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  • feedwordpress 17:11:23 on 2019/01/25 Permalink
    Tags: , , columbia, data, , , , governance, , , ,   

    Our Data Governance Is Broken. Let’s Reinvent It. 


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    This is an edited version of a series of talks I first gave in New York over the past week, outlining my work at Columbia. Many thanks to Reinvent, Pete Leyden, Cap Gemini, Columbia University, Cossette/Vision7, and the New York Times for hosting and helping me.

    Prelude. 

    I have spent 30-plus years in the tech and media industries, mainly as a journalist, observer, and founder of companies that either make or support journalism and storytelling. When it comes to many of the things I am going to talk about here, I am not an expert. If I am expert at anything at all, it’s asking questions of technology, and of the media and marketing platforms created by technology. In that spirit I offer the questions I am currently pursuing, in the hope of sparking a dialog with this esteemed audience to further better answers.

    Some context: Since 1986, I’ve spent my life chased one story: The impact of technology on society. For whatever reason, I did this by founding or co-founding companies. Wired was kind of a first album, as it were, and it focused on the story broadly told. The Industry Standard focused on the business of the Internet, as did my conference Web 2Federated Media was a tech and advertising platform for high quality “conversational” publishers, built with the idea that our social discourse was undergoing a fundamental shift, and that publishers and their audiences needed to be empowered to have a new kind of conversation. Sovrn, a company I still chair, has a similar mission, but with a serious data and tech focus. NewCo, my last company (well, I’ve got another one in the works, perhaps we can talk about that during Q&A) seeks to illuminate the impact of companies on society.

    It’s Broke. Let’s Fix It.

    And it is that impact that has led me to the work I am doing now, here in New York. I moved here just last Fall, seeking a change in the conversation. To be honest, the Valley was starting to feel a bit…cloistered.

    A huge story – the very same story, just expanded – is once again rising. Only it’s just … more urgent. 25 years after the launch of Wired, the wildest dreams of its pages have come true. Back in 1992 we asked ourselves: What would happen to the world when technology becomes the most fundamental driver of our society? Today, we are living in the answer. Turns out, we don’t always like the result.

    Most of my career has been spent evangelizing the power of technology to positively transform business, education, and politics. But five or so years ago, that job started to get harder. The externalities of technology’s grip on society were showing through the shiny optimism of the Wired era. Two years ago, in the aftermath of an election that I believe will prove to be the political equivalent of the Black Sox scandal, the world began to wake up to the same thing.

    So it’s time to ask ourselves a simple question: What can we do to fix this?

    Let’s start with some context. My current work is split between two projects: One has to do with data governance, the other political media. How might they be connected? I hope by the end of this talk, it’ll make sense. 

    So let’s go. In my work at Columbia, I’m currently obsessed with two things. First,

    Data.

    How much have you thought about that word in the past two years?

    Given how much it’s been in the news lately, likely quite a lot. Big data, data breaches, data mining, data science…Today, we’re all about the data.

    And second….

    Governance.

    When was the last time you thought about that word?

    Government – well for sure, I’d wager that’s increased given who’s been running the country these past two years. But Governance? Maybe not as much.

    But how often have you put the two words together?

    Data Governance.

    Likely not quite as much.

    It’s time to fix that.

    Why?

    Because we have slouched our way into an architecture of data governance that is broken, that severely retards economic and cultural innovation, and that harms society as a whole.

    Let’s unpack that and define our terms. We’ll start with Governance.

    What is governance? It’s an …

    Architecture of control

    A regulatory framework that manages how a system works. The word is most often used in relation to political governance – which we care about a lot for the purposes of this talk – but the word applies to all systems, and in particular to corporations, which is also a key point in the research we’re doing.

    Governance in corporate context is “the system of rules, practices and processes by which a firm is directed and controlled.

    But in my work, when I refer to governance, I am referring to the “the system of rules, practices and processes by which a firm controls its relationship to its community.” Who’s that community? You, me, developers and partners in the ecosystem, for the most part. More on that soon. 

    Now, what is data? I like to think of it as…

    Unrefined Information.

    I’m not in love with this phrase, but again, this is a first draft of what I hope will grow to more refined (ha) work. Data is the core commodity from which information is created, or processed. Data has many attributes, not all of which are agreed upon. But I think it’s inarguable that the difference between data and information is …

    Human meaning.

    That’s Socrates, who thought about this shit, a lot. Information is data that means something to us (and possibly the entire universe, as it relates to the second law of thermodynamics. But physics is not the focus of this talk, nor is a possible fourth law of thermodynamics….).

    As we’ve learned – the hard way – over the past decade, there are a few very large companies which have purview over a massive catalog of meaningful data, meaningful not only to us, but to society at large. And it’s this societal aspect that, until recently, we’ve actively overlooked.  We’re in the midst of a grand data renaissance, which if history remotely echoes, I fervently hope will give rise to …

    A (Data) Enlightenment

    That’s John Locke, an Enlightenment philosopher. Allow me to pull back for second and attempt to lay some context for the work I hope to advance in the next few years. It starts with the Enlightenment, a great leap forward in human history (and the subject of a robust defense by Steven Pinker last year).

    Arguably the crowning document of the Enlightenment is…

    The United States Constitution

    This declaration of the rights of humankind (well mankind for the first couple of centuries) itself took more than three centuries to emerge (and cribbed generously from the French and English, channeling Locke and Hume). Our current political and economic culture is, of course, a direct descendant of this living document. American democracy was founded upon Enlightenment principles. And the cornerstone of Enlightenment ideas is …

    The Scientific Method

    That’s Aristotle, often credited with originating the scientific method, which is based on considered thesis formation, rigorous observation, comprehensive data collection, healthy skepticism, and sharing/transparency. The scientific method is our best tool, so far, for advancing human progress and problem solving.

