 Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager for Data Diversity. We want to thank you for joining the latest in the Monthly Webinar Series, Data Architecture Strategies with Donna Burbank. Today, Donna will discuss emerging trends in data architecture. What's the next big thing sponsored today by Alation? 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 section. Or if you'd like to tweet, we encourage you to share our questions via Twitter using hashtag DA Strategies. And we very much encourage you to chat with us and with each other. And throughout the webinar, to do so, just click a chat icon in the bottom right hand corner of your screen for that feature. And as always, we will send a follow-up email within two business days containing links to the slides and the recording of the session and additional information requested throughout the webinar. Now let me turn it over to Ibi from Alation for a word from our sponsor, Ibi. Hello and welcome. Hi, Shannon. Thank you very much. With that, let's get started. Today we have for the agenda about transformation, data fabric, and data catalog. And we will be discussing how the three are related and how the data catalog makes a deep impact in both your data fabric strategy and the cloud transformation strategy. So when we talk to our customers and we talk to the analysts, what we have realized is the next big thing for them is cloud transformation and data fabric. Cloud transformation, those being there for a while, we see our customers are still looking into cloud transformation, they're either in the beginning of their journey or they're in the middle of their journey. Data fabric is an emerging trend. With that said, most analysts think that it is an important emerging trend and it requires the combination of multiple data and analytics tool to make this happen. We'll talk about this in a few minutes. So let's talk about cloud transformation. We've been hearing about cloud transformation for a while, but it is still a priority. The reason being is we know the benefits are compelling, whether it's reduction in the server cost, if you look on the left, 80% in some cases, or reduction in production cost. Furthermore, with DevOps, people are seeing significant cost reduction and they're able to deploy things faster and ship value to the customers faster and be competitive in their space as well. If you look on the right, the cloud is a priority for a lot of companies and we keep hearing that. According to this report, about 75% of all databases by the end of next year will be deployed or migrated to a cloud platform. A lot of our customers have IT budget dedicated for the cloud environment. In fact, 30% of IT budget is being allocated for the cloud platform. With all this focus, all this value, still cloud transformation is not easy. According to a survey by IDG in 2020, they found out that only 38% of the people were mostly in the cloud. They were not completely in the cloud. It was only 9% that were in the cloud today. So why is that? Because they have challenges and these challenges are, they don't know where to start. To start a successful cloud strategy, you need to start with data. You need to understand the data. The second thing is they don't understand the impact of migration, especially the business impact. You can't just take and move things around without understanding what impact it can have to the business. And finally, after you do the migration, are you able to drive adoption? From a user point of view, this thing has to be seamless. They don't have to worry about whether the data is in the cloud or in their on-prem location. So what is an ideal solution? What does an ideal solution look like? The ideal solution basically helps people understand and identify the critical assets that needs to be moved. It helps them understand the impact and gives them the ability to communicate it with their consumers. And finally, once they have moved the data to the cloud, it provides them a consolidated view of hybrid, multi-cloud assets, and it doesn't matter to the end user on what's going on. So let's see how Alation basically helps the cloud transformation journey. The way we do it is we basically divide it into three phases. The planning, migration, and adoption. All these three phases are equally important because without one another, you cannot really have a cloud transformation journey. So for planning, we know which data to move. We help our users understand if the data is not used, they don't have to move the data. And the result of that is they are able to start the projects faster. They're not spending six months, eight months trying to figure out what data to move, in some cases. And as a result, they also reduce the risk. And they reduce the IT cost. The second thing is the migration. One of the key things about migration is knowing if you're able to move data in an orderly manner. And we are able to track that, provide that visibility. Not only that, we are also transparent to the key stakeholders. So with our stakeholders in play, they are able to find and understand what's happening and they can communicate those changes through the Alation platform. The other thing is, it significantly helps reduce the migration time because of all of this. That's the impact, that's the result. And reduces the risk. Finally, when we talk about adoption, Alation brings in all technical users to the data. We drive that adoption because people can now trust their new system. And because of the rapid adoption, more people will use it. The increase in people using the data will increase. And obviously, you're able to sunset your legacy system. If this all sounds great, but there's still some challenge with the cloud. The challenge is really the same, that they still have mixed environment. They have data lakes, they have relational data. In addition to that, they have mixed applications. They're working with conditional and modern applications. They have different kind of applications. And finally, they still have limited resources, whether it's people, expertise, budget. So what do they do? This is where data fabric comes in. So according to Gartner, data fabric is the future of data management. Here's a textbook explanation of data fabric. It's a design concept that informs and automates the design, deployment, and use of integrated and reusable data objects, regardless of deployment, flexibility, deployment platform, or architectural approach. Okay, very good. Now let's focus on the keywords over here. The keywords over here is that it informs and automates household deployment, specifically around any platform, and it doesn't care about architectural approach. These are the key things to keep in mind with data fabric. It is not a single technology. It's a simple collection of tools that helps you achieve this. And that's why when we are talking about cloud migration, we're talking about people using different kind of technologies, this is where the data fabric comes in. So let's look at how the catalog helps provide the