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Big Data
Big data consists of large, complex data sets that cannot be processed using traditional data-processing methods and software.
Data Loading
Data Loading, Defined Data loading (the “L” in “ETL” or “ELT” ) is the process of packing up your data and moving it to a designated data warehouse. At the beginning of this transitory phase, you can plan a roadmap,…
Data Migration
Data Migration Definition Data migration is the process of moving your data from one location in a distinct format to another location in another format. While seemingly simple, data migration can be a highly complex and orchestrated process involving storage…
Data Analytics
Definition of Data Analytics Data analytics is the process of evaluating data to draw conclusions and identify ways to improve business operations. This process helps organizations uncover trends, find problems, optimize performance, and improve decision-making. Data analytics uses statistical analysis,…
Semantic Layer
Updated August 2026 A semantic layer represents your business in clear data form. It takes complex data structures and turns them into consistent, understandable terms, like revenue, churn, or customer lifetime value, for people and AI. The semantic layer provides…
Business Intelligence
Business Intelligence (BI) refers to the technologies and processes that collect, organize, and analyze business data to deliver insights that support better decision-making. Rather than simply reporting on past events, BI helps organizations uncover patterns, monitor performance, and identify opportunities…
Data Modeling
Data Modeling is the practice of modeling data to enable it to be physically structured to support analytical queries that provide business insights and create advanced analytics directed to address specific business questions. Data models are both logical and physical,…
Online Analytical Processing (OLAP)
Online Analytical Processing (OLAP) is a method for creating queries from multidimensional data, primarily for delivering insights for Business Intelligence . OLAP involves three core operations: aggregation / consolidation (roll-up), drill-down (from summary to detail), and slicing and dicing (snapshots…
Data Governance
Data governance is the framework an organization uses to keep its data accurate, secure, consistent, and properly managed. It is also required to ensure compliance with internal policies and legal requirements. Core Components of a Data Governance Framework Mature governance…
Data Visualization
Definition Data Visualization is a method for presenting data visually and compellingly in a way that highlights insights, including performance, change, trends, comparisons, patterns, correlations, and anomalies. Data visualization grew out of the statistics field, including descriptive statistics as a…
Data Storytelling
Definition Data Storytelling is a method for presenting data using a combination of visual and verbal techniques that are presented as a storyline where the story explains the context of the data, highlights key insights and may also present implications…
Data Literacy
Definition Data Literacy is a capability and set of skills that enable insights consumers, creators and enablers to understand what data is, how to use it and how to learn from it, including answering business questions to make decisions and…
Data Mesh
Definition In short, a Data Mesh is a framework and architecture for delivering data products as a service, supporting federated, domain-driven uses and users, and enabling decentralized insights created from centralized infrastructure configured to deliver data product components as microservices…
Data Fabric
A Data Fabric is a framework and network–based architecture (vs point-to-point connections) for delivering large, consistent, integrated data from a centralized technology infrastructure using a hybrid cloud. A data fabric is an architecture and set of data services that provide…
Feature Store
Definition The Feature Store is a singular facility where features are stored and organized for the explicit purpose of being used to either train models (by Data Scientists) or make predictions (by applications that have a trained model). It is…
Data Extraction
Definition Data Extraction is the practice of selecting data from one or more sources to store, transform, integrate, and analyze it for business intelligence or advanced analytics. Data extraction is the first step in the process referred to as ETL:…
Self Service BI
Self-service BI (SSBI) means that insight creators and consumers can create their own reports and analyses. In contrast, full-service BI requires direct assistance from technical resources. They might include data engineers, data modelers, data architects, platform architects, and business intelligence…
Data Transformation
Definition Data Transformation is the practice of enhancing data to improve its ability to address relevant business questions, including cleansing, filtering, attributing, and structuring to define, construct, and dimensionalize topically, semantically, and consistently for effective querying. Data Transformation is part…
Data Operations
Definition of Data Operations Data Operations is the practice (e.g., frameworks, methods, capabilities, resources, processes, and architecture) for delivering data to create insights and analytics with greater speed, scale, consistency, reliability, governance, security, and cost effectiveness using modern cloud-based data…
Cloud Data Warehouse
Definition of a Cloud Data Warehouse A Cloud Data Warehouse is a database of highly structured, ready-to-query data managed in a public cloud. Typically, cloud data warehouses represent the following features: Massively parallel processing (MPP) : Cloud-based data warehouses typically…
SQL Server Analysis Services (SSAS)
Definition Microsoft SQL Server Analysis Services (SSAS) offers online analytical processing (OLAP) and data mining capabilities, enabling business users to make sense of the data stored across their data warehouses, lakes, and lakehouses . It enables organizations to pull data…
Data Streaming
Data streaming is the continuous and near real-time transmission of data from a source to a destination, allowing for immediate analysis and decision-making, particularly in scenarios where delays could result in financial, operational, or safety risks.
