Dear participants, welcome to the course of Supply Chain Digitization. It is jointly taught by Professor Piyanka Burma, Professor Sushmita Narayana and myself Professor Devapratadas from Indian Institute of Management, Mumbai. So, in the last lecture of module 3, which is part of analytics in Supply Chain Management module, we talked about what is analytics, what are the various characteristics of analytics. We also talked about big data, what are its characteristics, various ways of big data. So, now in this session, we will focus mainly on various types of analytics, like how do we define various types of analytics and lot of example from supply chain management domain. Now, these are the main four types of analytics which you can see over here. The first one is descriptive analytics and then we have diagnostic analytics, then we have predictive analytics and then we have prescriptive analytics. So, these are the four important analytics which we have. So, in that throughout the next like few minutes, we will see like where these kind of analytics can be applied specifically in supply chain management domain because this course is on supply chain digitization. So, our focus would be in supply chain management and specifically where analytics can be used. So, let us understand these four types of analytics in detail. So, the first one is descriptive analytics. So, that means specifically if you have heard about descriptive statistics. So, this is nothing but in a simple manner descriptive statistics. It talks about what has happened in the past. So, I have data from the past. If I can capture it properly, I will get to know what is the average value. So, let us say if you have a demand data of various stock keeping units from the last 2-3 years, I can actually find out what was the average demand per year, what is the average demand per month, what is the median demand. Now, how the demand is changing month to month. So, all of these are possible through descriptive analytics based on the past data. That means whatever has happened, you have captured data from those points and then you try to find out how data is changing like what is the average value, what is the median value, what is the standard deviation of the data, is there any variability and so on. So, the good example could be I can use bar chart, I can use pie chart and nowadays lot of good software are coming which is actually creating dashboard. So, I have power BI, Tableau. So, descriptive analytics can be very nicely plotted in the dashboard and as a CEO level. So, let us see you are managing the supply chain of a big FMCG company. I need to know like in which DC, how many units are there, in which warehouse, how many products are lying and it is not being sold. I also want to know in which zone products are sold at a very fast rate. I also want to know in which month, how much was my sale. So, all of these can be easily captured using a dashboard and descriptive analytics can be useful for creating such dashboard. So, that is specifically about descriptive analytics. Now, we have second one called diagnostic analytics and as you can see the image of like microscope, we also have lens. So, this is basically saying like if something has happened, why it has happened, like why did it happen? So, let us say from here the bar chart, I can see that for this product A, my sale was low. So, I need to get to know why the demand went low, what was the reason? Similarly, I can see for this product B, the demand went up. So, what was the reason why the demand of product B went up? Obviously, I will be happy if demand goes up, but I also need to know why the demand went up. If I can know the reason, obviously next time I will try to like do the similar kind of activity. So, that sales of product B keeps on increasing. On the other hand, if I get to know the reason of product A, why it demand, why its demand went down, then obviously, I will take care of it. I will make sure that I will not do that activity in the future. I will take some corrective action. So, maybe some competitors have come in the market and they have taken away my demand. So, I have to make sure that they are like if I have to make sure that I am tracking my competitors movement and accordingly I am positioning myself.  So, that is being done through diagnostic analysis. So, if some event happened which are not as per my expectation, if I want to diagnose it, I will be able to do it through diagnostic analysis. So, root cause analysis also I can do to find out why there is an issue. Then the next part of analytics is called predictive analytics. It talks about what will happen. So, descriptive tells what has happened in the past. So, suppose from the past data, I can see that demand of product B is going up. Now, I have to predict whether the demand of product B will keep on going up in the future or what will happen. So, descriptive analytics is focusing only on the past data. Whereas, predictive analytics taking the past data and modeling it and predicting what will happen in the future. So, for product A, we saw that demand has gone down in the past. So, will it still go down or not. So, that is what the predictive analytics will do. Now, the fourth one is prescriptive analytics. So, what is it? It tells me what should be done. So, suppose for product B, descriptive analytics says the demand was very high, predictive analytics said that demand will keep on going up in the next 2, 3 years, then I have to think what should I do. So, let us say demand of product B is going up, going up, going up and I have a capacity very limited. So, obviously demand is more than capacity, then I will not be able to serve. So, I have to expand my capacity. So, the problem should be like how do I expand my capacity? Should I add a new plant or should I outsource it to some third party? So, all of this decision has to be taken through prescriptive analytics. So, this is basically in simple term descriptive, diagnostic, predictive and prescriptive. I am sure like you got some idea about this. So, in the next few slides what I will do will give example of each of these from the supply chain lanes. So, let us take this example part of descriptive analytics. So, first I will analyze the past demand of various products. So, I can use bar chart, I can use pie chart, I can also use dashboard as you talked about in the last slide. So, I will get to know what has happened in the past and as I can see. So, let us say this is for product B, the demand is up and demand is very high. So, through predictive analytics I will be able to predict whether demand will keep on going up or not. If demand keeps on going up as we discussed in the last slide. So, I have to decide first of all that whether should I have another plant or not first decision because if demand is more than my capacity I have to plan for it. So, if because plant cannot be installed in a day. So, I have to plan for it. So, prescriptive analytics helps me to plan. That means if demand goes up and crosses my capacity then the next question would be where should I have my next plant. Whether plant should be near to the demand location, plant should be near to the supplier location and where the plant location should be or to be the capacity of the next plant. If I do not want to have plant I have to outsourced. So, whom should I outsource like who will manufacture my products. I also have to take that decision and if my demand goes up I need more raw material more component. So, who will supply me this component like if there are multiple suppliers if there are multiple suppliers with me then the question is whom should I select. So, out of so many suppliers which are empaneled with me who will give me the extra material and then I have to select decide how much to order from them. So, let us say I selected 10 suppliers from each of these suppliers how many quantities of products should I take it from that is again decision. So, basically optimization. So, in a nutshell we do optimization over here as a part of prescriptive analysis which will minimize my cost which will maximize my profit minimize my risk. So, that all other constants are satisfied and it becomes efficient for my organization to do so. Then we will give another example let us say again on example of descriptive analytics from supply chain point of view there are cases where quality failure happened. So, many a time in manufacturing setup quality failure happens and although digitization happened all the automation has come, but still there are products which fails in the quality inspection. So, let us say I have product x, y, z. So, and I have found out that the product z finished product z which is being manufactured in my factory are having lot of quality issues although x and y quality is perfect, but for product j there are lot of quality issues specific in the last 4, 5 months. So, I am producing 3 products, but only product j is having lot lot quality issues. So, that I get to know through descriptive analytics. So, now once I get to know that product z is having lot of quality issues then I can do diagnostic analytics to check for it like why there is a quality issues I need to find out the root cause for