Data Commons — The Emerging Infrastructure of AI

In this episode of The Macro AI Podcast, Gary and Scott dive deep into the emerging concept of Data Commons — shared, governed ecosystems that make data interoperable, trusted, and ready for AI. 

They explain what a Data Commons is, how it differs from traditional data lakes, and why it’s essential to the next phase of AI transformation. From Google’s global Data Commons and the NIH’s biomedical repositories to emerging “Private Data Commons” inside enterprises, the hosts show how these ecosystems are reshaping trust, governance, and efficiency. 

Listeners will learn how Data Commons reduce AI hallucination, enable grounding, improve reproducibility, and support ethical AI. Gary and Scott also explore governance models, global equity, and the rise of AI agents that automatically fetch verified data from commons networks. 

If you’re a CIO, CTO, or business leader preparing your organization for AI, this episode offers the strategic framework you’ll need to understand the infrastructure of the future. 

🔗 Links mentioned: 

• Google Data Commons 
• Open Data Policy Lab — AI Data Commons Blueprint 
• Therapeutics Data Commons 
• NIH Data Commons 

About your AI Guides

Gary Sloper

https://www.linkedin.com/in/gsloper/

Scott Bryan

https://www.linkedin.com/in/scottjbryan/

Macro AI Website:

https://www.macroaipodcast.com/

Macro AI LinkedIn Page:

https://www.linkedin.com/company/macro-ai-podcast/

Gary's Free AI Readiness Assessment:

https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness

Scott's Content & Blog

https://www.macronomics.ai/blog

Transcript

00:00
Welcome to the Macro AI Podcast,  where your expert guides Gary Sloper and Scott Bryan navigate the ever-evolving world of artificial intelligence.  Step into the future with us  as we uncover how AI is revolutionizing the global business landscape  from nimble startups to Fortune 500 giants.  Whether you're a seasoned executive,  an ambitious entrepreneur,

00:27
or simply eager to harness AI's potential,  we've got you covered.  Expect actionable insights,  conversations with industry trailblazers  and service providers,  and proven strategies to keep you ahead in a world being shaped rapidly by innovation.  Gary and Scott are here to decode the complexities of AI  and to bring forward ideas that can transform cutting-edge technology  into real-world business success.

00:57
So join us,  let's explore, learn and lead together.  Welcome back to the Macro AI podcast, where we decode what's really happening at the intersection of business strategy and artificial intelligence. I'm Gary Sloper, joined as always by my co-host, Scott Bryan. And today we're exploring a term that's quickly becoming foundational to the AI economy, data commons. Yeah, Gary, this one might sound a little academic at first, but it's actually something

01:26
Every CIO, CFO, CEO  should understand. ah So as AI becomes really truly embedded into business processes, the idea of a shared governed and high quality data infrastructure, which is what is now commonly being called data commons,  is  actually starting to shape how we'll build trust, transparency, and competitive advantage that a lot of entrepreneurs are going to tap into.

01:56
Yeah, you're absolutely right. And think of a data commons as the new digital utility. Similar to the electrical grid  or the internet backbone, but for trustworthy interoperable data. It's  what allows AI systems to access verified information instead of making educated guesses. So just kind of like that one new digital utility that you could expect here in the future. Yeah.

02:25
So let's  define it a bit.  A data commons  is  much more than just a data warehouse  or an open data portal. uh It's an ecosystem. So more like a governed shared platform  where data, infrastructure and tools  live together. So people in AI systems can really discover, analyze and reuse information responsibly. Right.

02:54
The key is governance, right? A data commons  isn't just a pile of spreadsheets, ah you know, sitting somewhere on a public server. It's curated, versioned,  and most importantly tracked. So you know who contributed to the data, where it came from, and really under  what license it can be used  and, uh you know, for the intended folks looking to tap into it.

03:21
Yep, good point. And that governance layer makes all the difference for AI. So when models are trained or grounded  in data commons data sets, they inherit the quality, the context  and the provenance that are baked into that ecosystem, which is critical for reducing bias  and also for improving trust.  And I think if you're listening right now, you're like, why does this matter to AI? uh

03:49
And here's why we want to talk about this today. This concept is really exploding. AI models are only as good as the data that powers them. We all know this.  When we talk about hallucinations,  the model confidently spits out the wrong answer. So, you know, that often happens because the model doesn't have access to real structured data at runtime. So this is a really important concept. Yeah, exactly. um

04:16
Imagine  an AI assistant that can pull,  for example, live unemployment or live inflation data from a data commons instead of just  guessing based on some pre-trained sets. uh that's really not  science fiction. It's what Google's data commons is already enabling. uh So  Google's data commons integrates data from the Census Bureau, the World Bank, the UN.

04:44
and hundreds of other public sources into one searchable knowledge graph. Yeah, you're right. So if you were to ask it, what was the unemployment rate in Massachusetts compared to the national average in 2024? The AI can query the commons, pull correct numbers, say 3.2 % versus 3.8%, and cite the Bureau of Labor Statistics as part of the output. Yep.

05:11
Yep. And that, that single example that you just gave Gary, why data commons are so powerful. They, they make AI that is,  you they make it grounded, they make it auditable, they make it trustworthy.  And, um, that's really exactly what business leaders are demanding as they prepare to deploy enterprise AI at scale.  I think if you were to look at some of the leading examples out there, talked about Google, Google's project is the big one.

05:40
A unified public data commons that connects economic, uh demographic, ah climate and health statistics. ah They've even built a model context protocol, MCP. So  AI agents can plug directly into that data set in real time. So we've talked about MCP in the past, but this  is really powerful.

