🪙Business Intelligence: Data Sharing (Just the Quotes)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

🪙Business Intelligence: Predictive Analytics (Just the Quotes)

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

"When the purpose of forecasting is to generate accurate forecasts, it is useful to define performance metrics that measure predictive accuracy. Such metrics can tell us how well a particular method performs in general, as well as compared to benchmarks or forecasts from other methods." (Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide", 2011)

"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) 

"Most statistical models assume error free measurement, at least of independent (predictor) variables. However, as we all know, measurements are seldom if ever perfect. Particularly when dealing with noisy data such as questionnaire responses or processes which are difficult to measure precisely, we need to pay close attention to the effects of measurement errors. Two characteristics of measurement which are particularly important in psychological measurement are reliability and validity." (Clay Helberg, "Pitfalls of Data Analysis (or How to Avoid Lies and Damned Lies)", 1995)

"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)

"Any presentation of data, whether a simple calculated metric or a complex predictive model, is going to have a set of assumptions and choices that the producer has made to get to the output. The more that these can be made explicit, the more the audience of the data will be open to accepting the message offered by the presenter." (Zach Gemignani et al, "Data Fluency", 2014)

"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)

"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)

"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)

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

"Data scientists have extensive backgrounds in computer science, coding, machine learning, and statistics. Such an extensive background takes a long time to develop and is usually highly compensated. This has posed a tremendous barrier to many organizations. But this isn’t the only problem with relying solely on data experts. As organizations become more advanced, they are becoming more interested in using data to predict future outcomes. Merely relying on data from the past won’t suffice. Businesses must be forward thinking in how they collect their data to best serve predictive analytics. This means the employees who collect the data (e.g., those rating performance or creating and administering an engagement survey) need to understand how those data will later be analyzed." (Shonna D Watters et al, "The Practical Guide for HR Analytics: Using data to inform, transform, and empower HR decisions", 2019)

"Advanced analytics pivots around machine learning methods. Machine learning employs statistical learning methods to perform analysis on data. These statistical methods utilize algorithms that predict what may happen based on historical data or extract complex mathematical relationships from data to generate insights." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"Many argue that model drift is best monitored by monitoring the data drift in incoming data and the drift in the generated features. As and when the ground truth is available, it is joined by some primary key criteria with the inference data in a Delta table. Again, the update and merge operation support in Delta makes this a breeze. Now the actual and predicted values of the inference data are computed to see how well the model is doing in terms of the quality of insight generation. The feature engineering pipeline is completely in-house and is easier to monitor for drift. The model interpretability may indicate that some columns contributing to the predictive power are incorrect, and it may be necessary to add or remove features. In such cases, a threshold of tolerance is violated, which signals a need for model retraining." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"A Data Lakehouse provides a unified platform for various data workloads, such as descriptive, predictive, and prescriptive analytics. It can handle structured and unstructured data and enforce schema at both read and write times, enabling traditional business intelligence tasks and advanced analytics on the same platform." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"Predictive analytics, involves the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Neural networks, with their remarkable ability to learn and model complex patterns, have become the backbone of modern forecasting methods. Their application ranges from predicting consumer behavior in retail to forecasting the stock market trends, from anticipating weather patterns to foreseeing potential healthcare outbreaks." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"Data transformation sits at the heart of every successful data platform, serving as the critical bridge between data ingestion and data consumption. While basic transformations might involve simple cleaning and formatting, advanced transformation techniques encompass complex operations such as data enrichment, sophisticated deduplication, machine learning-based predictions, and the creation of derived metrics that weren’t present in the original data sources. These processes are essential for organizations looking to extract maximum value from their data investments." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

🪙Business Intelligence: Data Silos (Just the Quotes)

"Silos are everywhere. […] Silos are common because they are simple, reliable, and unambiguous." (Scott Rosenberg, "Dreaming in Code", 2007)

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

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

"A data silo is an isolated source of data that is only accessible to a single line of business (LOB) or department. It leads to inefficiencies, wasted resources, and obstacles in the form of incomplete data profiles and the inability to construct deep insights. [...] On the other hand, a data swamp is a large body of data that is ungoverned and unreliable. It is hard to find data and even harder to use it, which is why it's often used out of context. This is the opposite of data silos in the sense that the data is there and has been brought together, but because it has been done without adequate process and policy, it is as good as not being there. That would be a wasted investment." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 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)

