Data Monetization in Practice: A Nonprofit Guide
Introduction
In the evolving landscape of nonprofits and small businesses, diversifying or finding new revenue streams is crucial for sustainability and growth. One innovative approach is data monetization, which involves leveraging the data your organization collects to generate income. In this article, we will explore what data monetization is, how it can be used to scaffold your overall data strategy, and some of the concrete steps that your organization can take to develop and implement a monetization plan. Along the way, I’ll share a few stories of monetization plan creation and implementation from my work as a Product Manager, Engineer, and Data Strategy Consultant.
Internal vs External Monetization
As an early career nonprofit program manager, I was obsessed with figuring out how to collect and use data to create more strategic focus, to tell stories of mission fullfillment, and to help organization leadership make more informed decisions. These activities are well aligned with how you can think about internal data monetization. Internal data monetization is all about using your existing data better. It outlines ways that your organization can use the data it collects to make better decisions and improve day-to-day operations, saving employees time and energy, and hopefully helping you to better serve your audience.
External data monetization is typically what organizations have in mind when they think about data monetization. Here, we are talking about turning internal data into products or services that you can sell. This is often done by creating analytics services, new data products, or offering valuable insights to your target audience. However, it can be as simple as creating whitepapers, benchmarking reports, or even selling one-off datasets. As a data analyst helping companies go from creating time consuming ad hoc reports in excel to building out full analyst platforms that generate both insights and revenue.
What do these approaches have in common? Both are achieved by carefully constructing data that meets the needs of a specific audience that sees the dataset as value. The value is often derived from the fact that your target audience cannot or does not have interest in, or an ability to, construct the data themselves.
Data Monetization and Your Data Strategy
To be judicious, and to discourage siloed initiative creation, I almost always advise organizations to think of their data monetization plan as a small, but important part of a larger, more comprehensive data strategy. A data strategy is a plan for how your organization will use its data to achieve its goals. There are a number of resources online that go into detail about data strategies, what they should include, and how to create them. I’ve provided links to a few in the resources section below.
During my time at Apple, millions of dollars and lots of time was being invested in initiatives that sought to democratize Machine Learning. Conceptually, this meant removing the barriers internal teams encountered when trying to build AI. As a Product Manager for Apple’s Machine Learning Platform Technology group, I owned key aspects of an internal data monetization strategy which sought to help the organization save on costs related to data acquisition, storage, management and governance. I helped co-lead a team of engineers charged with building a centralized, well-governed data platform, which could be used by anyone within the Apple ecosystem. Everyone would know what data was available preventing duplicate purchases, teams could share data saving compute expenses for large datasets, and legal and compliance requirements for each dataset would be publicly available, minimizing risks and ensuring that contractual obligations were clear and that data used could be audited. This product saved an estimated $11MM in data related costs annually before being made generally available. However the real money came from the fact that the data platform helped teams at Apple create much of the AI used on iPhone, iPads and other products.
Your organization is unlikely to see profits in the millions from your data strategy. But, that doesn't mean your data monetization efforts will lack true impact. I worked with a nonprofit organization that wanted to monetize its data, but the data had many quality issues. I helped to define a data strategy and plan for monetization which outlined data quality standards, engineering work to be done, tools to onboard, and data policies to document and share. As part of the plan, I was also able to identify optimizations that would decrease their annual spend on data storage by about $18K. The organization's external data monetization plan was not expected to see profits for at least 3 quarters, but in the interim, these cost savings would help finance the development of much needed infrastructure.
Creating a Monetization Plan
Creating a data monetization plan is not a linear process- plan on iterating. You should be defining and testing hypotheses, building your learning’s data literacy, and revising your approach at each inflection point along the way. The next section of this article covers five key steps for creating a monetization plan. The order should and will change based on the realities of your organization and the type of data you are working with. What should not change, however, is the first step - talking to your team.
Step 1: Talk to your team.
Always, always, always start your data monetization planning process by speaking with the people within your organization who know the data best. It’s highly unlikely that a single person or team will have all of the context you need. At minimum, you should be seeking answers to the following questions:
What data do you use to do your job? Does the data come from an internal system, or do you have to source it from outside of our organization?