    And the scientific method – the pursuit of truth and progress – all that turns on the data. Prompting the question….

    Who Has the Most (and Best) Data?

    This is the question we are finally asking ourselves, the answer to which is sounding alarms.  As we all know, we are in a renaissance, a deluge, an orgy of data creation. We have invented sophisticated new data sensing organs  –  digital technologies – that have delivered us superhuman powers for the discovery, classification, and sense-making of data.

    Not surprisingly, it is technology companies, driven as they are by the raw economics of profit-seeking capital and armed with these self-fulfilling tools of digital exploration and capture – that have initially taken ownership of this emerging resource. And that is a problem, one we’ve only begun to understand and respond to as a society. Which leads to an important question:

    Who Is Governing Data?

    In the US, anyway, the truth is, we don’t have a clear answer to this question. Our light touch regulatory framework created a tech-driven frenzy of company building, but it failed to anticipate massive externalities, now that these companies have come to dominate our capital markets. Clearly, the Tech Platform Companies have the most valuable data – at least if the capital markets are to be believed. Companies like Google. Facebook. Amazon. Apple.

    All of these companies have very strong governance structures in place for the data they control. These structures are set internally, and are not subject to much (if any) government regulation. And by extension, nearly all companies that manage data, no matter their size, have similar governance models because they are all drafting off those companies’ work (and success). This has created a phenomenon in our society, one I’ve recently come to call …

    The Default Internet Constitution

    Without really thinking critically about it, the technology and finance industries have delivered us a new Constitution, a fundamental governance document controlling how information flows through the Internet. It was never ratified by anyone, never debated publicly, never published with a flourish of the pen, and it’s damn hard to read. But, it is based on a discoverable corpus. That corpus, at its core, is based on …

    Terms of Service and EULAs

    Like it or not, there is a governance model for the US Internet and the data which flows across it: Terms of Service and End User Licensing Agreements. Of course, we actively ignore them – who on earth would ever read them? One researcher did the math, and figured it’d take 76 work days for the average American to read all of the policies she clicks past (and that was six years ago!).

    Of course, ignoring begets ignorance, and we’ve ignored Terms of Service at our peril. No one understands them, but we certainly should – because if we’re going to make change, we’ll want to change these Terms of Service, dramatically. They create the architecture that determines how data, and therefore societal innovation and value, flow around the Internet.

    And let’s be clear, these terms of service have hemmed data into silos. They’re built by lawyers, based on the desires of engineers who are – for the most part – far more interested in the product they are creating than any externalities those products might create.

    And what are the lawyers concerned with? Well, they have one True North: Protect the core business model of their companies.

    And what is that business model? Engagement. Attention. And for most, data-driven personalized advertising. (Don’t get me started about Apple being different. The company is utterly dependent on those apps animating that otherwise black slate of glass they call an iPhone).

    So what insures engagement and attention? Information refined from data.

    So let’s take a look at a rough map of what this Terms of Service-driven architecture looks like:

    The Mainframe Architecture

    Does this look familiar? If you’re a student of technology industry history, it should, because this is how mainframes worked in the early days of computing. Data compute, data storage, and data transport is handled by the big processor in the sky. The “dumb terminal” lives at the edge of the system, a ‘thin client’ for data input and application output. Intelligence, control, and value exchange lives in the center. The center determines all that occurs at the edge.

    Remind you of any apps you’ve used lately?

    But it wasn’t always this way. The Internet used to look like this:

    The Internet 1.0 Architecture

    I’m one of the early true believers in the open Internet. Do you remember that world? It’s mostly gone now, but there was a time, from about 1994 to 2012, when the Internet ran on a different architecture, one based on the idea that the intelligence should reside in the nodes – the site – not at the center. Data was shared laterally between sites. Of course, back then the tech was not that great, and there was a lot of work to be done. But we all knew we’d get there….

    …Till the platforms got there first. And they got there very, very well – their stuff was both elegant and addictive.

    But could we learn from Internet 1.0, and imagine a scenario inspired by its core lessons? Technologically, the answer is “of course.” This is why so many folks are excited by blockchain, after all (well that, and ICO ponzi schemes…). 

    But it might be too late, because we’ve already ceded massive value to a broken model. The top five technology firms dominate our capital markets. We’re seriously (over)invested in the current architecture of data control. Changing it would be a massive disruption. But what if we can imagine how such change might occur?

    This is the question of my work.

    So…what is my work?

    A New Architecture

    If we’re stuck in an architecture that limits the potential of data in our society, we must envision a world under a different kind of architecture, one that pushes control, agency, and value exchange back out to the node.

    Those of us old enough to remember the heady days of Web 1.0 foolishly assumed such a world would emerge unimpeded. But as Tim Wu has pointed out, media and technology run in cycles, ultimately consolidating into a handful of companies with their hands on the Master Switch – we live in a system that rewards the Curse of Bigness. If we are going to change that system, we have to think hard about what we want in its place.

    I’ve given this some thought, and I know what I want.

    Let The Data Flow

    Imagine a scenario where you can securely share your Amazon purchase data with Walmart, and receive significant economic value for doing so (I’ve written this idea up at length here). Of course, this idea is entirely impossible today. This represents a major economic innovation blocked.

