🪙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)

🪙Business Intelligence: Accountability (Just the Quotes)

"The chaos without a data strategy is not as obvious, but the indicators abound: dirty data, redundant data, inconsistent data, the inability to integrate, poor performance, terrible availability, little accountability, users who are increasingly dissatisfied with the performance of IT, and the general feeling that things are out of control." (Sid Adelman et al, "Data Strategy", 2005)

"Good governance is less about structure and rules than being focused, effective and accountable." (Pearl Zhu,  "Digitizing Boardroom: The Multifaceted Aspects of Digital Ready Boards", 2016)

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

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

"[...] the governance function is accountable to define what constitutes data quality and how each data product communicates that in a standard way. It’s no longer accountable for the quality of each data product. The platform team is accountable to build capabilities to validate the quality of the data and communicate its quality metrics, and each domain (data product owner) is accountable to adhere to the quality standards and provide quality data products." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"The premise of OKRs is to keep objectives and results simple and flexible, ensuring they align with business goals and enterprise initiatives guided by regular reviews to assess progress during the quarter. The intent is to keep OKRs clear and accountable, as well as measurable, with between three and five objectives recommended at a high level that can each be tracked by three to five key measures. They should be ambitious goals, even uncomfortable, in challenging aspirations, making them stretch targets." (Ian Wallis, "Data Strategy: From definition to execution", 2021)

"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)

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

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

"Effective management of data quality at scale requires a clear delineation of organizational roles and operational frameworks that ensure accountability, consistency, and continuous improvement. Central to this structure are the interrelated concepts of data governance, data stewardship, and operational ownership. Each serves distinct, yet complementary purposes in embedding responsibility within technology platforms, business processes, and organizational culture." (William Smith, "Great Expectations for Modern Data Quality: The Complete Guide for Developers and Engineers", 2025) 

🪙Business Intelligence: Availability (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)

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

"The chaos without a data strategy is not as obvious, but the indicators abound: dirty data, redundant data, inconsistent data, the inability to integrate, poor performance, terrible availability, little accountability, users who are increasingly dissatisfied with the performance of IT, and the general feeling that things are out of control." (Sid Adelman et al, "Data Strategy", 2005)

"A good data strategy is not determined by what data is readily or potentially available –​​​​​​​ it’​​​​​​​s about what your business wants to achieve, and how data can help you get there." (Bernard Marr, ​​​​​​​"Data Strategy", 2017)

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

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

"Using data science, we can uncover the important patterns in a data set, and these patterns can reveal the important attributes in the domain. The reason why data science is used in so many domains is that it doesn’t matter what the problem domain is: if the right data are available and the problem can be clearly defined, then data science can help."  (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"It’s important to look at multiple hypotheses. If your initial hypothesis isn’t supported, you’ll have little direction for moving forward. […] Thinking through viable alternative hypotheses will help you get a sense of where to find data in your organization. […] start by brainstorming. Think about the information that may be available. Is there a way it might be relevant? What sources are available to you?" (Shonna D Watters et al, "The Practical Guide for HR Analytics: Using data to inform, transform, and empower HR decisions", 2019)

"DataOps requires that data scientists, data analysts, and data engineers have quick access to data, tools, and infrastructure to eliminate bottlenecks. That is, they need to be able to access, add, or modify data quickly by themselves. We term this availability to data self-service. Through self-service, data analytics professionals can create data products in far less time than with traditional approaches." (Harvinder Atwal, "Practical DataOps: Delivering Agile Data Science at Scale", 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)

"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)

"The term data governance is used for the processes and responsibilities that define, manage, and enforce access, privacy, availability, and security of the organization’s data. It typically includes a set of policies, rules, and data classifications and functionality to monitor and enforce compliance. As stated earlier, we use the term AI governance in a broader sense, also including AI artefacts." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Be aware that data product is not the same thing as data as a product. Data as a product describes the idea that data owners treat data as a fully contained product that they are responsible for, rather than a byproduct of a process that others manage, and should make the data available to other domains and consumers. Data product refers to the architecture of implementing data as a product." (James Serra, "Deciphering Data Architectures", 2024)

"The data fabric architecture is an evolution of the modern data warehouse (MDW) architecture: an advanced layer built onto the MDW to enhance data accessibility, security, discoverability, and availability. [...] The most important aspect of the data fabric philosophy is that a data fabric solution can consume any and all data within the organization." (James Serra, "Deciphering Data Architectures", 2024)

"Timeliness captures the degree to which data is available when needed and reflects the relevant time frame of the underlying phenomena." (William Smith, "Soda Core for Modern Data Quality and Observability: The Complete Guide for Developers and Engineers", 2025)

🪙Business Intelligence: Single Source of Truth [SSoT] (Just the Quotes)

