When made accessible and re-used responsibly, privately held data has the potential to generate enormous public value. Whether it’s enabling better science, supporting evidence-based government programs, or helping community groups to identify people who need help, data can be used to make better public interest decisions and improve people’s lives. 

Yet, for all the discussion of the societal value of having organizations provide access to their data, there’s been little discussion of the business case on why to make data available for reuse. What motivates an organization to make its datasets accessible for societal purposes? How does doing so support their organizational goals? What’s the return on investment of using organizational resources to make data available to others? 

The Open Data Policy Lab addresses these questions with its “9R Framework,” a method for describing and identifying the business case for data reuse for the public good.  The 9R Framework consists of nine motivations identified through several years of studying and establishing data collaboratives,  categorized by different types of return on investment:  license to operate, brand equity, or knowledge and insights. Considered together, these nine motivations add up to a model to help organizations understand the business value of making their data assets accessible. 

9R Framework: Nine Incentives to Open and Re-Use Data in and for the Public Interest

Following several years of researching and designing data collaboratives, we have identified the following nine incentives (see earlier versions of this here):

ROI Case: Knowledge and Insights: Ways in which data reuse improves an organization’s ability to pursue larger strategic goals.

1. Reciprocity: Gaining access to data sources and other assets held by organizations whose data may be important to business decisions. 

2. Rectifying Errors and Improving Data Quality: Identifying errors in datasets by letting others access and use them. 

3. Research and Insights: Generating new answers to questions, and providing organizations with insights that may not have otherwise been extracted. 

4. Reproducibility: Testing results of analysis by allowing others to conduct identical or related work. 

ROI Case: Brand Equity: Ways in which data reuse promotes an organization’s image for internal and external stakeholders.

5. Reputation: Enhancing an organization’s image and reputation, attracting media,  new users, customers, and investors who value socially conscious corporate actors.

6. Responsibility and Philanthropy: Fulfilling an organization’s social responsibilities, improving the environment in which it operates, and bolstering its reputation.

7. Recruiting and Retaining Talent: Attracting and retaining data science talent with projects that are compelling and socially relevant.  

ROI Case: License to Operate:Ways in which data reuse strengthens the “social license” for an organization to operate.

8. Regulatory Compliance: Helping organizations comply with regulations, become more transparent, or otherwise promote responsible data management.  

9. Revenue Maximization: Providing opportunities to generate new income or cut costs.

Using the 9 R’s Framework

This framework is useful not just for describing the rationales why organizations have made certain data assets re-usable but also for helping organizations articulate their motivations and build internal buy-in for data reuse. 

By evaluating and weighing each of the 9 R’s on a chart (see figure), data stewards—practitioners tasked with working on responsible data reuse—can better design their business case for providing access to data and establish the best methods or vehicles for doing so through, for instance, data collaboratives.

Above is the example of a spider chart that can be used to evaluate and weigh the 9 R’s in action. The figure above shows that brand equity and license-to-operate arguments are less important for this hypothetical organization, while knowledge and insights provide far stronger motivations for providing access to data. 

We encourage you to use similar methods to understand the motivations for reuse. A blank version of the above graphic can be found here.

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Over the next few weeks, we will focus on each “R” and provide further examples of how this has played out in practice. These pieces will draw in interviews from prominent experts as well as existing research and literature. 

We invite you to provide your own thoughts on the “9R Framework” and let us know if it resonates with you. Feel free to contact the Open Data Policy Lab team at [email protected] with your own thoughts and reflections on the framework, the graphics, and how both might facilitate data reuse. We are soliciting input from experts and practitioners.

Otherwise, we hope you will check this site regularly for updates.

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