 Hello, and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. We would like to thank you for joining the latest installment of the Monthly Data Diversity Webinar Series, Advanced Analytics with William McKnight. Today, William will be discussing what happened of note in the first half of 2020 in Enterprise Advanced Analytics. 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. For questions, we will be collecting them via the Q&A in the bottom right-hand corner of your screen, or if you'd like to tweet, we encourage you to share how it's a question via Twitter using hashtag A-V-V analytics. And if you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the bottom middle of your screen for that feature. And if you'd like to continue the conversation after the webinar, you can follow William and each other at community.dativersity.net. 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 speaker for the series, William McKnight. William is the President of McKnight Consulting Group. He takes corporate information and turns it into a bottom line producing asset. He's worked with major companies worldwide, 15 of the Global 2000 and many others. The McKnight Consulting Group focuses on delivering business value and solving business problems utilizing proven streamlined approaches in information management. His teams have won several best practice competitions for their implementations. He has been helping companies adopt big data solutions. And with that, I will give the floor to William to get today's webinar started. Hello and welcome. Hello and thank you Shannon. Welcome everybody. Thank you for joining us here for another installment of the Advanced Analytics webinar series with me. And this is probably number 18, something like 19 in the series. So I'm really proud of the work we've laid out here. And for this opportunity to share with you each and every month what's going on. Now, when Shannon asked me back in, I think it was September of last year for my titles for this year's series, and I gave her this one. I thought, well, come July, I'm going to have a lot of exciting advancements in the industry to talk about. There's going to be so much going on because so much has gone on and I knew some things that were coming to the surface that were imminent that I wanted to see play out and share with you and I do have some of that. I really do. There are things we need to know, but of course I had no idea that we would have an elephant in the room here that has just affected all of our lives. And of course I'm talking about 19 because that has happened most notably in the first half of this year and it takes center stage. I've had many conversations, obviously our business is still thriving and humming and I'm still talking to people in this industry all the time and this takes center stage. I mean, we have to take a step back sometimes in terms of how we operated before driving in business and all this and that and deal with this that's in front of us. And if you're feeling this way, you're not alone. And I'm no COVID expert or anything like that, but it is something that since we are corporate citizens, first and foremost, I might add. We have to deal with it. As a matter of fact, it wouldn't matter if I were here trying to talk to you about what happened of note in first half 2020 in supply chain management or in demand forecasting or in product management or what have you. There is the one thing that has affected all of us, every industry, every dimension of every industry and so this will be, you know, flavored a bit with with that I have to address it. And I want to bring you up to speed as well because I've talked to a lot of people in the industry and they said, well, you know, COVID made some changes here and I've learned what those changes are. I want to share that with you, but a lot of people are just unaware of what they need to know about what's changed and how to how to deal with the changes. So I'm going to touch on some of that as we go along here. I've been introduced and I will move on. Yes, my consulting group, we do strategy training and implementation. So, without further ado, let's dive into the, the number 1 thing that's happened here to all of us. COVID-19 and how it's affected us as information management professionals. What has it done to the industry vendors or everybody still most for the most part, you know, still working. Although I must say some things have been, been muted and other things have been accelerated. So, let's see what's been going on. Well, first of all, let me just say this on a personal level. Most of the time actually probably all the time I come on this webinar and I'm talking about if we look at Maslow's hierarchy of needs, I'm talking about self actualization stuff. I'm talking about building data lakes and master data management and all forms of analytics and virtualization and so on and all the exciting changes that's going on. And it's fun to do that. It's really fun. However, many of the conversations that I've had recently and I'm sure you've had as well have been kind of across the board here in terms of the hierarchy of needs. So, when something like this little fellow COVID-19 is impacting the world as it is, we are our foundation is shook. And we are dealing with a physiological level, the safety level of course, first and foremost, and I find some people are we're kind of stuck down there. And it's sad. It's sad to see and I want to encourage you if you're if you're not actively, you know, doing something for your family for this this menace that's upon us to take time to make sure that you drive yourself up towards the self actualization that we used to do. And do some time slicing across these parameters here of the hierarchy so that you're not getting stuck and you're learning what the changes are and some changes are going to be permanent. And for some of us to in order to do this and keep on our our path of productivity, we need to take some media breaks potentially. You know, things like this that help help us to to know that for the most part things are still going to be happening as they did before with some of these changes going on. So, anyway, and also on a personal level, if myself or Shannon can do anything for you in this time of need, please do reach out and let us know we are here for you. We are community driven. There we go. All right. So, let's talk, let's talk about this. COVID-19 has impacted worldwide operations. You're not alone. It's impacted you and your company. It's been so sudden. It was so sudden and with such accelerated disruption that we've never seen in our lifetimes and probably will not again. I think I know I remember the day that it really at the moment. This is major. This