"It is important to remember that the 'single version of the truth' - or enterprise logical data model - is not and should not be built all at once (that would take too long), but that it evolves over time as the project-specific logical data models are merged, one-by-one, a project at a time." (Sid Adelman et al, "Data Strategy", 2005

"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 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)

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

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

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

"Unlike other analytical data management paradigms, data mesh does not embrace the concept of the mythical single source of truth. Every data product provides a truthful portion of the reality - for a particular domain - to the best of its ability, a single slice of truth." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"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)

"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)

"The hub and spoke, or 'star network', is a data architecture model that centralizes data from various sources into a single hub, such as a data warehouse or data lake. The hub serves as the source of truth for data and provides standardized schemas and formats. The spokes are the various applications or services that consume data from the hub for different purposes, such as analytics, reporting, or ma-chine learning. Spokes can also perform transformations or aggregations on data before presenting it to end users. The hub and spoke architecture aims to simplify data integration and management by reducing complexity and redundancy in data pipelines" (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Traditional paradigms build systems for specific purposes with structured, homogeneous databases. This approach works for tailored needs but is impractical for complex domains that need to adapt to user characteristics and integrate heterogeneous data. KGs capture connections, enabling relationship discovery through graph pattern matching and traversal. Both the Resource Description Framework (RDF) and Labeled Property Graphs (LPGs) provide machine-readable formats that humans can interpret. KGs emphasize rich, meaningful data representations usable by both humans and machines, enabling a paradigm shift where intelligent behavior is encoded in a unique source of truth." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)

🪙Business Intelligence: Signals (Just the Quotes)

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

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

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

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

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

"Data catalogs that focus on search bring techniques and methods from information retrieval and web search engines to the data domain within enterprises. Some of those catalogs, such as Facebook Nemo, use advanced machine learning and NLP tools to provide personalized search of data within an enterprise. The search can also use data-specific signals such as usage, popularity, and freshness to rank data assets by usefulness." (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)

"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)

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

"A defining attribute of the domain-oriented ownership principle is the focus on context preservation in Data Management. This aspect accentuates the importance of keeping data within its native domain environment, allowing it to retain its original context, value, and meaning. When data is managed close to its source, its contextual richness is preserved. This sharply contrasts centralized models, where data is often abstracted from its source, leading to potential loss of signal or context. When data remains within its generating domain, it retains the nuances and specificities unique to its activities, challenges, and goals." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

 More on Signals in Data Science, Graphical Representation, Systems Engineering

🪙Business Intelligence: Accessibility (Just the Quotes)

"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)

"Blissful data consist of information that is accurate, meaningful, useful, and easily accessible to many people in an organization. These data are used by the organization’s employees to analyze information and support their decision-making processes to strategic action. It is easy to see that organizations that have reached their goal of maximum productivity with blissful data can triumph over their competition. Thus, blissful data provide a competitive advantage.". (Margaret Y Chu, "Blissful Data", 2004)

"[…] from a data strategy point of view, you need to describe the ideal data sets that would help you achieve your strategic objectives. You can then choose the best options for you based on how well they help you achieve your objectives, how easy it is to access or gather that data, and how cost effective it is." (Bernard Marr, ​​​​​​​"Data Strategy", 2017)

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

"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)

"A data product’s primary job is to consume data from upstream sources using its input data ports, transform it, and serve the result as permanently accessible data via its output data ports." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

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

"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)

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

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

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

"A data architecture defines data standards in an organization, including how data is accessed and consumed. It furthermore describes the data structures used by the business units. Data integration also depends on the defined data architecture standards since data integration requires interaction between data." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023) 

"Any project execution would be very difficult without implementation and usage of the right product capabilities. The selected products should support the data sources and platforms in your organization and provide AI-augmented functionality to ingest and automatically enrich metadata, allowing business users to easily understand, collaborate, enrich, and access the right data, to quickly establish an environment for highly automated and consistent governance and automatically secure data across the organization."(Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

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

"Data Fabric is a distributed data architecture that connects scattered data across tools and systems with the objective of providing governed access to fit-for-purpose data at speed. Data Fabric focuses on Data Governance, Data Integration, and Self-Service data sharing. It leverages a sophisticated active metadata layer that captures knowledge derived from data and its operations, data relationships, and business context. Data Fabric continuously analyzes data management activities to recommend value-driven improvements. Data Fabric works with both centralized and decentralized data systems and supports diverse operational models." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

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

"Semantic enrichment is the process of adding meaning to data, which is represented as additional metadata in the knowledge catalog. The intent of semantic enrichment is to simplify and optimize some of the key Data Fabric and Data Mesh tasks, such as search and discovery of assets, access, and consumption of assets by applications and business users to build corresponding data products." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"The data fabric is an approach that addresses today’s data management and scalability challenges by adding intelligence and simplifying data access using self-service. In contrast to the data mesh, it focuses more on the technology layer. It’s an architectural vision using unified metadata with an end-to-end integrated layer (fabric) for easily accessing, integrating, provisioning, and using data."  (Piethein Strengholt, "Data Management at Scale: Modern Data Architecture with Data Mesh and Data Fabric" 2nd Ed., 2023)

