Hello and welcome. My name is Shannon Kemp and I'm the Executive Editor of DataVercity. We would like to thank you for joining today's DataVercity webinar, what CDOs need to know, Foundations of Data Governance, sponsored today by CA Technologies, Makers of Irwin, and Sandhill Consultants. A couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them by the Q&A in the bottom right-hand corner of your screen. We encourage you to share our highlights or questions via Twitter using hashtag DataVercity. 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, Hamilton Hay, Senior Consultant at Sandhill Consultants, and Marcy Barkin, Goodwin, President and CEO of Access Software Designs. Over time, Ham has led much of the evolution of the CA Irwin Product Suite and its supporting education courses. He has provided his extensive expertise in information, process, and enterprise modeling to numerous major North American corporations and government agencies. The focus of his consulting and teaching has helped enterprises bridge the space between technical modeling and business success. Marcy founded Access Software Designs in 1989, a model management services and education company specializing in modeling environments, their infrastructures, and the fostering of communication to ensure successful projects, and has provided education and consulting services to Fortune 1000 companies as well as the government for over 20 years. Access, in conjunction with partner Sandhill Consultants, offers EMSOS, Enterprise Modeling Set of Standards, an infrastructure product which provides blueprints, model templates, and over 25 critical standards, prestige forms and templates for implementing a customizable model management infrastructure. Access is also a trusted CA services partner, offering expertise in model management services to CA customers. We are very lucky to have them both here with us today, and with that, I will give the floor to Ham and Marcy. Hello and welcome. Hi. Hello. Thank you, Shannon. Thank you very much, Shannon. Great introduction. I was going to say, she can introduce us anytime. We're really thrilled to be here with you guys today. Marcy and I have been colleagues and friends for several decades now, and we share a lot of experiences with regard to data process and enterprise modeling and all of these kinds of activities. Of course, I just wanted to acknowledge Marcy because maybe about 15 years ago, Marcy took on the task of really trying to begin to compile industry-based knowledge around best practices and standards for data, and that work has culminated in what we're going to be talking about today, which is that knowledge that we've accumulated, how we put it together, and how we think some of these notions and ideas will be of help to a number of clients and organizations, and especially their chief data officers or equivalents. In fact, let's plunge ahead, and we'll talk about low-CDO and friends. So what is a chief data officer? Well, I think this is an evolving role, an evolving role in an organization. But the standpoint that we look at is that the function of managing data, seeing the design of data that plays into the success of an organization, critical function which has existed as long as certainly I've been involved with enterprise designs and structures. So we think this functionality is bubbling up to being recognized at the executive level as something that merits focus by this notion of a chief data officer. Someone or some process that is, in fact, responsible for assuring that this corporate asset or corporate asset called data is, in fact, properly serving the needs of the business. What we're doing in our journeys here through the information world is, in fact, there are a lot of organizations that are still looking for help in terms of topics around data management and then data concept of data governance. How do we know where we are? How do we know we're on the right path to meet our business needs? I think that we pass on some information that will be of benefit to everybody that serves in that role, whether they have the title or the function.  You find that there is no standard definition, which is ironic given the fact that we're really talking about standards and best practices and how you know where you are, how you measure yourself. But there are some similarities when you tackle together various information from definitions from experts in the field and talking to organizations. So there seem to be four principles that come to the fore. The first principle is data is an asset. It's not just something that's expendable. It's not a consumable. It has a lot of persistence. It has to have some integrity over time and space and location. And it is probably, in many cases, the most valuable asset of an organization. It is more persistent than the machines, than the computers. The data is going to be around a lot longer than that laptop that you have or that server that you have, which will generally evolve. We know data does evolve. And, of course, that's principle two, which says fundamentally, we have to keep track of what is the purpose of the data. That is, what is its context? What does it mean to us? The information that we can get from these things that we call data needs to be managed because the environment changes. So business rules change, regulatory environments change, community world changes. If we talk about a concept like customer, we need to understand that we need to manage that concept because it will evolve. And sometimes consciously and sometimes inadvertently or accidentally. And I want to be axle about managing this asset. The third principle is that organizations that don't really define and implement policies around data and data management, they are going to, in fact, be very vulnerable and they may be struggling to succeed. We've seen a number of examples of this. Marcy, you've got one that you were talking about the other day. Yeah, actually, the idea of defining policies to have them govern and to be able to prove, for instance, an audit trail, that kind of situation, I ran into the exact problem. I was working with a customer on Wall Street and obviously a lot of regulations and so forth. And they were required to go through audits, obviously financial audits and so forth. But as far as what we concern ourselves with the data, they were supposed to actually provide proof that they had a handle on what their data structures were, how they were being used, when they had been changed. What the impact of that metadata change was across their different businesses and so forth. And they really didn't have a way of doing that. Documentation for standards and procedures is one thing and we quickly start there. We can get that fully organized, documented, and accessible. But without a way in which you can prove that you have all of that change control and going in is actually working and so forth, that is not at all going to satisfy an audit. So this company was in a lot of trouble. And after we organized it for them, put EMSOS in place and so forth, gave them a structure whereby they could say to the auditors, so here's the day on which we made the change. This is what the impact was and so forth. They passed the audit and in fact I got a lovely email from my team there that said that for the first time in years, they not only passed that data management audit, but they did it in half an hour. So there is a way to manage this and there is a way to prove the effectiveness of the infrastructure and the standards and the procedures and so forth that you put in place. So that's the good news. That's excellent, that's excellent. It says also, it really illustrates the fourth principle event also, which is, hey, ignorance is not a bliss. Funny as we work with clients, what we find is that there's no awareness, but in many cases that awareness is very limited and you really need to know what it is that you have in place, your existence or as is a data management system or governance system. And in your example here, we're getting to an environment which is more regulatory. To many of those regulators, you need to prove what you have in place and that in fact that it is serving the purposes that are needed to manage this critical asset. So if we look at what are the critical factors that are influencing our data systems and we recognize that governance is really a very broad term in terms of the scope involved in governance. It has to operate, governance has to operate as a function in this incredibly changeable systems that we're dealing with, the exponential increase in data, the exponential increase in the volatility of businesses. Things are bigger and they're faster. They're growing at incredible speed. So we're able to respond to the various dynamics that are going on. This reaches up in a number of our experience about organizations that are adopting agile development technologies to meet the velocity requirements of their business changes. And this is now really crashing into some of the more stable data management practices. The data stewards have to and whether they're DBAs or whatever roles that they have, they have to maintain the quality and integrity of their data structures. So the velocity is kind of banging up against the need for integrity and stability. Something that a number of our clients are talking about and trying to deal with. Governance has to comprehend all of that complexity. So some of the aspects of data governance in terms of a framework, we actually want to be looking at data quality, the data management, policies around data, whether they're security policies or efficient sharing policies or whatever. There's also aspects of data in process or interactive. They're mutually interdependent. So you cannot look at data in a vacuum, just you cannot look at business processes in a vacuum. And of course, a lot of the aspects of governance all boil up to what are the risks of the choices that we make, the decisions that we make. So these critical factors are things that the folks in governance and or data governance have to comprehend. The government landscape of governance, if we kind of like parse it up into some of the components, this little pyramid or triangular diagram, some of the major elements that we've seen and experienced with our clients. So we recognize that there's things like business requirements and the measures of business, which tend to be economic measures, not exclusively that. We also see, you know, there's the whole issue of these policies, what we're talking about in terms of governance and stewardship. But there are also other elements that like taxonomy, the organization of knowledge, different organizations organize their knowledge in different ways. We think it may all be uniform, but in fact it's quite variable, not only from organization to organization, but in lines of business. We see significant variations. And some of those were aligned in the past where we had really stand-alone systems, but now being large-scale integration of lines of business in order to meet the economic challenges that Congress is saying. The neatest, of course, is, okay, how do we do business? How do we in the data world do business? What kind of standards and procedures do we need? And most of you will say, yeah, we need something in this nature. The question is really serving the expanding role of our business. The foundation, the ultimate foundation here is really the consumer of data information. We say consumer validator because your customers will let you know if the information isn't very valid if you're not doing a good job and you need to be, again, tuned into that baseline. So we need each one of these components as individuals, but they're also a collective system, and we really, from a governance standpoint, need to make sure we pull them all together. Now, one of them, we've got a fundamentally little details here that says some of the paths that