    Or imagine a free marketplace for data that allows a would-be restaurant owner to model her customer base’s preferences and unique taste? (I’ve written this idea up at length here). Of course, this is also impossible today, representing a major cultural and small business innovation is impeded.

    Neither of these kinds of ideas are even remotely possible – nor are the products of thousands of similar questions entrepreneurs might ask of the data rotting in plain sight across our poorly architected data economy.

    We all lose when the data can’t flow. We lose collectively, and we lose individually. 

    But imagine if it was possible?!

    How might such scenarios become reality?

    We’re at a key inflection point in answering that question.

    2019 is the year of data regulation. I don’t believe any meaningful regulation will pass here in the US, but it’ll be the year everyone talks about it. It started with the CA/Facebook hearings, and now every self-respecting committee chair wants a tech CEO in their hot seat. Congress and the American people have woken up to the problem, and any number of regulatory fixes are being debated. Beyond the privacy shitstorm and its associated regulatory response, which I’d love to toss around during Q&A, the most discussed regulatory relief is anti-trust – the curse of bigness is best fixed by breaking up the big guys. I understand the goal, and might even support it, but I don’t think we need to even do that. Instead, I submit for your consideration one improbable, crazy, and possibly elegant solution.

    The Token Act

    I’m calling it the Token Act.

    It requires one thing: Every data processing service at a certain scale must deliver back to its customers any co-created data in machine readable format, easily portable to any other data processing service.

    Imagine the economic value unlocked, the exponential impact on innovation such a simple rule would have. Of course we must acknowledge the negative short term impact such a policy would have on the big guys. But it also creates an unparalleled opportunity for them – the token of course can include a vig – a percentage of all future revenue associated with that data, for the value the platform helped to create. This model could drive a far bigger business in the long run, and a far healthier one for all parties concerned.

    I can’t prove it yet, but I sense this approach could 10 to 100X our economy. We’ve got some work to do on proving that, but I think we can.

    Imagine what would occur if the data was allowed to flow freely. Imagine the upleveling of how firms would have to compete. They’d have to move beyond mere data hoarding, beyond the tending of miniature walled gardens (most app makers) and massive walled agribusinesses (in the case of the platforms – and ADM and Monsanto, but that’s another chapter in the book, one of many).

    Instead, firms would have to compete on creating more valuable tokens  – more valuable units of human meaning. And they’d encourage sharing those tokens widely – with the fundamental check of user agency and control governing the entire system.

    The bit has flipped, and the intelligence would once again be driven to the nodes.

    To us!

    But the Token Act is just an exercise in envisioning a society governed by a different kind of data architecture. There are certainly better or more refined ideas.

    And to get to them, we really need to understand how we’re governed today. And now that I’ve gotten nearly to the end of my prepared remarks, I’ll tell you what I’m working on at Columbia with several super smart grad students:

    Mapping Data Flows

    If we are going to understand how to change our broken architecture of data flows, we need to deeply understand where we are today. And that means visualizing a complex mess. I’m working with a small team of researchers at Columbia, and together we are turning the Terms of Service at Amazon, Apple, Facebook and Google into a database that will drive an interactive visualization – a blueprint of sorts for how data is governed across the US internet. We’re focusing on the advertising market, for obvious reasons, but it’s my hope we might create a model that can be applied to nearly any information rich market. It’s early stages, but our goal is to have something published by the end of May.

    Finally, Advertising

    I’ve not spoken much about advertising during this talk, and that was purposeful. I’ve written at length about how we came to the place we now inhabit, and the role of programmatic advertising in getting us there.

    Truth is, I don’t see advertising as the cause of this problem, but rather an outgrowth of it. If you offer any company a deal that puts new customers on a platter, as Google did with AdWords, or Facebook has with NewsFeed, well, there’s no way those companies will refuse. Every major advertiser has embraced search and social, as have millions of smaller ones.

    Our problem is simply this: The people who run technology platforms don’t actually understand the power and limitations of their systems, and let’s be honest, nor do we. Renee Di Resta has pointed this out in recent work around Russian interference in our national dialog and elections: Any system that allows for automated processing of messages is subject to directed, sophisticated abuse. The place for regulation is not in advertising (even though that’s where it’s begun with the Honest Ads Act), it’s in how the system works architecturally.

    But advertisers must be highly aware of this transitional phase in the architecture of a system that has been a major source of revenue and business results. We must imagine what comes next, we must prepare for it, and perhaps, just perhaps, we should invent it, or at the very least play a far more active role than we’re playing currently.

    I believe that if together – industry, government, media and consumers collectively – if we unite to address the core architectural issues inherent to how we manage data, in the process giving consumers economic, creative, and personal agency over the data they co create with platforms, the question of toxic advertising will disappear faster than it arose.

    But I’ve talked (or written) long enough. Thank you so much for coming (for reading), and for being part of this conversation. Now, let’s start it.

     
  • feedwordpress 18:01:49 on 2019/01/02 Permalink
    Tags: , cannabis, , data, , , , , , , , , , , ,   

    Predictions 2019: Stay Stoney, My Friends. 


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    If predictions are like baseball, I’m bound to have a bad year in 2019, given how well things went the last time around. And given how my own interests, work life, and physical location have changed of late, I’m not entirely sure what might spring from this particular session at the keyboard.

    But as I’ve noted in previous versions of this post (all 15 of them are linked at the bottom), I do these predictions in something of a fugue state – I don’t prepare in advance. I just sit down, stare at a blank page, and start to write.

