 Oh, digital officer of diversity. We would like to thank you for joining the latest installment of the monthly diversity webinar series advanced analytics with William McKnight sponsored today by Tamer. Today, William will be discussing the 2023 trends and enterprise 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 by the Q&A section, or if you'd like to tweet, we encourage you to share highlights or questions by your favorite social media platform using hashtag ADV analytics. And if you'd like to chat with us or with each other, we certainly encourage you to do so to open the Q&A panel or the chat panel, you will find those icons in the bottom of your screen for those features. And just to note the chat defaults is under just the panelists, but you may absolutely change that to network with everyone. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and any additional information requested throughout the webinar. Now let me turn it over to Anthony from Tamer for a brief word from our sponsor. Anthony, hello and welcome. You're muted. Are you there Anthony? Can y'all hear me? Anthony, are you there? You just muted again. Okay, looks like everyone can hear me. Are you there? Anthony? Should I go first? Well, we can get Anthony on the line. Anthony, you're unmuted, but we're not hearing it. Did you put your, I think I want to make sure you're, we tested everything before he... I don't know. There we go. There we are. Woo! Result. I love me. You're all good. Okay. Oh, you just muted again Anthony. Yes. Nope. Thank you. All right. Hello and welcome. When you're ready to share your slides. Awesome. I will do that. Sorry about the audio problems there. So, I assume I missed a brilliant introduction. Okay. So, welcome. And I thought I would take a moment and share. We've recently, Tamer's recently published a document called 23 Trends for 2023 in Enterprise Analytics. And of course what I wanted to do is spend, you know, 45 minutes going over each of the 23 different trends, but that's probably not possible. So, I thought I would share just just a little bit. And to start, just quick introduction. I'm the Chief Product Officer here at Tamer of a long history in the enterprise software business started working at a place called Siebel System. I worked for many years at CLIC and the analytics user driven analytics space. More recently at Solonus and then I joined Tamer. I mentioned that partly because what excited me about Tamer is also what I think is a really interesting challenge in the analytics space. So, having spent many years at CLIC and at Solonus I have a lot of experience on the front end of the analytics challenge on how we create dashboards and analysis and reports for people to make meaning out of data. My experience, however, was that in many cases having done all that wonderful work creating a dashboard, for example, the users didn't trust or believe the data behind that dashboard. And so, at its most basic what Tamer set up to do is to help improve the quality of enterprise data. And we do that having built on foundational technology developed at MIT that developed a set of machine learning algorithms for matching data across a silo data sets and improving the quality of that underlying data. So, the opportunity to spend some time thinking about how we actually get better data in the hands of people was an exciting opportunity. And the reason that this is such an important problem is that data has a huge amount of value inside every organization and probably not something I need to convince anyone on this call about. When you think about the opportunity that most organizations have as they become more digital and take advantage of the data, using data can obviously drive bottom line results in terms of revenue so you can think about things like cross on upsell opportunities. I can, it can deliver results in terms of cost so you know thinking about how we optimize spend, how we drive operational efficiency of the old adage if it's not measured it won't it won't change. And it can help avoid discontinuous adverse effects, reducing risk so you know looking at places where we can improve corporate risk management at the center of all of these sorts of concepts. All of these opportunities really is clean, curated continuously up to date data. And that's really, you know, the big opportunity and I suspect a lot of the reason that everyone is on this call is thinking about how your organization how you can think about taking advantage of data to capture these opportunities. So, it's in that spirit that, as I indicated at the top of the top of the call that Tamer spent some time and published the 23 predictions for calendar 2023. And, you know, there's obviously not enough time to go through all 23, but one of the things we did is we took the opportunity to go and canvas a whole set of professionals outside of, you know, really across the industry and getting their perspective on what's really changing in the data analytics space so these are not just predictions from the perspective of Tamer but really, you know, how we think about the data analytics space of changing. They also include what I think is pretty interesting forward by Dr. Michael Stonebreaker indicated that Tamer started an academic research at MIT. That was the was the academic that started and actually did that that research. Anyway, so picking on a couple of the trends and then I'm going to deep dive on one of them. But, you know, there are a couple of sort of just quick highlights so, you know, for example, trend number two around the role of the chief data officer. And I've spent a lot of time over the last year, both talking to chief data officers and spending time with them. What I think you're starting to see is what we've listed as prediction number two, that the focus of chief data officers is going to shift from really technical problems towards business problems business opportunities, or sort of prediction around marketplaces. So, our view is that data marketplaces are extremely valuable and you're going to see significant growth in them but the missing component in data marketplaces is match. It's the ability to know what of the data that marketplaces relevant for your internal data platform. So, I think part of the reason why companies tend sort of very related to that is and I think part of the reason marketplaces are going to be so successful is that many of the most companies will recognize that the best version of their data doesn't sit in their ERP, or in their data lake, or in their system of record it sits outside their organization. And so if you can take advantage of data that sits outside of your organization. Or number 17. This, finally, we're going to see organizations move away from source based governance and towards consumption based governance. So focusing energy on where data is used as a mechanism of providing governance and oversight, versus its source of creation point. Or lastly, prediction number 21, that data storage costs will continue to fall precipitously and effectively storing data is, you know, effectively free. So, I thought I would drill in on, however, prediction number eight, which is around data product partly because I just think it's particularly interesting and so from our perspective. 