are associated with each one of these categories involve a variety of specific information that needs to be managed and monitored. Think about it. We need policies. We need to understand accountability. We need to understand what our partner roles are. We need to understand what the communications policy is. When we're already talking about governance and stewardship, of course, they all need to come together and have the ability to work in terms of data architecture, design for the enterprise, whether they're a business analyst or whether they're a DBA or anything in between, the consumer included, need to be able to see the map, the landscape, and sort of how all these things fit together. They may not be doing that particular role in terms of what their job or role is, but they need to understand, however, how they interact with these other roles and other elements of the system. So, we're bringing up some of the requirements, Marcy. Yeah. So, in this slide, all of you are looking at my life for the last 20 years, which is why Ram and I are both laughing. So, I think I'll take this one, Ham, if that's all right with you. But we'll go into companies. I've been doing this for a long time and talk about standards and procedures and practices and forth. And it's amazing to me, although it shouldn't be at this point, that when I do walk into a company and I say, you know, give me your meek, your humble, your poor, you know, give me all of your documentation about any standards and procedures that you have around data management, data modeling, and so forth, I'll receive a certain amount of documentation, but it's always, always far less than what is really critical for what the company needs to be able to be successful. When I say successful, I'm saying what is the message of reuse? Are you getting out of your data structures? Everybody is spending so much time on analysis and so forth. If one team doesn't know what the other team is doing, if that information isn't brought forward, if it's not shared, a tremendous amount of time and money is being lost with each model that's being created, it's going to be shared. So when I look at standards and procedures, I say, are they getting reuse? Are they creating redundant data? A certain amount of redundancy is necessary, of course, but are we constantly reinventing the wheel here when we don't have to? So what I see is that folks think that they have standards and generally they do have some. Everybody knows what their naming standard is for an entity or a table or whatever, but that's only really the tip of the iceberg. And when I start to do a real assessment of the environment and speak to people, look at what they've gotten and so forth, inevitably there are many disconnects. There's gaps between how they're using the data, what they think is standardized, who's using those standards, and so forth. That gap, I call it the reality gap, between where you are and where you need to be because my attitude is this. Every company is an enterprise. I don't care if it's a small company or I go up to global companies or government agencies and so forth, it doesn't matter. Every company is its own enterprise and treating data should always be treated from an enterprise point of view, as opposed to the kind of siloed departments or even the siloed projects that we run into. So that gap of what we have and how a lot of people are using it and how are they sharing it and so forth is usually quite large, that's what I've found. So there's only a subset that's being used that has a piece to do with the fact that data governance, although we've used this term for many years, there's different kinds of governance and even in a lot of governance programs that I've seen, sometimes the data seems to be the stem child and that's a shame because there's a huge amount of loose that's taking place in terms of what the company can deliver as a whole if their data is not being taken care of and not governed properly. So my thought is that truly we have all of these issues. The biggest issue of all for me is that without the kind of data governance that we need to put in place, we can't trust the integrity of the data. We can't see when it's changing, we don't know whether I'm defining customer here and it means the same thing across the board or a different line of business is calling a client. We don't know any of this and that affects not only obviously our source systems but particularly data warehouses and so forth. So it's a huge issue for me and that to me is the biggest problem out of everything that I see in the way of issues in the field. So it's important about clearing that up. Okay, take it away. Okay, Marcy. Yeah, how do we know what we don't know? You say, okay, all these points are really great. We really recognize the need to govern what we do, govern in the sense of manage, in the sense of design. We need to do all these kinds of things. But how do we know about doing that? How do we know where we are at this point? Well, we use the concept of an assessment in terms of how we think through this kind of problem. By the way, you can take a free assessment to see where you are, self-assessment. Go to thesanthealkonsultans.com website and look under our standards section. And there's a little online thing that you can take just for you. It's basically based upon, here's what we have found over the years, to be the standards and procedures that seem to keep coming back as the critical core standards and procedures that are needed. And you can just go through and do a check on, well, what do we have in place? And that'll give you at least the first step in terms of seeing where you can be in this. Second question is, how do I determine where I want to go? In terms of, again, managing data and governing what we do. Well, the destination is, of course, really dependent upon your business, your context. It's also dependent upon what are those industry standards and practices that have been proven to help organizations be more successful. A combination of your context. And again, that's a process that, if thought process you need to go through with your organization, that's okay, we know where we are. Now, what's there to be? Where is it that we want to get to? And you should go through an assessment and a little vision planning kind of thing of where we want to go, but we really commit a large amount of resources to implementing your changes in your system. We can certainly, we all tend to, we have an immediate problem. We need to come up with an immediate fix. We may often find ourselves sub-optimizing, just propagating, actually, more of a problem in terms of inconsistencies from the business to the line of business or from IE to business or between our subcontractors and our organization. All kinds of things are, if we continue to do the same small-scale, highly localized changes to the system, we never attack what Marcy calls, you really got to start thinking like an enterprise here. Some things you need to be able to use across your overall enterprise. So, we call this creating a flight plan. This is not every flight in the U.S., there are thousands and thousands of routes in the U.S. under the plan, but the G is kind of interesting because it means that if we want to go from a destination point, you'll know where we are and where we want to go before we choose that route, but that's the next step. So, let's say we want to go from Nirvana in Seattle to Nirvana in South Florida. We identify those two points, the as is being our origin and our to be being our destination. So, we all recognize that there are several possible routes. Some are slower than others and they may have a lot of directional changes. But each one of those represents time that it takes to develop and implement what it is that we want. We may be misdirecting ourselves because we don't really capture a full picture of our case of route. And some of the routes might be a lot faster. It all depends upon how we define and think through the problem. So, the key thing is to understand really what's important to you to your organization with regard to the data. We'll talk a little bit more about this, but I already made reference to it in terms of development speed versus information stability and integrity. So, we all recognize that what is important to you, economics, the time to market, time to development, time is a big factor in everything that we do. Competence, are we ahead of the curve, behind the curve, trying to catch up where in that particular area the other factors include not only those external factors, but also our internal factors. What level of skills and knowledge do we have about developing systems or enterprise and our enterprise data? What are our capabilities? And we'll find some cases where we're maybe going to be short of those knowledgeable resources, both at the implementation level, at the technical level and at the managerial level. So, there are some possible routes. And what we want to share with you is of all of the possible routes, we're finding some very specific research choices that, in fact, tend to be most expedient for many of our customers. So, let me make sure that I didn't advance one slide too many. Yep, sorry about that. If I had a slide number, I'd see where I am, right, Martin? That's why I always say put numbers on them. That's true. There are some notions that we want to emphasize here, tracking your progress. If you identify what's important to you, but you also need to do the same theory of which is you need to track what's important to you. You need to pay attention to your measures or metrics. So, if you want to find those up front, if you know what's important to you, what are your best objectives, everything from schedule to cost, your budget, you're serving your customers, your consumers' needs. Your ability to track gives you the opportunity to identify before you get too far downstream or along your path, whether you've missed something or there's something that you may have had before you leave the assumptions or missing initial conditions you didn't understand. So, having the kind of tracking mechanism for these things is a crucial thing. And we find that, we talked with one customer, said, well, what do you have in the way of metrics? I said, we're talking with your people. They said, well, we don't really measure what we do here. And we talked to the senior management and said, of course we have metrics. I'm looking at my costs all the time. And the point, of course, the point was, what is it that each of your units is producing in terms of data objects? Are they serving the purpose? Do they have to be reworked? Those are important kinds of questions that need to be addressed. We're thinking that there's also some implementation strategies which seem to work well. The first one is designing for interoperability. This is fundamentally boils down to, if I define a data object, say, it's a customer name as an object, as an attribute or column, is it, in fact, the same usable across my multiple lines of business? That's re-use, designing for re-use. And it's re-use, design for re-use, helps us in terms of data and information quality. We wonder that information that needs to be shareable and that benefits the business from being shareable across our multiple lines of business is, in fact, designed that way. We also, you know, Marcy has found a lot of her travels that managing the data lifecycle is a key element. Actually, if you don't mind, let me address this a bit. You know, all of these are key implementation strategies here, but managing that data lifecycle is not something that is generally done quite honestly or really thought about. Pieces of it here and there, perhaps. But not as an overall plan, an overall structure for how we do these things. So, you know, we need things, for instance, like approval procedures. You know, perhaps a logical model is going to get approved before