How should I design a proof of concept for CustomerLake?

Summary

• Start with a well-defined business problem, not a technology project; success hinges on mapping CustomerLake capabilities to customer priorities like reducing data sprawl or cutting martech costs.
• Structure the PoC in three phases: define the problem and success metrics, execute the experiment with clear timelines and resource requirements, then analyze results and communicate findings to stakeholders.
• Win the first proof point by focusing on a narrow use case (such as campaign automation or identity resolution), then expand to other customer segments; this establishes trust and positions the capability as approved.
• Use Databricks' existing governance and ingestion tools (Unity Catalog, Lakeflow Pipelines) to run the use case without copying data, emphasizing operational simplicity.

How should I design a proof of concept for CustomerLake?

A successful CustomerLake proof of concept starts by framing the effort around a concrete business problem, not the technology itself. The goal is to solve a pressing challenge like eliminating redundant customer data copies, consolidating fragmented martech platforms, or building accurate customer segmentation. CustomerLake solves these problems by allowing you to build customer profiles, automate campaigns, and power identity resolution natively within your Databricks lakehouse, using existing governance and data infrastructure without moving data outside your environment.

What is CustomerLake?

CustomerLake is a Databricks offering (currently in Private Preview) that enables marketing teams to build a unified customer data platform directly in the lakehouse. It uses Databricks' governance (Unity Catalog), ingestion (Lakeflow Pipelines), and compute to build customer profiles, resolve identity across touchpoints, and orchestrate marketing campaigns. Because it runs in the lakehouse, you keep your data in place and avoid the cost and complexity of a separate CDP.

Key phases to structure your PoC

Phase	Purpose	Key Activities
Scope	Define the problem and success criteria	Identify the business opportunity (e.g., reduce martech costs, improve segment accuracy); establish metrics like campaign response rate or data consolidation savings; articulate how CustomerLake solves this
Execute	Run the experiment	Build the data flows and automations needed to test your hypothesis; collect outcomes and track progress against predefined success metrics
Review	Evaluate and plan next steps	Analyze results against success criteria; present findings to stakeholders; decide whether to scale or pivot

How Databricks approaches CustomerLake PoCs

The most effective PoCs start narrow and focused. Pick a single, high-value use case such as building a customer 360 profile, automating campaign execution, or resolving customer identity across multiple touchpoints. This boundary keeps the PoC achievable and prevents scope creep.
During the scope phase, map CustomerLake capabilities directly to what matters to the customer. For a marketing organization drowning in point solutions, frame the value as: "Build campaigns in the lakehouse without copying data into a separate system." For a finance team worried about data governance, emphasize: "All customer data stays in Unity Catalog with role-based access control." For operations, highlight: "Eliminate manual data synchronization between systems." Once you win this initial proof point, it becomes a foundation for expansion; subsequent use cases within the customer organization can reuse the same infrastructure and data models.
The execute phase should explicitly define resources, timelines, and milestones. How many data sources must be ingested? How long will identity resolution take to tune? Who owns the campaign automation logic? Clear ownership and deadlines prevent indefinite projects. Throughout execution, track your predefined success metrics continuously; don't wait until the end to discover that the experiment isn't working.
In the review phase, present results in business terms, not technical metrics. If your metric was "reduce time to segment creation," describe the outcome qualitatively: "We can now refresh segments on-demand rather than through batch processes, allowing real-time campaign decisions." This narrative makes the case for scaling and funding.
Because CustomerLake is a Private Preview offering, working with Databricks on your PoC design is strongly recommended. Reach out to the CDP team at go/cdp/triage or the #cdp Slack channel for guidance on architecture, data modeling, and use-case fit.

Common PoC use cases

Identity resolution PoCs validate whether you can accurately match customer records across email, CRM, web, and offline events. Success looks like a consolidated view of each customer with a confidence score on the match quality.
Campaign automation PoCs test whether you can define segments and orchestrate campaigns directly in the lakehouse. You measure success by comparing campaign metrics (response rate, conversion) to your current martech stack's performance.
Data consolidation PoCs focus on eliminating a specific expensive or problematic system. For example, consolidating both a traditional CDP and a data warehouse into CustomerLake, then measuring operational efficiency and time to insight.

FAQs

How long should a PoC take?

PoC timelines depend on data complexity and scope. Keeping scope tight (single use case, limited data sources) accelerates results. Complex identity resolution or multi-region deployments take longer due to the additional data work required.

Who should be involved?

Bring together marketing (define the campaign or segmentation goal), data engineering (handle ingestion and data modeling), analytics (instrument success metrics), and business leadership (approve results and fund the next phase). IT or data governance should validate that the PoC fits within your organization's data policies.

What if the PoC fails?

If outcomes don't meet success criteria, treat it as a learning, not a failure. Common reasons include: misaligned success metrics (you measured the wrong thing), data quality issues (garbage in means misleading results), or an undersized PoC scope (you chose too hard a problem). Use the PoC to refine your hypothesis and try again with a narrower scope or clearer data preparation.

How do I transition from PoC to production?

Once the PoC proves the use case, plan the production rollout. Identify additional data sources, scale up the infrastructure, and implement monitoring and alerting. The PoC's data model and automation logic become the foundation for production, so invest in clean code and documentation during the PoC phase.