🪙Business Intelligence: Dashboards (Just the Quotes)

"The real value of dashboard products lies in their ability to replace hunt‐and‐peck data‐gathering techniques with a tireless, adaptable, information‐flow mechanism. Dashboards transform data repositories into consumable information." (Gregory L Hovis, "Stop Searching for InformationMonitor it with Dashboard Technology," DM Direct, 2002)

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

“Dashboards aren't all that different from some of the other means of presenting information, but when properly designed the single-screen display of integrated and finely tuned data can deliver insight in an especially powerful way.” (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)

"Alerts are integral to the dashboard concept in that they transform the dashboard from a graphical information presentation into a live console for managing organizational processes and performance. Effective dashboard deployment must facilitate easy management of alerts. This management process involves three components: (1) rules, (2) actions, and (3) recipients." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)

"Although it does so much more, the central purpose of a dashboard is to warn the user when any relevant metrics are out of acceptable boundaries. In the dashboard terminology, these alerts consisting of rules and actions add critical value to an enterprise dashboard deployment complemented with strong visual indicators of warnings." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)

"The term dashboard has acquired a vibrant new meaning in the field of information management as leading organizations worldwide embrace the idea of empowerment through improved real-time information systems. In the current corporate vocabulary, a dashboard is a rich computer interface with charts, reports, visual indicators, and alert mechanisms that are consolidated into a dynamic and relevant information platform." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)

"An effective dashboard is the product not of cute gauges, meters, and traffic lights, but rather of informed design: more science than art, more simplicity than dazzle. It is, above all else, about communication." (Stephen Few, "Information Dashboard Design", 2006)

"Most dashboards fail to communicate efficiently and effectively, not because of inadequate technology (at least not primarily), but because of poorly designed implementations. No matter how great the technology, a dashboard's success as a medium of communication is a product of design, a result of a display that speaks clearly and immediately. Dashboards can tap into the tremendous power of visual perception to communicate, but only if those who implement them understand visual perception and apply that understanding through design principles and practices that are aligned with the way people see and think." (Stephen Few, "Information Dashboard Design", 2006) 

"Having a purposeless or poorly performing dashboard is more common than not. This happens when the underlying architecture is not designed properly to support the needs of dashboard interaction. There is an obvious disconnect between the design of the data warehouse and the design of the dashboards. The people who design the data warehouse do not know what the dashboard will do; and the people who design the dashboards do not know how the data warehouse was designed, resulting in a lack of cohesion between the two. A similar disconnect can also exist between the dashboard designer and the business analyst, resulting in a dashboard that may look beautiful and dazzling but brings very little business value." (Nils H Rasmussen et al, "Business Dashboards: A visual catalog for design and deployment", 2009)

"In general, it still holds true that 'there is no such thing as a free lunch'. What this means is that the most advanced dashboard solutions with the most features and flexibility are generally also the technologies that require more setup and more skill sets from the administrators and the end users. In some cases companies 'dumb down' their dashboard application in the initial stages of deployment so as not to scare their users with too many options. Later, when a dashboard culture has developed, they open up more of the functionality." (Nils H Rasmussen et al, "Business Dashboards: A visual catalog for design and deployment", 2009)

"Dashboarding is an area that has been using information design to communicate key business metrics for decades. In terms of purpose, these interfaces have embodied many of the best practices surrounding visual communication. Yet their aesthetic and creative value is often lacking. This is an area of great opportunity in business communication; while well intentioned, the dashboard’s traditional format and appearance could benefit from a bit of a makeover." (Jason Lankow et al, "Infographics: The power of visual storytelling", 2012)

"There are myriad questions that we can ask from data today. As such, it’s impossible to write enough reports or design a functioning dashboard that takes into account every conceivable contingency and answers every possible question." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"A dashboard is like the executive summary of a report. We read executive summaries and skip the body of the report if the summary is more or less in line with our expectations. Trouble is, measurement is never exhaustive. It is only when we dive in that we realize what areas may have been missed." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"[…] an overall green status indicator doesn’t mean anything most of the time. All it says is that the things under measurement seem okay. But there always will be many more things not under measurement. To celebrate green indicators is to ignore the unknowns. […] The tendency to roll up metrics into dashboards promotes ignorance of the real situation on the ground. We forget that we only see what is under measurement. We only act when something is not green." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Rolling up fine-grained metrics to create high-level dashboards puts pressure on teams to keep the fine-grained metrics green even when it might not be the best use of their time." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"A performance dashboard is a practical tool to improve management effectiveness and efficiency, not just a pretty retrospective picture in an annual report." (Pearl Zhu, "Performance Master: Take a Holistic Approach to Unlock Digital Performance", 2017)

"All human storytellers bring their subjectivity to their narratives. All have bias, and possibly error. Acknowledging and defusing that bias is a vital part of successfully using data stories. By debating a data story collaboratively and subjecting it to critical thinking, organizations can get much higher levels of engagement with data and analytics and impact their decision making much more than with reports and dashboards alone." (James Richardson, 2017)

"Dashboards are a type of multiform visualization used to summarize and monitor data. These are most useful when proxies have been well validated and the task is well understood. This design pattern brings a number of carefully selected attributes together for fast, and often continuous, monitoring - dashboards are often linked to updating data streams. While many allow interactivity for further investigation, they typically do not depend on it. Dashboards are often used for presenting and monitoring data and are typically designed for at-a-glance analysis rather than deep exploration and analysis." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

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

"Dashboards are collections of several linked visualizations all in one place. The idea is very popular as part of business intelligence: having current data on activity summarized and presented all inone place. One danger of cramming a lot of disparate information into one place is that you will quickly hit information overload. Interactivity and small multiples are definitely worth considering as ways of simplifying the information a reader has to digest in a dashboard. As with so many other visualizations, layering the detail for different readers is valuable." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"[Dashboards] are popular methods for displaying multiple visualizations and statistical information. Dashboards often take the form of some organizational instrument that offers both at-a-glance and detailed views of many different analytical and information dimensions. Dashboards are not a unique chart type themselves, but rather should be considered compositions that comprise multiple chart types." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)