2023 is the year of managing data as a product. And this is a sort of an important idea but something I've always said is that every business at its core is fundamentally a data business. So, if every business is a data business like you might think you're a retailer or you might think you're a hospital or a healthcare provider or manufacturing company, actually, you're in the data business and the sooner you recognize you're in the data business, the better. And if you recognize you're in the data business, then one of your most important assets is the data you generate. And that produces this opportunity to manage that data as a product and connect it to the business value you generate for your customers. So at a practical level, what does that mean? Like what is a data product? And our view of the data product has a couple of really key components. First, is that it's organized around the logical entities that you use for managing your business. So things like companies or customers, suppliers, products. If you're in the oil and gas, it might be wells, it could be travelers if you're in the hospitality industry, etc. Organized on the business entities that matter to you. And the data product includes several key components. So obviously it needs to have an industry and use case specifics. It's a fully trained machine learning model for doing entity resolution, data cleaning and enrichment capabilities, and rules for consolidating those records into those key entities. So that's what's necessary for the data product. And what is the data product for to do? What is it deliver? You could think of this as JIRA for your data. There's a mechanism under which the organization manages the delivery of data from a whole variety of sources to these key logical entities. And it empowers someone in your organization to be, hopefully more than one person, to be the data product owner. And the data product owner really you think of it can be thought of as the product manager for the data. So in similar, similar to what you might think about for a product manager, the data product owner thinks about establishing a vision for what this data is relevant for inside the organization. Engages the business in understanding their requirements, maps those requirements into tasks that data engineering teams need to deliver, manages a backlog of work to deliver that. And helps to create this iterative cycle between the business challenge that's been trying to be addressed and the data that's being produced by that organization. And typically and hopefully data product owners are pretty hands on so they're actually spending time testing and evaluating that. So my last few seconds. If this was interesting and you're interested in the other sort of 22 I guess predictions that we have in the, in the work and in the, in the report, you can go to tamer.com slash predictions you can see the URL right here. Super tech savvy you can scan this QR code and it will magically take you there and if you download the report now. When you fill out the form we're going to send you one of these cool swig mugs I can tell you from personal experience as a personal user of my swig mug. It is super awesome. I love my coffee mug. It's exactly the right size for holding my coffee. Sometimes I find these mugs are like too big or too small. This is like, perfect. Anyway, so scan the QR code, go to the URL. It's actually a very interesting report filled with some fun and interesting prediction that you can agree with or disagree with as at your leisure so thanks for the time I guess I hit stop share. That's right. So let's see what happens. Hopefully I did that right. Thank you so much for this great kickoff and for the presentation. I hope you all get your free mud. That's amazing. I love it. And thanks to tamer for sponsoring these webinars and helping to make these webinars happen. If you have any questions for Anthony, feel free to submit them in the Q&A section of your screen as he'll be joining us in the Q&A at the end of the webinar today. And let me introduce to you our speaker for the series, William McKnight. William has advised many of the world's best known organizations, his strategies form the information management plan for leading companies in numerous industries. He has a prolific author and a popular keynote speaker and trainer. He has performed dozens of benchmarks on leading database data lake streaming and data integration products. And with that, I will give the floor to William to get his presentation started. Hello and welcome. Thank you, Shannon. And thank you, Anthony, for that great introduction there and beginning. I will also add my two cents and recommend that everybody go ahead and get that report from tamer. It's a very good one. And I like how digestible each prediction is. Okay, so today we're here to talk about 2023 trends in enterprise advanced analytics. And by now you have probably suffered through. Oh, I don't know, maybe a dozen prediction articles or webinars or whatnot. I know I have, and I've thrown most of them away and I've added a few, and you're going to get my perspective today so I thank you very much. Here it is January 12 we like to wait until you're back to work and you're already kind of get back in the groove of things before we give you our predictions so. So here we go hopefully you're more paying attention because of the because of the time. This is our technology stack just one brief minute on this so if you're choosing any of these technologies if you want to run a POC on any of them or if you're implementing