Is there any data that you could use to do your job better?
Who do you go to when you need access to new data or information?
What systems, tools and technology do you use when working with this data?
What pain points do you encounter at each stage of the data lifecycle?
What data or information did you use to do this role at other companies?
Step 2: Document, Organize and Map your Data.
If your organization has lots of data, you may want to determine a method of conceptually grouping your data and documenting its connectedness. These groups might be as simple as “marketing analytics”, “constituent surveys”, “paid search outcomes”, and so on. You should take care to note which datasets contain Personally Identifiable Information or PII. Although nonprofits are not always under the regulations of GDPR et al. I strongly advise you to err on the siding of maintaining the trust of your constituent base and doing everything in your power to preserve their privacy. Lots of data governance resources and toolkits exist online for nonprofits. If your organization is a government contractor, you may want to speak with legal counsel to ensure you maintain compliance requirements.
Step 3: Conduct Market Research
Start researching the market for each category of data that your organization regularly produces. This is the hard part, but doing a thorough job here can save you and your teams lots of effort, time and energy down the road. Here, your goal is to gain an understanding of what makes data value and how value is determined within various markets. Research how similar data is being used in the industry. Look for case studies and examples. Determine who might benefit from your data (e.g., marketers, think tanks, financial analysts, researchers). The information you gathered in step 1 might point you in the right direction. Identify potential applications for your data (e.g., improving customer segmentation, enhancing operational efficiency, predictive analytics etc). If this is your first monetization project, It's a good idea to start by learning about macro data acquisition trends.
Step 4: Decide What to Monetize
Evaluate Market Demand
At this point, you are not trying to establish a true value for your data. Instead, you should aim to understand your target market's needs, the demand for your data, how your data will be made available to purchasers, how your market talks about your data (jargon, key words, etc), and what quantities of the data are typically needed for it to be considered valuable. Consider engaging in the following activities:
Reach out to potential buyers to understand their needs and willingness to pay for your data. Start with universities or peer organizations.
If your budget allows, you might consider running smoke tests such as a google ad to gauge interest in your data. Analyze industry reports and market trends to gauge the demand for your type of data.
Study competitors or peers who are monetizing similar data. Understand their pricing models and market strategies.
This step can take some time. I typically advise teams to be intentional about outlining a research plan, and to proactively socialize associated timelines. You may not have a network or industry connections that can expedite these efforts. You might have to build an engagement plan. Push back here is often a signal unrealistic expectations which can be detrimental to the long term success of monetization projects. Be as clear as possible on the whens, whys and how longs. Be prepared to move forward without a perfect plan.
Assessing Data Quality
With a more informed understanding of your data and your target market's needs, it's time to develop you Data Value Proposition. Your data value proposition is a concise and compelling way to communicate the benefits of your data to your target audience. Data volume, variety and creation velocity are often key benefits. You should identify and consider what unique insights your data can provide. You should also determine how accurate, comprehensive, relevant and timely your data is compared to alternatives.
The following data monetization quality assessment matrix is a simplified example of how you can visually plot and prioritize datasets being considered for monetization. If you’ve followed the steps above, you should be armed with all of the information needed to create a matrix that is unique for your organization and its plans.
Optimal Monetization
Data in this quadrant is of high quality and there is a high market demand for it.
Characteristics:
Accurate, complete, and timely data.
High relevance and utility for potential buyers.
Ready for immediate monetization with minimal additional investment.
Examples:
Real-time customer transaction data.
Detailed industry-specific analytics.
Strategic Investment
Data in this quadrant is of high quality but has low market demand.
Characteristics:
Accurate, complete, and timely data.
Potential to create or stimulate demand through marketing or developing new use cases.
Requires strategic efforts to identify and cultivate potential markets.
Examples:
Niche market data with potential for future growth.
Highly specialized research data.
Quality Improvement Needed
Data in this quadrant has high market demand but is of low quality.
Characteristics:
Data may be incomplete, outdated, or inaccurate.
Significant potential for monetization if data quality issues are addressed.
Investment needed to clean, enrich, or standardize data before monetization.