it goes physical, and perhaps that physical model will get, quote, approved before we get the DDL and go live with it. This inadvertent change that happens to data is something that people have not really taken to heart, and it's very difficult to manage data when you don't know when it's been changed or why it's been changed. So, by putting certain procedures in place, approval procedures at critical points or change control procedures, and I'm not just talking about the code. I'm talking about a model-centric environment where we're looking at how does the structure in a model change, and does that structure affect the physical side as well? Does the logical and physical need to be synchronized? Certainly, the database and the physical model need to be that sort of thing. So, a synchronization process for how we know and how we can trust that that physical model, for instance, is really what is in production, and that everything hasn't changed beneath us and we're not aware of that. You know, how do we notify people that a change has been made and so forth? Included in those procedures, we always have roles and responsibilities so that it's easy to know, you know, what am I responsible for? What does this model have to have to be approved? Who's going to do that and who do I hand it off to? That sort of thing. Governments really require accountability, and so roles and responsibilities are critical to procedures. We don't just say this is what has to be done. We have to say who does it and the group does it and so forth. And the last thing I want to say about this changing the data lifecycle is if we don't put that in place, it's very difficult to manage the data when we have changing resources, somebody leaves, somebody else joins. And the organizations that outsource a lot of their modeling have a terrible time if they can't say to the folks in India or wherever, look, here is the way we do it. Here is how we know our data has integrity. We practice governance here. So here, these are our standards and procedures. This is our lifecycle, and this is what we all adhere to, whether we're onsite or we're outsourced. So the managing of the data lifecycle is really, for me, the key to be able to get all of the other pieces working. And that really leads into, again, we need to be aware of what's happening. And that is, we need to understand what is important and figure out how to measure that. We need to monitor that. Measuring is one thing, but if we put the data in a drawer and don't look at it and don't go back into the organization, here's what's happening. Here's what we see in terms of our choice. Hey, we have an initiative to speed up our development process. What's it really working? So we need to measure that. We need to assess the changes that we make so we know that we're on the right path and we're also maintaining or improving the integrity of our system, of our data architecture. With that principles and these notions and concepts, what we'd like to share with you now is a technology solution to consider. We recognize that there's, again, many paths in terms of approaches to implementation of data management, data standards, the governance that overlooks all of that, hierarchical or overarching enterprise of you of what we're doing with regard to data. So let's look at some of the things that we're finding work for a number of our clients. The first thing we want to talk about is this notion of designing for interoperability. How do we, in fact, can we, can we approach concept of what we call error-free design? Well, we think that that is an approachable goal and what we want to look at, of course, is how do we, in fact, design our information structures, our data structures so that they are predictable, they're reliable, they're repeatable. They have integrity in the sense that they have captured the requirements from the business. And what we find is that you need a really good set of technology to be able to do that and then we're convinced that our data modeler is a technology solution that definitely serves that purpose. So we're going to take a peek now at what that really means in terms of, as a technology supporting the concept of governance. So let's look at the concept in governance of designing, of designing design standards that support interoperability of my data structures. Let's look at Erwin and I say, okay, what are the elements within Erwin or the principal elements that support that? Certainly in Erwin, the concept is instantiated of templates for standards so that you can take what you have as a defined naming standard and you can implement it in a template in Erwin that you can use to start new activities, new modeling efforts to upgrade existing models to a set of standards or to use as a comparison tool in terms of, let me take a look at whether any particular model or design, in fact, is compliant with some standards that are key. We also look at Erwin from the standpoint of does it support this lifecycle process we're talking about? Erwin has got a model repository architecture in which you can lay out the structure for storing models and data objects that support the lifecycle. We satisfy things like accountability. Yeah, the roles and permissions in a repository. Can we support audit trails? We can use things like user-defined properties to specify particular properties which are used to monitor progress, accountability, and so forth. And then the category is metrics. What can we do in terms of metrics? Well, with Erwin you not only have standard report formats but you can create additional report formats since you have access to the metamodel that allow you to do things like quantify some of the important parameters that you want to look at. So if you take a look at some of the specific design standards, the naming of certain errors that Erwin instantiates including writing specialized macros that you do a complex naming options or use of glossaries. Equivalent on the data type, you know, master data type standards for your particular relational database systems or custom systems that you wish to develop. You know, it's substantial technology supporting domains and use of domains as part of your naming standards for data objects. And of course things like standardized templates, option sets and things of that nature which we're going to be controlling the generation of DD also. So just design standard areas we see forager and there are other areas within Erwin that support, you know, how do you improve that interoperability in terms of your design? If we look at Erwin's support to data design management we look at the lifecycle process. I would see, as I mentioned, hey, this is an architecture within the model repository. It allows you to do everything from storing templates to a record or baseline models or individual data objects through, you know, a model architecture, standardized glossaries, all these elements basically you can manage and control within the repository. Accessibility, again, the roles, permissions, assigning users to these roles and permissions and controlling what they can and can't do in terms of modification of the objects that are in your repository. Also, as I mentioned, user defined properties, everything from, you know, creation dates to who the information stewards or data stewards was the approval for this particular object or some of the objects, all of those elements can be included in things like user defined properties. Here's a typical data warehouse model involving almost a thousand tables and 16,000 columns. Let's say, well, I don't know how good that is. Well, you know, if you use the ability within Erwin to generate reports, you can do things like standardize reports, modify them, create new reports, which allow you to do things like say, okay, measure how many duplicates are there in this particular model. How many of the tables or columns have definitions? If they're your standards to have a definition and we think it should be, then you can see that, hey, you've only got like 28% compliance on your total number of attributes in terms of having definitions. It can cause some of your operational systems problems if there isn't a definition and it certainly causes you problems over time. From the standpoint of looking at it now from Sandhills Enterprise Modeling Set of Standards, EMSS, where we've compiled this knowledge over an extensive period of time, this is in fact the leading, and it may be the only tool, as many of you know it might be, a knowledge base that has this kind of collection with regard to managing your standards and procedures. So we have questions, things like, from the government's perspective, okay, where am I? That's an interesting question. We talked about that at the very beginning in terms of creating a flight plan. Certainly the same general topics in terms of how does EMSS address things like design standards for interoperability, the life cycle process, the accountability. EMSS addresses each one of the major categories in some detail. You want to kind of reflect a little bit more because you've been the principal architect for EMSS. I think what I'd like to add here is that, yes, EMSS is a really robust, customizable framework and it's got all of the critical standards and procedures and templates and blueprints and forms and so forth. EMSS was designed from a governance point of view. Because that's always our starting point. Without governance, I can tell you to do this and that, but if there's no superstructure, if you will, for how that will be governed, how it will be implemented and so forth, that it really isn't going to help you very well. What I'd like to point out here is that, again, whether you're a small or a medium or a global company, you really must deal with governance issues, best practices and so forth and that's really how EMSS came into being. So, you look with companies such as Pfizer and Wells Fargo, Social Security Administration, Cummins down in Tennessee, all of these companies, these are rather huge, SSA obviously, Social Security Administration is a very large government agency. Each one of the customers that we work with has a different scenario in terms of their organizational structure, who's going to be in charge of this stuff, who's going to do it, what their roles and responsibilities are called and so forth. But the common thread that goes through all of this is that they all basically need the same types of standards, procedures, best practices and so forth. It's a question of how they do it that varies and that's why EMSS is customizable. But we've identified all of the critical aspects of managing the data lifecycle and so forth and that's what's included. The only other thing is, too, that it doesn't really matter what type of industry you are, healthcare or insurance, retail, manufacturing, all of these different businesses, either public or private, need to have a way in which they can get a handle on all of this and to make their modeling environment work for them as opposed to being something with a collection of different odd kinds of models here and there. We don't know whether it represents an enhancement. That's just for an application or whether it's something that could be used later because of a larger system requirement and so forth. So, in the size or the type of the company doesn't matter, this internal infrastructure of the governance and for modeling is something that we're seeing just a tremendous need for and out of all of that, this product was built. So, I just wanted to get into that. Thank you very much, Marcy. So, let's take a peek. Let's open the covers a little bit and just see what it looks like. There's a browser-based technology so that everybody needs to have access to the standards. And it's a set of not only the standards and procedures and forms of basic core content but also is included definitions and examples of things like report formats and documents, templates