is major change to the world. You know, we all we heard it coming. We've heard about it happening different places and whatnot. And when when I was watching some NBA basketball and suddenly the game was called off, you know, before it began, I knew that this was going to be huge. And we all kind of know where we were in that moment when we realized that this was going to be big and that we had to step back and deal with it and we all would have to step back and deal with it. We think no discontinuity events like this before and may never again, it's just put a pause in everything and it's impacted our customers as well. And so now when we're talking to our customer, when I'm talking to my customers, I'm trying to help them. I'm always trying to help customers, right? But now I'm trying to help them in different ways. And I think that's the approach that winning companies are going to take. Maybe they have to scale back some quotas. Maybe have to have to scale back some expectations right now and stay really customer focused because your customer needs have changed. And some things have changed in terms of their foundation and you got to change with them. And I'm going to try to share with you some of the major things that's been happening to my customers, probably your customers as well. And I want to give you a framework for operating in this COVID world. That's what I want to do to the data professional here today and give you all the different data points that you need to be aware of and start to, you know, figure out what you're going to do about them in your day to day work. Because COVID impacts your health and your well-being. Obviously it impacts your health if you have it or are around people that do or are susceptible to it, but it just didn't just in your day to day thinking about, I know for me, it's like every time I cough, I'm like, oh my God, do I have it? You know, it's impacting our well-being. So we have to acknowledge that we need to step back and we need to plan. We might need to change those plans we just made for this year and maybe modeling along at this point. So let's get back on track here. And the major thing about COVID for a data professional, a corporate professional is a lot of working from home. You're probably a home right now. And there are pros and cons to this. Some of us feel like we've lost that personal touch. I know I feel that way. For whatever reason, and I think it's just because we've lost our personal touch, seems like all the meetings that I've had in the past couple months, people want to turn on their video. And I turn mine on right along with it. Seems like all the webinars, not this one, not yet, but most of the webinars that I'm giving, I'm on video anymore. And that's been new. That's been a change. And we're trying to regain maybe some of that personal touch. On the bright side, I feel more connected to some people, some members of my team, some of my clients and so forth, because life has slowed down. There's a little less of that. I don't have time, but let me get back to that. We're meeting each other's families. We're talking more personally, hopefully establishing stronger connections that's going to be better for us in the long haul. We're meeting each other's pets. They come in on the video and so forth. That's really cool. I enjoyed that. But life has slowed down. There's little less of I don't have time. We're doing things that we never had time to do before. Hopefully we're doing some of these things, right? We're doing our upgrades. We're doing our maintenance. We're looking at our processes that maybe had some holes in it before. Maybe that path to production process, maybe our agile processes are needing a little bit of our attention. Now's the time. We're doing some documentation that, you know, we need to do and we're doing some learning. We're skilling up a little bit and I'll come back to that a little bit as well here because that has not abated the need for your learning. My learning has not abated in this time of COVID. What I want to say about this. Yeah, if we get out of this crisis, you know, quote unquote get out whenever that is. Hopefully it does happen. No guarantees, of course, but if we come to a year down the road or whatever and we have not, we're still back level on major pieces of software in our company. That's kind of like a neighbor that never mows their grass and it just keeps growing and growing. Okay, that's a big no no. Now's the time to hop on some of this. Now's the time to think out of the box. Now's the time to say, okay, for the last several years I've been heads down. I've been working to get away from the thing right in front of my face. And now I have maybe the opportunity to think a little bit broader, to think maybe a few feet out from my face or maybe some months or years and put in place some plans and be a part of that. So do think outside the box. Do think a little bit longer term now that you hopefully have the opportunity. There's virtual everything going on right virtual meetings, virtual hiring, there are people I know that have been in their companies for months now that have not seen the building that they supposedly work in and not seen any of their coworkers face to face, except over zoom and whatnot. So that is just a new reality and guess what it's kind of working. It's kind of working. So I think some of some of what we're doing now by necessity is going to stick around. Remote conferences. Yeah, conferences are happening remotely learning opportunities are still out there still prevalent. And for me, conferences being remote. I have mixed feelings about it because I really like seeing people face to face talking to people having that personal interaction, but I don't mind. I don't mind being able to get up in the morning and be at a conference and skip all the travel day, you know, there and back. So there's good and bad to it. And I think a lot of companies that I've talked to, they're going to be adopting some level of remote conferences there might, for example, we might expect that the big companies will have the one big one a year. And all or maybe some more, maybe they'll go more regional, but they won't have as many in person as we go forward until we figure this out. So that's what you can really expect. And we're also, I skipped this bullet here. This is important. You might think, well, you know, we don't have a commute anymore. We're not going to the office. So that's more time for us, right? But no, I find that information management professionals now just start working earlier. And they keep working till later and some of them are burning out. And that's a problem. Yes. But a bigger problem I see is that we're training ourselves to work at a slower