Overview

Provide data analytics services based on research expertise or generated datasets. Create curated research data marketplaces for purchase by external researchers or companies (balancing open science principles).

Core Concept

• This initiative aims to generate revenue by leveraging the university's data assets and analytical capabilities. It encompasses two main approaches:
  
  
  • A) Data Analytics Services: Offering specialized data analysis, modeling, or interpretation services to external clients, utilizing university expertise and potentially unique datasets.
    
    
  • B) Data Licensing/Marketplaces: Creating curated, often anonymized or aggregated datasets derived from research, and licensing access to these datasets to external researchers or companies via platforms or direct agreements, while strictly adhering to ethical guidelines, privacy regulations, and balancing open science principles.
    
    
• Revenue generated from both approaches is intended to support research activities, data stewardship, and related infrastructure.

Implementation Strategy & Key Steps

• A. Data Analytics Services: (Builds upon RaaS but with specific data focus)
  
  
  • Phase 1: Planning & Setup: Identify departments/labs with unique data analytics expertise (e.g., bioinformatics, health informatics, AI/ML, social science statistics) or valuable proprietary datasets suitable for service offerings. Define specific services and establish pricing models considering personnel time, computational resources, data access fees (if applicable), and university F&A. Develop service agreement templates clarifying scope, deliverables, data usage rights, IP ownership (typically client owns analysis results/reports), and confidentiality. Designate a managing unit (e.g., Data Science Institute, specific core facility, Corporate Relations).
    
    
  • Phase 2: Launch & Operations: Market analytics services to targeted industries or client types. Secure initial pilot projects. Establish secure workflows for data handling (intake, storage, analysis, delivery). Ensure compliance with data security protocols.
    
    
  • Phase 3: Scaling & Sustainability: Expand client base and service offerings based on demand and capacity. Standardize common analytical procedures. Invest in computational infrastructure and personnel as needed. Refine client management and project workflows.
    
    
• B. Data Licensing/Marketplaces: (Requires extensive groundwork)
  
  
  • Phase 1: Foundational Work & Governance:
    
    
    • Establish Data Governance Framework: Create a university-wide committee and comprehensive policies governing data classification, ownership, privacy, security, ethical review, access, sharing, and retention.
      
      
    • Legal & Ethical Review: Involve Legal Counsel, Privacy Office, and IRB/Ethics Committees from the very beginning to define permissible data types, required anonymization/aggregation levels (HIPAA, GDPR, FERPA, etc.), consent requirements, and ethical boundaries.
      
      
    • Identify & Curate Potential Datasets: Inventory research datasets with potential external value. Develop rigorous curation standards (metadata, quality control, formatting) and processes. Assess the rights associated with each dataset (sponsor restrictions, consent limitations).
      
      
    • Develop Model & Pricing: Choose a model (e.g., dedicated platform, TTO-managed licensing, tiered access). Develop standard Data Use Agreement (DUA) templates with clear usage restrictions, security requirements, attribution, and liability clauses. Establish a pricing strategy (e.g., subscription, per-dataset fee, tiered by use case).
      
      
  • Phase 2: Pilot Launch:
    
    
    • Select Pilot Datasets: Choose 1-3 well-characterized, low-risk datasets where sharing rights and anonymization procedures are clear.
      
      
    • Build/Configure Platform: Develop or configure the necessary technical infrastructure for secure access, user management, and potentially payment processing.
      
      
    • Documentation & Marketing: Create clear documentation for data users. Market the pilot offering to a specific target audience (e.g., researchers in a related field, specific industry segment).
      
      
  • Phase 3: Evaluate & Scale Cautiously:
    
    
    • Rigorous Pilot Review: Thoroughly evaluate the pilot's technical performance, user compliance with DUAs, legal/ethical integrity, and market response.
      
      
    • Gradual Expansion: Based only on a successful and compliant pilot, cautiously consider adding more curated datasets after they undergo the full governance, legal, and ethical review process. Continuously monitor compliance and refine processes. Ensure long-term funding for data stewardship.

Key Stakeholders & Roles

• Internal:
  • Researchers/Data Generators: Provide data and subject matter expertise; crucial for understanding data context and limitations.
    
    
  • Data Governance Committee: Sets policy, oversees compliance, provides strategic direction.
    
    
  • Office of Research: Policy development, oversight, compliance framework, potentially houses governance function.
    
    
  • Legal Counsel: Essential for contracts, DUA terms, privacy law compliance (HIPAA, GDPR, etc.), IP issues, risk assessment.
    
    
  • IRB/Ethics Committees: Critical gatekeeper for human subjects data; reviews protocols for data sharing.
    
    
  • Chief Privacy Officer/Data Security Officer: Oversees privacy compliance and data security implementation.
    
    
  • IT Services: Provides secure storage, computing infrastructure, network security, platform support.
    
    
  • University Library: Often leads data curation, metadata standards, and data management planning efforts.
    
    
  • TTO: Manages licensing agreements, IP aspects related to data/databases, potentially manages marketplace operations.
    
    
  • Finance Office: Handles billing, revenue collection, and distribution for services and licenses.
    
    
• External:
  • Clients (Analytics Services): Companies, non-profits, or agencies needing data analysis.
    
    
  • Licensees (Data Marketplaces): Academic researchers, industry R&D departments, data analytics companies.
    