Unified Data
Unified data combines disparate data sources, both cloud-based and on-premise, into a single, virtualized view, enabling comprehensive and accurate analysis across an enterprise.
Semantic Model
A semantic model is a conceptual framework representing the meanings and relationships of terms and concepts within a particular domain.
Retrieval-Augmented Generation (RAG)
Definition of Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is an AI framework that enhances the capabilities of Large Language Models (LLMs) by integrating external knowledge sources. This technique allows LLMs to access and incorporate up-to-date, domain-specific information beyond their initial…
Single Source of Truth (SSOT)
Many businesses leverage operational data to glean business insights to support decision-making. Teams may face challenges in organizing data from multiple sources. When data is not centralized, it can affect collaboration, lower data accuracy, and impact accessibility. Businesses must ensure…
Data Democratization
In an era where data is a powerful undercurrent driving all aspects of a business, its accessibility and comprehension are paramount. Business owners must understand the importance of data democratization in modern organizations to support positive outcomes. What is data…
Data Virtualization
Modern businesses rely on data virtualization to get up-to-date information and improve agility. This essential tool enables businesses to respond quickly to changing market conditions or regulatory requirements. Read on to learn about data virtualization, its key benefits, common use…
Text-to-SQL
Text-to-SQL systems translate natural language queries into SQL commands, enabling users to interact with databases using everyday language rather than SQL syntax. This breakthrough in data accessibility bridges the gap between human communication and database querying, democratizing access to valuable…
Composable Analytics
Composable analytics allows organizations to build flexible, customized analytics solutions by combining modular components. This methodology represents a paradigm shift from traditional monolithic platforms to a more adaptable framework that allows businesses to assemble (and reassemble) custom analytics capabilities as…
Natural Language Query (NLQ)
Natural language query (NLQ) allows users to access and analyze complex databases using everyday language, eliminating the need for specialized query languages or technical expertise. NLQ serves as an intuitive interface between humans and data systems. This technology allows users…
Data Engineering
Data Engineering Defined Data engineering is a process that involves the design, creation, and maintenance of infrastructure and systems to support the full data lifecycle. This process includes the collection, storage, processing, and delivery of data for analysis and decision-making.…
ETL (Extract, Transform, Load)
Definition of ETL (Extract, Transform, Load) ETL, which stands for Extract, Transform, Load, is a data integration process that forms the backbone of modern data warehousing and analytics. This three-phase computing process involves extracting data from various sources, transforming it…
Large Language Model (LLM)
What Is a Large Language Model (LLM)? A large language model (LLM) is a type of artificial intelligence system trained on enormous amounts of data to effectively understand and generate human language text. LLMs are built on a machine learning…
Semantic Modeling Language (SML)
Understanding SML The Semantic Modeling Language (SML) is an open-source, YAML-based language designed to define and manage semantic models . As a universal standard, SML enables different platforms to share semantic models, fostering portability and collaboration. By describing data in…
Generative AI (GenAI)
Generative AI (GenAI) Definition Generative artificial intelligence, or generative AI, is a cutting-edge form of AI capable of creating original content by identifying and replicating patterns within existing data. Unlike traditional AI systems that primarily summarize information or predict responses…
Structured Query Language (SQL)
Definition SQL, or Structured Query Language, is a standardized programming language specifically designed for “querying” or managing relational databases. In simple terms, it lets users ask questions of databases as well as update records, insert new data, and delete existing…
Predictive Analytics
Definition of Predictive Analytics Predictive analytics is transforming how organizations make decisions, uncover opportunities, and stay ahead of the curve. Analyzing historical data using statistical algorithms, machine learning (ML), and artificial intelligence helps forecast what’s likely to happen next. Rather…
Cloud Data Platform
Definition of a Cloud Data Platform A cloud data platform is a comprehensive and integrated suite of cloud-based services and technologies designed to manage the entire data lifecycle of an organization. This includes ingestion, storage, processing, analysis, governance, and security…
Key Performance Indicators (KPIs)
Definition of Key Performance Indicators (KPIs) Key performance indicators or KPIs are critical metrics used by organizations to measure their effectiveness in achieving key business objectives. These are the goalposts organizations use to compare themselves against over a given timeframe…
Machine Learning
