Good morning. I am Deepak Phatak. I am the principal investigator of the National Mission Project on Training Teachers on a large scale the T10KT program as we call it. I'm very happy to welcome all of you here with me on the dais are the two faculty colleagues who are going to steer this workshop for the coordinators. First one is Professor Barnard Menizis. Like me and like many of us here, he's an alumnus of IIT Bombay. After doing his PhD in U.S., working there for some time, then decided to come back and contribute to nation building. Curiously spent a few years in building the computer science department from scratch in a small college before coming to IIT Bombay and joining our school of IIT. Why I say this is, like many of us who interact with other colleges, he has a first-hand view of the problems faced by the faculty in smaller places, the ethos at those places. The desire of several people at such places to do extraordinarily well and he brings with it the complete understanding and hopes to use that in translating all of our desires to empower our colleague teachers. Next to him is Professor G. Shiv Kumar. G. Shiv Kumar comes from a wrong IIT for me. He comes from IIT Madras. But we share a passion where both chess players used to play for his team. I used to play for my team here. More importantly, ever since he has come here, he has contributed significantly to the development of the computing ethos of this place. He has not only led our signature software lab to greater glory, he works in multiple areas actually. So networks and network security could be just one of his areas, but he is also involved in formal verification. He is involved in too many things in fact. And like many of us, he has been contributing significantly to the building of expertise in other sister institutions by participating in either faculty recruitment or participating in the workshops such as this. He is also a stalwart advisor to a large number of industries on issues related to many things including cyber security. So welcome Bernard, welcome Shiv Kumar. Thank you so much for agreeing to conduct this workshop. When the national mission asked me to conduct a workshop on cyber security, I was slightly perplexed because our primary mission is to empower teachers who teach basic courses in engineering and science, primary. We do of course look at a few electives which are more popular, but mostly that was our mandate. Cyber security is certainly not a matter of introductory course in any engineering college. In fact, in many colleges the cyber security per se is not a separate subject. It is included in the discussions on either networks or some other. But what happened and this background is important for you to know is that the Prime Minister's office realized that we need to spruce up our expertise and ability to manage our servers under the dire threats of security that are emerging day by day. All of you, how many of you have heard of the problems in Estonia? I think it was 2002, Shiva, the complete Estonian commercial system broke down for a day, complete computerized system. Initially people blamed it on hackers, as you all know there are hackers everywhere in the world. It was subsequently discovered, it couldn't be legally proven, but it was subsequently discovered and almost proven that there was a state actor behind that meltdown. And the state actor was implicitly assumed to be the government of Soviet Union and Russia then, not Soviet Union 2002, it was Russia. The Russians of course never admitted to it. Subsequently it was discovered that China and the United States are also while safeguarding their own installations against the threats from outside, they're also engaged in creating threats for others. What it meant to the government of India is that the next decades wars will not be limited to conventional wars, but they might even be fought on the turf of cyberspace. Now how do you secure your N number of installations, particularly in a country where security is itself considered a very sort of low key thing? How many people you have seen who routinely share their passwords for example, have seen that in the banking sector for a long time? So on one hand it is necessary to build the awareness of the computer security, but on the other hand it is necessary to safeguard a large number of servers that exist in the distributed environment that pertains in the country. So Prime Minister's office is of the opinion that at the block panchayat level, where there is a server, there has to be somebody who is in charge of the security of the content of that server. And such a person would be called information security officer, ISO. The ISOs need not necessarily be high-floating engineers, they could be technical people trained into the art, but the estimate of the PMO is that we would require about four lakh such information security officers in next three to four years in order to safeguard the country's interest against the possible threat either by hackers or by the nation states or what. Now how do you train three lakh four lakh people in three years? These people need not as I said be top-end engineers, hello. There could be diploma holders from polytechnics, there could be even ITI trained people in the appropriate manner. Now how do you train them? So naturally the first task is to train the trainers who would eventually train these people. Unfortunately the government of the PMO was not aware of any mechanism which could train people on large number and which could train even teachers on large scale. Then M.H.R.D. told them that we have IIT Bombay which is running our T-10KT program and although not strictly part of the T-10KT mandate, it may be possible for IIT Bombay to conduct such a program. I went to see cert, Dr. Gulshan Rai, a good friend of mine, you cert in, you know the designation cert in, he is effectively a constitutional authority which looks at the cyberspace issues at the government level. It initially started with allocation of domain names and whatever whatever but effectively they also shortlist and certify agencies which can handle information audit, information security audit and so on. I was hoping that they will be able to send some of their experts to conduct these trainings. Unfortunately while Gulshan Rai believes that this training is important and in fact he personally requested me that we should actually conduct this program, they do not have adequate expertise at the level of training the trainers. We also contacted CDAC and my friend Prasar Rajat Munna is the director general of CDAC has promised that a couple of their experts might come and join us either in this workshop or subsequently in the main workshop for 10,000 teachers. I am giving you all this background to tell you that it has not been an easy task for us to do it till I found out Dr. Barnard and I found out Dr. Shokumar to be enthusiastic enough and to provide a helping hand to conduct this program. So on behalf of the government of India and on all our behalf I would like to give them a big hand for agreeing to conduct this workshop. Barnard has been a security buff for quite some time and in fact he has written a book. Have we got those books or they are distributed? Let me admit that whenever in the past we have decided to get some learning material we have never been able to get that in advance. This is the first time that all the coordinators are getting the book on the first day and I must thank my workshop team for handling the logistics. I am also glad to announce that books have been ordered for all participants. So you will know the exact numbers which will come to your respective centers and those many books will be dispatched to you before the actual participants come to your place. Ideally we would like to have the books with the teachers ahead of the workshop but that is not possible because these 10,000 people come from various places and I suppose as coordinators you will undertake the task of distributing the books in time to them and so on. So that in a nutshell is the backdrop preparation about the workshop itself, the content and the extent of the activities that you will do. Professor Barnard will mention those things in the introduction.  Since Barnard is going to speak to you at a great length, in fact even Professor Shukumar later but Barnard is going to kick start the session. At this juncture I would request Professor Shukumar to say a few words and then I will give some concluding remarks. Thank you very much Professor Fartak and very warm good morning to all of you. It's a pleasure and an honour and a privilege to be associated with this course and once you see the timetable and the schedule of lectures and labs and so on you will find that Professor Barnard is the one doing the bulk of the hard work and I am just making some appearances here and there is a reason for that. I think many of you would have read or heard about this book Accidental Prime Minister Sanjay Bharu. So in the context of yesterday's news it is all the more relevant. So Professor Fartak also hinted at it I am an accidental networking person and an even more accidental security person and I can tell you why and how and maybe many of you may have faced similar situations in your own universities and campuses. So when I joined here quite some time back Professor Fartak was ahead and we were asking ERnet was just getting started so you can guess how many years over back and so on and I kept asking for facilities I want this I want it and that I want email and then I didn't realise I was young that it's not a good thing to do because if you keep asking they'll say why don't you do it why don't you build it. So accidentally I got drawn into helping the setup of the networking infrastructure in our campus and it's been a long journey and to keep a long story short that's how you learn on the job we started with 64 kbps van link so all of us know 64 kbps what it can do that was considered a big jump in those days and therefore accidentally it became very important to manage that link well so what is the contention for the van link at that time or even today academic usage usage for good purposes versus misuse within codes and a campus with students like this were so bright and who know much more than the faculty in issues like this it was a challenging job to ensure that our internet resources were used properly or at least most of it was available for proper use and we could at least control if not eliminate the misuses so this led me to the second role which again I'm not unhappy don't take this as a complaint I became head of the computer centre and had to for two three years make sure that our internet services ran properly and that was a real challenging job email works beautifully 29 days of the month you don't hear a word on the 30th day there's an outage for one and a half hours then what do you hear okay all the choices to abuse more than what happened in the election campaign so you make sure that you learn how to secure your services how to make sure security has several meanings security does not always mean only attack there are bonds and securities right what is your secure you feel secure means what safe that there is no disruption in your you get used to having power here so you feel secure if power goes every five minutes so running a smooth service which is in the in the other sense a denial of service attack and so on and so all that will come later in the course so keeping services