Beyond Monetization: Building a Holistic Healthcare Data Value Strategy

The healthcare industry data landscape is unique. With an estimated 30% of world data generated by healthcare-related industries, information management is a major challenge. Early adopters of electronic health records (EHRs) now store two decades of clinical data and numerous other distinct and disparate datasets. As data volumes expand, new solutions are emerging to improve data insights, storage, and retrieval. EHRs are improving at collecting structured data, and emerging AI tools are opening up new opportunities to process unstructured data such as physician notes, laboratory notes, and clinical images. AI Agents also create and aggregate information, a set of secondary data that most organizations are only beginning to unpack.

Even so, adequately managing data differs from extracting maximum value. Payers, providers, and health tech companies alike are ramping up their investments in data monetization. They are developing three-dimensional strategies that go far beyond using data solely to support legacy operations, traditional revenue streams, and internal reporting.

Healthcare companies need to think beyond traditional monetization models and explore how to create new value with data outside of conventional methods. For example, a longitudinal record of patient data can inform many member-centric initiatives, while the data itself can be monetized through appropriate APIs and services.

 Healthcare Data

The good news is that there are few technological barriers preventing healthcare organizations from dramatically increasing the value of their data. With current data solutions, appropriate consent-based synthetic data exposure is relatively easy.  The real challenge is driving a wholesale organizational culture shift. Healthcare leaders should leverage their healthcare data as a distinct service line and create a “Data Value Center of Excellence” to generate pragmatic strategies for data value. Enabling a centralized, focused team and framework that continuously evaluates and unlocks the full value of an organization’s evolving data landscape is the first step on the journey. This team will also be responsible for equipping peers across the enterprise with the tools, training, and guidance needed to use data and AI solutions more efficiently and creatively.

Identifying Data Value Opportunities

When identifying the most attractive (or lucrative) opportunities to achieve new value from data, healthcare companies should consider opportunities across three dimensions: 

1. Reselling data. Transactional data monetization is what most organizations envision when they think about data monetization. Healthcare companies are rightly excited about these opportunities, though also realistic about the regulatory and infrastructure roadblocks. After data anonymization (critical for compliance purposes), healthcare companies can create data packages that can be sold to third parties to inform research and marketing efforts. These efforts will generate revenue and create exciting new partnerships that drive new therapies and improve health outcomes. Clinical trial organizations, for example, are now constantly looking for synthetic data from lab organizations to enhance their risk algorithms.

2. Data-driven optimization. In addition to providing a direct revenue stream, data can empower healthcare organizations to improve internal processes, identify cost-reduction opportunities, improve employee retention, increase patient satisfaction, and understand/predict risks. Often, the challenge is bringing disparate data sources into a more unified view that can serve risk modeling, value-based care, and patient engagement while providing a better framework for considering social determinants of health (SDOH). Healthcare organizations should build their data value strategies around those organizational pain points most likely to benefit from a data-driven solution. 

3. Healthcare innovation. In addition to generating revenue and delivering new efficiencies, data can open up entirely new service offerings and lines of business. As Artificial Intelligence models continue to evolve and reshape healthcare delivery, organizations will discover new opportunities to improve patient outcomes through Agentic AI, predictive analytics, precision medicine, clinical decision support, and personalized medicine. Many of these use cases require IT investments that align real-time data with ongoing research and development activities.  However, as AI becomes more accessible and widely adopted, organizations have countless opportunities to build this acumen without the need for considerable change management.

Building a Healthcare Data Value Creation Strategy

To craft next-generation data value creation strategies, healthcare organizations should begin by analyzing how they can achieve new value with the datasets at their disposal. They might veer toward data monetization if they are facing a pressing need for new revenue streams. On the other hand, if patient experience metrics are declining, data monetization will not be a panacea. The organization might be better served by asking how specific data types could be used to improve the patient experience to drive long-term customer experience and retention imperatives.

 Data Value Opportunities

Additionally, organizations must ensure that the prioritized data value opportunities match the sophistication of their data landscapes. Do they have the proper supporting infrastructure or, at the very least, the capital to invest in developing that infrastructure? Organizations will need to realistically assess their ability to effectively manage data consumption, analysis, cleansing, and transit internally and (often) externally.

On the technology side, organizations should consider cloud solutions, emerging data analytics and monetization platforms, data privacy and compliance tools, data security and encryption tools, consent management platforms, and data de-identification software. The most complex data value plays will require a considerable technology integration effort. To build the optimal data value tech stack, healthcare organizations will often be best served by working with partners that understand data governance, region-based data compliance, data transformation, and the potential of the toolsets above.

Of course, data maturity is about people and processes as much as technology. Leaders should not forget to ask: Are any organizational impediments likely to prevent return on investment? Clinicians, for example, may resist an effort to standardize data entry procedures if it requires them to work differently. Such people-related impediments are rarely insurmountable, but they will require an intentional investment in change management. This is where advancements in AI can potentially play a role in aggregating and classifying information to support these efforts.

Healthcare organizations must also carefully consider the ethical and legal implications of using data in new ways and decide what their appetite for risk is. All innovations must align with existing data protection regulations and data consent clauses. Tracking these data restrictions across geographies, legacy systems, and customer scenarios can be much more complicated than healthcare leaders anticipate. In some cases, pending litigation will also affect the path forward.

Beyond Monetization: The New Data Imperative

Healthcare organizations are increasingly exploring opportunities for data monetization; however, their focus should extend beyond purely transactional models. A comprehensive evaluation of data value can uncover alternative paths to return on investment. By adopting a strategic perspective, utilizing external expertise, and aligning promising data value initiatives with appropriate emerging technologies, healthcare organizations can unlock additional value—enhancing patient experiences, lowering operational costs, mitigating risks, and generating new revenue streams in alignment with their organizational goals. Ultimately, realizing meaningful value from data requires a cultural shift that unites teams around data-driven objectives, rather than treating data value as an incidental byproduct of other priorities.

About the Author

Philip Handal
Senior Partner, Healthcare Consulting Leader

Phil has over 20 years of experience in healthcare technology consulting and care delivery. He is a leader in Wipro’s Payor Strategic Consulting services, focusing on transforming technology to deliver value for the future of healthcare delivery. He has worked extensively with payors, providers, and life sciences organizations to develop strategic initiatives, implement technology, and execute data-driven digital solutions.