 Hello, this is Gerrit Leonhardt. Welcome to another edition of Meeting of the Minds. Today I have with me from Sydney, Australia, Ross Dawson, futurist, author, strategist and a good friend. Volume, variety and velocity. This is the thing that's happening now with data, is that data is exploding from what it used to be with the information that people have available at the fingertips and mobile devices. Now all of a sudden this volume is exponentially growing. Now we're at the takeoff point from 4 to 8, not from 4 to 5. So in a few years it means basically common global data pool is going to be absolutely humongous. Financial information, personal information, health information, traffic information, sensor networks, all these things. So data is truly the new oil now, switching from the energy economy to the data economy. That's driving lots of businesses and lots of companies are looking at this and saying how can this actually change what we do and how will it change what we do? Education, banking, research, media, essentially being data driven. Big data is making big differences between how effective organizations and individuals are. So Eric Brignolfsson of MIT says that he did a study which shows that those organizations that do what he called data driven decision making have 5 to 6% higher productivity than those organizations that don't. So there's a big divide now between those organizations that are able to capture and to analyze and to find the insights and make better decisions from data compared with those organizations that don't. As individuals, this idea of the quantified self, we can learn far more than ever before about what we are eating and when we are eating and what exercise we do and how we are sitting and what media we are receiving and how we are sleeping, all of which enables us to make better decisions, to act better so that we can be healthier. And that data can not just help individuals and organizations to be more effective and make better decisions. They can actually help them to know more about themselves. I think we are finding that computer systems actually often know more about individuals than they know about themselves about their preferences and what they like. So the potential of this is extraordinarily valuable. So clearly we also need to take into mind the implications for privacy. Big data potential has been clarified I think by McKinsey to be the neighborhood of 4 to 8 trillion dollars economy a year by 2025. I think one of the real issues with the collection and the use of data of course is the fact that it turns a lot of things into algorithms. So we can be quantified how valuable we are to our employer. We can be quantified how valuable we are to society whether we should receive money to do XYZ, how valuable our art is and so on and so on. So it makes things quantifiable, which in turn makes us quantifiable. So one of the issues of big data is how do we stay human on top of this pool of data that gives lots of objective, supposedly objective information. So we don't end up with what I call a bullshit algorithms like cloud that use a bad algorithm to describe something that isn't actually working. So for me big data has a lot of upside but it also really needs some sort of way of humifying you could say making it human so that we can actually get a result from it that fits us rather than us fitting the result. One of the massive domains of explosion of data is social data and we tell not very long ago we had almost no data about what people felt about each other, how they interacted, how they communicated, the value they placed on each other. So the social media as we're experiencing and the way it's growing is giving us extraordinary amounts of information about amongst other things how reputable a person is, how good are they, what they do, are they somebody you'd want to go out on a date with, are they somebody you would trust to do business with. And we're still at the beginning of really what's this big explosion. Part of that is going to give us the beginnings of this true reputation economy, one where we are aggregating extraordinary amounts of data that we never had before. How much people look at the eye when they meet is part of that data set. So the challenge is that this reputation data will always be flawed. We'll never be right, people place a lot of trust in it and it will be valuable. We do have to be cautious about where these numbers lead us. There's one really big issue about data and this issue is the big data, big brother issue. Clearly because as humans we tend to value hard information and numbers and knowledge and science as being the truth and parenthesis. Well we don't trust so much the other part which is intuition or feelings or philosophy or whatever. So the problem with big data is that it amplifies this left brain thinking that now that we have the data we can clearly say that this is the best fit to be my future wife based on DNA and all these things. And of course when you think this too it's quite ridiculous in that we're leaving out, we're using this data but we're not using the sentiment, the actual thing in between the data. And this is one of the big issues about data driven economies is that it puts us all into little boxes and then we have to perform according to the box. And I think this is one of the major challenges going forward is to use that data but there are lots of factors that will make it, you have to amplify it in a human way. Otherwise we have to actually perform according to the data. But the question is why and how do we need this? For example there's an app that allows me to monitor if my baby's diaper is wet. So the diaper has a chip in it. The chip says I am wet, send a message to my iPhone that baby has a wet diaper. And after you worry about this basically saying that doesn't a mother know when it's time to change the diaper anyhow or are they miles away doing something else? I mean this is kind of a bizarre thing when we start needing those things it's like we're going to create a little bubble to do something that we could do just as well without the bubble just for the purpose of creating a business. I think sometimes we can say these are things that we can do perfectly well ourselves which we will get computers to do. I mean the use of calculator. We are very poor at mental arithmetic now because calculators can do that. So we can outsource some of the tasks we've done before. Others we can actually get better. For example now we have rain alerts. We can know when the rain is coming. We don't have to keep on looking out the window to see where the clouds are coming. We can say alright the rain is coming I need to take in the laundry. If we have the information to be able to run our lives better there clearly is a risk we can start becoming too dependent on that. Well the question is where do you draw the line? For me personally of course I'm 52, I'm not 15 but I'm worried about people drawing the line pretty much nowhere so that whatever comes along we'll do. So right now we can get an implant to hear better or to see better