for the models and things of that nature. So, let's take a quick peek. We're going to talk about blueprints and the data model lifecycle. As a repository of knowledge, it's good for people to understand what their lifecycle looks like and various elements of it. This is just an example of a shared object, enterprise-level lifecycle that involves application development, data development and also the qualification of objects to be shareable across an enterprise. If we look at some of the standards, this is just a little snippet that shows you the detailed definition of what an attribute looks like. If we look at the lifecycle management, say an overlay of that data model lifecycle that shows where data governance roles are played, information asset owners, information stewards. And again, as Marcy said, each organization is going to have a different flavor, but what we have is a core set here that can be tailored for the particular organizational requirements. The accountability, the particular roles are defined within the standards and those roles are also defined in terms of where they play a role with regard to the procedures and all those are defined. And of course, all of that winds up being instantiated in Irwin in terms of things like the roles and permissions within the repository as well as with user-defined properties. Irwin instantiates things like accountability. Let's see what else we got here. Auditrails. You want to manage your audits. EMSOS has got things like change control forms that specify the information or checklists for submissions of objects for approval or consideration for things being a globally shared object. We look at metrics. Irwin has a set of standardized model validation forms. What we do in the standard side is to say which ones are in fact needed for particular needs in terms of the measures that you're collecting. And I showed you an earlier version of a metrics report that just added the number of objects with definitions of the number of duplicate objects in the system. Just the quick snapshot of, you know, there's the elements that are in both of these technologies that support the concept of governance. We certainly see that, you know, adopting an approach to governance, adopting, you know, a methodology, the technologies, you know, the approach like that we would recommend or some other approach, you're looking for expected, some expected benefits. We certainly look for things like reduced investment in terms of being a total lifecycle cost of ownership. We want to reduce cost as much as we can and improve productivity. So to do that, we don't want to reinvent the same object multiple times within a system, and we see that time and time again with our customers in terms of what the past is. Well, the future has to be different. We're not going to be able to reduce costs and improve our productivity. We need to reduce scrap and rework. People who are constantly repairing designs, you know, after they've been in the field, they're just throwing money through the sieve down the drain. We want to be able to improve our resource utilization and our productivity. All these things also focus on getting us to time to market or time to solution to the development a lot faster with a higher level of quality and integrity. The consistency and mutual understanding of using of data and usage across the enterprise we find is core to have lines of business share, information and knowledge across their lines of business and across the enterprise. And this is becoming more and more critical as our systems become more complex and more extended globally. And more mergers. Absolutely. Mergers all the time in terms of a very, very complex task to bring this kind of harmonization and alignment. As we mentioned earlier, the regulatory audit, regulatory environments are getting more critical, more complex, whether it's the financial, pipeline safety, pharmaceuticals. That's the one we ran the other day, vehicle identification number, you know, tracking and being able to track, you know, particular items that show up in the marketplace. Food safety is coming into the foreplay. And of course, things like increased complex, I can't even talk anymore here, increased competitiveness. If we need flexibility in our systems, we need to be able to turn the data architecture and the application architecture to be able to enhance those or modify those to meet the changing marketplace requirements. And that's always always at the end of the day. If we lose our data integrity and we lose our ability to share information, it's going to have a tremendously deleterious effect on the success of our business. Yes, design and distribute our data hopefully in a shared environment. And how we do that is really critical to the success of not only the modeling environment, but the larger overall governance objectives. So it really starts at home. It starts in the specific way in which we tackle looking at our data, using the data and distributing it. That's all about that. I want to leave a couple minutes, you know, a few minutes for questions. And just to note that if you send questions that don't get answered in real time, we will respond through e-mail and so forth. So how it started. Well, we talked about that. Take a little self-assessment that's available in sandhulaconsultans.com. To drill in a little further, we'll be more than happy to talk with you about approach to data governance. It might be right for you. And we're sure that we're really confident that you can benefit from these kinds of discussions. And we also offer in-depth price modeling assessments as well that we can talk to you about. So, you know, in conclusion, we've got everywhere you want to go. And of course you've got to know where you are. But if you don't understand your business context, you stop right now and go back and find out what your business context is, because that ultimately is the driver.  So know where you are. Know where you want to go. Create that flight plan to get your destination. Begin self-assessment. If you really think that there's things that you need to do and want some help on, so today that's really the important aspect. The question, I'll turn it back to you. We really appreciate being able to talk to you folks. Questions? We have a few minutes. We'll try to respond. Okay. We have several questions that have come in. And of course some of the common questions are people going to get copies of the slides. And yes, we'll be sending out a follow-up email within two business days also by end of day. Thursday with links to the slides, links to the recording session, and anything else requested throughout. Including if you keep, and I just have a couple minutes left here. So if you keep submitting your questions, Ham and Marcy have graciously offered to write up answers and I'll get those out to you in the follow-up email as well. So keep the questions coming. The first question is how do you deal with senior, A, senior management buy-in, and B, data quality issues, and other common issues in the field to get data governance in place? That's an excellent question because it's really an educational question. You know, senior management, if you are serving in this role, you'll be able to speak the language of business. You may not have those that skills and knowledge fully. So you need to, you know, get them from colleagues or from patriots that can help you express the best value of governance. Because fundamentally they're going to say, you know, we're going to have to fund this and you've got to show me where it's going to pay off. And so there's the conversation you need to have with business and it'll make you a little while to develop that. But it's doable. We help our customers understand how to express the importance of doing this. I think intuitively we all know it's valuable. The point is, is it how much of a priority is it? Do you want to add to that? You deal with CIOs all the time to kind of explain this. And this may sound conceptual, but it really is to the heart of it, which is that if I explain to a CIO, look, you've got all of these people spending all of this time, which is money to do all of this analysis and so forth. And if you are not getting reasons out of that, you are spending a general time and money that is completely unnecessary. It's more expensive to fix something than it is to do it right from the beginning. Now, I've been in the business long enough to have a certain amount of reality. I'm not trying to be a Pollyanna here, certainly not. I've been on the battlefield for a long time. However, if I have a project and it requires a data model, and at the end, and I have procedures in place to say, hey, these things now are considered shareable. Why? Because they have integrity. They have definitions. They've gone through approvals and so forth. At that point, if I make everyone aware of the fact that, hey, guys, we have these data structures, we can reuse these. I don't care if you're using different databases or whatever. I can start with these on the logical side, make sure that I'm satisfying the business requirements, and then do what I have to do to implement that in different physical ways. That's fine. But if we all go back and work with a structure that already exists rather than spending a month or whatever it takes to re-creating that as if it didn't exist, you cannot tell me that that does not save you time and money. That it's just the way it is. And we can do much more detail than that, but if we want to start with just that premise with a CIO, they get it. So you do have to address the bottom line. You do have to address us, and we bring metrics and so forth and so on, but conceptually, everyone in the upper-level management for the most part will get it, and that's where the discussion starts. And I'm pretty sure we're just past the top of the hour here. Thank you to MMRC for this great presentation and Q&A again. I'll get to all the questions that we didn't have a chance to get to, to you guys so we can get that out on the follow-up e-mail. Thanks, everybody, for your questions. I just love always, of course, when you guys are so interactive and really participate in the webinars. And of course, another big thank you for today's sponsor, CA Technologies and Sandhill, who, of course, enabled us to provide these great webinars to everybody for free. And just to remind everyone, again, we will be posting the recording and the slides to DataVersity.net within two days, and I'll get that follow-up e-mail out to you as soon as those things are up. And a quick thank you from Ham and I for everybody who attended. Thank you, everybody. Absolutely. And thank you guys for this great presentation. It was very engaging and I think everybody really enjoyed it. So I will give you guys that information and I hope everybody has a great day. Thank you. Bye-bye.


The upper and lower motor neurons. Injuries to the motor pathways result in paralysis. The extent of the paralysis depends on the type and location of the injury. Damage to the motor neurons in the frontal cortex or their tracts results in upper motor neuron syndrome. For example, if the left lateral corticospinal tract is damaged at the second cervical vertebra, control of the left upper and lower limbs is lost. Upper motor neuron syndrome is characterized by spastic paralysis where little atrophy of the muscle occurs. However, uncoordinated hyperreflex activities where muscle tone is increased cause spastic movements, reflexors and extensors alternately contract. Injuries to the neurons of the fibers in the vertical horn of the cord result in a different type of paralysis. Problems of this sort result in lower motor syndrome. Lower motor neuron syndrome results in flaccid paralysis with loss of movement, tone and reflex activity. The muscle becomes atrophied and flaccid. You have completed this learning activity on the upper and lower motor neurons.