foundation for the data fabric. Here's a diagram from Gartner. And again, there are a verbiage from here from their report that says metadata management tools are crucial for dynamic data fabric design and therefore partnership with data catalog vendors that can extract metadata for multiple sources. So if you look on the right, you'll see that there's a data source and there's a data consumer and there are different kinds of technologies in between, like data prep tools, knowledge drop and whatnot. But if you look right next to the data source, you will see that data catalog is the one that is actually providing the foundation. And that is key takeaway from this. So because the catalog is the foundation, let's understand what is a catalog. So again, a textbook explanation of a catalog is a repository of metadata on information sources across an organization. Key words to keep in mind is metadata and information. So metadata is nothing but data body of data, like schema information, column name, description that people can use and leverage to take advantage of. Second word over here to keep in mind is information services. Notice we use the word information sources and not data. That is because our view is the data itself, data catalog itself is a misnomer. In fact, we just don't catalog the data. We also catalog a lot of important information across the enterprise. If you look at the second bullet, you'll see it's tables, articles, reports, queries, visualizations and conversations. And if you go back to the use cases that is on the first bullet where you see search discovery, data governance and curation. Data catalog have been pivotal where they're able to provide you relevant information across huge volume of enterprise data. They really make search and discovery easy. When it comes to curation and governance, specifically our relation, what we do is we apply governance at the point of data use and that what it does is it helps organizations use the data accurately and properly and comply with organizational and regulatory policies. Final part over there is collaboration. Data catalog basically helps data stakeholders to work with each other. They're no more working in isolation and that becomes very important in today's global and remote workforce because to us, what they can do is they can both look at Viki like articles, see the ratings, reviews, who's used it and it provides that collaboration for people to get more use out of data. Finally in the bottom, you can see the data catalog includes common functionalities such as business glossary, lineage, catalog page, search. And business glossary is basically a common term. It's a common vocabulary within an organization and it helps ensure that people are using the same terms throughout, no matter what the situation is. We go a step further where we do is we do, we provide automatic suggestion of new and popular business term. In fact, we help scale business glossary efforts and lineage is a place where you can capture the illustration of the transformation. So you can understand the original data, who's uses, used it, how it is being used and how it's been changed throughout the life cycle. Really important as well. And again, we kind of fit this in the cloud transformation. The catalog pages, this is where people can put in their travel information. You can see the subject matter experts, other contributors, understand how the data has been used. And finally the search, we already talked about the search, which is very critical to a data catalog. Data catalog basically answers all this kind of questions. How do you find the information? Can it be used? Should it be used? These are the key things to look for in a data catalog. Finally, I want to take the opportunity to showcase how Elysian has done over the last few years. Here are some reports that we have won and awards that we have won from Gartner Forester. We've gotten a Magic Quadrant Leader for the last four years, Forester two years. In fact, 2019, they didn't have a machine learning data catalog category of wave. Otherwise, we could have had three. And we have won other awards as well. Basically, Elysian started the machine learning data catalog trend, and this is what Forester is saying. So before I leave, I want to leave you with the thought that so far people have been seeing a data catalog as a place to do search and discovery, but it is a platform for data intelligence solution where you can learn and do things around digital transformation, data governance, analyst productivity, cloud transformation, privacy, risk and compliance. Something that Donna is going to cover later on on these use cases as well, so you can get benefits over there as well. With that, I want to thank you for your time, and I'll hand it back over to Shannon. Debbie, thank you so much. And I see a couple of questions coming in. If you have any additional questions for Ibi or about Elysian, you may submit them in the bottom right-hand corner in the Q&A section. Ibi will be joining Donna in the Q&A at the end of the presentation today. Now, let me introduce to you our speaker for the monthly series, Donna Burving. Donna is 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 Limited, where she assists organizations around the globe in driving value from their data. With that, I will turn it over to Donna to start her presentation. Donna, hello and welcome. Thank you so much, and I guess happy new year to a lot of folks. This is the first session we've had of the year. Before I go further, Shannon, just a sound check. Is everything fine? Yeah, it's on good. Great, okay. So, as Shannon mentioned, today's topic is on emerging trends in data architecture. What is the next big thing? This is sort of a trend. We've been doing a great diversity in the past few years to really start out the year with a nice look forward and a little bit of a look back. So, each year, again, we also do with Data Diversity a survey that we will be covering that really talks about trends in data management that we've seen, what's next, what's looking back. And so, this webinar will go through those different survey responses. I'll also give some of my color commentary. I do this as my day job. I run a consulting practice called Global Data Strategy. And so, I think that's a benefit of kind of some of the insights we've been seeing from real world customers have added on to this survey and report. All of this report, if you'd like to get it after the facts, Shannon generally sends out links. That's often one of the questions, yes, this is on demand. Yes, you'll get a link to this report, but you can also download it either at the Data Diversity site or also at our site on Global Data Strategy. The other thing that's worth noting, but I just skimmed past, but this is part of a yearly series. And many of you join us each month. And thank you for that. It's always nice to see the familiar names on the chat. And generally, you often have nice chats going on during these sessions. But this is one of many. So, take a look at