pace. We're training ourselves to be less productive because, hey, we've got more time now. Suddenly, we're just expand the work that we need to do to cover more time that we supposedly have. And I want to caution against that. I want to keep up a strong, hard pace. And maybe even, you know, if you must stretch that out over more hours and be even that much more productive. So that's the way to go, I think, versus just slowing down completely here. All right. Data protection, speaking of things that are the COVID impact. Yeah, data perfection. It used to be with some people occasionally working sometimes from their house. This wasn't such a big issue now that most people are working in their houses. There are security concerns now about data and about all forms of communication. There's also high speed access issues, especially when we're geographically distributed. And not everybody has the same level of high speed access. So these are different things that are now coming to the forefront that I think, I T departments are dealing with. And then there was zoom, there was there is zoom, but it was great. We were all over it in that first month of lockdown and then the security concern came up Google famously. Got off them because of it and so many followed, but nonetheless, whether it's zoom or we're on WebEx right now. Go to meeting, etc. These are all booming right now. And these are different tools now that we are learning to use in the very best ways as sharing confidential info suddenly became a big issue because we're not doing it all inside corporate walls. And we're reconsidering our tooling. We're balancing familiarity with security. So security has become a huge data point that we're looking at in terms of considering software for purchase within our company. So if your favorite piece of software doesn't have great security, you know, that software that you were looking at, that may be a problem at attaining that now. Now we are starting to some of us anyway, preparing offices for the return of employees, but I say it's going to be different. And we have so many different geographical situations. I mean, how many different legislative profiles. Right now, do we have in our country, United States, we probably have 100. And so they're all different have to be dealt with. So I think we're going to start to have to see some really different profiles of returning to the office. Maybe more in some areas less in some other areas. There'll be special distancing parameters inside of offices. There'll be limited population limited death. We're going to be stockpiling sanitizer. There's going to be sanitizer stations. And I also say I didn't have it on here, but ventilation. We've learned ventilation is huge at eliminating. So you have to be have to be some innovation in ventilation of our building. And if you're in the cleaning business. I think you're pretty busy right now. Cleaning these offices on a much more regular basis. It has to happen. But at the same time, since distance is working. Coming returning to the office is not going to be something that we're going to kind of herd out and do here ASAP. So surprise distance is working and some percent will stay off site. So now suddenly when management didn't have to deal with detailed work from home policies. Now they do. Now they do. So some percent of us are going to be staying off site, or we might have multiple people to one seat type of arrangements when we come back in order to social distance. And some projects can be done all remote. So there's going to be many factors that go into the equation of whether you're going to be working from home or working in the office. Going forward. So keeping for focus. I touched on this a little bit ago. Staying in the head of your customers. Get out of your own company's head. Okay. This is the sign of a mature organization. Get into the head of your customers. How are they affected by COVID and what can you do about that? Release the pressure from sales forces for quota this quarter and maybe the next quarter and the one beyond. Encourage more personal touch. Encourage more of this helping customers where they are and where they are right now. It's quite possibly in a quite different place than they were five, six months ago. Help them survive. Help them thrive in this environment. Now resilient companies are going to come out of come out ahead. They always do. This is the time in in my life, probably all our lives when the Warren Buffett quote about when the tide goes out, you know, we see who's who's wearing pants or whatever it is. We are doing that right now. And unfortunately, this is cycling a bit of innovation in our software market because there are some companies that needed sales this quarter to keep the thing going. They've got great technology. They have interesting technology. They have artificial intelligence, but with slow down, they are now suddenly right for the picking. And we are seeing large vendors going on shopping sprees and and I won't say specifically, but I see more of that activity about to happen. We have a lot of planning going on for absorption of companies. So the question, I don't know the answer, but the question is, will big tech, which is now seemingly the only major category of tech that's surviving. Will they crowd out some innovation in our market. So that remains to be seen. Let's slide on on on code. All right, those who are less impacted are those of us who are cloud first already who are microservices based where data is already a separate function. And by the way, I'm calling out some things here that are in my maturity model. The presentation on that I gave in April, I believe you want to go look that up on YouTube. There and some of us who are more mature about or have been more mature about our data are now less impacted by COVID. And we're not scrambling in the way that others will be. What about agile development? A lot of companies have been putting that off or doing it haphazardly listening to naysayers inside the organization about it. But now with the workforce suddenly exploding becoming very remote, it's become pretty important. So if you're already doing great agile development, you are ahead of that game. And you're able to think about some more progressive things during this time and master data. I can't leave that off. That's a very important. If you have that cold, cold off as a separate discipline, ready to go ready to hit projects. And I've had presentation on this as well. Ready to support all these projects going forward. You've de-risk those projects. You've done a great thing there. So these are some of those who are less impacted by COVID. Hopefully you are that if you've been doing everything that I've been