of course a cataract or whatever and people can do that but very soon I can get a Wikipedia implant and I'm advantaged writing my speaking to an audience because I have a Wikipedia implant and you don't. So you're not as good as I am in parentheses because you don't and that eventually leads to the matrix or leads to real science fiction scenarios to where I'm worthless because I'm not beefed up. And that has a lot to do I think with this question of ethics and of course the question of the technology imperative because basically what we're seeing right now and I'll be wondering what you think about this is that we're creating a very large matrix of services and platforms that are all benefitting from this and generating new businesses from this, the data economy. So does it actually have use for us or does it actually mostly have use for those that are providing it to us? I think one of the big factors in the future of humanity is that divergence between those who choose to augment themselves with technology and connect and be meshed in the matrix and those who choose not. And it's a perfectly valid choice to say okay I don't want to be connected all the time I don't want to plug my brain into different things yet clearly those people that choose to take that path supposedly the old human path have an massive disadvantage to those people that choose to take that other path. There are choices to be made and I think that the reality is those who take the choices are moving away from the potential of technology to augment themselves are simply going to certainly be massively economically disadvantaged so it's possible to live a virtual life. This is the Amish thing, right? Are they disadvantaged because they don't use cars? I think they are, they don't think they are but they're certainly not the mainstream. And the problem is I think this is a really matrix problem I'm going to be on the grid or off the grid and I can't be on LinkedIn for example and receive your ping about a deal at the same time where I don't fill out my profile I don't put anything in, you won't ping me. So me not being as open is a disadvantage and then sooner or later me not being connected or having the Wikipedia implant is a disadvantage and sooner or later not having an electronic arm that can lift a car is a disadvantage and so who gets to do all these things? It creates also massive inequality because if I have lots of money I can beef myself up. So two divergences, one is the choice. So there are some people who make the choice. I do want to take every advantage of every technology possible or saying no that's too much I want to get off I just want to remain as I was born. The other one is in terms of wealth. There are so many possibilities that are coming only to those who have sufficient wealth. One of the most pointed of those examples is longevity. So I think there is a lot of promising techniques and technologies to be able to extend our lives. Almost certainly they will be very, very expensive. Insurance companies certainly won't want to pay for them. So big data is a very real phenomenon as I say. Data is the new oil. If this actually happens and we have by 2025 we have a five to seven trillion dollar data economy as McKinsey has projected. How do we get from here to there? What do you think companies need to do today so that they can actually use this concept of the data economy? A few basic stages for companies. One is identifying not just what data is the ones they currently can get but also what they could get. What's the data which could be valuable? You're able to make sure that that is all tagged at source to be able to filter that. One of the really critical things is getting the data analysis effectively and I think more and more that's going to go to crowdsourcing. Not every company can get the pool of data scientists internally so being able to get the mechanisms where you can crowdsource the analysis of that data. But the biggest single challenge I think and what organizations need to develop as a capability is how do you get from the insights from big data to better decisions and better actions and that's a very human and cultural aspect. I think there's a few organizations that are good even that are good at data analysis to really taking that to better decisions. I think just to add on to this I think the idea of what's happening with data being like oil when I take it out and I mine it and I slice and dice it that's the intelligence but I still can't drive it and if I don't have a license I don't get to use the gas so to me what companies need to do is to create a sense-making mechanism from this data because most of the data mining and the slicing and dicing will be done by software agents and machines and robots like IBM Watson and so on and they're far superior on the speed of this but the sense-making and taking all the data and saying don't pay attention to this, pay attention to that and this is a distinctly intuitive imaginary basically a composition process you could say in my view and this is going to be our work as humans in the future we're not going to do that digging stuff anymore I think for a lot of jobs that means that they're going to merge over into the creative part into the sense-making and most companies will actually not sell the software the software will be free essentially they will sell the sense-making around it I think this is part of this whole domain of the shift of where is the human value and the human are good at either making decisions or checking decisions and so the computers can be good at the analysis but flowing through the human part so it's in a way this issue of how do you bring the humans and the qualities of the humans the creativity and imagination of the decisions together with that data I think there's a key trend here in saying that we can observe essentially that computers were never good enough to do this now they are and they're getting to be good enough we can talk to them, they can translate us they can measure our electronic streams they can prick our blood and do all these things so now they're getting good enough now we should let them do all that manual stuff and all that crunching stuff and we have to evolve to the next level letting go of the crunching stuff and focus on what we do when it's already crunched so rather than being the ones digging out the oil or refining it, we should be the one driving the car deciding where to go and this is a whole different cup of tea don't understand that they have to get out of this business of mining because basically software will take that over completely and hopefully what this brings us to is where there's more power to the people exactly so this concludes today's episode of Meeting of the Minds thanks very much to Ross Dawson for being part of this today if you want to know more about the show you can go to meetingoftheminds.tv we're also taking questions and inputs for the next show just use the twitter hashtag meetingoftheminds and we'll be responding and trying to work your comments into our next show thanks very much for joining us