the year's lineup. Hopefully, you'll be able to join us for some more. We also have some nice case studies this year, which is always kind of something for real world. As always, these are all available after the fact as well. And just have a nice library of assets that they personally teach longer term. So, starting out, we're talking about trends in data management. And if we are good data managers and if you talked about the value of glossaries, we always talk about definitions. So, let's start with what we're even putting in context in the terms of what is data management. So, many of you on this call I know are active members of the DEMA or Data Management Association. And they have published a body of knowledge around data management. And if you look at their data management definition, they talk about the development and execution and supervisions as you read it yourself. But one of the things I like to highlight is really is that idea of delivering, controlling, protecting the value of data and information assets throughout their life cycle. And we'll talk more about that in the session that data management really is about protecting data as an asset to the organization that really drives business transformation as well as the technical. So, I like that definition and I thought it was worth highlighting. It's always good to start with what we're talking about, putting things in context. What's also helpful from the survey is that we allow survey respondents to provide their definition. And this is generally a very popular part of the paper. Again, we work with the data architects and data managers of definitions. So, we called out a few that I think are interesting from different aspects that I agree with. Well, one talks about the first one, the corporate asset that enables informed, accurate, and confident decisions. The rest of the survey and the rest of this presentation will talk a lot about that, how businesses are really trying to work together, which is that second point. The business and IT work together to make decisions and you really need obviously accurate data to make those decisions. So, that's one aspect. So, data is definitely a business asset. Also, data is obviously a technical aspect. And one of the definitions I like was that data management really is that technical side of data governance. I think data governance can be challenging because it's a business issue and you need committees and stewards and things, but it's also a technical issue. And so, things like data modeling, data architecture, master data management, there are data governance too, and there's a lot of both that will happen. And whenever there's an end to condition, that sort of makes people's heads spin, right? So, you really need all of those in order to be successful. And we will talk about that throughout the session, that this is a state of management, this is much a business issue as a technical one. And when they fit nicely together, that's really where organizations see the benefit. So, I thought it would also be interesting if we go with more definitions, but this is a data architecture webinar, and maybe some of you on the call are thinking there's almost a difference between data management and data architecture. So, it's similar, but it's a good sister initiative. And so, when you look at data architecture, this is again from the data management body of knowledge. If you think of architecture as representing data at different levels of abstraction, so it can be understood, that really guides those different data requirements that really understand the data strategy. And so, you'll see, again, linking, and architecture is a nice artifact because it links business and technical in an abstracted way that people know about decisions. So, to me, that's a cornerstone of data management, but really a specialty in that itself, that again, we have a whole series on that this year. So, I thought that it was worth making that distinction. So, moving on to some of the findings, again, there's a lot more in the document itself. I'm pulling out a few, again, we're data people, we like figures and numbers. So, I thought this might be helpful. And also, when you're making some business cases in your organization, numbers and facts are always popular. So, when we look at sort of one of the questions we ask in the survey is why you're using data, what are some of your top data-driven initiatives? What's a little bit new in this paper is because we've been doing this for several years, we're able to show some year-over-year trends, which this year, or last year, the 2020 report, was particularly an interesting year. So, seeing the trends from 2019 was particularly interesting. So, when you look at the initiative, this has been the same every year we've done the survey, that the main driver for data is gain insights through reporting analytics. So, the cynic in you can say, gosh, that seems old school. We've been doing business intelligence for years. Well, yes, we have, but that's really one of the drivers. We're still not there yet, I guess, after all these years. And there's still innovation in data management. So, I would say, gaining insights or reporting is still important. And interestingly, that's grown year-over-year from 2019. So, I think that's still a driver with the data management. Again, not surprisingly, 68% are looking to save money in increased efficiency. No matter how good the economy is saving money, it's always going to be popular. And that was pretty much the same year-over-year. It was literally 68% last year. So, that's something that's going to be the same. I bet we'll see that in every single report. It wasn't just interesting that it was literally the same year-over-year. Again, what was also hot last year and has grown even more is digital transformation. And we'll talk a lot about that as a driver in this economy, especially with things like COVID. So, we'll talk more about it in the session, but many initiatives have gone down. But digital transformation was one of the big ones that has actually gone up. So, you'll see a big jump in the year-over-year from last year that it's 11% in 2019 that has really grown. So, moving on from that, I guess you've probably all seen this meme if we've been on the internet for more than about five minutes. So, I didn't create this, but I'll feel it, right? So, you've probably seen this meme that's gone around with who in this new world of data management is leading digital transformation in your organization? CEO, CTO, or COVID, right? So, I think a lot of organizations have really upped their digital transformation out of necessity. So, suddenly we're all working from home. Suddenly everything's digital. Suddenly everyone's ordering things online. So, you'll see in terms of industries that are growing, generally you'll see that there's a online trend. Amazon is boom, right? People are ordering groceries online. People are working from home and on Zoom, large part of our day, right? So, I'll talk more about this in our practice. We're