Earlier this year I posted about an idea we’ve come up with to create a new “data layer” on top of last year’s popular “Points of Control” map. We created this map to visualize the theme of the Web 2 Summit conference, which is coming up again in a few weeks.

As you can see from the map, we’ve visualized eight key Internet players as cities, with each of the buildings representing storehouses of key data types. Cities are scaled by the size and engagement of their audiences, with data driven by our partner Nielsen and also company-reported sources. A detailed legend is here.

The map is still a work in progress, and there’s plenty of opportunity for you to comment on it. And there’s more coming — soon anyone will be able to create their own city, based on their own company, or one they think should join the map. Check it out, and stay tuned for more news.

Web 2.0 Summit, being held October 17-19 in San Francisco, will examine “The Data Frame” — focusing on the impact of data in today’s networked economy.

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Related:

• How can we visualize the big players in the Web 2.0 data layer?
• What are the key data categories companies want to control?
• The state of the Internet Operating System
• State of the Internet Operating System Part Two: Handicapping the Internet Platform Wars

As I wrote last month, I’m working with a team of folks to redesign the Web 2 Points of Control map along the lines of this year’s theme: “The Data Frame.” In the past few weeks I’ve been talking to scores of interesting people, including CEOs of data-driven start ups (TrialPay and Corda, for example), academics in the public data space, policy folks, and VCs. Along the way I’ve solidified my thinking about how best to visualize the “data layer” we’ll be adding to the map, and I wanted to bounce it off all of you. So here, in my best narrative voice, is what I’m thinking.

First, of course, some data.

On the left hand side are eight major players in the Internet Economy, along with two categories of players that are critical, but which I’ve lumped together — payment players such as Visa, Amex, and Mastercard, and carriers or ISP players such as Comcast, AT&T, and Verizon.

I’ve given each company my own “finger in the air” score for seven major data categories, which are shown across the top (I don’t claim these are correct, rather, clay on the wheel for an ongoing dialog). The first six scores are in essence percentages, answering the question “What percentage of this company’s holdings are in this type of data?” The seventh, which I’ve called Wildcard data, is a 1-10 ranking of the potency of that company’s “wildcard” data that it’s not currently leveraging, but might in the future. I’ll get to more detail on each data category later.

Toward the far right, I’ve noted each company’s overall global uniques (from Doubleclick, for now, save the carriers and payment guys — I’ve proxied their size with the reach of Google). There is also an “engagement” score (again, more on that soon). The final score is a very rough tabulation computing engagement over uniques against the sum of the data scores. There are pivots to be built from this data around each of the scores for various types of data, but I’ll leave that for later. This is meant to be a relatively simple introduction to my rough thinking about the data layer. Hopefully, it’ll spark some input from you.

Now, before you rip it apart, which I fully invite (especially those of you who are data quants, because I am clearly not, and I am likely mixing some apples and watermelons here), allow me to continue to narrate what I’m trying to visualize.

As you know, the map is a metaphor, showing key territories as “points of control.” The companies I’ve highlighted in the chart all have “home territories” where they dominate a sector — Google in search, Facebook in social, Amazon and eBay in commerce, etc. What I plan to do is create a layer based on the data in the chart that, when activated, shows those companies’ relative size and strength.

But how?

Web 2.0 Summit, being held October 17-19 in San Francisco, will examine “The Data Frame” — focusing on the impact of data in today’s networked economy.

Save $300 on registration with the code RADAR

Well, the best idea we’ve come up with so far is to show each as a small city of sorts, where the relative height of the buildings is determined by a corresponding data point. So Twitter, for example, will have a tall building in the middle of its city, representing “Interest data.” Google’s tallest building will be search. Facebook’s will be social, and so on. And of course the cities can’t be all on the same scale, hence our use of total global uniques, and total engagement. Yahoo may be nearly as big as Facebook, but it doesn’t have nearly the engagement per user. So its city will be smaller, relatively, than Facebook’s.

What is interesting about this approach is that each company’s “cityscape” emerges as distinct. Microsoft’s is wide but not tall — they have a lot of data in a number of areas. It will probably end up looking like a suburban office park — funnily enough, that’s what Microsoft really looks like, for the most part. Amazon and eBay will have high towers of payment data, with a smattering of shorter buildings. And so on. I don’t have a good visualization of this yet, but the designers I’m working with at Blend have sketched out a very rough early version just so you can get the idea (see image to the right). The structures will be more whimsical, and of course be keyed with color. But I think you get the idea.

I’m even thinking of adding other features, like “openness” — i.e., can you access, gain copies of, share, and mash up the data controlled by each company? If so, the city won’t be walled. Apple, on the other hand, may well end up a walled city, with a moat, on top of a hill.

Now, a bit more detail on the data categories. You all gave me a lot of really good input on my earlier post, where I posited these original categories. But I’ve kept them the same, save the addition of the wildcard data. Why? Because I think each can be interpreted as larger buckets containing a lot of other data. I’ll go through each briefly in turn:

Purchase Data: This is information about who buys what, in essence. But it’s also who almost buys what (abandoned carts), when they buy, in what context, and so on.

