Google, WalMart, and MyBarackObama.com: The Power of the Real Time Enterprise

by Tim O'Reilly | @timoreilly | comments: 22

What do Google, WalMart, and MyBarackObama.com have in common, besides their extraordinary success? They are organizations that are infused with IT in such a way that it leads to a qualitative change in their entire business.

I get frustrated when I see people highlighting use of social media--blogging, wikis, twitter, customer feedback systems like Dell IdeaStorm or MyStarbucksIdea--as if they were exemplars of what has come to be called "Enterprise 2.0."

As I said in my keynote at the Web 2.0 Expo NY (and in a followup radar post), WalMart is a better example of Enterprise 2.0 than any of these more trendy examples of user contribution systems. If Google's key innovation with PageRank was to recognize that a link was a vote, which could be counted and measured to get better search results, so too, WalMart recognized early on that a purchase was a vote. Each company built real-time information systems to capture and respond to that vote. WalMart built a supply chain in which goods are automatically re-ordered as they go out the door, with algorithms based on rate of sale controlling the reorders. Google built a better search engine, in which pages that were "better linked" were given priority over the ones produced by pure keyword matches. They went on to build real-time systems to measure what John Battelle called the database of intentions, as expressed by people's queries and subsequent clickstream data, as well as an ad auction system that prices ads in real-time based on the predicted likelihood of the ad being clicked on.

I came to see just how closely MyBarackObama.com emulated these ideas of the real-time enterprise in accounts of the Houdini project, a bold program in which poll watchers eliminated the names from voters who had actually made it to the polling station from the "get out the vote" call lists:

While the hot line was too overwhelmed to be of much use, the source said the program itself still proved a smashing success....the campaign was able to clean 1.6 million voters from the call lists they distributed to canvassers that afternoon, making those lists 25 percent shorter on average.

While the infrastructure for data reporting broke down under the pressure of the election, the general trend is clear here: competitive advantage comes from capturing data more quickly, and building systems to respond automatically to that data.

Consider MyBarackObama.com as a kind of vast machine, with humans as extensions of the programmatic brain: volunteers log in to get their get-out-the-vote call lists. They place their calls, then use the web to report back their results. Those results modify the call lists for the next volunteer. At the other end, the Houdini volunteers are taking note of who is actually coming out to vote, allowing the system to dispatch additional attention to hot spots, for example where there is an undervote compared to the campaign's projections. Meanwhile, the pruned call lists make the volunteers more effective. Inside the machine, programmers are tuning the algorithms, while top campaign staffers are making key decisions to adjust the resource mix.

Now put these three examples, Google, WalMart, and MyBarackObama together, and ask yourself what they tell you about the future of business, military operations, or any large organization.

Sensing, processing, and responding (based on pre-built models of what matters, "the database of expectations," so to speak) is arguably the hallmark of living things. We're now starting to build computers that work the same way. And we're building enterprises around this new kind of sense-and-respond computing infrastructure. In this sense, you can argue that Microsoft's term "Live Software" is the best name yet for the kind of software-infused enterprise we're building.

It's essential to recognize that each of these systems is a hybrid human-machine system, in which human actions are part of the computational loop. Back in 1998, when I was trying to understand just how people were using Perl and other scripting languages on the web, I came to recognize that web applications, unlike desktop applications, still have the programmers inside them. Perl was called "the duct tape of the internet" precisely because it was used for programming that was only expected to last a short time; the programmers were still there, constantly tweaking the application. (I first began using the image of "the Mechanical Turk" in my talks about this aspect of web applications in 2003.)

What became clear in the ensuing decade is that humans are not just part of the programming, but also sensors and actuators for computers. Our aggregate behavior is measured, monitored, and becomes feedback that improves the overall intelligence of the system. That is why I've said that the defining characteristic of Web 2.0 applications is that they "harness collective intelligence."

Aside: I seem to have lost the battle to define Web 2.0 as" the use of the network as platform to build systems that get better the more people use them. Perhaps its the lure of the obvious: companies and products that harness explicit user contribution are easier to recognize than those that pursue the more subtle and difficult task of harnessing implicit contribution. Or perhaps it's the persistent gravitational tug of the idea that the heart of Web 2.0 is ad-supported business models; therefore, enterprise features that look like those of well-known companies featuring user contribution and ad-supported business models must by definition also be "2.0." For me, the far more profound and powerful systems come from harnessing both explicit and implicit human contribution.

Again, consider MyBarackObama.com. It definitely harnessed explicit contribution, providing a platform for volunteers to organize and host local calling parties, to blog, or perform other campaign activities. But ultimately, Obama's ground game--old fashioned precinct-level organizing, amped up to a new level by an army of distributed volunteers armed with mobile phones and coordinated via a web application--was the key to his victory. The "explicit" social media elements of MyBarackObama.com paled in impact compared to the development of a next generation electronic nervous system, in which volunteers were trained, deployed, and managed by a web application who used them, in John Sean McMullen's memorable phrase, as "souls in the great machine."

Why Dell.com (was) More Enterprise 2.0 Than Dell IdeaStorm

by Tim O'Reilly | @timoreilly | comments: 9

In my keynote last week at Web 2.0 Expo New York, I made the comment that, cool as Dell Ideastorm is, the fundamental supply-chain approach behind dell.com is actually a better example of how Web 2.0 applies to the enterprise. I also made the provocative assertion that WalMart is a Web 2.0 company (or at least a model of how Web 2.0 principles apply to the enterprise.)

