Hello and welcome. My name is Shannon Kemp. I'm the Chief Digital Manager for Data Diversity. Thank you for joining the latest in the monthly webinar series, Lessons in Data Modeling with Donna Burbank. Today, Donna will discuss the evolving role of the data architect. What does it mean for your career? Just 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. And we very much encourage you to chat with us and with each other throughout the webinar to do so. Just click the chat icon in the upper right-hand corner to activate that feature. For questions, we will be collecting them via the Q&A section. Or, if you'd like to tweet, we encourage you to share highlights and questions via Twitter using hashtag lessonsdm. As always, we will send a follow-up email within two business days, containing links to the recording of the session and additional information requested throughout the webinar. Now, let me introduce to you our speaker, Donna Burbank. She's a recognized industry expert in information management with over 20 years of experience helping organizations enrich their business opportunities through data and information. She is currently the managing director of Global Data Strategy and Limited, where she assists organizations around the globe in driving value from their data. She has worked with dozens of Fortune 500 companies worldwide in the Americas, Europe, Asia, and Africa, and speaks regularly at industry conferences. In fact, she just spoke at Enterprise Data World at our conference in Atlanta last month and is doing so much with us, we think, as always. So with that, I will turn it over to Donna to get today's webinar started. Hello and welcome. Hello, thank you. Always a pleasure to do these, and always a pleasure to see so many people joining us. As you mentioned, I was able to see a few folks at Enterprise Data World this year and put a few names with faces, because we often get some of the same folks, and that's great. So yeah, we will just jump in. Shannon already introduced me, and I think you guys, oh, most of you know me, and we will actually talk a little bit more about me at the end, so I won't talk much here. I will say that I am on Twitter at Donna Burbank, and as Shannon mentioned, there is a hashtag today at Lessons DM if you kind of want to have an online chat or questions as we go along. A little bit about the series. If you're not aware, this is an every month kind of thing. So a couple of things I wanted to point out here.  And the ones coming up, you can see you may want to join. And you'll see that the one we highlighted here today is a little different than most. And this one, I want to leave time for questions, A, because we seem to be getting more and more questions each month as people kind of join and get used to the concept. And I've actually had pre-questions this time that folks have kind of emailed me at a time and said, could you cover this, or I wanted to ask you about this. And the other thing I wanted to point out about the series, it won't be as meaty this one in terms of content, but I will be referring and actually blatantly stealing some slides from some of the other sessions because I think the whole point of the series is that the role of the data architect is evolving, and there is tons of hot, cool stuff that you could be doing that relate to data model in the organization. And this series almost summarizes that from enterprise architecture to BI to MDM and data integration. So data architecture in and of itself is interesting and cool, but there's also, I find it more interesting when you apply it to other either business problems or technical challenges. And if you do want to kind of broaden out across the enterprise, this is a great way to do it. So hopefully you can, if anything, piques your interest today. Some of it you can go back to previous, either this year or even last year.  So it's not going to be as detailed and techy as maybe some of the ones in the past. It's hopefully going to be more kind of about you guys. So please do jump in with Q&A if we don't cover it, and during this session we'll cover it at the end. So this slide I've actually used before, but I think it's important. Data is hot, right? Data is now, we've been talking for old folks like me, we've been talking about data-driven business for years. I've been in the business over 20 years, and I think smart people have always known that data is important. But I think the rest of the universe is catching up to us finally, and partly because of the technology. It is just growing so fast, and there are so many opportunities. So I think unless you lived under a rock, you have heard the Harvard Business Review statement about the data scientist being the sexiest job of the 21st century. You know, I always carry out that they have a much different definition of sexy than I do. I think data is cool, I don't know if it's sexy. But it's not just them. Forbes, Wall Street Journal Leaders, a few, I just googled quickly and you'll see everybody wants to be a data-driven business. So that is a great opportunity for us. And I don't want people to get hung up. I think people reviewing this webinar later don't get offended by crossing up the word data scientist. Nothing wrong with data scientists, they're awesome people, awesome role. If you want to aspire to be one, or if you are one on the call, that's great. But I think we maybe overthink that. I think most people in the business or people who are finding out that data is cool just think data is cool. So I don't think whether you're a data architect, data engineer, database administrator, et cetera, et cetera, data science, I don't think that necessarily matters. I think there's opportunity for all of us. So for anyone listening to this later, nothing wrong with data scientists. I'm crossing them out. But I think that is important to know that this is our time in the sun and I think we should take it. And there's a lot of opportunity, which is one of the reasons we hosted this webcast. I get personally so many questions. What are the opportunities? What can I do? How do I grow? And I think the world's your oyster, finally. It has been for a while, but especially now. And I want to talk a little bit more about that because I think finally that the data-driven business has come to the front. And I think this is a great opportunity for data professionals to sort of have a sheet at the table, as they say. And I'll talk more about this. That there's this idea of becoming a data-driven company, kind of making the business more efficient. And then more and more companies are really seeing this as transformative and really becoming a data company. I've worked with several companies, but that is on the wall. I wish I had come up with it. But before I even came in, we are now a data company. And I think folks like Facebook and Uber and Amazon, they sort of transformed the marketplace through data. And I think a lot of companies are saying, oh, I want some of that too. Success breeds success. And I think we are well-placed to help with that. And so I think if you want to see the table and really want to start, kind of really making a change in your business now is the time, I think, because a lot of people who want to really be data-driven, I often am brought in with the clients, and these are kind of my more fun projects. Not that any project is not fun, but I love these when people say, you know, I want to be a data-driven business because you helped me understand what that means. And because I'll talk about this later. I'm an economics major initially, and I think the whole business side of it's really fun. And so kind of mixing the two is kind of my personal day in the sun. So I kind of think that's neat. So a little bit more about that. When we think about how can we transform our business through data, as I mentioned, I see these two separate things. There's probably a lot of different flavors of that. But one is, you know, how do we do better business optimization? To me, that's becoming a data-driven company, which is taking what we do and doing it more efficiently. So I'm going to have better marketing campaigns to sell what we're selling, or I'm going to make better products because now, you know, think of, I did some work for a telecom company, and you can actually, well, creep factor or good data, depending on how you're looking at it, you can actually see the click-through of who's using what and what products people are using and how people are using it. So if you're a product manager, what better feedback than that? So if we want to really optimize our product, we have the data to do that, Internet of Things. You know, do a lot of this type of thing. Better customer support. So some of my companies or my customers are using, you know, big data and can I mine support logs and see sentiment analysis of my customers? Can I use big data to kind of predict network outages and make sure that I have more efficient service? You know, so many things we can do with data because we just couldn't, didn't have the technology to do that, you know, some of this we could do before, but you can do it a whole lot better. You know, some of these new technologies, and you can do it at a lot lower cost. Or, you know, I think a lot of companies are getting more and more now, too, is that you can make, generate a lot of efficiencies through data. Can I optimize the supply chain of my data by getting the data of my company by getting the data flow better? You know, I often start with a project, and we'll talk a little bit about this later. I often start with a process, business process model, because data and process are unrelated. So before we ask what, how do you become a data driven business, show me what your business is, and let's walk through that business process, where is data touched, and then how can you use that data to really optimize those processes? Or is there a piece we didn't see, where if we could get data earlier, we could make better decisions, all of that. So I would say that's more of the category of how do we do what we do better. But I think the even cooler new stuff that I think people are doing is, how do we become a data company? How do we transform and actually do something completely different? So, you know, the Telco company that I worked with earlier, they were one that did have that, that would be a data company in our wall, their wall. So yes, they could become more efficient by looking at network outages and linking customer support with usage and that kind of thing. But they were actually monetizing some of their data. They were selling, when you think of it, anyone who traveled anywhere to get to work generally has their cell phone. So anonymized, of course, they did take privacy seriously. I think more and more companies are getting that. But they were able to kind of monetize, anonymize and monetize fault traffic and sell that back to some city planners and say, if we're going to build a new rail system, where do people need to go? Where are they going now? So is there data you are sitting upon in your company? This one I'll talk about a little later, an energy company that is really using data for smart metering and it's the data. It's really the valuable piece of the company. And I think, you know, that was a hot topic at EDW. How do we monetize data? How do we become, you know, more, the monetization of data is a hot topic. And I think a lot of companies