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

"Authentication means validating a user by using credentials to ensure that they are a valid user on the enterprise system. Authorization validates their rights to access a particular resource or perform certain operations on it. [...] Authorization is the process of granting or denying a set of actions that can be performed on a resource based on a set of permissions." (Aniruddha Deswandikar, "Engineering Data Mesh in Azure Cloud", 2024)

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

"Data security is about preventing unauthorized access to data and the policies and methods surrounding this access. It also protects the system from hackers and malicious users who could steal data. Data privacy, on the other hand, is about collecting, retaining, and recycling personal and sensitive data. There could be a few overlaps between security and privacy." (Aniruddha Deswandikar, "Engineering Data Mesh in Azure Cloud", 2024)

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

"Empowering with self-serve data infrastructure: The Data Mesh champions the ethos of self-reliance. By empowering teams to construct and oversee their data infrastructure, organizations can foster a culture of speed, autonomy, and accountability." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

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

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

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

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

🪙Business Intelligence: Generative AI [GenAI] (Just the Quotes)

"The art of mega-prompts spanning multiple written pages and looking like essays has become commonplace for complex tasks when building applications to get things 'just right'. Unfortunately, they bring with them lots of issues: errors, portability, complexity, and more. The GenAI world didn’t plan for mega-prompts. They have simply evolved into what they’ve become today because practitioners kept wanting to do more and more complex things, and their only way to express those intents was with a prompt. But step back and look at some of these prompts [...] Lurking just below the surface are a bunch of classical computing concepts like data, programming instructions, control flows, memory, and stora - all the components typically associated with classical computing elements." (Rob Thomas et al, "AI Value Creators: Beyond the Generative AI User Mindset", 2025)

"A 'hallucination' in the context of generative AI refers to the phenomenon where a model produces information that is factually incorrect, nonsensical, or not grounded in its input data or pre-existing knowledge. These are not mere typos or minor inaccuracies; they are confident, coherent, and often persuasive fabrications. In high-stakes domains like healthcare, law, or finance, a single hallucination can have severe consequences, eroding user trust and leading to catastrophic decision-making. While all LLMs are prone to this, the RAG architecture is specifically designed to combat it by tethering the model’s output to an external, verifiable knowledge base. Understanding why hallucinations occur is the essential first step to building more reliable and truthful AI systems." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026) 

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

"Agentic AI extends GenAI by embedding intelligence within autonomous or semi-autonomous systems that can plan, reason, and take actions within defined boundaries. Instead of simply generating a report, an agentic system might determine which data it needs, retrieve that data from multiple sources, perform analysis, summarize the results, and then trigger follow-up workflows, all while maintaining auditability and alignment with governance policies." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"GenAI, powered by large foundation models, is revolutionizing the way people interact with data and technology. Organizations are using GenAI to democratize AI through natural language interfaces, copilots, assistants, and creative tools that can draft text, summarize reports, or generate images on demand. Yet as transformative as this is, these systems remain largely reactive. They respond to prompts but do not decide what to do next. They do not plan, reason over time, or act toward goals. In short, they generate but they don’t take action." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Generative AI has a lot of problems because it needs a lot of data to work. Generative models like GPT and GANs need a lot of training data to learn patterns and make good outputs. This data dependency can cause problems like overfitting, which happens when the model does well on training data but doesn’t work well with new, unseen data. Also, the quality of the content that is generated is directly related to the diversity and representativeness of the training data. This means that biased or incomplete datasets can lead to outputs that are wrong or unfair." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"Generative AI has changed a lot of fields, but it is especially helpful for NLP, making pictures, and writing code. In NLP, models like GPT and BERT (bidirectional encoder representations from transformers) have changed how computers understand and write human language. These models help chatbots, virtual assistants, and tools that translate languages work. No matter what language or situation they are in, they make it easy for people to talk to each other. They can also be used to create content, such as articles, marketing copy, and even poetry. This saves time and boosts creativity." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"Generative AI is essential because it can make creative processes quicker and better, which saves time and money and pushes the limits of what machines can do. For example, it can transform text descriptions into realistic pictures, write articles, make music, or even code software. This technology is changing how content is made and making it possible to have personalized experiences like custom learning materials or marketing campaigns. Generative AI is also a big part of the progress that is being made in other areas of AI, like computer vision and natural language processing (NLP). This is why it is an important tool for solving hard problems in the real world." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"Generative artificial intelligence (AI) is a type of AI that learns from existing data and uses that knowledge to make new things, like text, images, music, or code. Generative models make new data points that look like the training data, while discriminative models only focus on classifying or predicting outcomes. This ability is game-changing because it lets machines copy how people are creative and solve problems in ways that were thought to be impossible before. Generative AI is a key part of modern AI applications and is pushing new ideas in fields like healthcare, entertainment, education, and more." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"Generative artificial intelligence (GenAI), powered by large language models (LLMs) like Google’s Gemini and OpenAI’s GPT, has transformed how we work and live, revolutionizing business after business. Despite this success, generative AI falls short in domains where specific domain knowledge, high accuracy, and explainability are essential. And it has other significant limitations, including hallucinations and a lack of context and relations. This is where knowledge graphs (KGs) come in, provid-ing contextual information - such as experiences, environmental characteristics, cultural aspects, and social normsneeded to build the 'third wave of AI' for mission-critical applications." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)