the failure. So, I will see through a microscope and see like what is the reason. So, I can go to the root cause and find out like which part or which component is having a problem. If the product is failed which part is giving issue, which part is not correct. So, after doing lot of this diagnostic analytics suppose I find out that there is a component C 1, there is a component C 1 which is there in part which is there in product j which is causing me problem. So, the part the component C 1 is giving me a problem. So, component C 1 is not of good quality. So, because of since component C 1 is part of product j because of that product j is also getting failed. So, now I need to find out who are my supplier who is supplying me component 1. So, let us say I found out that supplier 1 is giving me component 1. So, then I will talk to the supplier 1 and give them the data. Then the last 3, 4 months my product z is getting failed repeatedly and you have component C 1 which I got it from you is causing the problem. So, then the supplier will be notified will have a talk with the suppliers and then obviously, depending upon the contract you will take a action. Now the question is obviously, I cannot carry forward with the supply supplier 1. So, whom to select as a supplier. So, supplier 1 obviously, I will not go with I have supplier 2, I have supplier 3, I have supplier 4, I have supplier 5 and so on. So, out of this suppliers supplier 1 is cancelled obviously, because of the quality. So, now out of this so many suppliers whom should I select who will be able to give me a component C 1. So, I have to decide that for that I will use prescriptive analytics. So, prescriptive analytics is nothing, but optimization as we talked about in the last slide. So, I need to optimize and find out which suppliers would I select out of so many suppliers which are listed with me who can give me component 1. I have to see what I have to see, I have to see their cost, I have to see their delivery time, I have to see their responsiveness and so on. So, there are multiple factors based on which I would select the supplier. So, I will take this data and optimize and find out who would be my base supplier who can give me the C 1 at a minimum cost you know. So, shortest delivery time having good responsiveness and the last, but not the least the quality and there will be some other parameters also. So, I am not listing it down all the parameters, but let us see these are 4 major parameters based on that you have to decide who would be my supplier. So, this is a second example of starting from descriptive analytics we are doing diagnostic analytics and finally, prescriptive analytics. So, this is another set of problem which is related to the quality failure in manufacturing setup. Then I will give another example from supply chain again and it can be extended to any other domain also this is related to the performance of worker. Basically in Indian context like lot of manual labor work is there although automation is happening, but still like we have lot of like labor who are working in the factories warehouses. So, we need to check their performance. So, how can I check their performance? So, first based on the past data I can see like how they were utilized, how much efficient they were if you go to a let us say sorting facility of an e-commerce company you will see lot of pick like pickers, packers are there who are doing like sorting activity. So, everybody has some goal in mind because e-commerce company wants like 2 hours delivery, 12 hours delivery, 1 day delivery, 48 hours delivery. So, if I have to fulfill that then in the back end I also have to sort the product in that rate, I also have to pack it in that rate, I have to bag it in that rate. So, therefore, these worker who are working in the sorting facility has to make sure that they are following the timeline. So, if I have the past data I know who has followed the timeline, who has not followed the time, who is efficient, who is not efficient. So, the script analytics will give me that idea based on their past performance, who is efficient, who is not so efficient. Then I can develop and predictive analytics model, let us prediction model to find out what would be their efficiency in the future. So, although they are efficient in the past some of them, but will they remain efficient. So, I can predict that and find out in the next 6 months who are the worker, who is most efficient, who are the worker, who are not so efficient, who are the worker, who are mediocre efficient. I can get the efficiency score which is the predicted efficiency score, not the actual, but predicted efficiency score. So, if I have the predicted efficiency score, then whenever the next work happen, I can allocate them, I can do allocation of work based on the predicted efficiency score. So, now, let us say when the Diwali season comes in India, the demand of electronics item, demand of e-commerce products goes up like anything. So, that time I need the employee who is very very efficient, because if you make some delay, obviously, will fall behind. So, I have to make sure the best of the best resources are there available to me, who can do peaking process very efficient, who can do bagging of items very fast and efficient manner, who cannot make mistake, who should not like, who does not make much mistake. So, what happens in the specifically in the e-commerce industry, you will have their customers address and pin code, that is how the sorting happens. And if I pack wrong items and send it to wrong person, obviously, the customers will not be happy and satisfied. Therefore, I have to make sure that at sorting facility, I am sorting the correct items and sending it to the correct pin code and that error is minimized. So, if that is the objective, I can use these till starting with descriptive analytics, which will help me to find out, who are the best performer, who are the worst performers, who are not so good performers. I can predict their efficiency score in the future, I can use this score and find out and allocate, who would be the workers, who would be efficient and which job to be given to which worker. So, these are a flow of like different types of analytics which we use in the context of supply chain and specifically in the worker allocations. So, lot of employees are there, how to allocate the work to the employees, so that they can do the job more efficiently. So, these are few examples. Now, we will move to the next slide, where we will summarize starting from procurement to the last mail, since till it reaches to the customers, how different kind of analytics are being used. So, we have procurement. So, here how analytics are being used, as we saw in the last few slides also. So, I have many suppliers. So, out of these suppliers, I have to select which supplier would be the best one for me. So, I have to see like their cost component, like some supplier may give me the product at a lower cost, but the delivery time will be more. Some other customers may be giving me the product at higher price, but they can deliver the product at a shortest time, also quality will be good. Some customers might give me at lower price, but they are not responsive. So, therefore, how do I find out which supplier to be selected, from which I can procure my raw material, I can procure my component and so on. So, here I can select prescript analytics to find out who would be my best suppliers, from which I have to procure my items. Then the next question is, if you select 3, 4 suppliers, I have to decide which supplier, how much quantity should I procure it from, that is also an optimization model, which can be part of prescript analytics. Then also I can predict their behavior, like in future how will they behave, what would be their predicted cost, if I take the products from them. Then next in the context of manufacturing, once you get the products, raw materials component from the various suppliers, in manufacturing facility, I start producing the products, producing the finished goods, for each finished goods, I have to check their quality, I have to inspect, whether each part is good component or bad component or so on. So, as we discussed that image analytics are being used nowadays, for checking quality of the parts. And in the last class of analytics module, we saw the example of cheap manufacturing facility, where image analytics are being used to check whether good quality cheap is being produced or not. In the manufacturing setup, there is another example of predictive maintenance. So, what happens in predictive maintenance? So, in the manufacturing facility, like many a time, like parts that machine gets broken down. So, if I do predictive maintenance, then I will be alerted that this machine might be broken down in future or in next 2, 3 days, I can take action accordingly. So, this is one good example of predictive maintenance, which is a part of predictive analytics in the manufacturing setup. Then we have warehousing. So, once your product is finished product is done, equality is checked, then it comes to the warehouse. So, in warehouse, lot of analytics can be used. So, we will start with slotting optimization. So, what