are getting that. And I think Uber, really, it's the data. They actually had a great presentation here in the Denver area a couple of weeks ago and they really explained how they did that. It's fascinating. And it really was all data driven. And stuff we just didn't have before with some of this new real-time big data platforms. And I think that is an opportunity for us in the data field is that a lot of the companies that come to me say, I know I want to be data driven. Where I have the gap is I don't quite get what that means. You know, what does that mean moving my on-prem to the cloud to Hadoop? And I think that's where us in the data architecture realm, we do get that. So if we can be seen as a trusted advisor to help with that, that is an amazing opportunity. And we do play a unique role. So you might, as with many of my slides, if you've joined, you might be wondering, what the heck is she getting at here? So if you're familiar with the God Janus, way back in your history class, comes from the month of January, comes from this God. Because the idea of January at the beginning of the year, he has one face looking at the new year, and one face looking back at the old year. So it's kind of this two-headed, not a monster, actually. He looks like a good guy. So it's a two-headed God, actually. We are the gods of data. Where I see data architects are a bit of the Janus of the data world in a few ways. One is I think most data architects I've worked with are kind of that unique role that they can flip their head, or that doesn't gross you out. It's not a 360 kind of horror movie here. But you can flip your head in one direction and talk tech. I want to know how do I optimize by platform? Should it be an on-premise solution, or should it be in the cloud? Should I use Hadoop, or should I use a relational database? So we can kind of switch to that. Should I have an appliance? All these type of things. And then you can switch and talk to the business person. I'm talking business rules. I'm talking regulations. I'm looking at business opportunities. I might be creating a business glossary. So on the same day you might be talking about the database administrator about taking your logical model and optimizing it for poor performance and tuning. And you may be speaking to the business of what do we mean by what is the default credit swap and how do I publish that in the glossary. So I think that's why data architecture is fun, because I think you can have that unique role. And I think that's why we have a very unique place in the business that I think if we can leverage that part of our skill set and personality, that's what people are looking for right now. Think of anything. I want to buy a new car. Wouldn't I love to go with a friend who's a car mechanic or a car engineer that can explain all the things or I want to buy a new pair of skis? I would love to go with a ski expert that knew all the different models and makes. Sometimes this is salesperson, but I think anything that's technical. You'd love to have that friend that kind of knew what you wanted and could explain it to you in an easy way. Was it that anyone listens to national public radio? The car talk guys? They're kind of classic at that. People call and they want someone they can just kind of trust and explain it to in a clever, funny way that you get. And I think people want, maybe we're the car talk guys of data. Anyway, I think people are looking for that and we have that role. I think also it may be more literally of the one head looking forward and one head looking backwards. I think we do play a really unique role in that we do understand the core architectural principles. I mean there are so many new technologies now it does make my head spin. And I think that's why it's an exciting time to be in data management. I'm a big old nerd. I don't mind saying it and I am still learning new things every day. There seems to be a new technology coming out or a new way to store data or make it faster or more real time or to split it and parse it and do time series analysis in different ways. But I think I can understand that more quickly than the average person on the street because I do have a computer science degree and understand some of the core principles in all of us in the business have had some of that history that we can build from. So we can kind of help companies or your own organization understand where this new technology fit. So I think that's fun and I think that's a great opportunity. I think it's always, it's hard in this industry and I'll talk a little bit more about this. You can say, oh I have 20 years of experience and that can be the nail in the coffin. Because you've got these new young whippersnappers in Silicon Valley and these technologies didn't exist 20 years ago. So you don't want to be seen as outdated yet. Again, no one starts something from scratch. These are all built upon previous technologies. So here in the Boulder area where I live, Boulder, Colorado, for some reason it's sort of a hotbed of data folks and they have data meetups where I kid you not, 200 people show up and then talk data science and big data analytics and a lot of the new technologies that are coming out. I have to admit I was almost shy to go over a few of these because I was so old. I was not going to be a 20-year-old. And they're worse than 20-year-olds but there were a lot of gray hairs and there were a lot of four-year-olds and a lot of folks in between. And when I sort of explained what I did and how long I've been in the business and doing things like governance and some of those things, I expected a bit of poo-pooing the kids. I'll call them the kids that's 20-year-old.  Because that's how old people know how long life is. And then the real old people with them, you're probably laughing at me. You think you're old. But they were very fascinated to hear, oh, wow, I would love to know how companies are actually applying. You've actually worked for the Wall Street Bank that's doing some of this real-time data analysis because you explained how that works or how do you govern this data. So I found that it's really a fun way to kind of share. And they were doing some of the new hot-tech advances that I hadn't heard of yet. So again, if we have an open mind, and we can share our experiences both with the business and with other technologists in the organization, again, we have a lot of opportunity. So some of you have seen this slide before. Repetition helps ingrain it in your mind, but somebody, I haven't checked if they're on the call, be careful saying you like something. You'll see it again. They kind of like my little third normal form guy. You might wonder what this is. But the more I work in IT, I consider myself a techie person. I realize so much of it is the people side. And there are different personalities, and the more we think about that and realize that and work with that appropriately, as well as our own personality, I think that will help us. So you might wonder about this little guy in the corner. I'll explain that. So I think the good thing about data architects is that we do, are able to speak both technology and we are able to talk business. We are often not shy.  And I said, oh, you don't know data architects. There won't be questions, right? I think that is the beauty of our many architects' personalities of we do like to talk. We get passionate about our models, but we can also maybe be a little too passionate. So that little guy, he's just that strange guy on the street corner wearing the sign saying, the world is not going to end. The world is going to end if your data model is not in third normal form, right? And I think we sometimes scare people. I've scared my friends. I mean, what did you do at work today?  So I think we have to remember that not everybody loves our data model. I actually, you might have heard me on these webinars before saying nobody cares about your data model, and I don't mean that because people do, but nobody does really. And sometimes I actually said that to myself in my head. Remember, Donna, nobody cares about your data model. Keep it simple. Apply it to their business. Because the other thing we tend to do is we often find, we're sort of paid to find problems, right? Which is good. That's what an engineer sort of does. You find a problem and you fix it. But we can also often become seen as negative, and we can be sometimes seen as the old fogies, right? Oh, those architecture people are always telling us what we can't do. Those governance people always say no. You don't want to be the person always saying no. There might be a reason to say no. There might be problems, but can you come with a solution, right? So when you think of the business executive in the middle, like any human being, it's the what's in it for me. But almost by definition, when you think of an entrepreneur or a business person, they are very optimistic, sort of trained to be optimistic. They're looking for opportunities. Oh, I know we're going to be at data and business, and we're going to be just like Uber, and we can do what they don't want, and if the architect can go, no, can't do that. We have relational database. It's hard. We have six million data attributes, and just to rationalize them is going to take, you're out of the room. You're out of the discussion. So not to say that isn't hard, but can you put on your opportunity hat? I guess it's my advice there, right? So can you say yes and, right? Yes, it's hard, and we could maybe start with a sandbox, and later we may have to integrate the premise customer data, but we could start here. And as I said, we should look at personalities. We should look at our own. Are we being an old curmudgeon? Right? I have to do that with myself. I remember when I first came into tech, I was so excited. There was so much cool stuff. Anything new I was absorbing. And then I think we get a little complacent, and I have to remember, do you still have that excitement you did when you and University studying this stuff, because it is even more exciting now. So you don't want to say, I've been up in it for 20 years. Nothing changes. It's all the same. It is not all the same. There's some same fundamentals, but I think if I could give one piece of advice, it would be that, try to put on the opportunity hat. Don't be all Pollyanna, and everything's perfect if it isn't, but just think of the person who's definitely wasn't it for me. And to that third column, can you be seen more as that data advisor? Right? So they'd architect of course, but the advisor, can you say, and I think companies are well I know, because they call my company in to do this a lot, but be more of that data advisor so that you might say, you know, I know you want to be data driven. Have you seen these new graph databases? I know you're looking to do some customer relationship analytics. This would be an awesome thing. Don't want to bore you with the details, but I think this is something you might want to look at. I think this is, people are open to that. So the NALGI news, I know it's sort of an overused analogy, the one of the architect of the house. But I think, you know, especially with something like the rise of big data, self-service BI, a lot of people are starting to do it themselves and kind of look at the data and get more interested. I kind of have the analogy of a house, right? So I might start to build a shed in my yard or I'm going to paint in my office or I want to add some siding to my house. You know, I can do some of those things. I think most people can kind of do a certain level. But if I'm going to build an apartment building or a major addition on my house, I'm going to hire an architect. And I think I'm going to hire an architect that I trust. And I think that's where we want to be is that, you know, don't poo-poo folks saying, oh, you can never do your own self-service BI, but you want to help them with that. So I did do a big renovation to my house and did interview a lot of different architects or contractors, types of things and rolled a few out because a few acted like somebody did architects that are the negative ones that, oh, it's too hard.  