one, or do let us know we have expertise across the board big and analytic data platforms operational data and all your data management tools so we're talking about trends why are they important why should you care. Why should you care what everybody else around you might be doing or where things are going you want to see where that puck is headed. You want to know where budgets might be going towards over the next year. It's imperative to see these trends that affect your business and make a plan. Make a plan do you agree or not, and you need to pick your winners, pick the things that you agree are going to be trends that make sense for your business and get on board with them because keep in mind that if you're picking a trend. It's very likely that the vendor community is going to be supporting that trend is going to be doing more with that trend so what you see today is not where everything is going to go. For example, you're going to see quite a smattering of artificial intelligence based trends in my presentation. And that's because I am a huge believer that that is a real trend a real trend setter. And I want to make leaders out of the community here, not followers. And I want you to grow your business ideas as well. Maybe you can see in some of these trends from capabilities that you didn't know were very possible today. And maybe that can help shape the direction of your company's products and your company as a whole. So trends are important because of the efficiencies that you're going to gain in the capabilities that you're going to gain with them. Information management leaders, you hear me talk about this a lot. Information management leaders of tomorrow can advance maturity while also solving business issues and that is the big conundrum that you face as a leader is satisfying the short term, and not, not taking away too much from the long term while you do that. Unfortunately, many of our leaders tend to focus only on the short term and lead behind a bit of a scorched earth when it comes to architecture when it comes to tool selection, when it comes to overlap within the organization so on. So try to stay out of stay out of that as much as you can, while continuing to make your short term gains so I'm very well aware that the ship must keep sailing down the river here while you're picking on picking some of these trends to add so what I like to do is I like to look at the roadmap and think about the trends and see where can I start to bring those trends into my organization, which product projects that are coming up. Can I influence to be part of a future trend so I can kill two birds with one stone. So let's look first at last year's trends now I am going to spend a few minutes on this slide. So, I do want to do this because I think it's important to know where we've come from know what I was thinking what we were all thinking really last year at this time, and also see, well, how did we do. So the first three here I've got edge AI and edge computing dominate architectures. I'll give myself maybe be on this one because I didn't specify industry industries that are completely dominated their architectures are dominated by edge computing, include things like industrial automation and manufacturing, transportation logistics, energy and utilities, healthcare, telecom public sector and there's also a lot of smart city work going on so those are definitely all about edge, but not everything is data to start doing more data science and data cultivation yes I believe this is true. Now, data scientists used to be kind of glorified data integration architects, weren't they. But they have been able to use more of their calling now, now that we are doing a better job I think generally across the board with our data architectures. Oh yeah they're still kind of messy. We've added a lot of things and gotten a lot more data under control, at least to the point where I think the trend from last year is true. Why did adoption of containerized data. This is data that's packaged and stored in a container format such as a Docker container or Kubernetes pod, maybe a be on this one as well because while containerized applications are all the rage. Not all of the data that we're using yet is in this containerized format, although I think that this is trending as a matter of fact if I look across the board at these trends from last year. I think I was probably just a little bit ahead of the game right a little bit ahead of myself because a lot of these are going to be more true this year maybe than last year. You're going to see some more of these I can couldn't help it you're going to see some more of these come back up this year, and it's perfectly okay to have a trend over multiple years. Kubernetes. Yes, I just mentioned Kubernetes really taking off synthetic data use for training AI models. Yeah, you're going to be seeing this again. I wouldn't say I get myself, maybe somewhere between an a and a be on this because I think the synthetic data community, the vendor community that is for this stuff is really blown up and I think we're going to start seeing more uptake in this year. Data fabric sees uptake. Yes, the data fabric as an architectural distributed architectural adoption adaptation I should say of fundamental architecture that is definitely seeing an uptake. But at the same time I'm going to say that I think, I think more enterprises more people are claiming a data fabric then I've actually lifted a finger to make it into what I guess the science of the data fabric says so watch out for that if we raised hands and see a lot more raised hands around data fabric then what I suppose an auditor might find moving right along AI enable applications. Oh yeah, the applications within our enterprises and the ones that we're developing are definitely going to see some more of that today. Data catalogs cross chasm in the data stack. Yes, I think everybody at least thinks about their data catalog and I would say most organizations at least that I come across have a fairly well developed, maybe literature but are really putting effort into their data catalog and building it out to support all the artifacts that we have now around data. Data quality subsumed into data observability. Now, this is a trend that really I'm not saying data observability is taking off. I will say that this year by the way, but I was saying the data quality is going to be subsumed into that