    So Happy New Year, and here we go.

    1/ Global warming gets really, really, really real. I don’t know how this isn’t the first thing on everyone’s mind already, with all the historic fires, hurricanes, floods, and other related climate catastrophes of 2018. But nature won’t relent in 2019, and we’ll endure something so devastating, right here in the US, that we won’t be able to ignore it anymore. I’m not happy about making this prediction, but it’ll likely take a super Sandy or a king-sized Katrina to slap some sense into America’s body politic. 2019 will be the year it happens.

    2/ Mark Zuckerberg resigns as Chairman of Facebook, and relinquishes his supermajority voting rights. Related, Sheryl Sandberg stays right where she is. I honestly don’t see any other way Facebook pulls out of its nosedive. I’ve written about this at length elsewhere, so I will just summarize: Facebook’s only salvation is through a new system of governance. And I mean that word liberally – new governance of how it manages data across its platform, new governance of how it works with communities, governments, and other key actors across its reach, and most fundamentally, new governance as to how it works as a corporate entity. It all starts with the Board asserting its proper role as the governors of the company. At present, the Board is fundamentally toothless.

    3/ Despite a ton of noise and smoke from DC, no significant federal legislation is signed around how data is managed in the United States. I  know I predicted just a few posts ago that 2019 will be the year the tech sector has to finally contend with Washington. And it will be…but in the end, nothing definitive will emerge, because we’ll all be utterly distracted by the Trump show (see below). Because of this, unhappily, we’ll end up governed by both GDPR and California’s homespun privacy law, neither of which actually force the kind of change we really need.

    4/ The Trump show gets cancelled. Last year, I said Trump would blow up, but not leave. This year, I’m with Fred, Trump’s in his final season. We all love watching a slow motion car wreck, but 2019 is the year most of us realize the car’s careening into a school bus full of our loved ones. Donald Trump, you’re fired.

    5/ Cannabis for the win. With Sessions gone and politicians of all stripes looking for an easy win, Congress will pass legislation legalizing cannabis. Huzzah!!!! Just in time, because…

    6/ China implodes, the world wobbles. Look, I’m utterly out of my depth here, but something just feels wrong with the whole China picture. Half the world’s experts are warning us that China’s fusion of capitalism and authoritarianism is already taking over the world, and the other half are clinging to the long-held notion that China’s approach to nation building is simply too fragile to withstand democratic capitalism’s demands for transparency. But I think there may be other reasons China’s reach will extend its grasp: It depends on global growth and optimistic debt markets. And both of those things will fail this year, exposing what is a marvelous but unsustainable experiment in managed markets. This is a long way of backing into a related prediction:

    7/ 2019 will be a terrible year for financial markets. This is the ultimate conventional wisdom amongst my colleagues in SF and NY, even though I’ve seen plenty of predictions that Wall St. will have a pretty good year. I have no particular insight as to why I feel this way, it’s mainly a gut call: Things have been too good, for too long. It’s time for a serious correction.

    8/ At least one major tech IPO is pulled, the rest disappoint as a class. Uber, Lyft, Slack, Pinterest et al are all expected this year. But it won’t be a good year to go public. Some will have no choice, but others may simply resize their businesses to focus on cash flow, so as to find a better window down the road.

    9/ New forms of journalistic media flourish. It’s well past time those of us in the media world take responsibility for the shit we make, and start to try significant new approaches to information delivery vehicles. We have been hostages to the toxic business models of engagement for engagement’s sake. We’ll continue to shake that off in various ways this year – with at least one new format taking off explosively. Will it have lasting power? That won’t be clear by year’s end. But the world is ready to embrace the new, and it’s our jobs to invest, invent, support, and experiment with how we inform ourselves through the media. Related, but not exactly the same…

    10/A new “social network” emerges by the end of the year. Likely based on messaging and encryption (a la Signal or Confide), the network will have many of the same features as the original Facebook, but will be based on a paid model. There’ll be some clever new angle – there always is – but in the end, it’s a way to manage your social life digitally. There are simply too many pissed off and guilt-ridden social media billionaires with the means to launch such a network – I mean, Insta’s Kevin Systrom, WhatsApp’s Jan and Brian, not to mention the legions of mere multi-millionaires who have bled out of Facebook’s battered body of late.

    So that’s it. On a personal note, I’ll be happily busy this year. Since moving to NY this past September, I’ve got several new projects in the works, some still under wraps, some already in process. NewCo and the Shift Forum will continue, but in reconstituted forms.  I’ll keep up with my writing as best I can; more likely than not most of it will focus the governance of data and how its effect our national dialog. Thanks, as always, for reading and for your emails, comments, and tweets. I read each of them and am inspired by all. May your 2019 bring fulfillment, peace, and gratitude.

    Previous predictions:

    Predictions 2018

    2018: How I Did

    Predictions 2017

    2017: How I Did

    Predictions 2016

    2016: How I Did

    Predictions 2015

    2015: How I Did

    Predictions 2014

    2014: How I Did

    Predictions 2013

    2013: How I Did

    Predictions 2012

    2012: How I Did

    Predictions 2011

    2011: How I Did

    Predictions 2010

    2010: How I Did

    2009 Predictions

    2009 How I Did

    2008 Predictions

    2008 How I Did

    2007 Predictions

    2007 How I Did

    2006 Predictions

    2006 How I Did

    2005 Predictions

    2005 How I Did

    2004 Predictions

    2004 How I Did

     
  • feedwordpress 17:36:20 on 2018/11/05 Permalink
    Tags: , data, , , ,   

    Lazy Ad Buying Is Killing The Open Web. 


