Asset Mapping & Introduction to Earned Revenue Models
This module focuses on market analysis, data licensing, and pricing. Participants will learn about strategies for engaging in market analysis and landscape research, and will gain a clear understanding of data licensing regulations and parameters. Lastly, participants will build an understanding of how to price individual unique assets, services, and/or products.
DATE
July 31, 2026
TIME
12:00 pm - 1:30 pm ET
VENUE
PRESENTERS
Managing Director at Envoy
Salomon Moreno-Rosa
Salomon is a Managing Director at Envoy. He uses his background and expertise in nonprofit administration and policy development to inform inclusive hiring, philanthropy, and strategic planning engagements that help drive organizational goals and advance local outcomes.
Alejandra Piers-Torres
Manager at Envoy
Alejandra is a Manager of Strategy & Philanthropy at Envoy. She brings experience in local government, public/private partnerships, and program development to support social impact initiatives. Alejandra holds a BA in International Relations and Hispanic Studies from Brown University.
Rothschild Toussaint
Associate at Envoy
Rothschild is an Associate in the Strategy & Philanthropy sector at Envoy. He brings experience in economic development, affordable housing, research, and policy analysis. He employs a mixed methods research and data-driven approach to tackling social challenges. He holds a BA in Economic Geography from Dartmouth College.
Session Agenda​
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Welcome and Ice Breaker
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Review of Last Module
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Module 5: Asset Mapping & Intro to Earned Revenue Models
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Closing and Next Steps
Revenue Planning and Organization
Pricing Calculator
This spreadsheet is a planning and management tool used to monitor project progress, timelines, and resource allocation. It typically includes sections for task names, start and end dates, responsible parties, completion status, and budget tracking. This type of tool helps teams stay organized, assess performance against milestones, and ensure that projects are completed on time and within budget.
This financial planning tool is designed to help early-stage businesses project their income, expenses, and cash flow over a set period (typically 3–5 years). It usually includes sections for revenue forecasts, cost of goods sold, operating expenses, capital expenditures, funding sources, and break-even analysis. This template supports strategic decision-making, investor presentations, and loan applications by providing a clear, data-driven view of a startup’s expected financial trajectory.
Data Licensing and School Resourcing
This document explains the importance of open data licenses in reducing legal friction, supporting FAIR (Findable, Accessible, Interoperable, Reusable) data principles, and maximizing the utility of data. The guide outlines a three-step checklist for selecting licenses, explains key terms (permissions, conditions, and limitations), and compares common license types like Creative Commons, Open Data Commons, and government licenses. It also includes tools, decision trees, and examples to simplify the licensing process.
The Example of a Data System Software License Agreement outlines the terms under which software and related data systems can be used, licensed, and distributed. It typically includes sections on license scope (e.g., usage rights, restrictions), intellectual property ownership, confidentiality, warranty disclaimers, limitations of liability, and termination clauses. The agreement is designed to protect the rights of the software provider while clearly defining the obligations and permissions granted to the licensee. This type of document is crucial for organizations managing proprietary data systems to ensure legal compliance and operational clarity.
This document is a legal template outlining terms for granting temporary use of a property or premises. It defines key elements such as license fee, permitted use, term length, and responsibilities of both the owner and licensee. The agreement includes clauses on payment, maintenance, compliance with laws, insurance, liability, termination, and dispute resolution. Unlike a lease, it establishes a license—not a tenancy—meaning the licensee does not gain property rights. This document is structured to protect the owner’s interests while granting limited access and use rights to the licensee.
Licensing 101 toolkits
This document offers legal and strategic guidance for creating effective data licensing agreements. It explains what a data license is, compares it with data sales, and outlines key motivations for licensors and licensees. The document highlights critical considerations—rationale, complexity, and compliance—emphasizing intellectual property protections, permitted uses, and evolving data privacy laws. It also reviews state-specific legal trends, best practices, and essential contract terms like confidentiality, indemnification, and governing law. The resource is tailored to help organizations manage data rights while minimizing legal and operational risks.
The How to License Research Data guide by the Digital Curation Centre provides a comprehensive overview of how to legally and effectively license research data. It explains the importance of clear licensing for enabling data reuse, protecting intellectual property, and promoting open science. The guide reviews different types of licenses—such as Creative Commons, Open Data Commons, and government licenses—and highlights considerations like attribution stacking, commercial restrictions, and compatibility issues. It also discusses practical strategies for marking data with licensing terms, using rights expression languages, and implementing multiple licensing models when needed.
