Democratizing Data: A New Approach for Trustworthy Information ft. Julia Lane

[00:00:07] Julia

No one can tell you what data sources to use and no one can tell you what data sources are any good. We can tell you a little bit, but what you really want is the wisdom of the crowds. Can anyone tell you how to build an energy efficient building? No.

There are thousands of standards and there’s a community of practice that comes in and says these are the characteristics that make an energy efficient building a LEED building. What we need to do is apply that same standard of discipline that is used to build buildings, in building a new data infrastructure.

[00:00:54] Intro

Imagine spending an hour with the world’s greatest traders. Imagine learning from their experiences, their successes and their failures. Imagine no more.

Welcome to Top Traders Unplugged, the place where you can learn from the best hedge fund managers in the world so you can take your manager due diligence or investment career to the next level.

Before we begin today’s conversation, remember to keep two things in mind, all the discussion we’ll have about investment performance is about the past, and past performance does not guarantee or even infer anything about future performance. Also, understand that there’s a significant risk of financial loss with all investment strategies and you need to request and understand the specific risks from the investment manager about their products before you make investment decisions. Here’s your host, veteran hedge fund manager Niels Kaastrup-Larsen.

[00:01:49] Niels

For me, the best part of my podcasting journey has been the opportunity to speak to a huge range of extraordinary people from all around the world.

In this series, I have invited one of them, namely Kevin Coldiron, to host a series of in-depth conversations to help uncover and explain new ideas to make you a better investor. In the series, Kevin will be speaking to authors of new books and research papers to better understand the global economy and the dynamics that shape it so that we can all successfully navigate the challenges within it. And with that, please welcome Kevin Coldiron.

Okay, thanks Niels and welcome everyone.

Trustworthy, high quality, unbiased data are the foundations for a healthy democracy. They help government understand what’s working and what isn’t. And they help citizens hold the government to account and pressure it to change.

Our guest today is a world expert in designing data systems and tracking their effectiveness. And she says the current system for producing that high-quality data in the US is broken and needs to change right away.

Luckily, she’s written a book that explains how we can do that. Our guest is Dr. Julia Lane. She’s a professor at NYU’s Wagner School of Public Service and her book is called Democratizing Our Data: A Manifesto, and she’s going to explain why our system doesn’t work right now and what we can do about it. Dr. Lane, thanks so much for taking the time to join us on the show today.

[00:03:23] Julia

I’m thrilled to be here and thrilled to find out that we have a New Zealand connection, so.

[00:03:27] Kevin

We do have a New Zealand connection. I’m married to a Kiwi. And where I’m actually recording this is from New Zealand. And you are from Palmerston North, is that right?

[00:03:37] Julia

Yes, the hub of the Manawatu.

[00:03:39] Kevin

Okay, great. Well, so I found your book via an article that you posted talking about AI. And you said, on the one hand, we’ve got Goldman Sachs coming out saying AI is going to replace 300 million jobs. On the other hand, we’ve got economists at MIT saying it’s actually going to be good for low-income workers. And the bottom line is we don’t know who’s going to be right, but we do know that it’s very important and we do know we need to get great data on this.

And then within that same article, you posted an excerpt from a survey that the Census Bureau sends out to firms to try to get at this question. And I tried to follow the instructions on the survey, but I couldn’t do it.

[00:04:24] Julia

That’s the point. Yeah.

[00:04:27] Kevin

And so, can you tell us a little bit about that survey method for collecting data? Because as I read your book, it became apparent that is how a lot of the data that we use is actually put together, collected and put together.

[00:04:47] Julia

That was the primary motive for writing the book. And the article to which you refer, or the MIT Press reader, is kind of just the tip of the iceberg. So let me roll back the video.

And really the key point that I want to make is just that measurement is the basis of just about any activity that we want to think about, developing standards and so on. Data is absolutely critical for decision making.

And you mentioned a lot of the people listening to your podcast are in finance and investments and you need data to make decisions to make sure resources are allocated. And a lot of the issues that we have now are that our data are not particularly useful for decision making.

And a large part of it goes back to what kind of framework are we operating in? What are we measuring and who is it for and how can it be trusted?
Because if you’re going to make decisions, you want to make sure that they’re trusted.

So, I’ve been, by way of background, I’ve been working for most of my career as an academic economist, but worked a great deal with statistical agencies, the biggest one obviously being the US Census Bureau.

