 my style. My challenge today is to actually build on what was just said to say why don't actually think about the term big data any more because to me big data is absolutely useless. Big data is only useful as we transform it into big decisions. So the challenge for Australian livestock species is to do exactly as I said before is to merge what I consider big data into big decisions and actually equip the livestock industries to enhance the productivity. In the area that I work with there's major changes that are happening in the livestock area. Supplies to say that we've probably seen in the last 12 months changes that we wouldn't have expected in the last 10 years. My challenge is that producers are starting to operate more and sophisticated brand change. They're starting to think about quality supplying on the way from factory demographics but also about how do they fit into the brand architecture? How do they fit into the brand DNA? How do they fit into the brand ecology? Now all of those three things are starting to really differentiate producers and how they interact with supply chains. Productions driven by brand and products pretending to block more in the brand. So when you actually become closer to the brand one of the real challenges is you actually start to move away from that little bit of diversity that producers actually like. They like the opportunity of diversity but with diversity comes cost production and with that diversity also comes risks of being aligned to brand assets ups and downs. Successful producers are really changing. They're starting to manage more information in this kind of quick themselves to handle this information. There's more and more of sensors coming in. I'll show you some examples of that. There's this big challenge of managing more and more of panels with less labour so corporate agriculture is moving into this area. The cultural change that's happening in our particular space is that we are changing 300 or 400 years of agricultural production. Most farmers and most family farms that you spoke about this morning have built themselves on dealing with complexity around visualisation of that complexity. They walk out into the paddock, they observe what their animals are doing, they observe what their bees doing, they observe what's happening. With the advance of all of these sensors you're changing that dynamic because they no longer walk out into the paddock to observe things. They're getting this tsunami of information presented to them. So I'll show you that in a couple of these pictures that are coming up. So we have to see the cultural change of changing from visual agriculture to what I say is its best as information driven agriculture built around these paddock chains. So transforming big data. So big data requires analysis in information, in information and then interpretation, insights into decisions. Now the size of those, I've tried to capture a little bit, there's a heap of data, there's a little bit of information and I have to put decisions into that circle, landing the big decisions in. Now I really wanted to say the size of decisions would be a pinpoint on that particular side. So we are investing heaps of money into agricultural data, we're not investing heaps of money into agricultural decisions. And in our area the biggest goal that we'll have, particularly in southern livestock species, is the integration of what I call the livestock enterprises, with the cropping enterprises, with the pasture enterprises, with soil enterprises, all of those things which are the multi-faceted areas of new farming businesses, and there is not one single entity that's actually taking that role and responsibility on it. MLA, the company I work with, we're very happy working about livestock decisions, GRDC, happy working about cropping decisions. The interplace between our two areas will drive southern agriculture and yet we still haven't had those discussions, so that is the role we need to do. So emerging technologies, objective measurements change in the area and I know Tom and Troy will talk about that. Genetics and genomics, a couple of years ago when we first started the CRC, we started characterising animals and we thought in one or two genes that we could measure that would be fantastic. Then we got something that was measuring 100 genes, then we got something that measured 1000 genes, then we got something that was measuring 50,000 genes, then we got something that was measuring 700,000 genes. Now we're doing full sequencing at 3.5 million genes and the interesting thing, full sequencing costs now less than what it was to get one or two genes five years ago. So all of that information is coming. And then when you say to the average, a C-stop producer, I know there are several in the room, not only are we going to give you this information around beta types, we're going to tell you about full sequence information, that full sequence information is basically one gigabyte of information. You've got 100 bouts, so we want to transfer down 100 gigabytes of information down on the current download speeds. They have a little heart attack. All of those things are emerging in that area. Now with this information management, can we actually get all this information in? So now we're talking in terms of lean meat yield, we're talking single figures around percentages of lean meat yield. What happens if I actually gave you percentages of lean meat yield in 39 muscles across the body? I also gave you the meat colour of all those muscles, I gave you the pH of all those muscles, I gave you the marmalade store of all those muscles, I gave you the consumer ready quality stores of all those muscles. And then I said, here's all this information, there's at