and yes I believe that that did happen quite a bit. So, streaming analytics growth with IOT kind of back to the edge architectures. Definitely, if you're in IOT you're doing that with streaming data there's no other way. And so yes on that one sensors and automation drive data volume kind of similar to the prior bullet yes, that did happen medicine jumps to sharpen neurological disorders leading to a DNA revolution well the wording might be a little bit out there. I'm not sure about that but definitely this is a continued trend DNA being of course that genetic material that carries the instructions for the development of us as living organisms is present in almost all cells in the body is made up of a long chain of genetic information. So, in medicine DNA is going to do things like diagnosis and treatment of genetic disorders lead to personalized medicine gene therapy and forensics so all of this. That's coming. Finally, artificial intelligence based on data moves hard into design, we're going to be using artificial intelligence to do more than natural language processing and looking things up and it's a little bit automation here and there, it's going to be designing doing the real brain work that we need it to do. Not quite there yet and didn't win on that one I don't think yet I think we're going to see more of that this year. That design extends to tech and software and yes, I think the vendor community is definitely picking this up more so than the enterprise community auto ML cements itself is the future of ML. I'm not quite so sure on this one I miss a lot of promise in auto ML I still do. I still think that that's what's coming by still see a lot of enterprises not ready to say we trust it. We trust the algorithm selection of auto ML we're going to go ahead and pick our own algorithm. So we're developing a lot of expertise out there in the data science community about various algorithms and that's a great thing. And so I suppose that that creates a better foundation to move into auto ML, maybe more this year GPT three becomes the premier NLP. Well, I think I. Okay, GPT three, if I just said GPT three would be big, I would have hit it out of the park. If I but I also believe that GPT three has become the premier NLP there are competitors. But oh my gosh GPT three. I mean, what can't be said about what that's what that's already done. If anything I think some of the predictions that I've made for the year 2050 and the episode from, I think it was August of last year go and check it out on YouTube. I think some of that I might have to bring that data in because of the power that we see in GPT three right now. Okay, let's get into the trends for this year. Data democratization. Okay, yeah, that's an easy one right. Of course. Yes, everybody's going to realize that what we do in data and analytics is important. I do believe that that will happen. I think one of the important trends will be the continued empowerment of the entire workforce rather than just data engineers and data scientists, putting analytics to work within the enterprise so this is given new rise to all forms of augmented working or tools applications and devices push intelligent insights into the hands of everybody to allow them to do their jobs, more effectively and efficiently. But I think with with my next trend is going to be about CDOs just a look ahead there, but it's, it has to do with the importance of data within the organization, and that the realization of the importance of that, all the way to the executive level. We're going to see that in in 2023 if we have not seen it already many organizations I would say the leading organizations have already accepted this and already doing a lot of things for the toward that end, building a culture of data, building self service analytics. In 2023 enabling the non technical end user will become critical to the survival of that company, which leads us to the chief data officer. Now, I'm going to put kind of my people oriented trends up here at the beginning and then we'll move to more architecture ones chief data officers will turn their focus to building a data culture. What we're going to go up to date is CDOs kind of all over the place if I'm being honest in terms of what they do for an organization. And that's fine. They fit in where the organization has a gaping need. However, there is some science behind the CDO role there is some commonality of need across organizations, and that commonality of need in 2023 is going to be building that data culture, building a support culture for what I just talked about on the screen, the importance of data. So, these challenges are going to remain of trying to get a focus around the CDO role. One of the biggest challenges will continue to be tackling that they'll be tackling is trying to build an institute a data culture within the organization. And this will be addressed in 2023. I don't know that it will be fully solved. I do believe that they will turn their focus to it, and that will make some headway will improve data literacy. This is already an important part of the chief data officers role with 2023 will be the year when improving every employee's understanding of the importance of data becomes the top priority. Instituting a data culture, every level will help overcome some of the foundational causes of other data challenges while also ensuring that the data and the innovations of the past few years can be properly leveraged by business users. There's a role for the CDO the CDO role by the way will continue enforce across organization after organization, it'll be right up there. The order, the ongoing democratization of AI yes not just data. It's going to be democratize. It's going to be AI itself if there isn't an app that does what you need, then it's increasingly simple to create your own. Even if you don't know how to code. Thanks to the growing number of no coded low code platforms will come back to that a little bit later, but these are examples of this is like sway AI. Accio, and oh my the chat GPI. API is chat GPI API is are going to be really what builds the next generation of software vendors, because that API is powerful if you can take what chat GPT does. And you can focus on just a small slice of it, and really build an app around that and put the proper UI on the customer experience and so on. There is a lot to be done around that and I think that that will drive the next