And it was clear 30 years ago that surveys were not the way in which data could be collected going forward in the future, both because the concepts and the ideas that they were trying to measure didn’t make any sense, they weren’t timely, they weren’t actionable, and that the quality was going down substantially. And so, I wrote the book and talked to a whole bunch of people and kind of read the book to get the real issues.

I’ll talk briefly, shortly, about the two examples that I used, which was workforce data and measures of economic activity. What the challenges were with that.

And then the challenge that I kept running into when I talked with people is the data infrastructure just isn’t going to change because people are used to doing the same thing. That’s what they’re mandated by Congress to do, and it’s impossible to change.

So, we really need to rethink the way in which we are generating information in a way that can be trusted, because if we don’t, we’re going to be stuck. The most classic example was in the most recent presidential election, the polls were wrong yet again. That’s the same methodology that we’re using to collect economic and demographic data. They’re systematically biased. They’re systematically wrong, and we need to rethink how we’re doing this work.

[00:07:52] Kevin

You know, one of the things that a lot of people in the investment world have been wrestling with in the run up to the election was that, on the one hand, we had the standard statistics that we all look at: GDP, unemployment in particular (which you just referenced), and they’re saying, hey, the economy’s doing pretty well, doing really well. On the other hand, we’ve got sentiment data, pulled together from different sources, saying people are really unhappy.

You know, sentiment, a year or so ago, was kind of as low as it had been in a long time, and that that didn’t map into what the statistics were telling us. So, I mean, is that an example of the sort of, I guess, problem that you’re referring to? We’ve got kind of two different tools that are giving us different pictures, or do we just don’t know what the true state of play is in some sense?

[00:08:44] Julia

Well, I think the issue is that regardless of where you turn, we don’t have a sense of what’s going on. The economy has changed so rapidly over the past 20 years, and we’re using a system that was developed a hundred years ago to deal with measures, like measures of employment and unemployment, that Were developed in the 1920s and 30s and a data collection methodology that was developed in the 20s and 30s. And it’s just no longer fit for prime time.

For example, the unemployment measure is ‘actively looking for work in the survey week’. They call people on a phone for employment. They say, were you paid for at least one hour in the survey week? Are you in paid work for one hour in the survey week? Those are not measures of what’s going on in the labor market, which is much more complex than that. And they try and do a better job of it in the surveys.

But there is much richer information about what’s going on with modern technologies that can be captured and used to inform the decision making of business, of individuals and of government agencies. And the example that you used about AI is a really good one.

So, we’ve got this transformation. People are talking about the transformation that has happened and that is happening from their own observation in the labor market. But we don’t have statistics on that. So how is anyone going to know what is happening?

You know, some people say 80% of jobs are going to be fundamentally changed. And the Census Bureau says, well, only 5% of firms are using AI, which is correct. But you go back and (part of the reason I got into this is I was on the National AI Research Resources Task Force) government wants to spend a lot of money. We recommended $2.6 billion for spending in research so that the work that is being done in AI is not just for a few companies, but is spread more broadly, and that we could understand it.

So, I’m an economist like you, and of course, the first thing that you’re being told to do, in this presidential and congressionally chartered committee, is that if you’re telling us to figure out how much to spend, you want to know, okay, what’s the current state of affairs in AI? What’s the size of the workforce? How many firms are using it? What kind of work is being done? There’s no information.

So, that seemed useful, so I went to see where it was. I looked at this survey that the Census Bureau did. And you said you didn’t understand it. Let me reflect for the audience what the question was. It is a question that is sent, a questionnaire where they ask respondent to answer in a firm.

And it says, tell me about the impact of AI in your firm (which could be a thousand people), has it increased employment, decreased employment, or had it about the same? How is one person going to be able to answer that?

Or what’s happened to the skills of the people as a result of AI, increased, decreased, or about the same? You’re like, whatever they’re saying, if I’m the survey respondent, I’m going to skip that question. Which may be why the Census Bureau is saying only 5% of firms are using AI because the human being that had to answer it skipped it.

[00:12:49] Kevin

So, maybe just give us a little, you know, it sometimes helps to kind of visualize things. Like what? One thing that really surprised me about your book, and I should have known this because I’m kind of a user of all these statistics, but you said that most countries have a national statistical agency, a single source for gathering data. In the U. we’ve got, I believe, 13 statistical agencies. Some departments have multiple agencies within a department.