"Data silos often start to develop as the gap between data engineering activities and data science activities begins to grow. Data scientists frequently spend the majority of their time creating separate ETL and data pipelines that clean and transform data and prepare it into features for their models. These silos usually develop because the tools and technologies used for data engineering don’t support the same activities for data scientists." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Definition of data and AI governance policies, rules, and classifications is critical to break down data silos, allow for a uniform data consumption, and prevent misuse of data. It includes monitoring of compliance and enforcement of data and AI rules and policies on an ongoing basis, as well as ensuring compliance with regulations and laws." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Where Data Mesh differs from Data Fabric is that it has fixed requirements for the Self-Service platform focused on organizing and managing Data Products by business domain. Another difference is Data Fabric supports managing data as an asset and as a product. A Data Product can be composed of assets that have been governed and managed in a Data Fabric architecture. Data Fabric does not have these fixed requirements, although it inherently supports isolating data and Data Governance enforcement via metadata by business domain. You can think of a Data Mesh Self-Service data platform as supporting separate, independent companies (business domains), although the key criteria are that it does not create data silos and attains data sharing across these companies in a secure, quick, and easy manner. In Data Mesh, Data Products are created and managed by federated business domains and a data platform requires capabilities that enable data and policy federation. This is where a Data Fabric solution can also address Data Mesh’s requirements." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

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

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

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

"Promoting domain-oriented ownership is to combat the common problem of organizational silos. Silos can significantly hinder the free flow of data and expertise, making decision-making and innovation more challenging. We aim to break down these barriers by advocating for domain-oriented ownership and creating a more dynamic and collaborative data management landscape." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

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

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

🔭Data Science: Graphs (Just the Quotes)

"A semantic network or net represents knowledge as a net-like graph. An idea, event, situation or object almost always has a composite structure; this is represented in a semantic network by a corresponding structure of nodes (drawn as circles or boxes) representing conceptual units, and directed links (drawn as arrows between the nodes) representing the relations between the units. […] An abstract (graph-theoretic) network can be diagrammed, defined mathematically, programmed in a computer, or hard-wired electronically. It becomes semantic when you assign a meaning to each node and link. Unlike specialized networks and diagrams, semantic networks aim to represent any kind of knowledge which can be described in natural language. A semantic network system includes not only the explicitly stored net structure but also methods for automatically deriving from that a much larger structure or body of implied knowledge." (Fritz Lehman, "Semantic Networks", Computers & Mathematics with Applications Vol. 23 (2-5), 1992)

"The essential idea of semantic networks is that the graph-theoretic structure of relations and. abstractions can be used for inference as well as understanding. […] A semantic network is a discrete structure as is any linguistic description. Representation of the continuous 'outside world' with such a structure is necessarily incomplete, and requires decisions as to which information is kept and which is lost." (Fritz Lehman, "Semantic Networks",  Computers & Mathematics with Applications Vol. 23 (2-5), 1992)

"A graph enables us to visualize a relation over a set, which makes the characteristics of relations such as transitivity and symmetry easier to understand. […] Notions such as paths and cycles are key to understanding the more complex and powerful concepts of graph theory. There are many degrees of connectedness that apply to a graph; understanding these types of connectedness enables the engineer to understand the basic properties that can be defined for the graph representing some aspect of his or her system. The concepts of adjacency and reachability are the first steps to understanding the ability of an allocated architecture of a system to execute properly." (Dennis M Buede, "The Engineering Design of Systems: Models and methods", 2009)

"Graphs can embed complex semantic representations in a compact form. As such, modeling data as networks of related entities is a powerful mechanism for analytics, both for visual analyses and machine learning. Part of this power comes from performance advantages of using a graph data structure, and the other part comes from an inherent human ability to intuitively interact with small networks." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"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)

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

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

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

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

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

🪙Business Intelligence: Persuation (Just the Quotes)

"In many presentations it is not a question of saving time to the reader but a question of placing the arguments in such form that results may surely be obtained. For matters affecting public welfare, it is hard to estimate the benefits which may accrue if a little care be used in presenting data so that they will be convincing to the reader." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"There is a magic in graphs. The profile of a curve reveals in a flash a whole situation - the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces." (Henry D Hubbard [in William Brinton's "Graphic Presentation", 1939])