promoting on this webinar series, which is probably possible. But if you've been doing a lot of it, you are possibly less impacted. I hope anyway. But there are some things that's happened in the first half of this year that I want to share with you some things I'm excited about. So let's get into some of those specific events that I want to put on your radar. I want you to think about it as not only it's happened, but these are trends that's happening in our space of analytics. And some of our consortiums are happening. Here's one in particular that I want to call your attention to because I'm excited about it very timely, isn't it? Right? The COVID-19 high-performance computing consortium where companies like, and I'm afraid I'm going to leave some off here, but some of the ones that you would know about in this discussion working together IBM, Amazon, Amazon Web Services, AMD, Intel, Google Cloud, HPE, Dell, Microsoft, and there are others don't mean to leave any of them off. There's about 20, I guess, but they're working together to bring together the federal government industry and academia to provide access to the world's most powerful high-performance computing resources. Yeah, it's an open community and this is one of many. We see open communities now beginning to happen across our spectrum of information management. This one in particular has 30 plus members, I said 20, 24, 30. 400 plus petaflops of data and a petaflop is the ability of a computer to do one quadrillion floating point operations per second. That's a lot of computing power that's been put behind this. I hope it does something that's very visible for us. Of course, we all hope COVID goes away and that would be the ultimate goal or something like this. 100,000 plus nodes, 50 plus projects that are incorporated into this consortium. So let's keep an eye on that and all consortiums going forward because I think there's going to be a lot of them. Now, talking about industries that have been impacted in the first half of this year, healthcare has been impacted, possibly the most. Yeah, I'll say the most. But there's been not consortiums, but partnerships that have arisen that's pretty interesting between tech and the science community, the health science communities. Here's one particular that I'm excited about. Microsoft and Jack's lab for healthcare AI. So this is genomic medicine researchers at the lab have been using AI to help manage the vast amount of research data needed to powers precision oncology initiatives. So this is all about cancer. All right, so this is the core of Microsoft project called Hanover. It's the capability to comb through the thousands of documents published each day. And if you were with me last month, I believe I mentioned how great AI was doing it reading. Yes, reading and research. And this is putting it to work. Another thing that's happening in healthcare is virtual visits. I haven't had one, but I know many people have and that is now a technology adoption that's happened and all for the better. Right. I said different things were going, going virtual. Well, our healthcare visits are going virtual nuts meant projects. Telehealth has also meant a project and why has that gotten, you know, exploded other than COVID. It's because now we have more ability to build for telehealth, whereas before there were more restrictions in place around that. So we're seeing a loosening of those restrictions by necessity and again, this is something I think it's going to be sticking around. So healthcare is going through a lot of changes. We might know about this personally, but do know about it as an industry that there are changes there. There are changes in cyber security. Yes, a lot of security concerns companies have placed big bets on securing applications and unmanaged devices as well as risk and compliance in the first half of 2020. So I try to keep an eye on where money's going in our, in our industry, taking a broad view of the industry and our technology titans and private equity Goliath and platform security powerhouses that spent more than 8.5 billion on this year's top cyber security acquisitions. This has been a very high number one area really of spend. So where spend happening, happening in cyber security. And the point of this is all to discover malicious activity and tackle cyber threats in their early stages. And there are many of them, actually eight of the period for staff. Top 10 acquisitions have been focused around cyber security. How do you like that? So that tells you something about where things are going. So that means I'm keeping an eye on that. We all need to keep an eye on that. We all need to realize that there are threats out there to all the data projects, analytics projects that we're working on. And there may be some new technology solutions such as Amazon guard duty or Amazon web services purchasing cyber security software company squirrel. There may be some new technologies that can help us. Squirrel uses link data machine learning user and entity behavior analysis and so on to do what it does. And so here again, we see artificial intelligence at work. Yeah, that's, that's what it's all about these days. We are placing big bets on artificial intelligence. And I'm going to come to that in a little while. Another industry that's been highly impacted is transportation. And in different way, transportation has been impacted. A big thing that hit my radar that I want to share is Amazon acquiring the auto vendor zoo. I hope I'm saying that right. Here's a picture of what zoos is looking out there and seeing in traffic cars, lights, various different things that it doesn't want to hit apparently with the self driving car. Amazon is not alone. All the major vendors are making big bets on this. So transportation alphabet. The Google spin out is has spun out way more GM has crews Uber is doing this Tesla is doing this and even Apple is doing this. So those kind of are to figure out what they're doing. They are doing something way more. For example, the alphabet spin out raised 2.25 billion in outside funding just in the month of March. And so this is this is long haul stuff. This stuff about self driving cars. The money is going there. So that tells you something speaking of transportation. We have seen supply chain disruption. Those of you that are in manufacturing in retail in anything really that has a supply chain. You are seeing disruption now as a result of COVID. What's happening. I see is that distributors. The screws are being tightened on them screws are being tightened on the distributors because being a pass through like they were before is not enough anymore because manufacturers are taking advantage of the fact that we are now buying more and more. Online. We