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A customer success manager (CSM) opens a retention report and sees one of her accounts flagged as high churn risk. However, she knows a few details the model doesn't:
- Her team cut that account's support cases in half last month.
- Product usage climbed.
- The last SLA breach is a distant memory.
Based on that context, the score in the retention report is wrong, and she can explain exactly why. Yet she has no way to fix it, because the prediction lives in a data warehouse she can't touch. And, by the time she emails the data team to correct it, the quarter has moved on.
This is the gap between what the data knows and what people in the business know. Chris Hunter, a manager on Domo's solution engineering team, joined a Domo livestream to explore it.
Chris showed us how companies extend their Snowflake investment past the data team to everyone who makes decisions, such as the CSM manager. A key piece of this is a governed writeback loop, which lets business teams sharpen AI and ML predictions, then send those improvements back to Snowflake with a full record of who changed what.
In this guide, we'll break that loop down into steps you can take yourself.
How the governed write-back loop works
The loop connects two groups who rarely touch the same system. Your data team builds and owns the models in Snowflake. Your business teams carry context those models cannot see yet. The five steps below hand that context a safe path back to the source, with a person in control at every stage.
Surface the prediction where people already work
The loop starts by bringing the model's output to the person who can judge it. In the demo, Chris opened a revenue command center, a dashboard of the KPIs a revenue leader checks first: net revenue, revenue at risk, forecast versus actuals. An ML model running in Snowflake surfaced regional renewal risk right inside that view.
Two details make this step matter:
- Access follows identity: When someone signs in through OAuth, the view subsets down to the accounts and numbers that belong to that person.
- Prediction sits in context: The risk score appears next to KPIs people already trust, so it reads as insight rather than a number from nowhere.
Let people adjust the model with context the data lacks
The business perspective gets entered in at this step. During the livestream, Chris signed in as a member of the customer success team and opened the model at the account level. He could run the prediction as it stood, but he also knew things the data had not captured.
The tool allowed him to adjust the inputs to match what his team had actually done: support cases reduced, SLA breaches minimized, usage up. That knowledge lives in people's heads and in last week's work, not yet in the warehouse. The loop gives it a place to land.
Rerun the prediction and review the new score
With the inputs updated, the business user can rerun the model and get a new score. Nothing commits on its own. They can compare the new result against the original and decides whether it reflects the account more accurately than the first pass.
Note that this step can stay exploratory. A change doesn't have to go straight into production. Teams can experiment to see what an adjustment looks like, then hand the result to the data team or route it through an approval process.
Write the accepted prediction back to the governed source
When the score looks right, you can click accept. That single action writes the updated prediction back to a governed Snowflake table. There's no copy of the data, no export happening, and no side spreadsheet. The data team keeps one source of truth, and it's now sharper because the people closest to the account contributed to it.
As Chris put it, the loop means "not losing out on the investment that my data team has put in place." Domo makes that pushdown possible through Cloud Integrations, which federates to Snowflake without moving or copying the data. The warehouse stays the foundation. Domo helps more people use it.
Keep a full audit trail
Governance separates this loop from a free-for-all. Every write-back records who ran the model, what they changed, and what the outcome was. The data team can see each business-side prediction, review it, and decide whether to fold it into the production model.
Chris stayed direct about the guardrails, describing the exchange between the data team and business teams as "all governed, safe, secure, auditable." Domo builds human oversight into the loop in three places: a person judges the model, a person accepts the change, and a person can trace it later. That combination lets you invite more contributors without loosening control.
Where the loop goes next
The write-back loop is one piece of a larger pattern the session covered. Chris also walked through a Cortex agent that drafts a 90-day retention plan for at-risk accounts. The agent then pauses for a person to approve, edit, or reject it before anything runs. Nothing executes on autopilot, since a human signs off first.
He also showed how the Cortex Analyst chat returns the same answer inside Domo that the data team gets inside Snowflake. Both groups work from one consistent story.
Those pieces are easier to appreciate in motion. Watch the full session to see the complete architecture and find the first loop your own team could close.