"The modern data environment includes structured data as well as text, machine-generated logs and events, images, and audio. The rapid adoption of generative AI (GenAI) has accelerated both the creation and consumption of text data, increasing volume and variability. Additionally, hybrid and multicloud architectures are now standard. Data are stored and processed across warehouses, lakehouses, SaaS platforms, and edge services. Governance must operate consistently across these environments. In practice, this requires policies that can be enforced programmatically, with end-to-end lineage (where the data came from and how it has been changed) that is visible to both technical and business stakeholders." (Fern Halper, "Data Makes the World Go 'Round", 2026)

🪙Business Intelligence: Operations (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)

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

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

"Processes must be implemented to prevent bad data from entering the system as well as propagating to other systems. That is, dirty data must be intercepted at its source. The operational systems are often the source of informational data; thus dirty data must be fixed at the operational data level. Implementing the right processes to cleanse data is, however, not easy." (Margaret Y Chu, "Blissful Data", 2004)

"Variance establishes the comparison benchmark for each KPI. It has two requirements: (1) the basis for change and (2) change calculation. The most commonly applied references for the basis are relative periodic comparisons: year ago, quarter ago, and month ago. Other types of change basis are forecast, operational plan, quota, and so on. The most commonly applied values for change calculations are Difference, Percentage Change, and Percent Point Change." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)

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

"Metrics can serve two purposes: identifying problems and measuring performance. When the goal is to identify problems and pinpoint areas of operational inefficiency and ineffectiveness, defining the right metric requires a bit of detective work. It requires you to uncover the data residue of a problem and to determine what evidence can be found and how exactly it shows up. When the goal is to measure performance, the right success metrics focus on measures that can be controlled and where improvement in the metric is an unambiguously good thing." (Zach Gemignani et al, "Data Fluency", 2014)

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

"Data strategy is even less understood [thank business strategy], so the chances of success can be further decreased, simply because you need organisation-wide commitment and buy-in to succeed. Data does not exist in a bubble; it is not the preserve of a function that can fix it for all, detached from touching everyone else. It is core to how you run the organisation, and without a focus on where you are heading, it is going to trip the organisation up at every turn - regulatory compliance; operational effectiveness; financial performance; customer and employee experience; essentially, the efficiency in managing virtually every activity in the organisation." (Ian Wallis, "Data Strategy: From definition to execution", 2021)

"A data architecture needs to have the robustness and ability to support multiple data management and operational models to provide the necessary business value and agility to support an enterprise’s business strategy and capabilities." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

"A data mesh is inherently multimodal, and data products can be provided via a variety of means. Event streams remain the best option for the majority of data products, as it is far easier to power both operational and analytical use cases through a stream than a batch of files at rest." (Adam Bellemare, "Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)

"Data Fabric is a distributed data architecture that connects scattered data across tools and systems with the objective of providing governed access to fit-for-purpose data at speed. Data Fabric focuses on Data Governance, Data Integration, and Self-Service data sharing. It leverages a sophisticated active metadata layer that captures knowledge derived from data and its operations, data relationships, and business context. Data Fabric continuously analyzes data management activities to recommend value-driven improvements. Data Fabric works with both centralized and decentralized data systems and supports diverse operational models." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 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)

"Over 80% of models are never operationalized because the efforts involved in deploying them are enormous and the models are deployed and found to produce drift or fairness issues that outweigh the benefits."  (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

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

🔬Data Science: Median (Definitions)

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

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

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

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

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

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

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

Statistics: Mean (Definitions)

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

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

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

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

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

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

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

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

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

🔬Data Science: Moving Average (Definitions)

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

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

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

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

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

📊Business Intelligence: Advanced Analytics (Definitions)

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

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

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

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

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

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

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

📊Business Intelligence: Data Ingestion (Defintions)

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

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

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

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

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

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

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

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

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

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

📊Business Intelligence: Report Snapshot (Definitions)

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

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

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

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