So, can you just kind of give an overview of what data collection looks like in the United States and some of the inefficiencies that that creates? And then maybe we can use that as a foundation to think about a better way to do that.

[00:13:39] Julia

So, there are some very good reasons why this US statistical system is fragmented. The basic idea was that the people who were gathering the data should be closer to the communities that needed the data so that it could be use driven. That makes a whole lot of sense. So, you’ve got, US department of Agriculture has two statistical agencies. You’ve got energy, you’ve got justice statistics, you’ve got education statistics and so on. It makes a whole lot of sense. The problem comes when you’re trying to knit the things together.

I don’t think actually that’s a bad thing. The question is, how could you use different technologies to make sure that the agencies are accountable and responsive and how can you combine the information and produce it in a sensible way? And that’s kind of what the book is about. And that’s how I think a whole set of recommendations, that are implementable now, could be put together.

So, part of the problem with the agencies is they are set up like sausages in a sausage factory. They have been told by Congress, and given line funding, to produce data in a particular way. And they’re not given a whole lot of chance to innovate and change. And the workforce is not structured to innovate and change. They are survey methodologists and SAS statisticians who know how to collect data from surveys.

They are not trained to think about the different ways in which data are generated nowadays, which is, you think about the digital footprints that you throw off, even on this call. It’s being recorded by Zoom, and God knows who else, but they know lots of information about us.

You don’t have to ask people the answers to questions. What you have to be able to do is to figure out what data… First of all, you have to define the question, then you have to figure out what data are available to answer the question. And then you have to figure out how to harness the data in a way that is going to make sense and be able to answer the question in particular.

[00:16:14] Kevin

That’s interesting. So, is what you’re saying almost that the answers are out there and it’s better just to figure out a way to answer those questions with the data rather than in some sense asking people directly.

It’s actually more efficient to collect the implied answer from the data than the “actual answer” from a direct question, if that makes sense.

[00:16:45] Julia

That’s exactly right. So, let’s flip it on its head. So, start with the question that’s being asked. And I’m a big fan, I know that you have an international audience, but in the United States there are 50 states and there’s this great line that Lewis Brandeis, Supreme Court justice had, which is, “States are the laboratories of democracy”.

And so, you could imagine (and this is what we’ve been doing with the States, and this is what I recommend in the book), that States understand what questions need to be answered. What’s going on, and what local economic activity is there in my town or in my area? What’s the workforce conditions and what data are available to answer those questions in my area, what are the administrative records? The records are generated from the administrative programs like tax data, which is free flowing. What could I pull in from credit card transactions or many, many other sources so that I could answer the questions in a timely and actionable fashion rather than asking people like the AI question that you had. How could we figure that out from observing people’s behavior rather than asking them?

So, think about this question about how do we figure out what firms are working in artificial intelligence?

To go back to your economic activity example, a hundred years ago we classified what firms did by what was produced, by the standard industrial classification. So that’s why we have so many SIC codes for agricultural manufacturing, because that’s what we did 100 years ago, that so much more of the economy was.

Then about 50 years ago, we started to classify firms. We said, oh, it’s a service economy. So, we’re going to describe them or classify them by how things were produced. That’s where we got the North American Industrial Classification codes, NAICS codes. But really, nowadays, we’re not producing things as much anymore. It’s an innovation economy.

What’s innovation? It’s the creation of ideas. That’s what Paul Romer got the Nobel Prize in Economics for in 2018. He talked about the economics of ideas.

So, the way of thinking of the way that firms are organized is they have people having ideas about how to put things together. What Heidi Williams has referred to as ‘breaking the shackles of scarcity’. That’s what innovation is, putting things together in clever different ways. The endogenous technological change.

So, if we’re going to group firms by what innovative work they’re doing, say artificial intelligence which might be artificial intelligence in legal services, in health services, in financial services or in retail. How are we even going to get at that because AI is a complex thing?

So, what we’ve been doing is thinking about how can we group firms into industries of ideas paralleling economics of ideas? How can you group firms by people who are thinking about solving a problem in a new way? That’s innovation.

So, for the past 10 years, the way in which you build that system is you trace who is funded to do science in a particular area: quantum computing, AI, synthetic biology, or whatever you can. It’s not just the principal investigator, it’s the graduate students, undergraduate students, postdocs, clinical scientists. And then you trace how those people who’ve been trained to think about a problem, where are they being hired?

