How should I run a proof of concept for a new BI platform?

Summary

• Start with a pre-built demo using dbdemos to launch a working BI environment in minutes and explore natural-language analytics immediately.
• Organize your POC around one real use case with curated data, key metrics, and your actual business questions.
• Prioritize adoption and business value over breadth: the goal is durable assets that deliver real insights, not one-off demos.
• Combine hands-on builds with training workshops to ensure your teams can sustain the platform independently after the POC ends.

How should I run a proof of concept for a new BI platform?
A successful BI platform proof of concept validates whether the platform meets your business and technical requirements while your teams learn to use it independently. The fastest path is to combine a pre-built demo with your own data, structured governance, and focused training.

What is AI/BI?

AI/BI refers to self-service analytics powered by natural language. Instead of manually building dashboards, business users ask questions conversationally and receive instant answers backed by governed enterprise data. Databricks AI/BI combines Genie Agents for conversational exploration with AI/BI Dashboards for visual reporting, all on live lakehouse data without extraction or duplication.

Key capabilities to evaluate

When assessing a BI platform for your POC, consider these vendor-neutral dimensions:

Capability	Why it matters	What to test
Data access and governance	Your BI tool must respect row-level security, column masking, and data lineage. Users should only see data they have permission to access.	Test with a restricted user account. Verify masked columns remain hidden in reports and conversational queries.
Natural language support	Business users should ask questions in plain English and get accurate, instant answers without learning SQL or data structure.	Ask the platform 10-15 realistic business questions. Measure accuracy and response time.
Extensibility to your data	The platform should work with your existing data (in a lakehouse, warehouse, or database) without requiring data duplication or transformation.	Load one of your own tables and run queries against it in the POC environment.
Semantic layer and metrics	The platform should allow analysts to define KPIs, business metrics, and semantic models once, reusable across all queries and reports.	Build a simple semantic layer with 3-5 metrics. Verify they appear correctly in natural-language queries and dashboards.
Collaborative features	Teams should be able to share insights, collaborate on reports, and embed results in applications or documents.	Create a shared report. Have another team member access and modify it.
Performance and scale	The platform should return query results in seconds, not minutes, even on large datasets.	Query a large table. Measure time to result.

How Databricks approaches BI POCs

Databricks offers a rapid-launch approach:
Phase 1: Quick Start
Use the dbdemos library to install a pre-built sample BI environment in your Databricks workspace. This creates a Genie Agent (for conversational exploration), an AI/BI Dashboard, and sample tables, all ready to query in about 10 minutes. The samples include realistic use cases like portfolio review, customer support, and supply-chain forecasting, so your team can immediately see what AI/BI looks like in action.
Phase 2: Bring Your Data
Migrate one high-value use case to your own curated data. Work with your data team to prepare a "gold-layer" table with business-relevant columns and metrics. Once loaded into Unity Catalog, AI/BI operates directly on that data in real time, with no extraction or copying required. Teams can then ask questions against your actual business data.
Phase 3: Semantic Modeling and Hands-On Build
Work with your analytics team to define key performance indicators (KPIs) and build a simple semantic model. Create one Genie Agent workspace and one AI/BI Dashboard using your data. This is where your teams learn the platform and begin to understand what insights are possible. Include optional guidance on embedding results into applications for cross-functional collaboration.
Phase 4: Training and Adoption (Ongoing)
Databricks Academy offers free self-paced courses and instructor-led workshops, including the "Dashboard in a Day" hands-on workshop. This full-day training teaches end-to-end BI workflows through a story-driven case study, ensuring your teams can design and maintain dashboards independently.

Critical success factors

Start small. Pick one business domain with 5-10 high-impact questions your team asks regularly. Don't attempt a platform-wide migration in a POC.
Focus on adoption, not just capability. The goal is to create sustainable, valuable assets that your business teams will use regularly. The most successful POCs focus on delivering real business value, not just showcasing technical capability.
Use live data. Govemed enterprise data means you get real-time, trustworthy insights immediately. No data migration, no staleness, no separate analytics copy.
Enable business users. The power of AI/BI is that it lets non-technical users explore data conversationally. Genie Agents accelerate this by allowing iterative, interactive discovery. Demonstrate speed: answers to questions that normally take a week.

FAQs

How long does a typical POC take?

Most teams move through capability discovery and training, hands-on builds with your data, and adoption cycles. The exact pace depends on data readiness and team availability. Many customers accelerate by using pre-built demos first, then bringing their own data.

Do we need to move our data to use AI/BI?

No. AI/BI operates directly on data in your lakehouse or data warehouse via Unity Catalog. There's no extraction, duplication, or staging required.

What if we're currently using a different BI platform?

AI/BI is designed to work alongside existing tools during a POC. You can test it on a subset of data without disrupting your current analytics workflows.

Who should be involved in the POC?

Include a mix: your analytics team (to handle data preparation and semantic modeling), business users (to validate the platform meets their needs), and IT/governance (to ensure security and compliance are working).

What happens after the POC succeeds?

Databricks provides ongoing training, consulting, and enablement resources to help your teams scale from a POC to a production analytics platform. Many customers expand to additional business domains and integrate AI/BI deeper into their applications.