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

"The more complex the shape of any object. the more difficult it is to perceive it. The nature of thought based on the visual apprehension of objective forms suggests, therefore, the necessity to keep all graphics as simple as possible. Otherwise, their meaning will be lost or ambiguous, and the ability to convey the intended information and to persuade will be inhibited." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"To see is to reason. Thus, the use of visual forms of communication has great potential for influencing what a person thinks. Graphic presentation is always much more than a way to present just facts or information. Rather, it is a way to influence thought, and, as such, graphics can be a powerful mode of persuasion." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"Always bear in mind that the purposes of any chart are (1) to help gather, organize or visualize the facts; (2) to aid in analyzing them; (3) to help in developing the better method and evaluating it; (4) to assist in convincing management of the improvement’s value." (Ben B Graham, "Detail Process Charting: Speaking the Language of Process", 2004)

"We need [graphic] techniques because figures do not speak for them. selves. Numbers alone seldom make a convincing case or polish their author's image - the twin goals of that other great mind bender, rhetoric. While rhetoric deals in qualitative argument, its quantitative equivalent is graphics. As rhetoric has declined in popularity, so graphics have risen along with our acceptance of quantitative arguments. In graphics, figures finally find their own means of expression." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"Oftentimes a statistical graphic provides the evidence for a plausible story, and the evidence, though perhaps only circumstantial, can be quite convincing. […] But such graphical arguments are not always valid. Knowledge of the underlying phenomena and additional facts may be required." (Howard Wainer, "Graphic Discovery: A trout in the milk and other visuals" 2nd, 2008)

"A persuasive visualization primarily serves the relationship between the designer and the reader. It is useful when the designer wishes to change the reader’s mind about something. It represents a very specific point of view, and advocates a change of opinion or action on the part of the reader. In this category of visualization, the data represented is specifically chosen for the purpose of supporting the designer’s point of view, and is presented carefully so as to convince the reader of same." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"An informative visualization primarily serves the relationship between the reader and the data. It aims for a neutral presentation of the facts in such a way that will educate the reader (though not necessarily persuade him). Informative visualizations are often associated with broad data sets, and seek to distill the content into a manageably consumable form." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"If you do succeed in persuading them, you’ve only done so on an intellectual basis. That’s not good enough, because people are not inspired to act by reason alone." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

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

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

"Data visualization isn’t just about informing, it’s also about persuading." (Steve Wexler, "The Big Picture: How to use data visualization to make better decisions - faster", 2021)

"The lack of focus and commitment to color is a perplexing thing. When used correctly, color has no equal as a visualization tool - in advertising, in branding, in getting the message across to any audience you seek. Data analysts can make numbers dance and sing on command, but they sometimes struggle to create visually stimulating environments that convince the intended audience to tap their feet in time." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

🪙Business Intelligence: Transactions (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)

"More and more data is exchanged between the systems through real-time (or near real-time) interfaces. As soon as the data enters one database, it triggers procedures necessary to send transactions to Other downstream databases. The advantage is immediate propagation of data to all relevant databases. Data is less likely to be out-of-sync. [...] The basic problem is that data is propagated too fast. There is little time to verify that the data is accurate. At best, the validity of individual attributes is usually checked. Even if a data problem can be identified. there is often nobody at the other end of the line to react. The transaction must be either accepted or rejectcd (whatever the consequences). If data is rejected, it may be lost forever!" (Arkady Maydanchik, "Data Quality Assessment", 2007)

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

"A Data Fabric needs to serve analytical and transactional data consumption patterns to, for instance, address MLOps, trustworthy AI, MDM, inferencing, IoT, edge, and 5G." (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)

"Delta Lake brings capabilities such as transactional reliability and support for UPSERTs and MERGEs to data lakes while maintaining the dynamic horizontal scalability and separation of storage and compute of data lakes. Delta Lake is one solution for building data lakehouses, an open data architecture combining the best of data warehouses and data lakes." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