happens here? So, warehousing typically are large warehouses, the 1 lakh square feet, 1.5 lakh, 2 lakh square feet, huge space is there. So, there are multiple slots. So, which slot, which product to be kept so that I can optimize my space, I can also be quick to pick the items and I can find it very easily. So, let us take an example of slow moving versus fast moving. So, if the item is fast moving, I have to keep the slots in such location. So, that quickly I can pick it up and send it for the delivery. If I keep the fast moving item in the back side of the warehouse, where I need to take lot of time to go pick up from there and send it to the customers, obviously it will be not a good idea, because I have to go back all the way to the back end of the warehouse, bring the product and give it to the delivery location. So, therefore, where should I locate my fast moving items is an important decision. Where should I keep the slow moving? Let us say for a particular item, demand occurs once in a month. So, obviously, I will not keep it near the delivery station, I will keep it at the back side, so that the movement is not being hampered. Then, there are options like zone, zoning. So, in warehouse like clustering algorithm are being used for defining like which zone should be used for which items. So, there are many products like specifically those are managing e-commerce warehouses like few products are ordered together. I know based on my past experience, past data, there are few items which are ordered together. So, if the items are ordered together, I will keep them together in the warehouse, so that it becomes easy for me to picking up during the picking up process. So, therefore, I need to zone, create zones in the warehouse, so that I can keep the items which are ordered together, I will keep them together, so that pickers can pick them together and send it. So, my total picking time will be minimized and searching cost also will be minimized inside the warehouse. Then, we also saw the example of video analytics in warehouse. So, we saw this in the context of inventory count in the last lecture. So, if there is inventory discrepancy between ERP inventory count as well as physical inventory count, that can be done using video analytics like a drone can be fitted, a video camera can be fitted on top of drone, then the drone can go along the aisles and scan the products each and every SQ and comes back and gives me the report of inventory discrepancy. So, these are interesting examples of analytics in the context of warehousing. Then, we have logistics and transportation, here also like a lot of opportunities are there. So, I will give one example which is not discussed in the last class related to the delivery. So, of course, there are some manufacturing facility are there in India, their suppliers are located outside India. So, some of the suppliers are coming from Europe, some of them from America, some of them from China. So, obviously, they come through sea route. So, what happens when you bring the products through sea route? Obviously, there will be delay sometimes because of emergency situation like now sewage canal incident happened, then Panama canal incident happened. Since this is coming through sea route there might be delay. So, let us take an example that you get to know the supplier told you that the product will be delivered to you in 50 days ok, 50 days the part or component will come to you. If it comes on 50th day exactly I am happy because it is coming as per my plan. So, I do not have to change anything. Suppose it comes on 55 days, suppose the product from the supplier comes 55th day, then 5 days I have to wait for the parts to arrive. So, means sometimes it may happen that I have to stop my production because part is not available I have been delayed by 5 days. So, many a time I may have to outsource it from the local suppliers who will charge me hefty amount. So, therefore, it is not good for me. Similarly, let us say sometimes it may come on 46th days also. Let us see was good everything was smooth and I got the product on 46th day. Is it good for me? Yes, no it is again not good for me because my part has arrived in the facility 4 days before. So, I have to build up the inventory and my space will be lost. So, therefore, if the product comes before it is not good because my inventory holding cost have to pay. If the product comes after the expected delivery day that is also not good. So, therefore, I need to make sure that it is coming exactly on the same day. So, lot of analytics are being used nowadays specifically the predictive analytics to predict when my part will be arriving in my manufacturing facility. If it is coming from foreign suppliers overseas suppliers specifically there will be delays because of long distance travel. So, lot of mathematical model AML based models are being used to predict when the parts will be arriving in our facility. Then we also talked about few examples from transportation in the last lecture related to drivers fatigue. So, through image analytics through image analytics I can check drivers fatigue. So, if I can track their eyes movement continuously take the image and there is no movement in like eyeball movements are not there continuously for sometime I will be able to predict that the driver may fall asleep. So, therefore, I will alarm them and make them aware that you may fall asleep. So, take rest wash your eyes and then again drive. Then another interesting example of analytics in demand planning and lot of energy and time are being spent in demand planning. It is a very important aspect and most of the companies will have a demand planning team itself.  So, they have been seeing lot of advertisement in the TV media about their competitors product brand customer nowadays are very much aware about all the brand which are coming customers are aware because they are using the social media. So, they are getting information. So, customer is having lot of information about each and every product. So, they can take the decision based on their own understanding and since customer behavior are changing and it is being influenced by social media and then advertisement. So, obviously, very difficult to plan for it. So, nowadays demand sensing is very important topic like FMCG industries are actually like they are putting lot of efforts to make sure that they can estimate their demand properly. Not only FMCG, other industries also are spending equal amount of money and preparing data scientist creating data science team to make sure that their planning is done properly. So, if they can plan properly, if they estimate the demand properly the half of that thing is done. So, therefore, lot of efforts are being given for the demand planning and forecasting. So, for each and every excuse what will be my demand? What will be my demand in 6 months time? What will be my demand in 2 months time? What will be my demand in next 15 days? So, continuously I have to keep on tracking and I may change or update the demand also. So, lot of prediction modeling are being used for demand planning and since consumer behavior are changing the traditional forecasting tool may not be applied in every context ok. So, traditional forecasting tool can capture seasonality, it can capture trend, but since there are lot of variability in the demand and if I have to capture those ML model would be a good fit for that and obviously, companies are going in that direction. So, in the summary like what we did in the last lecture and this lecture we talked about what is analytics? We discussed what are the various components of analytics like I need to have data then from the data I will develop model. The model will help me to give better decision and using the decision it should create value for the organization. Then we also talked about big data and its characteristic, especially the 6 V of big data like volume, variety, velocity, variability, value and veracity. So, these are the 6 V's then we talked about type of analytics, descriptive, diagnostic, predictive and prescriptive and we gave lot of examples from supply chain management domain how these analytics can be used. So, I am sure like you have enjoyed this session. So, thank you and look forward to seeing you in the next lecture. If you have any doubt these write it to us we will be happy to answer your queries. Thank you and see you in the next class. Thank you so much.


Moodle 4. Activity completion. Activity completion is enhanced in Moodle 4 with completion requirements clearly displayed on the right and completion indicators in the course index. When our student accesses her course on a Moodle 4 site, she sees the activities and resources neatly displayed in cards. And when an item has a completion requirement, this is shown on the right-hand side. And there is a corresponding completion indicator in the course index. Once she's completed an activity, it displays as done in the course central area and it changes colour in the completion indicator in the course index, giving our student a quick view of her progress in the course. To summarise, clear a display of completion requirements helps students manage their learning more effectively.


