Building a Data & AI-Driven Marketing Organization

Published by Databricks on September 9, 2026 · Updated September 9, 2026

• marketing-analytics
• customer-data-platforms
• data-intelligence
• martech-stack
• ai-marketing

Why are marketing organizations moving toward data and AI?

Marketing organizations are moving toward data and AI because AI, new privacy regulations, and changing consumer expectations are making rigid systems and fragmented data harder to sustain. Databricks says marketers are often forced to trade off privacy and personalization.

Over the past five years, customer acquisition costs have risen by 60%, privacy regulations like GDPR and CCPA have tightened, and fragmented customer data has slowed personalization and real-time analytics. Those pressures are compounded by shrinking marketing budgets, which fell from 11% of company revenue in 2020 to 7.7% in 2024, increasing competition, and eroding brand loyalty. Databricks also cites that 1 in 3 customers leave after a single bad experience.

Data Intelligence for Marketing is Databricks’ answer to these pressures. The platform gives data teams purpose-built tools to collect and unify data, build models, and conduct analytics, while marketing teams can give every individual self-serve insights and AI agents that drive more relevant and efficient campaigns at scale.

What is the North Star of marketing in Databricks’ view?

The North Star is to drive personalization at scale, deliver smarter campaigns with bigger results, empower non-technical users with self-serve access to data and insights, and scale limited resources with AI.

Databricks says organizations often run into four blockers when they try to get there:

• Compromised brand and customer experiences from inadequate identity resolution and fragmented customer interactions.
• Long lead times for new campaigns when data and consent are fragmented across the organization.
• Difficulty scaling self-service because governance is managed in multiple places.
• AI initiatives that stay in experimentation instead of production, which limits marketing execution, optimization, and personalized engagement.

Why is a data intelligence layer better than point martech tools?

A data intelligence layer is better because it unifies data, analytics, AI, governance, and activation in one platform instead of scattering them across disconnected tools. Databricks says its unified data platform delivers unmatched flexibility, scalability, and cost efficiency.

The Databricks open Lakehouse architecture unifies first-party, second-, and third-party data, helps eliminate redundant infrastructure, reduces total cost of ownership, and accelerates time to value by integrating with existing data ecosystems. Real-time data processing is a core capability of the Databricks Data Intelligence Platform, which helps teams make AI-driven decisions faster while maintaining privacy and governance frameworks.

Why Databricks says it is the best foundation for marketing data:

• The most complete view of customer and campaign data, with enrichment across channels and seamless integration into existing martech tools and services.
• Self-serve data and insights that let every marketer ask questions in natural language and analyze customer and campaign data in new ways.
• More relevant, efficient campaigns at scale, powered by AI tools and AI agents that improve planning, execution, and optimization.

Databricks also says this approach future-proofs organizations as business needs and regulatory landscapes change.

What is a composable CDP, and why does Databricks recommend it as a starting point?

A composable Customer Data Platform is a CDP that uses data in the Databricks Lakehouse directly in preferred marketing tools, with no data movement. Databricks presents composable CDPs as a practical starting point because the modern data stack makes it easier to harness data where it already lives.

The approach lets marketing teams maximize the investment in favorite martech tools while keeping the data foundation unified and governed. Marketing engineers can collect, unify, and govern marketing data. Marketing scientists can build customer models such as propensity scoring, lifetime value, churn, media mix modeling, and generative AI applications. Marketing analysts can use Databricks SQL to run analytics, create and schedule data pipelines, and build dashboards in Databricks or external tools like Tableau and Power BI. Marketing executives can use natural language querying through Genie Spaces and AI-enhanced dashboards through AI/BI.

“The Databricks Data Intelligence Platform has everything marketers wish they had in their martech tools but don’t. It can affordably store all of the rich and complex data about their customers, handles huge volumes of data in real-time, and has a powerful AI/ML workbench. Marketers adopt all these strengths when they use a Composable CDP, like Hightouch, that integrates directly with Databricks.” — Tejas Manohar, Co-Founder & Co-CEO at Hightouch

How does Databricks support marketers, analysts, scientists, and executives?

Databricks supports each role with tools matched to its workflow, so the same governed data foundation can serve both technical and business users.