are seeing retail clothes all around us. And online buying just becoming so successful and so huge that manufacturers are figuring out how to go direct to consumer cutting out the distributors so distributors are really having to be innovative to be sustained in this market. Experts predict that Amazon will focus more integrating the technology, this self driving stuff into its distribution network than building a fleet of cars. Okay, so into and so we see self driving coming into distribution as well. So that's another major impact on the distribution industry. So broadly speaking, the transportation industry is undergoing major change and AI is the foundation of that we see conversational AI for customer service. We see propensity models being built by manufacturers for personas that are their customers. We see IOT enabled sensors that are giving us much more fine tune information about our products and so on. And we're seeing it help reduce the cost of bad quality. And so all of this means a major impact on our supply chains and manufacturers that are out there. But what about in the data enterprise. So I've been bringing it from big picture down to smaller picture for us in our day to day work as data professionals. What is going on in terms of trends and so forth in the in the 1st half of 2020 that's bound to continue for the 2nd half of 2020. Now, some of this I am borrowing from my presentation that I gave back in January where I talked about the trends for the upcoming year. And no, I did not have anything in there like a a virus. I don't think anybody did, but these are some trends that actually have hit quite pretty strongly in my in my circles. And I think they're going to continue graph solutions graph databases for big graph connected data problems. These are being adopted at a very high pace. Data visualization. Yes, I know it's a it's been around a while, but it's been kind of slow to rub out the the reporting mentality that so many companies have, but it's really beginning to make some strides there. And so we just see continued innovative uses of data visualization. Personally, we used to do a lot of this as a company, but I've sort of bowed out of it because the pace of innovation has been such that I like the I like the back end a little bit more. I don't know about you, but I like the back end a little bit more. It's fun to dabble in data visualization, but there's a lot of creative types that are so good at it and and just taking that so many great and wonderful places. So I love, I love what they're doing and presenting them data to do what they do. And so data visualizations going to continue stream processing is really picking up and the whole data integration space. I'll come back to this, but it's really blowing up right now. And I mean that in different ways. I mean, it's getting big. Yeah, getting big bigger than ever, but also the means by which we're doing that is blowing up. And we're doing a lot of stream processing as a result. A lot of kind of hub and spoke type of approaches like Kafka type approaches stream Leo type approaches MQ rabbit MQ etc type approaches to stream processing as opposed to the old ETL approaches and actually stream processing. In my architectures, it's necessary if you have quote unquote big data to absorb. It's just going to come at too rapid a pace to deal with in a ETL fashion. So many of us are adopting that level of great detailed granular data and we need stream processing. And of course it goes without saying maybe, but artificial intelligence now needs to be a part of every project or at least considered considered for every project and considered for everything that you have going on. We see projects continuing, but we see things that are in production starting to get blown up as a result of the artificial intelligence possibilities. The possibilities to do what we now have, you name it, 10,000 lines of code in production for now, let's cut that down to 100 lines of code. That's what artificial intelligence can do. It can vastly simplify and we can so called catapult a lot of the problems that we may have going on right now in production. So artificial intelligence, it's the gift that's going to keep on giving to the analytics and data industry. So you may have everything in production. You may feel like you got it all under control. But now that the competition is going to be picking up artificial intelligence and all the great things that it can do. Yeah, it's time to, I don't want to say blow up again, but I'll just say it blow up those projects and bring in artificial intelligence. Yeah, what's hot in terms of projects. Okay, those were technologies and approaches, but in terms of projects, what are companies still doing out there at a very high pace. These are four that come to mind that we're seeing fraud detection. Maybe this harkens a little bit back to earlier in the presentation I was talking about threads and and breaches and things like this and all the money that's going into the cybersecurity industry. Well, fraud detection is there now as a very hot project always has been I shouldn't say always, but for the past several years, it certainly has been it remains there supply chain optimization. Now we're we're seeing the supply chains are just changing as a result of COVID and other things. And we're really looking to optimize that supply chain. Sometimes that means different partners in our supply chain. So it's just just a thing and awful lot of work for us out there in the supply chain area. Preventive maintenance. Yeah, we're getting away from this. Well, let's let's do it on the schedule. Let's actually really look, really look at what's going on and and put in place. The right balance between swapping out too early and swapping out too late for our overall goals as an organization and getting ahead of maintenance, not letting things break. And that's just, that's just kind of the worst right when things break airplane parts, et cetera, et cetera. Right. We don't want to see that happen. So getting in front of that has become I don't want to say easy. None of this is easy, but it's become something that can be done at a higher level of specificity with artificial intelligence. And customer churn. Now, with all the online retail going on, all the various other things that are going on that's online, we're dropping data points out there at even a much more higher rate than say, even last year, which was at an all time high. So this business of big data just continues to happen. And those companies that are that are able to harness that data and take advantage of it and actually turn that into things like preventing churn, which is huge. They're, they're the ones that are getting ahead. They're