You need to hire me to paint all the walls and, you know, I can paint walls. The one I did hire was, oh, that would be cool. I see where you're going with that. We could build this. And I trusted his advice of can't kind of take down that wall. It's load-bearing. Good. Thank you. That's why I hired an architect, right? So I think that's what the business wants. Oh, don't use a graph database for your accounting system. That might not work. Or maybe you should. These are the pros and cons. I think people are looking for some of the get data because I think people see the opportunity. So spend a lot of time in the slide, but there's a couple things to it. One is, you know, personality-wise, try to look at the positive. Try to look at what's in it for them. So can you focus your conversation on their marketing campaign, their need to be data-driven. They're busy and you can make this faster for them.  One of the architects I hired said, are you doing yourself because you should hire me to do that? Well, I didn't hire that person. I can do certain things myself. But I think I've also seen architects be heroes in data architecture. And we'll talk about the for-business self-service BI because people just like me with a house. I know what I don't know. You get to a certain point and you call somebody and use the expert, and I think that's where architects can be. So I don't want to beat the point home, but I do think it's an important one with this. Can you move from data architect where you're kind of the nerdy kid that they bring in because they have to? Or are you the data advisor where they bring in, that person's got cool ideas? I know they could help us. I know we don't want to do this certain thing. They can explain how. Or maybe they'll have a new idea I haven't thought of. And I think that's where a lot of the opportunity in your career can come. And we do, too. It's not like people hate data or anything, and you might have seen this. I think it was last month I showed this. But I think it hits the point home, right? Why don't people care about my data? Do you really care about anybody else's job? Showing you how accountant comes to you is like, well, you know, we recently switched from an accrual price to counting to a cash pay. I just want my paycheck, right? And think of almost anything else. Think of the engineer running the heating of your office. You just want the heat to work. You just want your car to start. You might be kind of interested in your car if you're a car person, but really you kind of just want it to start. So think about that. We don't get too much into the details, but when you do apply it to their job and how you can help them, right? We're all people. We all have our own things to care about. Again, some of these are reuse slides, but I think they all fit together. And can we tell a story? Again, that Janice of the fact that we can often communicate and explain the business, the fact that data models, if you're a data modeler, kind of naturally are graphical. That we should have evolved. I heard this on the radio a few weeks ago that someone had said that we can't even dream without dreaming in stories, right? We can't even sleep without dreaming in stories. We always have pictures in our head. We're always thinking. We're always going. So if you can relate the data to a real-world impact or scenario, the reason we need governance is because, you know, it's kind of, we can get a better view of our customer if we're all talking together and know where the data is. We can get better lineage or you're not going to get in trouble with this regulation or something. But I think you need to relate it to a story. And I think we, as architects, are kind of well-positioned to do that. A, we often have the big picture in our heads, and often we're sort of well-suited to explain that. I've used these before and kind of showed them before. And I think, remember, be creative in your discussion. You might just show a high-level business model. Can we take that architecture we know, take the business rules that we often know and tell that story to the business? You know, I'm, is a customer the same as a client? In the picture, it looks like the same person. Are they different? Oh, well, client is what support uses. You know, customer is what sales uses. Are they the same thing? Yeah, we can probably rationalize that. Or no, they're not, because you're not a client on a support maintenance clause or something. You know, but that's where you start to get those rules out of people's heads. And again, start being that trusted advisor. You can explain why that matters. You can really explain how they can see opportunities of, oh, did you know that if we could link your prospect database with your product sales, we could kind of see patterns. You know, let me get, because you are in there seeing this data that, you know, the business people might not know. And again, you know, some folks, you might think you could go tell them, we can't get this because, and sometimes that's valid, but do we always turn it around and say, did you know if you could get this in reporting by linking A with B? We're all humans. We all want us to be opportunity. We want us to benefit. So kind of flipping it around that way sometimes can be very helpful. And if you've ever been in one of my classes and I have talked about this a lot, and you know, I've done a lot in my career and we'll talk with that in a bit. And training sales people and selling stuff. And that was a valuable background, because we're always selling. We can poo-poo marketing. We can poo-poo selling. We're always being sold to. You know, think of it, is everyone here on the call wearing generic clothes? Probably not. You're probably a brand you like. Your car is probably a branded. And yes, we're affected by marketing and sales pitches. So think of your sales pitch for your data project. And I've told this story before too, but you know, I actually had this moment early in my career where the CEO was riding up the elevator with me and asked, what are you working on? And I explained to him and he said, well, you know, what's the bigger picture? What does that do?  But a couple tips on that. So you could, you know, explain to the CEO, well, I'm working on a project to rationalize metadata across sources to ensure consistency. You know, you've lost them. That might be what you're doing. Or could you flip it around and say, I'm working on a project to get a better view of customers for that big campaign you're working on for the big marketing launch? Then they're interested, right? And you may explain what you're doing later, but they probably don't care at a very high level and you need good data to do that. So across the board, all of us do this. So when I do do this in the class, everybody who is an architect or a technology always does the former even though we just spent the whole module saying, hey, ladies and gentlemen, back to the person, we always start with, well, I'm building a data warehouse to help with your marketing campaign. And then we stepped back and we realized what we didn't like. We didn't start with the first one. We said, it's your marketing campaign. And I do this all the time. I used to do demos, you know, and I was a solutions consultant. And I'd spent all night working on a problem and fixing it. And I'd go to show it to the customer. And the first thing I do is talk with a problem. I'd just fix it, which is the worst thing to start with, you know, start with a problem. But that's what was on my head. And it was so hard to step back and just take a good breath and think what do they want to see? What's their point of view? And I think that's valuable, especially when you're trying to sell your product. What do they care about? How do you set it up quickly in two minutes and make sure they care? And frustrating or not, I have seen clients work, but lucky enough to work with a lot of different customers and different industries, countries, languages around the world. And I've seen some awesome technology and I've seen some awesome technology get no attention because no one sold it appropriately. And I remember in university what I'd learned, we'd learn about these different database options. How come we don't use that one? That one seemed cool. They didn't have the better marketing team. So this meant some great technology. They went by the wayside. So make sure that's not your project and sell it, sell it, sell it. It might feel uncomfortable, but I think you have to let people know what you're doing. And again, that might be outside. We as data architects tend to be good communicators, but a lot of us, myself included, might be an introvert and kind of would rather just be coding on your computer. It might be outside our comfort zone. So go a little outside your comfort zone and maybe try that little sales pitch or take a sales class or I'll get to that towards the end of kind of expanding your horizons can really be helpful. And that's what I love about my job is that I do go to a lot of odd and various and sundry different industries. And it's amazing what you learn and one industry applies to something that's completely irrelevant. I worked with a water company and what water piping actually helped me with a restaurant company because the supply chain was similar. I mean, it wouldn't have stopped that initially. So I think a lot of the weird jobs we have in our life can actually help in strange ways. So broaden your horizons there. But more about data and what's with people. The other great thing about architecture is when we think of these new hot technologies and trends, do they apply to us? Yes. I think everything from, I think I've already explained this idea of the data-driven business and data strategy and data optimization and business optimization. Yes, that applies to you because you get the data. And a lot of people don't. And I think, and I still do that, I think we forget that a lot of people don't because anything you know seems easy. And being able to explain that to somebody who probably is a data newbie because data is new to a lot of people, that's an amazing skill. And so don't belittle that just because you know how these databases work. I think a lot of people want to know what's in your head. And then some of the more traditional things we've heard about that are coming to the forefront over and over. Data governance, master data management, data analytics, BI, et cetera, et cetera. I'll go into some of the newer ones at the end. Yes, they all apply to data architecture. So we'll go through a few examples again, as I said, if you missed the beginning. There are webinars on each one of these practically throughout the