns are based on spurious patterns present in the training data used to induce the model. Overfitting happens for a number of reasons, including sampling variance and noise in the training set. The problem of overfitting can affect any machine learning algorithm; however, the fact that decision tree induction algorithms work by recursively splitting the training data means that they have a natural tendency to segregate noisy instances and to create leaf nodes around these instances. Consequently, decision trees overfit by splitting the data on irrelevant features that only appear relevant due to noise or sampling variance in the training data. The likelihood of overfitting occurring increases as a tree gets deeper because the resulting predictions are based on smaller and smaller subsets as the dataset is partitioned after each feature test in the path." (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", 2015)

"The main advantage of decision tree models is that they are interpretable. It is relatively easy to understand the sequences of tests a decision tree carried out in order to make a prediction. This interpretability is very important in some domains. [...] Decision tree models can be used for datasets that contain both categorical and continuous descriptive features. A real advantage of the decision tree approach is that it has the ability to model the interactions between descriptive features. This arises from the fact that the tests carried out at each node in the tree are performed in the context of the results of the tests on the other descriptive features that were tested at the preceding nodes on the path from the root. Consequently, if there is an interaction effect between two or more descriptive features, a decision tree can model this."  (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", 2015)

"There are two kinds of mistakes that an inappropriate inductive bias can lead to: underfitting and overfitting. Underfitting occurs when the prediction model selected by the algorithm is too simplistic to represent the underlying relationship in the dataset between the descriptive features and the target feature. Overfitting, by contrast, occurs when the prediction model selected by the algorithm is so complex that the model fits to the dataset too closely and becomes sensitive to noise in the data."(John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", 2015)