generation of, as I said, software vendors that are winners out there. Ultimately, the democratization of AI will enable businesses and organizations to overcome the challenges posed by the AI skills gap created by the shortage of workers that we're all well aware by empowering different people to become armchair data scientists and engineers. The power and utility of AI will become within reach for us all. By the way, on that point, I see no diminishing of the role of the data scientist in 2023, even though some more data science is going to be done by many more people. Okay, augmented working, and I hope you enjoy my doll II artwork this month it is the theme is Picasso. So in 2023 more of us were going to are going to find ourselves working alongside robots and smart machines. And there's many examples of this already in place. AI powered virtual assistants will be able, they'll be more prevalent and able to quickly answer questions is suggest alternative methods in terms of what we're doing. And developing this ability to work with and alongside intelligent smart machines will become an increasingly indispensable workspace. So if you don't use the tools that are available to you, you will become increasingly dispensable. Take a look at the possibilities around you for your organization for the role that your department does your organization does and see how that that can be supported by the technology that is possible today through AI and all the other things that we're talking about here, make sure you're fully using the technology automation, speaking of fully use utilizing the technology automation is happening in our space of data. I'm sure we're all kind of well aware that machines can code and machines can build quite complex code now we still need coders, not to worry about in 2023, but automation is happening around us for the some of the other things that we do to like data quality. We see more processes becoming automated across the entire data management industry, and the primary reason for it is of course time savings resources and data engineers are scarce companies need out of the box solutions that can automate some of their tax. Right now, we see many processes getting automated with AI and metadata data discovery and data source onboarding data quality monitoring data matching and golden golden record creation in master data management. So as we move into 2023, we can expect to see more companies switch to automated data and analytics, a system that uses advanced computer programs and simulations to discover and analyze digital information with like it or not little or no human intervention. So you want to be behind this technology to keep up with all these demands it will implement collaborative processes with stakeholders across many different departments, some of the ones I can think about or finance marketing, legal research HR data workflow automation is another big part of automation. So automating all of the handoffs that occur in any kind of workflow process to get something done within the organization, saving time and money, and ensuring quality and ensuring consistency of the process data governance and regulation. So we all know about GDPR. I think the rest of the world will kind of pick up on this and have their own GDP GDPR is in place. And there's not only for example, GDPR in Europe, there's Canadian what they call PIP EDA, and in China the PIPO other countries are likely to follow suit. So these are our guardrails around going haywire with the technology and doing quite anything and everything that we want. But most of the world's population will be covered by a GDP GDPR type regulation in 2023. So we need to move to ensure that we are in compliance with that. Check our internal data processing and handling procedures, make sure they're adequately documented and understood. For many businesses, this will mean auditing exactly what information they have, how it is collected, where it is stored, and what is done with it. So looking back to my last year prediction about data catalogs, that's going to support this kind of work, for example. The cloud providers themselves are delivering compliance systems now we're shifting that burden now to the cloud provider to make sure that they are in compliance on our behalf for the data and the processes that we do with them. So this realization is particularly acute for the public cloud deployments, of course, they ensure every data is stored in a particular geography, for example, because of the distributed nature of cloud computing and the fact that it. That doesn't have to be the case. Global cloud providers have the geographic footprint to support this. We're also going to be looking at the workflow capabilities of cloud environments and their regulatory compliance. So the cloud knows that I'm working on a specific project and I'm supposed to perform my task after no other person has performed it etc etc it knows what needs to be done. And it's a matter of making sure that the tasks are done in the right way and they are reproducible. So that's my data governance and regulation trend, real time data, if you're not doing it already, you will be doing it most likely in 2023. When you dig into data, in search of insights, as we do in analytics right. It's better to know what's going on now rather than yesterday, I know that's kind of a broad statement, kind of an obvious statement but then we have to break that down how is it better. Well, you'll hear me say many times, and I speak to architects alive. We'll be waiting for the business users to say this is what we need, we need to be bringing them the possibilities. And if they're not asking for real time data that's not the reason to not be delivering more real time data need to grow within the user community, the need to have the ability to deal with real time data, because that is the way that the world is going that is the way that our data needs to go real time data is increasingly becoming the most valuable source of information for the business. And working with this data often requires more sophisticated data and analytics infrastructure, which may mean more expense nobody likes that but the benefit is that we're able to act on the information as it happens. And this could be clickstream data, this could be monitoring transactions as they take place to make sure that we're not getting into a fraud type situation. The data fabric