least 39 muscles I'll give you, 10 pieces of information, go ahead and breathe and open care. Producers can't even do one, get it? They're a bit like Mariners, they get to one and they can't count past that. So we're throwing all this information directly into their laps and we're asking them to make much more informed decisions. And that you get through the panel. I'm a bit worried about that particularly here. So we've got RFI intakes and they're coming things, the fifth piece for livestock, I made a joke about three months ago in another forum, I said there'll be fifth piece for livestock and I know it was yesterday or two days ago that there was a major article coming up, we've actually done exactly that. They've now got fifth piece for livestock and they're actually thinking they're going to get improved, it's a breeding efficiency very kind of like about 10% through knowledge of that information. So sometimes when you make a joke you've got to be careful about the outcome. GPS tracking, we're doing all of these things so we now know virtual stocking rates, we're now changing our fencing lines, we're changing our pattern of resource utilization based on this information. We've got drones and sensors that are going up in that area. We've got water and fence sensors that are happening all the time. And the area that I'm most excited about is this biological tattoos. When you start to put in tattoos on individual animals with biologically active inks that they caress at different frequencies and that caress tells you about the well-being of animals. Now wouldn't that be exciting? You walk out in the paddock and all the heifers are bright green so you know that they're all happening. Now or more importantly it wouldn't be really good if you put the drone up and they saw all these bright green heifers and they actually reported back to you in your office when you're sitting inside the pool having a drink, all your heifers are happening. Right, now that's not too far off. Again that's going to change the place of agriculture and particularly the life of the animals. Again it's going to take a visualization. It'll change management skills. As Philip said earlier I'm starting to see that we're going to change away from farm managers to what I call a system as engineers. Systems engineers actually take all of this information that makes effective decisions. They're not managing anymore, they're decision makers. And again that's a change in agriculture we have here today. The areas are again, Kristen, people talk about in her session, I encourage you to go and listen to that later on. There's all of these hyperspectral cameras and new decks of x-rays. All of this information that's going on in the system and throw this amount of information back to livestock producers. More importantly, how do they make effective decisions about all of this information? How do they balance up the fact that we tell them that lean meat yield and eating quality have a big negative correlation? If you're not admitted into value based marketing you have to adjust to that negative correlation. The real value is actually optimizing those two principles. You throw a neural network at it, here's the optimal profile for exactly the numbers of animals that you need in each of these sequences. And in the northern meat industry we're doing exactly that. We're starting to say based on your genetic profile, based on the grade of grade you've had up to 200 days, you go on a boat and you go to a processing office. That's the type of information that's going to come forward. Again, pace of technology. That little thing, that's a nicks color sensor. That costs about $300 US. It links to your iPhone. It does a really good job of objective color in both beef and sheep. We think that we're able to put all of these little nicks sensors into a range of supermarkets, basically get crowdsourced information back as each of the consumers picks up a piece of meat, tells us whether they like the meat color or not. They come back next time, they use a little sensor, they tell us whether they like the product or not. So instead of having one or two thousand pieces of information, something we might have a hundred thousand pieces of information for consumers' capability of eating quality meat off on a daily basis. Won't that transform our agricultural production or go? Won't that transform our company's integral? So it's becoming increasingly complex. So by the biology of agriculture, particularly livestock systems, I mean producers and particularly livestock producers do a fantastic job of dealing with complexity in decision making. What they've done to the world is actually handle the complication of complexity and we are throwing more and more complication to that complexity. We are making decisions metric by the minute and that is going to influence how we do those areas. So it doesn't matter where your sickness supply chain is increasingly complex system or moving from complex to complicated, it's going to be a real challenge. And to show you, here's the red bean language value chain. This is all the information we've got that's going across there. It does a whole heap of information. All of these trains are coming in place. The really interesting thing is there is not one common set of language or standards that goes across that whole agricultural profile. There's bits of information collected from that and not one part of each of those sectors falls to each other. There's no standard in the making of your life the way across that speaker. So goal of decision making, understanding this whole area. So livestock enterprises are my challenge for the livestock producers in order to really grapple with big decisions and big data, understand