okay so this is okay okay this is lecture what's the number 39 okay all right so some reason the volume seems to be a little bit low and I'm worried about that but I think it should be okay okay so so we're going to begin to see OFDM which is which is related to what's called multi-carrier modulation okay I said it's related to this reason why I said it's related to which it is not multi-carrier modulation in the strict sense of the word but it's kind of related to it gives you the flexibility that provides okay so so far what is it that we've been seeing we've been seeing a situation where you produced symbols from a certain alphabet right at a certain rate okay and then you sent it through a transmit filter okay which was then up converted right it did a up conversion you multiplied by a 2 pi fct there was only one up conversion then maybe there's a real part here okay this went through the channel okay to this noise was added of course at the receiver noise gets added whatever you do and then this goes through a processing first I'm going to call it front end which is presumably analog or very highly sampled okay and then you have a equalizer stroke slicer okay the reason why I'm not writing equalizer and slicer differently is there are configurations where the slicer is built into the equalizer and use feedback okay so I'm saying equalizer slicer and and and then you're able to make an estimate of SK at the output of the slicer this might be a hard estimate or it can also be a soft estimate in case you're producing LLR here so this is the kind of system that we've been looking at so essentially what will happen is if you if you see from here to here somewhere in the middle here so here here I mean this is the transmit constellation so say some some four points and here if I look at the input to the slicer you will get what's what could be called the received constellation okay so if all your processing is going well nothing very dangerous is happening what you will get that the input to the slicer will be you'll get on average maybe the points of the input constellation and then but actually it's going to be bunched up around that okay right this is how the whole picture looks and you know what each of these components are and how they're going to be designed okay so ideally if the variance of this received constellation is equal to the variance of the additive noise right roughly okay maybe it should be slightly different because the channel might introduce some advantages for you right if you're using all the multipath to the channel or several things to the channel properly you might get something slightly better like what the match filter bound does right it gives you some distance from the channel also if you can gather everything properly then you would have a certain variance for these noise so maybe the variance is equal to the noise that was added in fact it should be slightly better than that because the channel is giving you some maybe giving you something okay so if it's as good as that then there's no noise enhancement and you've done the best possible thing that can be done okay but we saw the way we did the calculations unless the channel is flat you're not going to get anywhere close to that kind of performance if the channel shows a lot of dips and if it goes to zero then you will have noise enhancement your MSE calculations involve the arithmetic and geometric mean of the received spectrum in some way and it translates into the channel spectrum also and if it shows a lot of dips and changes the means are going to fall they will not be very good numbers okay so we saw there that the channel does something non-trivial then you are going to suffer because of the channel itself and not just because of the additive noise and you're not going to be able to gain a lot of mileage from the way you're doing the processing okay so so this is this is a traditional single carrier system that we've but we saw so far okay so so if you if you have to summarize in in a few lines if channel is flat then there is a good performance the channel varies very widely then a lot of things that you lose in this okay so this is what we saw that's one thing another thing is today you keep getting larger and larger bandwidth to use around your carrier okay your carrier is going to the gigahertz range okay so which means your bandwidth is going to be at least in the megahertz range okay so you're getting large bandwidths around a central carrier which is moving further and further to the right okay so the channel will never be flat or such a large frequency range so many things are happening to it to do the so many circuitries and so many multipaths and that all those things get added so there's no way the channel is going to remain flat over such a large frequency range okay and you know your receiver processing is going to be simple and efficient and good performs well only when the channel is flat okay so a nice alternative is to do multiple carriers okay so you have a bunch of bandwidth and you think of it as one carrier instead of thinking of it as one carrier you think of it as several carriers but with small bandwidth around them right so instead of dealing with the whole channel as one chunk of frequencies and having one center center frequency and worrying about a channel which is fluctuating a lot I'm going to limit my bandwidth but I'm going to increase my carriers okay so then I can hope since I'm using very small bandwidth around each carrier that the channel will be very flat and I'll get good performance okay so that's the motivation behind moving from single carrier to multi-carrier okay so towards if you want to move towards multiple carriers the advantages are okay channel is flat in in a small bandwidth around each carrier okay so that's the motivation and you know a receiver processing is going to become simpler everything is going to become nice if you do that that's what it's that's the first point that the other point is from a capacity point of view right from a capacity point of view it turns out if you have a large range of frequencies with you but whichever very varying response right channel frequency is very different it turns out what you have to do is you have to do different things in different bands that's what capacity tells you okay so from an information theory point of view if you look at capacity of such channels you learn that you have to do different things in different brands you have to put more power in certain bands you have to put less power in certain other bands okay right so that's what information theory tells you I know you have not studied that much of it but that's just believe believe it when I tell you okay so if you have a lot of frequencies a lot of variations information theory tells you that you have to do different things in different channel regions okay and if you're doing one carrier then the only control you have over putting different power and different frequencies is what which is the point in this block diagram where you can do that control where you can change the transmit filter right that's the only thing you can do okay so it turns out if you want to do that you have to keep adapting the transmit filter in a in strange ways of the channel changes right so there are so many things that are not very easy to do according to the channel in one transmit filter okay so if you do multi-carriers then naturally what do you have you have naturally different frequencies and different transmit filters so to speak okay so in fact in multiple carriers usually people choose a rectangular pulse the reason why you can choose a rectangular pulse is what such a small bandwidth then you spend sending a very large time so you can choose as well a rectangular pulse okay so the one so just by adjusting the amplitude of the rectangular pulse you can get nice control over the overall spectrum that you're using over the band okay so that's the other point in this multiple carriers so it gives you better control over subbands okay and this is required from a information theoretic point of view okay so so these two advantages are are very good in multiple carriers but there are a lot of disadvantages also okay so we'll slowly go towards the disadvantages and then how to make sure all the disadvantages go away is also what we'll see okay so so let me briefly talk about multi-carrier modulation how such a block diagram looks and then we'll talk about some disadvantages of doing multiple carriers over just one carry okay so but these two things are very important okay you get better control over the subbands that are available to you okay so instead of looking at the entire band then each subband is very easy to equalize and process it's going to be flat and your transmit filter can be just a rectangular pulse you don't have to do anything fancy there so so many things are nice in this in this kind of a thing okay well I have to be careful when I say rectangular pulse maybe it's not quite a rectangular pulse anymore but anyway we'll see we'll see how it's done later okay so don't get carried away by the rectangular pulsing okay so many maybe you don't want to do that okay so we'll see this as we go along okay so this is how a multi-carrier modulation picture would look so if you look at multi-carrier modulation basically here instead of doing one up conversion you imagine doing several up conversions and let me write down carefully how this is going to look okay so I have several brands and several transmit filters okay so I'll write down okay I'll do three of them and then put dot dot to show I have several of them so how many of them do I have first one takes a symbol s1 of k second one is s2k takes a symbol sequence right s1 of k s2 of k so until maybe I'll call this snc of k so nc denotes the number of carriers okay so so one can imagine each signal each symbol sequence coming from a different alphabet okay need not be from the same alphabet you can imagine different alphabets so I'll just put x1 can you see this x1 x2 okay so maybe just about you can see it okay x1 x2 xnc okay so these are the alphabets from which these symbols are coming okay they can all be the same for instance in most cases maybe they are all the same but they can also be different if you want to change some things okay and then I'm going to multiply by a carrier I'm going to use a special case here so I'm going to say the first carrier is e power j 2 pi n1 delta f t okay the next carrier is e power j 2 pi n1 plus 1 delta f t so until the last carrier is going to be e power j 2 pi n2 t delta f t I'm sorry okay so my nc carriers are all multiples of a small frequency delta f okay n1 delta f 2 and 2 delta f so nc equals what n2 minus n1 plus okay so have this many carriers and delta f you think of as a small frequency okay so delta f is actually the what the bandwidth of each subband okay so this is there's also another way of viewing it previously you had one channel and one carrier now you have a bunch of parallel channels okay so you have nc parallel channels and each channel you deal with separately okay and all the channels can exist simultaneously because in bandwidth or in frequency they don't overlap okay so you make sure they don't overlap in frequency and then you can use them simultaneously it turns out when you do OFDM you'll actually let them overlap in frequency also okay so so keep that in mind also a clever baseband processing you might even be able to overlap it but first I'm going to talk about a situation where you don't allow these different bands to overlap in frequency okay so then you do what then you do the standard real part conversion here and then you add them all up okay you have a summation okay and you produce your pass band transmit signal X of t okay so this is going to go through a channel then noise is going to get added okay and it signal goes into the receiver okay so this is how a general multi-carrier modulation picture looks and and and and and what okay so if you look at it