Industry Insights

In private equity–backed healthcare startups, data is too often treated as an expense—necessary for compliance, reporting, and basic analytics, but not recognized as a driver of top-line growth. This “cost center” mindset leaves massive value creation opportunities untapped.

When designed strategically, a data science function is not just a support role—it becomes a growth engine. Data can accelerate patient acquisition, optimize care delivery, reduce waste, and uncover entirely new revenue streams. For PE investors and operators, reframing data as a value creation lever is essential for scaling and maximizing exit value.

Why the “Cost Center” Mindset Holds Startups Back

Private equity playbooks are built on rapid scaling and value creation before exit. Yet many decisions—pricing, product expansion, patient engagement—are still driven by intuition or lagging reports. Without modern data science, startups limit their ability to:

• Improve forecasting accuracy.
• Identify new product opportunities.
  
  
• Optimize patient acquisition costs (PAC).
• Model churn risk and improve retention.
  
  
• Pinpoint cross-sell and upsell opportunities.
  
  
• Expand margins by reducing operational waste.
  
  

McKinsey & Company estimates AI and advanced analytics could generate $200–$360 billion in net healthcare savings. Still, 75% of health organizations cite lack of resources and planning as barriers to execution. For PE-backed companies, that gap is a direct threat to value creation.

Where Data Science Delivers the Biggest ROI

1. Accelerating Patient Acquisition

Predictive models can identify high-value referral sources, optimize marketing spend, and personalize outreach—reducing acquisition costs while increasing conversions.

2. Optimizing Care Pathways

Analyzing clinical and engagement data allows startups to redesign care pathways, improve outcomes, and strengthen payer contract value.

3. Reducing Operational Waste

Data science uncovers inefficiencies in staffing, workflows, and supply chains. For PE firms focused on margin expansion, these gains are immediate and measurable.

4. Identifying New Revenue Streams

From launching new service lines to monetizing anonymized datasets, data transforms growth strategies from reactive to proactive.

Building a Revenue-Centric Data Team

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PE-backed startups often face a post-close talent gap in data functions. While its necessary to start by identifying the types of Data Science and AI roles you need, the most impactful teams share these traits and guiding principles:

Core Traits of High-Impact Data Teams

• Clear Line of Sight to KPIs – every initiative ties directly to revenue, margin, or valuation.
  
  
• Agility & Scalability – capable of pivoting as strategies evolve.
  
  
• Cross-Functional Integration – embedded with product, marketing, and operations.
  
  
• Executive Visibility – data experts at the table for business and technical decisions.
  
  
• Strong Communication – translating insights from the boardroom to the frontline staff.
  
  

Anatomy of the Team

A diversified mix of data engineers, ML/AI engineers, data scientists, analysts, and visualization specialists is key—layer in project managers to enforce discipline, deadlines, and cross-functional alignment.

Data Science as a department should be built with consideration that they live separately from technology or IT, providing a high-visibility seat at the table to facilitate management that can reveal the value data brings to an organization when managed effectively. Data teams need leadership that understands and values their people and their idiosyncrasies. They are expensive talent that needs to be managed wisely to reduce costly turnover.  

Retention matters. Replacing a data scientist costs 1.5–2x annual salary (Josh Bersin). PE-backed leaders who treat these professionals as high-value contributors see stronger performance and lower turnover.

The Data Maturity Framework for PE Firms

When evaluating a portfolio company’s data function, use this 3-level framework:

• Level 1: Reactive Reporting – manual pulls, lagging indicators, Excel as primary tool.
  
  
• Level 2: Predictive Analytics – forecasting models, churn/acquisition insights, partial integration into decision-making.
  
  
• Level 3: Prescriptive & Autonomous Insights – advanced ML tied to KPIs, real-time decision support, proven revenue impact.
  
  

Post-close value creation plans should always include a roadmap from Level 1 → Level 3.

Metrics That Matter for PE-Backed Startups

To measure ROI on data investments, track:

• Patient Acquisition Cost (PAC) vs Lifetime Value (LTV)
  
  
• Revenue per Patient growth rate
  
  
• Margin expansion from operational efficiencies
  
  
• Revenue from new products/services
  
  
• Speed-to-decision (insight → execution cycle time)
  
  

Conclusion: From Overhead to Value Driver

In today’s competitive healthtech environment, data science is not a cost center—it’s a competitive advantage. PE-backed startups that integrate data into their core strategy unlock faster growth, more substantial margins, and higher valuations at exit.

Ready to transform your data team into a growth engine?
Contact Burtch Works to connect with top data scientists and AI talent who deliver measurable revenue, margin, and valuation uplift in PE-backed healthtech.

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About the Author
Maria Marsala Herlihy is the Chief Analytics and Data Officer at PatientPoint and the President and PGx Genomic Sciences Leader at Q5 Division.Her mission is to help providers, patients, and pharmaceutical companies better understand each person's unique genetic blueprint so they can be better informed along their life journey. She brings a new dimension to how we see and care for ourselves by translating raw, unintelligible healthcare data combined with an individual’s genetics data into customized actionable information. She is driven to empower people, scientists, medical doctors, and researchers by translating cutting-edge scientific research into products that drive precision medicine, healthcare, epigenetics, pharmacokinetics, and pharmacogenomics applications. She received her BSc in Applied Mathematics and Statistics from New York State University at Stony Brook and an MSc degree in Statistics and Operations Research from New York University. Her work history includes multiple senior leadership roles across the data sciences and health industry. She is a contributor on several scientific peer-reviewed publications and lay articles on health-related subjects.Certified Pharmacogenomics Professional.

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