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

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

"As we enter into certain types of analytical conversations, we expect the conversations to flow in a predictable and cohesive manner. A KPI dashboard, for example, uses redundant structures across specific dimensions or measures to convey information. A dashboard with a top-down exposition style provides high-level information first and clarifies downward, while a bottom-up dashboard starts with the details and clarifies them against the larger picture." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Chart choices can also create weight within the entire composition. Presenting information as a comprehensive visualization, such as in a dashboard, requires thinking beyond individual charts. In writing, we not only craft sentences, but write the composition as an entire piece. Certain sentences may drive the writing more, but all sentences play a role in conveying the message." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"The sizes of charts in space reflect how we convey information to a reader. In a dashboard context, the content, size, and space that the various charts occupy should reflect the form and function of the main message. As you saw with the bento box metaphor from the introduction, there needs to be deliberate thought put into the placement and size of each individual chart so that they all work together in harmony." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"When integrating written text with charts in a functionally aesthetic way, the reader should be able to find the key takeaways from the chart or dashboard, taking into account the context, constraints, and reading objectives of the overall message."  (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"It turns out that the visual part of [gauges] does not convey significant information; it merely serves as an aesthetically pleasing frame for the total value. However, on a dashboard, we typically need to save space and highlight key information, and a standard card would be better suited for this task." (Alex Kolokolov & Maxim Zelensky, "Data Visualization with Microsoft Power BI", 2024)

"Data may indeed be the new oil. But just like crude oil, data needs refining. It must be transformed into information. This is why we clean, combine, model, and visualize data. The output of all this work - whether you do it on your own, get some help, or use a (semi-)automatic process - includes reports and dashboards that provide insights into various aspects of the organization’s dealings, which decision-makers can then consume to make critical business decisions." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

See also the definitions on "dashboards". 

🪙Business Intelligence: Data Products (Just the Quotes)

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

"Discovery is the key to building great data products, as opposed to products that are merely good." (Mike Loukides, "The Evolution of Data Products", 2011)

"New interfaces for data products are all about hiding the data itself, and getting to what the user wants." (Mike Loukides, "The Evolution of Data Products", 2011)

"[...] a good definition of a data product is a product that facilitates an end goal through the use of data. It’s tempting to think of a data product purely as a data problem. After all, there’s nothing more fun than throwing a lot of technical expertise and fancy algorithmic work at a difficult problem." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"As data scientists, we prefer to interact with the raw data. We know how to import it, transform it, mash it up with other data sources, and visualize it. Most of your customers can’t do that. One of the biggest challenges of developing a data product is figuring out how to give data back to the user. Giving back too much data in a way that’s overwhelming and paralyzing is 'data vomit'. It’s natural to build the product that you would want, but it’s very easy to overestimate the abilities of your users. The product you want may not be the product they want." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"Generalizing beyond advertising, when building any data product in which the data is obfuscated (where there isn’t a clear relationship between the user and the result), you can compromise on precision, but not on recall. But when the data is exposed, focus on high precision." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"Ideas for data products tend to start simple and become complex; if they start complex, they become impossible." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"In an emergency, a data product that just produces more data is of little use. Data scientists now have the predictive tools to build products that increase the common good, but they need to be aware that building the models is not enough if they do not also produce optimized, implementable outcomes." (Jeremy Howard et al, "Designing Great Data Products", 2012)

"The best way to avoid data vomit is to focus on actionability of data. That is, what action do you want the user to take? If you want them to be impressed with the number of things that you can do with the data, then you’re likely producing data vomit. If you’re able to lead them to a clear set of actions, then you’ve built a product with a clear focus." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"The key aspect of making a data product is putting the 'product' first and 'data' second. Saying it another way, data is one mechanism by which you make the product user-focused. With all products, you should ask yourself the following three questions: (1) What do you want the user to take away from this product? (2) What action do you want the user to take because of the product? (3) How should the user feel during and after using your product?" (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"You can give your data product a better chance of success by carefully setting the users’ expectations. [...] One under-appreciated facet of designing data products is how the user feels after using the product. Does he feel good? Empowered? Or disempowered and dejected?" (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"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)

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

"A data product is based on semantically related raw data that is transformed into a meaningful business context and easily discoverable and consumable by business users." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Data products should remain stable and be decoupled from the operational/transactional applications. This requires a mechanism for detecting schema drift, and avoiding disruptive changes. It also requires versioning and, in some cases, independent pipelines to run in parallel, giving your data consumers time to migrate from one version to another." (Piethein Strengholt, "Data Management at Scale: Modern Data Architecture with Data Mesh and Data Fabric" 2nd Ed., 2023)

"In Exploiting semantic knowledge graphs can support interpretability and explainability of nearly all AI model types (including DL models) by discovering and depicting semantic and non-obvious relationships or depicting an ML model in a simplified and more readable, explainable way., a Data Mesh solution organizes data around business domain owners and transforms relevant data assets (data sources) to data products that can be consumed by distributed business users from various business domains or functions. These data products are created, governed, and used in an autonomous, decentralized, and self-service manner. Self-service capabilities, which we have already referenced as a Data Fabric capability, enable business organizations to entertain a data marketplace with shopping-for-data characteristics." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"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)

"Domain-oriented ownership is a core principle of data mesh. It entails that data producers, experts in their business domains, are responsible for the entire lifecycle of their produced data. Specifically, they take ownership of the data from the point of ingestion through transformation, serving, quality assurance, and governance. Moreover, they are responsible for the data products created from their data, which serve as units of data consumption for other domains or users." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 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)

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

"Data product usage is growing quickly, doubling every year. Obviously, since we made the investment, we'll work with our customer to find applications." (Ken Shelton)

🪙Business Intelligence: Measures (Just the Quotes)

"The most important and frequently stressed prescription for avoiding pitfalls in the use of economic statistics, is that one should find out before using any set of published statistics, how they have been collected, analysed and tabulated. This is especially important, as you know, when the statistics arise not from a special statistical enquiry, but are a by-product of law or administration. Only in this way can one be sure of discovering what exactly it is that the figures measure, avoid comparing the non-comparable, take account of changes in definition and coverage, and as a consequence not be misled into mistaken interpretations and analysis of the events which the statistics portray." (Ely Devons, "Essays in Economics", 1961)