properly running meant hands on so this is the second message that I want to say that my learning is not because I did research in this area and did work in this area and wrote my phd in this area but because I was by circumstance forced to learn hands on so the second point that I want to make in this brief inaugural address is that while lectures will is a must and listening to the theory part is a must the labs that we have planned for you I think will have tremendous value if you put in the effort so the labs to the best extent possible we have tried to design so that at least half of it or maybe two-thirds of it is doable by you in the lab but there'll always be a one-third which you have to take back and continue learning and continue you know trying and so on so forth so all I want to say is that this is a there's a small story again which all of you know probably but anyway let me repeat it that anxious mother brought her young you know son eight years or ten years to Ramakrishna and said this guy is eating too many sweets I've tried to hide it in the kitchen here there he finds it he rides everything and it's not good for him why don't you advise him to eat less how many of you have heard the story good so just for the benefit of others so he says okay bring him back after one week after one week the mother brings him back and then Ramakrishna tells him she is not good for you your health eat only two per day and then when you're big you'll feel by all sorts of things and the boy agrees so the mother is puzzled why didn't you say this one week back what was the answer I myself could not control I wanted to try for one week and see that I know how to control and only then I can teach a boy how to control this urge similarly if you are going to teach network security it would be great if it was push button technology that you just buy a device you plug it in and you're secure it would be great right that is the holy grail most technology wants to be invisible it should work that's all and our job should be to simply at the most put it on yeah is there any technology there like that if it is whether it is there or not security is not like that okay it is a knowledge oriented thing and you have to know you have to know what is happening how the tool works what it does what it is capable of and the human is not yet out of the loop okay so again I want to emphasize that the lab component of this course is something you should take very seriously and that is where the learning will happen if not right here in this workshop when you go back you should not assume that you know that that's the only thing or the tools that we give for you to try are the only ones and so on so forth so I think that will that will happen so the last point I want to make before handing the floor to Bernard who is done like I said most of the work for this course is that two things while it is a pack schedule and I'm pretty sure that most of you will find it a little demanding if you take it seriously do not you know lose the opportunity to enjoy some of the beauty of this campus okay most of the students are away campus is empty and nice it's beautiful there's a lake on that side lake on this side there's a mountain okay so do spend some time spend some time and the last part is what I think Mukta also told you that learning is not always this way from the stage to the audience much more learning happens between you so you are from different institutions different colleges so try to learn from each other the best practices or inputs which will also happen in the labs and so on so forth so with those two inputs to you I will conclude my initial address I'll be back on Monday morning to teach you one part of the course which is to how to set up services securely especially in a campus like ours so I'll hand the floor back to Bernard thank you President Shukumar I thought in the remaining 12 minutes of the time I'll take about seven or eight minutes to share with you some general thoughts I'll start with this 64 kbps link that he mentioned I do not know how many of you are aware of the prevailing costs for the so-called 64 kbps pipeline which BSNL used to give those days it was department of telecom that time it used to cost 950,000 rupees per year to have a dedicated 64 kbps link between Mumbai and Delhi life insurance corporation which I used to advise refused to implement a wide area network considering the cost running costs of that fortunately the prices have come down significantly and we can all talk about this but since he mentioned the early ERnet days let me share with you a very hilarious experience it was very frustrating Dr. Ramani at NCST and I at IIT Bombay we had our labs trying to establish an email service and we needed modems in those days you could not just buy a modem and install it and use it you required each modem to be certified by the department of communication as worthy of being used for whatever reason and they would take 15 to 20 days to certify a modem we went through that process we got two modem pair we put one here we put one in NCST and funny things happened either the both modems will transmit simultaneously or both would receive simultaneously so we couldn't get anything then we decided to use another set of modems we went to DOT again and they again took 20 days and Ramani and I felt so frustrated because these modems also did not work for somebody so finally Ramani says what is this nonsense let's go and working pair of modems doesn't matter and we'll go to DOT post fact say actually surreptitiously use the modems connected them without taking permission we are generally very very legal so we were ourselves were worried but our desire was to push the email exchanges in the when the first email was exchanged between our software lab and NCST's lab we actually danced with joy and Ramani pointed out the danger that we are on the wrong side of the law later on in a CSI conference I remember Dr. FC Kohli was chairing that session and I said sir we could have gone to jail for doing this and then he said you would have been the first martyrs of free India to get another freedom for the people of the country so that was the ethos there getting a modem working nowadays the kids buy modems and put them at homes all of us do that all of our technical people do that about the security I remember in hilarious incidents when I set up the first UNIX environment here was it 83 84 Shiva around that time we had three terminals and two machines each or something like that and we opened these terminals for people giving slots for half an hour and these slots were 24 by 7 so 2 to 2 30 a m 2 30 to 3 a m etc etc now one of the problems was that the kids would like to play games they're not great games the full graphic terminals were not there only text terminals there but the snake was one of the popular games for example and so on now as present Shukumar said we wanted to insist on good academic usage policy not for anything else but for the reason that the terminals were in short supply and there were other students who would like to compile their programs and none and so on so a battle royal ensue people would surreptitiously store the executables in their delight they're all UNIX machines we're a team of four very very smart programmers to assist us they were called Fata Chandal Chowkadi at that time it became Shukumar Chandal Chowkadi later when he took over as head of so the the best of them was called a wizard because he could write device drivers on the screen Hattu he was called Dr. Pradeep Hathkenangalekar so he wrote a script to automatically go into every account and find out an executable with the size of the game and delete that the next we saw that students first tried to shift the what you call the games in some other directories but with minus all option on the UNIX you could actually search everywhere else then students came up with this brilliant idea of breaking that into two or three parts storing them into under three different names and then concatenating them just before executing Hattu wrote another script to find out all executables and find various permutations and combinations which will amount to the same size of that file and started deleting those from the student director then students form collaborative unions so they will break it up into three parts one part will be with one student another part will be with another student so yeah so you see this is a choresy pie game which goes on perpetual and what you see in terms of hackers and even national players doing state players doing such funny thing is nothing but a extension of the desire of individuals to beat the system and the system to beat the individuals and this is a perpetual game it will never stop the point is that while we could successfully ensure that some symbolums of security happens here what is very very important to remember is what Professor Shukumar said at the end there is no shortcut to dirtying our own hands okay many of you may not be cyber security experts it's all right none of us were by the way okay I'm a hardcore electrical engineer converted to computer science Professor Shukumar actually is a regular computer person but as he told you he was handling the networks incidentally but it is possible even if you are required circumstantially to handle something it is possible to dedicate yourself learn things but do things by your own hand so that you're on top of things please do not leave these only to the technical staff who support you and in fact the technical staff will support you with greater admiration and greater commitment if they know that at any point in time you can roll up your sleeves and do exactly what you expect them to there is no shortcut to dirtying our hands in engineering and I would urge all of you to use the opportunity here in the labs to sit down and do things for yourself and there is absolutely no harm if you make mistakes all of us do that one great syndrome of the IIT system is that we do not hesitate in learning from anyone young or old as he said many of our students are smarter and we learn from them many of our technical staff are smarter and we learn from them as long as we show adequate humility to accept that look I don't understand this and I do not mind learning from you in fact people are encouraged to tell us more tell us better and that way a rapport is established and better systems are established having said this the general nature of the technology that is being increasingly used in education is mind-blowing and I would like you to also go back and reflect on how much of technology is being effectively used for educational purposes network and network security is one part of it but take the services for example email services I am allergic to an email ID which does not end in .in for Indians yet 90 percent of the people I come across use either gmail.com or yahoo.com or at best yahoo.co.in and when that happens in our academic institutions I feel ashamed my institute gives a login ID for lifelong for every faculty member for every student for every alumnus all those login IDs end in .ac.in IITB.ac. Why because IIT Bombay runs an efficient email service which caters to all the requirements people like President Shukumar had a big hand in building that establishing that service how many of your institutions run this first-class service how many of your students and your teachers do not feel compelled to go outside to some other domain to get an email