Machine Learning Defined Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data, spot patterns, and make decisions, all without being explicitly programmed. It powers everything from personalized recommendations and fraud detection to…
Feature Engineering
Definition In the world of data science and machine learning (ML) , raw data on its own is rarely enough to drive meaningful insights. Feature engineering is a critical process that turns raw data into usable inputs for ML models.…
Self-Service Analytics
Self-Service Analytics Defined Self-service analytics are business intelligence (BI) tools that empower all users to access, analyze, and visualize data independently, without relying on IT or data specialists. These intuitive, user-friendly platforms allow people to generate reports, create dashboards, and…
Embedded Analytics
Embedded Analytics Definition Embedded analytics, often referred to as embedded BI , is the integration of data analysis directly into applications, websites, or portals. Unlike standalone analytics tools, embedded analytics seamlessly incorporates visualizations , reports, and dashboards into existing platforms.…
Agentic AI
Agentic AI refers to artificial intelligence systems that possess autonomy, engage in goal-directed action, and operate with minimal human oversight. Unlike traditional AI models that simply generate outputs or predictions, agentic AI interprets objectives, plans multi-step workflows, interacts with external…
Model Context Protocol (MCP)
What is the Model Context Protocol (MCP)? MCP (Model Context Protocol) is an open-source standard that enables the connection of AI applications (e.g., assistants, agents, copilots, etc.) to external systems, like large language models (LLMs) and enterprise data systems. Using…
AI Analytics
AI analytics combines artificial intelligence with data analytics to revolutionize how organizations discover insights in their data. Think of it as your data’s personal assistant — one that uses machine learning algorithms, natural language processing (NLP), large language models (LLMs)…
Augmented Analytics
Augmented analytics is a transformative approach to data analysis that blends AI, ML , and natural language processing (NLP) into analytics workflows to make insights faster and more accessible. Rather than requiring specialized data science expertise, augmented analytics democratizes data…
Cloud Migration
Cloud migration is the process of moving data, applications, or workloads from on-premises infrastructure to a cloud environment — or between cloud providers — to improve scalability, performance, and analytics readiness. This strategic transformation enables organizations to leverage external cloud…
Data Quality
Data quality is the degree to which data meets established criteria for accuracy, completeness, consistency, timeliness, uniqueness, validity, and fitness for use. For enterprises managing complex data ecosystems, data quality includes both the technical aspects of data integrity and the…
Knowledge Graph
A knowledge graph is a semantic data model that organizes information into interconnected entities (nodes) and their relationships (edges), creating a structured network that both humans and machines can understand. Unlike traditional databases that store data in rigid tables, knowledge…
AI Governance
AI governance is a collection of policies, procedures, and mechanisms that ensure that various AI systems function ethically and transparently, along with accomplishing business objectives. Governance provides clarity and explainability for the integration and risk management of AI adoption within…
Data Lineage
Data lineage is both the process and record of data movement between systems, starting from where the data is initially sourced to where it is finally stored. It captures a comprehensive architecture that shows the data’s origin, the transforms during…
Data Layer
A data layer is an intermediary that sits between raw data (from multiple sources) and the applications that will consume it. It simplifies complex database data models so that business users can easily consume and rely on the information. The…
Autonomous AI Agents
What Are Autonomous AI Agents? Autonomous AI agents are software systems that can perceive their environment, create decision-making models, and act on them in order to reach a goal or set of objectives with no need for continuous human oversight. …
Generative BI (Generative Business Intelligence)
Generative BI, or Generative Business Intelligence, uses generative AI models to automatically create analytics insights, explanations, and visualizations from data. It allows business users to ask natural questions and get clear, context-aware answers without waiting for manual report building or…
Enterprise Analytics
What is Enterprise Analytics? Enterprise analytics (also known as enterprise business intelligence) refers to the systematic collection, integration, analysis, and interpretation of an organization’s data to enable strategic decision-making and operational improvement. In a way, enterprise analytics can be thought…
Cloud Analytics
What is Cloud Analytics? Cloud analytics is the process of analyzing data using connected computing resources and analytical tools hosted on online, cloud-based platforms rather than on local servers or personal computers. Data teams everywhere face the same challenge. They…
LLM Agents