booming as well and a lot of our customer, I mean, gosh, as a business owner, I sort of had a little bit of a heart palpitation in about March, April, last year. But interestingly, not only did most of our customers stay with us, a lot of customers, and we'll talk about a few, have actually grown their practice in data management, which leads me to my own edit of this particular slide, which hasn't been data management or data that's really driving the digital transformation or supporting, it's probably a better support. So, I'll give some great examples of companies that have their data in order really have either weathered this storm very well of COVID or in many cases, I mentioned, even increased in this new world of COVID. So, getting your data in order has always been a good idea. And I think those who are doing that now are even better. Again, if you're following the internet, I have to put this in, especially for the American group. That really doesn't make any sense, but I wanted to have my own Bernie Sanders meme. I don't not see that driving digital transformation, but he'd never know. And that's not a political state that I just don't see that. So, and if you have not seen that meme, you might not understand the joke, but he's sort of been everywhere since the collaboration. Okay, so maybe more practical for this is what do we mean by digital transformation? So, that is one of those terms that I sort of cringe when I hear because it's overused and we all sort of cringe with plus words and data management and it allows. So, but this is a realistic thing that's happening. And it has a lot been driven by COVID. It sort of made it a sudden reality for a lot of organizations. Some were very ready. Some were not. It had to really get their act together. So, I'll give a couple of examples. Some are more surprising than others. So, one on the left is a nonprofit we're looking, we're working with. And they've been very, very advanced. They're not your typical type of organization. You should have expected financial institutions and retail organizations to really be focused on a data-driven model, but this is a social services organization. And they have been working with us for many years to really build their data governance and their data-driven culture. The data governance is going well. They've been building dashboards. They're using master data management. A lot of really advanced things for a nonprofit. And they're a smaller nonprofit. They're not. They're doing each type of foundation. They're just a regular mid-size nonprofit. But because they were prepared, a few things. They were able to move to a telehealth model million within weeks, not only from a data transformation. They were doing a digital transformation, putting all of their health records online, getting more towards online sign-up and things like that. So that type of sweat, even doing Zoom meetings at all of the data governance meetings were on Zoom. So getting to that early, I really put them at a good advantage. And more importantly, some of the challenge was how do we build a data-driven culture for folks who are really more people, right? So a lot of folks are going to nonprofit. I mean, many are data-driven. A lot of social analysis is data-driven, but someone who maybe wants to be a school teacher or working with people in crisis tends to not necessarily want to look at a dashboard. But because of COVID, they were actually able to make some very data-driven decisions on when do we open our centers? When do we stay online? Where can we give services to our people most in need? Taking external data from COVID trends and putting it on a response dashboard so that you can really see where we're making the most benefit. And it really made some talking about buzzwords, something like, what does it mean to be data-driven? Very, very tactical. And people are sort of understanding how they can use dashboards and use it forward. I don't know, myself just kind of anecdotally from different friends that suddenly, I think everyone in the U.S. and one of the popular ones is kind of the Johns Hopkins COVID tracker. Suddenly people are looking at dashboards and talking about trends and analytics more than ever. So in a kind of an ironic way, kind of building this data-driven culture has been very, an ironic response, but it can put it into perspective. So that was one good success story. Another was an international bank that they have been around for decades, but as with many organizations, they had a lot of this sort of an old school culture where the management still wanted the report printed out and put on their desk. They had a lot of even mainframe systems, not actually uncommon in kind of a banking world. Again, in some ways banking is very advanced in terms of they've been doing data management for a while. The ironic corollary to that is a lot of those systems are very old. So you're trying to put their right in the business. So again, because of COVID, suddenly these executives who wanted their report printed out and put on their desk, couldn't do that anymore. There was no desk to put it on. So that was really driving a data-driven culture through having to go digital. So a lot of the workflows were very quickly and they had a very strong IT department that was working with kind of a more digital model. I'm getting a lot of questions about sound. So I'm gonna try something else. Do you think this is a good idea? Shannon, I'm sure you can do this. Okay, the other thing was, again, like the nonprofit, these two companies couldn't be more different in terms of size, in terms of culture, in terms of mission. But similarly, this culture, ironically, even though they were a bank, they really could not, they weren't really data-driven. A lot of it was doing by gut feel. They sort of had spreadsheets abounding, but they really weren't a culture that looked at shared dashboards and made decisions. Well, suddenly, they had their own COVID response. Their customers, how are they helping their customers through COVID? How many people were working from home? How were they supporting working from home? So that was a way that they could very tactically understand what data-driven meant. It was something that related to them. So this was a way of kind of growing that data-driven culture through dashboards. Hey, Donna, that didn't work. I don't know what's going on now. We've had so many challenges up here, Sam. It sounds like it's something hitting your mic. Or it sounds like typing, but I think it's something hitting your mic. Is this better? Yes. Okay, we will try this way. Okay, thank you. Sorry about that. So this last group, actually, it's a conglomeration of several clients mixed together, but very similar use case. So these were all organizations that were very in-person focused. One was an in-person, actually, both of them that were kind of conglomerating, they relied their company on in-person events. So that was a very big switch. But of course, these companies can't do in-person events anymore. So both of them were