🪙Business Intelligence: Data Analysis (Just the Quotes)

"As in Mathematics, so in Natural Philosophy, the Investigation of difficult Things by the Method of Analysis, ought ever to precede the Method of Composition. This Analysis consists in making Experiments and Observations, and in drawing general Conclusions from them by Induction, and admitting of no Objections against the Conclusions but such as are taken from Experiments, or other certain Truths." (Sir Isaac Newton, "Opticks", 1704)

"The errors which arise from the absence of facts are far more numerous and more durable than those which result from unsound reasoning respecting true data." (Charles Babbage, "On the Economy of Machinery and Manufactures", 1832)

"In every branch of knowledge the progress is proportional to the amount of facts on which to build, and therefore to the facility of obtaining data." (James C Maxwell, [letter to Lewis Campbell] 1851)

"Not even the most subtle and skilled analysis can overcome completely the unreliability of basic data." (Roy D G Allen, "Statistics for Economists", 1951)

"The technical analysis of any large collection of data is a task for a highly trained and expensive man who knows the mathematical theory of statistics inside and out. Otherwise the outcome is likely to be a collection of drawings - quartered pies, cute little battleships, and tapering rows of sturdy soldiers in diversified uniforms - interesting enough in the colored Sunday supplement, but hardly the sort of thing from which to draw reliable inferences." (Eric T Bell, "Mathematics: Queen and Servant of Science", 1951)

"If data analysis is to be well done, much of it must be a matter of judgment, and ‘theory’ whether statistical or non-statistical, will have to guide, not command." (John W Tukey, "The Future of Data Analysis", Annals of Mathematical Statistics, Vol. 33 (1), 1962)

"If one technique of data analysis were to be exalted above all others for its ability to be revealing to the mind in connection with each of many different models, there is little doubt which one would be chosen. The simple graph has brought more information to the data analyst’s mind than any other device. It specializes in providing indications of unexpected phenomena." (John W Tukey, "The Future of Data Analysis", Annals of Mathematical Statistics Vol. 33 (1), 1962)

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

"The first step in data analysis is often an omnibus step. We dare not expect otherwise, but we equally dare not forget that this step, and that step, and other step, are all omnibus steps and that we owe the users of such techniques a deep and important obligation to develop ways, often varied and competitive, of replacing omnibus procedures by ones that are more sharply focused." (John W Tukey, "The Future of Processes of Data Analysis", 1965)

"The basic general intent of data analysis is simply stated: to seek through a body of data for interesting relationships and information and to exhibit the results in such a way as to make them recognizable to the data analyzer and recordable for posterity. Its creative task is to be productively descriptive, with as much attention as possible to previous knowledge, and thus to contribute to the mysterious process called insight." (John W Tukey & Martin B Wilk, "Data Analysis and Statistics: An Expository Overview", 1966)

"Comparable objectives in data analysis are (l) to achieve more specific description of what is loosely known or suspected; (2) to find unanticipated aspects in the data, and to suggest unthought-of-models for the data's summarization and exposure; (3) to employ the data to assess the (always incomplete) adequacy of a contemplated model; (4) to provide both incentives and guidance for further analysis of the data; and (5) to keep the investigator usefully stimulated while he absorbs the feeling of his data and considers what to do next." (John W Tukey & Martin B Wilk, "Data Analysis and Statistics: An Expository Overview", 1966)

"Data analysis must be iterative to be effective. [...] The iterative and interactive interplay of summarizing by fit and exposing by residuals is vital to effective data analysis. Summarizing and exposing are complementary and pervasive." (John W Tukey & Martin B Wilk, "Data Analysis and Statistics: An Expository Overview", 1966)

"Every student of the art of data analysis repeatedly needs to build upon his previous statistical knowledge and to reform that foundation through fresh insights and emphasis." (John W Tukey, "Data Analysis, Including Statistics", 1968)

"[...] bending the question to fit the analysis is to be shunned at all costs." (John W Tukey, "Analyzing Data: Sanctification or Detective Work?", 1969)

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

"Almost all efforts at data analysis seek, at some point, to generalize the results and extend the reach of the conclusions beyond a particular set of data. The inferential leap may be from past experiences to future ones, from a sample of a population to the whole population, or from a narrow range of a variable to a wider range. The real difficulty is in deciding when the extrapolation beyond the range of the variables is warranted and when it is merely naive. As usual, it is largely a matter of substantive judgment - or, as it is sometimes more delicately put, a matter of 'a priori nonstatistical considerations'." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"[…] it is not enough to say: 'There's error in the data and therefore the study must be terribly dubious'. A good critic and data analyst must do more: he or she must also show how the error in the measurement or the analysis affects the inferences made on the basis of that data and analysis." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"The use of statistical methods to analyze data does not make a study any more 'scientific', 'rigorous', or 'objective'. The purpose of quantitative analysis is not to sanctify a set of findings. Unfortunately, some studies, in the words of one critic, 'use statistics as a drunk uses a street lamp, for support rather than illumination'. Quantitative techniques will be more likely to illuminate if the data analyst is guided in methodological choices by a substantive understanding of the problem he or she is trying to learn about. Good procedures in data analysis involve techniques that help to (a) answer the substantive questions at hand, (b) squeeze all the relevant information out of the data, and (c) learn something new about the world." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"Typically, data analysis is messy, and little details clutter it. Not only confounding factors, but also deviant cases, minor problems in measurement, and ambiguous results lead to frustration and discouragement, so that more data are collected than analyzed. Neglecting or hiding the messy details of the data reduces the researcher's chances of discovering something new." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"[...] be wary of analysts that try to quantify the unquantifiable." (Ralph Keeney & Raiffa Howard, "Decisions with Multiple Objectives: Preferences and Value Trade-offs", 1976)

"[...] exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as for those we believe might be there. Except for its emphasis on graphs, its tools are secondary to its purpose." (John W Tukey, [comment] 1979)

"[...] any hope that we are smart enough to find even transiently optimum solutions to our data analysis problems is doomed to failure, and, indeed, if taken seriously, will mislead us in the allocation of effort, thus wasting both intellectual and computational effort." (John W Tukey, "Choosing Techniques for the Analysis of Data", 1981)

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

"Exploratory data analysis, EDA, calls for a relatively free hand in exploring the data, together with dual obligations: (•) to look for all plausible alternatives and oddities - and a few implausible ones, (graphic techniques can be most helpful here) and (•) to remove each appearance that seems large enough to be meaningful - ordinarily by some form of fitting, adjustment, or standardization [...] so that what remains, the residuals, can be examined for further appearances." (John W Tukey, "Introduction to Styles of Data Analysis Techniques", 1982)

"A competent data analysis of an even moderately complex set of data is a thing of trials and retreats, of dead ends and branches." (John W Tukey, Computer Science and Statistics: Proceedings of the 14th Symposium on the Interface, 1983)

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

"Iteration and experimentation are important for all of data analysis, including graphical data display. In many cases when we make a graph it is immediately clear that some aspect is inadequate and we regraph the data. In many other cases we make a graph, and all is well, but we get an idea for studying the data in a different way with a different graph; one successful graph often suggests another." (William S Cleveland, "The Elements of Graphing Data", 1985)

"There are some who argue that a graph is a success only if the important information in the data can be seen within a few seconds. While there is a place for rapidly-understood graphs, it is too limiting to make speed a requirement in science and technology, where the use of graphs ranges from, detailed, in-depth data analysis to quick presentation." (William S Cleveland, "The Elements of Graphing Data", 1985)

"A first analysis of experimental results should, I believe, invariably be conducted using flexible data analytical techniques - looking at graphs and simple statistics - that so far as possible allow the data to 'speak for themselves'. The unexpected phenomena that such a approach often uncovers can be of the greatest importance in shaping and sometimes redirecting the course of an ongoing investigation." (George Box, "Signal to Noise Ratios, Performance Criteria, and Transformations", Technometrics 30, 1988)

"Data analysis is an art practiced by individuals who are skilled at quantitative reasoning and have much experience in looking at numbers and detecting  patterns in data. Usually these individuals have some background in statistics." (David Lubinsky, Daryl Pregibon , "Data analysis as search", Journal of Econometrics Vol. 38 (1–2), 1988)

"Like a detective, a data analyst will experience many dead ends, retrace his steps, and explore many alternatives before settling on a single description of the evidence in front of him." (David Lubinsky & Daryl Pregibon , "Data analysis as search", Journal of Econometrics Vol. 38 (1–2), 1988)

"[…] data analysis in the context of basic mathematical concepts and skills. The ability to use and interpret simple graphical and numerical descriptions of data is the foundation of numeracy […] Meaningful data aid in replacing an emphasis on calculation by the exercise of judgement and a stress on interpreting and communicating results." (David S Moore, "Statistics for All: Why, What and How?", 1990)

"Data analysis is rarely as simple in practice as it appears in books. Like other statistical techniques, regression rests on certain assumptions and may produce unrealistic results if those assumptions are false. Furthermore it is not always obvious how to translate a research question into a regression model." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"Data analysis typically begins with straight-line models because they are simplest, not because we believe reality is inherently linear. Theory or data may suggest otherwise [...]" (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"90 percent of all problems can be solved by using the techniques of data stratification, histograms, and control charts. Among the causes of nonconformance, only one-fifth or less are attributable to the workers." (Kaoru Ishikawa, The Quality Management Journal Vol. 1, 1993)

"Probabilistic inference is the classical paradigm for data analysis in science and technology. It rests on a foundation of randomness; variation in data is ascribed to a random process in which nature generates data according to a probability distribution. This leads to a codification of uncertainly by confidence intervals and hypothesis tests." (William S Cleveland, "Visualizing Data", 1993)