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

"[Management by objectives is] a process whereby the superior and the subordinate managers of an enterprise jointly identify its common goals, define each individual's major areas of responsibility in terms of the results expected of him, and use these measures as guides for operating the unit and assessing the contribution of each of its members." (Robert House, "Administrative Science Quarterly", 1971)

"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)

"Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes." (Charles Goodhart, "Problems of Monetary Management: the U.K. Experience", 1975)

"The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor." (Donald T Campbell, "Assessing the impact of planned social change", 1976)

"Reengineering is the fundamental rethinking and radical redesign of business processes to achieve dramatic improvements in critical contemporary measures of performance such as cost, quality, service and speed." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"Industrial managers faced with a problem in production control invariably expect a solution to be devised that is simple and unidimensional. They seek the variable in the situation whose control will achieve control of the whole system: tons of throughput, for example. Business managers seek to do the same thing in controlling a company; they hope they have found the measure of the entire system when they say 'everything can be reduced to monetary terms'." (Stanford Beer, "Decision and Control", 1994)

"A strategy is a set of hypotheses about cause and effect. The measurement system should make the relationships (hypotheses) among objectives (and measures) in the various perspectives explicit so that they can be managed and validated. The chain of cause and effect should pervade all four perspectives of a Balanced Scorecard." (Robert S Kaplan & David P Norton, "The Balanced Scorecard", Harvard Business Review, 1996)

"The Balanced Scorecard has its greatest impact when it is deployed to drive organizational change. [...] The Balanced Scorecard is primarily a mechanism for strategy implementation, not for strategy formulation. It can accommodate either approach for formulating business unit strategy-starting from the customer perspective, or starting from excellent internal-business-process capabilities. For whatever approach that SBU senior executives use to formulate their strategy, the Balanced Scorecard will provide an invaluable mechanism for translating that strategy into specific objectives, measures, and targets, and monitoring the implementation of that strategy during subsequent periods." (Robert S Kaplan & David P Norton, "The Balanced Scorecard", Harvard Business Review, 1996)

"The Balanced Scorecard translates mission and strategy into objectives and measures, organized into four different perspectives: financial, customer, internal business process, and learning and growth. The scorecard provides a framework, a language, to communicate mission and strategy; it uses measurement to inform employees about the drivers of current and future success." (Robert S Kaplan & David P Norton, "The Balanced Scorecard", Harvard Business Review, 1996)

"When a measure becomes a target, it ceases to be a good measure." (Marilyn Strathern, "‘Improving ratings’: audit in the British University system", 1997)

"Since the average is a measure of location, it is common to use averages to compare two data sets. The set with the greater average is thought to ‘exceed’ the other set. While such comparisons may be helpful, they must be used with caution. After all, for any given data set, most of the values will not be equal to the average." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"First, good statistics are based on more than guessing. [...] Second, good statistics are based on clear, reasonable definitions. Remember, every statistic has to define its subject. Those definitions ought to be clear and made public. [...] Third, good statistics are based on clear, reasonable measures. Again, every statistic involves some sort of measurement; while all measures are imperfect, not all flaws are equally serious. [...] Finally, good statistics are based on good samples." (Joel Best, "Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"Statistics depend on collecting information. If questions go unasked, or if they are asked in ways that limit responses, or if measures count some cases but exclude others, information goes ungathered, and missing numbers result. Nevertheless, choices regarding which data to collect and how to go about collecting the information are inevitable." (Joel Best, "More Damned Lies and Statistics: How numbers confuse public issues", 2004)

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

"Key performance indicators (KPIs) are the vital navigation instruments used by managers to understand whether their business is on a successful voyage or whether it is veering off the prosperous path. The right set of indicators will shine light on performance and highlight areas that need attention. ‘What gets measured gets done’ and ‘if you can’t measure it, you can’t manage it’ are just two of the popular sayings used to highlight the critical importance of metrics. Without the right KPIs managers are sailing blind." (Bernard Marr, "Key Performance Indicators (KPI): The 75 measures every manager needs to know", 2011)

"A statistical index has all the potential pitfalls of any descriptive statistic - plus the distortions introduced by combining multiple indicators into a single number. By definition, any index is going to be sensitive to how it is constructed; it will be affected both by what measures go into the index and by how each of those measures is weighted." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"Even if you have a solid indicator of what you are trying to measure and manage, the challenges are not over. The good news is that 'managing by statistics' can change the underlying behavior of the person or institution being managed for the better. If you can measure the proportion of defective products coming off an assembly line, and if those defects are a function of things happening at the plant, then some kind of bonus for workers that is tied to a reduction in defective products would presumably change behavior in the right kinds of ways. Each of us responds to incentives (even if it is just praise or a better parking spot). Statistics measure the outcomes that matter; incentives give us a reason to improve those outcomes." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"Once these different measures of performance are consolidated into a single number, that statistic can be used to make comparisons […] The advantage of any index is that it consolidates lots of complex information into a single number. We can then rank things that otherwise defy simple comparison […] Any index is highly sensitive to the descriptive statistics that are cobbled together to build it, and to the weight given to each of those components. As a result, indices range from useful but imperfect tools to complete charades." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

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

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

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

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

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

"GIGO is a famous saying coined by early computer scientists: garbage in, garbage out. At the time, people would blindly put their trust into anything a computer output indicated because the output had the illusion of precision and certainty. If a statistic is composed of a series of poorly defined measures, guesses, misunderstandings, oversimplifications, mismeasurements, or flawed estimates, the resulting conclusion will be flawed." (Daniel J Levitin, "Weaponized Lies", 2017)

"To be any good, a sample has to be representative. A sample is representative if every person or thing in the group you’re studying has an equally likely chance of being chosen. If not, your sample is biased. […] The job of the statistician is to formulate an inventory of all those things that matter in order to obtain a representative sample. Researchers have to avoid the tendency to capture variables that are easy to identify or collect data on - sometimes the things that matter are not obvious or are difficult to measure." (Daniel J Levitin, "Weaponized Lies", 2017)

🪙Business Intelligence: Reporting (Just the Quotes)

