Reid Hoffman on pervasive data and how it will impact business in the future

In addition to being the founder of LinkedIn, Reid Hoffman is a Silicon Valley insider with rich insight into technology trends, markets and building companies.

I attended his presentation at SxSW, where his main message was that the future was bearing down on us, and he prophesied that it would “arrive sooner and be stranger than we think.”

• He painted the context for his theme, “Web 3.0 as data,” with this timeline:
  • Web 1.0 was a low bandwidth environment in which individuals searched for files online (and on demand). The concept of “cyberspace” was separate from the “real” world. It was an anonymous world in which many people participated as animes.
  • Web 2.0 was a shift in which people increasingly participated with their real identities (MySpace notwithstanding), and the online world became increasingly integrated with the offline world. Social networks mapped social graphs (again, with real people), and most people blogged as themselves. Online became firmly embedded in offline life, as a way to help manage and navigate by using reviews and other buying tools. Wikileaks and the current revolutions in the Middle East are part of this larger trend.
  • Web 3.0 is mostly to do with the massive amounts of active and passive data we are generating. An example of passive data is phone calls from mobile devices. Bandwidth is increasing, which enables video, audio and graphic sharing and data. Hoffman advocates thinking hard about it and acting to protect data. Think about what kind of future we want to create.
• Web 3.0’s data introduces significant risks to privacy because every transaction, passive and active, is linked to our real identities. Mobile device transactions are constantly tracked, and this is relevant because they are tied to real identities.
• Hoffman’s biggest fear is how governments could use information to control people. Governments are organizations that are closest to what he called “pure power” (because they integrate information, legal authority and military/police power). They can mine email, text and all other digital data to learn anyone’s social graph.
• Unlike corporations, government is not incented to care for citizens; he implied it is less accountable.
• He introduced an interesting wrinkle to this: data is data, and the Web is global, so citizens can be outed and exposed by governments that are not bound by their laws. For example, if (U.S.) Americans pass legislation that controls how their government can use data, nothing prevents another government from using the data. This also suggests to me that the threat is far greater that hacker groups will use the data because they are outside the law.
• Ask yourself what kind of Web 3.0 world we should make. Our decisions may determine whether we find ourselves in a “Genuine New World” or a “Brave New World.”
• Hoffman offered two guiding principles to mitigate risk: 1) as a company executive, make sure you never “ambush your users” by exposing them and thus betraying their trust in you (for not safeguarding their data). 2) Realize that all data is not equal, things like credit card numbers need different protection than age or gender or physical address.
• The data we are producing falls into three types: explicit data is information that we understand is about us and we want to protect; implicit data is less known, we are unaware of it, examples are mobile calls, payments; analytics is data about data that tries to analyze and create meaning. Sources of all three types are exploding.
• Networks are data graphs.
• He would like a data dashboard of the information “the government” has on him. Analogous to credit reports, so you could correct errors.

How data will become more significant in Web 3.0

Next, Hoffman gave some examples of what he meant by Web 3.0 and its data.

• LinkedIn Skills is new; the site introduced a new section of the LinkedIn Profile, skills that are keywords and hotlinked. It encourages users to use the keywords to describe their capabilities. This enables LinkedIn to show skills graphs that are tied to various entities. For example, “blogging” and “reverse mergers” are associated with companies and geographies, which enables people with that skill to see how relevant it may be to a prospective employer; likewise, if they want a job with that employer, they can see how they can increase their chances by developing new skills. Moreover, people can see how skills are related to each other. They are mashing skills up with Wikipedia, too, to provide additional insights.
• Waze is an Israeli company that (through opt-in) tracks drivers’ velocity and location so that users can see real-time traffic flow. Here is another example of the importance of safeguarding data. People who are speeding self-incriminate if Waze doesn’t safeguard their identities. (I wonder how they prevent police from using the anonymous data to zap people in various locations.)
• There are numerous examples of sharing financial information to gain insight from the crowd; sites like Wasabi invite users to upload bank statements, so they can discover opportunities to buy and manage their finances better.
• Redfin is the Charles Schwab of real estate; its users share anonymized information about home prices, values, payments, etc., so they get a better idea for how to buy and sell in real time.
• He suggested that top-down topologies are inferior to bottom up. With LinkedIn Skills, they analyzed existing profiles to create the “skills” (i.e. keywords).