    
  • Platform Technology Vendors: Providers of data marketplace or secure enclave software.
    
    
  • Regulators: Entities overseeing privacy laws (e.g., HHS Office for Civil Rights).

Resource Requirements

• Personnel: Data scientists/analysts, data curators/stewards, IT security specialists, legal experts specializing in privacy and data law, compliance officers, TTO licensing staff, administrative support. Strong leadership for Data Governance.
  
  
• Financial: Significant investment required for: robust data security infrastructure, data curation efforts, potential platform development/licensing fees, ongoing legal consultation, compliance monitoring, personnel time. Pricing must reflect these substantial costs.
  
  
• Infrastructure/Technology: Secure, high-capacity data storage and backup; potentially high-performance computing resources; data curation and management tools; secure data access platforms/enclaves; robust cybersecurity monitoring and controls; potentially blockchain for provenance tracking (if deemed appropriate).
  
  
• Policy/Administrative: Mandatory comprehensive Data Governance Policy. Specific policies/procedures for data classification, de-identification/anonymization, data security standards, incident response, ethical review for data sharing, data licensing terms (DUAs), and revenue distribution.

Potential Challenges & Mitigation

• Privacy Violations & Regulatory Non-Compliance (Highest Risk): Failure to adequately anonymize data, unauthorized access, violating HIPAA, GDPR, etc.
  • Mitigation: Primacy of Legal/Ethical/Privacy review. Implement state-of-the-art anonymization techniques; utilize secure data enclaves; employ strict access controls; mandate strong DUAs with usage audits; err on the side of caution – do not share if compliance is uncertain. Continuous training for personnel. Robust incident response plan.
    
    
• Ethical Misuse of Data: Data used for purposes conflicting with university values or original consent.
  • Mitigation: Strong ethical review process; clear usage restrictions in DUAs; vetting potential licensees; reserving audit rights; transparency in data sources and limitations.
    
    
• Data Quality, Documentation & Curation Burden: Datasets may be poorly documented, inconsistent, or require significant effort to make usable and shareable.
  • Mitigation: Invest in dedicated data curation resources; establish mandatory metadata standards; implement quality checks; factor curation costs into pricing/resource allocation; start with highest quality datasets.
    
    
• IP Rights & Permissions: Unclear ownership or lack of rights to share/license data (e.g., due to prior agreements, funding restrictions, complex collaborations).
  • Mitigation: Thorough rights review for each dataset before considering it for monetization; clarify data ownership in university IP policies and grant agreements; negotiate appropriate data sharing rights in future agreements.
    
    
• Tension with Open Science Norms: Charging for data access may conflict with expectations for free sharing within the research community.
  • Mitigation: Adopt tiered access models (free/low-cost for non-commercial research, fees for commercial use); focus fees on value-added aspects (curation, aggregation, platform); reinvest revenue into research and data stewardship; be transparent about the model and rationale.
    
    
• Data Security Breaches: Unauthorized access to sensitive data repositories.
  • Mitigation: Implement multi-layered security controls (technical and administrative); conduct regular vulnerability assessments and penetration testing; ensure robust access control and monitoring; encrypt sensitive data; have a well-rehearsed incident response plan.
    
    
• Market Viability & Pricing: Uncertainty about demand or appropriate pricing for specific datasets or services.
  • Mitigation: Conduct market research; engage potential users early; use pilot programs to test pricing; clearly articulate the unique value of the data/service.

Success Metrics & Evaluation

• Financial: Revenue generated from analytics services; revenue from data licenses/subscriptions; cost recovery rate (including curation/compliance costs).
  
  
• Operational: # of analytics projects completed; # of datasets available for license; # of active data licenses/subscriptions; platform uptime/performance; data quality metrics.
  
  
• Compliance & Ethics: # of privacy/security incidents (target: zero); results of compliance audits; user adherence to DUA terms; IRB/Ethics committee review outcomes.
  
  
• Impact: Revenue distributed to research/stewardship; documented use of data by licensees (citations, products developed – if trackable); client satisfaction (services).
  
  
• Evaluation: Frequent (at least annual) reviews by the Data Governance Committee, involving Legal, Privacy, IT Security, Research, and Finance leadership. Independent security and compliance audits are highly recommended. Continuous assessment of alignment with ethical principles and university mission.

University Policy Considerations

• Data Governance Policy: The absolute cornerstone, defining roles, responsibilities, and rules for the entire data lifecycle.
  
  
• Intellectual Property Policy: Must explicitly cover datasets, databases, and software related to data analysis, including licensing provisions.
  
  
• Privacy Policies (HIPAA, GDPR, FERPA Compliance): Detailed implementation procedures reflecting regulatory requirements.
  
  
• Information Security Policy: Standards for data classification, access control, encryption, incident response, etc.
  
  
• Research Data Management Policy: Requirements for documentation, storage, sharing (often influenced by funding agencies).
  
  
• Data Use Agreement (DUA) Policy: Standardized templates and processes for data licensing, specifying user obligations and permissions.
  
  
• Ethical Conduct Policies/IRB Procedures: Specific guidelines and review processes for the secondary use and sharing of research data, especially human subjects data.
  
  
• F&A/Revenue Distribution Policy: Clear rules for allocating revenue generated from data monetization activities.