here is again, because I think it's going to be a strong trend this is, I like to say it's data virtualization on steroids. Data management and connects all data sources of data management components through the metadata. And once you connect it in a frictionless as a frictionless asset, providing access to an enterprise data to all relevant stakeholders, you have a data fabric. When fully integrated this can create a user friendly and predominantly autonomous enterprise wide data coverage interface. By the way, I'm not saying this to the exclusion of the data mesh. These are analogous concepts, and definitely they can work together. The mesh is more distributed, whereas the fabric is something that overlays data wherever it is. They both can work together so I do believe the mesh is a strong trend as well. So we're going to be taking more advantage of the multi model capabilities within databases, more than more more databases than not have multi model capabilities. So, maybe we're going to start to see a trend towards utilizing fewer databases than more databases and this will be the reason multi model. For example, you can use a key value store for shopping cart and session data, a document or maybe a column store for consuming the completed orders a relational database for the inventory and the financials and a graph store for customer relationships for marketing, or you could use maybe one database that does all of these two at least a sufficient degree for the needs of the application. So typically, we find multi model databases in the no sequel arena, and some of the leading ones are going to be MongoDB couch base or in DB or angle DB more logic Neo4j, Redis and Amazon document DB. So, we're going to be using those databases force more their multi model capabilities data observability. I talked last year about this data data quality becoming subsumed into observability and observability now I'm going to say is going to take off data quality is constantly involving and quality initiatives begin with a rule based approach later organizations will grow their use of data and start working on rule less solutions that rely more on AI and ML to find that low quality data. And that's a trend, but this trend of towards observability is it looks at data quality issue detection and resolution holistically and employees various techniques to monitor data health. That's data observability, observability, if I can say it. It's your organization's ability to understand the state of your data, based on the information that you're collecting. There's a bunch of tools out there that that claim observability, check them out, coming to a shop near you. Next, cloud native technologies and containerized applications. Yeah, this is a bit of an extension from last year's predictions where we're still here we're still doing it is still a trend. It still was a trend last year continues to be a strong trend this year as I look around cloud native data management technologies present several advantages and because of this cloud adoption is accelerating in all industries. And cloud databases account for most database revenue growth, for example, interest and success in the cloud, come down to the benefits of scalability. Low upfront costs, easier to use the good customer experience and consumption based pricing, occasionally we like that, where you only pay for what you use another growing trend is using containerized applications and I throw them together because they work together containerized applications allow you to deploy using Docker Kubernetes and app on any hardware without needing to change the code base, and it's tiny bits of code compared to full applications. And I think containerized applications are the way to go and the way that I see most applications being built in 2023 right now I would say it's probably on the order of a little bit less than half. But I think as time goes on, this is sticking and we're going to see more containerized applications so if you work on the data side the analytics side of things. Get to know your containerized applications and how you can most effectively support that type of application, because I expect the number of organizations that have containerized apps to be a majority, and for that majority to be doing a majority of their applications as containerized. You can go to no code by making the apps more straightforward required less coding. You can take you can make data management processes available to more users and roles. And there are various low code no code data apps the list is long, but Microsoft has its power apps there's air table notion, different things like that. These are examples of low code no code apps that almost any user can learn to use. There's some, there's others that I think have really embraced this low code no code in our space at a comma data observability is one another one is one data. One data provides an easy way for business users to onboard data improve data collaboratively collaboratively check its quality in an automated way and provide that data to other applications or users. So pretty cool to think about doing stuff like that, without coding. Organizations are also creating localized apps of their own with simple workflows localized apps can lead to localized databases that can manage minor local problems. What else is a strong trend yet there's so many. There's so many trends going on right now we we are definitely at the precipice of change serverless computing by abstracting away the underlying infrastructure serverless computing allows users to focus on the development of the application. It makes it easier there's that theme again, you get your cost savings, you get scalability, flexibility, improved reliability, increased speed of development and deployment. And it's event driven serverless computing computer is built around event triggers, it allows you to build highly responsive and efficient systems that could respond to changes in real time. So can you see the power of all these together means that you become a much more efficient organization. Absolutely, the leadership of technology within these large and midsize organizations needs to be driving this kind of change within the organization this year, in order for that company company to be successful, comprehensive data protection well beyond the database grants and revokes that we used to do, given the reliance on the on virtual