the ones that are going to survive and thrive in this era as COVID is upon us. So let me see if that's all I want to say. Yeah, these are projects that apply broadly to all industries in one way shape or form. And then there are so many projects within certain industries that that continue, continue forward. So still a ton of work for the data and analytics professional. It's just changed a bit. Data is new use. New highest use is in artificial intelligence algorithms. Yes, training artificial intelligence algorithms. So this is where you get to know behavior of people and companies out there in, in regards to us. In regards to our company, our products, our people, and we get to figure out how do we change that and make it better, make it better for them, make it better for us. It involves a lot of data collection. Let me put this up here. Data is the foundation to this future with artificial intelligence. I think we've kind of learned this. Some of us are still on the fence about this. Some of us are not getting our data act together before we embark on artificial intelligence. And then it's like, oh, we don't have the data right to do the artificial intelligence that we want to do. But data must be there. It's definitely a foundational part and 80% of customers surveyed that are investing in AI say the hardest part of AI is cleaning up your data, cleaning up your data. Organization. Hopefully, hopefully you've been architecting your data. Well, you don't have a crazy architecture that it looks like spaghetti and hopefully you have sensible allocation of workloads to platform and sensible data integration going on. And maybe some central sensible data virtualization over the top. These are elements that make a great architecture. I'm going to come back to architecture a little bit here. But my final point on this is going to be organizations are becoming algorithm libraries. That's right. cataloging and sharing algorithms because it's those algorithms that are optimizing the company. That's really going to make a difference going forward. So hopefully you're not doing your algorithms in silos. You're adopting some form of machine learning ops. I'm going to come back to that in a minute as well, but you're learning ways to share your algorithms across your enterprise. That's going to be really important. Okay, what else is really going on out here? Excuse me. Yeah. And let's just, let's just use the word the rise of the lake house. And this is the combination of the lake and the warehouse where the warehouse is reaching into the lake for the data that it needs. We get a water. Okay. Hopefully that's better. Maybe not. But the data warehouse, which is obviously on a relational database is able to reach into cloud based storage, which is kind of a euphemism for the data lake now. And we are seeing an explosion in the data that's going into our data lake and explosion in sensor based time series data in particular and edge based AI. All this means our data lakes are exploding with data that doesn't belong in a relational data warehouse, but yet occasionally, or maybe even frequently, depending on your architecture, the warehouse has to reach into the data lake. At the same time, and probably a conduit for the big changes in data integration is the data lake. We're seeing a lot of changes in data integration as a result of the data lake. Another way to say it, right? Automation is happening all around data integration, where we're leveraging the data integration that not only we've done before, but that others have done before because we're able to look across a spectrum of possibilities for our data integration out there. Retaining structure and structured data. So data lakes. Yeah, we can drop any data in there. We don't have to have structure in there. But what we're kind of coming to what people are realizing is, as a general rule of thumb, we want to retain the structure for already structured data, not blow it away and take the time to build a schema for some data that has high business or analytic value or is often queried by users. So this comes down to me. This comes down to learning the difference between cloud storage and a relational cloud database and learning their strengths and weaknesses. And the cost of each and where to place data. That's the balancing point, the very important point for determining the success of a data architecture. So I spent a lot of time talking about pricing cloud data warehouses, cloud data lakes because it is so important. It's not rocket science, but there are things that you have to know about it. I'll come to that a little bit more in the next slide. And finally, we're also learning the data quality is important to data in our data lakes as well, maybe not to the same level. But it's important and we just can't lock and load data. We're learning that we have to do some fundamental. Look, look at data that's going into the data lake in order to make it not become, let me just say, if the data swamp, okay, not become that. So data lakes are rising within enterprises and are working together with data warehouses. Now, here's something else that's settling in out there this as I talked to people and look across this industry, the realization that full best of breed has a price. It used to be the stack was pretty simple. There was ETL, there was a database, relational database, and there was a BI tool. The stack was pretty simple. Now the stack is much more complicated. I'm going to show you the stack in a minute, the new stack in a minute, but because it's so much more complicated, we have so many more pieces. Some of us have these kind of Frankenstein's, okay. And I'm not putting it down completely. I mean, these things come together over time and it's the way it is. But sometimes you have to take a step back and look at what you're doing and see if this, you know, are you truly best of breed or are you kind of best of breed across a spectrum of years, which is not really best of breed. And best of breed in and of itself, something we promote quite often, actually. And, but what, what we mean by it is to open yourself up to the possibility of not being stuck into your enterprise soft enterprise vendors offerings. So let's say you have an enterprise license for, I won't even mention one because that's not important, but for one of these companies, right. One of these full fact companies, you know, is that all you're going to consider, I say no, I say consider even some of the smaller vendors that are out there and what they can do for you. And so that's what we kind of mean by best of breed. But full on best of breed is full of some challenges. All right. And you're going to see some of those challenges in terms of interoperability, cost