"A man's judgment cannot be better than the information on which he has based it. Give him no news, or present him only with distorted and incomplete data, with ignorant, sloppy, or biased reporting, with propaganda and deliberate falsehoods, and you destroy his whole reasoning process and make him somewhat less than a man." (Arthur H Sulzberger, [speech] 1948)

"The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify. Statistical methods and statistical terms are necessary in reporting the mass data of social and economic trends, business conditions, 'opinion' polls, the census. But without writers who use the words with honesty and understanding and readers who know what they mean, the result can only be semantic nonsense." (Darell Huff, "How to Lie with Statistics", 1954)

"To be worth much, a report based on sampling must use a representative sample, which is one from which every source of bias has been removed." (Darell Huff, "How to Lie with Statistics", 1954)

"It is probable that one day we shall begin to draw organization charts as a series of linked groups rather than as a hierarchical structure of individual 'reporting' relationships." (Douglas McGregor, "The Human Side of Enterprise", 1960)

"[...] as the planning process proceeds to a specific financial or marketing state, it is usually discovered that a considerable body of 'numbers' is missing, but needed numbers for which there has been no regular system of collection and reporting; numbers that must be collected outside the firm in some cases. This serendipity usually pays off in a much better management information system in the form of reports which will be collected and reviewed routinely." (William H. Franklin Jr., Financial Strategies, 1987)

"Intangible assets [...] surpass physical assets in most business enterprises, both in value and contribution to growth, yet they are routinely expensed in the financial reports and hence remain absent from corporate balance sheets. This asymmetric treatment of capitalizing (considering as assets) physical and financial investment while expensing intangibles leads to biased and deficient reporting of firms’ performance and value." (Baruch Lev, "Intangibles: Management, Measurement, and Reporting", 2000)

"Project planning is the key to effective project management. Detailed and accurate planning of a project produces the managerial information that is the basis of project justification (costs, benefits, strategic impact, etc.) and the defining of the business drivers (scope, objectives) that form the context for the technical solution. In addition, project planning also produces the project schedules and resource allocations that are the framework for the other project management processes: tracking, reporting, and review." (Rob Thomsett, "Radical Project Management", 2002)

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

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

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

"Another way to secure statistical significance is to use the data to discover a theory. Statistical tests assume that the researcher starts with a theory, collects data to test the theory, and reports the results - whether statistically significant or not. Many people work in the other direction, scrutinizing the data until they find a pattern and then making up a theory that fits the pattern." (Gary Smith, "Standard Deviations", 2014)

"These practices - selective reporting and data pillaging - are known as data grubbing. The discovery of statistical significance by data grubbing shows little other than the researcher’s endurance. We cannot tell whether a data grubbing marathon demonstrates the validity of a useful theory or the perseverance of a determined researcher until independent tests confirm or refute the finding. But more often than not, the tests stop there. After all, you won’t become a star by confirming other people’s research, so why not spend your time discovering new theories? The data-grubbed theory consequently sits out there, untested and unchallenged." (Gary Smith, "Standard Deviations", 2014)

"A dashboard is like the executive summary of a report. We read executive summaries and skip the body of the report if the summary is more or less in line with our expectations. Trouble is, measurement is never exhaustive. It is only when we dive in that we realize what areas may have been missed." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

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

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

"Statistics, because they are numbers, appear to us to be cold, hard facts. It seems that they represent facts given to us by nature and it’s just a matter of finding them. But it’s important to remember that people gather statistics. People choose what to count, how to go about counting, which of the resulting numbers they will share with us, and which words they will use to describe and interpret those numbers. Statistics are not facts. They are interpretations. And your interpretation may be just as good as, or better than, that of the person reporting them to you." (Daniel J Levitin, "Weaponized Lies", 2017)

"Data may indeed be the new oil. But just like crude oil, data needs refining. It must be transformed into information. This is why we clean, combine, model, and visualize data. The output of all this work - whether you do it on your own, get some help, or use a (semi-)automatic process - includes reports and dashboards that provide insights into various aspects of the organization’s dealings, which decision-makers can then consume to make critical business decisions." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

🪙Business Intelligence: Measure (Just the Quotes)

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

"Quantify. If whatever it is you’re explaining has some measure, some numerical quantity attached to it, you’ll be much better able to discriminate among competing hypotheses. What is vague and qualitative is open to many explanations." (Carl Sagan, "The Demon-Haunted World: Science as a Candle in the Dark", 1995)

"Clearly, the mean is greatly influenced by extreme values, but it can be appropriate for many situations where extreme values do not arise. To avoid misuse, it is essential to know which summary measure best reflects the data and to use it carefully. Understanding the situation is necessary for making the right choice. Know the subject!" (Herbert F Spirer et al, "Misused Statistics" 2nd Ed, 1998)

"Balanced Scorecard theory is flawed because it presents managers with a scorecard which gives no score – that is no single-valued measure how they have performed. Thus managers evaluated with such a system […] have no way to make principled or purposeful decisions." (Michael Jensen,"Value Maximization, Stakeholder Theory, and the Corporate Objective Function", Journal of Applied Corporate Finance, 2001)

"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)

"Our culture, obsessed with numbers, has given us the idea that what we can measure is more important than what we can't measure. Think about that for a minute. It means that we make quantity more important than quality." (Donella Meadows, "Thinking in Systems: A Primer", 2008)

"What gets measured gets managed - even when it’s pointless to measure and manage it, and even if it harms the purpose of the organisation to do so." (Simon Caulkin, "The rule is simple: be careful what you measure", 2008) [source]

"What gets measured gets managed - so be sure you have the right measures, because the wrong ones kill." (Simon Caulkin, "The rule is simple: be careful what you measure", 2008) [source]

"Once these different measures of performance are consolidated into a single number, that statistic can be used to make comparisons […] The advantage of any index is that it consolidates lots of complex information into a single number. We can then rank things that otherwise defy simple comparison […] Any index is highly sensitive to the descriptive statistics that are cobbled together to build it, and to the weight given to each of those components. As a result, indices range from useful but imperfect tools to complete charades." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"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)

"Selecting the right measure and measuring things right are both art and science. And KPIs influence management behavior as well as business culture." (Pearl Zhu, "CIO Master: Unleash the Digital Potential of It", 2016)