tools to support hybrid work environments across the cloud, increasing adoption of software as a service, and the continued growth of enterprise data. It's inevitable that cybersecurity threats will persist and become increasingly complex in 2023. It's nearly impossible to prevent all the ways that bad actors can infiltrate organizations must deploy security strategies that include not just prevention and detection, but data protection backup and recovery as well. I expect to see more it and security decision makers adopting cloud based backup, for example, and a security threats remain persistent cloud data management protection features like cross region replication, an object lock in mutability will be increasingly important tools for security and infrastructure. And there's a whole host of tools that you'll want to add to your stack, including those that do attribute based access control. So there's different types of access control. And this is probably the slide that gets gets a little bit into the weeds, more than the others. But I felt like it was a really strong trend. And that's attribute based access control, as opposed to the alternative. And role based access control. So, all of the above. Yes, if you do them well, you're good, but some are going to require more work on your part than others and let's face it. Work equals doesn't get done. And when it doesn't get done in security that means vulnerability so I think there's an approach here within those three possibilities that works in my view a little bit better than the others and that's the attribute based access asset access control, which probably should be selected before you get into selecting your access control tool. And that is the methodology by which that tool goes about doing its access control is probably more important than the tool itself. Neural network machine learning model for text GPT three I'm back to that. I'm back to that. It's a massive neural network that has the capability of 175 billion machine learning parameters sounds like a lot sounds sounds important. Well surely you know by now, a little bit about what that is, what that's all about it was trained on hundreds of billions of words cookbooks Wikipedia, and the general web books in general, and coding. And the training data is all encompassing. It's a, it's been a matter of time since all of this data which is in one internet is harvested and used for more purposes than I'm going to look up something here and there. And that's today. That is absolutely today GPT through calculates for example how likely one word is to appear in a text, given the other words in the text, and boy does it have a lot of examples at its disposal. And this is what's known as the conditional probability of words, it allows it to quote unquote right for us. And I fear that writing. I fear that art. I fear that music and I love music. I fear that all these things are going the way of AI. Remains to be seen what that all means for us as people enjoying these sorts of things. The public can still use GPT three, even though Microsoft has an exclusive license. Of course it's all of the, all of the news right now that Microsoft is about to make an investment in GPT three that will value the company open AI at $29 billion and stay tuned on that. So synthetic data used for training AI models. So this gets a little bit into Anthony's prediction earlier about data products. How do we build those data products, synthetic data will be a requirement to build the enterprise. The enterprise cannot be built without the use of synthetic data it's going to become an important part of the stack. I don't have a lot of stack diagrams yet, but I feel like it needs to be there, because if you're creating AI that you need to identify the mundane things like, I don't know, cars and buildings and trees and so forth. You don't want to, you don't have the data for that. But, but that doesn't mean you can't have that capability. If you want to be able to understand different languages. If you want to be able to understand the weather, etc. Just about anything that is not in your core competency you have access to that data and can train your AI on that data by going to the synthetic data marketplace. And it's just fascinating. They, this company has had people all over the world. Thousands of people go ahead and read some general text of about 10 to 20 minutes that a customer service professional would would say like thank you very much for calling. What are you calling about today, all this sort of thing, and it's able to create the voice or you're able to create the voice with this by training it with this kind of data in and make your apps say the things that you want them to say I hope I said that right. But synthetic data is very important. Finally, last prediction, and then we'll get to some Q&A and by the way if you have any questions go ahead and put them in the Q&A panel for Anthony and I will get there in just a few minutes. AI infusion. AI will continue to be prominent in traditional BI and analytics solutions. Heck, if Microsoft is thinking about adding chat GPT to word, isn't it logical that we're going to see AI infusion into the analytics that we that we deploy as well. AI will continue to be prominent in traditional BI and analytics solutions in particular self service tools will directly integrate advanced analytics functionality from poor AI portfolios to make it easier for users to identify provision prepare and understand data. Some things are going to get easier, which might mean some other things are going to get harder in 2023. The complexity is not abating at all in terms of what we do in terms of what we, what we have to be to be the best in 2023. I hope that you feel that you are up to the challenge of 2023. And you are going to get yourself the education that's required. You're going to have a true north that you're guiding, not only your enterprise but yourself towards in 2023. And you're going to find ways to bring some of this into your organization into your world and enjoy it. And finally, I'll say embedded analytics, building on the general trend towards data as an API service companies will see more opportunities to embed analytical charts within line of business processes. Many of these will be prebuilt and supported by use case specific AI outcomes. There's more maturity moving in perfectly than a merely perfectly defining the