Examples:
Incomplete customer profiles with high relevance.
Raw data sets with potential for valuable insights but need processing.
Limited Potential
Data in this quadrant has low quality and low market demand.
Characteristics:
Poor data quality: incomplete, outdated, or inaccurate.
Low relevance or utility for potential buyers.
Limited immediate potential for monetization.
Might need to pivot strategy or find niche markets.
Examples:
Outdated survey data with limited applicability.
Internal operational data with limited external relevance.
You may find that your data value proposition is aspirational. If this is the case, you should identify actionable steps that will need to be taken to help achieve the level or data quality needed to make your data value proposition real. These should be socialized as part of the organization's overall data strategy.
Get Early Feedback
To get early feedback on your data monetize plan, it is often helpful to create a document that outlines what you plan to monetize, the financial impact that the company should expect, how you plan to realize those goals, and what metrics the team can use to track progress along the way. When you create and eventually socialize a business case for the monetization, it should be specific to the selection of the data that you and your team identify as having high market potential. This is best done as a team. Your main goal at this point is to get a document in front of key stakeholders and subject matter experts so that they can provide input, identify any holes or gaps in your working plan or understanding, and so that you can begin the work of evangelizing the plan. If your organization is composed of teams that use data, but do not possess a high level of data literacy, you will also use this document as an opportunity to introduce and define key concepts that your stakeholders will need to understand when participating in decisioning making and strategy finalization.
As you work to build the business case, make sure you detail any legal, privacy and other compliance considerations that your leadership should be made aware of in addition to any risks that you have identified. Lots of resources exist that can help you create a business case so I want to cover them here.
Within the plan, you should also describe how purchasers will access the data and how it will need to be secured. This section of your document should be considered a work in progress until after your organization creates a concrete data monetization roadmap or strategy. You’ll want to get input from subject matter experts, such as legal team members, HR, engineers, and other data security professions. You should clearly identify any software subscriptions, licensing for tooling that might be needed to address access and security. If these roles do not exist within your company, I strongly suggest you find a reputable expert to work with. If your technology stack uses Amazon Web Services, consider reading through some guides on data monetization and speaking with your solutions architect. Organizations like TechSoup will also be a great learning resource.
The business case should summarize your findings from market research, demand evaluations, and data value positioning work. It should also include your data assessment, your data value proposition and any assumptions you’ve made along the way. If early income or sales projections need to be included, take care to clearly describe how any calculations or assumptions related to time-to-value, or return on investments have been made.
Be Intentional
Opinions and processes differ across organizations, but at this point, I like to stop and make sure that I'm socializing the business case, getting lots of buy-in and input. I like to give the business case a few weeks minimum, ideally 2-3 months of discussion and QA before moving on to actually attempting to quantify the value of your organization's data.
A key conversation that will need to be had is about your organization staff capacity and ability. Some questions to answer during this time include:
Does leadership clearly understand what skills and experience will be needed to bring this plan to life?
Are they prepared to make investments in its existing staff so that they can grow their skills appropriately?
Are they receptive to the realities of data monetization? Do they understand what data is and is not valuable, in what forms, and why?
Are the organization’s goals for monetization realistic based on market findings?
Although you may be tempted or pressured to move on to actually quantifying the value of your data. I strongly suggest you get a sense of how well aligned your stakeholders are, and whether or not they have constructed realistic expectations before doing. This wait and watch approach is a good test of your organization’s patience with the process of defining and realizing a data monetization strategy. Given the state and quality of your data and your organization's readiness, realizing revenue from data may take multiple months, quarters or even years. This is especially the case if you need to generate and prepare new data and or insights, scale or build out completely new platforms or services.
The processes of researching the market, aligning stakeholders, setting expectations, and speaking with subject matter experts will undoubtedly prove utilitarian in helping your team grow its data literacy and its understanding of the true value of its data. Don’t rush through these steps. Take care, and try to gain as much as possible during the journey.
Step 5: Quantifying the Real Market Value of Your Data
With a selection of specific datasets defined, you should now begin the task of quantifying the value of your organization’s data. You should consider:
Cost Savings: Estimate the cost savings your data can provide to a potential buyer.