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

"The Data Fabric architecture needs to guarantee this single version of the truth within the application and transactional landscape, which – depending on the deployment option of an MDM solution – could also mean to assemble this single version of the truth based on core information that is dispersed and maintained in various data stores." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"The main goal of the transaction log is to enable multiple readers and writers to operate on a given version of a dataset file simultaneously and to provide additional information, like data skipping indexes to the execution engine for more performant operations. The Delta Lake transaction log always shows the user a consistent view of the data and serves as a single source of truth. It is the central repository that tracks all changes the user makes to a Delta table." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"When you leverage Delta Lake with Structured Streaming, you get both the transactional guarantees of Delta Lake and the powerful programming model of Apache Spark Structured Streaming. With Delta Lake, you can now use Delta tables as both streaming sources and sinks, enabling a continuous processing model that processes your data through the Raw, Bronze, Silver, and Gold data lake layers in a streaming fashion, eliminating the need for batch jobs, resulting in a simplified solution architecture."(Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

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

🪙Business Intelligence: Delivery (Just the Quotes)

"Depending on the extent of the data quality issues, be careful about where you deploy BI. Without a reasonable degree of confidence in the data quality, BI should be kept in the hands of knowledge workers and not extended to frontline workers and certainly not to customers and suppliers. Deploy BI in this limited fashion as data quality issues are gradually exposed, understood, and ultimately, addressed. Don’t wait for every last data quality issue to be resolved; if you do, you will never deliver any BI capabilities, business users will never see the problem, and quality will never improve." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)

"If you give users with low data literacy access to a business query tool and they create incorrect queries because they didn’t understand the different ways revenue could be calculated, the BI tool will be perceived as delivering bad data." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)

"Apply DataOps principles to the development and delivery of data. DataOps is a best practice framework that accelerates the development of data and quality across its entire life cycle with high efficiency and quality. This is especially important when integrating data across distributed complex systems and environments." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

"Data Fabric is a distributed and composable architecture that is metadata and event driven. It’s use case agnostic and excels in managing and governing distributed data. It integrates dispersed data with automation, strong Data Governance, protection, and security. Data Fabric focuses on the Self-Service delivery of governed data." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

"Data management is the process of developing, implementing, and monitoring systems, procedures, and practices to deliver and enhance the value of data and assets throughout their lifecycle, while data and AI governance is defined as the exercise of authority and control during the management of data and assets." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Data Mesh self-service capabilities are business- and domain-centric; they are geared toward building, delivering, and managing data products in a concrete business, domain, or industry context." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

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

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

"A transformative perspective offered by the Data Mesh is envisioning data as a product. This section underscores the significance of curating data with the meticulousness and vision akin to product development, ensuring it delivers tangible value to its consumers. The ripple effects of this paradigm shift, spanning roles, processes, and technologies, are also meticulously unpacked." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"If the product discipline is concerned with what we build and why, the delivery discipline speaks to how we execute our ideas. The mechanics of delivering an ML product involve multiple disciplines: delivery planning, engineering, product, ML, security, data, and so on. We use the term delivery here to refer to the delivery planning aspects of how we build ML solutions." (David Tan et al,"Effective Machine Learning Teams: Best Practices for ML Practitioners", 2024) 

"ML product delivery presents a unique challenge because business and customers may not know exactly what they need, or can expect, of an ML product - it’s hard to know a priori how well an ML system will perform with the data that we might curate. On one hand, we may go through product ideation but end up with an idea that is not technically achievable. On the other hand, we may not know what product features are even possible with ML before having done some experiments and seeing some working software." (David Tan et al,"Effective Machine Learning Teams: Best Practices for ML Practitioners", 2024)

"Refactoring is an essential habit of effective teams because it helps them regularly reduce technical debt and complexity, but without tests, refactoring is highly risky. Often, this leads teams to follow the path of least resistance - i.e., not refactoring - and the codebase becomes increasingly convoluted. As a result, executing on our ideas becomes harder and slower over time. On the other hand, the safety harness of comprehensive tests makes it easy for us to make a change, validate the change, and regularly reduce technical debt as we deliver new features." (David Tan et al,"Effective Machine Learning Teams: Best Practices for ML Practitioners", 2024)

🪙Business Intelligence: Duplicates (Just the Quotes)

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

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

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

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

"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)

