Why We Wrote This Guide
Marketing technology is evolving quickly and data lakehouses are at the center of that shift. But for many marketers, the terminology can feel more like alphabet soup than actionable insight. That’s why we created this guide: to demystify the jargon and give business and marketing leaders a clear, digestible reference for understanding the core concepts behind the concept of a marketing data lakehouse. Whether you’re contemplating an upgrade to an older, more rigid martech stack or trying to future-proof your data strategy, this glossary is here to help you stay ahead of where the world of martech data is going.
This glossary is intentionally non-technical and intended for business and marketing professionals.
What is a Data Lakehouse?
What is a Marketing Data Lakehouse?
A marketing data lakehouse applies this architecture specifically to the needs of marketing teams. It unifies disparate data sources—like CRM, web analytics, media performance, and customer transactions—into one centralized platform. This gives marketers a complete, real-time view of their audiences, enabling smarter segmentation, personalization, and campaign optimization. It’s the foundation for data-driven marketing at scale. For a full description, check out our marketing data lakehouse overview.
To help you navigate the technology, we’ve compiled the most comprehensive glossary of lakehouse-related terms, covering everything from architecture to analytics, campaign readiness to compliance.
Use these links to jump directly to the section below:
Data Infrastructure Basics
Lakehouse – A hybrid data architecture that combines the storage flexibility of a data lake with the query performance of a data warehouse.
Data Lake – A repository that stores raw, unstructured, and semi-structured data in its native format.
Data Warehouse – A system optimized for querying structured data for analytics and business intelligence.
Data Stack – The collection of tools and technologies used to collect, store, process, and activate data across an organization. In marketing, this includes platforms like CRMs, CDPs, analytics tools, and data lakehouses.
Delta Lake – An open-source storage layer that adds reliability to data lakes with features like transactions, versioning, and schema enforcement. It’s a foundational element to many modern data lakehouses.
Schema-on-Read – A method where structure is applied only when data is accessed or queried.
Data Lineage – A visual trace of where data came from, how it was transformed, and where it’s stored.
Data Catalog – A searchable inventory of datasets, fields, and metadata across your ecosystem, designed identify and describe the data available for use.
Metadata Management – Organizing descriptive information about your data (e.g., source, format, tags).
Query Federation – Functionality that queries multiple data sources at once without moving the data—ideal for hybrid marketing data stacks
Data Quality & Governance
Governance – Enforcing rules and procedures to ensure data integrity, security, and compliance.
Data Cleaning – The process of identifying and correcting inaccuracies or inconsistencies in your datasets.
Golden Record – The most complete and accurate version of a customer or prospect record after deduplication and enrichment.
Normalization – Standardizing formats (e.g., phone numbers, addresses) across records.
Data Enrichment – Adding external third-party data to enhance the depth of insights available.
Data Appending – Enhancing customer or prospect records with additional attributes like demographics or lifestyle data.
Negative Data Suppression – Removing vulgar, deceased, or undeliverable records from marketing datasets.
USPS-Compliant Data – Address data formatted to meet postal standards for delivery and campaign execution.
Identity Resolution & Matching
Identity Graph – A system that connects multiple identifiers (email, device ID, phone number) to the same individual or household.
Customer Stitching – The process of combining multiple identifiers such as name, address, email, cookie, mobile ID, or phone number into a single customer profile. Essential for creating unified views of consumers across channels and devices.
Deterministic Matching – A match based on exact identifiers (e.g., same email address or loyalty number).
Probabilistic Matching – Uses algorithms to infer likely matches when exact identifiers aren’t available.
Deduplication – Removing duplicate records to create a single version of each customer or household.
Householding – Grouping individuals under a shared household for targeting or analysis purposes.
Firewall-Resident Data Processing – Processing sensitive data inside a company’s firewall, eliminating exposure to third-party systems.
Analytics & Data Science
Audience Segmentation – Dividing your audience into groups based on attributes, behaviors, or preferences.
Propensity Scoring – Predicting the likelihood that a customer will take a specific action (e.g., purchase, churn).
Lookalike Modeling – Identifying prospects who resemble your highest-value customers.
Feature Store – A centralized system for storing, managing, and sharing predictive variables used in machine learning models, often powering campaign scoring and personalization.
Acquisition Analytics – Proprietary models that score prospects for likelihood to convert.
Attribution Modeling – Analyzing how different channels contribute to conversions and ROI, helping marketers allocate budget and credit more effectively across the funnel.
Engagement Scoring – Measuring how active or engaged a customer is with your brand.
Activation & Omnichannel Integration
Data Activation – Making data actionable by pushing it to marketing platforms like CRMs, DSPs, and email platforms.
Reverse ETL – The process of syncing data from a lakehouse or warehouse back into operational systems like CRMs, ad platforms, and email tools.
Onboarding – The process of matching offline data to digital IDs for online targeting.