The Data Licensing Guidance document, developed by the GEO Data Working Group, provides clear recommendations on standardized licenses for sharing open Earth Observation (EO) data. It emphasizes the need for legal clarity and interoperability, discouraging custom or restrictive End User License Agreements (EULAs).
The 5 Takeaways from the NYC Bar Association’s CLE on Data Licensing summarizes key legal insights on how to protect and monetize data through licensing. It highlights that data licenses must clearly define data ownership, scope of use, permitted users, and specific terms regarding data delivery, updates, and formats. The guide emphasizes the need to address rights over derived data, exclusivity, confidentiality, and data security—especially when sensitive or personal information is involved. It concludes that while data is not traditionally protected as intellectual property, careful contract structuring makes it a highly monetizable asset.
The Data as IP and Data License Agreements guide from Practical Law provides an in-depth legal and strategic framework for treating data as a form of intellectual property. It covers how data can be protected under trade secret, copyright, and tort law, and outlines best practices for drafting data license agreements. Key topics include defining data ownership, scope of license rights, handling derived and usage data, sub licensing, exclusivity, data delivery, security, indemnification, and confidentiality.
This document provides a comprehensive framework for organizations to ethically and responsibly share data. It outlines best practices for data classification, licensing terms, privacy and security considerations, consent management, and legal compliance. The guide emphasizes transparency, ethical use (including AI considerations), and minimizing reputational and logistical risks. It also introduces the Montreal Data License as a model for standardizing data licensing in AI and machine learning contexts.
Data Agreements templates and MOUs (Memorandums of Understanding)​
This document is designed to streamline legal negotiations between investment managers and data vendors during data evaluation periods. It addresses the use of alternative data (e.g., transaction, web, geo-location data) and includes key legal provisions related to confidentiality, compliance with privacy laws (e.g., GDPR), prevention of insider trading, permitted use, termination, and liability.
This guide offers detailed guidance on how to structure and negotiate data use agreements (DUAs) between researchers and data providers. It explains the key components of DUAs—purpose, scope, legal context, data protection, and ethical considerations—while aligning with the "Five Safes" framework: safe projects, people, settings, data, and outputs. The guide also includes sample language, a checklist for agreement development, and references to templates and repositories of existing DUAs. It serves as a foundational resource for ethical and legally sound data sharing in research and policymaking.
This guide provides a comprehensive, user-centered framework for creating effective data dashboards. It outlines a step-by-step process—from identifying users and understanding their needs to selecting data, choosing software platforms, and developing prototypes. The guide emphasizes best practices in data cleaning, visual design, and collaborative development, while also highlighting common pitfalls to avoid. It is a practical resource for organizations aiming to build dashboards that are actionable, accessible, and tailored to specific decision-making contexts.
This document is an example that provides how to sustain effective data governance policies. It offers editable templates, policy guides, and best practices across key areas such as: - Purpose, structure, and charter of governance bodies - Data quality, access, security, and breach response - Public reporting and data request management - Data retention/destruction - Electronic communication protocols - Data system changes and governance of data partnerships
This document outlines the purpose of the collaboration, the roles and responsibilities of each party, specific goals and how they will be achieved, and terms for oversight and amendment. The template includes prompts and example sections to help organizations clearly define shared objectives, allocate duties, and establish mechanisms for evaluation and adaptation. It serves as a flexible, working agreement to promote aligned, transparent, and effective cooperation.
The Third Sample MOU Letter is a formal agreement template between a nonprofit fiscal sponsor and a sponsored organization outlining their roles in administering a project, typically for grant-funded activities. It establishes that the fiscal agent assumes financial and legal responsibility while the applicant implements the project and handles reporting.
This document offers a detailed, practical roadmap for creating effective interagency MOUs, especially within human services and aging sectors. It explains the purposes and types of MOUs, outlines stages of negotiation (planning, drafting, and finalization), and provides legal and operational guidance.
This document provides a comprehensive framework for establishing formal partnerships between academic institutions and employer partners. It defines mutual responsibilities, program structure, intellectual property rights, FERPA compliance, data security, marketing, and revenue sharing. The template includes detailed clauses on governance, confidentiality, legal indemnity, payment terms, and joint oversight, tailored for educational programs co-developed with industry.
The ACT Government Data Sharing Agreement Template is a formalized framework for documenting and managing data sharing between two or more parties. It outlines key components including purpose, project details, roles of data custodians and requestors, data handling protocols, data security measures, legal compliance (e.g., privacy laws and intellectual property), and expected outputs.