"Visualization is an approach to data analysis that stresses a penetrating look at the structure of data. No other approach conveys as much information. […] Conclusions spring from data when this information is combined with the prior knowledge of the subject under investigation." (William S Cleveland, "Visualizing Data", 1993)

"When the distributions of two or more groups of univariate data are skewed, it is common to have the spread increase monotonically with location. This behavior is monotone spread. Strictly speaking, monotone spread includes the case where the spread decreases monotonically with location, but such a decrease is much less common for raw data. Monotone spread, as with skewness, adds to the difficulty of data analysis. For example, it means that we cannot fit just location estimates to produce homogeneous residuals; we must fit spread estimates as well. Furthermore, the distributions cannot be compared by a number of standard methods of probabilistic inference that are based on an assumption of equal spreads; the standard t-test is one example. Fortunately, remedies for skewness can cure monotone spread as well." (William S Cleveland, "Visualizing Data", 1993)

"A careful and sophisticated analysis of the data is often quite useless if the statistician cannot communicate the essential features of the data to a client for whom statistics is an entirely foreign language." (Christopher J Wild, "Embracing the ‘Wider view’ of Statistics", The American Statistician 48, 1994)

"Science is not impressed with a conglomeration of data. It likes carefully constructed analysis of each problem." (Daniel E Koshland Jr, Science Vol. 263 (5144), [editorial] 1994)

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

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

"No matter what the data, and no matter how the values are arranged and presented, you must always use some method of analysis to come up with an interpretation of the data.

While every data set contains noise, some data sets may contain signals. Therefore, before you can detect a signal within any given data set, you must first filter out the noise." (Donald J Wheeler," Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"The purpose of analysis is insight. The best analysis is the simplest analysis which provides the needed insight." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Without meaningful data there can be no meaningful analysis. The interpretation of any data set must be based upon the context of those data." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Statistical analysis of data can only be performed within the context of selected assumptions, models, and/or prior distributions. A statistical analysis is actually the extraction of substantive information from data and assumptions. And herein lies the rub, understood well by Disraeli and others skeptical of our work: For given data, an analysis can usually be selected which will result in 'information' more favorable to the owner of the analysis then is objectively warranted." (Stephen B Vardeman & Max D Morris, "Statistics and Ethics: Some Advice for Young Statisticians", The American Statistician vol 57, 2003)

 "The dashboard framework must also facilitate a retracing of the drill-down path. A user should be easily able to get to the previous chart from the destination chart. This recursive capacity helps create a better self-guided analysis experience. If users are not able to retrieve the previous chart easily during a drill-down path, they may lose track of their thought sequence. An inability to retrace may lead to user frustration and a dysfunctional self-guided analysis." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)

"Exploratory Data Analysis is more than just a collection of data-analysis techniques; it provides a philosophy of how to dissect a data set. It stresses the power of visualisation and aspects such as what to look for, how to look for it and how to interpret the information it contains. Most EDA techniques are graphical in nature, because the main aim of EDA is to explore data in an open-minded way. Using graphics, rather than calculations, keeps open possibilities of spotting interesting patterns or anomalies that would not be apparent with a calculation (where assumptions and decisions about the nature of the data tend to be made in advance)." (Alan Graham, "Developing Thinking in Statistics", 2006)

"It is the aim of all data analysis that a result is given in form of the best estimate of the true value. Only in simple cases is it possible to use the data value itself as result and thus as best estimate." (Manfred Drosg, "Dealing with Uncertainties: A Guide to Error Analysis", 2007)

"Put simply, statistics is a range of procedures for gathering, organizing, analyzing and presenting quantitative data. […] Essentially […], statistics is a scientific approach to analyzing numerical data in order to enable us to maximize our interpretation, understanding and use. This means that statistics helps us turn data into information; that is, data that have been interpreted, understood and are useful to the recipient. Put formally, for your project, statistics is the systematic collection and analysis of numerical data, in order to investigate or discover relationships among phenomena so as to explain, predict and control their occurrence." (Reva B Brown & Mark Saunders, "Dealing with Statistics: What You Need to Know", 2008)

"Data analysis is careful thinking about evidence." (Michael Milton, "Head First Data Analysis", 2009)

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

"The discrepancy between our mental models and the real world may be a major problem of our times; especially in view of the difficulty of collecting, analyzing, and making sense of the unbelievable amount of data to which we have access today." (Ugo Bardi, "The Limits to Growth Revisited", 2011)

"Too often there is a disconnect between the people who run a study and those who do the data analysis. This is as predictable as it is unfortunate. If data are gathered with particular hypotheses in mind, too often they (the data) are passed on to someone who is tasked with testing those hypotheses and who has only marginal knowledge of the subject matter. Graphical displays, if prepared at all, are just summaries or tests of the assumptions underlying the tests being done. Broader displays, that have the potential of showing us things that we had not expected, are either not done at all, or their message is not able to be fully appreciated by the data analyst." (Howard Wainer, Comment, Journal of Computational and Graphical Statistics Vol. 20(1), 2011)

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

"The four questions of data analysis are the questions of description, probability, inference, and homogeneity. Any data analyst needs to know how to organize and use these four questions in order to obtain meaningful and correct results. [...] 

THE DESCRIPTION QUESTION: Given a collection of numbers, are there arithmetic values that will summarize the information contained in those numbers in some meaningful way?

THE PROBABILITY QUESTION: Given a known universe, what can we say about samples drawn from this universe? [...] 

THE INFERENCE QUESTION: Given an unknown universe, and given a sample that is known to have been drawn from that unknown universe, and given that we know everything about the sample, what can we say about the unknown universe? [...] 

THE HOMOGENEITY QUESTION: Given a collection of observations, is it reasonable to assume that they came from one universe, or do they show evidence of having come from multiple universes?" (Donald J Wheeler," Myths About Data Analysis", International Lean & Six Sigma Conference, 2012)

"Each systems archetype embodies a particular theory about dynamic behavior that can serve as a starting point for selecting and formulating raw data into a coherent set of interrelationships. Once those relationships are made explicit and precise, the 'theory' of the archetype can then further guide us in our data-gathering process to test the causal relationships through direct observation, data analysis, or group deliberation." (Daniel H Kim, "Systems Archetypes as Dynamic Theories", The Systems Thinker Vol. 24 (1), 2013)

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

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

"In general, analytic processing is known as 'heuristic' processing. In heuristic processing the requirements for analysis are discovered by the results of the current iteration of processing. […] In heuristic processing you start with some requirements. You build a system to analyze those requirements. Then, after you have results, you sit back and rethink your requirements after you have had time to reflect on the results that have been achieved. You then restate the requirements and redevelop and reanalyze again. Each time you go through the redevelopment exercise is called an 'iteration'. You continue the process of building different iterations of processing until such time as you achieve the results that satisfy the organization that is sponsoring the exercise." (William H Inmon & Daniel Linstedt, "Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault", 2015)

"Statistics is an integral part of the quantitative approach to knowledge. The field of statistics is concerned with the scientific study of collecting, organizing, analyzing, and drawing conclusions from data." (Kandethody M Ramachandran & Chris P Tsokos, "Mathematical Statistics with Applications in R" 2nd Ed., 2015)

"The dialectical interplay of experiment and theory is a key driving force of modern science. Experimental data do only have meaning in the light of a particular model or at least a theoretical background. Reversely theoretical considerations may be logically consistent as well as intellectually elegant: Without experimental evidence they are a mere exercise of thought no matter how difficult they are. Data analysis is a connector between experiment and theory: Its techniques advise possibilities of model extraction as well as model testing with experimental data." (Achim Zielesny, "From Curve Fitting to Machine Learning" 2nd Ed., 2016)

"[…] the data itself can lead to new questions too. In exploratory data analysis (EDA), for example, the data analyst discovers new questions based on the data. The process of looking at the data to address some of these questions generates incidental visualizations - odd patterns, outliers, or surprising correlations that are worth looking into further." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. The data sets themselves are explicitly linked as a form of representation to an observational or otherwise empirical domain of interest. 'Structure' has long been understood as symmetry which can take many forms with respect to any transformation, including point, translational, rotational, and many others. Symmetries directly point to invariants, which pinpoint intrinsic properties of the data and of the background empirical domain of interest. As our data models change, so too do our perspectives on analysing data." (Fionn Murtagh, "Data Science Foundations: Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics", 2018)

"Analysis is a two-step process that has an exploratory and an explanatory phase. In order to create a powerful data story, you must effectively transition from data discovery (when you’re finding insights) to data communication (when you’re explaining them to an audience). If you don’t properly traverse these two phases, you may end up with something that resembles a data story but doesn’t have the same effect. Yes, it may have numbers, charts, and annotations, but because it’s poorly formed, it won’t achieve the same results." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Analytics provides a way to demonstrate the linkage between people and business outcomes. HR analytics (also called people analytics or talent analytics) use measurement and analysis techniques to understand, improve, and optimize the people side of business. Data are the raw numbers you track. [...] Metrics focus on counting, tracking, and presenting past data. Analytics uses statistics to help you see patterns in the data." (Shonna D Watters et al, "The Practical Guide for HR Analytics: Using data to inform, transform, and empower HR decisions", 2019)