"It’d be nice to fondly imagine that high-quality statistics simply appear in a spreadsheet somewhere, divine providence from the numerical heavens. Yet any dataset begins with somebody deciding to collect the numbers. What numbers are and aren’t collected, what is and isn’t measured, and who is included or excluded are the result of all-too-human assumptions, preconceptions, and oversights." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"People do care about how they are measured. What can we do about this? If you are in the position to measure something, think about whether measuring it will change people’s behaviors in ways that undermine the value of your results. If you are looking at quantitative indicators that others have compiled, ask yourself: Are these numbers measuring what they are intended to measure? Or are people gaming the system and rendering this measure useless?" (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"As long as measurements are abused as a tool of control, measuring will remain the weakest area in a manager’s performance." (Peter Drucker)

"For although it is certainly true that quantitative measurements are of great importance, it is a grave error to suppose that the whole of experimental physics can be brought under this heading. We can start measuring only when we know what to measure: qualitative observation has to precede quantitative measurement, and by making experimental arrangements for quantitative measurements we may even eliminate the possibility of new phenomena appearing." (Heinrich B G Casimir)

🪙Business Intelligence: Analytics (Just the Quotes)

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

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

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

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

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

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

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

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

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

"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)

"Data analytics is a powerful tool to increase the likelihood that you have the right problem. Both quantitative and qualitative data serve a purpose in supporting a hypothesis. They allow you to objectively measure and identify patterns and relationships." (Shonna D Watters et al, "The Practical Guide for HR Analytics: Using data to inform, transform, and empower HR decisions", 2019)

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

"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)

"Data literacy is not a change in an individual’s abilities, talents, or skills within their careers, but more of an enhancement and empowerment of the individual to succeed with data. When it comes to data and analytics succeeding in an organization’s culture, the increase in the workforces’ skills with data literacy will help individuals to succeed with the strategy laid in front of them. In this way, organizations are not trying to run large change management programs; the process is more of an evolution and strengthening of individual’s talents with data. When we help individuals do more with data, we in turn help the organization’s culture do more with data." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"In the world of data and analytics, people get enamored by the nice, shiny object. We are pulled around by the wind of the latest technology, but in so doing we are pulled away from the sound and intelligent path that can lead us to data and analytical success. The data and analytical world is full of examples of overhyped technology or processes, thinking this thing will solve all of the data and analytical needs for an individual or organization. Such topics include big data or data science. These two were pushed into our minds and down our throats so incessantly over the past decade that they are somewhat of a myth, or people finally saw the light. In reality, both have a place and do matter, but they are not the only solution to your data and analytical needs. Unfortunately, though, organizations bit into them, thinking they would solve everything, and were left at the alter, if you will, when it came time for the marriage of data and analytical success with tools." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"Pure data science is the use of data to test, hypothesize, utilize statistics and more, to predict, model, build algorithms, and so forth. This is the technical part of the puzzle. We need this within each organization. By having it, we can utilize the power that these technical aspects bring to data and analytics. Then, with the power to communicate effectively, the analysis can flow throughout the needed parts of an organization." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

♟️Strategic Management: Methods (Just the Quotes)

"The writer has found, in analyzing and diagnosing organization and accounting work, that charts can express more on one page than is sometimes expressed in several chapters of writing, and has been the author and originator of many methods of charting industrial expressions. It is necessary, as a first step, for analytical and other purposes, to make a chart expressing all of the relations governing the organization of a business so as to show the very foundation upon which all authorities, accounting, and business transactions are based and conducted. There have been more failures scored both personally and financially for lack of these very elements in a business than by reason of any other one thing. As well try to build a house without a foundation as to try to conduct a business, especially a manufacturing business, without proper organization." (Clinton E. Woods, "Organizing a factory", 1905)

"It is only through enforced standardization of methods, enforced adoption of the best implements and working conditions, and enforced cooperation that this faster work can be assured. And the duty of enforcing the adoption of standards and enforcing this cooperation rests with management alone." (Frederick W Taylor, "Principles of Scientific Management", 1911)

"Motion study is the science of eliminating wastefulness resulting from using unnecessary, ill-directed, and inefficient motions. The aim of motion study is to find and perpetuate the scheme of least waste methods of labor." (Frank B Gilbreth, "Primer of scientific management", 1912) 

"For any manager to utilize graphic methods for visualizing the vital facts of his business, in the first place it must be impressed upon his that the method will produce the results for him and then he must know how to get up a chart correctly, and last, but far from least, he must know what the essential facts of his business are. Charts, in themselves, mean little and like many another force for the accomplishment of good, if misdirected, may result unprofitably." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"Business executives cannot afford to ignore the merits of graphical representation which have for so long been accepted by the engineer and man of science. They must look behind the graphical method and study the conditions leading to the picture along with the picture itself. No business is too small to profit by an examination which shall analyze and scrutinize nor too large to ignore its possibilities. Each business must adjust the graphical methods to its own peculiarities and each diagram must be adjusted to the individual for whom it is prepared or the individual must be educated up to the use and importance of these methods of analysis." (William C Marshall, "Graphical methods for schools, colleges, statisticians, engineers and executives", 1921)

"Do not confuse objectives with methods. When the nation becomes substantially united in favor of planning the broad objectives of civilization, then true leadership must unite thought behind definite methods." (Franklin D Roosevelt, 1937)

"When an active individual of sound common sense perceives the sordid state of the world, desire to change it becomes the guiding principle by which he organizes given facts and shapes them into a theory. The methods and categories as well as the transformation of the theory can be understood only in connection with his taking of sides. This, in turn, discloses both his sound common sense and the character of the world. Right thinking depends as much on right willing as right willing on right thinking." (Max Horkheimer, "The Latest Attack on Metaphysics", 1937)

"The concern of OR with finding an optimum decision, policy, or design is one of its essential characteristics. It does not seek merely to define a better solution to a problem than the one in use; it seeks the best solution... [It] can be characterized as the application of scientific methods, techniques, and tools to problems involving the operations of systems so as to provide those in control of the operations with optimum solutions to the problems." (C West Churchman et al, "Introduction to Operations Research", 1957)