Based on questions I've heard since, I thought I should explain further.

Many people have been seduced into the ideas that Web 2.0 is all about explicit collaboration, contribution, and "the wisdom of crowds." So, for example, on his Web 2.0 Watch List, Seth Godin wrote: "For our purposes, my definition is that most of these companies are, as the wikipedia says, sites that 'let people collaborate and share information online in a new way.' So," he says, "Google doesn't make the cut, because most of their traffic comes to their search engine."

Now, I will say categorically that any Web 2.0 definition that excludes the Google search engine is broken. But it's broken in an instructive way, one that shows what the problem is.

Web 2.0 is ultimately about understanding the rules of business in the network era. I define Web 2.0 as the design of systems that harness network effects to get better the more people use them, or more colloquially, as "harnessing collective intelligence." This includes explicit network-enabled collaboration, to be sure, but it should encompass every way that people connected to a network create synergistic effects. So let's take Google:

• The very act of creating a search engine via spidering relies on a user-contributed network. Where does Google get its raw material except from us? When I publish this blog post, I'm contributing to Google (and to every other search engine.)

• Google's PageRank algorithm extracts an additional level of implied metadata contributed by links. All spiders follow links to discover new content; PageRank taught us that some links matter more than others. The engine became smarter by understanding a bit more about what people were contributing even when they didn't know they were doing so. Many a new breakthrough in Web 2.0 (e.g. Facebook's social graph) comes from making implicit contribution explicit in some way, gathering its benefit and amplifying it.

• Google's real-time ad auction is the heart of their economic engine. Their stroke of genius, which gave them their seemingly insuperable lead over the other search engines in ad monetization, was to understand that selling the top ad position to the highest bidder was actually leaving money on the table. Given that advertisers only pay for clicks, Google realized that if they could project the likely click-rate on an ad, they could sell top position to the best combination of price and click rate. A $5 cpc ad clicked on 3x as often as a $10 cpc ad will make $15. By instrumenting, measuring, and responding to the click rate, Google made ad auctions smarter - through harnessing implicit user contribution.

• Google also measures click-through behavior, surfing habits, and everything else they can get their hands on that will help them improve search results or ad performance. All of this is fed back into a real-time loop that is constantly trying to automate and optimize the user experience. Sounds a lot like the human brain, eh? (At least one with lots of learning plasticity.)

  If there is only one thing that enterprises ought to learn about Web 2.0, it's this one: building information systems that allow you to adjust in real time based on interaction with your customers is the true mark of a networked enterprise.

If you understand the following three things, you know everything you need to know about Web 2.0 and the enterprise:

1. Harvest every bit of user contribution, not just the explicit. Your business has thousands of touch points with customers. When they buy from you, they contribute data as well as money. When your suppliers increase their prices, or change their delivery times, they contribute data to you. When you advertise, and people respond (or don't), they contribute to you. When you introduce a new product, when you do something your customers love, or hate, and people talk about it, they contribute.

   Your data is one of your most critical business assets. Are you doing everything you can to wrest competitive advantage from it? I'll remind you again: PageRank and the real time Adwords auction were both hidden in plain sight. Understanding what data you have, and what meaning you can extract from it, is the holy grail of Web 2.0.

2. The era of IT as a back-office function is over. It's no longer good enough to gather data and analyze it, then propose and adjust strategies over the next budget cycle. You must infuse your organization with IT, so that, like Walmart, your supply chain responds every time a customer rings up an item at the cash register. This is how Walmart is like Google. No, not the website, but the live enterprise, which learns and responds.

   That's why in my enterprise 2.0 talks, I usually end by saying "turn your IT department inside out - or wait for some innovative startup to do it for you." Banks could be building something like Wesabe's Value Engine and tips feature, which extracts collective intelligence from credit card data; phone companies could be doing something like Skydeck's extraction of your social network from your phone bill. In fact, they'd be in a way better position to build integrated services against this data than startups that are having to first extract the data from corporate databases one customer at a time!

3. Web 2.0 thrives on network effects (also known as virtuous circles): data begetting more data, services getting better in such a way that they are used more often, until you are so far ahead of the next guy that he can't catch up. That network effect is enhanced by letting other people use and build on your data, not by keeping it private. What we've seen is that the first company to create network effects in a particular class of data tends to end up owning that data simply through having the biggest pile, or the best results, not because they have unique data. (Again, Google: Microsoft and Yahoo! have the same data for the most part; Google is better at creating value for others from it.)

I will note that Dell recently announced that they were abandoning their long-heralded build-to-order methodology in favor of standardized commodity boxes. But I still stand by my statement.

There are two possibilities: first, that Dell is wrong, and their new supply chain approach will not save them, just make them more like everyone else. It could be that their "live suppy chain" approach just got too crufty, too complex - the article linked above suggests more than 5000 possible configurations. Maybe what they needed to do was to make the system smarter again by streamlining and simplifying.

But it's possible too that the competitive advantage to be wrung from a live enterprise only takes you so far, and that in certain circumstances other advantages are more important. It may well be that the PC market has reduced itself to such commodity status that standardization trumps customization. It may well be that the costs of physical goods mean that the laws of virtual networks are only partly true in that realm.

I haven't studied Dell's situation and market sufficiently to have a fixed opinion. But the answer is knowable - a good field of study for business school cases. It's worth repeating something I once said about open source, but now for Web 2.0: This is science, not ideology. Our goal is to understand what works, and why.

P.S. I also talked about the ideas here in my piece from last year, What Would Google Do?