very clever. Both of them are using their downtime to really understand their customer segmentation because things will come back. People will come back to in-person events and they're preparing for, and I agree, I think in-person events will increase because people are sick of being cooped up at home. So they're using this valuable time where things are a little slower to look at their data, their customer segmentation, and look at dashboards to really understand their customer profile. In addition, because of that, many of them are also doing online services and suddenly their customer data is in a very different way of who wants to see online services, what is the thing we can change to support them? So interestingly, these particular companies that their whole business model is around in-person events. Think of museums, for example, that are, what do you do with a museum? You go there in person, but they're either offering different services online or understanding, in some cases, donors and things like that. So I thought this was a helpful example to maybe show, A, what does digital transformation mean? What does data-driven mean? And what I'd like to do in all of these webinars is not only look at some kind of facts and figures, but some very practical things we're seeing in the real world. And that's one of the benefits of why I like my job is that you actually get to see these things in real time all over the globe in very, very different companies, which is kind of fun. So the trend across all of these is good old-fashioned tried-and-true meat and potatoes, everywhere you wanna say it, tofu and beans, business intelligence and analytics, right? So when we looked at the respondents, still a huge driver are things like reporting and analytics, which is a key driver for data management. More specifically, 72% of those are specifically BI, of business intelligence, little disheartening that those who have BI only 69 have a data warehouse to support that. This wasn't a trend in the report, something I'm seeing that's a little disheartening is a lot of the younger folks, the good news, a lot of the younger folks in university and training classes are learning things like BI tools and how to do great analytics with R and see the visualization, data storytelling and visualization is very, very hot. But unfortunately, because of that, it's easier to create the visualization than to get the data right behind the scenes. That's why we're very busy. Not so many people love to do the hard work of getting the data right or don't understand that. It seems easy enough, can I just put it in a BI tool and do the joins there and make it look pretty, really not going to be a long-term scalable issue without having a data warehouse. Data Lake is something that I guess I feel old enough that I've seen that grow and then see the disillusionment. It's a great use case for the right use case, things like IoT, things for like large scale streaming unstructured data. There was a crazy moment in our data management career that those of us in the business called it when it happened and we're called an old school folks, that just having a data lake where the sort of vendors were saying, just put everything in the data lake. You don't need this old fashion thing like a data warehouse. And I think that was seen that doesn't make sense. You don't put something like your annual report figures in the data lake. There's a greatest case for a data lake, but unfortunately it seems when there's a new tool, everything needs to be that when you have a hammer, everything looks like a nail. So my gut feel is that's a good percentage, right? Maybe a little higher, but I don't think everybody needs a data lake. Companies who do is an excellent, excellent tool, but it's not the right tool for things like standard reporting and business analytics of kind of trying to get your financial figures and things like that. So just kind of an intro, but all of them have the same use case that we're trying to get better insights around our business. So some of the figures from the report, data management, what's hot and what's not. So just covered it, but there's some more data around that. The top initiatives when we ask people, what are you currently right now implementing in your organization behind reporting? Actually, number one has been number one for the past as long as you've been running this report. Data warehouse again, coming in second. And then the other ones that are high, I think is a positive and makes a lot of sense. In that data security, obviously, especially if this is things like financial information, data governance and architecture, again, are sort of supporting solutions to that. The other thing you'll see in all these reports is two colors. So 2020 is the red and 2019 is the gray. You'll see most of, actually all of the 2020 are lower. There has been a softening for just about everything. Nothing really grew. Gosh, we just survived 2020. So I don't think that's a surprise, but it is something we'll talk about and it is something to note. So what has dropped the most? Knowing that everything dropped, what were sort of the big candidates of what's not hot anymore? Self-service data prep actually surprised me a little because I actually am seeing a lot of need for self-service, but back to my earlier comment, I think there's a lot of demand for self-service reported. I want to, gosh, the data is clean, the data is curated. Now I want to go slice and dice it. I understand how to analyze that data. Not so many people want to do the hard dirty work of cleaning the data and the governance of it, which is why we're popular. My analogy is that I bought a house years ago and I was going to do it all myself and I was going to put up the walls and the piping and the electrical and that lasted about six months until I realized it took me three weekends of my valuable time, took an actual electrician about 20 minutes and they did it right. So it might be that, that DIY maybe this is so popular for that foundation. And that feels to me like a right balance, right? If the data team is building the platform for that curated data and the governing team can help govern it, really that should make the visualization of it easier, but you do need that foundation. So that one, it wasn't a surprise, but thinking through maybe wasn't so much. Big data going down, I think a couple of things. I mentioned that disillusionment, that big data doesn't solve everything. It's a tool in the toolbox, but not the only one. And I think that there's a little disillusionment with it, it doesn't do everything. Document management surprised me a little until I thought about it further. That went way down almost as much as the self-service data prep. But think of that bank that I gave as an example. I think as people go more digital, what I'm seeing is a lot of companies are moving away from documents clearly have their place, but I think there was an overuse of documents where things could be automated and maybe a workflow or other tools that