"Linking the basic parts are communication, balance or system parts maintained in harmonious relationship with each other and decision making. The system theory include both man-machine and interpersonal relationships. Goals, man, machine, method, and process are woven together into a dynamic unity which reacts." (George R Terry, "Principles of Management", 1960)

"The essential task of management is to arrange organizational conditions and methods of operations so that people can achieve their own goals best by directing their own efforts toward organizational objectives." (Douglas McGregor, "The Human Side of Enterprise", 1960)

"The unique feature of the decision tree is that it allows management to combine analytical techniques such as discounted cash flow and present value methods with a clear portrayal of the impact of future decision alternatives and events. Using the decision tree, management can consider various courses of action with greater ease and clarity. The interactions between present decision alternatives, uncertain events, and future choices and their results become more visible." (John F Magee, "Decision Trees for Decision Making", Harvard Business Review, 1964)

"The concept of leadership has an ambiguous status in organizational practice, as it does in organizational theory. In practice, management appears to be of two minds about the exercise of leadership. Many jobs are so specified in content and method that within very broad limits differences among individuals become irrelevant, and acts of leadership are regarded as gratuitous at best, and at worst insubordinate." (Daniel Katz & Robert L Kahn, "The Social Psychology of Organizations", 1966)

"For the scientist a model is also a way in which the human though processes can be amplified. This method often takes the form of models that can be programmed into computers. At no point, however, the scientist intend to loose control of the situation because off the computer does some of his thinking for him. The scientist controls the basic assumptions and the computer only derives some of the more complicated implications." (C West Churchman, "The Systems Approach", 1968)

"It is an axiom of program budgeting that the budget should facilitate the process of alternative methods of obtaining objectives." (Chester Wright, "Program Budgeting and Cost Benefit Analysis", 1969)

"Statistics is a body of methods and theory applied to numerical evidence in making decisions in the face of uncertainty." (Lawrence Lapin, "Statistics for Modern Business Decisions", 1973)

"Strategic planning is not the 'application of scientific methods to business decision' […] . It is the application of thought, analysis, imagination, and judgment. It is responsibility, rather than technique. […] Strategy planning is not forecasting. […] Strategic planning is necessary precisely because we cannot forecast. […] Strategic planning does nor deal with future decisions. It deals with the futurity of present decisions. […] Strategic planning is not an attempt to eliminate risk. It is not even an attempt to minimize risk." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"Perhaps the fault [for the poor implementation record for models] lies in the origins of managerial model-making - the translation of methods and principles of the physical sciences into wartime operations research. [...] If hypothesis, data, and analysis lead to proof and new knowledge in science, shouldn’t similar processes lead to change in organizations? The answer is obvious-NO! Organizational changes" (or decisions or policies) do not instantly pow from evidence, deductive logic, and mathematical optimization." (Edward B Roberts, "Interface", 1977)

"Someone adhering to the values of a corporate culture - an intelligent corporate citizen - will behave in consistent fashion under similar conditions, which means that managers don’t have to suffer the inefficiencies engendered by formal rules, procedures, and regulations. […] management has to develop and nurture the common set of values, objectives, and methods essential to the existence of trust. How do we do that? One way is by articulation, by spelling [them] out. […] The other even more important way is by example." (Andrew S Grove, "High Output Management", 1983)

"The formal structure of a decision problem in any area can be put into four parts:" (1) the choice of an objective function denning the relative desirability of different outcomes;" (2) specification of the policy alternatives which are available to the agent, or decisionmaker," (3) specification of the model, that is, empirical relations that link the objective function, or the variables that enter into it, with the policy alternatives and possibly other variables; and" (4) computational methods for choosing among the policy alternatives that one which performs best as measured by the objective function." (Kenneth Arrow, "The Economics of Information", 1984)

"A real challenge for some organizations is to build more qualitative information into their formal systems. One method used in some companies is to request a written narrative with each submission of statistics from the field. Another method is to hold periodic, indepth discussions involving several managers from different levels so that each can contribute whatever qualitative data are available to him." (Larry E Greiner et al, "Human Relations", 1986)

"Enterprise Engineering is not a single methodology, but a sophisticated synthesis of the most important and successful of today's change methods. 'Enterprise Engineering' first explains in detail all the critical disciplines (including continuous improvement, radical reinvention of business processes, enterprise redesign, and strategic visioning). It then illustrates how to custom-design the right combination of these change methods for your organization's specific needs." (James Martin, "The Great Transition, 1995)

"Enterprise Engineering is defined as that body of knowledge, principles, and practices having to do with the analysis, design, implementation and operation of an enterprise. In a continually changing and unpredictable competitive environment, the Enterprise Engineer addresses a fundamental question: 'how to design and improve all elements associated with the total enterprise through the use of engineering and analysis methods and tools to more effectively achieve its goals and objectives' [...]" (Donald H Liles, "The Enterprise Engineering Discipline", 1996)

"Change pressures arise from different sectors of a system. At times it is mandated from the top of a hierarchy, other times it forms from participants at a grass-roots level. Some changes are absorbed by the organization without significant impact on, or alterations of, existing methods. In other cases, change takes root. It causes the formation of new methods" (how things are done and what is possible) within the organization." (George Siemens, "Knowing Knowledge", 2006)

"It's not enough to be talented. It's not enough to work hard and to study late into the night. You must also become intimately aware of the methods you use to reach your decisions." (Garry Kasparov, "How Life Imitates Chess", 2007)

"The goal of enterprise architecture is to create a unified IT environment" (standardized hardware and software systems) across the firm or all of the firm's business units, with tight symbiotic links to the business side of the organization" (which typically is 90% of the firm […] at least by way of budget). More specifically, the goals are to promote alignment, standardization, reuse of existing IT assets, and the sharing of common methods for project management and software development across the organization." (Daniel Minoli, "Enterprise architecture A to Z: frameworks, business process modeling", 2008)

"Enterprise architecture [is] a coherent whole of principles, methods, and models that are used in the design and realisation of an enterprise's organisational structure, business processes, information systems, and infrastructure. […] The most important characteristic of an enterprise architecture is that it provides a holistic view of the enterprise. […] To achieve this quality in enterprise architecture, bringing together information from formerly unrelated domains necessitates an approach that is understood by all those involved from those different domains." (Marc Lankhorst, "Enterprise Architecture at Work: Modelling, Communication and Analysis", 2009)