ID including you yourselves I think this should be a matter of great concern to all I would expect the following within the next six months and six months a long time within the next six months each one of you should undertake the responsibility of going back convincing your institution institute administration convincing yourselves that to run a first-class email service for all the constituents of my institute is my personal mission and that we would make our students and our colleague teachers and our colleague staff proud to use my institute .ac.in or yahoo.in or whatever as a email I will tell you why this is important in all international conferences that are held everywhere in the world including those which are held in India the only people who have their email IDs ending in their nation's name are people from outside India whether it is Japan whether it is Zekoslovakia whether it is South Africa smallest countries you take that tremendously proud their email IDs will end with their nations only in our case and one more case that is United States but they believe .com means .com.us they believe that .org means .org.us so this is something I would like to leave behind as an added important activity to be undertaken if nothing else at least at these 220 remote centers if that is established and if you can pass on this message to all 10,000 teachers who come to attend this workshop I think we can make a a different sense of pride in the usage of all but please remember you cannot just set up a service and say email IDs are given the email service better work properly 24 by 7 people have to have faith in that service they're only people will people don't use emails just like that they have faith in that service they must have faith in our services and unless we can establish a faith in such simple services how can we establish the user's faith in the security that we'll set up the security will come only after services so this is what I would I would like to say I I just wanted to know how many of you these colleges are have participated in the QEE program that MHRD had recently started one two three four four not very many all the QEE institutions there is a special workshop that we are arranging on pedagogy my colleagues process either a year and process Anna Murthy will be conducting it we will be offering this educational pedagogy workshop later for all the teachers but we ourselves have discovered how teaching learning has become so very different and can be so very effective if we understand not the conventional pedagogy or methodology of teaching but the modern methodology of learning and teaching in the context of heavy use of technology so that will be a workshop which will follow the QEE colleges will come to know about it but we will be offering this workshop for the general teachers across the country probably in this December or next summer so I already taken more than my fair share of time let me formally close this inaugural session and hand over mic to Krisha Bernard wishing you well for a very successful workshop as Shiva said please try to use this opportunity to interact with other elements of the institute live behind some of the good practices that you have at your places so that we learn from you take back with you whatever good that you find in this institution with an intention to try and establish those good practices in your own institute thank you very much God bless you


Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVersity. We'd like to thank you for joining the DataVersity Webinar today for Ways to Optimize Your Business and Drive Impactful Results through Spatial Analytics sponsored today by Altrix. Just a couple of points to get us started. Due to the large number of people that attend these sessions, he will be muted during the Webinar. For questions, we'll be collecting them by the Q&A in the bottom right-hand corner of your screen. If you'd like to tweet, we encourage you to share the questions via Twitter using hashtag DataVersity. And if you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the upper-hand corner for that feature. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the Webinar. Now let me introduce to you our speakers for today, Lisa Aguilera and Jessie Cho. Lisa is a Senior Product Marketing Manager at Altrix. She is passionate about the analytics space and showing how innovative analytic technology can help analysts move past mundane data tasks, elevate their skills and expertise, and deliver ever-increasingly sophisticated insights. Jessie is a Solutions Consultant at Altrix. Prior to joining the Altrix team, Jessie was a business analyst and Altrix user for over two years. She understands firsthand the need of business analysts as well as how to maximize Altrix to drive faster business insights. In her current role, Jessie leverages her unique experience to help organizations realize the benefits of self-service data analytics. And with that, let me turn it over to Lisa to get us started. Hello and welcome. Thank you so much, Shannon. And welcome, everyone. I want to say thank you very much for taking time out of your very busy day to join us. I do hope that you will be able to pick up some useful bits of information in some of the materials that we present today.  You can just go ahead and pop it in the Q&A. It'll help kind of guide our conversation today as well. So to level up that, we're going to spend a quick moment on explaining what spatial analytics is. And then I'm going to dive into four common use cases of spatial analytics to help you kind of think about other ways you could be using location-based information. Then we're going to go into four different case studies of real-world examples of unique and varied ways that analysts like yourselves may be doing spatial analytics or deriving some of the location data and blending it with their transactional low-point sale data in order to get new insights. After which, I'll pass this on to Jesse for a quick demonstration and then we'll get to the Q&A. So thank you all for participating in the chat. I see a couple of you are actually doing spatial analytics and a couple of you are not. So I hope that we'll cover both sides of the house in this conversation. So for those of you who may not know, there is actually a secret underlying piece of information in those data sets. It's the fact that 70% to 80% of all data has some type of spatial component as part of it. And some of this location information is really there to give relevance to the data sets that you're using. Now, if you're not using this data to understand the where of things when you're performing your analytics, this is going to be a great session for you to understand why you'd want to maybe tap into that. So tapping into spatial analytics is more than just mapping. It's more than creating, you know, drive time analysis. It's more than what you would typically see. It's really about helping you help organizations drive efficiencies in the offerings, in the operations, and in order to help enhance profitability within your organization as an analyst. Now, some seem to think that you really have to be an expert in spatial analytics or you have to know mapping or you have to have special mapping tools. And what we're going to talk through today and what we're going to showcase in some of these cases is there's a lot of analysts like yourself at other organizations that you'll hear more about today that don't have spatial background, that don't have big tools to do things with, that have been able to tap into the location information within their data set to really derive some creative and deeper insights that have driven some really impactful bottom-line benefits. Now, when you look at spatial analytics for location-based data, you can use it to do things like customized location services or inventory based on key customer habits within your organization. You can use it to help improve store service or asset location strategies. You can also use it to ensure availability and improve customer experiences that'll help drive down latencies or gaps in services within your organization and drive efficiencies in marketing and sales programs or offerings within your organization. So let's take a quick look at one of our first use cases here. This one is a pretty basic one that can impact many organizations. So for those of you on the call, how many of you actually do drive-time analysis as of right now? And I'm not talking about point-to-point location analysis. I'm really talking about drive-time. So taking into consideration routes and traffic and everything else as part of your analysis, you can go ahead and informally type in. Great. I'm seeing a few yeses. Excellent. Wonderful. So for those of you who are already doing this, you understand the value that this does. For those of you I see here, a few of you are saying no, maybe not yet. Maybe you should be looking into it. So let's talk a little bit about this use case. Drive-time analysis is really important in terms of helping determine the physical location of a new site or a new building to evaluate competitive mix in your area. It's also extremely important if your organization is... if you're working in an organization that uses real estate as part of a franchise model or something along those lines. Now, for those of you who went through the housing bubble in the last recession, I'm sure you can all attest to the fact that real estate is an expensive and risky proposition. Now, if you are in a hyper-competitive industry or a franchise type industry or a chain type industry, location, as you know, is key to the success of those organizations. And a good location can be competitive to acquire. It also can be quite time-sensitive to acquire because it can be very difficult to find right commercial locations at the right price in the right areas before competition comes in and sweeps it out. Now, to offset some of the real estate or location risk, location analytics must balance ROI against some demographic information, area competition, operational costs, and changes to things like roads and zoning. So as I mentioned earlier, we're going to use a bit of an example here. Let's say you're in a hyper-competitive market in analysts that work that, let's say, a coffee chain. A lot of these variables take on an even higher level of importance. So using our coffee chain example as an analyst working in an organization like this, if you were able to visualize and understand the location selection risk, you could use spatial analytics to understand the interface key variables and help make suggestions in refining location selection strategy as more of the data starts to become available and you start to become more comfortable with it. Now, one key way of determining a new location would be using drive-time analysis, like I said earlier. This is important because you may have a location that may look physically close based on a straight line, but road by road, it may actually be further than you would prefer. Your organization would prefer. It may not even be tapping into the right, maybe cluster of demographics that you're trying to target within that drive-time. That's why it's really important to use drive-time analysis with your information of your user and demographic data. You could also look at it in terms of competing options. Again, using the example if you were an analyst at a coffee chain organization, you would have to take in considerations like restaurants