"While visuals are an essential part of data storytelling, data visualizations can serve a variety of purposes from analysis to communication to even art. Most data charts are designed to disseminate information in a visual manner. Only a subset of data compositions is focused on presenting specific insights as opposed to just general information. When most data compositions combine both visualizations and text, it can be difficult to discern whether a particular scenario falls into the realm of data storytelling or not." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"If the data that go into the analysis are flawed, the specific technical details of the analysis don’t matter. One can obtain stupid results from bad data without any statistical trickery. And this is often how bullshit arguments are created, deliberately or otherwise. To catch this sort of bullshit, you don’t have to unpack the black box. All you have to do is think carefully about the data that went into the black box and the results that came out. Are the data unbiased, reasonable, and relevant to the problem at hand? Do the results pass basic plausibility checks? Do they support whatever conclusions are drawn?" (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"We all know that the numerical values on each side of an equation have to be the same. The key to dimensional analysis is that the units have to be the same as well. This provides a convenient way to keep careful track of units when making calculations in engineering and other quantitative disciplines, to make sure one is computing what one thinks one is computing. When an equation exists only for the sake of mathiness, dimensional analysis often makes no sense." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Overall [...] everyone also has a need to analyze data. The ability to analyze data is vital in its understanding of product launch success. Everyone needs the ability to find trends and patterns in the data and information. Everyone has a need to ‘discover or reveal (something) through detailed examination’, as our definition says. Not everyone needs to be a data scientist, but everyone needs to drive questions and analysis. Everyone needs to dig into the information to be successful with diagnostic analytics. This is one of the biggest keys of data literacy: analyzing data." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"Advanced analytics pivots around machine learning methods. Machine learning employs statistical learning methods to perform analysis on data. These statistical methods utilize algorithms that predict what may happen based on historical data or extract complex mathematical relationships from data to generate insights." (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)

"A Data Fabric has its focus more on the architectural underpinning, technical capabilities, and intelligent analysis to produce active metadata supporting a smarter, AI-infused system to orchestrate various data integration styles, enabling trusted and reusable data in a hybrid cloud landscape to be consumed by humans, applications, or other downstream systems. Data cataloging to generate and leverage active metadata is seen as a vital component of any Data Fabric." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Querying by timestamp makes it easy to perform time-series analysis because we can compare the data of the same table to itself at two different points in time. And while there are other ETL patterns we can follow to capture historical data and enable time-series analysis (e.g., slowly changing dimensions and change data feeds), time travel provides a quick and easy way to perform ad hoc analysis for tables that may not have these ETL patterns in place." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"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)

"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)

[Murphy’s Laws of Analysis:] "(1) In any collection of data, the figures that are obviously correct contain errors. (2) It is customary for a decimal to be misplaced. (3) An error that can creep into a calculation, will. Also, it will always be in the direction that will cause the most damage to the calculation." (G C Deakly)

"We must include in any language with which we hope to describe complex data-processing situations the capability for describing data. We must also include a mechanism for determining the priorities to be applied to the data. These priorities are not fixed and are indicated in many cases by the data." (Grace Hopper) 

See also: Analysis

🪙Business Intelligence: Data Pipelines (Just the Quotes)

"Data Lake is a single window snapshot of all enterprise data in its raw format, be it structured, semi-structured, or unstructured. Starting from curating the data ingestion pipeline to the transformation layer for analytical consumption, every aspect of data gets addressed in a data lake ecosystem. It is supposed to hold enormous volumes of data of varied structures." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018

"The quality of data that flows within a data pipeline is as important as the functionality of the pipeline. If the data that flows within the pipeline is not a valid representation of the source data set(s), the pipeline doesn’t serve any real purpose. It’s very important to incorporate data quality checks within different phases of the pipeline. These checks should verify the correctness of data at every phase of the pipeline. There should be clear isolation between checks at different parts of the pipeline. The checks include checks like row count, structure, and data type validation." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"For advanced analytics, a well-designed data pipeline is a prerequisite, so a large part of your focus should be on automation. This is also the most difficult work. To be successful, you need to stitch everything together." (Piethein Strengholt, "Data Management at Scale: Best Practices for Enterprise Architecture", 2020)

"A data pipeline is a series of transformation steps (functions) executed as the data flows from one step to another. Data mesh refrains from using pipelines as a top-level architectural paradigm and in between data products. The challenge with pipelines as currently used is that they don’t create clear interfaces, contracts, and abstractions that can be maintained easily as the pipeline complexity complexity grows. Due to lack of abstractions, single failure in the pipeline causes cascading failures." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data lake architecture suffers from complexity and deterioration. It creates complex and unwieldy pipelines of batch or streaming jobs operated by a central team of hyper-specialized data engineers. It deteriorates over time. Its unmanaged datasets, which are often untrusted and inaccessible, provide little value. The data lineage and dependencies are obscured and hard to track." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data mesh [...] reduces points of centralization that act as coordination bottlenecks. It finds a new way of decomposing the data architecture without slowing the organization down with synchronizations. It removes the gap between where the data originates and where it gets used and removes the accidental complexities - aka pipelines - that happen in between the two planes of data. Data mesh departs from data myths such as a single source of truth, or one tightly controlled canonical data model." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"A data pipeline is an artifact of a data engineering process. It transforms raw data into data ready for analytics. These in turn help solve problems, aid support decisions, and make our lives more convenient. In some ways, it can be thought of as the stitch between the OLTP and OLAP systems. Data pipelines are sometimes referred to as ETL, which stands for extract, transform, load, and it has a variation called extract, load, transform (ELT). The main difference between the two is whether the incoming data is first saved to disk and then transformed (data wrangling) or vice versa. The processing is loosely referred to as ETL. Although, it is fair to say ELT is relevant in the context of Data Lakes and unstructured data, whereas ETL is used for Data Warehouses." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Historically, for their analytics needs, enterprises relied upon a set of tightly coupled tools, typically provided by a single vendor. Nowadays, nearly all of the components of a traditional data warehouse are independent and interchangeable. Those independent tools can be flexibly combined to provide a modern data stack. It is common for current enterprises to have separate tools for data ingestion, data pipelines, data storage and querying, data visualization and business intelligence, and data quality. Furthermore, data can flow in the opposite direction out of the data warehouse in what is referred to as reverse extract, transform, and load (ETL)." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Data has historically been treated as a second-class citizen, as a form of exhaust or by-product emitted by business applications. This application-first thinking remains the major source of problems in today’s computing environments, leading to ad hoc data pipelines, cobbled together data access mechanisms, and inconsistent sources of similar-yet-different truths. Data mesh addresses these shortcomings head-on, by fundamentally altering the relationships we have with our data. Instead of a secondary by-product, data, and the access to it, is promoted to a first-class citizen on par with any other business service." (Adam Bellemare,"Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)

"Gaining more insight into data, simplifying data access, enabling shopping-for-data, augmenting traditional data governance, generating active metadata, and accelerating development of products and services are enabled by infusing AI into the Data Fabric architecture. An AI-infused Data Fabric is not only leveraging AI but also likewise an architecture to manage and deal with AI artefacts, including AI models, pipelines, etc." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"While a Data Fabric is an architecture that facilitates the end-to-end integration of various data and AI pipelines across hybrid cloud environments through the use of intelligent and automated systems and applications, a Data Mesh should be seen as a solution, which is geared toward delivering data-as-a-product in an organizational federated approach." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Fabric Pipelines provide reliable and efficient end-to-end orchestration of data flows, managing ingestion, transformation, and loading through a sequence of steps that can leverage various data processing engines. They allow centralizing and orchestrating data movements from various sources, thanks to advanced connectivity features, and with great scalability. Built-in monitoring tools enable real-time tracking of data flow status and quick detection of anomalies or errors." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"It should be noted that, unlike Dataflow Gen2, in pipelines, it is not mandatory to enable staging to load data into a warehouse. Indeed, pipelines are designed for more general orchestration scenarios where you can combine various activities such as transformations, API calls, and so on to create complex workflows. They are not specifically focused on data preparation but rather on end-to-end process automation. Pipelines are more flexible and used for a variety of orchestration tasks, whereas Dataflow Gen2 is specifically designed for data preparation and transformation, hence the requirement for staging in that case." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

🪙Business Intelligence: Storytelling (Just the Quotes)

"Storytelling reveals meaning without committing the error of defining it." (Hannah Arendt, "Men in Dark Times", 1968)

"Scientific practice may be considered a kind of storytelling practice [...]" (Donna Haraway, "Primate Visions", 1989)

"Storytelling is the art of unfolding knowledge in a way that makes each piece contribute to a larger truth." (Philip Gerard, "Writing a Book That Makes a Difference", 2000)

"The human mind is a wanton storyteller and even more, a profligate seeker after pattern. We see faces in clouds and tortillas, fortunes in tea leaves and planetary movements. It is quite difficult to prove a real pattern as distinct from a superficial illusion." (Richard Dawkins, "A Devil's Chaplain", 2003)

"The world of a story is not merely the sum of all the words we put on a page, or on many pages. When we talk about entering the world of a story as a reader we refer to things we picture, or imagine, and responses we form - to characters, events - all of which are prompted by, but not entirely encompassed by, the words on the page." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

"We have, as human beings, a storytelling problem. We're a bit too quick to come up with explanations for things we don't really have an explanation for." (Malcolm Gladwell, "Blink: The Power of Thinking Without Thinking", 2005)

"There is an extraordinary power in storytelling that stirs the imagination and makes an indelible impression on the mind." (Brennan Manning, "The Ragamuffin Gospel: Good News for the Bedraggled, Beat-Up, and Burnt Out", 2008)

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

"The purpose of a storyteller is not to tell you how to think, but to give you questions to think upon." (Brandon Sanderson, "The Way of Kings", 2010)

"Visualizations act as a campfire around which we gather to tell stories." (Al Shalloway, 2011)

"The storytelling mind is allergic to uncertainty, randomness, and coincidence. It is addicted to meaning. If the storytelling mind cannot find meaningful patterns in the world, it will try to impose them. In short, the storytelling mind is a factory that churns out true stories when it can, but will manufacture lies when it can't." (Jonathan Gottschall, "The Storytelling Animal: How Stories Make Us Human", 2012)

