This session looks at how technology allows for rapid insights into huge swaths of data, and what this means for business and society. Read excerpts from the discussion below, or download the full transcript.

Fox: Why are we all suddenly—it’s not suddenly, but it’s definitely the last year, two, it’s this big data moment. And, Rick, I’ll start with you. Clearly you thought that this was an interesting moment.

Smolan: Imagine you touched your finger to the stove and it wasn’t until six months later that the stove burned your finger. A million things happen in-between those two data points. It seems like this is the first time in human history where we’ve had the ability to basically touch our finger to the stove and get the information while our finger is still on the stove.

I think the implications of that are so dramatic. It’s almost like you’re going to cross the street and your knowledge of where the cars are is five minutes out of date. That’s incredibly dangerous. Well, what if it’s six months or six years or completely wrong? So that cause and effect is suddenly getting connected in a way that I think it’s just the right tool at the right time to help us address these enormous problems we’re facing as a species.

Fox: Vivek was up here on this stage last year. He was talking in such forceful terms about how great this was and how we had so much data now that we didn’t need science anymore. We didn’t need the scientific method. We’d just look at what had happened and we’d know the future. Did you really mean that?

Ranadivé:  I was exaggerating a little bit to make the point. But even since I said that, there’s all kinds of examples of that. And so I’d like to say that math is starting to trump science and that you don’t really need to know the why, you just need to know the what. Like if A and B happen, then C will happen.

So for years AIDS researchers were trying to find how the AIDS virus mutated and they couldn’t. So then about a year ago they converted it into a math problem, put it into a game called Foldit, and then within one week, gamers found the solution to the problem.

Fox:  Gil, you’re involved with one of the world’s great fishing expeditions, it seems like, at Factual. Describe it to me, like a day in the life of your company, going out and finding another data source to plum.

Elbaz:  The key opportunity is better decisions, and increasingly these better decisions are automated, they’re happening in real-time. What we’re seeing is that these automated systems, we’re trusting them with more and more of our lives. And the better—the more data you have, the better the model that you will be able to build. Of course the algorithm is important, but usually whoever has the most data will win.

So Factual, one of the things we do, why we’re getting interest, is we can provide additional data in certain verticals, specifically places and product data. And our customers use this to enrich their own data in order to build better business intelligence, better predictive modeling.

Björk: I think an important thing in this big data discussion is most of the information for you at that point is noise. How do you filter out the stuff that’s not relevant to the decision you want to make right now?

Fox: Where is the balance in thinking about big data—and thinking about how to get useful things out of it—between actual domain expertise in medicine or whatever else, and just being a good data scientist?

Smolan: I heard Francis Collins from the NIH talking at TEDMED last year, and he was talking about the fact that drug companies spend of billions of dollars looking for cures for some of the biggest diseases facing humanity. And along the way, as they test these drugs, they get to a point where they do clinical trials, and then unfortunately very often some of these drugs have serious harmful or deadly side effects. So .001 percent of the people taking the drugs will die, therefore, the drug is never released. Now, because we are able to actually do generic sequencing, right, and decode individual DNA at a level that soon will be pretty affordable, he’s saying in five years you go to your local drugstore and for $40 they will basically tell you which drugs will work for you and which ones won’t.

Fox: All this data that’s out there, how do you, or should we, think about ownership? Who owns it?

Elbaz:  Yeah, certainly ownership is important. The dimension of it that I think is very important, from a business standpoint, is how much of it do you want to own? Too often a company sees all of their data as their asset. But it turns out that ownership comes with a price. In many cases, that’s a price of huge costs for maintenance.

Fox: If somebody can collect stuff about me that turns out to be really useful, that’s great. But shouldn’t it be my decision? Shouldn’t I own it?

Elbaz:  I think the contrarian view would be that people will make a living in the future just selling their data exhaust. I think that’s a direction we’ll get to.

Björk: Most of the things that we solve in this area have nothing to do with personalized information. Most people don’t even have access to relevant data in their day-to-day life to make a good decision.

Smolan: The fact that everyone else is trading in our data except for us and we have no control of it, it’s sort of unsettling. Especially as it looks like data is becoming more and more valuable and important.

Fox:  There’s a big business opportunity out there for a middleman, basically, between consumers and all of these people who want our data. And I mean, to me it’s immediately very attractive because then there would be someone with an incentive to make these things clear.