Search Data: The original database of intentions — query data, path from query data, “intent” data, and tons more search signals.

Social Data: Social graph, but also identity data. Not to mention how people interact inside their graphs, etc.

Interest Data: This is data that describes what is generally called “the interest graph” — declarations of what people are interested in. It’s related to content, but it’s not just content consumption. It includes active production of interest datapoints, like tweets, status updates, checkins, etc.

Location Data: This is data about where people are, to be sure, but also data about how often we are there, and other correlated data — i.e., what apps we use in location context, who else is there and when, etc.

Content Data: Content is still a king in our world, and knowing patterns of content consumption is a powerful signal. This is data about who reads/watches/consumes what, when, and in what patterns.

Wildcard Data: This is data that is uncategorized, but could have huge implications. For example, Microsoft knows how people interact with their applications and OS. Microsoft and Google have a ton of language data (phonemes, etc.). Carriers see just about everything that passes across their servers, though their ability to use it might be regulated. Google, Yahoo and Microsoft have tons of email interaction data. And so on …

Now, of course all these data categories get more powerful as they are leveraged one against the other, and of course, I’ve left tons of really big data players off the map entirely (small startups like Tynt, Quora, or Sharethis have massive amounts of data, as do very large companies like Nielsen, Quantcast, etc.). But you have to make choices to make something like this work.

So, that’s where we are with the Web 2 Summit map data layer. Naturally, once the data layer is live, it will be driven by a database, so we can tweak the size and scope of the cities and buildings based on the collective intelligence of the map users’ feedback.

What do you think? What’s your input? We’ll be building this over the next two months, and I’d love your feedback before we get too far down the line. Thanks!

Related:

• What are the key data categories companies want to control?
• Points of Control: The Web 2.0 Summit Map
• The state of the Internet Operating System
• State of the Internet Operating System Part Two: Handicapping the Internet Platform Wars

Many of you probably remember the “Points of Control” Web 2 Summit Map from last year. It was very well received. Hundreds of thousands of folks came to check it out, and the average engagement time was north of six minutes per visitor. It was a really fun way to make the conference theme come to life, and given the work that went into its creation, we thought it’d be a shame to retire it simply because Web 2 has moved on to a new theme:

For 2011, our theme is “The Data Frame” — focusing on the impact of data in today’s networked economy. We live in a world clothed in data, and as we interact with it, we create more — data is not only the web’s core resource, it is at once both renewable and boundless.

Consumers now create and consume extraordinary amounts of data. Hundreds of millions of mobile phones weave infinite tapestries of data, in real time. Each purchase, search, status update, and check-in layers our world with more of it. How our industries respond to this opportunity will define not only success and failure in the networked economy, but also the future texture of our culture. And as we’re already seeing, these interactions raise complicated questions of consumer privacy, corporate trust, and our governments’ approach to balancing the two.

How, I wondered, might we update the Points of Control map such that it can express this theme? Well, first of all, it’s clear the game is still afoot between the major players. Some boundaries may have moved, and progress has been made (Bing has gained search share, Facebook and Google have moved into social commerce, etc.), but the map in essence is intact as a thought piece.

Web 2.0 Summit, being held October 17-19 in San Francisco, will examine “The Data Frame” — focusing on the impact of data in today’s networked economy.

Save $300 on registration with the code RADAR

Then it struck me — each of the major players, and most of the upstarts, have as a core asset in their arsenals data, often many types of it. In addition, most of them covet data that they’ve either not got access to, or are in the process of building out (think Google in social, for example, or in deals, which to my mind is a major play for local as well as purchase data.) Why not apply the “Data Frame” to the map itself, a lens of sorts that when overlaid upon the topography, shows the data assets and aspirations of each player?

So here’s where you come in. If we’re going to add a layer of data to each player on the map, the question becomes — what kind of data? And how should we visualize it? My initial thoughts on types of data hew somewhat to my post on the Database of Intentions, so that would include:

• Purchase Data (including credit card info)
• Search Data (query, path taken, history)
• Social Graph Data (identity, friend data)
• Interest Data (likes, tweets, recommendations, links)
• Location Data (ambient as well as declared/checked in)
• Content Data (journey through content, likes, engagement, “behavioral”)

Those are some of the big buckets. Clearly, we can debate if, for example, identity should be its own category, separate from social, etc, and that’s exactly the kind of argument I hope to spark. I’m sure I’ve missed huge swaths of landscape, but I’m writing this in a rush (have a meeting in five minutes!) and wanted to get the engine started, so to speak.

I’m gathering a small group of industry folks at my home in the next week to further this debate, but I most certainly want to invite my closest collaborators — readers here at Searchblog, to help us out as we build the next version of the map. Which, by the way, will be open sourced and ready for hacking …

So please dive into the comments and tell me, what are the key categories of data that companies are looking to control?

Related:

• Points of Control: The Web 2.0 Summit Map
• The state of the Internet Operating System
• State of the Internet Operating System Part Two: Handicapping the Internet Platform Wars