Case Studies
Learn how dbt fuels sensemaking for organizations of all sizes.

“Our evergreen platform, built on dbt and Databricks, lets our teams around the world work from the same data sets. That translates to higher productivity and greater trust in our data as we work to become a global enterprise.”Nana Essuman, Senior Director of Data Engineering & Data Warehouse at Condé Nast

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80% Reduction in data engineering hours, reducing costs
95% Reduction in time to onboard new employees
6-Figure annual savings in IT headcount costs


40 hours saved weekly on maintaining models
10% decrease in warehouse computing costs
15% saved in ingestion costs with dbt snapshots


+100 data team NPS, rated by internal stakeholders
1 week to build new dashboards, down from months
2 hours to debug, down from half a day


3500 models running every 15 minutes
45,000 data analysts contributing to dbt models
92% reduction in weekly bugs with the help of automated dbt testing


300% enhancement in data pipeline project delivery efficiency
10x increase in reporting dashboard performance
100 new sources added following a major company merger in just 1 month


Near-zero data pipeline & infrastructure maintenance time
>50% reduction in time to introduce new data sources


90% reduction in data maintenance time
3500 dbt tests applied, up from 150
83 centralized metrics implemented in the dbt Semantic Layer


3-month migration of 26 data sources, 1200 models, and 6300 tests to dbt
$0 increase in total cost of ownership
6-8 hour data infrastructure maintenance windows reduced to 0


11x reduction in data runtime
10s of issues already detected in near real-time
125% reduction in time to identify business-critical data issues


7 omnichannel marketing campaigns launched
5 data sources centralized in the data warehouse
2-3% target topline impact for brand, customers, and markets


Time-to-insight reduced from weeks to days
12.5-hour reduction in idle time per day for clients
60,000 liters of fuel saved in six months


€1.3bn in sales tracked across restaurants, drive-throughs, and delivery
4 markets with different data stacks centralized
5x faster delivery times for historical data


100-125 billion transactions operated per day
55 business users onboarded to modeling and reporting


50% reduction in time to build and deliver a data integration
30% faster to deliver data sets, with a streamlined development process
2-4x fewer operational costs than running ETL pipelines


2x increase in the number of people collaborating on data modeling
3 weeks of work eliminated from regulatory reporting
$10M reallocated back into the business


Increased speed to delivery by 30%
80% decrease in inconsistent reports
10 inconsistent data sources identified and resolved per month


99% decline in data pipeline breaks since implementing automated end-to-end testing
70% data team growth in the last 3 years
20 reporting views modeled from 1000s of raw tables


20% reduction in data platform costs
33% increase in data transformation speed
25% of all sales pipeline generated by their new personalization engine


80% reduction in licensing costs by migrating from Matillion to dbt Cloud + Stitch
6 months to implement and launch new data stack
3-4 weeks per year dedicated to maintenance saved by the data team


100 hours saved on a typical data integration
80% less time spent on data processing jobs
30% more clients supported without increasing IT head count


14 Analysts developing in dbt Cloud
5 Analyst-powered production datasets
3-Month cross-team project reduced to 3 weeks


600% decrease in time to actionable data
8x increase in engineering contribution
$110,500 saved in annual engineering costs


10 engineers and 40 analysts share 1 development framework
50% reduction in engineering tickets for data issues
75% acceleration in time to deployment


500x more rides in 4 years
6 to 60 people on the data team in under 2 years
1 hour to onboard new data team members to dbt


6 months to implement and launch a new data stack
80% of work to create new data products can be self-served by analytics team
1 day to trace the root cause of issues, down from two weeks

7x increase in the number of data models produced in a year
30,000 smart meters tracked in 1 dashboard


Saved 27 hours of manual data work, leading to huge cost savings
Built over 300 models and 2,500 tests using dbt
Captured an additional $80,000 in revenue from two alerts


4-8x increase in speed from idea to production
1 week for new analytics engineer hires to start shipping
10x decrease in maintenance costs


5 analysts trained with no previous data engineering experience
5x more models developed
50% reduction in model build time

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