How a Bank Marketing Team Stopped Spending Half a Day Fixing Partner Data Every Week.
4 hrs
saved per week on data calls
0
engineering tickets
↑
client trust!
The Problem
Marketing teams collected data from multiple partners categorized by DMA, channel, tactic, and audience. Each partner varied their naming structure and changed values without proper communication, requiring weekly QA and ongoing corrections. This eroded trust in data tools and added hours of review time each week, delaying decision making.
The Solution
Data collection and transformation were automated to eliminate manual maintenance. JESTR was configured to monitor naming conventions across partner feeds and alert the responsible teams the moment an inconsistency appeared. No more waiting until a dashboard looked wrong.
When corrections were needed, marketing analysts handled them directly. They described the fix in plain English through JESTR's chat interface, and JESTR translated that into a dbt model edit with a GitHub pull request, reviewable, trackable, and live in minutes. No SQL. No engineering ticket. No waiting in a sprint queue.
The same chat interface replaced recurring data review meetings and ad hoc Slack requests. Analysts could ask questions about their data on demand and get answers immediately, turning a weekly 30-minute call into something they just didn't need anymore.
Results
Average correction time dropped from 2.5 days to under 30 minutes
Engineering tickets for data corrections down 100%
Analysts can now query the warehouse directly, no data engineer needed for questions that go beyond the dashboard
"We went from having a 10 message long email chain every week, setting up a meeting, describing the problem and waiting... to just describing what was wrong and watching it get fixed."
— Senior Marketing Analyst, Regional Banking Client