"Data transformation sits at the heart of every successful data platform, serving as the critical bridge between data ingestion and data consumption. While basic transformations might involve simple cleaning and formatting, advanced transformation techniques encompass complex operations such as data enrichment, sophisticated deduplication, machine learning-based predictions, and the creation of derived metrics that weren’t present in the original data sources. These processes are essential for organizations looking to extract maximum value from their data investments." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Data virtualization is a technique that allows users and applications to access and interact with data stored in multiple, physically separate locations as if it were all in one place. Instead of moving or duplicating data, virtualization creates a logical layer that connects to the original sources and presents them in a unified view. This means users can query, analyze, or combine data from different systems - cloud storage, databases, or other platforms - without needing to know where or how the data is stored." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"The problem with data lakes is that they have several drawbacks preventing them from being the perfect or ideal solution. The first drawback is an organizational problem: (•) How to organize data in the lake (•) How to classify, catalog, secure, document, and find it (•) How to avoid the lake turning into a swamp where data is mixed, duplicated, obsolete, or inaccessible (•) How to manage quality, governance, and traceability in the lake."(Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

🪙Business Intelligence: Self-Service (Just the Quotes)

"Enterprises typically become interested in data catalogs when they have a specific use case or need in mind. Data governance, self-service analytics, and cloud data migration are common examples. Having a specific need or use case helps focus efforts and measure impact. However, as with other technical efforts within enterprises, it is essential to prepare for long-term sustainable success and to have a plan to maximize successful adoption." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"In a self-service environment with multiple publishers, it’s impossible to completely avoid data redundancy and overlapping. Multiple data assets with similar content, but possibly with varying quality, will exist. A data catalog can guide users to trusted data that comes from a reliable source and is frequently used. A data catalog can also use various explicit and implicit quality signals when ranking datasets for recommendation. Some of those signals are discussed next. Furthermore, a data catalog can recommend domain experts who are automatically identified based on actual data usage." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Self-service BI initiatives help organizations become more data-driven and democratize access to data. But data can’t be used if it can’t be found. Search and discovery of trustworthy data is a core value of enterprise data catalogs, and the value extends well beyond business users." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

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

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

"In 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)

"The best approach to building self-services for domains is to follow what many SaaS services do. They follow a serverless model that is easier for their users to understand and utilize in their applications. The intention of the serverless SaaS providers is to not require their users to worry about the 'servers' that are allocated on their behalf. Users can focus more on their business rather than managing and tuning servers. This should be the same model for self-services: to make a streaming data mesh serverless so domains need to focus only on their business."(Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"Where Data Mesh differs from Data Fabric is that it has fixed requirements for the Self-Service platform focused on organizing and managing Data Products by business domain. Another difference is Data Fabric supports managing data as an asset and as a product. A Data Product can be composed of assets that have been governed and managed in a Data Fabric architecture. Data Fabric does not have these fixed requirements, although it inherently supports isolating data and Data Governance enforcement via metadata by business domain. You can think of a Data Mesh Self-Service data platform as supporting separate, independent companies (business domains), although the key criteria are that it does not create data silos and attains data sharing across these companies in a secure, quick, and easy manner. In Data Mesh, Data Products are created and managed by federated business domains and a data platform requires capabilities that enable data and policy federation. This is where a Data Fabric solution can also address Data Mesh’s requirements." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

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

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

🪙Business Intelligence: Data Swamps (Just the Quotes)

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

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

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

"A data silo is an isolated source of data that is only accessible to a single line of business (LOB) or department. It leads to inefficiencies, wasted resources, and obstacles in the form of incomplete data profiles and the inability to construct deep insights. [...] On the other hand, a data swamp is a large body of data that is ungoverned and unreliable. It is hard to find data and even harder to use it, which is why it's often used out of context. This is the opposite of data silos in the sense that the data is there and has been brought together, but because it has been done without adequate process and policy, it is as good as not being there. That would be a wasted investment." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

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

"The data swamp anti-pattern arises from indiscriminate ingestion of uncurated data, which rapidly dilutes data warehouse utility and complicates quality monitoring." (William Smith, "Soda Core for Modern Data Quality and Observability: The Complete Guide for Developers and Engineers", 2025)

"The problem with data lakes is that they have several drawbacks preventing them from being the perfect or ideal solution. The first drawback is an organizational problem: (•) How to organize data in the lake (•) How to classify, catalog, secure, document, and find it (•) How to avoid the lake turning into a swamp where data is mixed, duplicated, obsolete, or inaccessible (•) How to manage quality, governance, and traceability in the lake." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

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

"As the need to store and analyze new data types (e.g., semi-structured, unstructured, real-time streams) grew, organizations began turning to data lakes. These systems are designed to store high volumes of raw data at scale and use a schema-on-read approach, offering greater flexibility. Initially built on platforms like Apache Hadoop, many early data lakes fell short due to lack of governance, poor performance, and inadequate metadata management. As a result, these early deployments often became so-called data swamps, where users struggled to find, trust, or use the data effectively. Such deployments delivered little to no business value." (Fern Halper, "Data Makes the World Go 'Round", 2026)