can better manage what was done in a document. So that's just my color commentary on that. So I did want to say as I mentioned, overall most things have decreased. The things that have the smallest decrease, not too much of a surprise. Data science went down only 6%. I think there's a lot of interest in data science in new ways we can use analytics for insights. And then data governance went down not very much either. In fact, I might have thought that would have grown because so many companies, once they realize they need the data, they realize they need to govern the data and risk and opportunity are very closely linked to data governance. So just found that sort of an interesting corollary to a lot of this. So looking ahead, what are people going to be building? We sort of asked that very specific question. What are the top initiatives that you're looking to implement in the future? Wasn't a surprise to me because if we do all of these things in our practice on that list pretty much, and the top ones we're seeing are data strategy, partly because that's in our name, data architecture and data governance, maybe master data. I would have expected to be a little higher. That's fairly high. That's another thing we see a lot of. But to me, that makes a lot of sense. If you're going to have a successful data strategy, you really need data governance and data architecture to support that data governance. I would see going a little more on the business side and data architecture being more on the technical side, but they're both have overlap and they're very closely linked. The data strategy I'm not surprised with because everything we've been saying, right? Companies who want to be successful, especially in this new data digital world are need to be data driven. A digital transformation is driven by data and a good percentage of our clients are in the middle of a digital transformation and sort of brought us in because they need the data to support that. So that focus on data strategy, you may be rolling your eyes if you've been on more than one of my webinars. I can't really show this each time, but this is our framework that we do use across everything because it really touches all of it. So the top is it really aligning that business strategy with your data strategy, very much aligned with what the survey said, really using data governance and collaboration and I put those together, right? So data governance, maybe that's the stick and collaboration is the carrot. And I found once you get the business people together looking at the data, looking at those dashboards to make decisions, you're very gonna quickly see if that data is not right. So I find that the positive growing that data culture and letting people make decisions, the stick will sort of come on its own. Maybe I love too much of a polyana or a glass half full, but I like the things that people are adults and the more they see that this data is valuable, they're gonna be incented to make that data right rather than sort of lecturing people that data should be right. So that's just something in our practice we like to sort of see the carrot as much as the stick. And then all of these other areas that we've been talking about are foundational and relate together. So why we always use this framework is you'll see it across this data management survey, it's really hard to separate them, right? You can't do data governance without a good data architecture and you really can't do data quality without data governance and architecture and master data and you can't do master data without architecture and that's why sometimes some of this can be confusing but or metadata is a common one and speaking of the sponsor with data catalog and that really sits across everything. So but they are separate disciplines and they do have their place. So this is why we always pull out this chart because a customer might come to us and say, we really need to implement our data strategy and we say, great, what's your architecture behind that? Or a company might, we wanna even do better data analytics. And we might say, how's your metadata to support that? Do you have the lineage? Do you have the definition? So hopefully this is a helpful tool for you as well as you look through your data management and hopefully leading it up to your business strategy. So for those of you on the call who feel kind of from that level three to level five is already really great. And we've seen that as well. Wisconsin is frustrating. You can have a great metadata catalog and your architecture is perfect but the business might not get it. Is what you're building aligned with their strategy? Is it about marketing? Is it about risk? Really kind of, it might be for you the next step forward messaging what you have better rather than building more. And that's really, really where you look at, need to look at all of this holistically. So it ties into that idea of linking business and IT and really making data more of a business strategic asset. So I found this part of the survey really interesting as well. Is, and this has been sort of evolving and changing each time we do the survey more towards two things, more three things. One is that more and more disparate roles are being involved in data management or data strategy. It's not just your data architect which is sort of expected or your CIO. It's also your business stakeholders. Your project managers, your chief executive officer not just the chief data or information officer those would be expected. I would also add and we saw some of it in the comments chief marketing officer, chief operations officer. So a couple of things there. One is that their business roles. So not a large, I said there will be three things. One is a larger swath or different types of people involved. Two is more of a business focused. So not only IT, of course, IT should be involved. If it's only business, that's gonna be its own problem, right? We've seen that happen also kind of that shadow IT. And the third, which is really heartening is that more C level roles are being involved. And I think that's fair because who's driving it? It's not who's executing it. I don't think your CEO should be there kind of entering data into the catalog that would be probably a great problem to have if that's something they were interested in doing. But really in terms of driving this and being the champion for change, you'll see the CDO, CEO, CIO, again, some of the right in votes, which also aligned with what we're seeing in our practice, chief marketing officer, chief operations officer. And when you implement something like data governance, really think of that. Who's going to be your executive sponsor? And it should be a business person. The other thing which is interesting is that the one of the ones that grew the most was this idea of a chief data officer. And I know that is a common topic in our biz. Is that a thing? Is that a growing thing? So yes, it's a growing thing. My color commentary on that is we often start our implementations with sort of a maturity