"We are, as a species, addicted to story. Even when the body goes to sleep, the mind stays up all night, telling itself stories." (Jonathan Gottschall, "The Storytelling Animal", 2012)

"The fact of storytelling hints at a fundamental human unease, hints at human imperfection. Where there is perfection there is no story to tell." (Ben Okri, "A Way of Being Free", 2014)

"There is no such thing as a fact. There is only how you saw the fact, in a given moment. How you reported the fact. How your brain processed that fact. There is no extrication of the storyteller from the story." (Jodi Picoult, "Small Great Things", 2016)

"Stories can begin with a question or line of inquiry." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Data storytelling provides a bridge between the worlds of logic and emotion. A data story offers a safe passage for your insights to travel around emotional pitfalls and through analytical resistance that typically impede facts." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

🪙Business Intelligence: Insight (Just the Quotes)

"Knowledge workers and BI experts must continually evaluate the reports, dashboards, alerts, and other mechanisms for disseminating factual information to ensure the design facilitates insight." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)

"In fact, the analogy to storytelling is limited when applied to communicating with data. Data visualization has fundamental characteristics missing from traditional storytelling. For example, interactive data visualizations let audiences explore information to find insights that resonate with them. Visualizations take shape based to a large extent on the underlying data. And as this data changes, the emphasis and message of the visualization is likely to change." (Zach Gemignani et al, "Data Fluency", 2014)

"[…] the better insights are communicated, the more likely it is that data leads to positive action (in this case, better business decisions)." (Bernard Marr, ​​​​​​​"Data Strategy", 2017)

"Data Lake induces accessibility and catalyzes availability. It warrants data discovery platforms to soak the data trends at a horizontal scale and produce visual insights. It largely cuts down the time that goes into data preparation and exhaustive data analysis." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Bad data are expensive: my best estimate is that it costs a typical company 20% of revenue. Worse, they dilute trust - who would trust an exciting new insight if it is based on poor data! And worse still, sometimes bad data are simply dangerous; look at the damage brought on by the financial crisis, which had its roots in bad data." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)

"Data storytelling provides a bridge between the worlds of logic and emotion. A data story offers a safe passage for your insights to travel around emotional pitfalls and through analytical resistance that typically impede facts." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"First, from an ethos perspective, the success of your data story will be shaped by your own credibility and the trustworthiness of your data. Second, because your data story is based on facts and figures, the logos appeal will be integral to your message. Third, as you weave the data into a convincing narrative, the pathos or emotional appeal makes your message more engaging. Fourth, having a visualized insight at the core of your message adds the telos appeal, as it sharpens the focus and purpose of your communication. Fifth, when you share a relevant data story with the right audience at the right time (kairos), your message can be a powerful catalyst for change." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Ensure you build into your data literacy strategy learning on data quality. If the individuals who are using and working with data do not understand the purpose and need for data quality, we are not sitting in a strong position for great and powerful insight. What good will the insight be, if the data has no quality within the model?" (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"In the same vein, data strategy is often a misnomer for a much wider scope of coverage, but the lack of coherence in how we use the language has led to data strategy being perceived to cover data management activities all the way through to exploitation of data in the broadest sense. The occasional use of information strategy, intelligence strategy or even data exploitation strategy may differentiate, but the lack of a common definition on what we mean tends to lead to data strategy being used as a catch-all for the more widespread coverage such a document would typically include. Much of this is due to the generic use of the term ‘data’ to cover everything from its capture, management, governance through to reporting, analytics and insight." (Ian Wallis, "Data Strategy: From definition to execution", 2021)

"Current decision-making in business suffers from insight gaps. Organizations invest in data and analytics, hoping that will provide them with insights that they can use to make decisions, but in reality, there are many challenges and obstacles that get in the way of that process. One of the biggest challenges is that these organizations tend to focus on technology and hard skills only. They are definitely important, but you will not automatically get insights and better decisions with hard skills alone. Using data to make better data-informed decisions requires not only hard skills but also soft skills as well as mindsets." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)

"Data engineering is the process of converting raw data into analytics-ready data that is more accessible, usable, and consumable than its raw format. Modern companies are increasingly becoming data-driven, which means they use data to make business decisions to give them better insights into their customers and business operations. They can use these to improve profitability, reduce costs, and give them a competitive edge in the market. Behind the scenes, a series of tasks and processes are performed by a host of data personas who build reliable pipelines to source, transform, and analyze data so that it is a repeatable and mostly automated process." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Decision-makers are constantly provided data in the form of numbers or insights, or similar. The challenge is that we tend to believe every number or piece of data we hear, especially when it comes from a trusted source. However, even if the source is trusted and the data is correct, insights from the data are created when we put it in context and apply meaning to it. This means that we may have put incorrect meaning to the data and then made decisions based on that, which is not ideal. This is why anyone involved in the process needs to have the skills to think critically about the data, to try to understand the context, and to understand the complexity of the situation where the answer is not limited to just one specific thing. Critical thinking allows individuals to assess limitations of what was presented, as well as mitigate any cognitive bias that they may have." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)

"Understanding modern data architectures and sound data engineering principles and practices are crucial to ensure that your AI and BI strategies are reliable and defensible. Generated insights are going to be as good as the quality of the underlying data, so the upfront effort put into understanding the data, modeling it, and transforming it per the business needs goes a long way to foster innovation, productivity, and agility in your data teams." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"We are at the interesting conjunction of big data, the cloud, and artificial intelligence (AI), all of which are fueling tremendous innovation in every conceivable industry vertical and generating data exponentially. Data engineering is increasingly important as data drives business use cases in every industry vertical. You may argue that data scientists and machine learning practitioners are the unicorns of the industry, and they can work their magic for business. That is certainly a stretch of the imagination. Simple algorithms and a lot of good reliable data produce better insights than complicated algorithms with inadequate data." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Gaining more insight into data, simplifying data access, enabling shopping-for-data, augmenting traditional data governance, generating active metadata, and accelerating development of products and services are enabled by infusing AI into the Data Fabric architecture. An AI-infused Data Fabric is not only leveraging AI but also likewise an architecture to manage and deal with AI artefacts, including AI models, pipelines, etc." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Establishing a comprehensive observability architecture necessitates a systematic approach that spans the entirety of the data pipeline, from initial telemetry collection to actionable insights accessible by diverse stakeholders. The core objective is to unify distributed data sources - metrics, logs, traces, and quality signals - into a coherent framework that enables rapid diagnosis, continuous monitoring, and strategic decision-making." (William Smith, "Soda Core for Modern Data Quality and Observability: The Complete Guide for Developers and Engineers", 2025)

"The lakehouse combines the best elements of data lakes and data warehouses for OLAP workloads. It merges the scalability and flexibility of data lakes with the management features and performance optimization of data warehouses. [...] A lakehouse eliminates the need for disjointed systems and provides a single, coherent platform for all forms of data analysis. Lakehouses enhance the performance of data queries and simplify data management, making it easier for organizations to derive insights from their data." (Denny Lee et al, "Delta Lake: The Definitive Guide", 2025)

"Viewing the dendrograms in high dimensions provides insight into how the algorithm has joined points to clusters. For example, single linkage often has edges leading to a single focal point, which might not yield a useful clustering but might help to 

identify outliers. If the edges point to multiple focal points, with long edges bridging gaps in the data, the result is more likely yielding a useful clustering." (Dianne Cook & Ursula Laa, "Interactively Exploring High-Dimensional Data and Models in R", 2026)

🪙Business Intelligence: Measurement (Just the Quotes)

"There is no inquiry which is not finally reducible to a question of Numbers; for there is none which may not be conceived of as consisting in the determination of quantities by each other, according to certain relations." (Auguste Comte, “The Positive Philosophy”, 1830)

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

“Of itself an arithmetic average is more likely to conceal than to disclose important facts; it is the nature of an abbreviation, and is often an excuse for laziness.” (Arthur Lyon Bowley, “The Nature and Purpose of the Measurement of Social Phenomena”, 1915)

“Science depends upon measurement, and things not measurable are therefore excluded, or tend to be excluded, from its attention.” (Arthur J Balfour, “Address”, 1917)

“It is important to realize that it is not the one measurement, alone, but its relation to the rest of the sequence that is of interest.” (William E Deming, “Statistical Adjustment of Data”, 1943)

“The purpose of computing is insight, not numbers […] sometimes […] the purpose of computing numbers is not yet in sight.” (Richard Hamming, “Numerical Methods for Scientists and Engineers”, 1962)

“A quantity like time, or any other physical measurement, does not exist in a completely abstract way. We find no sense in talking about something unless we specify how we measure it. It is the definition by the method of measuring a quantity that is the one sure way of avoiding talking nonsense...” (Hermann Bondi, “Relativity and Common Sense”, 1964)

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

"A mature science, with respect to the matter of errors in variables, is not one that measures its variables without error, for this is impossible. It is, rather, a science which properly manages its errors, controlling their magnitudes and correctly calculating their implications for substantive conclusions." (Otis D Duncan, "Introduction to Structural Equation Models", 1975)

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

"Changing measures are a particularly common problem with comparisons over time, but measures also can cause problems of their own. [...] We cannot talk about change without making comparisons over time. We cannot avoid such comparisons, nor should we want to. However, there are several basic problems that can affect statistics about change. It is important to consider the problems posed by changing - and sometimes unchanging - measures, and it is also important to recognize the limits of predictions. Claims about change deserve critical inspection; we need to ask ourselves whether apples are being compared to apples - or to very different objects." (Joel Best, "Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"Measurement is often associated with the objectivity and neatness of numbers, and performance measurement efforts are typically accompanied by hope, great expectations and promises of change; however, these are then often followed by disbelief, frustration and what appears to be sheer madness." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"Measuring anything subjective always prompts perverse behavior. [...] All measurement systems are subject to abuse." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)