year or in the past. So if you want to do a deep dive on BI, for example, we did that a couple of months ago, I think, so you can always do a replay of that. So we're just going to touch the highlights. So I've showed this before as well. This is kind of our framework for my company of how we generally approach things. And you'll see at the top, it kind of really starts with that business strategy, business alignment. It goes right down to the bottom to all the hard stuff, too. Right? Not that business strategy is hard. That is very hard in itself. But from the databases, relational to big data, the unstructured, semi-structured, all of that. And you'll see the data architecture and related disciplines like data asset planning and data integration and metadata. They're right there at the center. Because if you want to do the other things in the diagram that we'll talk about like MDM or warehousing or governance, you need a good architecture. So, you know, it's kind of hard to put this together because everything's intertwined. It's all interlocking keys. But you really can't do any of it without a solid architecture. So, you know, architecture will continue to be relevant no matter what technology we're looking at. And when we do talk about enterprise data strategy, it always can. And in fact, last year, if you're under the replay, we did the webinar just on data strategy and how that applies to data modeling and where that fits. And this is a slide stolen from there. So, when you think of the business strategy, we start with a conceptual data model. What are the pieces of information we want for this strategy? Is it product, customer, online, social, trends, external data? We start at the top. And yes, business people will consume that. They'll understand it if you keep it at a high level and tell the story. Big fan of that. Again, back to the, I would work all night with a problem and start with a problem when I was demoing something. I think, maybe it's just me, but I think sometimes, you know, techy people, we can be self-deprecating and think, no one wants to hear about our models. So, I know you guys don't want to see this. One customer say, why do you keep saying that? I love these things. I love these models. They make so much sense to me. So, I think we should stop saying that because architecture is cool. I think maybe our time in the sun, we're not used to it. We're kind of blinking in that sunlight going, wait, we're suddenly cool. You do want to see my data model? Because that is happening. And I'm suddenly surprised. I know I've told this story before, but I will tell it over and over because it's awesome. So, I did a governance project, I guess it was last summer, for a big restaurant chain. And I walk in and it was the marketing team with the sponsor. We walk in the room. I kid you not, I can't exaggerate, but this is not an exaggeration. The marketing team goes, I am so excited to see you data governance people. I love data governance. You're going to help. I mean, we wouldn't have heard that before, but marketing put together is that they wanted to do these campaigns. They needed data. They started looking at the data, and they got that the data was a mess. And they needed an architecture, and they needed governance, and they needed all the hard stuff, and they wanted somebody to do that for them. And we were those people. And so, yes, you will have those words. They are out there. I think I still get surprised. So, yes, at the conceptual model, that's the modeling the business. At the bottom level, the physical model, that's important too. So, do I understand the database structures? Can we optimize the database structure? Should it be relational? Should it be graphs? Should it be a document database? And I think, and we'll talk about this at the end as well, if some of those terms aren't familiar with you to you, they should be, because not everything is a relational database. And I think a lot of folks grew up with a relational database who are an architecture, and there's always been more than that, but I think they all need models and they can all be modeled. And then all the things around the data modeling ecosystem, so things like lineage and impact analysis, and metadata management and standards and glossaries and all of that tend to fall in the realm of architecture and are very much needed. So, I talked about this a little bit. I thought it was worth while we're kind of on that topic of business transformation and how data can support that. This was a company we worked with in the U.K. in England. Almost a perfect example of the case I gave where they had on their wall with the poster, we are a data company. And one of the reasons is when you think about it, it's a little ironic that this is a company when you think of it was consumer energy like electricity and gas and that kind of thing. And they're trying to incent their customers to use less of it, right? So you have a product and you're trying to get your customers to use less, which is sort of the opposite of most people that are trying to sell more. So that's sort of not necessarily the best business model. So when they looked at it, what was an asset they could leverage? It was the data itself. So can you do things like smart meters, Internet of Things, have people control their own heat in your home, you want to turn down the heat on your cell phone and that kind of thing. And they were basically transforming the business to be a data company, new things like big data, Internet of Things. But very quickly they realized they need an architecture to do this and they really need, there's a lot of legacy data out there. Do they even know who their customers are to get, can you turn down the heat of your cell phone but do I have the right address, right? So one of the things we kind of started was what is that business critical data element? What does need to be architected? And a lot of it was we need to get our kind of small data right. We do need to have an architecture and governance to do that. And then which does make more sense on the big data platform and what needs to stay on-prem. They started a new data governance program that they never had. They put data quality in place. So again, sort of ironically, so when you think of it, this was all about business transformation. It was all about kind of the new stuff or I'm new anymore, right? The big data and Internet of Things and the first thing they started with was kind of the more traditional stuff because you're going to walk before you can run. And that wasn't negative. They were still doing the cool new stuff. They were just doing it in the more practical and transformed way. So it's not an either or. And I think more and more companies are realizing that. I mean, I've been in this business forever and things like architecture and governance are getting more popular, not less. So I think as people realize they want to do the cool stuff that you need to do that wisely. So moving on, if I could actually move my slides one of those days. Master Data Management, I think we're going to whiz through some of these, but this is almost classically an architecture problem that when you think about trying to get that single source of truth, well, A, what sources are out there? Do I have the source of all the different, say we want a single group product or a single view of customer? You need to get the architecture to do that. I think getting that common structure with an MDM if you do the hub system, for example, and that ability to talk to people. So where I've seen MDM hubs fail is that we get all the, do we have all the right attributes? Do we have a process model to integrate them? And I think that's where architecture can come into play. So again, this is an area where I've gone into companies and they haven't had an architecture, but they wanted the MDM and realized they need an architecture to do MDM. So it's almost impossible to do this well if you don't have some sort of architecture behind it. The other area that this often falls into, and I always, even if it's all this stuff, yes, you could boil the ocean, you can have all of this take forever. I always say start with an afternoon. Try doing it in a whiteboard in an hour, and you could probably do a lot more than you can expect just by starting there. If it needs to take more, you can. It's probably more than an hour in a whiteboard, but don't be scared away by doing it. When I get this sometimes with customers, I'll say, I need to do a process model and that seems really hard. And I say, okay, well, do a workflow and just show really quickly how this fits. Because I think data architecture does tie into things like enterprise architecture which we talked about, I think it was January. And I almost can't think of data without thinking of process or what business capabilities are we trying to support. When you're thinking of things like MBM, you'd better be thinking of this. What business capabilities are you using the data? What business processes are you using the data? That restaurant chain I mentioned, it almost turned more into a process. They didn't have a lot of data. They had a lot of data that was being touched, a little bit of data that was being touched by a lot of different people in different processes. So that could have been a recipe for failure, pun intended, a recipe for a restaurant. For failure, by not looking at that, you might have said, oh, it's not that hard. They only have, you know, 500 recipes and I could just do that really quickly. But the hard part was understanding the touch points. And I think that's where architecture comes into play. So no, you don't need to buy a huge fancy process tool. Some people do. The water company I mentioned did have a big fancy process tool because it was an engineering company, really, and you need to understand process. So, again, sometimes you need deep process. Some teams need a light process, but don't not look at it. Sometimes you need deep business capability models. If you don't know what those are, check out the January webinar. But if you're thinking of transforming the business and you don't understand what your core capabilities are and how data applies to that, you'd better at least start to think about these. So architecture is architecture and data is generally at the center of a lot of those different architectures. So business intelligence and self-service BI. Again, a lot of folks can say, you know, and I have, again, the good side of some of this huge rise and new tools is there are so many tools out there that can do so much so quickly. So in the BI session we talked about, you know, you can download open data. You can use these awesome new visualization tools and the awesome new munging tools and do a lot. But if there's no metadata behind it, and you have fields like F1, F2, and you don't know where that data came from and there's no code sets around it, and it takes a long time to run because you haven't really optimized the data structure, you're going to run into a bottleneck. And I think that's where architects can come in. You can create things like these code sets and the reference sets, and you can help describe how an architecture could be optimized and you can't just report on, you know, a million-day element and expect that to be fast if you don't architect it in a certain way. And I think I've seen a lot of architects be heroes in this scenario because I think the business wants that. Yes, in fact, one of my favorite quotes that I don't have been here was from a business user, and we were trying to explain the reason why we needed the glossary in architecture and metadata, and they looked at us and said, I mean, you're not doing that? Of course, yes. We want that.  