.png)
Commonly Well is a behavioral health research firm with a unique competitive advantage: its patient-reported recovery insights. They capture non-clinical data measuring what people feel and experience during and after addiction, recovery, or rehabilitation. This data transforms recovery programs into evidence-backed systems that behavioral health organizations need to prove their impact.
Their recovery insights were invaluable for program leaders, but Commonly Well’s engine was reaching its operational limits.
CEO David Whitesock, who founded Commonly Well five years ago, knew their data could shape policy, inform research, and change lives. He recognized that to reach the next stage of growth, he needed to invest in a scalable intelligence platform that could bring their data to life.
So he turned to Data Culture.
David lived and breathed data within behavioral health systems for years. He developed the Recovery Capital Index and collects, analyzes, and interprets participant data every day.
Commonly Well didn’t just want to tap into a data consultancy to fill in the knowledge gaps; he wanted a team that would understand his world, jump in, and bring fresh perspectives.
Data Cultures' expertise in technical plumbing and ability to dive into every part of a client's world is exactly what Commonly Well needed.
Data Culture helped Commonly graduate from Google spreadsheets to Snowflake and Sigma, acting like an extension of David’s team and building systems that would add value and set them up for the next stage of growth.
Together, Commonly Well and Data Culture:
David also wanted to explore the benefits of AI in a strategic way, building a bridge between the data warehouse and the power of LLMs, without compromising the safety and sensitive nature of patients' data.
Data Culture leveraged Snowflake’s secure AI capabilities and turned numerous survey data points into a cohesive automated recovery plan for coaches and providers, while ensuring the ‘plumbing’ was secure and appropriately de-identified.
“We have the backbone built on Snowflake. Snowflake is opening up those connections to GPT. You're only a step removed.” – David Whitesock, CEO Commonly Well
Commonly Well realized their data products and analytics foundation were their biggest asset: a scalable intelligence layer they could productize into data-driven services.
Commonly started using dashboard prototypes to provide packaged insights to small non-profits who needed powerful, enterprise-level data services but didn’t have the funds to pay for a full-time data analyst.
By building a scalable data tool, David’s team could “fractionalize” that talent and offer analytic insights as a service.
This enabled them to offer new services to customers, not just reports. This included…
Now that everything was automated, Commonly Well could focus on anticipating questions and delivering compelling evidence briefs and reports before clients asked, spurring Commonly Well’s data forward as trustworthy and nestled in credible data software.
And that was just the beginning. Unlike before, when high-stakes contracts were hard to secure, their new enterprise-grade platform gave Commonly Well credibility and leverage as well.
Commonly Well started to win high-stakes contracts with customers in universities and governments because their products were built in secure tools like Snowflake and Sigma. Data Culture ensured everything was HIPAA-compliant, and Commonly Well was able to pass through approval processes faster.
The data infrastructure offered immediate credibility, giving them a competitive advantage and opportunities to expand to larger, more regulated customers.
There were more opportunities to win customers they couldn’t previously serve, especially contracts that required specific data safety protocols. As long as they were within credible systems, this market expansion was huge.
The technical credibility didn’t just help with winning contracts; it allowed Commonly Well to have a seat at the government level, in policy-making.
When Palm Beach County, Florida, wanted to allocate 120 million dollars to support addiction treatment and recovery services, Commonly Well used the robust data platform they built with Data Culture to analyze years' worth of survey data to identify specific patterns and trends related to the needs of the population. They found insights that were contrary to traditional approaches to addiction funding.
Based on these findings, the county chose to invest in addressing the social determinants of health, including the non-medical factors like housing, employment, and community support. Key to long-term recovery for people overcoming addiction and mental health conditions.
Their data evolved from reporting what happened to policy-shaping evidence.
In highly regulated industries like healthcare, your infrastructure can either slow you down or earn trust fast. By building on "Gold Standard" tools like Snowflake and Sigma, Commonly Well earned well-deserved credibility, which opened new doors and unlocked bigger contracts.
As David mentioned, "Numbers can't speak for themselves." Simply having data isn't enough; the team must be curious about it and be able to play with it. Commonly Well moved from "pulling data" manually to "pushing insights" automatically, which changed how their customers interacted with the information.
Commonly Well realized their true value wasn't in the one-time survey deliverable, but in the on-demand intelligence layer they built on top of it. By moving from manual reporting to a productized data warehouse, offering scalable, recurring "Insights-as-a-Service" that their industry has never seen before.
