Developer Creativity, Personal Data and Open Source
Liberty. Equality. Data. Podcast #11 with Heather Meeker
In this episode, we are joined by Heather Meeker, an experienced technology lawyer and open source expert, as well as Founding Portfolio Partner at OSS Capital, an early-stage venture capital fund specializing in commercial open source development. Heather has a wealth of experience as a developer (“computer programmer” as she calls it), working on open source license strategies for some of the most recognized companies over the decades.
“Open source is eating software itself much faster than software is eating the world.” — OSS Capital
In this podcast episode, we talk about developer communities, early models for data and their evolution, open source as a way to develop and promulgate a kind of standards and, the power of open innovation, the impact it could have on the data that we generate was available to build innovative solutions upon.
You can listen to this episode on Spotify, Apple Podcasts, and Google Podcasts.
Here’s a short summary of the key points discussed on the podcast:
1. A unique approach to investing in OS
We started this Liberty. Equality. Data. podcast by asking about the unique angle of OSS Capital in evaluating which commercial open-source companies. Heather explained that there are quite a few VCs investing in open-source and developer tools; however, OSS is looking for companies whose founders are also original developers of the respective open-source project. In her own words:
“We see an unbelievable upside in this kind of business approach. This is like a new kind of company and we really believe that it’s the way to success while simultaneously doing a lot of good for the technology industry.” (Heather Meeker)
2. Opportunities with data: breaking silos and why traditional open source licensing models don’t work for data
As a technology lawyer, Heather has spent several decades building hardware and software solutions with clients. Data was one of the issues that were of particular interest to her. One of the first cases she handled concerned an individual who had type I diabetes and could not access his own blood records because it was protected by a proprietary data framework of the device maker. Heather explained that even then, this seemed bizarre that the data could not be released.
Heather went on to explain that hardware and software companies seek to protect their innovations (and data) by various intellectual property (“IP”) rights. However, in practice, such IP regimes in general and copyright rules, in particular, are not suitable for data:
“Data is one of those things where copyright law is not really a good fit… There are other legal regimes for data, but a lot of it is copyright. Copyright is supposed to cover expression as opposed to facts. So it’s kind of strange bedfellows with data.” (Heather Meeker)
Heather emphasized that data could have a lot of utility for users who generate such data; and that interoperability is one of the key components to unlocking data value. She also noted that much data is currently stuck in silos, and much innovation could happen if data could be freed and opened up for innovation.
More specifically, two intertwined considerations require more attention:
- The data must be in the usable form: there should be common data standards and frameworks for data; “usability” also means that the user of data is not sued for any IP infringement;
- Sensitivity of data and data privacy considerations: we need to balance data privacy and how to share data properly.
Heather emphasized that — at least from a legal/licensing point of view — we cannot treat data in the same way as we treat software or video content: Old licensing simply don’t work for data.
3. Standards for data interoperability and incentives for developers
Although the amount of data that is generated is increasing every day, it seems that the current regulations seem to impose more restrictions on the flow of data. Is it possible to bridge the gap between more data and increasingly stringent regulations?
It is obvious that for data to flow and generate value, it is important to establish a common set of open standards for data. Heather noted that this task could be bestowed upon specialized standard-setting organizations or leave it to the developer community: the latter path has been especially successful. Markus Lampinen added that developers could be especially fast in solving problems and delivering realistic results.
“If we can figure out how to use data properly, then we have a possibility of doing things that nobody before us could do to manage healthcare, environmental, and societal problems.” (Heather Meeker)
4. The Value of Data
The nature of data makes it a very unique type of asset in the new digital economy. Heather suggested that differently from the video content, for example, data could be understood from the perspective of “data-as-a-service”:
“We can think of data like something that is being hooked up to a fire hose; what’s valuable is what’s in the hose and how fast it’s running.” (Heather Meeker)
Heather noted that in most business deals around data, a lot of the value derives from the data provider “massaging” the data — correcting it, taking out things that shouldn’t be in there, putting different data sets together, and providing up-to-the-minute information. A lot of the data that’s involved in data deals is actually publicly available — there’s nothing proprietary about it — but the art is in figuring out what to use and when to sample it and where to get it and then moving it all around and putting it together, which, again, is more like a service than a piece of intellectual property.
Increasingly, there are companies that also say that they want a certain result, that doesn’t necessarily need all the raw data, or don’t even want all the raw data, and that someone else should keep it. It’s too much for them to handle. Ultimately, all the businesses want from data is recommendations. But importantly, they also want transparency regarding how the recommendations were created. And that’s where we get into issues about reproducible and responsible AI.
Such liability concerns with data and AI seem to have become obvious in such areas as autonomous vehicles, healthcare, finance, etc.
5. Individual agency around data
As new solutions emerge with data, new sets of challenges emerge in the realm of individual agency and empowerment. Heather suggested that — as we have learned from the past — developers will come up with new data models that will gradually be embedded in large-scale consumer-facing settings. The laws and regulations with catch up later.
You can listen to this episode on Spotify, Apple Podcasts, and Google Podcasts.
You can find the transcript of the podcast here.
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