Because that’s how their ideas are getting transmitted. Just as you and I are communicating now. Humans communicate, yes, to some extent through papers and patents, but largely through human interaction.

And when you look at economic activity being around universities, it’s largely because people are constrained. They like to be, in particular, geographies, they like to share ideas in person and so on. So, tracing those flows of people from areas where the research investments have been high is a way of tracing how AI has worked its way through the economy.

[00:22:03] Kevin

That’s interesting. So would an example be. I’ve got a… I’m going to say I got a… Let’s say I get a degree in machine learning from NYU or from Berkeley, and I end up working for a pharmaceutical company, or I end up working for, you know, maybe I work for Fonterra, which is the big milk company here in New Zealand. And by kind of tracing the flow of people into different industries, then you’re saying, okay, that gives us some way of measuring kind of the penetration of these various, I don’t know, foundational training across the economy or something like that.

[00:22:49] Julia

That’s exactly right. And so, then you can see who’s hiring them.

Now, Frontera or, you know, a company in the Bay Area, they are hiring someone with a degree in machine learning, not because of their good looks for personality, but because they want those skills. Now why do they want those skills?

And, you know, a PhD, by the way, in AI, coming out of Stanford, they’re starting at $800,000 a year, plus fringe benefits, plus bonuses. So, the firm is hiring them for a reason, because those ideas are getting transplanted in that firm and because of the existence of administrative records, data that are generated from the administration of government programs like unemployment wage records.

You actually have information (protected information, obviously) about all the workers in those firms, and you can get information about the skills of those workers. So, you get a sense of, well, that guy in machine learning, that’s a New Jersey guy. It could be male or female. They trained in machine learning, applied to agriculture, let’s say, and they get hired into a dairy company, or they’ve been using machine learning at UC Davis, and it’s wine.

So now, that firm wants to do something with AI in wine, maybe to figure out better times to pick the grapes, figuring out the features of the grapes. So, they’re doing something in that area and you got a sense of the skills.

So that’s a way, a much more powerful and timely (going back to my thing), more timely and actionable way of understanding what’s going on in the economy than asking people the question.

[00:24:53] Kevin

So, you’ve been involved in a number of different initiatives to try to create some of these alternative data collections and dissemination models. And in that article that you wrote about AI, you said that we need a center for data and evidence that sits outside the federal government, that’s nonpartisan and independently funded. And I thought that was interesting because it felt to me that was kind of a different approach than what you had suggested in the book.

And I was wondering that your mind had changed since you published the book, or if, what you’re proposing with AI, is just kind of, hey, we need something that we can set up quick and fast because this is such an immediately important topic. If you see where I’m getting at.

[00:25:46] Julia
Yeah, no.

[00:25:47] Kevin

Because it seems quite important because if you’re saying, hey, you’re throwing up your hand saying there’s just nothing we can do within the government then that’s quite worrying to me.

[00:25:58] Julia

Well, what I had recommended in the book was that a federally funded research data center be set up. And those are, for those of you who are listening, that is kind of an independent entity like a national lab or a think tank, but that is chartered by a federal agency so that it has closer ties.

I sat on two advisory committees between, when I wrote the book; two White House advisory committees, the NAIR and the Advisory Committee on Data for Evidence Building. And the feeling was pretty strong that if it was going to be a cross-cutting activity, an FFRDC wouldn’t do it.

[00:26:45] Kevin

And sorry, the FFRDC is the…

[00:26:50] Julia

The Federally Funded Research Data Center, that’s what I recommended in the book. Sorry. And to be honest, the challenge there is a federal government, it’s so slow and it’s going to become even slower than it has historically been.

So, as I talk to more and more people my thinking did evolve by saying, maybe it shouldn’t be directly, you know, even peripherally attached. It could be an independent think tank like the National Bureau of Economic Research. Which is very influential, or like the Urban Institute or the MDRC that were all set up outside the government, but, really, a lot of the ideas can spill in.

And then this idea of democratizing data with direct ties with the demand side, with the States and with the, with the businesses, I think that’s going to be more robust. Given five years of my ideas being floated out there and people saying, really you’d be much better off with this independent nonpartisan think tank.

[00:28:03] Kevin

What is the state of play on that? Are you, are we in the process of putting something like that together or is it still kind of in the idea stage?