and complexity, not to mention, or I guess I am mentioning time to value. But it's not all bad. There are some interoperability beacons of hope. For example, there is a Databricks snowflake connector for allowing you to bring snowflake data into a Databricks scale and notebook, which is great. And certainly high end data movement and integration tools like informatic and talent have connectors for many data sources and targets. Many of these top vendors realize their tools have to play nicely with others. Yeah, with each additional vendor and associated application added to the integrated stack and added layer complexity is an invariable to support it. So nobody's got a perfect architecture, by the way, nobody sitting here, you know, pointing fingers or anything like that. Architectures are complex, but we want to keep moving them in a sound direction in a true architecture direction. So what are the new technology facts? Here's the picture we're going forward with now. Maybe you've heard me say this in the past, but as a company, we've been walking around with nine, as the magic number, nine different relevant data architectures for enterprises today. And we've looked at that over the past year and we've, I'll say it again, we've blown it up because artificial intelligence and machine learning has really entered the fray and we're putting big bets on it. So we may have five or six of these relevant technology stacks and architectures going today as opposed to the nine, we'll grow that out. We'll grow that out, but it's going to include stuff like this stuff like data engineering data ingestion by streaming. So we're talking about vendors like Confluent Kafka stream sets data engineering. Yeah, we're talking about like data bricks, EMR, HD insight data proc, the data warehouse. I probably don't have to list those off for you. There's, there's a few machine learning data IQ data robot. How about an operational database? Now that has to work in here with the machine learning ecosystem. Now we're talking there about a Mongo, Cassandra, maybe even a Vertica, data security, Okara, Prevacera, okay, data governance. Yeah, now that's part of the stack, Calibra, Elation, Informatica, et cetera. And workload management may be part of the stack. So speaking of part of the stack, and for those of you that are Uber technical, this might be your favorite slide of the day here. But these are modern platform examples. And there's a single platform example. I'm using these are examples only examples only. I'm using an example of Cloudera platforms here. And they have offerings kind of across the board across the spectrum of need here. So I call that a single platform. Of course, Azure does as well, but they're, you know, different piecemeal things together on the Azure platform, not knocking it, just saying that's what it is saying with AWS. And then there's the multi vendor example. And so I'm just trying to put some flesh on what I've been saying that there are different ways to approach this. And you may be knowingly or unknowingly doing one of these four. And I encourage everybody to be, what's the word, head first or just cognizant of what decisions they're making at a big level. And then there are different sound decisions at a smaller level. Like, for example, which data science platform should we go with? Well, it depends kind of where you're going in terms of your platform. Are you going with a single platform? Are you going with Azure, AWS? Do you really want to try to run, I don't know, Azure, well, that's not a good example. I'm trying to find a good example here. So something that's native to Azure, do you really want to try to run that on AWS? I say no. I'd like to take meaningful risks, but that's not one that I think any enterprise should take too risky. Same for, you know, AWS tools on Azure. But here you see, I'm going to leave the footnotes off that level of detail is not important. You can take the slides, you can kind of, you know, study this a little bit more, but you can see that there are various new tools now that are in the stack, new players, new things that are interesting in this machine learning world that we're stepping into that are necessary that we're now acknowledging, talk about what's happened the first half of this year, we're now acknowledging this. And we're slowly moving on to stacks that look like this. Machine learning ops, I said I'd come back to it. Yeah. This is pretty important for those companies that are adopting AI and machine learning at a high level. It's the sharing part. It's also, it's not just sharing, but it's also enabling us to get what we're building into production and working and working for the bottom line of our business. It's the adoption of dev ops principles to machine learning delivery. And I will, I will say that yet dev ops is important. Machine learning ops may be more important to you as you go forward and don't try to just take your dev ops and erase the word, I don't know, development and put the word machine learning in there and say, well, this is our machine learning ops. It's different. That's why it's a different field. Machine learning processes primarily primarily revolves around creating training and deploying models. That's what it's about. Once trained and validated models of the point into an architecture such as what we just saw that can deal with large quantities of often stream data to enable insights to be derived. Okay. So we have different levels where the machine learning ops makes sense. As you can see here in our reports in our warehouses, our data legs and in our machine learning operations. So by the way, there's so much going on. So much going on in this industry. Another something I've learned to watch out for is if somebody's saying they know it all. Nobody knows it all. It's too much and it keeps changing. So if that's the approach that a vendor is taking with you, you need to run the other way from whatever advice that is, because it's all about trade off and putting on the table what you know right now and what you see coming and making some best decisions as a result of that. And if you do that with some great knowledge foundation that you get at places like hopefully this webinar and others, then, then you're going to be ahead and then you cannot take yourself. You will be ahead. All right. What else is happening close to home here. Our data team dynamics and this is only accelerated as a result of COVID because I feel like one thing that a lot of companies are taking advantage of this, this time to do is to reorganize. And to organize, I would say a lot of times what I see is more sensibly. So now we have business departments. They clearly state the claim and building their