in the surrounding area or other cafes or even other existing franchise locations and ensure that they're not situated too closely and that the customer base in that region isn't going to get cannibalized. What kind of data would you use when you build this type of analysis? You would want to pull in things like your customer loyalty program data, sales by store. If you were that analyst at a coffee chain, you would add parameters for competing businesses as well. You would then apply drive-time proximity to every geocoded customer address and location. Then you would look at the targeted customers segmented within a specific drive-time from two potential locations. More importantly, choose the location with most profitable potential if you're trying to build an analysis like this. Should you not have rich enough customer loyalty data or point of sales data? You could go further and augment your data using demographic and thermographic information, bringing in something from Dunn and Bradstreet, Experian Census, et cetera. What type of analysis should you be looking to build when you're trying to do drive-time analysis? You really want to look at drive-time. You want to look at cluster maps of users within a given area, plot mapping and competing offers, and potential customers who match key demographics within a certain drive time for your organization-specific offering that you're bringing to market. That's a bit of an overview on drive-time. Let's get into the next use case that location analytics can impact. This is really about mitigating risk and exposure, as well as really adapting to maybe disasters and being prepared and readiness within certain geographies. In this case, we're going to use a basic example of an insurance company. If you're an analyst at an insurance company, you really need to be on top of your game when it comes to setting up premiums and mitigating exposure. Spatial analytics is extremely invaluable for this, particularly in regards to disaster claims. When performing this type of analysis, you would need a couple of data sets. You would probably pull in your claim data, your customer data, your policy holder data, flood weather or seismic type data depending on the risk exposure you're looking to analyze and something like property value. After collecting all of this data, you would then create various analytics used. For an example, if you're an analyst or if you currently are an analyst at an insurance company, you probably already know this, you would want to understand the risk exposure for your company, let's say, regarding earthquake policies. I'm from California, so this is a big thing for us out here. How much risk is it going to be to cover certain households within a certain area for earthquake policies? You would do this by mapping the policy holders and geocoding your addresses from their policies, and then you would overlay a map of seismic information that denotes fault lines and maximum probable magnitudes. After you've kind of built out the seismic hazard map, you would then remix the reassemble data and use it to really understand where do you have certain clusters and you'd be able to pull that reporting information back to your decision makers to really help them understand their risk exposure in certain areas. So the other thing that you would be able to do with using this type of data is if you are an analyst at an insurance company, for example, you would want to set up response centers after disasters. So one of the things is, again, here in California, earthquakes are a big thing out here, they're real, so how many households do you have within certain areas that would need that kind of response time for your insurance claim company? Do you have enough staff in place to support those call zones in those areas where you need to quickly get to and make sure that you have not only the right response time, but the right number of support available within a certain area. So you could be able to take all of that information, look at how your staff within your organization blend all that data together, and pull these reports for your business decision makers that will really help them understand when risk exposure as well as support within a given area. You could also take all this data and then combine it with regions, depending on your high risk and areas that you're facing in terms of claims and claims management and be much more proactive and effective in terms of determining premiums for your organization. So there's multiple ways that you can look at this. Similarly, you could take a different direction. Real estate investors can use hazard data. So if you're working at a big real estate firm or a big development firm, you could take this data, bet it with the hazard data, such as flood plain maps, and evaluate which properties are valued appropriately if your organization is in the habit of purchasing land to develop on. You could also couple this information with predictive analytics and data sets such as listings and sales price or school enrollment if you're looking at home development, crime and more. And you could forecast trends on property values in different areas with relative ease by tapping into a lot of this information and pulling it together. So third use case that we're going to go into here. This is a bit surprising. There's a lot of organizations that do tap into customer profiling in order to influence sales and marketing, but there's a lot of organizations that don't. They don't take location information as a factor in a data point in order to determine what they're going to be doing from a customer marketing perspective or a sales perspective. So when using location analytics for this type of activity, you're going to hyper-target sales and marketing activities to microclimates within a region, which helps them resonate a lot better. So you'd want to find out who your most loyal customers are within a given region. You'd want to find out what's the best ROI for marketing spend and where should it be targeted and to whom should it be targeted. And then who's most likely to purchase a certain product within a certain region? As we know, people purchasing patterns and preferences actually differ quite a lot based on not just state-to-state or city-to-city, but also between county and counties. So what kind of data would you need to create this type of yield? You'd want to tap into a lot of your customer data, your sales by location, your responsiveness by location. And then, again, you best overlay demographic and formographic information on top of that. So done in Bradstude Experience and Census to be able to really paint a full 360 picture of not only just your existing customer, but potential customers within a given area. Now, typically customer information is gathered as, you know, the point of sale or the time of transaction or via online surveys, you know, where customers are required to enter their receipt transaction in exchange for a prize or a drawing in a sense sort. Or you may get information from loyalty card data. But if your face is a challenge that the customer data you need is not part of your company's original collected data set, what should you be doing?  Your company decides it wants to target homeowners for renovation-related campaigns and renters for home accessory promotions. But your current customer data set is unable to distinguish between renters and owners. So how can you really get that data and tease it out? Well, the secret is fuzzy matching. If those of you who haven't heard of fuzzy matching, Jesse will talk a little bit through that in the demo. Fuzzy matching allows you to match data assets that don't really have common shared identifiers. And using a waterfall matching process, a user can then change their access to possible data associations and refine the data until you have the highest probability matches and the best data possible. So this customer data can then be further enriched by overlaying it with third-party data, something like experiencing household data, where you would get over 300 different variable sets for demographics or psychographic attributes. So some of the analytics that you can perform once you've collected all this data is you could overlay drive time information with this. You could see who should get which type of offers or coupons or promotions based on their proximity to a certain location, store location. You could also determine purchase preferences and tendencies and use it to really drive suggestions regarding inventory optimization and preferences of anyone within a given region. So for example, you know, barbecues, maybe you carry barbecues year round in most of the Florida stores that you have as an analyst, but let's say you're in Nebraska, barbecues in the wintertime, maybe not such a great idea. So really kind of customizing based on purchasing preferences we're going to give a location. It's key in terms of operational efficiency. Now you could also use it to determine potential purchasing patterns. If you're using that experience helpful data and overlaying it with your customer data to try and find potential people within your store and customize and determine your offering based on their buying habits. It's another way to drive insights using location. So our last use case that I'm talking about before we get into some of the real-life case study scenarios and some of the insights that your staff have been able to build is talking about how spatial analytics or location analytics can be used and tied to the placement of physical assets such as property. So not necessarily an actual store location, but physical property, things like service coverage area. So if you were an analyst working within the telco business, you know, some data that you may want to pull in to perform your analysis would be customer data, maybe coverage, tower coverage within an area, data bandwidth coverage within an area, demographic information again and population density. And you would use all of this data and you could create a couple of different analytics for customer maps of users within a given area, demographics of customers within a given area, thought mapping of coverage, and then drive time to specific locations. So again, if you're an analyst and, you know, for example, you're working in a telco company, you could be tasked with how do you optimally play cell phone towers to reduce drop calls. Your company may send out data to that signal and you would then take that data, get that signal change, put it on a map and see where your coverage is lacking. You could then use this data and map it and combine it with population density, growth and customer locations. You could use it and you would find ideal cell tower placements or even usage. You could take that a little bit further. You could drop call tolerances and iterate on this model. You can overlay this with factors such as negotiated lease rates for placing cell towers on buildings. The other thing that you could do and an organization that we know of has done this for a telco area in New York is they looked at the demographic information and customer information within a given region. They looked at the best cell coverage as well as their data coverage to understand what types of coverage offering to make within a given region so that they weren't offering the best cell coverage and the best data plan coverage within their areas in which the demographic skewed much more heavily younger. They used