[00:28:14] Julia

I think there’s a lot of energy around it and largely because things, podcasts like the one that we’re having here, there are a lot of foundations who are starting to come together, a lot of businesses who are coming together and saying, just like MDRC and MBR were set up, they were set up to solve a set of problems and they can’t get solved inside the government. So, we’re going to jointly fund an independent institute.

And I think that institute would have kind of three elements. One would be it would be much closer to the demand side. So, I said that the strength and the weakness of the US system is how fragmented it is. The original idea was to be closer to the market, but this institute would be set up to be directly responsive. You set up a direct organizational infrastructure so that the demand side gets included from States and from businesses and from the workers.

Then the second piece is explicit recognition of the need for written down standards for this new type of data that we discussed. Stat agencies developed standards for survey data collection. We need the creativity of generating standards that can codify the tacit knowledge that we have about the new types of data to produce products that can be trusted. Just like we have an underwriter seal of approval for lab bulbs, have an underwriter seal of approval for the data, and community driven.

So, the challenge that people kept telling me about the centralized FFRDC, Federal Funded Research Data system, is that really the questions would still be coming from Washington. They would be centralized. And that’s why it wouldn’t be robust enough to produce the answers that we needed.

So, the democratization would be even stronger than I recommended in the book. It should be community driven and bottom-up. So, I think that’s critical.

[00:30:39] Kevin

I’ve got two questions. One just, well, I guess given that we’ve just had an election, it was very contentious. If you’ve got something that’s set up as a think tank, I mean, think tanks come under a lot of criticism because oftentimes they’re just kind of lightly veiled lobbying institutes, right? They’re funded by a particular, you know, someone’s got a particular point of view. How do you stop that from happening? How do you maintain trust from all sides of the political spectrum in something like this?

[00:31:09] Julia

That is, again, a great question. So, I think that the absolutely critical thing to generate trust, from an operational point of view, is set up a governance structure that is clearly nonpartisan. So, we have, the National Bureau of Economic Research is a good example of that, that does not set up policy recommendations. They provide the facts. They provide the answers.

Now, the way in which this would be different from the NBR is you would have two pieces, right? You’d have one being the plumbing, which is the data infrastructure, which takes all this noisy data and turns it into analytical high quality data set with a green checkmark seal of approval. That would be one piece. But then you have a broad range of people coming in whose mission is to solve the data problem and steer well away from policy recommendations. So, it’s data and evidence. That’s what explicitly calling that institute, and it can be nonpartisan, but NPR is very, very careful not to make any policy recommendations.

[00:32:38] Kevin

You’re a Kiwi, as we said at the beginning, and I know you were involved in working with their national data or statistical infrastructure. Are there other countries outside the US that do this really well? Is New Zealand an example? Are there countries in Europe that have, that have kind of modernized their data collection infrastructure that we could mimic in some way or take lessons from?

[00:33:04] Julia

Well, obviously I’m a fan of what the New Zealanders did, and that’s actually a really good example. So, I was working at the Census Bureau, and the way in which the integrated data infrastructure got started in New Zealand was I was on leave in New Zealand and could see that that system could get mimicked.

And then the Prime Minister, where he was Minister of Finance at the time, Bill English, saw that the data that had been built at the beginning in New Zealand could be used to inform the decision making of the New Zealand government. And Bill, Sir Bill English, Bill was absolutely brilliant at figuring out how it could get integrated across the government.

And now, with the new government, it’s turned into a Ministry of Social Investment. And much of the decision making has to be made on a cost benefit calculus. And they have integrated it into the entire decision making of New Zealand policy. So, in fact, that is aspirational. We looked at it a lot when I was sitting on those White House committees, what New Zealand has done.

I think there are other countries that are starting to head that way. I hate to say this, but the Australians, but yeah, I think in the UK, I sit on an advisory committee for the UK Data Service. They’re moving. This is inevitable. The Service is just too slow, too expensive, not granular enough, not timely enough.

So really the question is not whether it’s going to happen, but when and how and how strategic we can be? And I do think every time you get a new administration, they come in and they say, what can we do better? And I think this is clearly an area in which there’ll be quick returns.

So, the basic idea is democratize the data. You move it out of Washington, and responsive to businesses and the States, using maybe many of the tools that, as you mentioned, Audrey Tang has been developing to get that community input, and then develop standards that give you the green check mark, that this data can be trusted, that it’s sensible, and that it captures economic activity or demographic/social activity in a way that can be used and acted upon. Those are the kind of the critical elements.