“The value of having numbers - data - is that they aren't subject to someone else's interpretation. They are just the numbers. You can decide what they mean for you.” (Emily Oster, “Expecting Better”, 2013)

"Until a new metric generates a body of data, we cannot test its usefulness. Lots of novel measures hold promise only on paper." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)

"Usually, it is impossible to restate past data. As a result, all history must be whitewashed and measurement starts from scratch." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)

🪙Business Intelligence: Data Mesh (Just the quotes)

"Another myth is that we shall have a single source of truth for each concept or entity. […] This is a wonderful idea, and is placed to prevent multiple copies of out-of-date and untrustworthy data. But in reality it’s proved costly, an impediment to scale and speed, or simply unachievable. Data Mesh does not enforce the idea of one source of truth. However, it places multiple practices in place that reduces the likelihood of multiple copies of out-of-date data." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data Mesh attempts to strike a balance between team autonomy and inter-term interoperability and collaboration, with a few complementary techniques. It gives domain teams autonomy to have control of their local decision making, such as choosing the best data model for their data products. While it uses the computational governance policies to impose a consistent experience across all data products; for example, standardizing on the data modeling language that all domains utilize." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data mesh is a solution for organizations that experience scale and complexity, where existing data warehouse or lake solutions have become blockers in their ability to get value from data at scale and across many functions of their business, in a timely fashion and with less friction." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data Mesh must allow for data models to change continuously without fatal impact to downstream data consumers, or slowing down access to data as a result of synchronizing change of a shared global canonical model. Data Mesh achieves this by localizing change to domains by providing autonomy to domains to model their data based on their most intimate understanding of the business without the need for central coordinations of change to a single shared canonical model." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data mesh [...] reduces points of centralization that act as coordination bottlenecks. It finds a new way of decomposing the data architecture without slowing the organization down with synchronizations. It removes the gap between where the data originates and where it gets used and removes the accidental complexities - aka pipelines - that happen in between the two planes of data. Data mesh departs from data myths such as a single source of truth, or one tightly controlled canonical data model." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"A core premise of data mesh is federating data ownership among domain data owners who are responsible for their data as a product. Offering the data as a product requires the data to be discoverable and to have explicitly stated quality characteristics and a clearly defined access method. Such requirements are at the core of what data catalogs support. With support for data labeling, curation, and crowdsourced feedback, data catalogs are well positioned to offer data as a product. Furthermore, data catalogs support the enforcement of compliant data usage, which becomes more important when data ownership is not managed centrally." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Data mesh relies on a distributed architecture that consists of domains. Each domain is an independent unit of data and its associated storage and compute components. When an organization contains various product units, each with its own data needs, each product team owns a domain that is operated and governed independently by the product team. […] Data mesh has a unique value proposition, not just offering scale of infrastructure and scenarios but also helping shift the organization’s culture around data," (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 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)

"The data mesh pattern doesn't feature a central node and is loosely coupled compared to a hub-spoke architecture. It has different data lakehouse nodes that are independent of each other. The node data lakehouses are domain-driven. A domain can be oriented in multiple ways. The original idea of data mesh alludes to a source-oriented domain aligning to business processes. However, a more practical approach would be to define a domain based on the organizational setup and practicality; for example, a domain can be a product group, it can be separate organizational entities, and it can also be a specific business process, such as marketing. Each domain has its own data lakehouse that is managed and maintained by that domain." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"A data fabric is an architectural approach to provide data access across multiple technologies and platforms, and is based on a technology solution. One key contrast is that a data mesh is much more than just technology: it is a pattern that involves people and processes. Instead of taking ownership of an entire data platform, as in a data fabric, the data mesh allows data producers to focus on data production, allows data consumers to focus on consumption, and allows hybrid teams to consume other data products, blend other data to create even more interesting data products, and publish these data products - with some data governance considerations in place." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"A Data Mesh views data primarily as organized around domain owners who create business-focused data products, which can be aggregated and consumed across distributed consumers, organizations, and Line of Business (LoBs) in a self-service and shopping-for-data fashion. Transforming data from disparate data sources to be consumed as data-as-a-product is an essential paradigm of any Data Mesh." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Data has historically been treated as a second-class citizen, as a form of exhaust or by-product emitted by business applications. This application-first thinking remains the major source of problems in today’s computing environments, leading to ad hoc data pipelines, cobbled together data access mechanisms, and inconsistent sources of similar-yet-different truths. Data mesh addresses these shortcomings head-on, by fundamentally altering the relationships we have with our data. Instead of a secondary by-product, data, and the access to it, is promoted to a first-class citizen on par with any other business service." (Adam Bellemare,"Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)

"Data mesh architectures are inherently decentralized, and significant responsibility is delegated to the data product owners. A data mesh also benefits from a degree of centralization in the form of data product compatibility and common self-service tooling. Differing opinions, preferences, business requirements, legal constraints, technologies, and technical debt are just a few of the many factors that influence how we work together." (Adam Bellemare, "Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)

"In a data mesh, data is decentralized, while in a data fabric, centralization of data is allowed. And with data centralization like data lakes, you get the monolithic problems that come with it. Data mesh tries to apply a microservices approach to data by decomposing data domains into smaller and more agile groups." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"The data mesh is an exciting new methodology for managing data at large. The concept foresees an architecture in which data is highly distributed and a future in which scalability is achieved by federating responsibilities. It puts an emphasis on the human factor and addressing the challenges of managing the increasing complexity of data architectures." (Piethein Strengholt, "Data Management at Scale: Modern Data Architecture with Data Mesh and Data Fabric" 2nd Ed., 2023)

"The Data Fabric architecture can help enterprises address the challenges of data and AI governance effectively, including the orchestration and exchange of metadata across organizational implementations. First, Data Fabric pulls data from disparate data sources and orchestrates metadata exchange across organizational systems, thus providing a holistic view of data and AI at the enterprise level, which lays a solid technology foundation for a consistent and unified enterprise-level data and AI governance. Likewise, a Data Fabric architecture serves as a foundation for a Data Mesh solution, which is supporting organizational or departmental data and AI governance initiatives. Second, the advanced automation and AI technologies employed by a Data Fabric architecture can greatly simplify the implementation of data and AI governance at the enterprise or organizational level, enabling organizational federated Data Mesh initiatives, where orchestration and exchange of metadata across organizations need to be implemented as well." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"The terms Data Fabric and Data Mesh are often viewed as different, conflicting, or at the best overlapping data architectures or frameworks, data management concepts, or approaches to discover, explore, govern, and consume data. However, these concepts are related to each other, where each concept emphasizes specific imperatives or objectives."(Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"When building a data mesh, it is necessary to enable existing engineers in a domain to perform the tasks required. Domains have to capture data from their operational stores, transform (join or enrich, aggregate, balance) that data, and publish their data products to the data mesh. Self-service services are the “easy buttons” necessary to make data mesh easy to adopt with high usability. In summary, the self-services enable the domain engineers to take on many of the tasks the data engineer was responsible for across all lines of the business. A data mesh not only breaks up the monolithic data lake, but also breaks up the monolithic role of the data engineer into simple tasks the domain engineers can perform." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"While a data mesh seeks to solve many of the same problems that a data fabric addresses - namely, the ability to address data in a single, composite data environment—the approach is different. While a data fabric enables users to create a single, virtual layer on top of distributed data, a data mesh further empowers distributed groups of data producers to manage and publish data as they see fit. Data fabrics allow for a low-to-no-code data virtualization experience by applying data integration within APIs that reside within the data fabric. The data mesh, however, allows for data engineers to write code for APIs with which to interface further. Without clearly defined boundaries, domains appear to be too interconnected, and ownership becomes either political or subject to interpretation. For instance, a large retailer most likely has multiple domains. [...]" (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"A data mesh is a decentralized data architecture with four specific characteristics. First, it requires independent teams within designated domains to own their analytical data. Second, in a data mesh, data is treated and served as a product to help the data consumer to discover, trust, and utilize it for whatever purpose they like. Third, it relies on automated infrastructure provisioning. And fourth, it uses governance to ensure that all the independent data products are secure and follow global rules."(James Serra, "Deciphering Data Architectures", 2024)

"A data mesh splits the boundaries of the exchange of data into multiple data products. This provides a unique opportunity to partially distribute the responsibility of data security. Each data product team can be made responsible for how their data should be accessed and what privacy policies should be applied." (Aniruddha Deswandikar,"Engineering Data Mesh in Azure Cloud", 2024)

"At its core, a data fabric is an architectural framework, designed to be employed within one or more domains inside a data mesh. The data mesh, however, is a holistic concept, encompassing technology, strategies, and methodologies." (James Serra, "Deciphering Data Architectures", 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 emphasizes ensuring reliable, consistent, and interoperable data products. When data is treated as a product, quality is non-negotiable. High-quality data must meet the expectations and requirements of its users, both internally and externally. Additionally, data products must be designed with other products in mind, adhering to principles like loose coupling for easy interchangeability and high cohesion for strong functional relatedness. This feature enables the integration of different data products, ensuring seamless interoperability and greater usability. Data products should be reliable, complete, accurate, and accurate. They should also be integrated, compatible, and consistent rather than isolated, incompatible, or conflicting." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"It is very important to understand that data mesh is a concept, not a technology. It is all about an organizational and cultural shift within companies. The technology used to build a data mesh could follow the modern data warehouse, data fabric, or data lakehouse architecture - or domains could even follow different architectures." (James Serra, "Deciphering Data Architectures", 2024)