What is an LLM Agent? An LLM agent is a type of artificial intelligence that uses large language models (LLMs) to autonomously accomplish tasks, make decisions, and communicate with other systems or data repositories to achieve the objectives defined for…
Modern Data Stack
A modern data stack is a collective of cloud-native, open technologies that functionally gather, store, transform, and analyze large amounts of data. In practice, data stacks allow organizations to generate insights, govern their analytics, and power AI and machine learning…
AI Hallucinations
AI hallucinations happen when an AI system, particularly a large language model (LLM) , generates information that appears credible but is factually incorrect, fabricated, or unsupported by its training data or source context. The output looks legitimate. It sounds confident.…
Agentic Analytics
Agentic analytics uses the power of artificial intelligence (AI) to analyze enterprise data, employing autonomous AI agents to develop their own multi-step analytical workflows. These agents function independently to plan and execute decision-ready insights from enterprise data without requiring human…
AI Copilot
An AI copilot is a contextually embedded artificial intelligence system that assists users in conducting cognitive processing within the tools and workflows they already use. Commonly integrated in analytics dashboards and other software platforms, AI copilots interpret questions in natural…
Natural Language Processing (NLP)
Natural language processing, or NLP, is the field of artificial intelligence that enables computers to interpret and generate human language. NLP is the fundamental bridge that so many AI systems rely on to connect human communication to how machines process…
Explainable AI (XAI)
Explainable Artificial Intelligence (XAI) refers to a set of processes and methodologies for making AI decisions understandable to those who depend on them. The explainability of how a model arrived at its output must be traceable and understandable, which is…
AI Context
AI systems have a reputation for sounding naturally fluent and insightful. But without context, they can behave like an overconfident intern who skipped the company’s onboarding process. LLMs and AI agents are capable of distilling libraries of data on command.…
Context Engineering
Autonomous AI agents need context to carry out tasks. Context engineering is the practice of defining a model’s contextual understanding with the appropriate data at each step of an agent’s workflow. As widespread enterprise adoption of agentic AI and conversational…
Enterprise AI
The use of AI has progressed beyond consumer-forward answer engines and experimental projects that dominate headlines and day-to-day conversations. Across the enterprise, AI is now being embedded into numerous functions at scale, from AI-driven data analysis and financial reporting operations…
AI Architecture
Just as an architect’s blueprints inform the design and building composition of a physical structure, AI architecture uses a similar framework in determining how enterprise AI systems are assembled and governed. It represents the sum of every structural decision data…
AI Guardrails
As organizations race to integrate AI into their core operations, the need for quality control and trusted outputs has never been more critical. AI guardrails are the foundation of this control, as they serve as the safeguards organizations need to…
Semantic Drift
Context means everything when using artificial intelligence. Without it, AI can easily interpret the same word in different ways. That’s the premise behind semantic drift, where AI outputs no longer match the original meaning of your prompt or the agent’s…
AI Stack
The AI-related topics of conversation across most leadership boardroom meetings seem to center on models and their capabilities. AI architects are excited to share the latest iterations, and C-suite executives like talking about them. But what’s often not discussed is…
AI Infrastructure
AI infrastructure is the foundational layer on which every enterprise-level AI system is built. In simple terms, it represents the technology and data systems that enable AI to operate reliably in production. While data centers are a headline topic of…
AI Readiness
By now, most enterprises serious about AI have launched some semblance of their own model or initiative. Far fewer enterprises have successfully sustained one long-term. The difference centers on foundational AI readiness and the layers surrounding the model: trusted data,…
Decision Intelligence
Decision intelligence, or DI, is an emerging discipline that helps enterprises move from insight to action. From a high-level perspective, it’s about optimizing and orchestrating decision-making across an enterprise value chain, though you’ll see it applied across many industries and…
Data Gravity
As today’s enterprises continue to accumulate more data, managing and migrating that information keeps getting more difficult and costly. Cloud data warehouses and operational systems store massive amounts of information, and the location of that data increasingly determines where analytics…
AI-Ready Data
AI-ready data is enterprise data with attached business meaning (certified metric definitions, approved join paths, access controls, and governance policies) that AI systems can interpret and query without guessing at your schema or inventing their own definitions. Clean data isn’t…