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    But…I just *bought* a robe. I don’t want another one.

    If you’re read my rants for long enough, you know I’m fond of programmatic advertising. I’ve called it the most important artifact in human history, replacing  the Macintosh as the most significant tool ever created.

    So yes, I think programmatic advertising is a big deal. As I wrote in the aforementioned post:

    “I believe the very same technologies we’ve built to serve real time, data-driven advertising will soon be re-purposed across nearly every segment of our society. Programmatic adtech is the heir to the database of intentions – it’s that database turned real time and distributed far outside of search. And that’s a very, very big deal. (I just wish I had a cooler name for it than “adtech.”)” 

    But lately, I’m starting to wonder if perhaps adtech is failing, not for any technical reason, but because the people leveraging are complicit in what might best be called a massive failure of imagination.

    I’m about to go on a rant here, so please forgive me in advance.

    But honestly, who else out there is sick of being followed by ads so stupid a fourth grader could do a better job of targeting them?

    Case in point is the ad above. I took this screen shot from my phone this past weekend while I was reading a New York Times article. The image – of a robe Amazon wanted me to buy – was instantly annoying, because I had in fact purchased a robe on Amazon several days before. Why on earth was Amazon retargeting me for a product I just bought?!

    But wait, it gets worse! As I perused the next Times article, this ad shows up:

    That would have made sense *after I bought a robe, but…” I bought slippers two weeks ago. So WTF?

    You might think this ad makes more sense. If the dude buys a robe, makes sense to try to sell him a new pair of slippers, no? Well, sure, but only if that same dude didn’t buy a new pair of slippers two weeks ago. Which, in fact, I did just do.

    So, yeah, this ad sucks as well. Not only is it not useful or relevant, it’s downright annoying. The vast machinery of adtech has correctly identified me as a robe-and-slippers-buying customer. But it’s failed to realize *I’ve already bought the damn things.*

    Is it possible that adtech is this stupid? This poorly instrumented? I mean, are programmatic buyers simply tagging visitors who land on ecommerce pages (male robe intender?) without caring about whether those visitors actually bought anything?

    Are the human beings responsible for setting the dials of programmatic just this lazy?

    Yes.

    I’ve been a critical observer of adtech over the past ten or so years, and one consistent takeaway is this: If there’s a way for a buyer to cut corners, declare an easy win, and keep doing things they way the’ve always been done, well, they most certainly will.

    But why does it have to be this way? Digging into the examples above yields an extremely frustrating set of facts. Consider the data the adtech infrastructure either got *right* about me as a customer, or could have gotten right:

    • I am a frequent ecommerce customer, usually buying on Amazon
    • I recently purchased both a robe and some slippers
    • I am reading on the New York Times site as a logged on (IE data rich) customer of the Times‘ offerings

    These are just the obvious data points. My mobile ID and cookies, all of which are available to programmatic buyers, certainly indicate a high household income, a propensity to click on certain kinds of ads, a rich web browsing history reflecting a thickly veined lodestar of interest data, among countless other possible inputs.

    Imagine if a programmatic campaign actually paid attention to all this rich data? Start with the fact I just purchased a robe and slippers. What are products related to those two that Amazon might show me? Well, according to its own “people who bought this item also bought” algorithms, folks who bought men’s robes also bought robes for the women in their life. Now there’s a cool recommendation! I might have clicked on an ad that showed a cool robe for my wife. But no, I’m shown an ad for a product I already have.

    Why?

    I’ve got a few calls in to verify my hunch, but I suspect the ugly truth is pure laziness on the part of the folks responsible for buying ads. Consider: The average cost for a thousand views (CPM) of a targeted programmatic advertisement hovers between ten cents (yes, ten pennies) to $2.  With costs that low, the advertising community can afford to waste ad inventory.

    Let’s apply that reality to our robe example. Let’s say the robe costs $60, and yields a $20 profit for our e-commerce advertiser, not including marketing costs. That means that same advertiser is can spend upwards of $19.99 per unit on advertising (more, if a robe purchaser turns out to be a “big basket” e-commerce spender).  So what does our advertiser do? Well, they set a retargeting campaign aimed anyone who ever visited our erstwhile robe’s page.  With CPMs averaging around a buck, that robe’s going to follow nearly 20,000 folks around the internet, hoping that just one  of them converts.

    Put another way, programmatic advertising is a pure numbers game, and as long as the numbers show one penny of profit, no one is motivated to make the system any better. I’ve encountered many similar examples of ad buyers ignoring high-quality data signals, preferring instead to “waste reach” because, well, it’s just easier to set up campaigns on one or two factors. Inventory is cheap. Why not?

    This is problematic. What’s the point of having all that rich (and hard won) targeting data if buyers won’t use it, and consumers don’t benefit from it? An ecosystem that fails to encourage innovation will stagnate and lose share to walled gardens like Facebook, Google, and others. If the ads suck on the open web (and they do), then consumers will either install ad blockers (and they are), or abandon the open web altogether (and they are).

    We can do so much better. Shouldn’t we try?

     

     

     
  • feedwordpress 20:07:01 on 2018/10/31 Permalink
    Tags: , data, , , , food, , , , , , small business   

    After the Token Act: A New Data Economy Driven By Small Business Entrepreneurship 


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    Gramercy Tavern in New York City

    If Walmart can leverage data tokens to lure Amazon’s best customers away, what else is possible in a world of enabled by my fictional Token Act?