Connected Ecosystem – A system where data flows freely between platforms like CDPs, CRM, analytics, and activation tools. For example, syncing Salesforce, Adobe, and your ad platforms for seamless execution.
Omnichannel Data Readiness – Preparing consistent customer data for use across channels like email, social, direct mail, and display.
Data Feeds – Exported, structured data streams that feed into campaign execution platforms.
Real-Time Analytics – Monitoring and responding to data streams as they happen, rather than in batch.
APIs (Application Programming Interfaces) – Interfaces that allow systems to send and receive data.
Martech & Customer Data Platforms
CDP (Customer Data Platform) – A centralized platform for collecting and activating first-party data. Note: Traditional CDPs have significant limitations vs. a more modern, composable lakehouse approach as described here.
CRM (Customer Relationship Management) – Software for managing customer relationships and communications.
Martech Stack – The collection of software tools used for marketing planning, execution, and measurement.
Audience Distribution – The act of pushing audience segments to various marketing platforms for execution.
Custom Modeled Audiences – Segments created using client-specific data and predictive modeling.
Propensity Audiences – Pre-built audiences ranked by likelihood to convert or take a key action.
Data Contracts – Formal agreements between data producers (like IT or analytics teams) and consumers (like marketing) that define the structure, quality, and update frequency of shared datasets.
DMP (Data Management Platform) – A legacy system for anonymous audience targeting, largely replaced by CDPs and Lakehouses, especially as data privacy rules and the loss of third-party cookies limit their effectiveness.
Security, Privacy & Compliance
PII (Personally Identifiable Information) – Sensitive data that can be used to identify an individual (e.g., name, SSN).
Eliminate PII Transfers – Ensuring all processing happens within secure environments with no third-party exposure.
First-Party Data – Data collected directly by the brand (e.g., site visits, transactions).
Third-Party Data – Data purchased or acquired from an external source.
Zero-Party Data – Data willingly shared by customers (e.g., survey responses, preferences).
Data Minimization – Collecting only the data needed for a specific use case.
Data Tokenization – Replacing sensitive information with anonymized values or secure tokens, similar to encryption but specifically designed to make data useless outside of its secure context.
Enterprise-Grade Security – High-level protection standards such as AES-256 encryption and SOC 2 compliance.
HIPAA / GDPR / CCPA – Major data privacy regulations governing how data can be collected, stored, and used.
Lakehouse Deployment & Architecture
Composable – A modular approach to building a data lakehouse using interchangeable components—such as storage, compute, identity resolution, and activation—rather than relying on a single platform. This gives marketing teams more flexibility to customize their stack for speed, scale, and evolving needs, while avoiding vendor lock-in.
Medallion Architecture – A layered approach to organizing data into Bronze (raw data like web logs), Silver (cleaned and standardized tables), and Gold (analysis-ready or campaign-ready datasets).
Agile Implementation Model – A flexible, iterative deployment approach to shorten timelines and reduce risk.
Client-Hosted Deployment – The lakehouse resides in the client’s cloud environment for full control and compliance.
Platform Agnostic – Built to integrate with any infrastructure — Snowflake, Databricks, Azure Fabric, and more.
M&A Data Consolidation – Merging legacy data systems post-acquisition to create a unified marketing dataset.
Data Mesh – A decentralized data architecture approach where each team (e.g., marketing, product, finance) owns and manages its own data as a product.
Campaign Optimization & Insights
True Customer 360 – A holistic view of each customer across all channels and datasets, serving as the end goal of unifying your marketing, sales, and service data. This is the ultimate desired outcome of unifying customer data across sources.
Personalized CX at Scale – Enabling dynamic, one-to-one experiences across thousands or millions of users.
Cross-Channel ROI Measurement – Unified reporting across digital and terrestrial channels to measure performance accurately.
Marketing Efficiency Optimization – Using predictive analytics and real-time insights to reduce spend and increase ROI.
Marketing Automation – Using AI and machine learning to automate decisions like targeting, segmentation, and channel mix.
Direct Mail Attribution – Connecting direct mail campaigns to online and offline conversions for full-funnel visibility.
A Smart Future for Marketing Data
The marketing data lakehouse isn’t just another buzzword—it represents a fundamental shift in how modern marketing teams store, access, and activate their data. Legacy systems are often too rigid, too slow, or too fragmented to keep up with today’s customer expectations. The lakehouse model offers a unified, flexible, and scalable foundation that brings together raw data, real-time insights, and activation-ready audiences.
If some of the terms in this glossary felt new or technical, that’s okay—this resource is here to guide you as the industry evolves. The key takeaway? The future of marketing is data-first, and a well-designed lakehouse gives your team the power to turn that data into real business results.
Ready to modernize your marketing data stack?
At DDG, we help companies deploy marketing data lakehouses built for speed, scale, and smarter decisions. Whether you’re just getting started or looking to optimize your current setup, our team is here to help. Let’s talk.