Glossary

Big data consists of large, complex data sets that cannot be processed using traditional data-processing methods and software. Read more »

Business Intelligence (BI) is the practice of extracting insights from data to measure and improve business performance. Business intelligence uses data supported with technology to deliver insights at scale to multiple constituents: analysts, decision-makers and action-takers to improve business performance. … Read more »

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 support big data use cases and apply… Read more »

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… Read more »

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, AI, ML, and deep… Read more »

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… Read more »

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. At its core,… Read more »

Data Extract is the practice of selecting data from one or more sources for the purpose of storing it, transforming it, integrating it and analyzing it for business intelligence or advanced analytics. Data extracts take all or a portion of… Read more »

Data Fabric is a framework and network–based architecture (vs point-to-point connections) architecture 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… Read more »

Data Governance is a process for ensuring that data is acquired, stored, consumed and shared with controls based on policies considering security, privacy, permissioned access, usage and monitoring.   The purpose of Data Governance is designed to ensure that data is… Read more »

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 take… Read more »

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, outline where you would… Read more »

In short, Data Mesh is a framework and architecture for delivering data products as a service supporting federated, domain-driven uses and users, enabling de-centralized insights created from centralized infrastructure configured to deliver data product components as micro services supported by… Read more »

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 and database applications.… Read more »

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,… Read more »

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 platforms and tools applying… Read more »

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 and… Read more »

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. Read more »

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 of… Read more »

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… Read more »

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 way… Read more »

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 into a consistent format, and loading… Read more »

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 a… Read more »

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 to specific inputs, generative… Read more »

A large language model (LLM) is a deep learning model trained on vast amounts of data to understand and generate human language text. LLMs are built on machine learning (ML) and an underlying neural network called a “transformer model.” LLMs… Read more »

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… Read more »

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 across… Read more »

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 training data — as a… Read more »

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… Read more »

The data landscape has changed significantly in the last few years due to the increased adoption of big data, cloud data warehouses, self-serve analytics, data virtualization semantic layer, and more.  A semantic layer is a business representation of data and… Read more »

A semantic model is a conceptual framework representing the meanings and relationships of terms and concepts within a particular domain. Read more »

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 business-friendly terms, it… Read more »

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… Read more »

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 from across… Read more »

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 data.… Read more »

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… Read more »

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. Read more »