We should have thought you were. And I think until they saw the data, they didn't realize how bad it was, which is often the case. So that little quote in the lower right, a lot of the self-service BI professionals, data scientists, et cetera, they spend a huge part of their day cleaning, reformatting, munging data to make it fit for purpose, and they don't want to. They just want to do the report. So if you can take some of that heavy lifting off their plate and look like heroes and do some of that, you know, obviously there's governance around it, and they need to play some part. But I think that's a positive rather than a negative, and I have a lot of teams I work with where there is sort of a user element to that self-service BI. I mean, a support element. They can actually work with the users to do that, and that's a good place to be. Governance came up. This is one of the pre-questions that came in before the webinar that I thought was worth talking to. So governance is a big driver for architecture. I think there's, well, there's many parts of governance. A lot of it is the people process part. Is there a data governance steering committee? You know, are there data stewards, both business and technical? I think, you know, having an architecture background often is the architecture that's your governance. You know, is there a domain? Again, I often do a process model. I often look at capabilities whenever I come in to do a data governance. And sometimes some of these big problems were something like, well, there wasn't a dropdown on the user interface.  Well, that's something where an architect can come in and say, can we just do a dropdown list? And here's your standard reference set. And we can use it, and problem gone. So I think you do, again, you're that genus that you can, if you think of the role such as a technical data steward or that are kind of building the data structures or doing data quality checks, can, you know, and I've seen some groups where maybe that person is the technical data steward, is doing something like running a data cleansing tool, which is great. But sometimes you can say, well, you know, if you normalize that data, you wouldn't have these data problems. Or if I had a domain or a dropdown, or here's some data sets we can use, that's definitely in support of the data steward.  And this is an art, a science, right? So it's not just putting a bunch of terms up there and the definitions. There's ways to create taxonomies. And, you know, again, that's where architecture comes in. So I've seen data architects on the data governance, boards, steering committees, whatever you call them. I've seen them as voting members. And again, titles don't matter so much. It's people a lot. It's, do they trust you to be the person who knows? And often I've heard, you know, you've got to bring the architect in. They're the ones that know the business and the technology of the data. So I've seen them as voting members. I've also seen them as kind of non-voting advisors. You know, think back to that advisor slide. And for that reason, you know, it may be the, you know, marketing department that makes the final decision or the CFO that makes the decision, but they do want that trusted data advisor of the room going, guys, does this make sense? And before I make a decision, is this going to have any negative effects? So, and I kind of hit on this earlier, but I think architecture and metadata really make that governance actionable. Now, if you're going to have a policy procedure, the more you can embed that in the structures and the audit and the lineage and all that to make it actionable, that's where it really kind of has the teeth. And so you have an opportunity there. And I don't want to, the little business, there's only so much we can cover sort of by the time. Emerging technologies need architecture too. So it doesn't, you know, we tend to think relational. So, you know, there can and should be structure and big data, not always. So can you be the one to say, when do we need to structure a hive, do we need to have something in a hive table? Or when do we not? When is it really just a, you know, kind of a censored data stream? If I'm moving to the cloud, where is the PII information and need to track? You know, how do I, or one of our big clients in the UK, basically they're building data standards for in and out of things data and publishing that as open data sets. So yes, the structure behind the IoT. I mentioned there was an amazing presentation from Uber. I'm sure I could share it, but we signed this big old NDA. But it was, you know, and I thought they explained really well. They're using everything from taking censored data from the cars and some of the pictures in the cars and where they're going and map data to trying to optimize each step of the way with a different architecture. And the question was, what are we trying to do with it? Are we trying to store it quickly? Are we trying to read it quickly? Are we trying to report on it quickly? And each of those requires a different structure. And I think someone who understands that is super valuable. Yes, if you want to just store it quickly, you want to put it out in the S3 bucket and that's what you need to do. But you might want to put tags, metadata tags on that. So you know, for example, or if you want to report that in a data warehouse, you probably want to put that in a relational database and here are some options and star schema versus whatever, and it's getting back to that use case. And I think there's so few people that can understand those different architectures and why you use each one. I want to do real-time chats on my website and get instantaneous statistics and usage patterns unless, you know, that's not a relational database, right? So, you know, I think someone that can speak that is a huge value to any organization. And to do that, you need to stay educated and I think that's all of us. Yes, we're all busy. Put that out there. I've done this. I am a big fan of some of these online courses. Dataversity, of course. I know you're all here. But either things like Coursera where you can do online courses or MIT has opened up a lot of its open courseware and there is just an amazing wealth of stuff out there that's either free or really low cost. So there's almost no excuse to be really, not really smart and stuff. And I've seen, you know, we think this is supposed to be more about your career. I've seen some, I'm a big fan of university several degrees myself, but one of the younger gentlemen and one of my customers didn't have a, he only has a high school degree and he learned a lot of stuff online. And he's a whisket. Just didn't particularly have the means when he grew up but he's smart enough and he learned a lot online. So if you'd like to, there's a lot of opportunities. So a little bit in the chat because I know folks are using that a bit. Any I missed? Anyone have a good place they go online that's not a university or not? You know, Coursera, any of those? Chat it out. All right. Well, there's a few I would say just look and that's even changing over and over. So, you know, keep things, keep your eyes open because stuff edX someone mentioned is another one that people might look at. So I'll put that out there because I know we tend to be the learning type and there's a lot of good stuff available. So take advantage of it and if you're not, you're going to be a dinosaur pretty quickly. That stuff changes so fast. And the other thing is data architects do come from, TDWI was another one someone mentioned, eLearning. So data architects come from a wide range of backgrounds and I think that's what makes us special and effective. So for some reason I've seen a lot of musicians. You know, I have a friend that went to the Berkeley School of Music in Jazz Theory and he's a database administrator. So I think there's something about music that's logical to get creative that puts us in. So these are all titles that I have seen or actually worked with from chemistry to, there was one, it was actually at EDW a couple years ago and the lady said I was an accountant and I was sick of seeing bad data so I became a data architect. I sort of joined, I wanted to fix it. So anything I've missed, if people want to put in the chat, I saw one say engineering, more of just traditional engineering, building stuff. So I've seen, anyone else have kind of a different career that they've come from? I've seen, some of these might not be initially thinking like a social worker, right? In fact one of my customers, they said oh, we're the data people and all our customers are social workers, they won't get it. Well a lot of statistics and population science in social work, they got it. So I think there's a lot of things, physics is someone else that someone came back with. So again, and if you aren't initially in data, I went back and got a computer science degree partly because I just like the stuff. But you don't have to and there's a lot of stuff online and a lot of great, I think that often is our strength is that we, operations research is something else that people came back with. So a lot of good stuff. I'm going to quickly talk about me, it seems like really self-centered, but I will give a call. One of the reasons I thought to do this is if anyone was at EDW this year in Atlanta, we had Ursula Cartone, Cartone, it was my Italian. So she did the keynote and I think, I think Shannon will send out the link later if I'm not wrong, that you can get the videos of this later and it's probably worth listening to. She kind of did a day in the life of how she ended up being chief data officer at a fairly large organization and her crazy path to get there. It wasn't, you know, you don't go get a degree in chief data authorship, even anything, right? So I think most of us come back from a really odd way of doing things. So I'll quickly show you my crazy way to get where I got. So my initial degree was in economics and English. I don't mind saying it, English major, way back. But I think when you talk about communicating, that comes in really handy. I've written a few books, I think, learning to speak. I was on the debate team in college if we want to get nerd competition, right? I think it really helped in terms of public speaking and that kind of thing. So in college, my parents always told me this. In high school, I did a lot of weird jobs from, I was going to be, I was going to work on Wall Street. I was an economics person. I was going to be finance. So I did a lot of that. I also did some weird temp jobs. I actually did drive a fork truck, actually a truck. And I think, especially now, what I do for a living, having worked in some of these industries and understanding how a business operates is just super valuable.  