    Well, Walmart vs. Amazon is all about big business – a platform giant (Amazon) disrupting an OldBigCo (Walmart and its kin). Over the past two decades, Amazon bumped Walmart out of the race to a trillion-dollar market cap, and the OldCo from Bentonville had to reset and play the role of the upstart. The Token Act levels the playing field, forcing both to win where it really matters: In service to the customer.

    But while BigCos are sexy and well known, it’s the small and medium-sized business ecosystem that determines whether or not we have an economy of mass flourishing.  So let’s explore the Token Act from the point of view of a small business startup, in this case, a new neighborhood restaurant. I briefly touched upon this idea in my set up post, Don’t Break Up The Tech Oligarchs. Force Them To Share Instead.  (If you haven’t already, you might want to read that post before this one, as I lay out the framework in which this scenario would play out.) What I envision below assumes the Token Act has passed, and we’re at least a year or two into its adoption by most major data players. Here we go…

    ***

    Fresh off her $2,700 win from Walmart, Michelle decides she’s ready to lean into a lifelong dream: Starting a restaurant in her newly adopted neighborhood of Chelsea in New York City. Since moving to the area from California, she’s noticed two puzzling trends: First, a dearth of interesting mid- to high-end dinner spots walking distance from her new place, and second, what appears to be higher-than-average vacancy rates for the retail storefronts in the same general area. It appears to be a buyer’s market for retail restaurant space in Chelsea. So why aren’t new places launching? She read the Times’ piece on vacancies a few years ago (before the Token Act passed) and was left just as puzzled as before – seems like there’s no rhyme or reason to the market.

    Michelle wants to start a high end American gastro pub – the kind of place she loved back when she lived in Northern California (she’s fond of Danny Meyers’ Gramercy Tavern, pictured above, but it’s a bit too far away from her new place). She has a strong hunch that such a place would be a hit in her new neighborhood, but she’s not sure her new neighbors will agree.

    Now starting a restaurant requires a certain breed of insanity – they say the best way to make a small fortune in the business is to start with a large one. The truth is, launching restaurants has historically been a crap shoot – you might find the best talent, the best designer, and the best location – but if for some reason you don’t bring the je ne sai quois, the place will fail within months, leaving you and your partners millions of dollar poorer.

    It’s that  je ne sai quois that Michelle is determined to reveal.  The tools she will leverage? The newly liberated resources of data tokens.

    Before we continue, allow me to draw your attention back to the rise of search, indeed, the very era which begat Searchblog in the early 2000s. Google Adwords launched in 2000, and within a few years, the media world had been turned upside down by what I termed The Database of Intentions.  As if by magic, people everywhere could suddenly ask new kinds of questions, finding themselves both surprised and delighted by the answers they received.

    Gates-Line compliant ecosystem quickly developed on top of this new platform, driven by an emerging industry of search engine marketing and optimization. SEO/SEM sprung into existence to help small and medium sized businesses take advantage of the Google platform – by 2006 the industry stood at nearly $10 billion in spend, growing more than 60 percent year on year. Adwords grew from zero to millions of advertisers by connecting to a long tail of small businesses that took advantage of an entirely new class of revealed information: The intents, desires, and needs of tens of millions of consumers, who relentlessly poured their queries into Google’s placid and unblinking search box.

    Were you a limo service in the Bronx looking for new customers? It paid huge dividends to purchase Adwords like “car service bronx” and “best limo manhattan.” Were you a dry cleaner in West LA hoping to expand? Best be first in line when customers typed in “best cleaners Beverly Hills.” Selling heavy machinery to construction services in the midwest? If you don’t own keywords like “caterpillar dealer des moines” you’d lose, and quick, to whoever did optimize to phrases like that.

    My point is simply this: Adwords was a freaking revolution, but it ain’t nothing compared to what will happen if we unleash data tokens on the world.

    ***

    Ok, back to Michelle and her new restaurant. Of course Michelle will leverage Adwords, and Facebook, and any other advertising service to help her new business grow. But none of those services can help her figure out her je ne sai quois – for that, she needs something entirely novel. She needs a new question machine. And the ecosystem that develops around data tokens will offer it.

    Thanks to her Walmart experience, Michelle has become aware of the power of personal data. She’s also read up on the Token Act, the new law requiring all data players at scale to allow individuals to create machine-readable data tokens that can be exchanged for value as directed by the consumer. After doing a bit of research, she stumbles across a startup called OfferExchange, which manages “Token Offers” on behalf of anyone who might want to query TokenLand. OfferExchange is a spinout from ProtocolLabs, a pioneer in secure blockchain software platforms like Filecoin. It’s still early in TokenLand, so an at-scale Google of the space hasn’t emerged. OfferExchange works more like a bespoke yet platform-based research outfit – the firm has a sophisticated website and impressive client list. It uses Facebook, Twitter, LiveRamp, and Instagram to identify potential token-creating consumers, then solicits those individuals with offers of cash or other value in exchange for said tokens.