This is Stink Tech Hawaii, Community Matters here. Bingo, we're back. This is Stink Tech Hawaii, and today we're doing Community Matters, a special show at the one o'clock block. We're talking about energy. Actually, we're talking about more than energy. Our special guests are Michelle Daigle. She is the Hawaii Energy Policy Forum graduate research assistant for Sharon Moriwaki at UH Manoa. And Erin Bear, mostly Bear. Breach, is that right? That's correct. And he is also a graduate research assistant for Sharon Moriwaki at the Hawaii Energy Policy Forum at the University of Hawaii. And the title of our show, which is actually the title of the program we're going to discuss, it's the same thing. If it's different, there's something wrong. It's called Making Good Plans, that sort of word, plans for a sustainable, resilient Hawaii. And it's the title of the legislative briefing which these guys are working on, which is going to take place on January 10th, which is one week from today at the auditorium in the Hawaii Capital Building. Wow, exciting. As Hawaii Energy Policy Forum does every single year, just before the session opens, the session opens on January 17th, I want to say, and January 10th is before. And the idea here is to educate and sort of warm up, if you will, the legislators of the Hawaii State Legislature in 2018 on issues relating to energy, sustainability, and resilience. Okay, so let's get down to the details. What are you guys doing on this program first? Well, right now what we're trying to do is finalize our speakers and really come together on our three separate panels. We have a major panel in the beginning of keynote panel that will set up the entire day. And then followed by that, so we'll be touching on some key points. And one of our key people actually that we'll be presenting is, will be Denise Antolio. Yeah, we're going to go through that. Okay, so Erin, Bear, if she's doing that, what are you doing? Well, what am I doing? That's a good question. You came to ThinkTech to find out what Bear is doing. It's a cliffhanger question. Well, it's been a cooperative effort. We've been working with the speakers and organizing this event. It's really set in the foundation for providing information for the legislators and managers for learning about how Hawaii can be more resilient and more sustainable, especially with this unpredictable climate that we live in. Yeah, you bet. You know, it's interesting in some ways it's confusing that we have climate change, we have energy, and we have to establish the connection, you know? So this is a program differing from earlier programs in earlier years, you know, where the Hawaii Energy Policy Forum is, yes, it's doing energy, it always does energy, sort of in the concept of our community, our state, is more than just energy touches sustainability and resilience. So why don't you help me with that intersection, Michelle? Well, this year what we're doing is different. We are partnering with the Office of Planning. And so the State Office of Planning, Leo Asuncion, has come on board, and we're really trying to work at that intersection because the focus of all of that is having a good solid plan. If you don't have a plan, then it really makes it very ad hoc as you're going through and you're trying to address these issues of resiliency. A plan for integrating energy into Hawaii in response to climate change, in response to increasing hazards having to do with either vulnerability with oil prices spiking and also with these incredible weather patterns that we're seeing all in either the Atlantic or in the Pacific Ocean. It's getting very severe and Hawaii's right in the thick of things. Okay, Bear, how's she doing and how much of what she said you agree with? I agree with all of it so far. I thought you'd say. So what does sustainability mean? Well, that's kind of a loaded question in the terms of what sustainability, what are we looking for? Sustainable, what is sustainability? Sustainable is I guess it keeps on going. Yeah. If you have already battery, it just keeps on going so it's sustainable. It doesn't fall apart, right? What do you want? That sustainability. How does that apply in the world of social science? We want Hawaii to be like, they can do things on their own to be sustainable in energy, as Michelle mentioned, and we import 90% of the food that we have here. So if a weather event was to happen or some geopolitical crisis was to occur, we want to have a sustainable future so with all these external factors we can provide for ourselves. You're making the assumption that we will have, A, those events will happen. Yeah. And I'm not going to disagree. And B, that we are going to be on our own when, you know, likely on our own when those events happen. Well, is that what's implicit here? Being 2,000 miles away from the United States, it puts us in a unique situation of we are kind of on our own, but yet connected at the same time. Yeah. So with the increasing weather and extreme weather patterns that are happening, we've been so fortunate to not be hit by a major hurricane. Yeah. I always say every day, you know, it's a beautiful day. Today's a beautiful day. It's a day closer to the next big weather event. Yeah. So why do we care about this? Why don't we just wait for it to happen and deal with it then? Well, if you do that, then when something major does happen, we're not going to have a coordinated effort to deal with this. We're actually less resilient if we wait in this kind of, you know, response type of environment. Yeah. We want to preempt that type of situation. Yeah. And to do that, we really need to sit down and look at how we are all interconnected. It's supposed to be ignored. Excuse me. Wait for it to happen. And why do we, you know, so it'll happen. It'll be an interesting day. Well, interesting in the Chinese sense. It'll be very interesting. Why do we care? Why are you working so hard on this program? Why don't we just let it go? Because we don't want Hawaii to be like Puerto Rico. Ah. We don't want Hawaii to have a situation where you have an extreme weather situation where there are another type of hazard that comes in and throws us for a loop because we are waiting to see how we might react that destroys our grid. But let's look at Puerto Rico for a minute. Of course, they still, even now, what is it, three, four months already? Yeah. They don't have electricity. There's still 50% of electricity. Yeah. That's really an awful way to live. And it means there's no economy, zero economy. Yeah. And people are sick. They're not getting well. Exactly. Their lives are like on hold at best. Yeah. And maybe worse. But I guess what I get out of Puerto Rico is that you could die. Yeah. You could die. People you know could die. Lots of people could die. It depends on how extreme it is, right? It does. It depends on me. So you're trying to save lives. Orange you, Michelle. I got you now. I am. I'm trying to save. I'm trying to save lives. Trying to save economy. Trying to save. It's not just, it has to do with life or death, but it also has to do with standards of living, too, leading up to that. It doesn't wait for some type of an extreme event to change the ways in which we go about our daily lives. Why didn't you have a program like this last year or the year before? We just really weren't ready. It's, I think, with the state legislator going ahead with the Paris Climate Accord and really setting us up for this conversation. It's the right time. This is the right time to talk about it. What is the intersection that makes you say it's the right time, Bear? Well, this unique opportunity we're having, partnering with the state office of planning, is so unique. Having, we want the state office to be the center of all the planning that's going on in the state. I know it sounds kind of interesting, but we have a lot of silo planning that can occur. Some people can be stuck. You're trying to tell me that there's a lot of planning going on, but it's not coordinated, is that what you're trying to say? It happens. It couldn't happen. Yeah. And we want that. You're so politically correct, Bear. I'm trying to be, I guess. What kind of planning are we doing here in the state? I mean, around this area, around the notion of making a more sustainable, resilient Hawaii, who's doing what as we know today right now? Hawaii's in a unique situation being the largest metropolitan area and being one of the most isolated ones in the Pacific, and we feel the effects of climate change more than any other place in the world. Yeah. And having 1.2 million people on a walk, we need to start planning for more people and the smaller surface area of an island that we're going to have. So, planning for this and putting the correct policies and actions in place to protect the quality of life. But why now? Why now? Why is this important now? I think of this as a metaphor. Everybody does certain things to prevent a heart attack or heart disease.  