How to get your Medallia data ready for agentic AI

Medallia holds some of your most valuable experience data — survey responses, sentiment scores, feedback themes and topics, and the organizational structure that ties every comment back to a specific region, team, or unit. Getting Medallia data ready for agentic AI means centralizing it in a warehouse or data lake where AI agents can query your full feedback history, cross-reference it with other business data, and answer questions like "which regions saw customer sentiment drop this quarter, and why?" in seconds rather than weeks. Fivetran + dbt Labs deliver the complete data foundation agents need — Fivetran moves Medallia data reliably into your warehouse or data lake, and dbt transforms it into trusted, AI-ready tables.

Why Medallia data is critical for agentic AI

Every day, customers and employees leave feedback in Medallia — survey answers, sentiment signals, and the themes and topics that reveal what's actually driving satisfaction or churn. That feedback typically stays inside the Medallia application, disconnected from the sales, support, and product data that would give it real context. CX teams manually export reports, stitch feedback to account records in spreadsheets, and wait weeks to learn whether an issue is isolated or systemic. By the time a theme surfaces in a quarterly business review, the moment to act on it has often passed. Meanwhile, feedback volume keeps growing — responses and comments across every unit, region, and survey program — far more than any team can read end-to-end. Agentic AI closes that gap, but only when Medallia data sits in infrastructure built for agents, not just analytics: a governed store where an agent can join feedback with customer, product, and financial data on demand.

What agentic AI can do with Medallia data

Once Medallia data is centralized and prepared, AI agents put it to work directly.

A VoC program lead can ask, "which units saw sentiment decline in the past 30 days, and what themes are driving it?" and get an immediate answer instead of waiting for the next analyst-built report. A CX insights leader can have an agent flag emerging complaint themes the moment they spike, rather than discovering them at the next quarterly review. A regional CX manager can ask an agent to compare feedback history for a specific account over time, surfacing whether a customer's satisfaction is trending up or down across multiple survey cycles. A customer success leader can combine Medallia feedback with customer profile attributes — segment, tenure, or product usage — to see which customer types are least satisfied and why, without building a manual cross-reference.

Each of these tasks used to require an analyst, a spreadsheet, and days of waiting. With Medallia data centralized and AI-ready, an agent answers them directly, continuously, and across the organization's full feedback history — not just the slice a dashboard happens to show.

How Fivetran gets your Medallia data ready for agentic AI

Raw Medallia data isn't built for agent workloads. Feedback responses, sentiment scores, themes, and topics accumulate continuously across every survey program, unit, and region — too much volume for any team to export and reconcile by hand. The data also sits fragmented: survey configuration, customer profiles, and feedback records live in separate parts of the application, disconnected from an organization's other business systems.

Fivetran moves Medallia data reliably into your warehouse or data lake, syncing feedback, sentiment, and thematic data on an ongoing basis so agents always work from current information, while fully re-importing survey and customer profile data whenever it changes so nothing goes stale. Fivetran also preserves feedback history, giving agents the full record of how a customer's experience evolved rather than a single snapshot.

From there, Fivetran + dbt Labs complete the picture: dbt models, tests, and documents raw Medallia data, turning it into centralized, cleansed, and governed tables that are genuinely AI-ready. For organizations standardizing on an open architecture, the Fivetran Managed Data Lake Service extends that same reliability to a data lake foundation.

What your Medallia data unlocks for your team

With Medallia data in a central warehouse, AI agents can unlock capabilities your team couldn't access before.

• Real-time sentiment intelligence — agents track how customer and employee sentiment shifts across every region, unit, and program, not just the ones covered in a scheduled report.
• Thematic root-cause analysis — agents surface the specific themes and topics driving satisfaction or dissatisfaction, down to the individual comment.
• Organizational accountability — agents compare performance across units and groups, showing leadership exactly where experience is improving or slipping.
• Customer-level context — agents connect feedback to customer profile attributes, revealing which segments need the most attention.
• Longitudinal tracking — agents follow a single customer's feedback history over time, flagging when satisfaction trends move in the wrong direction.

FAQ

What does it mean for Medallia data to be AI agent-ready?

Medallia data is AI agent-ready once Fivetran centralizes it in a warehouse or data lake, refreshes it continuously, and models it into clean, trusted tables an agent can query directly. That means an agent pulls feedback, sentiment, and organizational data on demand instead of waiting for someone to build a report.

What can my team actually do with AI agents and Medallia data?

Teams ask agents direct questions about customer and employee sentiment — which regions are declining, which themes are driving dissatisfaction, or how a specific account's feedback has changed over time — and get immediate, accurate answers instead of waiting on manually built reports.

Is Medallia data ready for AI agents out of the box?

Not without preparation. Medallia data needs to be centralized, modeled, and governed before an agent can query it reliably.

Do we need a data engineering team to set this up?

No. Fivetran handles the connection and ongoing sync automatically, and dbt handles the transformation layer, so teams get Medallia data analysis-ready without building pipelines from scratch.

How does Fivetran get Medallia data ready for AI agents?

Fivetran moves Medallia data reliably into your warehouse or data lake, keeping feedback, sentiment, and organizational data continuously current. dbt Labs then transforms and governs that raw data into clean, tested, and documented tables. Together, Fivetran + dbt Labs deliver the full movement-to-transformation stack a business needs to make Medallia data genuinely AI agent-ready.

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