The Essential Data & AI Glossary: Beyond the Jargon

The Essential Data & AI Glossary: Beyond the Jargon

Man using a computer with a green and yellow background

Your guide to the evolving data & AI technology and major players in the ecosystem. 

The data and AI landscape moves fast, and staying ahead means understanding the terms that define it. This glossary breaks down key concepts—from data warehouses to generative AI—so you can keep pace with industry leaders, emerging trends, and the latest cloud innovations. 

More than just definitions, this is about data literacy—empowering everyone to understand, engage with, and make informed decisions in an AI-driven world. And as technology evolves, so will this glossary, expanding alongside innovation to keep you in the know.

Glossary Navigation


Data Warehouse

A data warehouse is a centralized system designed to store, organize, and analyze large volumes of structured data from multiple sources. It enables advanced reporting and decision-making. While historically pivotal in analytics, data warehouses are increasingly being complemented—or replaced—by Data Lakehouse cloud platforms for enhanced flexibility and scalability.

  • Synonyms: EDW (Enterprise Data Warehouse), Data Repository
  • Common Popular Clouds: SQL Server, Oracle, Amazon Redshift

Use Cases:

  1. Business Intelligence Reporting: Consolidate sales, marketing, and finance data to create comprehensive dashboards for tracking company performance and trends.
  2. Customer Insights Analysis: Integrate customer data from various channels to identify purchasing patterns and improve marketing strategies.

Data Lake

A Data Lake is a centralized repository that stores raw, unstructured, semi-structured, and structured data at scale. It provides the flexibility to process and analyze diverse datasets for advanced analytics and AI workloads.

  • Synonyms: Big Data Storage, Raw Data Storage, Unified Data Pool, Data Reservoir
  • Popular Clouds: Amazon S3, Google Cloud Storage, Azure Data Lake Storage

Use Cases:

  1. Machine Learning Model Training: Store raw data (e.g., images, text, or videos) to train and test AI and machine learning algorithms effectively.
  2. Big Data Analytics: Analyze logs, clickstream data, and IoT sensor data to uncover patterns, trends, and operational insights in real time.

Data Lakehouse

A Data Lakehouse is a unified data management platform that combines the scalability and flexibility of a data lake with the structured data management, governance, and performance features of a data warehouse. This architecture enables seamless analytics, AI/ML workflows, and data sharing.

  • Synonyms: Unified Analytics Platform, Integrated Data Architecture
  • Popular Clouds: Snowflake, Databricks, Google BigQuery, Amazon Redshift

Use Cases:

  1. Unified Data Analytics: Perform real-time analytics on structured and unstructured data in a single environment, such as analyzing sales trends alongside customer sentiment from social media.
  2. Data Sharing and Collaboration: Share data securely across teams or with external partners for collaborative insights while maintaining performance and governance.

Customer Data Platform (CDP)

A CDP is a centralized platform that collects and unifies customer data from multiple touchpoints, creating a single source of truth for personalized experiences and analytics.

  • Synonyms: Unified Customer Data System, Marketing Data Platform
  • Popular Tools: Salesforce Data Cloud, Adobe Experience Platform, Twilio Segment

Use Cases:

  1. Customer 360 Profiles: Create unified customer profiles for personalized marketing campaigns.
  2. Real-Time Personalization: Deliver tailored recommendations based on real-time customer interactions.

Master Data Management (MDM)

MDM is a set of tools and practices for ensuring that an organization’s critical data—such as customer, product, or supplier information—is consistent, accurate, and governed across systems.

  • Synonyms: Data Governance System, Golden Record Management
  • Popular Tools: Informatica MDM, SAP Master Data Governance, Reltio

Use Cases:

  1. Customer Data Consistency: Ensure consistent customer records across CRM and ERP systems.
  2. Regulatory Compliance: Maintain clean and auditable records for legal or compliance purposes.

