Back office ops · Production

SafetyCulture gets serious about company OKRs with dbt Cloud

The problem

SafetyCulture's data transformation layer was split across LookML and Airflow with no testing or dependency management, leaving stakeholders confused about where to find reliable data. Mistrust and rework slowed time-to-insights, while 97% of first-time users dropped off after 28 days without an analytical explanation.

First attempt

An attempt to schedule and orchestrate LookML models with Airflow quickly ran into accessibility and architecture challenges: the team had to manually build all dependency graphs and ended up with the transformation layer across two tools that only a few people could operate.

Workflow diagram · grounded in source
1
Scalability limit triggers rebuild
trigger
“We were using LookML for all of our transformations, which just wasn't scalable. There's also a limit to what you can do—no testing, no DAG... it required tons of supervision to ensure alignment with existing architecture”
2
Redshift infrastructure redesign
integration
“The team redesigned their data architecture in Redshift to increase compute by 50% and disk by 100x at the same cost. The re-platforming reduced their average pipeline query time from 17 to 4 minutes”
3
Dbt transformation consolidation
integration
“Consolidating all of their transformation work in one place, the team designed future state conceptual, logical, and physical data models in dbt”
4
AI customer variable analysis
ai_action
“We're using AI to better understand our customers, looking at about 150 different customer variables from demographics to product usage and behavior”
5
Customer segment identification
output
“Analysts who previously only focused on one business area collaborated in dbt and identified 7 customer segments”
6
Segments applied in Salesforce
integration
“These segments were applied in Salesforce and used by AEs and CSMs for customer expansion and retention”
7
Tableau goal-tracking dashboards
output
“built the Tableau dashboards that they use on a weekly basis to track performance and to take corrective action”
Reported outcome

SafetyCulture rebuilt 80% of their data in dbt and cut average pipeline query time from 17 to 4 minutes.
Data team eNPS rose from -20 to +69, an 89-point increase. New customer retention beyond 28 days more than doubled from 3 to 6.5%, with a projected 40% increase in MAU.

Reported metrics
data team eNPS-20 to +69, an increase of 89 points
New customer retention2x increase
Data rebuilt using dbt80%
Redshift compute increase50%
Show all 11 reported metrics
data team eNPS-20 to +69, an increase of 89 points
new customer retention2x increase
data rebuilt using dbt80%
Redshift compute increase50%
Redshift disk increase100x
pipeline query timefrom 17 to 4 minutes
first-time user drop-off rate97%
customer retention beyond 28 daysfrom 3 to 6.5%
projected MAU increase40%
data team headcount6 to 15 people
daily data processing scale3x
Reported stack
dbtRedshiftTableauLookerLookMLAirflowSalesforceQualtrics
Source
https://www.getdbt.com/case-studies/safetyculture
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

SafetyCulture rebuilt 80% of their data in dbt and cut average pipeline query time from 17 to 4 minutes.

What tools did this team use?

dbt, Redshift, Tableau, Looker, LookML, Airflow, Salesforce, Qualtrics.

What results were reported?

data team eNPS: -20 to +69, an increase of 89 points; New customer retention: 2x increase; Data rebuilt using dbt: 80%; Redshift compute increase: 50% (source-reported, not independently verified).

What failed first in this deployment?

An attempt to schedule and orchestrate LookML models with Airflow quickly ran into accessibility and architecture challenges: the team had to manually build all dependency graphs and ended up with the transformation l…

How is this back office ops AI workflow structured?

Scalability limit triggers rebuild → Redshift infrastructure redesign → Dbt transformation consolidation → AI customer variable analysis → Customer segment identification → Segments applied in Salesforce → Tableau goal-tracking dashboards.

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