Because it's amazing some of these things I have done. I worked in the fast food chain in high school and did a job at a fast food company. And it sort of helped because I kind of done the walk, right? So I graduated, went to D.C., worked in one of those economics, told him to think tanks. And we were doing, I guess you would have called me a data scientist back then. They didn't ask. I was sexy. I had data scientists. So, we did kind of economic analysis. For me, it was awesome. Worked with a lot of the top secret security clearance, a lot of the decision makers around the globe flew around and worked with these decision makers and built models and built software to do that. And I am not necessarily always good at predicting trends and my stock patterns have maybe proven that. But in this case, I did. I said, this computer thing is going to take off and data is really hot and it's really interesting. You can make a way a lot more money and we'll have more job opportunities and having to get a PhD in economics. So I went back and got a computer science degree and became a programmer and this thing, we were building models to do, I guess someone asked the question, yes, a content metric kind of analysis which is sort of data science and statistics and that kind of stuff. I thought programming was super fun. Did a little in high school and kind of went back to it. Let a programming team, but you know, and the bad thing about, not to knock government because I think there's a lot of great people in government, but the agency I was in was this wasn't able to use some of the new actually very locked down secret stuff and they couldn't do some of the new trends. So I went to a company that did some of the new trends, but it was often way back before some of the modern approaches and it was sitting in your cubicle and write code and don't talk to anybody and that if you guys know me is not me. So quickly left that when it became more of a consultant for a company called Platinum Technology if anyone remembers that and that was more my sweet spot of working with people, working with data, helping companies understand how to use their data. Spent some time in the US, also spent time overseas. I worked in Italy for a couple of years. It was a horrible, horrible place to have to live. I'm sarcastic. It was awesome. Highly recommend it. Came back to the US and they asked me to join product management. So I kind of did product design for one of the metadata repositories on the market back then. Also did product management for some of the data modeling tools and I think again that programming background that I had done having been in the field again who knows what's got to you know help you there. Back that I'd done data architecture I'd also done some product marketing for one of the data architecture tools out there which really when you talk to somebody and think about how do you sell a project? Well I literally was selling stuff, right? Found out that I really like the consulting because I like to get my hands dirty doing the stuff and I like to actually build things to help customers not that product management isn't building stuff. But for now I think that's really sort of fun. So I did some time at more of a business consulting information unless the data which I think was just eye opening when you think of how to talk to executives and how executives think and how you really want to transform business outside of data all together. But then I realized I like the data stuff so put the two together to do global data strategy where I think there is a niche in the market where can you take that business transformation experience and link it with data and link it with business and all the other weird and sundry stuff I've done and did that and that's just me I'm not saying that's the perfect or greater it's just a career it's next who knows right? There's so many opportunities is it building a new product with some of these new tools that are out there now I still think there's going to be some great new tool. So I think there are so many opportunities now just keep your eyes open I wouldn't have planned any of that when I was 16 but you kind of just get there and I think Ursula at EDW has an even better story than mine it's probably better speaker than me I really enjoyed it so if you weren't there to see it I would recommend that and listen to other sundry going off course so sometimes it might be a role that might not like some of my roles had nothing to do with data for a while but it was interesting when I came back to data that those were valuable so in summary hopefully I had a couple tidbits that might help people it's a great time to be in data management for us old folks it's finally our day in the sun this our little guy got told or so I knew you needed to manage your data better so capitalize on that keep learning use your strengths I think people need to communicate it and most importantly discover how you can add value to the organization so that's probably the best tip what's hot what do people care about how can you tie yourself into that just a few more things before we wrap up there's me if anyone has questions or wants to harass me later ask a question or praise or whatever there I am there's my company we do this for a living so if you need help let me know speaking of skills there is a training center I particularly have a course on metadata there's ones on governance and stewardship and a bunch of other stuff as well there's a whole other series throughout the year so if any of those things we kind of hit on as I mentioned MDM integration et cetera let us know and then without further ado if there's any time for questions we'll open that up now thanks Anna as always we always have another great presentation and we have a few questions coming in here we've got a couple minutes left just a reminder to answer some of the most popular questions is we will be sending out a link to the slides the recording and a follow-up email by end of day Monday I'll also get at you the links so you can purchase the links purchase the recordings from EDW and take a look at Ursula's talk the first question coming in here is how do you find the data advisor roles I think that's a great question and you don't sort of find them in a way or they're not out there I have not seen that as a title you have to almost become that so I think how you find that is you make some relationships with the business you find some problems that need to be solved and you put those selves in your situation I would say being approachable and showing some solutions to problems and then you sort of become I wouldn't say that's a title I would say it's almost a role use that correctly but I think it's becoming approachable finding the projects to help offering yourself up as a helper and showing your expertise and I think people will start coming back to that's probably my best advice there great question and I think most of the comments were just comments not a lot of questions coming in today everyone's quiet well I shouldn't say everyone's quiet this is not a lot of questions I think it's good Donna thank you so much for another fantastic webinar we so appreciate it everything and that you do with us we look forward to even more and hope to see you all and thanks to our attendees for being so engaged in everything that we do we hope to see you all next month in May as Donna mentioned hope everyone has a great day thank you thank you


What's going on guys, this is Coby from the surf station with a quick board review on the new Channel Island Zeus surf board It's just another great model from the Taylor Knox collection According to Taylor the Zeus is a quick and loose all-around performance shortboard from the looks of this board It's another great board for bigger surf and has a bit of foam opening up the flexibility of riding in the smaller surf as well While I wouldn't put this in the small wave board section of your quiver. This is definitely a great board to have The nose on the Zeus is kind of pulled in but it's got enough rocker door It's gonna make it much easier to drop it in on a bit steeper waves It has more of a thickness to it. So you might have a little bit more drive The Zeus just has a bit more of a tapered down tail. It's a bit thinner It's gonna give you a little bit more of a drive because you got a deep double B concave It's definitely a better board for bigger waves. It's got pretty hard rails It's definitely going to be much smaller and narrower of a tail than most the other Small wave gravel boards by Channel Islands This board is a little bit thicker in the body Which is going to give you more paddle power and definitely give you a little bit more drive But it does taper out to the tail so you have a bit of a thinner performance tail for bottom turns and such The Channel Island Zeus comes in two different things you can get it in a five-inch setup or a three-inch setup Each setup comes in either fcs one fcs two or futures the Zeus It's just a good all-round performance shoreboard. It's recommended for about waist to head high waves The good thing about this board is it has a little bit more of a thickness in the center of the board Which will make it a little bit easier for paddling into waves We have a lot of them here at the surf station come check us out or check us out online at surfstation store.com


what's good YouTube this your boy Chihuahua back at y'all again with another speed art today I did a picture of that boy Luzanne on my iPad Pro the program I used is called Procreate so go ahead and check that out in your app store you gotta pay for those about three dollars but I'm telling you it's worth every dollar man and I've been having a lot of fun using it so you finna see a lot more videos we can use in my iPad Pro than Adobe Illustrator because that's still my number one check it out this is worth it man you know I'm saying you can't be three dollars and if you're new to the channel go ahead and hit that like button subscribe make sure you click post notifications so you will be notified every time I drop some heat and welcome to the family


You're welcome back.  Against this major concern, international alerts with the support of United Kingdom UK AID say the aim is to stabilize and end gender-based violence. The said modern 20 percent of male children have been abused sexually, noting that their silence has impacted negatively hence the need for both genders to speak out for justice. The sharpest enough women for peace network woman Laura Bemosez, more attention has been given to female child in the fight against SGBV, adding that the boy child must be given priority. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


All right, so this is the full story. This is the middle of the drive, and I come out for a play, and I told my receivers coach, I was like, hey, I gotta go to the bathroom. He was like, you gotta pee or do a number two? I said, I gotta do a number two. And then it went away for like 10 seconds. So I was like, all right, I'll go back in the game. And when I was back in the game, the feeling came back up. And so I run back to the sideline and they had a cart ready for me. So the head trainer was like, you gotta go to the bathroom. And I was like, yeah, he was like, it's a cart down there. I'm like, bro, I'm not taking a cart. Then the equipment manager comes up and I was like, hey, it's a cart down there. I think you should go ahead and go. Pete wanted me to do it on the sideline. I'm like, you know, I got too much respect. And I said, I can't do it on the sideline. And so I hopped on the cart and went to the locker room. I was actually mic'd up that game too. So this is, this is like very... So NFL films has full pooping? Yeah, yeah, they got everything.