"To explain a data mesh in one sentence, a data mesh is a centrally managed network of decentralized data products. The data mesh breaks the central data lake into decentralized islands of data that are owned by the teams that generate the data. The data mesh architecture proposes that data be treated like a product, with each team producing its own data/output using its own choice of tools arranged in an architecture that works for them. This team completely owns the data/output they produce and exposes it for others to consume in a way they deem fit for their data." (Aniruddha Deswandikar,"Engineering Data Mesh in Azure Cloud", 2024)

"With all the hype, you would think building a data mesh is the answer to all of these 'problems' with data warehousing. The truth is that while data warehouse projects do fail, it is rarely because they can’t scale enough to handle big data or because the architecture or the technology isn’t capable. Failure is almost always because of problems with the people and/or the process, or that the organization chose the completely wrong technology." (James Serra, "Deciphering Data Architectures", 2024)

"Data mesh fundamentally reframes data governance and validation by distributing accountability to domain-oriented teams who act as custodians and producers of their respective data products. These teams possess intimate domain knowledge, which is essential for nuanced validation criteria that adapt to the semantics, context, and evolution of their datasets. By treating datasets as first-class products with clear ownership, interfaces, and service-level objectives, data mesh encourages autonomous validation workflows embedded directly within the domains where data originates and is consumed." (William Smith, "Great Expectations for Modern Data Quality: The Complete Guide for Developers and Engineers", 2025)

"Modern complex organizations increasingly confront the challenge of ensuring data quality at scale without centralizing validation activities into a single bottlenecked team. The data mesh paradigm and federated controls emerge as pivotal architectural styles and organizational patterns that enable decentralized, self-serve data quality validation while preserving coherence and reliability across diverse data products." (William Smith, "Great Expectations for Modern Data Quality: The Complete Guide for Developers and Engineers", 2025)

"Adopting a data mesh requires more than architecture; it involves changes to culture, roles, and accountability structures. [...] Many organizations will need to create new roles, such as data product managers, who combine stewardship, analytics, and product thinking. These individuals oversee the life cycle of a data product to ensure it delivers business value and meets usability standards. Success often depends on building a culture where data is treated as a shared asset across the enterprise and on putting clear lines of accountability in place. In practice, this can mean forming cross-domain councils to align on standards, tying performance objectives to data quality, or investing in training so domain teams are equipped to manage their products responsibly." (Fern Halper, "Data Makes the World Go 'Round", 2026)

🪙Business Intelligence: Data Marts (Just the Quotes)

"There are four levels of data in the architected environment - the operational level, the atomic (or the data warehouse) level, the departmental (or the data mart) level, and the individual level. These different levels of data are the basis of a larger architecture called the corporate information factory (CIF). The operational level of data holds application-oriented primitive data only and primarily serves the high-performance transaction-processing community. The data-warehouse level of data holds integrated, historical primitive data that cannot be updated. In addition, some derived data is found there. The departmental or data mart level of data contains derived data almost exclusively. The departmental or data mart level of data is shaped by end-user requirements into a form specifically suited to the needs of the department. And the individual level of data is where much heuristic analysis is done." (William H Inmon, "Building the Data Warehouse" 4th Ed., 2005)

"If you think of a Data Mart as a store of bottled water, cleansed and packaged and structured for easy consumption, the Data Lake is a large body of water in a more natural state. The contents of the Data Lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples." (James Dixon 2010)

"Many organizations need to create data warehouses - massive data stores of timeseries data for decision support. Data are imported from various external and internal resources and are cleansed and organized in a manner consistent with the organization’s needs. After the data are populated in the data warehouse, data marts can be loaded for a specific area or department. Alternatively, data marts can be created first, as needed, and then integrated into an EDW." (Ramesh Sharda et al, "Business Intelligence: A Managerial Perspective on Analytics" 3rd Ed., 2014)

"Whereas a data warehouse combines databases across an entire enterprise, a data mart is usually smaller and focuses on a particular subject or department. A data mart is a subset of a data warehouse, typically consisting of a single subject area (e.g., marketing, operations). A data mart can be either dependent or independent. A dependent data mart is a subset that is created directly from the data warehouse. It has the advantages of using a consistent data model and providing quality data. [...] An independent data mart is a small warehouse designed for a strategic business unit (SBU) or a department, but its source is not an EDW." (Ramesh Sharda et al, "Business Intelligence: A Managerial Perspective on Analytics" 3rd Ed., 2014)

"Data mart: A subset of a data warehouse that’s usually oriented to a business group or process rather than enterprise-wide views. They have value as part of the overall enterprise data architecture, but can cause problems when they sprout uncontrolled as data silos with their own data definitions, creating data shadow systems." (Rick Sherman, "Business Intelligence Guidebook: From Data Integration to Analytics, 2015)

"Data marts promised to be quicker and cheaper to build, and provided many more benefits - including the benefit of actually being able to finish building them! The data mart was primarily a backlash to the big, cumbersome CDW projects, with the key difference being that its scope was limited to a single business group rather than the entire enterprise. Of course, that shortcut did speed things up, but at the expense of obtaining agreement on consistent data definitions, thereby guaranteeing data silos." (Rick Sherman, "Business Intelligence Guidebook: From Data Integration to Analytics, 2015)

"There are, however, many problems with independent data marts. Independent data marts: (1) Do not have data that can be reconciled with other data marts (2) Require their own independent integration of raw data (3) Do not provide a foundation that can be built on whenever there are future analytical needs." (William H Inmon & Daniel Linstedt, "Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault", 2015)

"The data warehouse approach is also referred to as data marts with the usual distinction that a data mart serves a single department in an organization, while a data warehouse serves the larger organization integrating across multiple departments. Regardless of their scope, from the architectural modeling perspective they both have similar characteristics." (Zhamak Dehghani,"Data Mesh: Delivering Data-Driven Value at Scale", 2021) 

"Data marts are subject-oriented databases typically aligned with a particular business unit like sales, finance, or marketing. These are some-times called 'functional data marts' since they support specific business functions. Data marts accelerate business processes by allowing access to relevant information in a more timely nature since they are not aggregating the volume and variety (many data sources) that an EDW does. However, they are more transformed or normalized than an ODS." (Scott Burk et al, It’s All Analytics - Part II: Designing an Integrated AI, Analytics, and Data Science Architecture for Your Organization, 2022)

"Some sources try to distinguish the differences of data marts and EDWs by size. Size is a consequence and not a determinant. While EDWs are normally much larger, they are larger due to the fact they are pulling data from many sources and across business functions." (Scott Burk et al, It’s All Analytics - Part II: Designing an Integrated AI, Analytics, and Data Science Architecture for Your Organization, 2022)

"Traditional data stores used for analytics, such as data marts and data ware-houses, followed an ETL process, extract first by making a copy from the source, then transformations are made upon this copy, and then the data is loaded into the target system. ETL tools require processing engines for running transformations prior to loading data into a destination. Running these engines performing transformations before the load phase results in a more complex data replication process." (Scott Burk et al, It’s All Analytics - Part II: Designing an Integrated AI, Analytics, and Data Science Architecture for Your Organization, 2022)

"Data marts are used in most data warehouse approaches and focus on the data of a specific area, department, or domain of the business. A data warehouse focuses on building an SSOT for all data. A data warehouse can be made up of data marts (bottom-up approach), or data marts can be created from the data warehouse (top-down approach). In either case, data marts are smaller and less complicated than the data warehouse. As they only contain a subset of the data, data marts are often easier and quicker to establish. They are often managed by the departments the data mart focuses on." (Olivier Mertens & Breght Van Baelen, "Azure Data and AI Architect Handbook", 2023)

🪙Business Intelligence: Lakehouses (Just the Quotes)

"A data lakehouse is an amalgamation of the best components from both data lakes and data warehouses. A data lakehouse implements data structure and data management features from data warehouses into a cost-effective storage like a data lake. It tries to combine the best from both worlds - data lake - based Big Data analytics and a data warehouse." (Bhadresh Shiyal, "Beginning Azure Synapse Analytics: Transition from Data Warehouse to Data Lakehouse", 2021) 

"A defining characteristic of the data lakehouse architecture is allowing direct access to data as files while retaining the valuable properties of a data warehouse. Just do both!" (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"Once you combine the data lake along with analytical infrastructure, the entire infrastructure can be called a data lakehouse. [...] The data lake without the analytical infrastructure simply becomes a data swamp. And a data swamp does no one any good." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"The data lakehouse architecture presents an opportunity comparable to the one seen during the early years of the data warehouse market. The unique ability of the lakehouse to manage data in an open environment, blend all varieties of data from all parts of the enterprise, and combine the data science focus of the data lake with the end user analytics of the data warehouse will unlock incredible value for organizations. [...] The lakehouse architecture equally makes it natural to manage and apply models where the data lives." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"With the data lakehouse, it is possible to achieve a level of analytics and machine learning that is not feasible or possible any other way. But like all architectural structures, the data lakehouse requires an understanding of architecture and an ability to plan and create a blueprint." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"A data lakehouse stores a lot of data. It stores data in the data lake layer and the serving layer in structured and unstructured formats. The data needs to be processed with different types of compute engines. It can be a batch-based compute or a stream-based compute. A tightly coupled compute and storage layer strips off the flexibility required in a data lakehouse. Decoupling compute and storage also has a cost implication - storage is cheap and persistent but compute is expensive and ephemeral. It gives you the flexibility to spin up compute services on-demand and scale them as required, and also gives better cost control and cost predictability." (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)