06:04
Yeah, I mean, imagine lots of different data commons where you can just plug right in using MCP, know, standard, basically the standard protocol  and get those, those data sets in real time. That's, that's powerful.  And then there's a, let's just another example, the, the therapeutics data commons or TDC. uh That one's focused on AI  in drug discovery. And that includes  it, it, it hosts curated uh benchmark data sets that researchers can use to

06:34
to train models  and predicting  molecules and target interactions. So it's where  kind of open science meets machine learning.  The NIH Data Commons task takes it another step further. They're building shared biomedical repositories. And that allows uh secure

06:59
reproducible AI research, which  is obviously impactful for what they do and what they're serving  the entities for. In Europe, the open data commons movement underpins smart city and climate initiatives. So cities like Amsterdam and Barcelona are already using urban data commons for traffic flow, energy management, uh even carbon reduction  modeling.  you're not just seeing it just for healthcare, you're seeing it for things like climate, which is pretty cool.

07:29
Yeah, exactly. I think kind of at the macro level, you already have a global movement towards  shared  governed data ecosystems and each domain is building its own data commons tailored to its mission, which is really going to enable all kinds of startups around the world to take advantage of this level of uh up-to-date data.  Yep. You're absolutely right. So as we always do on every show,  if you're a business leader,

07:56
in trying to understand the business value and the enterprise implications here. ah What does it mean for a CIO or CFO building this  in their AI roadmap? Yeah, I think it means that  data infrastructure ah becomes  strategic infrastructure. So enterprises will start building private data commons  and they'll have, they'll be

08:22
Internal hubs where data from uh CRM, ERP, HR, and finance systems are all standardized, labeled, and made accessible under proper guidance.  that's, again, that's private data commons.  Well, think about this.  When you add AI agents on top of that, you're no longer  operating on inconsistent silos, right? They can reason over a single source of truth.

08:48
That's what enables reliable analytics,  AI co-pilots, and automation for your business. So another key point there.  Exactly. So that private data commons, that data infrastructure will then be that kind of that foundational building block for AI agents that are smart and accurate.  I think the ROI  obviously is huge. You'll have  across those silos, you'll have reduced  duplication of data cleaning. You'll have

09:16
faster decision cycles, you'll have lower regulatory risk. ah But beyond that, it's about trust. Executives need to know that  when their AI system gives a forecast or a compliance report, it's drawing from verifiable permissioned data. Hey, you can think of the data commons as the connective tissue of AI transformation.

09:43
infrastructure that allows organizations to scale AI responsibly, which is going to be, and already is, but will be even more of a requirement as  you need to be responsible around your artificial intelligence, both for regulators and for  your customers that want to continue working with you. Yeah. On that point, let's just shift the focus little to governance  and uh ethics, because I think now not all data commons will be created equal. uh

10:13
The governance question is huge. So who decides what goes in,  who has access, how do we ensure underrepresented regions and communities are also included in  public data commons? Yeah. I think that's where the policy world is catching up. uh Groups like  Open Data Policy Lab have proposed global AI data commons frameworks,  shared equitable infrastructures.

10:41
designed to make AI fairer and more inclusive. uh The idea is to prevent an AI divide where only the largest tech firms, for example, control the world's data supply, which  is not the intention here. Right. Yeah. So data is like oil  and the open  AI data commons frameworks will enable people to have to be able to access it, not just the largest corporations, like you mentioned. uh

11:10
And I think for enterprises, this translates into uh compliance and brand protection. The reason I'm saying that is because a well-governed data commons will demonstrate uh data provenance, consent management, and auditability is going to be key. ah It's really going to be critical under GDPR, CPRA,  and other emerging governance acts because it will have to be auditable. Exactly. This isn't just good ethics.

11:38
Good business transparency and traceability are fast becoming table stakes. Yep. All right. So let's kind of think about where this goes next. You know, we're heading toward an ecosystem where LLMs  and AI agents connect to data commons automatically.  They'll all have data hooks,  APIs that let them query live data sets,  you know, kind of the way browsers pull from the web and there'll be data commons everywhere. And like we said, in almost every domain.

12:07
Yeah, it's the next evolution. Agentic AI grounded in shared data. Instead of hallucinating agents  in theory or in practice will retrieve facts from common graph reason over it and even contribute to, to, to, you know, new data back  to the user. all governed and versioned in that particular environment from a agentic AI standpoint. Yep. And as live to real time as possible. really good.

12:37
good factual information  that's trustworthy.  And I think businesses that understand this early will have a huge advantage. They'll be able to deploy trusted AI faster. They'll probably have fewer compliance headaches and  they'll even have a more clear ROI  because they know how quickly they can access this data and how to use it. Yeah. So final thought,  Data commons  are to AI. What the power grid is,

13:05
the industrial age. They create the infrastructure for reliable, equitable access to the fuel of the future, which is data. So that's my parting gift there as we close out. Good summary. Good summary. And for our leaders listening, just start thinking about your own data commons strategy. You're going to need to identify what data commons you might need, where they are, who governs them. What do you need? Ask what data do we have? What

13:35
data should we share  and what governance do we need  and how can AI safely plug in? Yeah, that's the blueprint for AI success over the next decade. Yep. All right. Well, thanks again for joining us on the Macro AI podcast.  And if you found this episode helpful, please share it with a colleague or a board member who's shaping your company's AI strategy. Yeah. And as always, you can find more insights and resources on

14:01
either one of our business pages, uh mine on macronutservices.com and Scott's macronomics.ai, which I'll  link again in the show notes. And until next time, keep  leading in the AI era.