You plan so you can be properly prepared when a certain disaster hit. Yeah. Okay. So, I mean, it sounds like, I'm taking a wild guess here, it sounds like somehow these events are somehow closer to us and that we are more aware of them. We begin to acknowledge the realities, the danger, and so it would be a good time when you realize the danger, it would be a good time to try to plan. Yeah. Absolutely. But we really shouldn't wait for it to get to that peak point. Well, I mean, over the past... I mean, some of these things could wait decades. It could, but others... I mean, just last, I think it was just this past year, you, if you had a satellite picture of the Pacific Ocean, you saw three intense hurricanes. Yes, right. All lined up, one right south. Better than us. You know, it's just... We miss the bullet. We miss them all. We keep missing the bullet. And while we can be, we're extremely fortunate for that, who's to say that in the next moment, we're not going to have that. And so there is this inability to fully predict what will happen, but at the same time, that inability to not concretely predict what will occur should spring us to action. Okay. So, I mean, this intersection is integration of energy and all the other things. Why is the energy policy forum interested in, you know, climate change and sustainability, such as is going to be revealed in this program? That's a very good question. We are interested in this because at this juncture in time, we're understanding that if we just focus purely on energy itself, all by its own, we are in effect siloing ourselves as well. We have to start interconnecting. It's this cross-fertilization between the different departments, between different sectors. Just recently over at our Hawaii Clean Energy Day back in August, we were really trying to do this between the intersection between energy and transportation has been neglected. I'll agree with that. We really need to sit down and look at how both of these different sectors can work to support each other. That's something you said a minute ago. The economy, which, you know, affects our quality of life, getting up in the morning and prosecuting our day. So, if you don't have a ready supply of energy that you can afford, you don't have an economy. No, you don't. It's a direct proportion, good energy, good economy. So, if we lose energy for any reason, you know, then we are not sustainable as an economy. So, I think it's joined at the hips. It is. And specifically in Hawaii, where we, a lot of our economy is based on tourism, it actually uses quite a bit of energy to even bring in people to sustain our economy. We are reliant on large numbers of tourists from Europe, from Asia, from the mainland. It's relational. If we don't get the tourists, okay, we're not going to have an economy. And we can't get the tourists so we don't have energy. Exactly. So, this is a big effect from one thing only. Exactly. We really need to see it as a web where you may have an intersection of one or two strands that are really easily recognizable on one plane. And on another plane, you'll have another group. But if you tug on one place, you're going to have this movement at the center of the web. It's just we don't see the direct connection. We need to start thinking of energy, sustainability, transportation, agriculture, so biofuels. So what's the referendum term for?  It's an ecosystem. It's a social ecosystem. That's what you guys do at the School of Social Sciences, isn't it? It's what you study all day. You're both studying there, am I right? Yes. How much of what she said you agree with? 100%. What would you add to it? I would just say that, like Michelle mentioned, it is a web and there are ebbs and flows of what is going on. Yes, you pull one end and another end is going to be affected. Yes. But there needs to be communication of, hey, we're doing this over here. You might feel this effect. So can you please prepare for it? So the citizens and the residents might feel this effect. So if one part of the web doesn't properly planned, it's not just this, the DOE's not just going to be feeling the effect. It's the DOT and our public health and so on and so forth. Exactly. Well, that suggests that we have very complex problems because this involves everything in our society, directly or indirectly. It's a complex problem that can only be handled by a central authority. Somehow the central authority has to give us leadership to get together by consensus or otherwise and make it sustainable and resilient. And that's what director assumptions office is supposed to be. They are the state office of planning. They are supposed to be setting out all the plans for all of the different governmental agencies throughout the entire state that you then take a look at and check to see whether or not your plan dovetails it and achieves that overarching goal. There's an idea. All the plans should be consistent. They should be consistent and connected. You heard it here on Think Tech. Now you're going to hear a big break and when we come back, Michelle and Bear are going to take us through the actual program. How exciting. We're going to hear about the big name speakers and roughly decide or at least understand what they're going to say that's next Wednesday, January 10th. At what time? At 1 p.m. At 1 p.m. In the state legislature auditorium right there in the capital. Right after this break we're going to be right back. This is Think Tech Hawaii raising public awareness. Sounds like scuba divers are the poor man's astronaut. At dive heart we believe that to be true. We say forget the moon. Dive heart can help children, adults, and veterans of all abilities escape gravity right here on searchdiveheart.org and imagine the possibilities in your life. They said I could play so I had no chance to play at all. That's my life. I love music. That's how we do it. Likeable science on Think Tech Hawaii. Every Friday afternoon at 2 p.m. I hope you'll join me for likeable science where we'll dig into the science, dig into the meat of science, dig into the joy and delight of science. We'll discover why science is indeed fun, why science is interesting, why people should care about science and care about the research that's being done out there. It's all great, it's all entertaining, it's all educational, so I hope to join me for likeable science.  I'm kidding around. Okay, we'd be back and we are talking about making good plans for a sustainable, resilient Hawaii with our special guests, Michelle Dago and Aaron Bear Breach. They are both graduate research assistants at Hawaii Energy Policy Forum in the School of Social Sciences at UH Manoa and they're both involved in planning this legislative briefing January 10th next week in the Capitol auditorium. So it's divided into three parts. Yes. Tell me the three parts we'll rotate. Tell me the three parts and what the scope of each part is. Okay, so the first, we have three different panels. The first panel is a keynote panel. The second is our state panel, the state representatives on it. And the third one we'll be finishing with the county panel. First one, what's the scope? The second one is really meant to provide a background and a foundation for the discussion for the entire day. It's going to be discussing climate change. It's going to be discussing the role of legislation and mitigating that and connecting, cross-connecting all the different sectors we were talking about earlier in a cohesive manner. This is foundational stuff. It's foundational. So that the legislators who attend or listen on to get these things in their offices, they can listen to what's going on elsewhere in the Capitol. And if they look at the videos, we'll take video that they do. So they can get an idea of the connections, the interconnection of all these things. That ecosystem you're describing is a really good description. So tell us who's on this first panel. The keynoters. Okay. So the first person is Denise Antoli. And she's going to be talking about the intersection of climate change and renewable energy. And then next... She's a lawyer. She is a lawyer. She's a lawyer. She's a lawyer. She's a lawyer. She's a lawyer. She's a lawyer. She is a lawyer. She's a law professor. She's a law professor over at... She's the number two... I think she's the associate dean. ...at the University Law School. Yeah, she has done... She's done a lot of research on environmental science. Yeah, she's an environmental lawyer and teacher. Yeah. Yes. So she could talk about the legal structure. Yeah, she's... I think it's really important that whatever we plan, we have to execute. And if you're going to execute, you have to make statutes. And the statutes should be in their own landscape consistent with other statutes. Exactly. She's the next-to-action type of perspective where she's really... She's going to survey the field and then she's going to show us how we can translate that over into actionable policies and laws that we can use to help structure and plan. So we'll figure out what the existing, you know, statutory framework is to figure out what we do, what we take apart of that plan and try to implement through new laws. Exactly. So you want to... There's Leo Asunchin, which is the director of the State Office of Planning. He's going to talk about how... You mentioned him. Yeah. He's your co-partner in this whole deal. Yes. And he's going to talk about how the State Office of Planning is going to be the nexus of planning in the state of Hawaii and kind of walking us through the process and the co-teaching between planning through all the different counties and different silos, different departments. Yeah. So the operative word is planning. Yeah. You know, you can go ad hoc and sometimes the legislature does go ad hoc without considering the effect of what it's doing as against other statutes and other priorities. A plan helps you resolve the priorities and the timing. Exactly. And determine what's first and what's more important and how much money you should be spending on this, that and the other thing. Yeah, because sometimes what you want to do may cost more, but it's more essential than these lower-hanging fruit that you want to grab at. Right? Yeah. So it pays to take a look at that plan. It's like having a good outline when you're putting together a presentation. Yeah, but the problem is there's a lot of agencies in this state and other states that have their own planning group. So you're going to plan a group here, plan a group there. But that's the whole point with Leo Sunchin's office is that's actually part of it. You want to have a centralized plan but you need it to be adaptable to different situations. It's just you need it to have cohesive strands throughout all the disparate parts so they're not actually disparate. Yeah. They're working in synergy with the state. And you're also suggesting by implication that the plan is not static. Exactly. Because life is not static, history is not static, and geopolitical and ocean and earth science is not static. Right. And if you think of if it's static then it's not resilient. Resiliency is all about malleability, flexibility, flexibility, change, and making sure that you give and take with the situation at hand. I'm going to go to this program. Guarantee. I'll be there. Good to know. Okay. Who's after Leo? There's Chip