🪙Business Intelligence: Data Warehouse (Just the Quotes)

"Unfortunately, just collecting the data in one place and making it easily available isn’t enough. When operational data from transactions is loaded into the data warehouse, it often contains missing or inaccurate data. How good or bad the data is a function of the amount of input checking done in the application that generates the transaction. Unfortunately, many deployed applications are less than stellar when it comes to validating the inputs. To overcome this problem, the operational data must go through a 'cleansing' process, which takes care of missing or out-of-range values. If this cleansing step is not done before the data is loaded into the data warehouse, it will have to be performed repeatedly whenever that data is used in a data mining operation." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"Having a purposeless or poorly performing dashboard is more common than not. This happens when the underlying architecture is not designed properly to support the needs of dashboard interaction. There is an obvious disconnect between the design of the data warehouse and the design of the dashboards. The people who design the data warehouse do not know what the dashboard will do; and the people who design the dashboards do not know how the data warehouse was designed, resulting in a lack of cohesion between the two. A similar disconnect can also exist between the dashboard designer and the business analyst, resulting in a dashboard that may look beautiful and dazzling but brings very little business value." (Nils H Rasmussen et al, "Business Dashboards: A visual catalog for design and deployment", 2009)

"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)

"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)

"Data quality in warehousing and BI is typically defined in terms of the 4 C’s - is the data clean, correct, consistent, and complete? When it comes to big data, there are two schools of thought that have different views and expectations of data quality. The first school believes that the gold standard of the 4 C’s must apply to all data (big and little) used for clinical care and performance metrics. The second school believes that in big data environments, a stringent data quality standard is impossible, too costly, or not required. While diametrically opposite opinions may play well in panel discussions, they do little to reconcile the realities of healthcare data quality." (Prashant Natarajan et al, "Demystifying Big Data and Machine Learning for Healthcare", 2017) 

"Data warehousing has always been difficult, because leaders within an organization want to approach warehousing and analytics as just another technology or application buy. Viewed in this light, they fail to understand the complexity and interdependent nature of building an enterprise reporting environment." (Prashant Natarajan et al, "Demystifying Big Data and Machine Learning for Healthcare", 2017)

"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 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)

"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)

"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)

"A data warehouse service provides cleansed and transformed data that can be used for multiple purposes. First, it serves as a layer for reporting and BI. Second, it is a platform to query data for business or data analysis. Third, it serves as a repository to store historical data that needs to be online and available. Finally, it also acts as a source of transformed data for other downstream data marts that may cater to specific departmental requirements." (Pradeep Menon, "Data Lakehouse in Action", 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)

"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)

"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)

"A data warehouse is a centralized repository of structured, cleaned, and verified data that has been extracted, transformed, and loaded from various sources. These steps are commonly called ETL, which stands for Extract, Transform, Load. This data processing methodology involves extracting data from multiple sources, transforming it to meet business needs, and loading it into a destination for analysis and consultation." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"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 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 Science: Median (Definitions)

"The middle value in an ordered set of values for which there are an equal number of values." (Jennifer George-Palilonis, "A Practical Guide to Graphics Reporting", 2006)

"The center-most value in an ordered set of values. If the set quantity is even, then the average of the two center-most values." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The median is a statistical measure of variation. It represents the middle measurement when a set of measurements are collected in ascending order: 50% of the measurements are above the median and 50% are below it." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"The middle value in a set of ordered numbers. The median value is determined by choosing the smallest value such that at least half of the values in the set are no greater than the chosen value. If the number of values within the set is odd, the median value corresponds to a single value. If the number of values within the set is even, the median value corresponds to the sum of the two middle values divided by two." (Microsoft, "SQL Server 2012 Glossary", 2012)

"The middle value in a set of values. Half the values fall below the median, and half the values fall above the median. See also average; mode." (E C Nelson & Stephen L Nelson, "Excel Data Analysis For Dummies ", 2015)

"To find the median, list the values of the data set in numerical order and identify which value appears in the middle of the list." (Christopher Donohue et al, "Foundations of Financial Risk: An Overview of Financial Risk and Risk-based Financial Regulation, 2nd Ed", 2015)

"Middle score in a distribution." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

Statistics: Mean (Definitions)

"In a numerical sequence, the number that has an equal number of values before and after it. In the sequence 3, 5, 7, 9, 11, seven is the mean." (Dale Furtwengler, "Ten Minute Guide to Performance Appraisals", 2000)

"The average value of a sample of data that is typically gathered in a matrix experiment." (Clyde M Creveling, "Six Sigma for Technical Processes: An Overview for R Executives, Technical Leaders, and Engineering Managers", 2006)

"The sum of all values in a variable divided by the number of values." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"The average value of a sample of data that is typically gathered in a matrix experiment." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"The sum of all values in a variable divided by the number of values." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2007)

"The result of dividing the sum of all values within a set by the count of all values included." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The mean is a statistical measure of central tendency. It is most easily understood as the mathematical average. It is calculated by summing the value of a set of measurements and dividing by the number of measurements taken." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement", 2012)

"To find the mean add up the values in the data set and then divide by the number of values." (Christopher Donohue et al, "Foundations of Financial Risk: An Overview of Financial Risk and Risk-based Financial Regulation" 2nd Ed., 2015)

"Arithmetic averages of scores. The mean is the most commonly used measure of central tendency, but should be computed only for score data." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

🔬Data Science: Moving Average (Definitions)

"A trend-following indicator that works best in a trending environment. Moving averages smooth out price action but operate with a time lag. Any number of moving averages can be employed, with different time spans, to generate buy and sell signals. When only one average is employed, a buy signal is given when the price closes above the average. When two averages are employed, a buy signal is given when the shorter average crosses above the longer average. Technicians use three types: simple, weighted, and exponentially smoothed averages." (Guido Deboeck & Teuvo Kohonen (Eds), "Visual Explorations in Finance with Self-Organizing Maps 2nd Ed.", 2000)