Materialized Views

A Materialized View is a database object that stores the results of a query physically on disk, allowing for faster query performance by avoiding the need to recompute complex calculations or aggregations each time the data is queried. Unlike standard views, materialized views require periodic refreshing to stay up to date with source data.

  • Synonyms: Cached View, Precomputed View, Persistent View
  • Popular Tools: Snowflake, PostgreSQL, Oracle, Microsoft SQL Server, Amazon Redshift

Use Cases:

  1. Accelerating Business Intelligence Queries: Materialized views improve dashboard performance by storing aggregated results from large datasets, reducing query execution time in BI tools like Tableau and Power BI.
  2. Optimizing Reporting and Analytics: Companies use materialized views to precompute frequently accessed data, such as sales summaries or customer activity trends, ensuring real-time reporting efficiency.

Semantic Model

A Semantic Model is a structured representation of business data that defines relationships, hierarchies, and business logic to enable consistent and meaningful analysis across an organization. It acts as an abstraction layer between raw data and end-users, ensuring that metrics, dimensions, and calculations are standardized.

  • Synonyms: Data Abstraction Model, Analytical Model
  • Popular Tools: Microsoft Power BI, Looker, SAP BW, Snowflake, Tableau Semantic Layer

Use Cases:

  1. Ensuring Consistent Reporting Across Departments: By defining key metrics and business rules in a centralized model, organizations prevent discrepancies in reports generated by different teams.
  2. Enhancing Self-Service Analytics: A well-defined semantic model allows business users to query data using familiar business terms without needing deep technical knowledge of the underlying database structure.

Data Ingestion

Data Ingestion is the process of collecting, importing, and loading data from various sources into a data warehouse, data lake, or other storage systems for further processing and analysis. It can be performed in real-time (streaming) or in batches.

  • Synonyms: Data Collection, Data Loading, ETL (Extract, Transform, Load) Input
  • Popular Tools: Apache Kafka, Talend, AWS Glue, Fivetran, Snowflake Snowpipe

Use Cases:

  1. Streaming Customer Data for Real-Time Analytics: Companies use real-time ingestion pipelines to capture customer interactions, such as website activity or IoT device data, for immediate analysis and action.
  2. Consolidating Data from Multiple Sources: Organizations ingest structured and unstructured data from databases, APIs, and cloud storage into a centralized data warehouse for unified reporting and analytics.

Data Orchestration

Data Orchestration is the process of automating, coordinating, and managing data workflows across multiple systems, ensuring seamless movement, transformation, and integration of data. It helps organizations streamline complex data pipelines and optimize processing efficiency.

  • Synonyms: Data Workflow Automation, ETL Coordination, Data Pipeline Management
  • Popular Tools: Apache Airflow, Prefect, Dagster, AWS Step Functions, Google Cloud Composer

Use Cases:

  1. Automating Multi-Step Data Pipelines: Enterprises use data orchestration tools to manage dependencies across data ingestion, transformation, and storage processes, reducing manual effort and errors.
  2. Optimizing Cloud Data Workflows: Cloud-based data platforms use orchestration to ensure timely data movement and processing across distributed environments, improving efficiency and reliability.

Machine Learning (ML)

Machine Learning is a subset of artificial intelligence (AI) that enables systems to learn from data, identify patterns, and make predictions or decisions without being explicitly programmed.

  • Synonyms: Predictive Analytics, AI Modeling, Statistical Learning
  • Popular Tools: TensorFlow, PyTorch, Scikit-learn, AWS SageMaker, Google Vertex AI

Use Cases:

  1. Fraud Detection: Identify anomalies in transactions by analyzing patterns, flagging suspicious activity, and preventing fraudulent behavior.
  2. Sales Forecasting: Businesses use ML to analyze historical sales data and market trends to predict future revenue and optimize inventory management.

Business Intelligence (BI)

BI refers to technologies and tools for collecting, analyzing, and visualizing data to support strategic decision-making.