    Michelle does a Crunchbase search for OfferExchange and sees it’s backed by Union Square Ventures and Benchmark, which gives her some comfort – those firms don’t fund fly-by-night hucksters. And OfferExchange site is impressive – in less than five minutes, it guides her through the construction of an elegant query. Here’s how the process works:

    First, the site asks Michelle what her goal is. “Starting a restaurant in New York City,” she responds. The site reconstructs around her answer, showing suggested data repositories she might mine. “Restaurants, New York City,” reads the top layer of a directory-like page. Underneath are several categories, each populated with familiar company names:

    • Restaurant Reservation and Review Services
      • OpenTable Google Resy Yelp Eat24 Facebook (more)
    • Food Delivery Services
      • GrubHub Uber Eats PostMates InstaCart (more)
    • Transportation Services
      • Uber Lyft Juno Via (more)
    • Real Estate Services (Commercial)
      •  LoopNet DocuSign CompStak (more)
    • Location Services 
      • Foursquare Uber Lyft Google NinthDecimal (more)
    • Financial Services
      • American Express Visa Mastercard Apple Pay Diners Club (more)

    And so on – if she wished, Michelle could dig into dozens of categories related to her initial “restaurant New York City” search.

    Michelle’s imagination sparks – the kinds of queries she could ask of these services is mind blowing. She could  limit her query to people who live within walking distance of her neighborhood, asking her *actual neighbors* for tokens that tell her what restaurants they eat at, when they eat there, the size of their checks, related reviews, abandoned reservations, the works. She might discover that folks like Indian takeout on Mondays, that they rarely spend more than $100 on a meal on Tuesdays, but that they splurge on the weekends. She could discover the percentage of diners in Chelsea who travel more than two miles by car service to eat out at a place similar to the one she has in mind, and what the size of the check might be when they do. She can also check historical average rents for restaurants in her zip code, over time, which will certainly help with negotiating her lease. The possibilities are endless.

    Put another way, with OfferExchange’s services, Michelle can litigate the merde out of her je ne sai quois.

    *** 

    This post is getting long, so I’ll stop here and pull back for a spot of Thinking Out Loud. I could continue the story, imagining the process of the token offer Michelle would put out through OfferExchange’s platform, but suffice to say, she’d be willing to pay upwards of $5-20 per potential customer for their data. The marketing benefit alone – alerting potential customers in the neighborhood that she’s exploring a new restaurant in the area – is worth tens of thousands already. And of course, OfferExchange can connect anyone who offers their tokens to Michelle’s new project a discount on their first meal at the restaurant, should it actually launch. Cool!

    But let’s stop there and consider what happens when local entrepreneurs have access to the information currently silo’d across thousands of walled garden services like Uber, LoopNet, Resy, and of course Facebook and Google. While better data won’t insure that Michelle’s restaurant will succeed, it certainly increases the odds that it won’t fail. And it will give both Michelle and her investors – local banks, savvy friends and family members – much more conviction that her new enterprise is viable. Take this local restaurant example and apply it to all manner of small business – dry cleaners, hardware stores, bike shops – and this newly liberated class of information enables an explosion of efficiency, investment, and, well, flourishing in what has become, over the past four decades, a stagnant SMB environment.

    Is this Money Ball for SMB? Perhaps. And yes, I can imagine any number of downsides to this new data economy. But I also believe the benefits would far outweigh the downsides. Under the Token Act as I envision it, co-creators of the data – the services like Uber, OpenTable, or Facebook – have the right to charge a vig for the data being monetized. Sure, it’d be possible for an entrepreneur to steal customers via tokens, but I’m going to guess the economic value of allowing your customers to discover new use cases for their data will dwarf the downside of possibly losing those customers to a new competitor. Plus, this new competitive force will drive everyone to play at a higher level, focusing not on moats built on data silos, but instead on what really matters: A highly satisfied customer. That’s certainly Michelle’s goal, and the goal of every successful local business. Why shouldn’t it also be the goal of the data giants?

     
  • feedwordpress 02:36:10 on 2018/10/22 Permalink
    Tags: , data, data portability, , , , ,   

    Instead of Breaking Up The Tech Oligarchs, Let’s Try This One Simple Hack 


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    (image)

    Social conversations about difficult and complex topics have arcs – they tend to start scattered, with many threads and potential paths, then resolve over time toward consensus. This consensus differs based on groups within society – Fox News aficionados will cluster one way, NPR devotees another. Regardless of the group, such consensus then becomes presumption – and once a group of people presume, they fail to explore potentially difficult or presumably impossible alternative solutions.

    This is often a good thing – an efficient way to get to an answer. But it can also mean we fail to imagine a better solution, because our own biases are obstructing a more elegant path forward.

    This is my sense of the current conversation around the impact of what Professor Scott Galloway has named “The Four” – the largest and most powerful American companies in technology (they are Apple, Amazon, Google, and Facebook, for those just returning from a ten-year nap).  Over the past year or so, the conversation around technology has become one of “something must be done.” Tech was too powerful, it consumed too much of our data and too much of our economic growth. Europe passed GDPR, Congress held ineffectual hearings, Facebook kept screwing up, Google failed to show up…it was all of a piece.

    The conversation evolved into a debate about various remedies, and recently, it’s resolved into a pretty consistent consensus, at least amongst a certain class of tech observers: These companies need to be broken up. Antitrust, many now claim, is the best remedy for the market dominance these companies have amassed.

    It’s a seductive response, with seductive historical precedent. In the 1970s and 80s, antitrust broke up AT&T, ultimately paving the way for the Internet to flourish. In the 90s, antitrust provided the framework for the government’s case against Microsoft, opening the door for new companies like Google and Facebook to dominate the next version of the Internet. Why wouldn’t antitrust regulation usher in #Internet3? Imagine a world where YouTube, Instagram, and Amazon Web Services are all separate companies. Would not that world be better?