assessment. And it almost starts with no management, just sort of data governance lead and then eventually when you're sort of a high, if you think of the CMMI kind of a level three or four, that's often where a chief data officer makes sense. Often a question my customers ask is, do we need a chief data officer? And I often say no, because you don't want to do that too early. To me, that shows the evolution of data management is that companies are mature enough and they're doing enough data that they need that data officer role, which I think is a great all around. I think that's a good role that more organizations should have, but advice for people on the call, don't do that too early, make sure you have enough data to manage it and you have that culture first. Couple of other things before, I wanna make sure we have some time for questions. So one of my favorite questions we always ask is I'm a tech person at heart, I'll talk all day about business value and business strategy, but I'm a tech nerd. What is this why I'm in this business? So what tech are people using, right? Across the board, year over year, always, relational databases are the big driver of what companies are using. I don't think that's a bad thing. I think relational databases get a rat-bad rap again. Oh, that's legacy. I like to reword that in terms of it's foundational, right? Relational data stores are good for what they're good at, which is a large part of when you're looking at analytics and you're trying to get the data right, consistency, referential integrity, the fact that the data lines up correctly, relational databases were built on relational algebra and there's theory behind it and that's why they're popular. Does not mean they're the only thing. So another thing that's been happening over the years is you'll see those other lines are getting higher. It isn't only, it's and, right? So there should be things that augment a relational database. Again, it's not an either or, it's an and. So yes, can we use a graph database? Really cool technology. Can we do more real-time streaming? Of course. There's a lot more tools in our toolbox, but don't throw away the hammer. You still kind of need one. So you'll see there, relational databases are sort of the top. A little bit of set that spreadsheets is 71%, but we'll get to that later. I think that's just a fact of life that's been also up there all over the years. So maybe more interesting is where are people headed? What are people looking to do in the future? So this is current state in terms of the future. I think people are cautious. So you'll see that the big plans for, again, everything is many things are still sort of down, but you'll see relational databases are moving more to the cloud. So you'll see future people are looking to move to the cloud, but still a relational database. Again, that theory hasn't gone anywhere. I think a lot of package applications, things like ERP and CRM, which again can be good and bad. I just want to make sure they aren't used as a data management solution. They're not, I see that too much. Well, our CRM is our, we don't need master data management. We have a CRM, we can have a whole webinar on that one. Yes, you have customer data in a CRM, but it's certainly not a master data system. So, and lamentably, spreadsheets are still up there. Less so. You'll see that's only 35%. I just think it's because people don't want to admit they're still going to use their spreadsheets as a data platform. Again, to be fair to the spreadsheets, spreadsheets are wonderful. I use them all the time, but they are not a data platform. We have one unnamed large international financial institution that finance people of spreadsheets, but the number of databases people said they use when you looked at them were spreadsheets. A spreadsheet is not a database, it's a spreadsheet. So it's a good tool, but again, it's not a tool for enterprise-wide data management. So I do think with COVID, say the word, it's both good and bad. The good is that it has driven digital transformation. I do see this report more than previous, kind of a cautious, back-to-based approach, which in many ways is good. You maybe can't do machine learning until you've got some good clean data to do that machine learning on. And so it may be, again, I don't have a crystal ball, why a lot of these answers occurred. It could, that could have been a normal reaction anyway to a lot of companies who want to do the next generation things, but can't until their data is clean. Again, we sort of focus at our company, a lot of those back-to-basics, things like governance, things like metadata, and often we're brought in for a company who's trying to do AI or digital transformation because you can't do sexy stuff without the foundational stuff. So I'll say it again, a lot of the stuff is in old school, it's foundational, and you really can't run before you walk. So I do want to wrap up for some questions. So each year also I do some predictions of where we will be headed in the future. So I am putting myself out there and saying, in 2019 I did some predictions for 2020. How well did she do, or was she close? So I'll go back there, I think you're by me. And maybe they weren't like out there predictions, say maybe you could have made the same ones, but the things we kind of talked about in 2019 is this idea of blurring of a business in IT would continue absolutely seeing that, seeing a lot of more business people being involved, that blurring of data management in business, things like digital transformation, industry 4.0 for manufacturing, yes, maybe less industry 4.0, I could maybe give myself a half rating there, partly I know many of my manufacturing clients are the ones that sort of slow down, it's hard to be selling cars in this economy, right? Or building new bridges and things. But digital transformation, yes, and being data business driven. Organizations relying on a matrix set of tools, not only relational, I think that's still true. Again, relational is a big one, but I am seeing, and that graph is being more distributed of usage that people are trying new technologies, no SQL graph, et cetera, but it's AMP, it's not OR. The one I'm unhappy about, data governance and ethics will have an increased role in business. Data governance, yes, ethics. Maybe I'll give up on that one, maybe I was too, I am the sky, in fact, some of my friends I've been talking to, I talked to a friend the other day that had this great company who wanted to talk to me about where you could send people documents and then it would track their eyes and where they looked at the document and what clicks they made and all I could think of is creepy as heck, right? I just, I think we're going in the opposite direction with ethics, but that's my guess, these are my predictions that I'm curious what other people think. And the fact that analytics and BI will be a strong driver, with an evolving focus more toward AI and predictive rather than simple descriptive, maybe I should have given myself a half rating there. I