Fletcher from the SOAS. Oh, we've heard his name. He's been here. He's a very famous person in the world of sustainability and climate change. And he'll be discussing the impacts that climate change will have on the state of Hawaii and the kind of the factors that will be influencing Hawaii specifically. So hurricanes, rainfall, food security. And then I know he wants to touch on homelessness and what that means. Wow. That's a lot to bite off. Yeah. What is he speaking for six hours? I think he has eight minutes or so. Okay. That'd be very interesting. Yeah. He's been looking at this a long time even before it became coin of the realm here the way it was now. Yeah. And he helped with the sea level rise with the report the fiscal one. He made charts or innovation charts that we need to look at. And I'm sure the charts are more threatening now than they were when he first made them. Yeah. Here we are. We're going down that pike. Yeah. It doesn't stand still. Yeah. Okay. Who's after Chip? And then after Chip will be John Cole. He's over at HNEI. And he'll be looking at the scientific research behind energy and looking at how that can come into play in our plans in regards to sustainability, resiliency. Yeah. This is all consistent with the notion that Hawaii is a laboratory. Exactly. Because we're alone but also because we have these resources. We have guys like Fletcher. We have guys like John Cole at HNEI can find things out in connection with energy but the intersection of energy and other things that we could not only use ourselves but each to the mainland who may not have the same level of awareness about these things. Yeah. We could, I mean, Hawaii's a leader in so many different venues, so many different areas and arenas. This is where we can be a leader again. Yeah. Where we can really show the mainland what Hawaii has to offer in terms of research, in terms of driving the conversation. Yeah. And to be a beacon of hope in how we can figure. I want to tell you assuming we can garner that reputation, which I hope we can. Maybe this program the guys are working on will help us garner that reputation. If a bad storm happens and we go down the pike like Puerto Rico, we're not going to be a leader anymore. That's right. We're going to be into survival mode. So we have to be well prepared and we're going to demonstrate that we're well prepared. Exactly. Okay. That's the end of that panel. That's the end of that panel. That's the state panel. Yeah. And you mentioned just a moment ago the prioritization that needs to be done for the state. And that's exactly what they're going to be speaking about is how there are different projects that will be done and different plans that are proposed to them. But there needs to be a cost benefit analysis of showing this needs to be done. It'll benefit this amount of people and it should be prioritized one. That's for the planners to do that. You cannot expect to give a legislature a legislator or legislative committee to get down that way. You've got to have a careful plan with you've got to bring your resources and scientists and sociologists whatever together. Yeah. And so they figure out the short strokes and they make a recommendation to the legislation and then the legislature tests on that and looks into that and takes action on that. So that's the way the process I think should work and I hope I'm right when it comes out that way in this panel program. Okay. Oh, the second you want me to go through each of them. First will be Vice Chair Willis Sparrow. So he's going to be talking about the perspective of what needs to happen in the legislature from the senate perspective. And we just had a conversation with him today and he was saying how he's heard so many different projects over 200 and he can just name a few with just his hand that have actually come to fruition and part of that he said has to do with really laying out really clearly the steps that need to be taken to achieve these plans so it doesn't fizzle out. So he wants something really well laid out so he can turn around and assess it for the state of Hawaii and then implement it. And get public support. Get public support. Exactly. So and that's really key. Who else? There will be Chair Chris Lee from the House Committee on Energy and Environmental Protection. He'll be speaking on what risks are posing the state of Hawaii and what needs to be done to protect the shorelines in our land. He's a fellow who could actually do a lot of lawmaking. Yeah. As a chair of the Energy Environmental Committee. Okay, who else? Next we have Suzanne Case over at DLNR. She plans to address climate change and adaptation in relevance to your other programs that they have and looking at how that's going to create a sustainable, resilient Hawaii in regards to forestry, wildlife, land management, aquatic infrastructure. She is an extensive background in this, I mean, 28 years. Big job, big agency, big responsibility and it's really great that these are great people. Yeah, I think she's going to bring quite a bit to this panel. Who else? There's David Rodriguez from DOT and he'll be speaking on HDOT's role in sustainability in the airports, the roads and all the transportation sector in Hawaii. Yeah, well, there's a lot of energy involved in that and it's a catch-up game with transportation because we really haven't paid that much attention to it and we only have, what, five or six thousand electric cars in the state when we have a million fossil fuel cars we have to make progress and we can't achieve to your point we cannot achieve sustainability without solving that problem. Exactly. We can't keep on fossil fuel because fossil fuel is not sustainable so if you want to have a sustainable place you've got to make that shift. You do. And that's why DOT is so important. He is really important. Who else? Then we have Scott Enright over at the Department of Agriculture and he's going to be talking about the intersection of sustainability and food security and really looking at how we can create state, county and private partnerships to mitigate any types of hazards in that it's not working. Exactly. I think we only have about a week's worth of food at any one time. They have more. Well, McDonald's. It's not real food. It's plastic. Exactly. You can't live on that. Some of that food is okay but you can't live on that. So we have to figure out a way to grow agriculture. Exactly. Again, this touches everything, doesn't it? It touches everything. It's economy. It's locals working for other locals. It really creates that ohana type of thing I think. And we want these organizations to the legislature not only educating what makes suggestions about what are the important initiatives that should be considered. Exactly. And the actions that need to be taken. Yeah. Okay. Is that the end of the second panel? No. We have one more. Luis Salveria over at D-Bed. Another important department head who's going to be talking about D-Bed and their resiliency programs and how they are envisioning resiliency within their department and ostensibly it's the connection with business, all of business in the state of Hawaii so I'm sure he'll have I hope he'll have some really important ideas. Luis, you've got to have some important ideas here, okay? Come through for us. All right. That's the second panel. The third panel is the county panel. Yes. And they'll be speaking about what's going on in each county for resiliency and sustainability and how they're engaging different stakeholders and basically what needs or need to be met for each county to be sustainable and resilient. This is great. This hasn't happened before. Counties are getting together. I don't know what happened this year must be the water. Something changed, isn't it? They had a meeting a few weeks ago representatives of all the energy offices on all the neighborhood. I was at a while who would get together and compare notes of what they're doing but this is the most exciting part of the entire program because I mean I'll just I'll read them off to you. We have Ron Whitmore over at Hawaii County. We have Fred Riddell from Maui. He's the energy commissioner over there. We have Josh for our city and county of Honolulu. And then we have Mike Dolly over at the Department of Planning on Hawaii. And they have been in the thick of things and they're already communicating and they already have several different points that they need to bring to the legislature. Yeah. And the legislature needs to hear what they are doing and what they need. Exactly. Yeah. It's brilliant, you know, Michelle, there. Brilliant. It's a great, great program. Great panels, great thoughts, great ideas, a great need to discuss and achieve action. So this is the big intersection is not only between the subjects that will be covered it's between these people and the legislature who could actually do something. So close for me. Okay. What should people remember about this? We're really trying to make good plans for sustainability in Hawaii and it's essential for us to have these good plans and to get out of silos and to communicate to make sure that Hawaii is sustainable across broad sectors. Yeah. We want to avoid any type of severe hazards to our way of life. We want to survive. We want to survive It's not just survival. It's thriving. Right. Okay. That's good. And kudos to the School of Social Sciences for putting you guys on it for, you know, being the organizers of the Hawaii Energy Policy Forum and encouraging this kind of program. Bear, now you get to tell everybody where and when and how they can get there. Well, it is next week exactly one week from right now the Hawaii State Auditorium at the Hawaii State Capitol 1 to 4 p.m. You can get there. I believe it's Baritania is the best access to this. Yeah. You can do your you can ride Bickey over there. You can take the bus. You could ride your EV. You could walk. You could carpool. You could carpool. So many. So many ways to get there. Complete streets, Ron. Complete legislative streets. Okay. And also we'll make a video of it. And we'll play it on OZ16. So you can see it after the fact and bear. You guys are great. See you soon. Aloha.