10 rules of entrepreneurship in Web 3.0

He introduced by sharing ten rules of entrepreneurship because entrepreneurs would have a big part of developing Web 3.0.

1. Disruptive change should be a big part of the concept behind your business; a hallmark is that, if you succeed, you will spawn a “platform” from which numerous new businesses will be born.
2. Aim big because it takes the same effort to launch something small (incremental) as something really big. You also have more room.
3. Build a network around your company, and tap it for everything, thereby amplifying your company. Ask your network for advice and help, treat it as your board of advisors and distributed intelligence.
4. Plan for good and bad luck by anticipating things that disasters and fortuitous things that could happen. Be ready to capitalize on good things. He is an alum of PayPal, where they were not excited by “all these eBayers” using PayPal at first. Then they thought, “Wait a minute, maybe these are our customers.” Be paranoid, try to consider challenges and bad luck that could present, and consider how you could mitigate damage.
5. Practice “flexible persistence” by actively considering when you need to be in persistent mode and “go through walls” and when you need to realize that maybe you need to tweak your concept. The above eBay example also probably works for this.
6. Launch early, don’t let perfectionist tendencies delay you. You should be embarrassed by your first release. No matter how smart your team is, you won’t get it right in isolation of customers and users. You will lose valuable time getting it too perfect; you want to be early to start fast iteration cycles by interacting with users.
7. Be ambitious but don’t drink your own Kool-Aid because you’ll lose your way. Be paranoid, ask your network what’s wrong with your company to draw out faults.
8. Realize that product is important, but distribution is even more key because a great product that no one knows about will fail. Build a solid distribution concept into the DNA of the product.
9. Pay attention to culture from the beginning because the people you hire will hire all the rest of the company. Hire people who are adaptible rather than experienced.
10. Break the rules sometimes, nothing is holy here.

Analysis and conclusions

• This post and subject may seem like meaningless tech babble, but I also encourage you to think about it, and deeply. Wired has been covering this for a long time, pervasive information and data has been a major theme there, even predicting the death of the scientific method.

• You have undoubtedly heard of Moore’s Law and Metcalfe’s Law, which describe the falling cost of computing and the increasing value of networks respectively. Once you appreciate that every time every person and every machine hits a button of a digital interface, that represents a transaction that can be recorded and analyzed. Behavior can be deduced. Falling computing costs make it increasingly feasible to analyze these transactions and understand trends.
• Hoffman didn’t go down this road, but Chris Anderson’s and Wired’s big thesis is that we can eliminate a huge part of uncertainty by using statistics. To call this “disruptive” probably trivializes it, it is so immense in signficance.
• Hoffman’s clear message is exceptionally valuable: “Don’t let this happen to you. Take an active part in creating that future.” Influence it the way you think it can serve best, and try to mitigate threats. Assess how you can exert the most influence.
• Think about your business or the most important things in your life. Reflect on the sources of uncertainty. Research the types of digital transactions that are occurring and anticipate how data could change the rules. It won’t be even, but you will be ahead of the curve by thinking about this, constantly. Invest some cycles in learning and sharing with complementary thinkers.
• Hoffman’s Ten Rules are solid; I would wrap them in the larger context of seeing your company (or yourself) as a surfer. You are interacting with some kind of market (a body of water) that is far stronger than you. However, if you watch it and respect it, you can use its power to create amazing things. My favorite is probably #5. It’s an interaction. #3 is critical, too, building your network with purpose is probably the most important thing you can do. It’s a well known law in the Valley among people who are serially successful.

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