the analysis to decide that the demographic of younger users actually ends up using data more and they don't use actual calls so why would we want to increase our actual call coverage when we need to increase our data coverage and we're able to really customize and optimize their service offering that way by using location data, coverage data, customer data as well as demographic data. Now let's get into some actual analytics, facial use cases that hopefully if you are an analyst who has participated and done spatial analytics or location analytics may kind of see some creative ideas and how to, how other ways to use and think about your location information when you're building your analytic insights and for those of you who on the call don't have a spatial analytics background, these are analysts just like yourself. The majority of these use cases that I'm going to be speaking to don't come from analysts who have spatial analytics backgrounds or location intelligence backgrounds. They're able to perform these insights through a little bit of creativity and foresight in terms of how they're going to tackle their problems. So the first case today I want to talk about is Cardinal Health. This is a very interesting use case. The analysts at Cardinal Health did not have an actual spatial analytics background whatsoever. One of the ways that they were using it was in the nuclear pharmacy division of Cardinal Health and they were looking to improve the doctor-patient relationship for PET scans for oncology. Now for those of you on the call who are lucky enough to not know what a PET scan is, it is a powerful imaging technique that is typically used to diagnose and treat lots of diseases, but most notably cancer. And there are more than 1.5 million PET scans conducted each year. Now the problem that we're facing here wasn't the sheer number of PET scans that were being conducted. It was a little known fact that there is a huge logistics challenge with PET scans actually. The reason for that is F18 is a nuclear medicine needed to perform these PET scans. And you have to have just the right amount of F18 strategically placed around the country to make it to the hospitals in time for PET scans because the F18 drug actually only has a half-life of six hours. So Cardinal Health had the challenge of trying to figure out how to support any one of the 300 million people seeing any one of the 10,000 of physicians and any one of the 10,000 of hospitals or clinics in the country and ensure that they had this F18 with such a short shelf life readily and available for their PET scans when they were there with their doctors. Now how can they manufacture enough of this drug in a four-hour period and have it delivered on time without inconveniencing patients? And the other thing that they're trying to figure out is how to profitably price the drug knowing that some of the F18 may actually not make it to its location due to its shelf life and it would be useless once it got there. Now what they did was they used all of the data sources they could get their hands on, all of their Excel data, all of CRM data, location data, hospital data, patient data, number of appointment data, and they used this to analyze time of distance from the delivery and they blended that drive time data with root information and shelf life data as a product to come up with optimized groups and drive times of securing and delivering the drug. Now the power of tapping into not only just their traditional data sources but looking at the spatial and location data as part of the process at Cardinal Health resulted in huge savings. The value of this analytic output really drove visibility into operational gaps that they had and increased efficiency by 100x and their first project saved almost 1.1 million in logistics fees. So I hope this is an encouraging story for those of you who may not have tapped into the location information of your data to just really drive home and understand how valuable it can be. Now another use case that we have here is Dell Hayes. So if you are from the east coast on this call, you're probably familiar with the Dell Hayes brand but for those of you who like myself live on the west coast, you may not have heard of them before. They are a food supermarket operator. They have over 1,500 super markets in the eastern United States and they operate under several names like food line, Hanna Ford and bottom line dollar food brands. They're also located internationally in the Europe as well. So the analysts at Dell Hayes, they support the strategy, marketing, pricing, category management, merchandising, finance and legal departments within Dell Hayes and a lot of the analytics that they use is spatial or location data for a lot of the insights that they do create and they are a small team of analysts that they have been able to create some pretty interesting things. The analysis that they use location data and operational data is in test and control initiatives that they have taken on with Dell Hayes. Really seeing what types of activities might be suitable for certain stores and estimating the effectiveness of an initiative and change before rolling it out in math. So what they do is they take information and match store households in pre-period after the initiative. So one of the initiatives that they did was with their food lion branch. The food lion branch had a flyer in a small local newspaper and the organization wanted to evaluate the effectiveness of the flyer because they were spending about 1.5 million a week on the flyer. The analysts at Dell Hayes established a test market and they called any locations from that test list that had major things happening like opening, closing, remodels happening, or stores that had a typical high season volatility. What they did is they used the location data for a lot of these to create tests of using the flyer versus not. What they resulted in was the test of where the flyers were working actually showed a positive lift and they figured out there were certain demographics in areas where the flyer was not working. The areas where they did see a positive lift resulted in a minimum of $1.4,000 per share per week in sales lift. The story that it did not work and what they did was they stopped using the Sunday flyer and optimized their marketing spend and eliminating this in what they found was it didn't work in urban areas and that also resulted in a lift because they weren't inefficiently spending marketing dollars in areas where they didn't need to. Another test that they're doing with location-based information is they're exploring omnichannel service efforts and offering. Deciding if an online grocery shopping will work for certain locations and will provide a lift versus walking into physical locations. What they're doing there is they're taking their demographic information, customer information and considerations how far customers are coming from to perform this test. Now, sorry, before I get too far into this, I didn't call out at the bottom of each of the slides that I'm sharing with you cases. On slide share is the full story of how they're able to perform these analytic use cases that I'm talking about and they dive into a bit more detail than I'm diving in here so if you are interested in hearing a little bit more about what Dell Hayes is doing after the event you'll have this slide so you can click on those links there and find out more details. Now, the next use case we're going to look at is AAA. These are the site assistants that they offer to the insurance, to the travel help and DMV support at some of their locations. I know I'm a big fan of their DMV support at some of their locations. The analysts at AAA actually use location data for a lot of different regions. They've been able to tap into some of the spatial components of the data sets that they work with on a day-to-day and have a team of spatial analytic experts when they're pulling all of this in. What they do is they use location data to help determine their clubs and what kind of services they're going to be offering at their clubs, where to locate their clubs. They also use it to help understand the demographics and purchase history of their members near each branch office and location in order to find out how to support or what offerings to introduce within key locations. They also use it from physical alignment of services and people who are there helping at each of these club locations. What are peak hours for members coming into certain locations and do they have enough people on staff or do they have too little people on staff to support those kind of high traffic times and high traffic locations? Triple A use a lot of their data such as travel, ERS, auto buying insurance, vehicle repair data. They use income data, household count and other census data and they used it to really build out a comprehensive view of their customers and potential customers within each area. What they've done is been able to really optimize the various service offerings of each membership location. Make sure that there's ease of access to them. Make sure that the drive time distance is optimized as well as I said earlier that each of these services offered within each store area was optimized as well. To send out personalized communications to targeted marketing segments, triple A does tap into all of their existing day to day operational data plus they tap into some of the location information in that data in order to drive deeper efficiencies. The last use case and this one is my particular favorite. If you do have time to check out one I would highly suggest to click on the slide for a little bit more of a deep dive on the Cineplex story. For those of you who are not familiar with Cineplex, they are a leading entertainment company in Canada and they operate the most modern and fully digitalized motion picture theater circuits in the world actually. One of the objectives that the analysts were faced at Cineplex was in identifying key audiences for new films release to inform key marketing sales and other business decisions. The problem that Cineplex is facing is every week a new film comes out and there are very few data points available prior to release to really identify how successful or how popular a movie is going to be within any given location. The analysts were tasked with forecasting the demand for film production because Cineplex wanted to improve the guest experience not only in the theater but also drive staffing decisions based on the demand available for film products and make sure that they were more efficiently staffed predicting location specific demands for films that played any given weekend as well as make sure that they were accurately supported in their food and beverage in terms of marketing. To further ask the analysts at Cineplex were also tasked with helping to suggest personalized marketing efforts for upcoming releases as part of their customer loyalty program. The challenge they were looking at was how to determine who to communicate which new releases without past purchase data available or based on the type of movie that was being offered because they didn't have a lot of information to go off of.  