Live from Las Vegas, it's theCUBE, covering NAB 2017, brought to you by HGST. Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at NAB 2017 with 100,000 of our closest friends talking all about media, entertainment, and technology. The theme this year is MET, because the technology is so mixed in with everything else that you can't separate anymore. And we're really excited to do a deep dive into kind of the customer, or not the customer, excuse me, the consumer side of this whole world with Eve Burquist. He's the project director, data and analytics, entertainment technology center at USC. So Eve, welcome. Thank you, thanks for having me. So when I was doing some research on your segment, really interesting to see that you're involved very much in trying to figure out what people like to watch, how they like to watch, and get a bunch of data because now the choices for the consumers of media and entertainment are giant. Like never before. Yeah, there's a very, very basic question that I think not a lot of people in media and entertainment can't answer. It's like, why are people watching your stuff? And they have sort of surface level answers, but there's ways that the content out there that we watch resonates cognitively with us that is really important as very fundamental in how we consume media and entertainment. And even the decision making of why we decide to go watch a show on Netflix or play a mobile game or watch a YouTube video, why do we make these specific choices? What drives those choices? All these questions don't have a lot of really good answers right now and that's where we focus all of our work at ETC is to really understand people's drive to entertain themselves or decisions to entertain themselves at a very deep level and really understand how various narrative structures in film and trailers and brands and advertising resonate with people at a cognitive level. So it's pretty interesting, it really goes with the whole big data theme and the AI theme, because now you can capture, collect, measure data in ways and consumption in ways you could never do before. Yeah, that's a good point. So there's three things that are really impacting media and entertainment in every industry really. It's number one is the ability to think in systems, right? You know, we used to think about problems in a very sort of siloed manner, right? We think about problem in isolation with other forces like we look at the flu in isolation with the environment that we're in and stuff like that. There's another way to look at things and then more holistically it's a system called systems thinking and the ability to think of audiences as a system just like your body's a system inside a system, right? Is really revolutionizing the way we're looking at entertainment in media. The second thing is the availability of data. Just there's an enormous amount of data out there. A lot of it is unstructured, but there's, you know, the good thing about entertainment media is that it drives passion and drives conversations and anything that drives passion and conversation gets very rich in data. And the third thing that is impacting the industry is machine learning and AI and the ability to really look at all of these data points across the system holistically in a very intelligent, more semantic manner and make sure that you're measuring the right thing. So for a very, very long time, the media and entertainment industry has been measuring the wrong things and it's really now catching up very, very fast and making sure that it's measuring the right things. For example, how do we measure how specific narrative structures in film resonate with people cognitively in a way that translates into the box office, right? Is there a specific character journey that resonates better in an action movie with males versus females? And how does that matter for how a story is being told? Where do you innovate in script, right? Interesting point is the entertainment industry is very unique in that it has two major problems. It's number one, its clients, customers are absolute experts in the product because if you're 25 or 35, how many movies have you watched? Thousands of movies, right? So you're an expert in movies. Certainly the ones that you like. Exactly. If you're 25, you haven't bought hundreds or thousands of cars, right? So, but on the other hand, the supplier of the content doesn't know as much as the customer that the customer knows about the product. So you have two problems. You have a really, really, really highly expert client. But you don't know a lot about that client as a studio, right? Or a network or a media company. So that's a very, very unique, distinct challenge that they're starting to get very, very smart and very advanced in thinking about. The other thing is that I see in the movie industry, and I'm no expert by any stretch of imagination, but it seems like the compression pressure is huge. The budgets have grown to be giant. And the number of available weekends for your release are small. And the competition for attention and eyeballs around those weekends, it just seems to really have a really high kind of risk reward profile that's getting more and more extreme. And is that driving people more to kind of the known, or is it just my perception that, they're taking less risks on modifications from the script or modifications of kind of the norm especially around these big budget. I mean, just the fact that you got version one, two, three, four, five, six, pick your favorite theme. It seems to be a trend that continues and gets even more. I mean, Superman, how many Superman movies are there? Or Spider-Man? So that's really interesting, right? So the very natural tendency of the media and entertainment industry is when it doesn't know, as I was mentioning, right? It doesn't know as much as it could or should know about who its audience is. The tendency then becomes to just take less and less risks in telling stories exactly the same way. And that's why you see a lot of really, really formatted, very formulaic movies. What we're trying to do is, and the challenge with that is that, again, you have an audience of experts. And so if every single movie looks like the same one, looks like the other one, you're going to have a problem. People aren't going to go gravitate towards another kind of entertainment or some of your competitors. So you have to know where do you meet people's expectations in a movie and where do you innovate? Deadpool is a really interesting example. Deadpool has the structure of a basic superhero movie, but it has a lot of innovation underneath. And so for the studios, knowing where do you stick to the formula and where do you innovate in telling a story when you make a billion dollar movie is going to become more and more interesting because if you innovate too much, you're going to turn people off. If you don't innovate enough, you don't want to turn people off. So we actually have some research looking at the mathematical definition of why we think certain things are interesting and certain things are not interesting so we can separate. These are the things that you need in your movies. Whereas there's some aspects, if you go back to Deadpool, there's some aspects of Deadpool as a movie that are very traditional of the superhero genre and a lot of other aspects that are very, very innovative. So you have to innovate in certain areas and you have to not innovate in areas and that's a real challenge and so that's why we're really applying our work to looking at narrative structure and storytelling at ETC is because that's where a lot of the revenue opportunities and the de-risking opportunities are. It's interesting, before we went live, you were talking about thinking of storytelling and narrative as a little bit less art and a little bit more science in terms of thinking at it in terms of algorithms and rhythmically because there are patterns there, there is data there. So what are some of the data that you measure to get there? You mentioned earlier that in the past people were measuring the wrong thing. What are the right things to measure? What are some of the things you get because you're measuring now? Yeah, so it is still very much an art, right? It's making art a little bit more optimal. Optimizing art is what we're doing but it will remain art for a very long time. I think for, and since we're at any of these sort of broadcasting environment, I think a lot of the measurements and systems that have been in place for decades now are looking at demographics and demographics whether you're a male