from a spectrum point of view suppose you had a bandwidth that was initially available to you okay so this is the like the frequency line okay maybe zeroes here okay so maybe you had a bunch of bandwidth available to you which went it went from n1 delta f all the way to n2 delta f centered at that and so the total frequency that was available to you was maybe nc times delta f okay so this was the total frequency that was available to you maybe centered from n1 delta f to n2 delta f okay what you did was you separated this into nc bands okay of delta f each okay so nc bands of delta f each this is delta f and you want to treat each of this delta f hertz of frequency as a separate parallel channel okay and you don't want these channels to interfere or cause trouble to each other okay one way of doing them doing it is to make sure that they don't overlap in frequency okay so which is called frequency division multiplexing okay so that's the first approach which seems like the simple simple very very simple approach you can do something called frequency division multiplexing what you do in this case is basically treat each channel individually okay so if you have a frequency of delta f under your control what would you do the first thing to do is to okay so if you just look at the first first thing n1 delta f is my center frequency delta f is the bandwidth that is available okay so I'm going to first choose some transmit filter to occupy the delta f frequency I might choose a sync transmit filter but that might cause problems so maybe I choose a trace cosine with some excess bandwidth so the bandwidth the data rate that I'll be actually using will be 1 by t equals some fs which is less than delta f okay so each channel then each channel symbol rate on each channel would be what per channel or per carrier will be some 1 by t which is an fs which is less than delta f you won't be doing 1 by delta f which is the maximum possible you're doing some I'll put less than or equal to if you want to use full bandwidth also you can use it okay so that's the problem okay so this this is this is one reason but if you use full bandwidth there is another problem if you want to treat each of these guys as separate okay and you're using the full bandwidth there is another problem what is the problem okay what do you do at the receiver is the question so what do you do at the receiver you have to filter out the bands how can you filter out the band if you're using the entire frequency it's not possible you need some guard interval between them so typically fs is also going to use I mean some of some some reduction here is needed for the guard band as well so fs will definitely be less than delta f okay so the frequency that you'll be really using here will only be something smaller like this okay there will be a so if you use something like this there will be a guard here okay some guard is needed here okay so once you have a guard band you can define a designer band pass filter as a nice frontend for your receiver so for each channel you have a different band pass filter okay so if you have NC carriers at the transmitter you need NC up converters okay a bank of up converters and then at the receiver you need a bank of band pass filters at different center frequencies to bring out just extract your channel and then process just like before your processing will definitely be simpler because you expect your channel to be very flat and maybe a one tap or a two tap equalizer will take care of the equalization and you won't get any noise enhancement it'll be very nice okay so all that is fine but you need a guard band and the data rate goes down okay so this is frequency division multiplexing hopefully it's clear it's just like bunch of different channels working together simultaneously to transmit differently and you separate them at the receiver and you work okay so what are the possible advantages and disadvantages here okay so first thing you notice your symbol period for each transmitter is very long okay so your delta f is small even if your overall bandwidth was large your delta f is small and your symbol period is very very long so that gives you a lot of advantages in processing those are the advantages that I've been talking about okay so let's let's just try to rest down some advantages and disadvantages okay so so first thing is receiver processing is okay equalization is simple let me be very careful let me not say receiver processing okay simple equalization okay so maybe a one tap equalizer will do in most cases okay so you may not need anything more than one tap maybe two tap okay so then there's no noise enhancement well I'm saying no noise enhancement of course very minimal noise enhancement because the channel is very flat okay with any resummel processing you will get good performance okay and then the third point is about the symbol time okay so your symbol is really really long right so delta f can be very small and your symbol is very very long so because of that if you had some noise which was impulsive in time okay like for instance what you have in wireless channels right so it's gonna be a noise or channel fluctuations which are very impulsive in time it's gonna be a deep fade and then you're going to come back to the regular strength before so if you have that since your symbol duration is really really long and when you integrate things out right you're going to have an integrate and dump type equalizer right so as much as that okay so then all those time impulsive behavior will be will not affect your performance that much okay so it's immune to time impulsive distortions these are the advantages that we would like to keep okay so from multi-carrier modulation okay particularly in wireless communications this is very very useful okay so that's the first thing so disadvantages the first thing is guard bands result in a in wastage of bandwidth okay so and this is very very critical okay whatever you do your filters you're going to require a significant amount of guard band and from your precious in the NC times delta f bandwidth you're dropping something at the rate of NC even if you require a small guard band gets multiplied by NC okay and it plays a very significant factor into your whole trade-off right for in your trade-off you want your delta f to be small so you want your NC to be large and when you want your NC to be large the amount of bandwidth you're wasting also goes through the roof okay so that's a significant problem nobody will be willing to do this for that reason okay so if you have to put guard bands and frequency is so expensive nobody is going to nobody's money investment that's the first thing second problem is if you look at the transmitter and the receiver it looks like you need a bank of up converters and a bank of bandpass filters and down converters etc okay so these things are also extremely expensive in hardware okay so anything you want to build in the R of domain and analog is going to be extremely complex so maybe you can do some smart design so that you avoid the power amplifiers and each thing separately okay but still ultimately there is enough circuitry in there which is going to be very complex because of the bank of up converters and bandpass filters okay so that is a significant disadvantage okay so more things that I want to add to the advantages you get control over power of sub bands okay and this is an advantage like I said we didn't show why it was an advantage but from a capacity point of view there is an advantage here okay so all these things are nice advantages but there are two significant disadvantages need for a guard band and need for a bank of up converters and down converters okay the solution to this this whole problem well this is not really a problem you have four advantages and two disadvantages there is a technique which takes these disadvantages I mean gets rid of these disadvantages and keeps all the advantages okay so in that technique is what's called orthogonal frequency division multiplexing okay contrary to the definition it is not frequency division multiplexing there is no frequency division multiplexing that actually happens here you happily allow all the sub bands to overlap okay so that's the main principle there but still it gives you enough control and gets rid of the guard bands and the bank of converters and down converters needed okay so that's where the OFDM comes in okay so basically it gives you the best of both worlds okay but even though FDM is part of the acronym like I said it's not frequency division multiplexing you do something else you actually allow the bands to overlap significantly okay there is a very significant overlap between the bands okay which causes some trouble in implementation but all of that can be overcome but it gives you all gets rid of all the disadvantages in multi-carrier modulation gives you the advantages okay so so what's the idea here first of all okay so if you want to get rid of the guard bands what should your FSB your symbol rate 1 by T is to be equal to delta F right so that's the first thing that has to happen so you have to take that as the delta F okay so that's the first step in OFDM you set 1 by T which is a symbol rate per carrier to be equal to delta F the second and very surprising thing which may not be that that obvious is your transmit filter is said to be a rectangular pulse of width T okay your transmit filter becomes a rectangular pulse okay so so clearly when your transmit filter is a rectangular pulse with width 1 by delta F what's what's going to be its frequency response it's going to be a sink and it's going to overlap into the neighboring carriers okay so it's going to overlap in quite a significant fashion with every next carrier so on okay so you might say how do I get rid of it okay it turns out you can do very clever baseband processing to separate these carriers even though they are they get they start interfering with each other okay so that's the main idea okay so you do baseband processing to separate carriers okay so maybe you do this but still one of the problems that still remain is having these bank of up converters and band pass filters it turns out well this is not really because of OFDM even in the multi-carrier modulation world people knew it can be it could be done okay it turns out you can get rid of the bank of up converters and BPFs by doing by converting them into clever FFT and IFFT operation okay so that's possible like I said it was known even before OFDM came along but maybe in the context of OFDM it becomes much much more precise and nice okay so you use FFT IFFT to simplify transmitter and this here okay so so the whole I mean at least in my belief and belief for so many others the entire area of single processing rests on one algorithm okay which is the FFT and the efficiency of the FFT okay the fact that it can be done in N log N as opposed to N squared okay so the entire single processing area rests on that if that were not true single processing would have gone out of the window log back yes the entire thing is made possible because of the efficiency of and here is one more illustration of why this comes about okay so who who found out that could be done in N log N first time I'm sorry well that's all recent who did it prehistoric days it was known who was the first guy yeah came from Gauss again so okay so the whole of electrical engineering completely rests on Gauss' shoulders nobody else who played a role okay so even today is a very important character to get to know okay so all right so let's let's move on and see how this is done in so many so many different ways okay so the first thing I'm going to show you is how to convert these bank of up converters into an IFFT okay and then we'll see how FFT gets rid of gets rid of that okay so that's the first thing we'll see okay so how does an IFFT enter the transmitter okay so once again remember this picture this is my multi-carrier transmitter okay so I have each thing each transmitter each channel kind of