They actually combined it with film attribute data so genre and rating and they predicted the composition of the audience based on 13 age and gender segments. Then what they did was they combined these results from 13 models to predict the segments and created a forecasting module that consisted of multiple modules that were together to produce a forecast for each film location and business day. Now they then tested this forecasting model accuracy against a 52 week sample from the past and then used market basket analysis with advanced ticket purchase from loyalty members to identify companion films. For each member they generated an interest score for new movies using some of the consumer behavior data for companion films and other film attributes like actors, directors, and genres. With this they were really able to address the request of their business leaders to optimize the user experience at each location. Before I get in, let Jamie kind of, I'm sorry, let Jesse kind of show you how you can do this yourself. I just want to talk about the one thing all of these cases have in common. What they have in common is what a lot of you guys on the phone have in common today. Some of them came from a mix that's not having any location information background or spatial analytics background. Some of them came from it having had done some spatial analytics background. Some of these cases have in common is that they all used a solution, a self-service analytics platform that made it easy for them to actually take all of this location-based data and blend it together and create some of the insights they talked about. One of the key ways that they did that is they explored spatial allocation data. It was really about using the platform solution to help them use location information and spatial data just like they would any other data set and really treat data as it is, data, independent on whether it had a mapping or geolocation component to it. The problem is most existing business intelligence and analytics tools today, they do display some spatial data on a map, but they leave the analysis between the points up to a user, so not as well-rounded as most people like and they can be a little challenging to use. Then there's the other side of the spectrum where you have more sophisticated but niche tools that exist for mapping and spatial analytics, but there's a select number of experts who can use these tools, they can be a little bit difficult to explore if you've never had exposure to them in the past. You need to make sure that whatever you're using to perform your location intelligence is not narrow in scope or expensive or require you to have to work with a different tool set to work with this data set versus the location based data set that you're working with or that requires you to actually turn to somebody else for their analytic expertise. You want to look for a true analytic tool that will allow you to access and use all the data that you need to perform your insights, regardless of the size or the format that you need for analysis, which is what those for-use cases that I just talked about did. You also want something that's going to help you geocode data quickly and easily. You want to help you perform in-line visual litics. In-line visual litics is very different than you normally get with Tableau. It's about helping you visually build out your analytic models so you can quickly iterate on them, quickly create assisted analytic models, and then output your production to a visual consumption platform or to a map or to a PDF. The other thing is you want to make sure that whatever platform or tool that you're using to perform some of your use cases that has the information and that you can analyze data in new ways and easily share that information in the way that it needs to be consumed by other users. For those of you who don't know about Altric, it's easy to think of Altric as a repeatable workflow for accessing, profiling, prepping, blending, performing spatial analytics, predictive analytics and modeling all of your data. It's the comprehensive work that it really empowers analysts of all skill sets to be able to access all their data within their organizations in a secure and governed way so that your IT departments and your data architecture and data managers feel safe with you being able to directly connect to the data that you're connecting to in order to pull the insights you need. It's a code free way of liberating you to actually use spending hours working on your data and help you create and think of new ways of using your data to answer questions that just weren't answerable before, like the examples with Cineflex or with Cardinal Health and empowering you to be able to perform really limitless kind of analytic models and address a tremendous amount of business questions that may come your way without limiting you to online visual analytics that Alteryx offers as a platform. And then just as easy as it is to consume and connect into data and we're very agnostic as to where that data is in the cloud in a traditional data warehouse, Excel, access format doesn't really matter our size. Alteryx is actually agnostic in terms of sharing information and outputting that data and creating an analytic insight if no one is there to actually consume it. Your insights don't make any impacts to your organization if people aren't actually using them. So you need to be able to share them in a way that your business decision makers want to consume them. I'm sure many of you can raise your hand and say you have to create reports ad hoc all the time and Alteryx will help make that very easy for you. Some of you may be creating reports that need to get repeated on a regular basis that are consumed often through things like Tableau or Click or Power BI, Alteryx allows you to quickly output all of your analytic information into the visual platforms that your business decision makers want to consume information on or it can help you write information for downstream. So with that I'm going to pass this on to Jesse who's going to show you a bit about Alteryx and how easy we make the analytic process from end to end. Thank you, Lisa. Let me share my screen here. You all see my screen? Yes. You're coming through. Perfect. This is Alteryx Designer. It's a desktop software solution. When you first open it up you get this getting started and we do have tutorials to kind of get you familiar with the tool so you can open any of those up and walk through how you would build out a workflow. So this area we call the canvas. This is where you drag and drop your different tools. This is the configuration window. This is where you'll get any messages that occur while the workflow is running. To the left is the configuration window. This is where you'll configure each of the tools that you drag onto the canvas. For example, I bring in an input data tool. Then this configuration window on the left changes. So this is where you configure categories so we have the favorites tools. These can be customizable but most people start out with these favorite tools. We have our in and out tools, different preparation tools, joining tools, parsing, transforming. Our in database tools as well if you're processing a large dataset and you want to utilize the processing speed of, for example, your SQL server and do some manipulations and joining and then stream it back out into your computer's local working memory. We also have reporting tools and then our spatial tools so you can see there are also icons that you can drag and drop onto the canvas. Then we also have predictive tools as well and our connectors that can output directly to Salesforce. We also have connectors to share point. So that's a brief overview of our tools. Here I have a pre-built workflow just for sake of time. First we have our orders table so we have it by customer ID, order ID, store number, product, unit price, discount and quantity. We also have customer data with customer ID, their address location and then we have another input with our store information with address as well as latitude and longitude. So first we need to clean up our orders. We have some null values that we need to clean up which we can do with the data cleaning tool here. We calculate the total sales for each row and then we summarize our data based at the customer level. You can see down here in the results window we have customer ID, how many transactions they've ordered with us, their total spend as well as what store they shopped at. Then we join the customer data with our orders table with the join tool. So now we know what location our customers are in. We have a three geocoder which uses the TomTom data and indicate the address field to get the spatial object which is essentially all of our customer points here. So you can see in the browse that all of our customers are mapped out during the Denver area. So we join these two data sets together and then with the stores because we have lat and long already we can create a point in Altrix. Then we use the non-overlap drive time tool where we're creating a trade area essentially based off of a 10 minute drive time using the stores point. So here we can see we've created these trade areas based off of the 10 minute drive time for each of our stores. Then we're just renaming the field here so store point and then we have our store trade area. Then I want to see which of my customers are shopping within my trade area and outside my trade area. So to do that I can use the spatial matching tool and this will give me customers that match so I have around 1500 customers that were all within a 10 minute drive time of a store. Then I have these 800 customers or so that are outside that trade area. So I want to dive deeper into those customers. So here I just have another join tool to join back the spatial information here with the customer point, the store trade area and the store point. Then I wanted to calculate these customers because they're outside the store trade area. How far are they driving to the store that they actually shop that. So here I can use the distance tool, tell it which field I want to calculate the distance between. So I chose customer point and the store point and then I wanted to output the drive time based off of peak hours. You can actually specify off peak night or peak hours. So we have different options of the drive time calculation there. And you can also optimize your route based off of time or distance. And here in my units I chose miles. With UlterX you're able to output data just as you are to input data. You can output to a flat file space if you have right access to. So UlterX respects the read and write accesses of your databases. So no one can access databases they don't already have access to. You can also summarize spatial objects as well. So here I'm grouping by the stores. And I'm taking the sum of the spend, how much customers bought there, the average drive time, and the average drive distance by miles. And combining my customer points. To essentially create a report here that would show me PDF of which customers are outside each of the stores that they shop at. So here's my PDF report. So I can see for store 100 these blue dots are my customers that have outside the 10 minute drive time. And the orange are each of my stores. So I can see that some customers they actually live closer to another store, but for some reason they drove out to store 100 to buy a product. And then I have some tables here to show what the revenue outside the 10 minute drive time was and what it was inside. So we can see that total spend was a lot higher within the 10 minute drive time. And then those that were driving outside were spending at least 20 minutes to drive to the store. And this report is actually