This document is a template that details key contractual elements including specifications, delivery terms, inspection and acceptance, payment procedures, warranties, and compliance with laws and donor regulations (notably USAID). The contract emphasizes accountability through clauses on confidentiality, anti-corruption, anti-trafficking, audit access, dispute resolution via arbitration, and protection of intellectual property.
This template details the scope of work, compensation structure, reporting requirements, term and termination clauses, indemnification, and insurance requirements. The agreement includes strong provisions for compliance with federal, state, and local laws, nondiscrimination, confidentiality, and intellectual property rights. It also outlines audit rights, subcontracting rules, dispute resolution procedures, and ownership of deliverables. This contract ensures accountability, transparency, and legal compliance in consultant engagements for publicly funded projects.
Pricing Unique Assets and Products
This document encourages aligning pricing metrics with customer value, simplifying pricing and packaging structures, and tying sales incentives to pricing outcomes. Through survey data and case studies, it illustrates how companies that implement these strategies outperform peers in profitability and growth, especially amid economic uncertainty.
This document outlines critical steps for translating a pricing strategy into measurable business results, particularly in B2B settings. It emphasizes making pricing a top management priority, developing a phased and ambitious pricing roadmap, assigning an experienced program manager, and embedding tools and systems to support execution. The guide also stresses the importance of governance, salesforce training, KPI tracking, internal communication of early wins, and continuous improvement through pilot programs.
This document covers foundational concepts like the 5Cs of pricing, pricing throughout the product life cycle, and models such as cost-plus, skimming, penetration, and dynamic pricing. It emphasizes leveraging analytics to inform decisions, optimize promotions, and forecast outcomes using models like conjoint analysis, Van Westendorp, Gabor-Granger, and econometric modeling. The guide includes real-world case studies, optimization workflows, and pricing dashboards, positioning pricing analytics as a critical tool for improving profitability, customer segmentation, and long-term market success.
This document provides guidance on how pricing excellence can be a powerful driver of profitability across business-to-business industries. It outlines key strategies such as improving pricing transparency, aligning prices with customer value, training sales teams as value negotiators, and using advanced analytics and big data to set optimal prices. The report features case studies, practical frameworks, and expert interviews, showcasing how companies have achieved 2–7% increases in return on sales (RoS) through pricing transformations. It also provides a maturity model and strategic roadmap for evolving pricing capabilities over time.
This report explores evolving trends and executive insights around software pricing strategies, especially the rise of consumption-based models. It analyzes various pricing structures—like per-user, tiered, freemium, outcome-based, and usage-based—and distinguishes between pricing models and billing methods. It also outlines a decision-making framework for selecting pricing models and explores macro trends like AI, low-code platforms, and telemetry that are reshaping software monetization.
This academic review that explores the lifecycle of big data as a commercial asset. It examines key areas of data economics: pricing models (economic-based and game-theory based), trading mechanisms (including auction-based systems), and data protection techniques such as digital rights management (DRM), encryption, and watermarking. The paper categorizes market structures (monopoly, oligopoly, competition), highlights challenges in multi-owner data trading and privacy, and proposes frameworks for fair, efficient, and secure data markets.
This document contains information on pricing strategies for data products offered in digital marketplaces. It analyzes 15 academic studies to assess the maturity of data pricing models, explore contexts and types of data products, and identify mechanisms used for determining prices. The paper categorizes pricing models based on market structure (e.g., monopoly, duopoly), pricing metrics (e.g., usage, volume, flat fee), and quality attributes (e.g., accuracy, completeness).
This survey provides a comprehensive review and classification of data pricing strategies in data marketplaces. It categorizes pricing methods based on data granularity (dataset vs. query-based) and privacy sensitivity (with or without privacy considerations), covering flat-fee, tiered, versioning, auctions, contracts, and mechanism design approaches. The paper also distinguishes between pricing in markets with complete vs. asymmetric information and highlights techniques like VCG mechanisms, differential privacy, and provenance-based pricing. It serves as a foundational resource bridging academic theories with practical pricing strategies for data monetization.
This document is a review of academic literature on pricing strategies for data products in digital marketplaces. It categorizes mechanisms into single-request pricing, volume packages, time-based subscriptions, two-part tariffs, and freemium models, with further analysis of approaches like auctions, game theory, and privacy-aware pricing. The study highlights that most research focuses on single-request and query-based pricing, while practical applications remain under explored. It underscores the complexity of pricing intangible, shareable data assets and calls for more real-world research to bridge theory and practice in sustainable data monetization.