🪙Business Intelligence: Data Store (Just the Quotes)

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

"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)

"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)

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

"The lakehouse provides a key advantage over the modern data warehouse by eliminating the need to have two places to store the same data. [...] Data lakehouses offer the key benefit of being able to run performant BI/SQL-based scenarios directly on the data lake, right alongside the other exploratory data science and machine learning scenarios." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)

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

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

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

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

🪙Business Intelligence: Information Systems (Just the Quotes)

"[...] 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)

"The big part of the challenge is that data quality does not improve by itself or as a result of general IT advancements. Over the years, the onus of data quality improvement was placed on modern database technologies and better information systems. [...] In reality, most IT processes affect data quality negatively, Thus, if we do nothing, data quality will continuously deteriorate to the point where the data will become a huge liability." (Arkady Maydanchik, "Data Quality Assessment", 2007)

"The corporate data universe consists of numerous databases linked by countless real-time and batch data feeds. The data continuously move about and change. The databases are endlessly redesigned and upgraded, as are the programs responsible for data exchange. The typical result of this dynamic is that information systems get better, while data deteriorates. This is very unfortunate since it is the data quality that determines the intrinsic value of the data to the business and consumers. Information technology serves only as a magnifier for this intrinsic value. Thus, high quality data combined with effective technology is a great asset, but poor quality data combined with effective technology is an equally great liability." (Arkady Maydanchik, "Data Quality Assessment", 2007)

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

"The concept of programmed decisions is important because the ultimate (and unachievable) goal of information systems is to provide purely programmed decisions. Because this is not possible, we seek to provide the optimum type of information to the human decision-maker, who then makes non-programmable decisions. Decisions lend themselves to programming techniques if they are repetitive and routine, and if a procedurs can be worked out for handling them so that each is neither an ad hoc decision nor one to be treated as a new situation each time it arises." (C S V Murthy, "Data and Businesss Analytics", 2020)

"Timeliness means that information is available when it is needed. Most managers function in a dynamic environment of change, demands updated and current information. Computerised information systems have the ability to gather, sort, analyse, store, retrieve, and transmit large amounts of information in a very short period of time. Completeness of information is the extent to which information is all there." (C S V Murthy, "Data and Businesss Analytics", 2020)

"Understanding complex information systems begins with a clear understanding of information and its general characteristics. Information can be considered as the very blood of an organisation, but it must be properly understood and appropriately distinguished from data. Too many times, the terms ‘data’ and ‘information’ are used interchangeably, but the terms most clearly mean entirely different things. Data should be defined as raw, unsummarised and unanalysed facts. Information is data that has been presented in such a way as to alter the receiver’s understanding. Data are the raw materials from which information is derived. This is a necessary distinction for the manager to make, because loads of data can be generated, without producing even one iota of useful information." (C S V Murthy, "Data and Businesss Analytics", 2020)

"A data architecture defines a high-level architectural approach and concept to follow, outlines a set of technologies to use, and states the flow of data that will be used to build your data solution to capture big data. [...] Data architecture refers to the overall design and organization of data within an information system." (James Serra, "Deciphering Data Architectures", 2024)

🪙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)

"Modern data warehouses employ several techniques to deliver performance at scale. Columnar storage organizes data by column rather than row, dramatically improving efficiency for queries that analyze specific attributes across many records. Massively parallel processing (MPP) distributes queries across many computers, enabling analysis of enormous datasets. Intelligent partitioning and indexing strategies optimize data access based on common query patterns." (Michael J Peña, "Azure Data Fundamentals: A Guide to DP-900 Certification and Beyond", 2026)

"Real-time analytics fundamentally changes the relationship between data and decision making. Traditional analytics often involves collecting data over time, storing it in databases or data warehouses, and then periodically analyzing it to identify patterns and insights. This approach, while valuable for historical analysis and long-term planning, introduces significant delays between when events occur and when organizations can react to them. Realtime analytics eliminates this delay, enabling immediate awareness and response to events as they happen." (Michael J Peña, "Azure Data Fundamentals: A Guide to DP-900 Certification and Beyond", 2026)