or female, your age, your ethnicity or your income, used to predict what you would watch. It doesn't do that anymore, right? And if you have kids like me, you watch the same thing that they're watching, you're playing the same video games that they're playing. I think there's a new way to measure things more cognitively and semantically and neuroscience is starting to get into the issue of why do we think certain stories are more interesting or more appealing than others? Why do certain stories lead us to make actual decisions more than others? And so I think at a very, very basic level, right? You have to unpack this notion of why do people go see this movie? And it's a system, you know? That decision happens in a system where some of the system is demographics, demographics aren't going to go away, they're still predictive to a certain extent, but it's also, you know, cast, it's also who has recommended this movie, right? And what are the systems of influence in driving certain people to see a movie? So all of these things, and of course, what we're focusing on, which is sort of storytelling and narrative structure and how that sort of translates to making decisions to see this movie. A lot of, you know, we're still in the infancy of measuring all of this system in a very scientific, granular way, but we're making very, very quick progress. And so even things like understanding the ecosystem of influence around why certain communities are influenced to go see certain movies by other communities and what happens there, right? So I'll give you an example. We did, we pulled months of data on Reddit about where supporters of Clinton and where supporters of Donald Trump would engage on that topic. Are they talking about that amongst each other? Or are they really going out there and trying to convince other people to vote for Trump or vote for Hillary Clinton? And we saw some two radically different patterns. So pattern number one, the Clinton people would mostly engage with each other on Reddit. So that's cool and that has very little value because you're not being an ambassador. On the other hand, the Trump people were engaging far outside of the Trump subreddit and trying to convince people to join the movement to donate to vote for Trump. And so we think there's a model there that can be ported to the entertainment industry where if your fans, if your fan base is mostly engaging with each other, that has less value than if your fan base is really going out there and really trying to get other people excited about your movie and why do certain people get excited and how do your fans, what argument do your fans use out there to convince others to go see your movie? All these things we're looking at and it's a brand new world now for media fans because of all of these data points. The systems conversation is so interesting because it's not only the system of the individual, but it's like you said, it's all these systems of influence today. Look at the Yahoo reviews, the Rotten Tomato reviews. What are their Reddit as a system of influence? Who would have ever thought? Yeah, and we're going into a world where quickly we're going to be able to understand entertainment and storytelling and narrative and it's cognitive power almost on a neural network base and looking at what kind of neural network in our brains get fired when we are exposed to this type of character or this type of storyline or this type of narrative mechanics. And so this is a really exciting time. The other thing that's interesting, we've talked again a little bit before we turned the cameras on is about the trailers because that's kind of the story within the story. And depending on your objectives and the budget, they can make all kinds of number of trailers in very different ways to approach or to target very specific audiences. I wonder if you could get into that a little bit. Yeah, so in the media and intimate industry, decisions have been made. And if you think about it, it's amazing that the media and intimate industry has made so much money. So I think it's a testament of the enormous creative talent that's involved. But especially for trailers, a lot of the decisions about trailers are made sort of looking at what's worked in the past in a very sort of haphazard way. There really isn't a lot of data and analytics and science applied to, hey, what kind of trailer, what structure of trailer do we need to put out there in each channel for each target audience to get them really excited about the movie because there's many different ways you can present a movie, right? And we've all seen many different types of trailers from many different types of movies. What we're doing, and nobody's really worried about, hey, let's analyze, for example, the pace, right, the edit cuts, the structure of the edits for the trailer and how that resonates with people. And now we have the ability to do that because people will count views on YouTube, for example, or there'll be a way to measure how popular a trailer. So what we're doing is we're just measuring everything that we can't measure about a trailer. Is it a complete story? What is the percentage of the trailer is the main character and what is the percentage of the trailer that the influence character is? And we're looking at cast, is there's a trailer with Ben Affleck work better if Ben Affleck is a lot in the trailer or not a lot in the trailer? And what kind of trailer types work better for specific genres to target audience specific channels? So we're really unpacking that into a nice little spreadsheet and measuring all the things that we can measure. And the thing about this is, if you think about the amount of money that's involved in making these decisions, if you're a studio and you're spending three, four, five billion dollars a year in marketing expense, and my work can make it even 10% more efficient, that's like half a billion dollars in savings. That's enormous, right? So it's a really exciting time for media entertainment because all these things are on the horizon to help them make better decisions, more data-driven decisions and really free up creative because if we can tell the people who tell the stories in film, you can innovate so much more now because we know that we've boiled down to a science and we know that in this, if you have these four or five things in your script, everywhere else you can innovate, go nuts. I think it's going to free up a lot of creative talent and we're going to see a lot more interesting movies out there. The other piece, I think, I mean, obviously a trailer for a movie is one thing, but take that little genre of creative that's purely built to drive behavior and that's a commercial. And I always joke with my kids, I watch a lot of sports and there'll be a car ad and I'm like, just think if you're the poor guy that gets the assignment to make another car ad.  You've got to think creatively. But the data that you're talking about in terms of the narrative, what types of shots are cutting based on the demographic that you're trying to go after for that specific ad, that must be tremendously valuable information. Yeah, it is really valuable. So our philosophy is that everything is story, right?  We make decisions based on the narratives that other people tell us and we tell ourselves about how to represent the world.  So we have to simplify, to compress it into a set of behavioral script that says, okay, it's sort of an executive summary of the reality and that executive summary is a story. And it's especially powerful in driving what we buy and how we consume things. And so I've built a platform that looks at, that extracts very, very structured data from conversations about what is the narrative structure around a specific brand? Is it focused more on emotions? Is it focused more on ethics? Is it focused more on the utility of the product? And trying to correlate that to look at what kind of narrative structures around your brand and what kind of story around your brand drives more sales? And so that's really, really interesting and sort of understanding again that relationship, that cognitive relationship between stories and how efficient they are in driving specific behavior. That is exactly what my research is about. Eve, we could go on all day, but unfortunately we are out of time. So thank you for spending a few minutes and dropping by, fascinating conversation. All right, he's Eve Burques from USC where all the film stuff's happening. I'm Jeff Frick, you're watching theCUBE. We'll be back from NAB 2017 after this short break. Thanks for watching.