"Lakehouse is a new architecture and data storage paradigm that combines the characteristics of both data warehouses and data lakes to create a unified basis for all types of use cases to be built on top of it. There is no need to move data around. Data is curated and remains in an open format and serves as the single source of truth (SSOT) for all the consumption layers. A modern data platform has needs that span traditional data warehouses, data lakes, machine learning systems, and streaming systems and there is some overlap among these systems. A Lakehouse offers features that span all four systems [...]" (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Simply put, 'lakehouse' refers to an open data architecture that combines the best of data lakes and data warehouses on a single platform. At this point, it would be fair to say that a lakehouse is closer to a data lake than a data warehouse. In fact, it is an extension of your data lake to support all use cases, from BI to AI. All data science and ML personas who were shunted into downstream applications because the tools of their trade were so vastly different and can now share the same stage and have access to the same data as other data personas. This eliminates the need to stitch fragile systems together and leads to better data quality and end-to-end latencies since there is no need to copy data across disparate architectures." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"The data mesh pattern doesn't feature a central node and is loosely coupled compared to a hub-spoke architecture. It has different data lakehouse nodes that are independent of each other. The node data lakehouses are domain-driven. A domain can be oriented in multiple ways. The original idea of data mesh alludes to a source-oriented domain aligning to business processes. However, a more practical approach would be to define a domain based on the organizational setup and practicality; for example, a domain can be a product group, it can be separate organizational entities, and it can also be a specific business process, such as marketing. Each domain has its own data lakehouse that is managed and maintained by that domain." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"The lakehouse provides a key advantage over the modern data warehouse by eliminating the need to have two places to store the same data." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)

"Traditional data lakes provide the necessary scalability, but not the real-time concurrency and latency needed for BI use cases. Delta comes to the rescue once again by providing performance at scale with a host of optimization techniques, such as caching, data compaction, and indexing. Previously, a subset of the curated data would be pushed to a warehouse to satisfy the latency and concurrency requirements of known queries. What this meant was that if a consumer needed a different access pattern or a slightly older dataset that was not available, they would have to request that their IT or data team get involved. This took data democratization a step backward. Ideally, we should allow people to access any data that they have privileges to. Delta Lake goes a step forward and allows BI tools to access data directly from the lake instead of accessing a sliver of the data in their expensive warehouses." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Like data lakes, the lakehouse architecture leverages low-cost cloud storage systems with the inherent flexibility and horizontal scalability of those systems. The goal of a lakehouse is to use existing high-performance data formats, such as Parquet, while also enabling ACID transactions (and other features). To add these capabilities, lakehouses use an open-table format, which adds features like ACID transactions, record-level operations, indexing, and key metadata to those existing data formats. This enables data assets stored on low-cost storage systems to have the same reliability that used to be exclusive to the domain of an RDBMS. Delta Lake is an example of an open-table format that supports these types of capabilities." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Delta Lake is one solution for building data lakehouses, an open data architecture combining the best of data warehouses and data lakes." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running Modern Data Lakehouse Architectures with Delta Lake", 2024)

"It is very important to understand that data mesh is a concept, not a technology. It is all about an organizational and cultural shift within companies. The technology used to build a data mesh could follow the modern data warehouse, data fabric, or data lakehouse architecture - or domains could even follow different architectures." (James Serra, "Deciphering Data Architectures", 2024)

"The term data lakehouse is a portmanteau (blend) of data lake and data warehouse. [...] The concept of a lakehouse is to get rid of the relational data warehouse and use just one repository, a data lake, in your data architecture." (James Serra, "Deciphering Data Architectures", 2024)

"The lakehouse combines the best elements of data lakes and data warehouses for OLAP workloads. It merges the scalability and flexibility of data lakes with the management features and performance optimization of data warehouses. [...] A lakehouse eliminates the need for disjointed systems and provides a single, coherent platform for all forms of data analysis. Lakehouses enhance the performance of data queries and simplify data management, making it easier for organizations to derive insights from their data." (Denny Lee et al, "Delta Lake: The Definitive Guide", 2025)

"A lakehouse combines the scalability and flexibility of a data lake with the governance, structure, and performance of a data warehouse. It allows organizations to store both structured and unstructured data in one platform while supporting robust analytics, machine learning, and BI workloads. These lakehouses get by the data swamp problem by providing features that are database like. That includes support for ACID transactions." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"A solid data foundation is critical for AI success. The foundation can grow in phases, but it needs to be there. AI will add pressure to the data foundation. Modern workloads require data that is connected, interpretable, high quality, and governed. Modern workloads utilize diverse data, and this is especially true with the popularity of GenAI. Trustworthy AI depends on knowing where the data came from and how it has changed. Modern architectures including the lakehouse and the data fabric exist because the traditional ways of managing data no longer fit the scale or complexity of what organizations need to do today. In other words, there is no path toward enterprise AI without a strong data foundation. This was echoed in the expert advices. Experts spoke about the dangers of 'vanity projects', particularly with GenAI, when foundational data issues were ignored. Others described how their companies moved faster specifically because they had already invested in governance, semantic layers, lineage, and observability." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"[...] organizations are working to unify the data silos that often exist across their data ecosystem. Some are doing this through a centralized physical approach, such as implementing a data lakehouse pattern, which allows them to store and manage all types of data in one place. Many lakehouse implementations such as those from vendors such as Snowflake and Databricks have evolved into what is often referred to as the modern data platform. This is an architectural pattern that combines the lakehouse with tightly integrated tools for data ingestion, transformation, analytics, observability, and governance. This pattern reflects a shift toward cloud-native architectures that are designed to support end-to-end data workflows with scalability and flexibility." (Fern Halper, "Data Makes the World Go 'Round", 2026)

🪙Business Intelligence: Data Lake (Just the Quotes)

"If you think of a Data Mart as a store of bottled water, cleansed and packaged and structured for easy consumption, the Data Lake is a large body of water in a more natural state. [...] The contents of the Data Lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples." (James Dixon, "Pentaho, Hadoop, and Data Lakes", 2010) [sorce] [first known usage]

"A data lake represents an environment that collects and stores large volumes of structured and unstructured datasets, typically in their original, unaltered forms. More than a data depository, the data lake architecture enables the various users and data science teams to conduct data exploration and related analytical activities." (EMC Education Services, "Data Science & Big Data Analytics", 2015)

"A data lake strategy supports the introduction of a separate analytics environment that off-loads the analytics being done today on your overly expensive data warehouse. This separate analytics environment provides the data science team an on-demand, fail-fast environment for quickly ingesting and analyzing a wide variety of data sources in an attempt to address immediate business opportunities independent of the data warehouse's production schedule and service level agreement (SLA) rules." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"At its core, it is a data storage and processing repository in which all of the data in an organization can be placed so that every internal and external systems', partners', and collaborators' data flows into it and insights spring out. [...] Data Lake is a huge repository that holds every kind of data in its raw format until it is needed by anyone in the organization to analyze." (Beulah S Purra & Pradeep Pasupuleti, "Data Lake Development with Big Data", 2015) 

"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)

"[...] the real power of the data lake is to enable advanced analytics or data science on the detailed and complete history of data in an attempt to uncover new variables and metrics that are better predictors of business performance." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"The data lake is not an incremental enhancement to the data warehouse, and it is NOT data warehouse 2.0. The data lake enables entirely new capabilities that allow your organization to address data and analytic challenges that the data warehouse could not address." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"Unfortunately, some organizations are replicating the bad data warehouse practice by creating special-purpose data lakes - data lakes to address a specific business need. Resist that urge! Instead, source the data that is needed for that specific business need into an 'analytic sandbox' where the data scientists and the business users can collaborate to find those data variables and analytic models that are better predictors of the business performance. Within the 'analytic sandbox', the organization can bring together (ingest and integrate) the data that it wants to test, build the analytic models, test the model's goodness of fit, acquire new data, refine the analytic models, and retest the goodness of fit." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"A data lake is a storage repository that holds a very large amount of data, often from diverse sources, in native format until needed. In some respects, a data lake can be compared to a staging area of a data warehouse, but there are key differences. Just like a staging area, a data lake is a conglomeration point for raw data from diverse sources. However, a staging area only stores new data needed for addition to the data warehouse and is a transient data store. In contrast, a data lake typically stores all possible data that might be needed for an undefined amount of analysis and reporting, allowing analysts to explore new data relationships. In addition, a data lake is usually built on commodity hardware and software such as Hadoop, whereas traditional staging areas typically reside in structured databases that require specialized servers." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)

"A data warehouse follows a pre-built static structure to model source data. Any changes at the structural and configuration level must go through a stringent business review process and impact analysis. Data lakes are very agile. Consumption or analytical layer can be modified to fit in the model requirements. Consumers of a data lake are not constant; therefore, schema and modeling lies at the liberty of analysts and scientists." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data in the data lake should never get disposed. Data driven strategy must define steps to version the data and handle deletes and updates from the source systems." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"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)