think a lot of people are going back to the simple descriptive right now and maybe not doing this much predictive in AI but they would have hoped partly because of the data, you need to get that data right before you can really have good AI models. So without further ado, what are the predictions? Coming up, no, what are the next big thing for 2021 beyond according to me? So give that a grand and first all for what it's worth. For 2021, I do think this focus on foundations will continue even with a booming economy and with whatever, I think people are realizing that again, to do the sexy stuff you need the foundational stuff. I will predict that these companies that some of the examples I talked about who are looking at their data now will reap the benefits in the rebound. Folks that are taking this time either because of demand and they have to very quickly go digital or ones that are slowing and are using this time to make their data better. They will see the results of that when we sort of come back to another booming economy. Business insights will continue to be a driver. I don't see that ever changing. I see data governance increasing as well. Business being more involved. I reluctantly took off that because I've given up on that. I don't want to give up. Hopefully next year I'll be wrong on that one. And then investment and data management. I'm just saying again, I just talked about just one friend of many examples like this who are people who are maybe were laid off or are seeing a slowdown or are just seeing opportunities. I'm seeing a lot of kind of startups out there whether it's a catalog, get a quality cloud platforms, new AI solutions, I think we'll come out of this with some really cool things that people are using this time to be really creative. So I hope I'm right there. So wrapping up so that we can questions. What have we seen? That return to foundation, a return to using seeing insights, a continuing of a wide range of roles being involved and that foundational relational database in addition to everything else. So as Shannon gets ready for questions, I'll do my blatant plug that we do this for Levy 3 to help let us know. And another blatant plug to this is a series and if you enjoyed this one, please join us for the substitute ones we have one every month. If you want the white paper again, it's available both on the university and global data strategy. So Shannon without further ado are there questions, thoughts, ideas? Donna, thank you so much for another great presentation. Nope, I'm getting a echo from, you know. This is thanks for another great presentation to say I'm some of the most commonly asked questions. Just a reminder, I will send a follow-up email by end of day Monday with links to the slides and links to the recording. So dining in here, this question came in for you, Levy in during your presentation. Do we have any frameworks for planning stage? If any, what are they and how do we use them for a multi-cloud environment? Yeah, yes, we do have a framework which is offered through our consulting services, but from a product point of view, what we do is we help provide popularity. So this question is around migration, I'm assuming. And when I said one of the biggest challenge people have with migration is they don't know what to move and which data to move. So what we do is we provide popularity within the product and what that does is you know this is a 10% most used data and once you know that is the most used data, then you can move that data, those data and then you look at the next 10% and you can take a phase approach to that. As far as the second part around the multi-cloud is concerned, it doesn't matter where your data resides, the move can happen and you can make the move from a relations point of view, what it is is we connect and we see data and we know the usage of the data and we connect to everything, all the cloud, all the two of our connectors, all the popular clouds. Perfect, anything you wanna add to that, Donna? No, I think I think he handled that one well. So why a studio once organizations are more mature with data culture, will it also mean a pile of data that may also be all over the map and not as per defining the strategy? A fair point, I think I see this chief gate officer is maybe more strategic and that it would be hard to be that strategic without your house and order. And so I guess when our evolution is maybe the first is a data governance lead or a data governance officer that does get that house and order before you could see a strategic. So again, people have different names for different roles, but I think, yeah, if you think of the CDO as that more strategic, you kind of have to have a level of organization before that can even be helpful. And so that's where I think of as an evolution. It may be better suited if you are in those lower levels of maturity to focus on a more tactical role to really get that house and order with an executive champion who's not in the data world that might be more effective than starting right with an executive data role because that just might be too much of a jump if you're not data-centric already, but my two cents, if he has any thoughts on that one. I love it. Okay, great. All right, I'm gonna try and slip in one more question here. So I have a long term career as a data architect data modeler. I'm finding clients are not willing to give me a chance because I do not have cloud experience. Is there still a market for guys like me with no cloud? Should I make an effort to learn cloud? I would say yes and yes in that is there still a market? I think data, depends how one defines architecture. I think a good architect understands the business as much as technology and understands the foundation. So for example, we always start with a conceptual data model and some that understands the basic rules of data and things like that. To me that's also part of a data architect. Yet I know myself, I'm a huge back to basics foundational kind of person but when we're hiring for a data warehouse person I mean so much is in the cloud now it really is it does not a showstopper but in terms of if you're the type of architecture who likes to build probably having some Azure AWS snowflake on your CD is not a bad idea because I do think that's where a lot of things are having heading but also have the foundation. Like if I have just me because I'm someone who's hiring if I see CDMP and one of those platforms all the better. But having either one alone isn't as valuable because of the limitations we mentioned. Great and that is all the time we have slated for today. So thank you so much to both of you to Donna and Debbie for these great presentations and just a reminder to the attendees thank you for being so engaged in everything we do. I will send a follow-up email to all of you by end of the Monday with links to the slides and links to the recording. And I hope you all have a great day and stay safe out there. Thanks Donna. Thanks Elation for sponsoring. Thanks Debbie. Thank you. Thank you everyone.