Hello everyone. Once again I welcome you all to MSB lecture series on transformative chemistry. Today let me begin my lecture that is 53rd in the series on methods of characterization with more emphasis for spectroscopic methods. So far we discussed in length about coordination compounds and to an extent about organometallic compounds. In case if you make new organometallic compounds or coordination compounds to ascertain its purity and to know in what quantity we got and what is the yield, how we can improve to get all these things to get an insight into the synthetic procedure that was used and also to improve the method we should know what kind of compounds we got it. Of course the mechanistic path and all those things we already discussed in my last series of lectures. Now let us look into the characterization of these compounds. For that one we have to use analytical instruments as well as spectroscopic instruments. So let us start with UV visible spectroscopy. Before that I shall give little bit introduction to the methods of characterization or spectroscopic methods that we have at our disposal. So how to go with the spectroscopic approach to understand the structure of a molecule and also its purity. Nowadays the spectroscopic methods are widely used not only by inorganic chemists but also you can see synthetic chemists of course. Synthetic chemists when we talk it includes both organic chemists and inorganic chemists and forensic chemists also it is equally important to analyze the samples and also to ascertain toxicity and all those things and environmental chemists also it is very very vital and of course biological chemistry is insufferable with spectroscopic methods nowadays and natural products chemists also when they make new compounds or extract new chemicals from natural resources to understand what kind of compounds they got and what impurities are there and how to purify. So spectroscopic methods come very handy that means to identify new chemical compounds that we make and natural products from plants and animals that are made and drugs, toxins and explosives when it comes to forensic materials containing soil, water or the atmosphere agricultural science and mechanisms of biochemical process to understand mechanisms of biochemical process not only biochemical processing inorganic and organic process also the spectroscopy comes very very handy and it is an important tool in the present scenario. Let us look into spectral interpretation and general process we should follow for structural recidation of an unknown sample. First what we should do is let us begin about purification of the compound let us say we have pure compound how to ascertain we have to go for CHN analysis or mass spectrometry data and NMR that would give you information about molecular formula and then NMR IR UV can also give you insight into the functional groups present in it and NMR can also provide you information about substructures for example if we have whether we have methyl group aromatic group is there and then carboxylic group is there this all information comes and of course X-ray gives a complete if it is a solid compound if we can able to crystallize and get single crystal suitable for analyzing or performing X-ray crystallography then we can get molecular structure. So then about unsaturation we can get information from molecular formula and then possible structures come from all these avenues and then we can draw all possible isomers and then once of writing all possible structures we can again go back to NMR mass and IR data to arrive at most appropriate structure. So always we should have the habit of using more than one spectroscopic method the analytical method to ascertain the structure to understand the purity of the compound also to confirm that whatever the molecule we decide that we obtained in our synthetic method. So then NMR would give you reasonable structure and ultimately single crystal X-ray analysis would tell you what it is provided it is a solid sample so that means no molecular formula. So this is how we can go through various instruments and the data from those instruments to understand what kind of molecule we have made and also the details about that molecule. So to identify molecules that we prepare we can use an array of structural information elucidated by spectroscopic methods. We want to be able to get the information as rapidly as possible and we can deal with all phases of matter mixtures and pure compounds. So in that context again spectroscopic methods are very very vital in today's synthetic world. The process of determining structure is very deductive and is very much like solving puzzle. Often if we make new compounds certainly we can enjoy analyzing and interpreting the data from spectroscopic and analytical methods we get to understand what we have made. So one or more spectroscopic experiments is carried out and by analyzing the data we can hopefully determine the structure. The most common methods for structural determination are mass spectrometry and nuclear magnetic spectroscopy NMR, nuclear magnetic resonance spectroscopy and infrared spectroscopy IR and electronic spectroscopy UV and visible. Each method provides its own special kind of data that we can apply to molecular structure determination and each spectroscopic information you know gives a very interesting and a special kind of data and when you club together all this information without any ambiguity we can understand the structure of a molecule. So contributions from different form of spectroscopy comes towards understanding the purification of a sample. Among them nuclear magnetic resonance spectroscopy plays an important role when we have NMR active nuclei in that molecule. So NMR experiments apply to nuclei that have the quantum mechanical property of a spin. I shall elaborate more when I go to nuclear magnetic resonance spectroscopy after completing this UV visible spectroscopy. So time being let us know that all nuclear spin with I value half such as 1 H 13 C 31 P 15 N or nuclear spin I equals 1 for example 2 H 14 N. They provide information about the types numbers and connectivity of particular atoms. For example NMR can show ethanol has if you take simple ethanol if you record NMR spectrum 1 H NMR spectrum for this one. So it can tell you about the presence of three groups such as C H 3 C H 2 and OH. How two types of carbons in the ratio of one is to one that information comes and then three types of hydrogen in the ratio of 3 is to 2 is to 1 that information comes and C H 3 and C H 2 groups are bonded together this information also comes. So that means that is good enough to understand yes if I have ethanol in my hand. Similarly for isopropanol if you take isopropanol so what you can get from 1 H NMR is yes it has two methyl groups one C H group and one OH group for many molecules the entire structure can be reduced. So routine NMR experiments are performed on solutions of the molecules of interest normally in deuterated solvents such as C D Cl 3 C 6 D 6 D 2 O or even acetonitrile C D 3 C N and for many we only need a few milligrams and in all these cases when we perform NMR we need very few milligrams of samples about 10 percent of NMR active nuclei should be present in it. So this depends upon the abundance of the isotopes we are looking at this is very very important if it is 100 percent abundant very small quantity is sufficient but the percentage of NMR active nuclei is small then we need more samples. NMR can be used to study mechanisms and also we can get an idea about presence of transient states or even intermediates of chemical reactions in solution especially this holds good it can provide more information when you perform a chemical reaction in solution medium the samples may be mixtures. So NMR in general is a method but it is most useful spectroscopic approach for determining the structure. So NMR is a very general method but it is a most useful spectroscopic approach for determining structure of molecules. Infrared spectroscopy also called as vibrational spectroscopy so IR spectra results from the absorption of infrared radiation that causes vibrations of the molecules the spectra are typically presented as percentage transmittance or absorption the peaks carry a lot of information such as functional group identification structure information and even symmetry. So IR is a very sensitive method and is used widespread in part due to its easy sample preparation very easy to prepare sample we can record sample in solid state in solution and also for gaseous molecules we can still record IR spectrum. The equipment used is relatively inexpensive very compact and simple to use a single spectrum can be run in just a few minutes. It does not take very long duration of time for recording a spectrum or preparation of the sample. Samples can be prepared in few minutes and also we can complete recording a spectrum within a few minutes. The presence of functional group give rise to distinct features that can be identified within well different ranges of spectrum but IR is only limited to determining the presence and identification of functional groups it ends it can give you some information about presence of those functional groups that is it it cannot go beyond and it is very difficult to understand the structure and composition of the entire molecule simply by using a recording IR spectrum. Now let me give you approximate time scale for structure determination. You see for example here I have listed various techniques and also the time scale for that particular technique. So electron diffraction whatever the process that happens up to 10 to the power of minus 20 seconds one can record that means anything that is very very fast so that can be recorded with electron diffraction and x-ray 10 to the power of minus 18 up to that and uv visible up to 10 to 10 rise to minus 15 and visible 10 rise to minus 14 and IR and Raman 10 rise to minus 13 and ESR 10 rise to minus 4 to 10 rise to minus 8 and NMR 10 rise to minus 1 to 10 rise to minus 9. So that means whatever the dynamic process that happens in the time scale of 10 to the power of minus 1 to 10 to the power of minus 9 can be identified by NMR. If the process is very slow slower than 10 to the power of minus 1 or faster than 10 to the power of minus 9 then NMR fails to give any insight into those dynamics happening in solution. First kinetics 10 rise to minus 3 to 10 to the power of 2 and physical separation if any sample is stable for more than 100 seconds