"Data Lake induces accessibility and catalyzes availability. It warrants data discovery platforms to soak the data trends at a horizontal scale and produce visual insights. It largely cuts down the time that goes into data preparation and exhaustive data analysis." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data Lake is a single window snapshot of all enterprise data in its raw format, be it structured, semi-structured, or unstructured. Starting from curating the data ingestion pipeline to the transformation layer for analytical consumption, every aspect of data gets addressed in a data lake ecosystem. It is supposed to hold enormous volumes of data of varied structures." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data swamp, on the other hand, presents the devil side of a lake. A data lake in a state of anarchy is nothing but turns into a data swamp. It lacks stable data governance practices, lacks metadata management, and plays weak on ingestion framework. Uncontrolled and untracked access to source data may produce duplicate copies of data and impose pressure on storage systems." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data warehousing, as we are aware, is the traditional approach of consolidating data from multiple source systems and combining into one store that would serve as the source for analytical and business intelligence reporting. The concept of data warehousing resolved the problems of data heterogeneity and low-level integration. In terms of objectives, a data lake is no different from a data warehouse. Both are primary advocates of terms like 'single source of truth' and 'central data repository'." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"At first, we threw all of this data into a pit called the 'data lake'. But we soon discovered that merely throwing data into a pit was a pointless exercise. To be useful - to be analyzed - data needed to (1) be related to each other and (2) have its analytical infrastructure carefully arranged and made available to the end user. Unless we meet these two conditions, the data lake turns into a swamp, and swamps start to smell after a while. [...] In a data swamp, data just sits there are no one uses it. In the data swamp, data just rots over time." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"Data lake architecture suffers from complexity and deterioration. It creates complex and unwieldy pipelines of batch or streaming jobs operated by a central team of hyper-specialized data engineers. It deteriorates over time. Its unmanaged datasets, which are often untrusted and inaccessible, provide little value. The data lineage and dependencies are obscured and hard to track." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data lakes have been in existence for a while now, so their need is no longer questioned. What is more relevant is the specifics of the solution's implementation. Consolidating all the siloed data by itself does not constitute a data lake. However, it is a starting point. Layering in governance makes the data consumable and is a step toward a curated data lake. Big data systems provide scale out of the box but force us to make some accommodations for data quality. Age-old aspects of transactional integrity were compromised on a distributed system because it was very hard to maintain ACID compliance. Due to this, BASE properties were favored. All of this was moving the needle in the wrong direction and from pristine data lakes we were moving toward data swamps, where the data could not be trusted and hence insights that were generated on the data could not be trusted either." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Lakehouse is a new architecture and data storage paradigm that combines the characteristics of both data warehouses and data lakes to create a unified basis for all types of use cases to be built on top of it. There is no need to move data around. Data is curated and remains in an open format and serves as the single source of truth (SSOT) for all the consumption layers. A modern data platform has needs that span traditional data warehouses, data lakes, machine learning systems, and streaming systems and there is some overlap among these systems. A Lakehouse offers features that span all four systems [...]" (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Simply put, 'lakehouse' refers to an open data architecture that combines the best of data lakes and data warehouses on a single platform. At this point, it would be fair to say that a lakehouse is closer to a data lake than a data warehouse. In fact, it is an extension of your data lake to support all use cases, from BI to AI. All data science and ML personas who were shunted into downstream applications because the tools of their trade were so vastly different and can now share the same stage and have access to the same data as other data personas. This eliminates the need to stitch fragile systems together and leads to better data quality and end-to-end latencies since there is no need to copy data across disparate architectures." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"When it comes to data lakes, some things usually stay constant: the storage and processing patterns. Change could come in any of the following ways: Adding new components and processing or consumption patterns to respond to new requirements. […] Optimizing existing architecture for better cost or performance" (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)

"Data lakes have some very strong benefits. A data lake architecture enables the consolidation of an organization’s data assets into one central location. Data lakes are format agnostic and rely on open source formats, such as Parquet and Avro. These formats are well understood by a variety of tools, drivers, and libraries, enabling smooth interoperability. Data lakes are deployed on mature cloud storage subsystems, allowing them to benefit from the scalability, monitoring, ease of deployment, and low storage costs associated with these systems. Automated DevOps tools, such as Terraform, have well-established drivers, enabling automated deployments and maintenance." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Like data lakes, the lakehouse architecture leverages low-cost cloud storage systems with the inherent flexibility and horizontal scalability of those systems. The goal of a lakehouse is to use existing high-performance data formats, such as Parquet, while also enabling ACID transactions (and other features). To add these capabilities, lakehouses use an open-table format, which adds features like ACID transactions, record-level operations, indexing, and key metadata to those existing data formats. This enables data assets stored on low-cost storage systems to have the same reliability that used to be exclusive to the domain of an RDBMS. Delta Lake is an example of an open-table format that supports these types of capabilities." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Traditionally, data lakes have always operated under the principle of schema on read, but have always had challenges enforcing schema on write. This means there is no predefined schema when data is written to storage, and a schema is only adapted when the data is processed. It is imperative for the case of analytics and data platforms that your table formats enforce the schema on write to prevent introducing change-breaking processes, and to maintain proper data quality and integrity." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Unlike data warehouses, data lakes support all data types, including semi-structured and unstructured data, enabling workloads such as media processing. Because of their high throughput ingress channels, they are very well suited for streaming use cases, such as ingesting IoT sensor data, media streaming, or web clickstreams. However, as data lakes become more popular and widely used, organizations started recognizing some challenges with traditional data lakes. While the underlying cloud storage is relatively inexpensive, building and maintaining an effective data lake requires expert skills, resulting in high-end staffing or increased consulting services costs." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Unstructured and semi-structured data are often critical for AI and machine learning use cases, whereas structured and semi-structured data are critical for BI use cases. Because it natively supports all three types of data classifications, you can create a unified system that supports these diverse workloads in a data lake. These workloads can complement each other in a well-designed processing architecture, which you will learn about further on in this chapter. A data lake helps solve many of the challenges related to data volumes, types, and cost, and while Delta Lake runs on top of a data lake, it is optimized to run best on a cloud data lake." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"A common pitfall in ML delivery is the horizontal slicing of work, where we sequentially deliver functional layers of a technical solution - e.g., data lake, ML platform, ML models, UX interfaces - from the bottom-up. This is a risky delivery approach because customers can only experience the product and provide valuable feedback after months and even years of significant engineering investment. In addition, horizontal slicing naturally leads to late integration issues when horizontal slices come together, increasing the risk of release delays." (David Tan et al,"Effective Machine Learning Teams: Best Practices for ML Practitioners", 2024)

"A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is without having first to structure the data and run different types of analytics - from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"Delta Lake is a transactional storage software layer that runs on top of an existing data lake and adds RDW-like features that improve the lake’s reliability, security, and performance. Delta Lake itself is not storage. In most cases, it’s easy to turn a data lake into a Delta Lake; all you need to do is specify, when you are storing data to your data lake, that you want to save it in Delta Lake format (as opposed to other formats, like CSV or JSON)." (James Serra, "Deciphering Data Architectures", 2024)

"The allure of Data Lakes was their ability to store vast amounts of raw data. However, this advantage can become counterproductive without stringent governance and management protocols. In their zeal to harness the power of Big Data, some organizations indiscriminately dump data into their lakes. Without proper classification, curation, and quality checks, these lakes can become swamps - murky repositories filled with valuable data, redundant information, and outdated datasets. Navigating these data swamps becomes a significant challenge, leading to prolonged data retrieval times, increased chances of using obsolete or incorrect data, and a decline in the agility and efficiency of data-driven decision-making processes rather than facilitating quick and insightful analytics." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"A lake based on the medallion architecture combines the best of lakes and data warehouses. By breaking down silos and eliminating data duplication, it becomes a standard for building data platform architecture." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"A data lake is a distributed repository of raw and unprocessed data stored in its original format, with-out a predefined schema or structure. A data lake is designed to support a wide range of data types, sources, and use cases, such as exploration, discovery, and data experimentation. A data lake follows a 'schema on read' approach. Data is structured and processed only when it is accessed or consumed by a user or application (Extract, Load, Transform (ELT)). A data lake also enables data democratiza-tion, meaning data is accessible and available to anyone who needs it, without barriers or restrictions." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"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)

"Considered by many companies as the next generation of data architecture, the data mesh represents the natural evolution of traditional data lakes and data warehouses. While the latter are often limited by their centralized and monolithic structure, the data mesh aims to enable companies to deploy a more flexible, responsive, and massively scalable data strategy." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Data Lakes embrace a schema-on-read approach, storing vast volumes of raw or lightly processed data in native formats with minimal upfront constraints. This design significantly enhances ingestion velocity and accommodates diverse, unstructured, or semi-structured datasets. However, enforcing data quality at scale becomes more complex, as traditional static constraints are absent." (William Smith, "Great Expectations for Modern Data Quality: The Complete Guide for Developers and Engineers", 2025)

"The problem with data lakes is that they have several drawbacks preventing them from being the perfect or ideal solution. The first drawback is an organizational problem: (•) How to organize data in the lake (•) How to classify, catalog, secure, document, and find it (•) How to avoid the lake turning into a swamp where data is mixed, duplicated, obsolete, or inaccessible (•) How to manage quality, governance, and traceability in the lake."(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)

"Data lakes represent a fundamental shift in how organizations store data for analytics. Unlike traditional approaches that required data to be structured and organized before storage, data lakes provide a repository for raw, unprocessed data in its native format. They serve as the foundation for many large-scale analytics architectures, particularly when organizations need to preserve data in its original form. The concept emerged as a response to the increasing variety and volume of valuable data." (Michael J Peña, "Azure Data Fundamentals: A Guide to DP-900 Certification and Beyond", 2026)

"Fabric builds on Microsoft’s analytics evolution by unifying previouslyseparate services into an integrated experience that emphasizes simplicityand cohesion. At its foundation lies OneLake, a single data lake that servesas a unified storage layer across all analytical workloads. This approacheliminates the silos that traditionally separated different analytical tools, enabling seamless data sharing and collaboration across roles and teams. The platform brings together multiple workload types under a consistent experience. Data engineers can build and manage pipelines that ingest and transform information. Data scientists can develop and deploy machine learning models. Data analysts can create reports and dashboards. Business users can access self-service analytics. All these personas work within a unified platform that maintains consistent data definitions and governance across activities." (Michael J Peña, "Azure Data Fundamentals: A Guide to DP-900 Certification and Beyond", 2026)