broken out by store number. So I can see for each store how far customers are traveling to shop at that specific store. So maybe that's something I need to look into for those specific customers. I can ask why they drove to a further store when there's actually a closer store to them. But I'm able to see that with this drive time analytics. So this concludes the brief demo. We have some time for some Q&A. Thank you so much for this great presentation. Just a reminder if you have questions, go ahead and submit them in the Q&A in the bottom right hand corner of the screen. And to answer the most commonly asked questions, I will be sending a follow-up email by end of day Thursday with links to the recording and anything else requested throughout the webinar. So what will be some things to consider when looking at Ltrex for spatial analytics that you want to take that one? Sure. So there is a difference, and I'll give you an example of Dell Haze. Dell Haze actually uses a combination together. There is a bit of when it comes to being able to one, access data that you need securely and quickly without coding or waiting for your IT or data analytics department to extract that data. We are extremely respectful of all of the data governance and access rewrite policies that organizations have in place, but it is quite easy for you as an actual analyst to access your data. The other thing that is a big difference is Ltrex is a full analytics platform work bench. So not only do you have a community, you can perform the data blending, you can perform the data profiling, and it's one piece of feedback that we have for certain traditional spatial tools start to really kind of choke when you're working with larger and larger amounts of data, or you're bringing in lots and lots of different data points. So we have many customers who actually use Ltrex as if you will, their analytics engine and creating all their models and then outputting it out to things like Azure or map info or Tableau even for that matter. The other difference is all Ltrex as you saw with Jesse's demo here, it is a drag and drop environment. That's what we mean by inline visual litics. You get to do your full analytics work flow visually without coding, you can share this quickly easily and you can up skill your individual analytics abilities and the data profiling as you saw with Jesse's screen here is the same exact thing as spatial, you just drop a tool on the canvas and you're doing spatial analytics. It's the same with predictive as well, we have plenty of training courses that will help you up skill your predictive capabilities and you just can't get those with some of the more traditional spatial analytics tool. Where can I learn more on the tool? There's a lot of great resources to learn about the tools, I think I saw some questions and chat coming in about how do you know when to augment your data with demographic data and to getting a little bit more in depth about the spatial components and how to get them going. The Ltrex community as Jesse shows here, she can pop up and say thank you so much. The Ltrex community is a wonderful resource to go to. You don't have to be an Ltrex customer, you can download the free trial and everything you saw here that Jesse showed and there are plenty of free training that you can sign up for. You can ask the specific questions on when do I know when to augment my data set to the user group. That will answer your questions not just about Ltrex but just analytics in general. Highly recommend that you check out the Ltrex community to learn about this. This is the lunch and learn on spatial analytics. This is coming up this week on Thursday. Again, you don't have to be a customer. You can download the free trial and follow along and understand Ltrex and spatial capabilities. Certainly a great set of resources there. What processes do you have to determine the enriched part of your flow? Again, that's a great question. I think that the best place to really look at it here at the Ltrex community is going to depend on what type of analysis that you're looking to do and what you're trying to augment. I would very much highly recommend posting that specific question here. I think I also put it in the chat window too, a thread within the Ltrex community that did address this specific question. If you'd like to check out the chat box as well, you can click on the link I put in there. I certainly know the answer to this. I've done several webinars with Tableau. The question is, is Tableau your product as well? Maybe you can talk to that as well as how you interact. I'm going to let Jesse answer this question because she comes from an analytics background and she was sitting in your guys' shoes not too long ago performing a lot of insight work. Jesse, I'd like to hear your opinion on Tableau and Ltrex. I was at a company where we had Altrix server and Tableau server. I would build out a workflow process that updated operational dashboards on a daily basis so that our operations team could go look at the dashboard every morning and determine if they needed to make any changes to scheduling for our part-time staff. Once I built out the workflow in Altrix, I was able to schedule these workflows and actually have them automatically update to Tableau data extracts. As you can see here, I have a Tableau data extract output which then would automatically get updated onto Tableau server. They work hand-in-hand really well because Altrix is really good at manipulating, blending and joining all of the data sets. I was dealing with over 500 million rows of data. For me to do a join on Tableau server, it was somewhat difficult and clunky. I was able to utilize Altrix to do my nimbulations, joins, calculations because some calculations have to be on different levels of detail. Tableau, I'm sure some of you are familiar with it. I'm sure some of you have Tableau. You get that error that says you cannot aggregate on a non-aggregate on different levels of aggregation. So I was able to do all those calculations within Altrix and then push it directly to Tableau. We also do have a Tableau starter kit that is a complimentary download as well where you can output directly to a templated workbook where you just update your TDE. So if you already have a TWB that's built and you just want to refresh your TDE, we can use this icon as well. And then we also have a polygon for Tableau to build out your spatial visual dashboards. And we can output directly to Tableau server or Tableau online as well. And where did the time go? It's such a great presentation. I'm afraid we are at the top of the hour, however. Lisa and Jesse, I can certainly send the remaining questions over if you want to review those. Thanks so much for this great presentation. Thanks for our attendees for being so engaged in everything we do. We just love all the questions coming in as always. And just a reminder again to answer your questions, I'll be back. Jesse and Lisa, thank you so much. I hope you both have a great day and thanks to Altrix for sponsoring today's webinar. Thank you, Shannon. Thank you. Have a great day all.


Where's a chromatic CJ Stroud number to one out of eight? Boom there you go Texans will mag that one out of eight on the CJ There we go. Congrats. That's about as good as you can do on the Strouds That is gonna be for Christopher J with the Texans one out of eight There we go. Congrats there, Christopher CJ Stroud chromatic


Hello friends, myself Ilya Arsandhane, assistant professor from Department of Electronics, Valchin Institute of Technology, Sulapur. Today, we are going to see what are low frequency noises. So, these are the learning outcomes. At the end of this topic, students are able to explain the effect of noise and analyze different types of noises. Types of frequency noises such as low types of low frequency noises such as flicker noise, burst noise and avalanche noises. So, first we are going to see again what is meant by or how noise is defined. So, as we know noise may be defined as unwanted introduction of energy which tends to interfere with proper reception and reproduction of transmitted signal. So, noise hence it can limit the range of the systems. Noise may sometimes even force a reduction in the bandwidth of a system if it affects the sensitivity of the receiver also. Similarly, electrical noises are undesirable noises that fall within the passband of the signal. Passband basically when we are considering the filter then at these points the electrical noises may fall or get suppressed. Similarly, noise is can also be called as a random energy that interfere with the information signal. In radio receiver, in radio receiver noises may basically produce a hiss noise in the loudspeaker at the output as we have seen in our day to day life we can we can see this particular point that is a hiss is produced in a loudspeaker when there is a no input voice from the mic. So, that particular hiss noise is called as the it is the unwanted signal which is called as the noise. So, these are the sources of the noise. So, as you can see noise is basically further divided into two parts internal and external. Similarly, the external is further divided as atmospheric noise, extraterrestrial noise, industrial the internal noise basically define further categorized as thermal noise and short noise. So, as we have seen atmospheric noise basically they include cosmic noise and solar noises, industrial noise the turning on and off of the electrical machine drilling, stamping all these noises come under the industrial noise, extraterrestrial noise basically these are due to the these occur in the space due to the stars and the planets. Cosmic noise is one type of the noise which come under the extraterrestrial type of the noise. Similarly, internal noise, thermal noise this particular thermal noise as we have seen it occurs due to the it it basically occurs due to the different electronic as well as electrical components in a circuit such as resistors capacitors inductors transistors etcetera and short noise these are the noise which are occurring or which affect the low frequency type of the signals and these are majorly affecting the low frequency signals. So, low frequency signals or flicker noise, active devices or integrated circuit devices, diode transistors etcetera also exhibit a low frequency noise which is frequency dependent that is it is not uniform and hence this type of noise is called as a flicker noise. When we consider the spectrum density of the noise here the noise spectrum density increases as the frequency decreases hence it is given by 1 upon f into the noise. So, flicker noise is basically due to the impurities in the material which in turn cause charge carrier fluctuations. So, charge carrier fluctuation fluctuations is nothing but charge carrying components are electronic electrons and the holes. So, whenever there is a random movement between the electrons and the holes these movement basically called fluctuations and due to these fluctuations the flicker noise gets introduced. In semiconductors when we consider flicker noise arises due to the fluctuation in carrier densities such as whenever there is a current flow from in whenever we are considering a current flow in a transistor from emitter base to emitter collector at that particular point there is a flow of electrons as well as there is a flow of holes due to this due to this flow in semiconductors the flicker noise can get introduced. So, second type of noise