An optimist's view: What makes data good?

In Episode 4 of Series 7 of The Rights Track, Todd is in conversation with Sam Gilbert, an entrepreneur and affiliated researcher at the Bennett Institute for Public Policy at the University of Cambridge. Sam works on the intersection of politics and technology. His recent book – Good Data: An Optimist’s Guide to Our Future – explores the different ways data helps us, suggesting that “the data revolution could be the best thing that ever happened to us”. 

Transcript

Todd Landman  0:01 

Welcome to The Rights Track podcast which gets the hard facts about the human rights challenges facing us today. In Series 7, we're discussing human rights in a digital world. I'm Todd Landman, in the fourth episode of this series, I'm delighted to be joined by Sam Gilbert. Sam is an entrepreneur and affiliated researcher at the Bennett Institute for Public Policy at the University of Cambridge, working on the intersection of politics and technology. His recent book, Good Data: An Optimist's Guide to Our Future explores the different ways data helps us suggesting the data revolution could be the best thing that ever happened to us. And today, we're asking him, what makes data good? So Sam, welcome to this episode of The Rights Track.

Sam Gilbert  0:41 

Todd thanks so much for having me on. 

Todd Landman  0:44 

So I want to start really with the book around Good Data. And I'm going to start I suppose, with the negative perception first, and then you can make the argument for a more optimistic assessment. And this is this opening set of passages you have in the book around surveillance capitalism. Could you explain to us what surveillance capitalism is and what it means? 

Sam Gilbert  1:01 

Sure. So surveillance capitalism is a concept that's been popularised by the Harvard Business School Professor, Shoshana Zuboff. And essentially, it's a critique of the power that big tech companies like Google and Facebook have. And what it says is that, that power is based on data about us that they accumulate, as we live our lives online. And by doing that produce data, which they collect, and analyse, and then sell to advertisers. And for proponents of surveillance capitalism theory, there's something sort of fundamentally illegitimate about that. In terms of the way that it, as they would see it, appropriates data from individuals for private gain on the path of tech companies. I think they would also say that it infringes individual's rights in a more fundamental way by subjecting them to surveillance. So that I would say is surveillance capitalism in a nutshell. 

Todd Landman  2:07 

Okay. So to give you a concrete example, if I'm searching for a flannel shirt from Cotton Trader, on Google, the next day, I open up my Facebook and I start to see ads for Cotton Trader, on my Facebook feed, or if I go on to CNN, suddenly I see an ad for another product that I might have been searching for on Google. Is that the sort of thing that he's talking about in this concept?

Sam Gilbert  2:29 

Yes, that's certainly one dimension to it. So that example that you just gave is an example of something that's called behaviour or retargeting. So this is when data about things you've searched for, or places you've visited on the internet, are used to remind you about products or services that you've browsed. So I guess this is probably the most straightforward type of what surveillance capitalists would call surveillance advertising. 

Todd Landman  2:57 

Yeah, I understand that, Sam, but you know when I'm internally in Amazon searching for things. And they say you bought this other people who bought this might like this, have you thought about, you know, getting this as well. But this is actually between platforms. This is, you know, might do a Google search one day. And then on Facebook or another platform, I see that same product being suggested to me. So how did, how did the data cross platforms? Are they selling data to each other? Is that how that works? 

Sam Gilbert  3:22 

So there's a variety of different technical mechanisms. So without wanting to get too much into the jargon of the ad tech world, there are all kinds of platforms, which put together data from different sources. And then in a programmatic or automated way, allow advertisers the opportunity to bid in an auction for the right to target people who the data suggests are interested in particular products. So it's quite a kind of complex ecosystem. I think maybe one of the things that gets lost a little bit in the discussion is some of the differences between the ways in which big tech companies like Facebook and Google and Amazon use data inside their own platforms, and the ways in which data flows out from those platforms and into the wider digital ecosystem. I guess maybe just to add one more thing about that. I think, probably many people would have a hard time thinking of something as straightforward as being retargeted with a product that they've already browsed for, they wouldn't necessarily see that as surveillance, or see that as being particularly problematic. I think what gets a bit more controversial, is where this enormous volume of data can have machine learning algorithms applied to it, in order to make predictions about products or services that people might be interested in as consumers that they themselves haven't even really considered. I think that's where critics of what they would call surveillance capitalism have a bigger problem with what's going on.

Todd Landman  4:58 

No I understand that's, that's a great great explanation. Thank you. And I guess just to round out this set of questions, really then it sounds to me like there's a tendency for accumulated value and expenditure here, that is really creating monopolies and cartels. To what degree is the language of monopoly and cartel being used? Because these are, you know, we rattle off the main platforms we use, but we use those because they have become so very big. And, you know, being a new platform, how does a new platform cut into that ecosystem? Because it feels like it's dominated by some really big players.

Sam Gilbert  5:32 

Yes. So I think this is a very important and quite complicated area. So it is certainly the case that a lot of Silicon Valley tech companies have deliberately pursued a strategy of trying to gain a monopoly. In fact, it might even be said that that's sort of inherent to the venture capital driven start-up business model to try and dominate particular market space. But I suppose the sense in which some of these companies, let's take Facebook as an example, are monopolies is really not so related to the way in which they monetize data or to their business model. So Facebook might reasonably be said to be a monopolist of encrypted messaging, because literally billions of people use Facebook's platform to communicate with each other. But it isn't really a monopolist of advertising space, because there are so many other alternatives available to advertisers who want to promote their products. I guess another dimension to this is the fact that although there are unquestionably concentrations of power with the big tech companies, they also provide somewhat of a useful service to the wider market, in that they allow smaller businesses to acquire customers much more effectively. So that actually militates against monopoly. Because now in the current digital advertising powered world, not every business has to be so big and so rich in terms of capital, that it can afford to do things like TV advertising. The platform's that Facebook and Google provides are also really helpful to small businesses that want to grow and compete with bigger players. 

Todd Landman  7:15 

Yeah, now I hear you shifting into the positive turn here. So I'm going to push you on this. So what is good data? And why are you an optimist about the good data elements to the work you've been doing?

Sam Gilbert  7:27 

Well, for me, when I talk about good data, what I'm really talking about is the positive public and social potential of data. And that really comes from my own professional experience. Because although at the moment, I spend most of my time researching and writing about these issues of data and digital technology, actually, my background is in the commercial sector. So I spent 18 years working in product and strategy and marketing roles, and particularly financial services. Also at the data company, Experian, also in a venture backed FinTech business called Bought By Many. And I learnt a lot about the ways in which data can be used to make businesses successful. And I learned a lot of techniques that, in general, at the moment, are only really put to use to achieve quite banal goals. So for example, to sell people more trainers, or to encourage them to buy more insurance products. And so one of the things that I'm really interested in is how some of those techniques and technologies can move across from the commercial sector, into the public sector, the third sector, and be put to work in ways that are more socially beneficial. So maybe just to give one example of that type of data that I think contains huge potential for public goods is search data. So this is the data set that is produced by all of us using Google and Bing and other search engines on a daily basis. Now, ordinarily, when this data is used, it is to do banal things like, target shoes more effectively. But