>> MC: Well good evening everyone my name is Craig Smart, I'll be your MC for this evening. I'm delighted to welcome you all on behalf of Curtin University, hear more about the industry lead course, of course the master of Predictive Analytics. Something new, something pretty exciting. This evenings event will give you an opportunity to hear from industry as well as from academics, it's a nice marriage of the two domains. So to kick off I'd like to welcome up to the stage the Pro Vice Chancellor for Science and Engineering Professor Andris Stelbovics if you could come forward, we'd love to hear from you. >> Andris: Right, good evening everybody. In the past fifteen years we've seen an enormous growth in the use of intelligent phones. If we go to a country like India, and I was in India three weeks ago, if you go into the poorest villages, the one thing you notice is no matter what the income or status of people is in India, that everybody's connected, they've got a phone. So what we actually see is that the new generation of people on this planet are using devices in a very sophisticated way, to actually provide information which may really be of great benefit to you as a business owner. The service that they are actually providing is actually benefiting us all. So if you're in the audience tonight as a parent or a prospective student I strongly recommend this degree to you. >> MC: Thanks Andris. And I think it's fair to say that to "innovation" has been a catchword for the Curtin University for a long time. And this is a good example of that happening, this marriage between science and engineering and the world of business. So without further ado, I'll ask Tony Travaglione to come forward and speak to us briefly about the, I guess the business dimension of the master Predictive Analytics. Thanks Tony. >> Tony: Just a little bit about the Business School. Our Business School more recently just been accredited internationally as a total business school by AACSB, which basically means we're in the top 5% of business schools in the world, only about 5% business schools have that accreditation. Organisations have really needed to maximise their access to get the most of their assets. It allows you know that big data to be used and analysed to effectively maximise the assets and productivity. And you've seen the success of those mining companies for example, oil and gas companies that have been able to do that and remain in the market in a very difficult time. The other screen that the business school is associated with or primarily responsible for is the financial and investment predictive analytics. And again this is sort of an area that's probably more readily understood in the business world and amongst your sort of general laymen in the sense that vast quantities of information allow financial markets to really effectively minimise risk and to maximise returns. That's all I've really got to say and leave it to the experts later on to talk a little bit more about those particulars streams. But thank you very much. >> MC: Thanks Tony. Now to move on I want to introduce our next speaker Mary Hackett, is the regional director of GE Oil and Gas. Mary joined GE in November 2014 from Woodside so very reputable firms, where she was senior vice president for the Australian Oil Business Unit. And in her 17 years with Woodside, she held a number of senior leadership roles including Project Manager of the Pluto Expansion, Engineering Services Manager and Facilities Engineering Manager And obviously well-known names is well placed to discuss the impact of smart technologies and data analytics to industry. So if I can ask Mary Hackett to come forward, we'd love to hear from you. >> Mary: To be honest and I'm just so excited when Andris talked about this course and the work Brian told me about it, unbelievable. And I seriously think WA, Curtin is going to put WA on map with something like this because it's incredibly important. I think the reason that we're lagging consumers is because it has to be a deep personal passionate need and it has to be a well-understood need. And that's why a course like this is really important to match industry with students to really get that deep learning and find out where the true needs are. Because it's not always immediately obvious. So I'll just give you an idea of where GE has worked across its whole spectrum of delivery. So GE works in healthcare, works in power utilities, so across the full gambit. So it gives them the power to really leverage the ecosystem. >> David: I want to ask you a couple of questions. You know what's the probability of having a little weather event, the sun bouncing off something that causes a traffic accident on the freeway. And therefore what's probability of being able to work out how much time it takes to get emergency services out there. We need an ambulance, we need a policeman. And how many policemen do we need out there and how many ambulance crew do we need out there? These questions, so what is predictive analytics? Can we tell the future? No. But it can tell us a lot about what may, what might, what's probable and was improbable to happen, and we can start allocating resources accordingly. So when we talked about the traffic accident and all the other things that went on from that imagine that you actually were working for government and strategic planning area and someone came to you and said "You need to tell me how much I need to spend over the next 10 years in this area." So what do you do? Predictive analytics is very good at looking backwards and looking forwards, but it's only based on the facts and data that you know. We need to try and integrate our thinking to look at things that might happen now and how they affect our future. And predictive analytics is more than the maths. It's about using your brain power to interpret what you see and build on that in ways that people just aren't thinking of at the moment. And that's the power of predictive analytics. >> Tom: I remember going to a presentation, most part of an event that Woodside were running and there was a presentation given about the use of technology in managing assets better within Woodside. And there were a couple of examples given from the electricity industry, where we said "Actually they are much further ahead than we are in the oil and gas industry" in terms of how they go about using condition monitoring data and so forth to make better decisions about the management and maintenance and so forth of those assets. So what we started to say was "Well actually, no, perhaps focusing down in industries isn't the right way to go." So whilst the oil and gas industry undoubtedly right now is facing a lot of challenges because of the downturn in the market, the fall in the price of crude oil, and the knock on effect that it's had in the gas markets. But actually when we think about the problems that we're trying to solve, they're very much across multiple industries. But undoubtedly these are sorts of skills that are going to be extremely relevant to businesses here in WA. I have absolutely no doubt about that. >> Brian: I'm in the petroleum engineering sector and as Mary explained it's clearly apparent that there needs to be a lot more automation both within that energy sector but also in the mining sector. We hear about talk of automated trucks driving around mine sites but hey, there's a big process plant in there that is not automated at all, has total manual labor governing it, much as many of the oil and gas process trains have offshore platforms in Western Australia. Offshore there is only one platform believe me in the order of 60 platforms only one is fully automated. In the North Sea there's only one platform automated and yet there are 400 platforms. And we need in the resources industry the scientists, the engineers and the data analysts, the people who make sense out of the data, that is collected to be used in a common-sense situation to make companies leaner and produce and basically a greater profit margin. That's what turns industry, keeps industry going. That's what keeps the economy going and jobs. >> Ling: As you can see, that's to do with the predictive analytics obviously we deal with data. We get data, we clean the data, we manage the data and then the security of the data and how to understand and basically how to understand and to draw conclusions, make predictions based on them. That's what you need the computing people to do and that's what we actually want to offer in the course as well. A big part of the course, basically the first year of the course, majority of the contents will be on how to deal with the data. >> Felix: So I work for the School of Economics and Finance at the Curtin Business School. I was trained as an econometrician so part of my role is to sort through thousands, if not millions of data points and try to make some sense of it. And over the last ten, twenty years, what we see is this increasing of that abilities of really really good quality data. So what got me excited about this is the ability to be able to sort through all those complex relationship between the financial market and our economy and then make some sense out of that. And potentially project into the future, allow us to make better decisions and management and either from our personal wealth persoective as well as even one of those policy role that be able to do policy, that be would be more robust to perhaps the latest global financial crisis. >> MC: So thank you everyone and I hope to see some of you on campus perhaps in the not-too-distant future, thank you very much.