resilience and adaptability of all your production system, understand where the key points are going to be. There's going to be huge opportunity not to leave productivity. I think people focus on productivity a lot in the supply chain, at least in the meat supply chain I was working. The cost of non-compliance to livestock production far outweighs the cost of any improvements we can make in productivity. So people producing things to a market end point after six or seven hundred days and getting that wrong is far greater cost to the industry than making productivity by two or three percent. And as an example with the cost of non-compliance, and a couple of supply chains I've worked in, it's about $52 an animal for the dark cutting, which is one industry issue. It's a $1.92 a kilo. It's a $50 million issue that can be solved just with the data that we've got right on the ground. So there are big gains in getting rid of non-compliance. So less questions and resources are probably the thing for a good culture more than anything else. We're going to try some of these things. As I said before, compliance to market specification, there's $30 to $85 a head, value-based marketing, well I've gone away from the term value-based marketing, in livestock it's value-based pricing. When you actually have value-based pricing then you allow the value chain to the market. Producers shouldn't get involved in that area. This is sufficiency and profitability. They're all going to leverage this information and data to information to decisions, identifying where those key areas are going to be quite important. And from an RSE point of view, when you actually use that type of area you will radically change investment portfolios in R&D. If you know where the key points of transactional efficiency are, you will change where you invest R&D dollars. And at the moment we apply a shotgun approach to that type of area. So lots of databases, MedStand or MLA, we're lucky to be the custodians of a range of different databases. So we've got NIS, MedStand, Australia, livestock data link, the National Livestock Recording System, we're the custodians of breed plan, we're the custodian, sorry for being ABRI, sheep genetics, on-farm software, breed societies have all involved that animal health disgrace. So there's all of these databases that come in on board. Very few have actually integrated together to form big decisions. So in one area, a little bit of on-farm software now is linking the National Livestock Recording Service with the Bureau of Meteorology to sell a little bit of information about when we sell and what to feed. Probably about 2% of livestock production is starting to actively make that product. And we get a little bit more complicated when we link those NIS with MedStand Australia with livestock data link together with breed plan and sheep genetics and the research databases to improve the information of any product. And to show you how complex our challenges are, in the area of MedStand Australia last year we braided 3.2 million animals. Each of those had measurements of intramuscular fat, measurements of colour, measurements of oscillation for all those other challenges. We have a national genetic database and breed plan with the breed societies which is basically the world's best at the moment. How many records out of that 3.2 million you think went back into the genetic database to influence genetic decision making? Well you know what I mean, it's greater than zero with no primer, so it's 611. So the LR2.9 million plus we lost in terms of that contribution to the genetic database. And our base of MLA manages both of them and we can't even connect them yet. So that's a challenge we have. And here's an example of where the real value actually lies. Tom will probably use this later on in his talk, value based marketing pilot. If we actually understood the complexity of leading that yield, we actually understood the complexity of eating quality. If we can apply that system, provide that information back to producers, over 105,000 animals is 18.8 million dollars difference between paying on weight and fat versus paying on real value based marketing. So there's about 120 to 130 dollars per animal by getting that decision wrong. That's a huge opportunity for the Australian agriculture industries to look at. So we've got a whole heap of things there, benchmarking, and I won't go through those, but benchmarking, raising systems, hospital compliance, the area that I want to see our industries get much more invoked in. The PCA is about supporting investment strategies. We saw this morning about investment in agriculture. The problem with our investment in agriculture is we are not using big decisions to make that investment decision for us. So we can do a lot more of those areas. Welcome to Welfare. The spoke about brand DNA earlier on, there's going to be this quantum need for Australia agriculture to start to underpin brands with information that supports the brand integrity in domestic and international communities. And all that requires information coming from farm right up through the value channel. So to deal with that complexity is going to be a challenge. Then you get measurement of R&D, investment, all those other things. And the borderline is total farm productivity or profitability. It's the key area of big decisions. Now at the moment with big data, we are making no minrides into that area. With big decisions, we can probably transform that with about a 5 or 10 percent increase. That's the type of quantum we can see in agriculture. And value channel efficiency is just a start. To give you an idea what's already out there, this is the Irish system. So the Irish, and you think the Irish are a