Revenue Generation: Project the additional revenue your data could help generate for a buyer.
Pricing Models: Consider various pricing models (e.g., subscription, pay-per-use, licensing) and determine which best fits your data.
You’ll want to perform data modeling to establish even more realistic expectations for ROI. This means very clearly identifying the costs associated with creating your data monetization program, and the sales projections with justifications. Pricing your datasets is something that you will want to get right as poorly priced or conceived plans can turn away purchasers.
Implementation
You’ve determined how to monetize your data, you’ve helped your organization leadership align on realistic expectations and delivery timelines, and gain buy-in and supporters for your plan. At this point, you are well positioned to lead your organization towards the realization of its goals.
If you’re not already familiar with hypothesis driven development, I encourage you to check Alex Cowen’s Hypothesis-Driven Development (Practitioner’s Guide). Hypothesis Driven development is an approach to software development where teams establish testable hypotheses or intentions that inform key product decisions as they iteratively build. Having clearly defined hypotheses to test at key milestones along your implementation path ensures that your team will not go too far down any given path without signal that your efforts are having the impact you intend. This approach can be easily adapted for data monetization projects, especially if your team plans to build a new product or introduce new tooling and technology.
I once worked with a nonprofit founder who asked me to help her create a plan for data monetization from a combination of publicly available and user generated content from her platform. The multi-phase plan would establish and test a number of hypotheses. Positive signals from these tests would validate our perception of the organization's data value, ensuring that investments in engineering head count, technology, infrastructure and marketing were sound. It would also give the small team time to learn how to support and talk about the new offering. The plan looked something like this:
Phase 1: 6 months
Hypothesis: There is an audience for this data as gauged by web interactions with Google Ads.
Phase 2: 3 months
Hypothesis: Users value data/insights enough to provide their email and affiliations before downloading an insights report.
Create a landing page providing gated access to a downloadable report. Offer marketing content opt/in.
Create an infographic of high-level insights and findings with a CTA/ redirect to the project landing page. These could be shared on social media.
Phase 3: 6-9 months
Hypothesis: 5% of users downloading the insights report will purchase or show interest in purchasing raw data used to create the insights report.
Automated marketing emails would be opt-in report consumers.
Sales support team members would attempt to build relationships and engage with data purchasers from prospects.
Phase 4: 1-2 years
Hypothesis: If we find priority prospects are willing to purchase 3 datasets over the course of 3 quarters, they will be willing to pay for subscription based access after 1.5 years.
The data analysts would manually prepare these reports and make them available to download online.
marketing efforts and budget would go towards promoting the offering.
Phase 5: 6+ months
Hypothesis: The organization can successfully onboard 90% of its subscription based users to the paid API while maintaining.
New subscriptions see a 25% QoQ growth.
Following this approach, the organization could expect to see around $45K in revenue in the first year from raw data downloads alone. Projections suggested that within 3 years, the organization could also generate additional income by offering dashboard services, increasing revenue from data by another 13%. Within 4 or 5 years the organization could also produce conferences, and other ticketed events. Returns would not be immediate, but they would be sustainable and significant amounting to approximately $220K in new recurring revenue within 3-5 years. We could test and refine our approach along the way.
Unfortunately, the founder wanted to bring in $325K immediately, and decided not to pursue the plan. She instead requested that I help create a price sheet and deck that could be used to encourage existing partners to pay her organization for data, which they had been receiving for free for 5 years. Needless to say, none of her partners could not justify the cost and decided to decrease their reliance on the organization’s data citing new data strategies of their own. I eventually ended my work with the organization realizing the founders unrealistic expectations prevented her from engaging in thoughtful, strategic planning.
Data Monetization is not an easy task. It can take months, if not years, for your work to reach its full revenue earning potential. Your organization will need to regularly check-in on and revise its data strategy, verify that internal practices ensuring data quality evolve, ensure data governance policies and procedures are upheld, and continually level up your team.
As a last piece of advice - make sure you take time to celebrate small wins along the way. 15 years ago, I would never have guessed that my passion for creating surveys would lead to a career helping build truly amazing products. Take care and enjoy the journey.