"For a time series, an average that is updated as new information is received. With the moving average, the manager employs the most recent observations to calculate an average, which is used as the forecast for the next period." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

[exponential moving average:] "A moving average of data that gives more weight to the more recent data in the period and less weight to the older data in the period. The formula applies weighting factors which decrease exponentially. The weighting for each older data point decreases exponentially, giving much more importance to recent observations while still not discarding older observations entirely." (SQL Server 2012 Glossary, "Microsoft", 2012)

"An average that’s calculated by using only a specified set of values, such as an average based on just the last three values." (E C Nelson & Stephen L Nelson, "Excel Data Analysis For Dummies ", 2015)

"A mathematical average of data points over a specified period of time. Moving averages are used on financial price charts to show the average price over a selected interval of time. Examples are the SMA(9), SMA(20), SMA(50), or SMA(200) referring to 9-, 20-, 50-, or 200-period simple moving averages. Other types of moving averages also exist, such as an exponential moving average (EMA) and triangular moving averages (TMA). The EMA places more emphasis on the most recent data points. The TMA places more emphasis on the center data points of the specified range, that is, 9, 20, 50, 200, and so on." (Russell A Stultz, "The Option Strategy Desk Reference", 2019)

📊Business Intelligence: Advanced Analytics (Definitions)

"A subset of analytical techniques that, among other things, often uses statistical methods to identify and quantify the influence and significance of relationships between items of interest, groups similar items together, creates predictions, and identifies mathematical optimal or near-optimal answers to business problems." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Algorithms for complex analysis of either structured or unstructured data. It includes sophisticated statistical models, machine learning, neural networks, text analytics, and other advanced data-mining techniques Advanced analytics does not include database query and reporting and OLAP cubes." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A subset of analytical techniques that, among other things, often uses statistical methods to identify and quantify the influence and significant of relationships between items of interest, group similar items together, create predictions, and identify mathematical optimal or near-optimal answers to business problems." (Evan Stubbs, "Big Data, Big Innovation", 2014)

"Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks. (Gartner)

"Analytic techniques and technologies that apply statistical and/or machine learning algorithms that allow firms to discover, evaluate, and optimize models that reveal and/or predict new insights." (Forrester)

"Advanced analytics describes data analysis that goes beyond simple mathematical calculations such as sums and averages, or filtering and sorting. Advanced analyses use mathematical and statistical formulas and algorithms to generate new information, to recognize patterns, and also to predict outcomes and their respective probabilities." (BI-Survey) [source]

"Advanced analytics is an umbrella term for a group of high-level methods and tools that can help you get more out of your data. The predictive capabilities of advanced analytics can be used to forecast trends, events, and behaviors. This gives organizations the ability to perform advanced statistical models such as 'what-if' calculations, as well as to future-proof various aspects of their operations." (Sisense) [source]

📊Business Intelligence: Data Ingestion (Defintions)

"Data ingestion is the first step in the data engineering lifecycle. It involves gathering data from diverse sources such as databases, SaaS applications, file sources, APIs and IoT devices into a centralized repository like a data lake, data warehouse or lakehouse. This enables organizations to clean and unify the data to leverage analytics and AI for data-driven decision-making." (Databricks) [link]

"Data ingestion is the import and collection of data from databases, APIs, sensors, logs, files, or other sources into a centralized storage or computing system. Data ingestion and transformation renders massive collections of data accessible and usable for analysis, processing, and visualization. It’s a fundamental step in data management and analytics workflows, enabling organizations to glean insights from their data." (ScyllaDB) [link] 

"Data ingestion is the process of collecting data from one or more sources and loading it into a staging area or object store for further processing and analysis. Ingestion is the first step of analytics-related data pipelines, where data is collected, loaded and transformed for insights." (Fivetran) [link] 

"Data ingestion is the process of collecting and importing data files from various sources into a database for storage, processing and analysis." (IBM) [link]

"Data ingestion is the process of transporting data from one or more sources to a target site for further processing and analysis. This data can originate from a range of sources, including data lakes, IoT devices, on-premises databases, and SaaS apps, and end up in different target environments, such as cloud data warehouses or data marts." (Striim) [link] 

"Data ingestion is the process of importing large, assorted data files from multiple sources into a single, cloud-based storage medium - a data warehouse, data mart or database - where it can be accessed and analyzed." (Cognizant) [link] 

"Data ingestion is the process of moving and replicating data from data sources to destination such as a cloud data lake or cloud data warehouse." (Informatica) [link] 

"Data ingestion refers to the tools & processes used to collect data from various sources and move it to a target site, either in batches or in real-time." (Qlik) [link]

"Data ingestion refers to collecting and importing data from multiple sources and moving it to a destination to be stored, processed, and analyzed." (Teradata) [link] 

"The process of obtaining, importing, and processing data for later use or storage in a database. This process often involves altering individual files by editing their content and/or formatting them to fit into a larger document. An effective data ingestion methodology begins by validating the individual files, then prioritizes the sources for optimum processing, and finally validates the results. When numerous data sources exist in diverse formats (the sources may number in the hundreds and the formats in the dozens), maintaining reasonable speed and efficiency can become a major challenge. To that end, several vendors offer programs tailored to the task of data ingestion in specific applications or environments.' (CODATA)

📊Business Intelligence: Report Snapshot (Definitions)

"A SQL Server Reporting Services report that contains data that was queried at a particular point in time and has been stored on the Report Server." (Victor Isakov et al, "MCITP Administrator: Microsoft SQL Server 2005 Optimization and Maintenance (70-444) Study Guide", 2007)

"A report that contains data captured at a specific point in time. Since report snapshots hold datasets instead of queries, report snapshots can be used to limit processing costs by running the snapshot during off-peak times." (Darril Gibson, "MCITP SQL Server 2005 Database Developer All-in-One Exam Guide", 2008)

"A report that contains data captured at a specific point in time. A report snapshot is stored in an intermediate format containing retrieved data rather than a query and rendering definitions." (Jim Joseph et al, "Microsoft® SQL Server™ 2008 Reporting Services Unleashed", 2009)

"A static report that contains data captured at a specific point in time." (Microsoft, "SQL Server 2012 Glossary", 2012)