architecture. Yeah, there's still the IT professional. We're still IT professionals at some level, right? But I don't mean that we all sit in central organizations anymore. We sit everywhere, but we do our thing. We're not, I mean, there's so much dedication that's authority technology. There are degrees in it. And you really got to, you really got to dive into this to be to be capable of that work with it. And so, you know, you're going to have to pair technology with business still that's still a thing. You know, high, high level data scientists can do both great. All right. Good for them. But for most people, it's really that smart pairing of technology with business that's going to get the job done. So the reporting structure, yes, become more complicated than ever. As a matter of fact, I think it's going to get even more complicated as we keep generating with our, with our org structures more and more here going into the second half of the year. So it's going to get more complicated before it gets less complicated. This is true for really everything we do, everything in this industry. So just be aware of that changes, changes real change is going to be with us for quite some time. And, and boy, has it ever been with us in the first half of this year, not the change we we hope for or the change we envisioned, but the change we got. And as we settle into the new, the new realities here, we're going to start getting back to some of these changes that are necessary in our organizations to mature data. Okay. So, let me finish off here, data team dynamics acknowledging the need for data deployments to be near the business unit. In the organization organizational charts. And we're seeing a reduction. Okay, I am seeing as a consultant who comes into companies to give advice about data platforms, data architectures, how to mature data and how to win with data, et cetera. I've always encountered a bit of, I'll call it internal grist and resistance to change. I see that abating recently. I think people are getting that they need to be data driven. They need change around their data architectures and what they're doing with data. And so I welcome that, of course, in terms of what I do, but you should welcome it as well and be a part of it. Don't be, don't be one of those that are part of the grist and the resistance to change. That is slowly, but surely a wearing off and wearing thin in organizations as they see the need to do a great job with the things that we do in analytics. And that brings me to my last slide. And I failed to mention to throw in your Q and a, but if you have any questions about anything here or really with a topic like this, it can be anything. Go ahead and put that into the Q and a I'm going to turn it back to to Shannon to see if we have any of this Q and a Shannon. William. Thank you so much. And just to answer the most commonly asked questions. And if you do, as well, you mentioned, if you have questions, feel free to spend in the bottom right hand corner in the Q and a section. And again, just answer the most commonly asked questions. Just a reminder, I will send a follow up email for this webinar by end of day Monday with links to the slides and links to the recordings. Um, William, we had a, you know, just a comment coming in here, you know, earlier when you're talking about COVID, you know, the sadness, the quote, quote, sadness of this, you know, what are the effects of this and and are they really all that bad. It was stressful, but generally clean if it postulated that the disruption and pain would have been far worse than we've been given a month's notice. Is that the comment. Yep. Yeah, I mean, I agree. Right. If it wasn't so sudden, we, we would have been able to plan for it. And who knows what, what, you know, life would have been like, I mean, the, when it, when it really seriously impacted us when we, when it all sunk in here. It's not like it wasn't, it wasn't creeping up. It's just, it's just, we ignored. So here we have it and and we're dealing with it now. I don't want to say the best we can, but we're dealing with it. And yeah, so who knows where we would have ended up. But yeah, probably in a slightly better place, a more organized place. And obviously, you know, we may have been feeling a lot better about it. If we, if we started getting on it before it hit us so hard. So, how would you compare the analytics of the individual against each of those specific to cloud platforms? Is there one better over the other and why? Okay. Each of the cloud platforms. I wonder if the person is speaking of these. Would you think so, Shannon? I would think so. Yeah. So definitely let us answer is yes. So this is a part of a major project that we're doing right now. And we're looking at all these platform approaches and looking at the total cost of ownership and that it's a mouthful. I don't want to quite, you know, go into it or spill it all right now, but we are researching those of you that are doing one versus the other of these. What is your ultimate cost for some major enterprise projects that that we're doing a lot of right now? And so far it looks like, you know, some of the the single platform example is done well. There's a lot of ifs and caveats right around this, but the single platform example provides some great lower total cost of ownership and really sticking within an Azure platform or AWS platform with what they recommend, what's native to them, what they've kind of embraced in their architecture such as the things that I show you here is more beneficial than a multi-vendor example such as what I show here. But not to say that can't be done very well as well and TCO is a it's a mouthful. I mean, we got you got to talk about the added capabilities that you get from tools and what that does to you and may open up some new possibilities which may expand scope or projects and so on. Right. So we wanted to try to keep that all keep that all reigned in as we try to do a try to do a TCO comparison here. But keep an eye on some of my social and you'll see this research come out. But yeah, that's kind of what I have to say about that right now. I love it. I'm afraid though that that brings us to the top of the hour. This is a couple other questions you're asking over to you, Liam. But thanks everybody for the thanks William for this great presentation and thanks everybody for being so engaged in everything we do and hanging out with us today. And as William said, you know, never hesitate to ping either of us if there's anything that you need. I was in the follow up email by end of day Monday with links to the slides and links to the recording as well as our contact info. So I hope you all have a great day and stay safe out there. Thanks, William. Thanks all.