then one can visually look into it and pick them hand picking can be done too and if they have different morphology one can do hand picking under a microscope. So this is very very important information as far as time scales are concerned for various techniques. So now this gives electromagnetic radiation applied in different techniques here so our attention is ultraviolet. So now let us try to look in more detail about UV visible spectroscopy. Whenever we have to study something the question pops up in our mind why should we learn this top and after all it makes sense here if I make this statement because nobody solves structure with UV any longer because just with UV you cannot solve the structure but you need proof from many other instrumental data. So that means why we should learn this top because many organic molecules have chromophores that absorb UV light and then UV observance is about 1000 times easier to detect per mole than NMR so that means very easy. Still used in following reactions where the chromophore changes useful because time scale is so fast and sensitivity is so high and kinetics especially in biochemistry and enzymeology use are depend heavily on UV visible spectroscopy for the same reason. Most quantitative analytic chemistry in RNA chemistry still conducted using HPLC UV detectors one should remember about that one. If we ask why is so important yes it is very very important because of this quantitative analytical chemistry involved in doing UV visible measurements. So one wavelength in that case what happens one wavelength may not be the best for all compounds in a mixture we have to use a range of wavelengths instead of using monochromatic we have to use a range of wavelengths so that it covers all absorption or transition that happens. So affects quantitative interpretation of HPLC peak heights this affects quantitative interpretation of HPLC peak heights. But one should know what compounds could and could not be detected by UV detector one of the best ways for identifying the presence of acidic or basic groups due to big shifts in lambda wavelength for a chromophore containing a phenol carboxylic acid etc. So what happens if the peaks are shifted towards left we call it as hypsochromic shift or we call this blue shift and if it is shifted other way around it is called bathochromic shift or it is also called as red shift. So now let us look into UV absorption process for saturated compounds. Saturated compounds anticipated electronic transitions are sigma to sigma star and sigma to pi star. Their high energy transitions accessible in vacuum lambda maximum is around less than 150 nanometer not usually observed in molecular UV visible. So now if we have double bonds and unsaturated systems less energy for to pi star as a result what happens what we see is n to sigma star and pi to sigma star transitions and non-bonding electrons are lone paced or wave length will be in the range of 150 to 250 nanometer region. And these things are most common transitions among double bonded or unsaturated systems are n to pi star and pi to pi star observed in organic molecular UV visible observed in compounds with lone paced and multiple bonds with lambda maximum will be in this range. In inorganics it is little different additionally transition between d-orbit split by presence of ligand feed. So we have seen in ligand field theory how d-orbit are split based on the influence of the ligand feed. So additionally in getting different transition I show you sigma sigma star pi to pi star and n to pi star we also come across another one that is called dd transitions. So usually dd transitions occur in visible region and besides dd transition in metal complexes we also come across charged transfer transitions. Of course in case of charged transfer transitions we come across two types of charged transfer transitions metal to ligand charged transfer transitions and also ligand to metal charged transfer transitions. And there are instances where if you have a bimetallic system metal to metal charged transfer transition also occurs. Of course in all these cases one can also anticipate ligand to ligand transition as well. So in those cases when whenever we talk about charged transfer transitions one must act as a donor and the other act as an acceptor. Any of these require that incoming protons match in energy the gap corresponding to a transition from ground to excited state. That is obvious for example if I consider highest occupied molecular orbital is here and lowest one is here or any transition for that matter that has to occur between two energy levels we have to supply the energy that matches the energy difference between those two levels. So the energies corresponds to one photon of 300 nanometer light or approximately 95 kilo calories per mole. So now I have given little bit more information about metal complexes and also about de-electronic configuration. Just you know I have segregated various de-electronic configurations into four categories and below that one I will come back to this one later. So let us look into mercury iodide this is brick red compound and it has a detain electronic configuration. And then let us look into K-mino4 potassium permanganate intense purple you all have seen here metal is in plus 7 oxen state as a result it has D0 electronic configuration. And if you look into bismuth triiodide orange red in color in this one what happens detain of course the S2 P3 electronic configurations in bismuth is in trivalent state we have two electrons are left. So now we have detain S2 electronic configuration but this is very beautiful orange red in color and then if you look into prussian blue we have intense blue color you can see and that means here I am talking about metal to ligand charge transfer transition ok and ligand to metal charge transfer transition and also we can come across as I mentioned ligand to ligand but here metal to metal charge transfer transition can be explained here for intense color of prussian blue molecule. Now look into the electronic configuration let us try to understand some similarities of groups segregated in four parts D0 detain yes D0 has no D electrons no electrons and D10 means all orbitals are completely filled ok they are usually colorless and now I have another group very interesting here so this group includes electronic configuration such as D1 D4 D6 and D9 whereas the third one in the series includes D2 D3 D7 D8 and another one fourth one is unique it has a D5 completely half filled electronic configuration. So completely half filled is fine and no electrons is fine and 10 electrons completely filled is also fine then how this second and third categories were brought together yes it is very interesting you can see one electron is there in D orbital and then if you look into this one one less than half filled electronic configuration. So one electron is in D1 and one electron less than half filled electronic configuration and one more than half filled electronic configuration and one less than completely filled electronic configuration is that clear in this group we have one electron system and one less than half filled electronic configuration and one more than half filled electronic configuration and one less than completely filled electronic configuration we have. So now you can see some similarities among these electronic configurations of D1 D4 D6 and D9 now let us look into D2 D3 D7 D8. Now two electrons are there here in the same way two less than half filled electronic configuration and two more than half filled electronic configuration and two less than completely filled electronic configuration. So now it appears like later if I have spectra from all this electronic configuration I should understand as probably in category 2 all these electronic configuration probably would show one type of spectrum or one type of absorption or one type of transitions and similarly category 3 where we have D2 D3 D7 D8 also probably show one type of so just remember the significance behind this kind of classification we will understand as we progress with this course. So now let us look into metal to ligand charge transfer MLCT we call it as if the metal is highly oxidizable the transition will occur at low energy if the metal has a low oxidation number so that means if the metal is easily oxidizable the transition will occur at low energy if the metal has low oxygen state or oxidation number. If the ligand is easily reducible with low laying empty orbitals like pi star in a case of aromatic ligands or carbon monoxide sigma star in PR3 etc that means basically metal to ligand charge transfer occurs if the metal is in lower oxygen state if it is in lower oxygen state it is electron rich and also it is in lower oxygen state and also when it is electron rich because of repulsion inter electron repulsion it is prone to oxidation that we have already seen in our oxidative addition reaction reductive elimination reaction scheme. So that is one criteria in order to see metal to ligand charge transfer transition on the other hand ligands should have empty orbitals to accept electrons given from the metal so that means metal to ligand charge transfer that obviously the name itself says that electrons are coming from metal to ligand usually when we make a metal complex ligands donate through sigma donation if the metal to ligand comes it has to be in the form of pi electrons you should remember so that means pi acceptor capable ligands can facilitate metal to ligand transfer in that case non-classical ligands such as carbon monoxide, phosphines, olefins, pyridines and all those things play a major role. Examples for metal to ligand charge transfer includes 2 to bipyridine, 110 phenanthroline, carbon monoxide, cyanide and thiocyanide and if you just look into this compound here this orange colored complex is being studied as the excited state resulting from this charge transfer has a lifetime of microseconds and the complex is a versatile photochemical reduction agent and then we can also see for example tetracharbonyl phenanthroline tungsten complex or tricarbonyl bipyridine iron complex etc. So let me stop here and come back in my next lecture discussing more about UV visible spectroscopy and now we shall discuss about ligand to metal and then move on to understanding DD transitions in more detail. Until then have excellent time and also keep reading. Reading should not be stopped as far as we are learning something and do well. Take care. Thank you for your kind attention.