little bit backwards and apologies for those people Irish in the room. But I have a lovely piece of legislation that says if you breed cattle in Ireland, you have to be part of that system. It's great. Wouldn't that be a great piece of legislation across Australia? But it means every animal in Ireland is fully genotype. Every animal, all the transactions right the way through its lifetime are made. When it goes to the avatar, every piece of information is actually captured in the avatar. All of that information is fed back into a national database and that's all made available back up into decision making around genetics and charters. Completely integrated right the way through the system. You have to be part of that. Now, the really good thing is the Irish captured the European Agricultural Commission and they got something like 70 million dollars to install one. Now, we don't have the luxury of doing that type of area. So that's our challenge. By my crew in New Zealand, the Ed City system with New Zealand, that's about 187 million dollars to do. Now, we're talking about in MLA about putting a system that's going to cost us two to three million dollars when I've even been in the right ballpark when it comes to these type of decision making. So the lesson we've learned from others about talking to things and we've got a review out at the moment. It's about getting everybody in some room and thinking about whether we in this situation actually are. So looking for people to make decisions and outcomes. And the real key about integration across the livestock chain is that you've got to create a weakling based on aggregation and aggregation to leads to decisions. At the moment, Nick might talk about later on about open access platforms around data. Well, the real key for asking livestock is open access around the algorithms that be involved in decision making. Because at the moment, there is no confidence in a lot of those algorithms that be involved in decision making. So we've got to get to that. No one really knows what your data is. Everybody thinks it's going to be important. Everybody is applying a decision approach to the principles to address that. There's democratic data, there's delivery through public available APIs and everything like that. He talked to everybody, everybody thinks yes, on about the big data. He talked to everybody about how to use the big data on collecting big data. Collecting big data is not using big data. Everybody wants to be part of it. And everybody thinks that because they've got data, they've got value in big data. And people have yet to work out the only value in data is when you aggregate that data to make a decision. And we have got to place a real cultural change in in agriculture about moving away from IP protectionism to an open source platform that allows big decisions to be made across agriculture. Because at the moment we're moving the other way around. Information about genuine value, creating genuine value on the supply chain on the value chain is a real critical thing. You can make decisions, you can give advice on those type of things, but unless they lead to tendual value there will be no impact on agriculture. So 74 of data by itself is not valuable. And people are putting all of these restrictions around data because I think they're finding the data. The real key is the finding algorithms that aggregate the data. That's where the real information is going to be collected. And actually making the public source for those types of areas is going to be a real thing. And people think that providing data and help entitles them to more data. So I've listened and we've worked with people recently that said I'm giving you this data and in return for this data I expect this. And in many cases you can't actually predict what you're going to give them. So how can you actually give the data without actually understanding what you're going to give them back. And that's going to be the learning experience we've worked over the next couple of years. And then industry owned data. I mean as an RDC about three months ago we've blindly went into the belief that we should set up a single national database set up all these algorithms to all these wonderful things to control the world. But a lot of concentrations led us to believe our role is firstly helping set the standards and secondly helping set the framework about how you can correct the algorithms to make decisions. It's not our role to pool the data together. Our role is to coordinate the civil data opportunity to do that. So my challenge for the industries are data is increasingly more complex it's complicated and volumetric and if you think farmers are struggling with data they're decision making they are. They're struggling with ground decisions they're struggling with all those things and suddenly we're going to put this huge pressure on them to cope with the information that's coming back to them. And I had already started to see some of the producers struggling particularly in the C-stop industry struggle with that transition from visual to the information group of decisions. And that is creating culture. Big data must generate capacity to drive big decisions. So I hope everybody walks out of the room thinking about big decisions rather than big data and the key barrier is engaging everybody along the value chain and listening to what Phil said earlier about changing the value chain that horizontal platform of information transfer is yet to be discussed because we haven't yet had a real conversation about agriculture about integrating livestock with pastures and crock in the soils and everybody's walking in their own silos and that big decision about integration is yet to come. Thanks very much.