How do I build a business case for moving customer data and marketing workloads onto one platform?

Summary

• The case for moving customer data and marketing workloads onto one platform starts with the cost of the fragmented stack: customer data and identity copied across redundant tools, marketers waiting on data teams, and governance spread across separate vendors. The value comes from removing those.
• Frame the value qualitatively: reclaim budget spent on redundant SaaS tools and data copies, reduce governance and privacy risk by keeping customer data in one governed place, free data teams from ad-hoc requests, and let marketers act at customer speed.
• The architectural shift is to run customer profiles, models, and activation where the data already lives, instead of copying it into separate marketing systems that each add a governance surface.
• Databricks' agentic CDP for this is CustomerLake, embedded in the lakehouse and governed by Unity Catalog. It's currently offered as a preview, with capabilities rolling out over time. See Introducing CustomerLake.
• Present the case per stakeholder: marketing hears self-serve segmentation and personalization at scale, data and IT hear one governed copy and less risk, and finance hears fewer redundant tools and data copies.

How do I build a business case for moving customer data and marketing workloads onto one platform?

The case starts with the cost of the fragmented stack you have today. Customer data and identity are usually scattered across redundant tools, each holding its own copy; marketers depend on data teams for every audience and report; and governance is spread across several vendors that each need access to sensitive customer data. Moving customer data and marketing workloads onto one governed platform removes that overhead: one governed copy of customer data feeds both analytics and activation, marketers can serve themselves, and agents can act on governed data continuously. Build the case qualitatively first, around reclaimed budget, reduced risk, and faster marketing, then attach figures only from your own measured baseline.

What moving onto one platform means

Traditional customer data platforms and martech tools tend to copy customer data into separate systems, each with its own governance and its own activation channels. Moving onto one platform means consolidating that data layer onto your governed data foundation, so customer profiles, predictive models, and campaign activation run where the data already lives rather than in a chain of downstream copies. The goal is one source of truth for customer data, governed once, that both your data teams and your marketers can use.

Value drivers to quantify

Build the model around drivers you can measure against your own baseline, and keep each qualitative until your numbers are real:

Value driver	What to measure
Eliminate redundant tools and data copies	The overlapping SaaS tools and the duplicate customer-data copies each one maintains.
Reduce governance and privacy risk	The number of external vendors holding customer data, and the effort to enforce policy consistently across them.
Free data teams	The share of data-team time spent on ad-hoc audience and reporting requests from marketing.
Democratize to marketers	How much sooner campaigns launch when marketers build audiences themselves instead of queuing work with IT.
Personalize at scale	The manual campaign work that limits how many customer journeys you can run at once.
Keep activation flexible	Whether you can activate across channels through connectors without locking data into a single vendor.

In most organizations the productivity and risk drivers matter more than tool-license savings, because customer data spread across vendors is both a cost and a liability.

How Databricks approaches this with CustomerLake

Databricks' agentic CDP for this is CustomerLake, embedded natively in the lakehouse. It's designed so marketing runs on the same governed data as everything else, rather than on copies:

• Customer 360 on your data foundation. Unified customer profiles are built directly on your governed data, without copying or moving it into a separate system, and inherit security and lineage from Unity Catalog.
• Agentic identity resolution. Agents turn raw, fragmented data into governed golden customer records and resolve identity to produce unified IDs for personalization.
• A self-serve workspace for marketers. Marketers build audiences and journeys in a dedicated workspace with Genie-powered natural-language segmentation, so they don't have to write SQL or wait on data teams.
• Agentic campaign workflows. Agents help plan, execute, and optimize campaigns, and recommend next-best actions from signals, context, and business objectives.
• Flexible activation. Connectors to a broad range of marketing and advertising tools activate segments across channels without building custom pipelines.

CustomerLake is currently offered as a preview, with capabilities rolling out over time. Learn the category in What is an agentic CDP?.

Building the narrative for stakeholders

Tailor the case to each audience. Marketing hears self-serve segmentation and personalization at scale. Data and IT hear one governed copy of customer data, consistent policy, and fewer ad-hoc requests. Finance hears fewer redundant SaaS tools and data copies. Start with a pilot: pick one high-value use case, such as a single audience or journey, run it on the platform, and measure launch time, effort, and governance overhead against your current approach. That turns the qualitative case into your own evidence.

FAQs

Is CustomerLake generally available?

CustomerLake is currently offered as a preview, with capabilities rolling out over time. Frame timelines and specific capabilities as preview when you build your business case, and confirm current availability with Databricks.

Why move customer data onto the same platform as analytics and AI?

Because copying customer data into separate marketing tools multiplies both cost and governance risk. Keeping one governed copy, where profiles, models, and activation run in place, reduces duplication and lets agents act on current, governed data.

Do marketers need to write SQL?

No. CustomerLake includes a self-serve workspace with Genie-powered natural-language segmentation, so marketers can build audiences and journeys without SQL and without waiting on data teams.

How does governance work when marketing runs on the platform?

Customer profiles are built on your governed data and inherit access control, lineage, and audit from Unity Catalog, so customer data is governed once rather than separately inside each marketing vendor.