producing an independent symbol it gets encoded but my transmit filter is going to be I'm going to set it to be a rectangular pulse and my delta f I mean one by t okay so one by t here is going to be equal to delta f okay so those are the things that I will use and my frequencies are all multiples of a given delta f n1 delta f 22 delta f okay so there's going to be a whole bunch of notation and I might get confused with the notation hopefully hopefully we'll see how to write okay so the signal x of t which was obtained by adding all the different channels okay so if you do that well after the real part also okay so I've done the real part also so it's going to be real part of summation i equals 1 to nc okay so don't worry too much about the subscripts and superscripts just come up with something a little change as we go along okay it's going to be sik e power j 2 pi n1 plus i minus 1 delta f t is that okay this is my entire thing okay so the transmit filter plays no role right it's a it's a rectangular pulse so sik just multiplies the transmit filter so this will be from for what time 0 to t which is one by delta f okay so that's the time okay so keep that in mind and this t is going to be large so this so okay so so looking at this before I proceed and show you all the simplification the first difference that should pop up into your head is what we did before was what we took each si of k then converted into kind of multiplied by a rectangular pulse and then converted into a carrier and then transmitted so here what's actually happening is you collect nc si of k okay so you don't send each sa of k separately you collect nc sa of k then multiply each of them by a separate carrier then add it up and send okay so this is an example of a block modulation scheme you don't do symbol level modulation each of them independently you take a block of symbols together and then you do one common modulation to it okay so right now it seems like there are several different carriers so but I'll pull the common term out and you'll see one carrier will come out so this is an example of a block modulation so OFDM is block modulation you don't do memory less symbol by symbol modulation you collect a bunch of symbols together and modulate them at one go and that has advantages particularly when your channel is behaving very differently that has an advantage okay so it's all natural in so many different intuitive ways the first time you see it it's always confusing okay definitely the notation is very confusing so try to stay with me we'll see how it goes okay so I'm going to what I'm going to try is from this n1 delta f to n2 delta f I'm going to pick a center frequency a convenient center frequency it doesn't matter what I pick okay so it's just for convenience I'm going to pick one center frequency and then I'm going to pull it out and see the remaining base band signal complex base band signal okay so that's what I'm going to do and like I said you can pick any center frequency you can even pick it to be equal to n1 delta f or n2 delta f I'm going to pick n1 plus n2 by 2 into delta f so you can pick anything you want okay it only affect it only it'll only introduce a phase factor and that will anyway get married to your carrier and you don't know the face of your carrier anyway so it doesn't really matter in in any significant way okay so that the center frequency I'm going to pick I'm going to write this as real part of okay I'm going to pull out a e power j 2 pi nc delta f t then write it as a summation I'll change my indices here I think just to make sure I don't run into any trouble later I'm going to write here as l plus 1 okay once again don't pay attention to the subscripts the reason why I'm putting l plus 1 is what I shifted from 1 to nc to 0 to nc minus 1 so I just wanted some confirmation there now I have to do e power j what 2 pi l minus what okay I have to I have to subtract something okay so that something turns out to be I think the way I choose it nc minus 1 by 2 like I said it doesn't really matter what this is don't worry too much about that okay the choice I've made for nc is n1 plus n2 by 2 okay so because of that this thing works out okay so that's a capital nc so like I said it's not really a great thing to worry too much about I could have made a minor mistake also but it doesn't really matter so this becomes your carrier okay and this you can call it x tilde of t is like your complex base band signal okay so you see why it's the complex bin I've just taken e power j nc delta of t and I've written that multi-carrier modulation signal as a complex envelope of e power j 2 pi nc delta of t okay so what you know about complex envelopes this nc is just an arbitrary choice you can change it but you'll still get something else the only thing that will change is any power j fixed phase term which anyway can be pulled into the carrier it's not a big deal don't worry about it maybe there is an extra delta f also okay don't don't worry about that center frequency it won't change what's inside very significantly you'll see soon enough okay so now when I want to generate this x of t at the transmitter all I have to do is generate this complex base band signal and use one up converter followed by a real part to get to my x of t I don't have to do each of them separately okay so this is clear even from multi-carrier modulation you don't need any OFDM or anything but it has been helped by the fact that you're doing rectangular pulse okay because you're doing rectangular pulse there's no strange factor showing up here okay so it's all very simple and straightforward because of that rectangular pulse okay so there are ways to get around that also but anyway the rectangular pulse is good enough okay so so now let's let's draw a brief picture to illustrate this okay so it will give you a nice sense of what's actually happening okay so you have in your frequency you had n1 delta f to n2 delta f okay so I've picked my nc delta f somewhere here okay so previously you were using the band from here to here okay so now I've pulled out this e power j nc delta f so this entire thing is nc times delta f so your origin will shift to nc delta f I have a past complex base band signal of bandwidth what x tilde of t has bandwidth which is equal to nc delta f by 2 okay well it's not quite nc delta f by 2 why sorry depends on yeah yeah the way I have chosen nc it's going to be nc delta f by 2 okay that's fine I've chosen it to be the center so it's nc delta f by 2 but there is one more thing which is missing here remember my I had a rectangular pulse shape so what will happen the bandwidth that's being used will be a sync on both sides so I have to bring in some extra lobes of sync on both sides so when I write nc delta f by 2 I'll also have some extra bandwidth on both sides for the some extra lobes of sync plus some extra sync lobes only then I'll be able to accurately represent my x of t in base band properly so maybe a few more lobes maybe some three or four on both sides you might want to pick to make sure the entire bandwidth that you're actually using gets nicely captured in base band also okay this is because of the rectangular pulse shape that I chose okay so that's going to spill over into into the as a side lobes okay is that okay it's fine right so you keep getting so a few multiples of delta f on both sides it's not to not be a huge bandwidth it'll be a multiples of delta f because my time is one by delta f right so you'll have a few lobes on both sides and that has to be brought in to accurately give my x tilde of t okay is there a question what's the question that's good to ask oh no no it'll be just some few sync lobes I mean the for instance the rectangular pulse right in the middle may not be you may not need extra sync lobes it's only for the edge a few sync lobes a few delta f's on both sides you will have to pick okay so now we have to generate this fancy looking base band signal okay which has the e power j term and all that so when you have something so fancy okay you don't want to keep multiplying with complex exponentials you don't want to do that but you know it's a base band signal what is a very easy way of generating such signals okay what's the principle we have used in generating base band signals we don't generate them in analog what do we do sample them over sample them generate them in digital sample them at Nyquist rate whatever rate is needed generate them in digital and then rely on your d to a interpolation to give you the continuous time signal okay so don't generate base band signals in analog you never do that right so you want to sample it at Nyquist rate generate the base band signal in digital and then rely on your d to a to give you the final conversion to analog okay so maybe you do any kind of interpolation d to a maybe you do a sync proper interpolation or you do just a point to point just linear interpolation maybe okay so maybe you do a sync interpolation that's that's actually better but you rely on your d to a to give you the actual analog signal okay so that's what we're going to do next I'm going to take this base band signal and sample it at Nyquist rate okay whatever rate is needed okay and then that will give me a discrete time base band signal and you'll see that is naturally generated by a by an IFF okay so that's what we're going to okay so we're going to sample x tilde of t at what rate okay I have bandwidth but it's not enough if you do NC right NC delta F the reason is you want to bring in the extra side loops of the sync so you have to put some larger number so I'm going to say I'm going to sample it at a frequency which is some n t o t so maybe I'll just call it n here okay maybe it's n t o t it's okay some larger number okay so this n t o t is going to be greater than NC okay so reasonably large so that so that you'll cover the entire thing okay so if I do this if I do this what is my x tilde of n going to be which is x tilde of n times what n times some t sampling the t sampling is going to be 1 by n total into delta F if you do that what will you get you get summation l equals 0 to NC minus 1 yes l plus 1 k e power j 2 pi well you have this l minus NC minus 1 all these things are not so crucial 2 times what delta F times n times t sam right so remember delta F is 1 by t okay so if you do all that you'll get simply n by n total okay so this is these are the samples that I have to generate okay what is the rate of these samples what is the separation in time between two of these samples t by n total okay so the samples are separated in time actually by t by n total t is your overall time which is 1 by delta F which is very large but these samples are coming out at t by n total not not that t okay so this is separated by t by n total so when it goes into the d2a it should go at with the clock which is n total times the clock that you are using for your symbol rate it cannot be the same clock okay so your over sampled it right because it has to fit into the bandwidth you're getting somewhere you have to pay you can't use a large bandwidth and happily skip sampling at t it won't happen so that's what you're doing okay so now I want to convert this this nasty looking expression into a nice ifft type expression okay so that's what I'm going to do next okay so for that for doing that you notice here you only have nc data symbols and your division is here by n total which is larger okay and you know in your ifft these two things have to match so what do you do you put zeros up on top and down below okay so you take your sl of k then add some zeros on top add some zeros below to make it equal to n total okay so this is a standard thing if you look at any OFDM standard today they will always put some zeros up on top and down below this is to bring in the things that you have okay so that's what we're going to do what we'll do next is we'll take we will set basically nc to be equal to n total to be equal to some n okay and then what about sl of k sl of k will be equal to zero for l equals some n1 minus