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Reflection on The “Money Path” for Open Data

ID-100207743-2Sunand Menon, previous Markets For Good guest author of one our most highly visited posts, has offered a recent thought piece (Stop Assuming Your Data Will Bring You Riches) that deserves a deeper examination. I’d like to use it to raise more questions (not give answers) about the pathways to sustainable business models for open data. If we are to move from the talk of potential (both in terms of social impact and sustainable revenue), consider this a juncture where we can stop and search for a few questions that might move the conversation forward. Before reading on, it might also be useful to take a quick look at our opening of the theme, just to frame your thoughts.

The fact that so many data sets are being opened up and made truly accessible is a welcome and prevailing theme on open data. Running just as fast is the progress toward using it to build applications that can improve quality of life. In parallel, however, it’s time to hone in on a definition of utility in market terms and how we can sustain the momentum with long-term funding to fuel not only utility but also the rapid iteration of experiments.

Let’s take another look at the argument in Sunand’s article in terms of preparation, targeting a market and optimizing revenue potential.


With other commodities, the ownership question is usually clear enough not to bring into question, but how do we reconcile “open” with “ownership”? Sunand recommends the following first steps as preparation for market viability:

  • “To evaluate data ownership, enlist the early help of domain experts — content specialists and legal counsel who understand how data is created, stored, manipulated, packaged, distributed, and commercialized.
  • Categorize the datasets you have identified into three buckets: “data we own”, “data customers own”, and “data third parties own” to ensure added clarity.
  • Quantify (as much as possible) the value-add of any derived data versus the original data, in order to be in a better position to create mutually agreeable data usage and revenue share agreements with suppliers and co-creators of the data.”

Question: How much of this work is currently happening in the social sector, whether explicitly labeled as such or not? Let us know your thoughts in the comment section below if any of these steps sound familiar.


An obvious step for building a business model is determining for whom the product (data) may be useful, i.e. not assuming that it is generally useful. Again, there are a number of steps enumerated with the goal of identifying and validating a market.

  • “Identify target customers by casting a wide net across potential users, and performing customer interviews to establish their “jobs to be done” (as Harvard Business School professor Clay Christensen says)…
  • Test user perceptions with a range of potential data offerings…
  • Ask prospective customers to assign a gut-check ranking — ”High”, “Medium” and “Low” — to the individual datasets and metrics and note these preferences…”

Question: The work proposed here assumes capacity and capability for data-specific work. A few of our other guest authors have noted that both are a concern, e.g., Laura Quinn with: “We all need to understand that if we as a sector lean on nonprofits to provide data they simply don’t have the infrastructure to provide, what we’ll get is not better data—in fact, we may data that’s worse.” Do you feel that the individual organizations and funders are moving quickly enough to reconcile the gap between the need for data infrastructure at the organization level and the demand to produce actionable data?


For those organizations that have advanced their data practice beyond the targeting level, the question is perfecting a market approach. Specifically, a business model must be robust enough in its surrounding feedback loops to produce reliable evaluation of revenue potential. Further, the model should accommodate the ability to then “test, learn and tweak” for optimum execution and to use the resulting knowledge to indicate when the model itself needs to be changed.

Question: Can you identify the feedback loops that drive your current service model?

These are just a few questions designed to sharpen our thinking on how we can unlock the potential of open data to create sustainable value for beneficiaries and organizations; that is, for you to be able to use it on an ongoing basis without it being simply another expense line. You might note that they share the theme of one of our first blog posts, Beth Kanter’s Doing The Math Ourselves, i.e. starting with our own intention and capability at the organizational level. Let’s talk about that.

A summary question:

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