• Marketing engineers use Databricks to collect, unify, and govern marketing data for a strong analytics foundation.
• Marketing scientists use advanced machine learning and AI capabilities for customer modeling, media mix modeling, and generative AI use cases.
• Marketing analysts use the SQL interface to run analytics, create and schedule pipelines with SQL alone, and build dashboards directly in Databricks or through Tableau and Power BI.
• Marketing executives use Genie Spaces for natural language querying and AI/BI for AI-enhanced dashboards that support strategic decision-making.

How does Databricks fit into the broader martech stack?

Databricks fits into the broader martech stack by making data access, collaboration, and governance easier across the tools marketers already use. Delta Sharing lets warehouse-native solutions be deployed in under an hour, which speeds data access and collaboration.

Unity Catalog extends existing governance policies automatically to marketer-friendly tools, giving teams consistent and secure data management across the platform. Databricks says this tight integration into the broader martech stack helps organizations enhance marketing outcomes while adapting to evolving business and regulatory needs.

What real-world success stories does Databricks cite?

Databricks cites customer examples and internal use cases that show how a unified data foundation can improve marketing performance.

The content highlights outcomes such as:

• A unified audience view for the GM Rewards program.
• Self-service segmentation, orchestration, and campaign optimization.
• HP centralizing data for faster campaigns with federated query pushdown.
• Retail clients converting prospects into customers more quickly.
• Real-time recommendations that boost player engagement.
• Easier adoption of marketing AI to deliver better shopping experiences.

Three customer quotes reinforce the point:

“We’re syncing millions of records directly to SFMC every single day to orchestrate personalized journeys for the 65+ million members in our loyalty program. We’re powering thousands of audiences and over four billion emails every year, and that’s not even factoring in what we do across other channels.” — Tino Tomasone, Sr. Manager, Database & Audience Management at PetSmart

“Integrating the ActionIQ Customer Data Platform with the Databricks Platform has empowered our marketers with data and analytics, enhancing their ability to engage customers and achieve higher customer lifetime value.” — Manish Agarwal, Vice President of Data, Analytics and Insights, Skechers

“Since HP previously had a lot of first-party data sitting dormant, we could finally tap into those insights and segment it in a way that unlocked value once we partnered with Databricks and ActionIQ.” — Kumar Ram, Global Head of Marketing Data Sciences, HP

How did Databricks build its own marketing organization on Databricks?

Databricks built its own marketing organization by creating a Marketing Lakehouse: a unified customer data foundation with AI-powered decision-making, privacy, and data governance. Databricks says this lakehouse became the trusted source of truth, broke down silos, and helped align marketing and sales.

Liz Dobbs, AVP of Marketing Technology, Data and Growth at Databricks, said the team had outdated legacy systems, inconsistent data across platforms, repeated breakdowns in data pipelines, and backward-looking reporting. Databricks used that challenge to reimagine what Databricks could be for marketing teams and to showcase the platform’s potential for the marketing industry.

The internal transformation produced four concrete results:

1. Data democratization with self-serve analytics: Databricks’ internal AI-driven analytics assistant, Marge, launched at the end of 2024 and has already prompted over 5,000 questions, giving non-technical users self-service access to insights.
2. Campaign intelligence that drives revenue impact: Databricks developed an internal lead-scoring model using graph neural networks, replaced a legacy black-box model, improved lead-to-opportunity conversion rates by 3x, and unlocked $15M in incremental pipeline.
3. Omnichannel activation with a composable CDP: Databricks reduced database update times from eight weeks to 2.5 hours, a 94% improvement, and testing and segmenting web audiences based on data-driven journeys led to a 20% increase in opportunity conversions.
4. AI-powered automation for content tagging: Databricks used an AI bot to retag more than 10 years of legacy marketing content, improved classification efficiency by 94%, and saved time for the product marketing team.

How can a marketing team get started with Databricks?

A marketing team can get started by scheduling a consultation with Databricks experts to discuss tailored solutions. Databricks also frames the path forward as building a composable CDP on Databricks, with leading brands already using that model to power marketing outcomes globally.

The call to action is simple: contact Databricks to evaluate how a unified data and AI foundation can support customer data unification, analytics, activation, and AI-driven marketing.

What is Databricks?

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Condé Nast, Rivian, Shell, and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI.

Databricks is headquartered in San Francisco, with offices around the globe, and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake, and MLflow.