[00:35:57] Kevin

I wondered if we could kind of, toward the end here, loop back and talk a little bit more about, you know, the current economic data. You know, a lot of the listeners here are investors and rely on things like the national income and product accounts that is basically being used to produce GDP statistics, unemployment, things like that. How confident should we be in that data when we’re analyzing it and trying to make judgments about the current state of the economy? I mean, what degree of skepticism should we apply to that?

[00:36:33] Julia

I think a lot. I don’t think we can be confident. And I think the people at the Bureau of Economic Analysis are very aware of it. It’s not that they’re not terrific people, and good economists, and good statisticians. Erik Brynjolfsson, who you may have heard of, who’s at Stanford, and Diane Coyle, who’s at Cambridge, we had a conference at Stanford in March, actually, about the measurement of AI. (I can send you the link after we get off the call so that you can provide it in the podcast.) And I asked Dipan Arora, who’s a great guy, who’s a director of BA, and his assistant associate director who does the measurement of the national income and product accounts, come and talk. And they said they’re really struggling with how to measure the changes in the economy. Part of it is they don’t have the resources. They don’t have a framework within which to really operate.

So, I think they’re as aware as anyone else of the challenges that they face and the problems, I highlight in the book, of it being stitched together not purposefully, but kind of haphazardly in order to meet the needs.

Steve Landerfeld, who is the director of BEA, Bureau of Economic Analysis, which generates the GDP accounts, wrote multiple times about how they should be reconceptualized, and nothing has been able to be done. Diane Coyle wrote a brief but affectionate history of GDP. She says, 4/5 of economic activity is not measured now; 80% is not properly measured.

[00:38:27] Kevin

Is the message then that you really ought to be searching for alternative ways to kind of track these things if you’re really serious about trying to understand the economy? I mean, are there data sources that you think do a better job, at least at a high level, that are out there that people could look at?

[00:38:49] Julia

So, you were kind enough to open up with saying, you know, I’m an expert in this area, so I’m going to say that I’m expert enough to tell you that no one can tell you what data sources to use, and no one could tell you what data sources are any good. We can tell you a little bit, but what you really want is the wisdom of the crowds.

Can anyone tell you how to buy, how to build an energy efficient building? No.

There are thousands of standards and there’s a community of practice that comes in and says, these are the characteristics that make an energy efficient building a LEED building. What we need to do is apply that same standards discipline that is used to build buildings in building a new data infrastructure. We know how to do it. We’ve got all the pieces, parts, we know what the needs are. You have businesses trying to figure out what the heck is going on with the workforce. You have workers, you have government agencies. Just instead of all these ad hoc efforts, have a systematic, standardized approach to building that infrastructure. And so that’s doable and feasible. And that’s what we’ve been talking about.

[00:40:10] Kevin

Well, I appreciate that. And what are your future plans in terms of involvement in these efforts?

[00:40:19] Julia

There are a lot of foundations who, as I indicated, who’ve been very interested in how do we get more organized? I think it’s going to be hard for the government, for all kinds of reasons, to be organizing this.

So, I’m working with a couple of think tanks: American Enterprise Institute, which is center right, and the Brookings Institution, which is center left, to bring together those foundations to develop the kind of institute that we’re talking about. So, you know, the MDRC and the Urban Institute were set up with funding from foundations and government agencies.

Rand, National Bureau of Economic Research, all of those were set up deliberately to solve a problem. And that same kind of energy is happening now because the current system just isn’t working.

[00:41:17] Kevin

I think that’s a good place to wrap up and just say thank you for highlighting these problems and working towards solutions. It’s very important for all of us. So, thanks for taking the time and joining us on the show today.

[00:41:32] Julia

Well, you’ll see some results in six months to a year, I expect. So, I’m very excited about the future.

[00:41:38] Kevin

Well, that’s really good to hear. That’s really good to hear. So that’s it for today. Julia’s book is called Democratizing Our Data: A Manifesto. Please make sure you get a copy of it and follow her work because I think you can tell from this conversation a lot of the ideas are not being discussed enough yet on mainstream media. So, for all of us here at Top Traders Unplugged, thanks for listening and we’ll see you again soon.

[00:42:07] Ending

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