is the burst noise this or popcorn it is also called as popcorn noise. So, burst noise is one kind of a low frequency noise. So, low frequency noise observed in BJT is called as burst noise. So, this noise basically occur in bi-junction bipolar junction transistor and hence it is called as burst noise noise in this noise appears as a series of burst at two or more levels when present in an audio system the noise produced popping sounds when this particular noise occurs in audio system then the noise produces a popping sound in the in the in the audio system. The spectral density for this when it is considered the spectral density increases as the frequency decreases it is same as that of as that of flicker noise. It is also a low frequency noise partition noise it occurs whenever there is a current this total current when it gets divided between two or more electrodes and this results in random fluctuations in the division or during the division. So, whenever we are considering a current which is flowing through a circuit and a and a point comes where the the current needs to be get divided in the circuit. So, during the division of the current at that particular point as soon as the current current gets divided there are some fluctuations produced there are some random fluctuations produced hence these fluctuations cause these these fluctuations cause noise to get introduced in the circuit such type of noise is called as the partition noise. The spectrum of partition noise is flat it is spectrum means spectrum noise spectrum density or the noise spectrum for the partition noise when it is considered it is very much flat. The diode diode is said to be less noisy than that of transistor for the same reason hence the third electrode me makes more noise. So, whenever we are considering a two terminal and a three terminal device when we compare these two electronic components then we find that the two terminal device which is nothing but a diode which has less partition noise whereas when we consider the three terminal electronic component which is a transistor it produces more partition noise because in that the current gets divided into three different terminals hence the partition noise is maximum. Avalanche noise so it it basically occurs due to due to avalanche that is ionizing where additional holes and electrons are produced which in turn contribute to the ionization process. So, whenever we are considering the density of electrons and holes then in P type in P type material and N type material if there are some specific some specific electrons and holes present then due to the then due to the energy flow or due to the due to the energy input signal input what exactly happens is there is the ionization takes place between the holes and the electrons and hence due to this noise gets introduced. So, the ionization process or the ionization phenomenon basically it produces a noise which is called as the avalanche noise here the noise spikes are present large noise spikes are present in the avalanche current sometimes these large noise spike currents are very much can cause a damage to the electrical circuit the spectrum density of avalanche noise is flat as that of the as that of the burst noise or it is also called as a popcorn noise when we consider zener diode then in zener diode avalanche noise is avoided is is majorly avoided. So, inter modulation noise so inter modulation noise or inter modulation distortion is the amplitude modulation of the signal containing two or more different frequencies in a system with non-linearities. So, basically the non-linearities occur in the frequencies itself modulating frequency is low and carrier frequency is high frequency the inter modulation between each frequency component will form additional signal at frequencies that are not just at harmonic frequencies of either, but also at the sum and different frequency of the original and are multiples of those sum and difference frequency. So, whenever we are considering the modulation process then there is a variation in the frequency with respect then there is a variations in carrier frequency as that of the modulating signal frequency due to the variations there are some additional frequencies produced which are basically called as harmonics and these are produced due to the sum and the difference that is variation variation is nothing, but in that we are considering sum and difference of the frequencies between carrier and the modulating frequencies. So, inter modulation is rarely desirable in radio and audio processing as it creates unwanted suppress emissions often in the form of sidements. So, the noise produced due to the inter modulation or variations in frequencies of carrier and modulating signal frequencies this is called as the inter modulation noise. Impulse noise is generally only minor and noise for analog data for example when noise voice transmission is corrupted by short clicks and crackles with loss of intelligibility. However, impulse noise is the primary source of error in digital data communication. So, whenever we consider a spike of energy then that would destroy any voice data of 50 bits or 4800 BPS. So, these are the references for you people for the further study. Thank you for watching the video.


What's up everybody I'm evil rabbit. We're here on our factor two with a couple of my buddies Nate Charles Jonathan and Taylor we're here gonna be doing a sprint series here in our factor two with some GT series cars We're about to get into qualifying. We have one lap of hot lap qualifying five laps sprint races We're gonna be doing about five of them. Make sure you guys follow me in the scrambits We're not all that found in the description box below. Let's get to it Dude why of all times normally I just hope I just open up Open up our factor and it opens up right away now. It's downloading all these workshop items Oh, yeah, I took like five minutes to download everything. Oh And we're about to start qualifying. No, you're not. No, you're not. No, you're not. We are No, please Don't boot me out of the first race. I'm literally gonna be on it two seconds You won't be booted out you're just gonna have to start from the back. No, don't do that What good of our world? Music that doesn't show up on you. Ah, no, you good Instant demonetization copyright I just want to get in front of them.  I think it's the outlet Here we go. I'm even gonna get one lap. I can't believe you guys were starting without you guys around I'm definitely driving I'm already on about to start my hot lap. Hey, look Taylor and her race No, this is so mean, honestly Time you got time Did you do them longer? Is it gonna be the same format? Because I remember you could barely get one lap in You got time Yeah, it's six minutes So you you still have time to get all your laps in the technically you only need two It'll count the second one the third one's just there for getting back to the bench Yeah Yep, now I'm starting my hot lap.  It's not that way anymore So I'm just curious. It's something? No, I didn't do anything. That hill is so disconcerting when you're on the brakes midair I'll be on the brakes midair God, I do not remember this track sketchy braking zone. Whoa I'm just gonna try and cut Would have been bad on your only out lap, right? This lap will not count 131.36 Dang charles 131.63 Wide-flag Wow, I only got a 133. 0.44 The guy behind is just gonna get that 0.00 All right, you two Oh god I forgot to start my traction control back on You know, it was actually nice being in that spot because I could uh see the lights for a change Yeah, a little bit of understeer That's all right. Let's build on it. Why am I getting understeer? Tires are cold Oh, yeah, that would be all right It's like I know this by now I'm running from me right now. Yeah, I got shut down on that last one Got a tiny bit of body work damage there. Just ignore it That was neutral I saw you like flick out. I was like, what is this? It's really hard to pass through these sections Oh, yeah, this is a lot Charles Sorry Play left All right. Yeah I didn't expect you to brake so soon. I felt bad about that. I'm sorry.  Oh my What happened? Why were all those cars spot out? I'm that That's honestly That's honestly.  Dude, they were literally just all in the middle of the train. The whole track was blocked. I couldn't do anything I mean, I guess it's good. It's a sprint series Kind of just like you're in the lead weaseled my way through Oh until that happens Oh boy That was not fun push push push. We can win this Well I lost it. I lost my focus Charles Oh, no You didn't see me in the dirt. You didn't see me in the dirt I was the way in his back. I saw a car in the dirt. I didn't know it was you.  I got screwed up too. You're right behind me.  Hold your line White flag mate, last lap. Bring it on home Clear left P3 I was like why am I in P3 and then I realized I'm racing in AI Pretty much shape. We might need to fix that White flag Right side Clear right Nice pass Andrew Oh, man Well done, Andrew. Nice movement. Thank you. I see you trying to get that third that third spot There's a white flag. I see you there, Taylor. There's a white flag Okay, I got it. There's a white flag. Jesus. How many times you gotta say it? You went from wrecking the finishing third. That's not bad, Taylor I was on a mission I'll take I'll take second Good win, Charles Nice, Charles Nate, where you at? Oh, man. I shouldn't Charles.


They can't physically drag you back in There's nothing they can do about it They're just bluffing They're just trying to intimidate you Because they're thinking that will make you do what they want They're thinking that will help them in controlling you They're just trying to intimidate you They're thinking that will help them in controlling you They will brainwash you into thinking That you can't move on without them They will try to make you think that you need to be in a relationship with them Because they think that will prevent you from abandoning them You need to be responsible You need to stop communicating with them When you do this You are giving them opportunities to remain in your life You need to forget about what the relationship used to be like And see it as it is now Look at the way that they're behaving towards you If a thug approached you and started harassing you on the street You would do whatever it takes to escape You would never return to them again You would avoid walking down that street So it should be the same thing with a narcissist Because there's really no difference between them They are bullies They seek to harm, intimidate and coerce you They want to make you afraid They feed off your fear They want to persuade you to do something That you are unwilling to do They want to obtain something from you By using force or threats But you should not tolerate this type of behaviour You need to get out of that situation You need to go no contact They may try to contact you on social media They may post insulting things about you But you need to ignore it They may even like your posts They may make good comments But either way You shouldn't let it affect you No matter if a narcissist likes something You should see it as something unpleasant You should see it as offensive Because you see through their game Ignore any kind of contact they try to make with you Don't interact with a narcissist It is your responsibility to disengage with them It is your responsibility to stay no contact And when you do that You will be forced to move on