    Perhaps. I’m not well read enough in antitrust law to argue one way or the other, but I know that antitrust turns on the idea of consumer harm (usually measured in terms of price), and there’s a strong argument to be made that a free service like Google or Facebook can’t possibly cause consumer harm. Then again, there are many who argue that data is in fact currency, and The Four have essentially monopolized a class of that currency.

    But even as I stare at the antitrust remedy, another solution keeps poking at me, one that on its face seems quite elegant and rather unexplored.

    The idea is simply this: Require all companies who’ve reached a certain scale to build machine-readable data portability into their platforms. The right to data portability is explicit in the EU’s newly enacted GDPR framework, but so far the impact has been slight: There’s enough wiggle room in the verbiage to hamper technical implementation and scope. Plus, let’s be honest: Europe has never really been a hotbed of open innovation in the first place.

    But what if we had a similar statute here? And I don’t mean all of GDPR – that’s certainly a non starter. But that one rule, that one requirement: That every data service at scale had to stand up an API that allowed consumers to access their co-created data, download a copy of it (which I am calling a token), and make that copy available to any service they deemed worthy?

    Imagine what might come of that in the United States?

    I’m not a policy expert, and the devil’s always in the details. So let me be clear in what I mean when I say “machine-readable data portability”: The right to take, via an API, what is essentially a “token” containing all (or a portion of) the data you’ve co created in one service, and offer it, with various protections, permission, and revocability, to another service. In my Senate testimony, I gave the example of a token that has all your Amazon purchases, which you then give to Walmart so it can do a historical price comparison and tell you how much money you would save if you shopped at its online service. Walmart would have a powerful incentive to get consumers to create and share that token – the most difficult problem in nearly all of business is getting a customer to switch to a similar service. That would be quite a valuable token, I’d wager*.

    Should be simple to do, no? I mean, don’t we at least co-own the information about what we bought at Amazon?

    Well, no. Not really. Between confusing terms of service, hard to find dashboards, and confounding data reporting standards, The Four can both claim we “own our own data” while at the same time ensuring there’ll never be a true market for the information they have about us.

    So yes, my idea is easily dismissed. The initial response I’ve had to it is always some variation of: “There’s no way The Four would let this happen.” That’s exactly the kind of biases I refer to above – we assume that The Four control the dialog, that they either will thwart this idea through intensive lobbying, clever terms of service, and soft power, or that the idea is practically impossible because of technical or market limitations. To that I ask….Why?

    Why is it impossible for me to tokenize all of my Lyft ride data, and give for free it to an academic project that is mapping the impact of ride sharing on congestion in major cities? Why is it impossible for a small business owner to create an RFP for all OpenTable, Resy, and other dining data, so she can determine the best kind of restaurant to open in her neighborhood? I’m pretty certain she’d pay a few bucks a head for that kind of data – so why can’t I sell that information to her (with a vig back to OpenTable and Resy) if the value exchange is there to be monetized? Why can’t I tokenize and sell my Twitter interactions to a brand (or more likely, an agency or research company) interested in understanding the mind of a father who lives in Manhattan? Why can’t I tokenize and trade my Spotify history for better recommendations on live shows to see, or movies to watch, or books to read? Or, simply give it to a free service that’s sprung up to give me suggestions about new music to check out?

    Why can’t an ecosystem of agents, startups, and data brokers emerge, a new industry of information processing not seen since the rise of search optimization in the early aughts, leveraging and arbitraging consumer information to create entirely new kinds of businesses driven by insights currently buried in today’s data monopolies?

    Such a world would be fascinating, exciting, sometimes sketchy, and a hell of a lot of fun. It’d be driven by the individual choices of millions of consumers – choosing which agents to trust, which tokens to create, which trades felt fair. There’s be fails, there’d be fraud, there’d be bad actors. But over time, the good would win over the bad, because the decision making is distributed across the entire population of Internet users. In short, we’d push the decision making to the node – to us. Sure, we’d do stupid things. And sure, the hucksters and the hustlers would make short term killings. But I’ll take an open system like this over a closed one any day of the week, especially if the open system is governed by an architecture empowering the individual to make their own decisions.

    It’s be a lot like the Internet was once imagined to be.

    I’ve been noodling on such an ecosystem, and I’m convinced it could dwarf our current Internet in terms of overall value created (and credit where credit is due, The Four have created a lot of value). It’d run laps around The Four when it comes to innovation – tens of thousands of new companies would form, all of them feeding off the newly liberated oxygen of high quality, structured, machine readable data. Trusted independent platforms for value exchange would arise. Independent third party agents would munge tokens from competing services, verifying claims and earning the trust of consumers (will Walmart really save you a thousand bucks a year?! We can prove it, or not!). Huge platforms would develop for the processing, securitization, permissioning, and validation of our data. Man, it’d feel like…well, like the recumbent, boring old Internet was finally exciting again.

    There’s no technical reason why this world doesn’t exist. The progenitors of the Web have already imagined it, heck, Tim Berners Lee recently announced he’s working pretty much full time on creating a system devoted to the foundational elements needed for it to blossom.

    But until we as a society write machine-readable data portability into law, such efforts will be relegated to interesting side shows. And more likely than not, we’ll spend the next few years arguing about breaking up The Four, and let’s be honest, that’s an argument The Four want us to have, because they’re going to win it (more money, better lawyers, etc. etc.). Instead, we should  just require them – and all other data services of scale – to free the data they’ve so far managed to imprison. One simple new law could change all of that. Shouldn’t we consider it?

    *In another post, I’ll explore this example in detail. It’s really, really fascinating. 

     
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