delta 1 so on till n1 minus 1 and then it will be equal to the transmit symbols no okay so I don't know wait wait wait wait okay so maybe you're getting confused by the notation here so let me not well I can I can do this it's not a big deal I'll show you how I'm doing it it's okay it's not a big deal just wait for wait for a few minutes it's equal to the transmitted symbol for n equals n12 and 2 see notice my sl of k is zero for those things so it's okay right so I'm adjusting my terminology if you don't like it just don't use it it's okay I'm not transmitting anything for n1 minus delta 1 to n1 minus 1 those extra things will actually be zero I'm not using those bandwidths okay so don't worry about this nc being equal to n total okay and then again once again set it to be equal to zero for l equals n2 plus 1 so on till n2 plus some delta 2 okay so you have have your actual carriers n2 minus n1 plus 1 which is right in the middle which are actually going to be your data carriers which are carrying something you want to send and then the initial few carriers you say you're actually using them but you only send zero and likewise in the last last few carriers you say you're using them but you're sending zero on it okay the reason is you're actually using those carriers okay so you can't say I'm not using that bandwidth I'm using that bandwidth okay so I'm sending nc to be that okay so it's a bit of a bad notation I know the notation is bad here I just want to show you how this works okay all right so once I do this the zero padding okay you'll see the whole thing will simplify so x tilde of n up to some phase term okay there'll be a phase term e power j some n tot minus 1 by n tot or some such thing there'll be a phase term up to a phase term x tilde of n will become l equals 0 to n minus 1 slk okay once again don't worry about the l and l plus 1 and all that okay we did this go here all right let's just hide it slk e power j 2 pi ln by n okay so it'll become that there'll be a phase term I know I'm not accounting for the phase term here the reason is ultimately the phase term can go and get absorbed in the carrier okay I don't care about the phase term when I produce my x tilde of n okay and my equalizer will take care of any phase term anyway so I'm going to have a one tap equalizer so it'll take care of my first term so I don't have to worry about any phase problems so up to a phase term this will be equal to this quantity x tilde of n okay so remember I'm going to now this the sample separation here is capital T by n okay so this will last for capital T seconds okay so it will still last for capital T seconds but I have to do a interpolation here with the d2a before I take my up conversion with the nc okay so this becomes an ifft okay so what will be a choice for capital n typically yeah some power of 2 so 250 6 5 12 10 24 whatever so one of those choices you can do whatever fft can be implement okay so there might also be a 1 by root 10 if you want to normalize your ifft anyway all these scaling things don't really matter in real life right so you just adjust them however you want finally all right so I'm going to come back and draw a picture to show you how this works in the next class but anyway so this conversion is what matters any questions okay all right so so what's the so so so what's coming next is to show you what do you do on the receiver okay so we've let the carriers overlap massively at the transmitter so what do you do at the receiver is not clear but even though I've left the even though I let the carriers overlap advantage 4 still remains okay the overlap but the significant lobe is still going to be at each of these things and that power you can control by your constellation so you do have control over the subband to an extent you haven't lost it at all okay right but they overlap you have to come up with a problem of dealing with them okay that's the one thing and then equalization is a problem you can only do suboptimal equalizes if you want to do optimal equalizes you'll once again have all these problems but suboptimal equalizes are very very very easy to build for these things so that's where the main strength comes okay suboptimal equalizes which don't lose too much on the optimal equalizes okay so that's the way it goes all right so let's stop


Hello subscribers! Today is Wednesday June 28th and I'm excited to show you today's trading video because today I made one of my biggest percent gainers since I started this YouTube channel and it feels great especially after making a bad trade on yesterday and not being able to trade on Monday. It feels great getting in there and making a big return. And with that being said I wanted to show you the possibilities of trading options. Maybe you don't understand exactly how or why but today's call options that I traded went up a total of 613% since yesterday's close. I hope you can see that in the camera pretty clearly. And that was on an underlying stock ETF excuse me that is trading at $243.49. It only moved $2.16 a total of not even 1%. The actual underlying stock only moved 0.9% not even a full percent and that's what the call options did 613%. Guys trading options is like trading penny stocks on steroids only a lot better. Why? Because the underlying stock is predictable unlike penny stocks. Here's another thing. Here's another advantage of trading options. They're super super liquid. Folks I truly believe that there's nobody else that trades like like me. I'm able to make these phenomenal returns with a little bit of money. Consistently that's the key consistently. Sure penny stocks can go up thousands of percent a day but how many people actually do that and do that consistently day after day after day with options I'm able to make 5 to 10% and that's my goal. That's my personal goal. Today I made much more but I'll let you watch the video just show so you know exactly how much I made. Spy SPY is essentially the S&P 500 and it's predictable. It's actually technicals at work, financials at work, real companies. It's a collection of other companies and it's predictable. That's the keyword predictable and when something is predictable you can exploit it to your advantage. And with that being said hit that subscribe button down below. It would help me out tremendously and also share and like my videos. Comment below what you traded today. Well what you lost money on, what you made money on and we can help each other out. I would greatly appreciate any feedback. Thank you so much for watching. Good morning everybody. Let's see.  This morning we have pending home sales at 10 o'clock eastern time and crude oil element towards at 1030 eastern time. With markets opening so high I am going to assume it's going to stay fairly flat between now and 1030 for the next hour and I'm going to move much I wouldn't think. And then after I was thinking about it after not being able to trade Monday and trading yesterday into that trade I was really looking at my trade from yesterday and just wasn't the right setup. And I told myself I was never going to trade just for the sake of trading. I have to be patient and disciplined in my trading and my setup. Gotta find my setup, find it, trade it, profit. Going to be looking at the calls obviously 4250. I have a 242.50 call. Interesting. I'm looking at Fridays. Oh this is interesting. I just now realized that the June 30th which is Friday those options I don't have 50 cent strike prices. Nothing seems strong around here. Well just as I thought there's not much, there's no momentum to the downside here. Sure you could have gotten in. It would have been a bit scary. You could have gotten in though and actually made a 10% return off of the 242.50 puts. Could have been buying around 39.40 cents selling at about 45 cents somewhere in there. But it was it was a really tough move here. And I want to trade with the longer trend overall. I'd like to confirm those moves using the 15 minute and the 15 minute it's really strong. And I'm just gonna sit here patiently waiting. Again we got about another half hour or so. Before we see the crude oil inventories maybe that might spark something to the upside. I'm really looking for a move to the upside here. We're starting to see a nice push here to the upside. This is exactly why I didn't want to trade up here. Even though the indicators were setting up just nothing was very strong and solid. And again you could have made some money here but right now all the momentum is to the upside. So there's a sort of satisfaction in being right. It feels good. It feels really really good. But let's see what happens here. We are looking for a move to the upside. Hopefully it'll set up in the next half hour or so. I'm really liking it around here. It looks really good. Looking at the 242.50 calls. Looking like it's losing a little bit of steam here. Not wanting to get that nice push. Just going to be watching it really closely here. Starting to look really good here. I'm waiting for momentum to kick in here. Nothing at all. Nothing happened with this move here. Potential move here. But let's wait another five minutes and see what that oil inventory number does for the markets. Nothing solid here currently. It's got a ways to go up for the setup to show up again. Everything's moving around but nothing to my liking right now. It's safer to stay out of the market until the setup shows up. Looks like it is going higher here. We're starting to get to that sweet spot. This is what I really hope that it trades sideways here for a little bit. This is really good right here. It is really good. I'm really liking it. Got in at 57 on the 242.50 calls. Looks like there's no momentum here. I'm watching it extremely closely here. Here we go. Okay now we're moving. There we go. There we go. Perfect. This is exactly what I was hoping for. I'll be getting out soon. Okay I got out. Bots at 57 and sold at 72 cents. Wow guys. That was a super, super fast trade. I'm really excited about this. I was extremely, extremely patient. I can't believe this. Wow trade. I want to show you. Let's see the 28242 calls. Wow they're going up higher. Amazing. There you go guys. There you go. I made my money back that I lost yesterday and much more guys. Wow. I was extremely patient, extremely disciplined in today's trading and I made out really, really well. I bought the 242.50 calls. I bought them at 57 cents and I sold them at 72 cents. That is one heck of a return guys. That was 26%. 26% guys. One of my biggest all-time gainers and that was a three-minute long trade. Wow. I am super excited guys. Wow. It feels so good. I can't even explain to you guys. After not being able to trade on Monday, making losing trade yesterday and making back my money that I lost yesterday and quite a bit more, I am so excited. It comes to show guys that I keep saying this. You have discipline. You have patience and you have a good strategy. You will make money. You will make a ton of money. I'm so excited in today's trade. Wow. That is huge, huge for me. Thank you so much for watching. I'm going to keep trading each and every day. My goal is to make enough money to where, first of all, my wife can quit her job and then I can quit my job. I still got a long road ahead of me. Like I said before yesterday, not one trade is going to make me or break me. What's going to make me is my persistence, my discipline and my patience. That is what makes a great trader guys. That is my belief anyways. Thank you so much for watching. Don't forget to hit that subscribe button down below. Please like and share my videos. I would greatly appreciate it. If you have any questions, please comment below. Please give me any feedback. Any likes, dislikes, what do you like about my videos, what do you hate about my videos? Let me know what you traded today. Let me know what you made money on, what you lost money on, what you think you need improvement on, what your strengths and weaknesses are. I would greatly appreciate it just so we can talk stocks. Thank you so much for watching.