  • Synonyms: Analytics Reporting, Data Visualization Tools
  • Popular Tools: Tableau, Salesforce CRMA, Looker, PowerBI

Use Cases:

  1. KPI Dashboards: Monitor key performance indicators in real time.
  2. Operational Insights

Identify process inefficiencies and areas for improvement.

AI (Artificial Intelligence)

AI refers to a broad field of technology that encompasses systems and algorithms capable of performing tasks that typically require human intelligence, such as reasoning, learning, decision-making, perception, and natural language processing. It spans multiple subfields, including machine learning, generative AI, robotics, and autonomous systems, and it drives innovation across industries.

  • Synonyms: Machine Learning, Cognitive Computing, Artificial Neural Networks, Intelligent Automation
  • Popular Tools: Salesforce Einstein, IBM Watson, Google AI, Snowflake Snowpark, OpenAI, NVIDIA Robotics Toolkit

Large Language Model (LLM)

A Large Language Model (LLM) is an advanced artificial intelligence model trained on massive text datasets to understand, generate, and process human-like language. It powers applications such as chatbots, content generation, and text analysis.

  • Synonyms: AI Language Model, Generative NLP Model
  • Popular Tools: Salesforce AgentStudio, OpenAI GPT, Google Gemini, Anthropic Claude

Use Cases:

  1. Automated Customer Support: LLMs can power chatbots that provide instant, AI-driven customer service responses.
  2. Content Generation & Summarization: Used in marketing to create blog posts, social media content, or summarize long documents efficiently.

Generative AI

Generative AI refers to machine learning models capable of producing new content, such as text, images, audio, or code, based on patterns and examples in training data. It is revolutionizing creative workflows and enhancing automation.

  • Synonyms: Content AI, AI Generative Models
  • Popular Tools: Salesforce AgentStudio, OpenAI GPT, Google Bard, Anthropic Claude

Use Cases:

  1. Content Generation: Automate the creation of product descriptions, marketing copy, or customer responses.
  2. Customer Experience Enhancements: Generate personalized emails or chat responses in real time.

Agentic Generative AI

Agentic Generative AI refers to AI systems that perform complex tasks independently and generate new content (text, images, code, etc.) or solutions without human intervention. These systems combine the adaptability of autonomous AI with the creative capabilities of generative AI, enabling self-governed decision-making and action alongside content creation.

  • Synonyms: Self-Learning Generative Systems, AI Autonomy with Creation
  • Popular Tools: Salesforce Agentforce GPT, IBM Watson Code Assistant, Google DeepMind Gemini

Use Cases:

  1. Autonomous IT Operations with Content Generation: Monitor and resolve infrastructure issues autonomously while generating documentation, reports, or action plans in real time.
  2. Dynamic Workflow Automation and Content Creation: Manage repetitive tasks or customer interactions autonomously, such as using bots to handle claims while generating personalized customer communication or resolving complex scenarios with tailored AI-created responses.

Predictive AI

Predictive AI uses historical and real-time data to identify patterns and forecast future outcomes, enabling proactive decision-making.

  • Synonyms: Predictive Analytics, Forecasting AI
  • Popular Tools: Salesforce Einstein Analytics, Azure AI, Google AI Platform,  Snowflake Snowpark

Use Cases:

  1. Sales Forecasting: Predict future revenue based on historical trends and current pipelines.
  2. Customer Churn Analysis: Identify at-risk customers to improve retention strategies.

Related

Team around a desk strategizing
How to Prepare for Revenue Cloud Implementation—and Get It Right
Featured_Image_Salesforce_Partner_Advisory_Board
Top Salesforce Consultancy Coastal Secures Four Prestigious Positions on Salesforce Partner Advisory Boards
Two people looking at an ipad together with colorful data imagery in the background
10 Big Bets on AI in the Salesforce Ecosystem for 2025