shortcomings a lot of us could sit back and say well that's wrong that's not. That's not doing something that's scalable to the future, but we need to step in and be able to say here's the better way. And we need to do that the right way within organizations, especially today is so many of us are working from home. Don't be afraid to fail. Don't talk yourself out of having a new beginning. It's okay. It's okay. I've reinvented myself many times, and it's time this year for you to reinvent yourself. This is your year. Have an open mind. No plateaus are comfortable for long so whatever plateau, you are on your enterprises on. Maybe it's comfortable now but it will not be comfortable for long. And so we want to be part of we don't want the discomfort to hit us like AI is going to hit so many people like a ton of bricks. They're not going to know what hit them. They have no idea of what AI can do for them. They think it's C3PO. It's not that at all, as most of us now know and are learning more about. So the resistance that you get to your change making, it's not necessarily about making progress, but it's the journey you want to be the one to carry them on that journey. So in summary, prepare to securely build or bring on more users of data. Yes, look for automation possibilities implemented data fabric over your data infrastructure. All native technologists and containerize applications all the way. Think low code no code first it's not going to solve everything. It's not going to be a solution out there and low code no code for everything but definitely check it out before you plunge ahead. Look at your data security options pick the right one for you. So what you're doing now, in terms of data security, think machine learning for text analysis, and really for everything infusing AI into all of your applications in 2023. That brings me to the end of my part and I'll turn it back to Shannon for the Q&A. William and Anthony thank you so much for these great presentations and for kicking off the year with some trends and things to look forward to. And just to answer the most commonly asked questions just a reminder I will send a follow up email to all registrants by the end of day Monday for this webinar with links to the slides and links to the recording, along with anything else requested. So diving in here. So what about data literacy, isn't it an important trend for 2023. Go ahead Anthony. I would say data literacy is absolutely something we've seen but as a kind of new trend for 2023. I think data literacy is something that we've been talking about for some time. I think that's certainly why it doesn't appear on our list. What do you think? I think it'll be a bit of a knock on to the trend that I said earlier about CDOs are going to turn their focus to building a data culture. They're not going to be able to do that without more data literacy in enterprises. So I think we'll struggle with that so and I think that not everybody's going to come around to that, that understanding in 2023 so it didn't make my list. I think it needs to be a part of what the CDO is driving in organizations. Okay, so once the difference between data mesh and data fabric. Okay. I think I'll probably have a whole webinar devoted to this topic. I'll talk about this later. By the way, we give these on the second Thursday of every month so I forget which month it is that I'll be talking more about this but the data. I think I talked about the data fabric in here I talked about both of them but the fabric is more of the metadata overlay on whatever data that you have enabling all of that data to be utilized together. And in queries in applications, and so on, creating an asset that comprises a lot of the organization's data, which is great. A mesh is, is the notion of, of departmentalizing the application, the data architecture so not one data warehouse and data lake and and path to pipeline that is to a machine learning environment but many within the organization but not completely independent. While there should be some sharing of resources. Maybe there could be an enterprise data warehouse that comprises some of the data that the organization needs, and so on, but it's a way of dividing and conquering this whole messy notion of having a data architecture within an organization by having parts of the data architecture everywhere but making them work together holistically. But the only thing I would add to that very briefly is that in the context of our data mesh with an overlay of the metadata in the fabric. One of the really important concepts that you need to think about is entity resolution and thinking about finding common data elements across that fabric or sorry across the mesh or within the mesh is that language. You know, that's something that tamer spends a lot of time thinking about that, and that's really the core IP behind the machine learning built, which is how to find those entities within them. And we've just got a couple minutes left but I'm going to throw a question in here for that elevator pitch answer. I see an earlier comment on biases and AI as most of what is called now, what is called AI now is information at scale. Not intelligence biases will be amplified by the products that are not fully explainable it chills my spine to hear a trend of slapping a UI and consolidated statistical natural language processing and vision models. I know it's happening I'm in our presenter and your take on that. Okay. Go ahead and meet. Okay, a lot of words there so I hope I'm answering the question. Yeah, yeah by bias of course there's there's bias in the data with there are different ways to support that. I don't quite know if the question was about. Is that going to be a problem in 2023. Is that going to be rectified is that does that bias, I guess where the architecture goes for our official intelligence in 2023. I just say about bias that that I think we're doing a, we're at least in 2023 we're going to become much more aware of the problems by us and probably we're going to feel it we're going to have some failures. We're going to have to do a lot of bias within our data, and we're going to have to do a better job at data. I'll say it again, data is absolutely number one importance to making artificial intelligence work so if that's not supporting the not supporting you, then you're just not going to have success and bias is a huge part of whether that's true or not. But the point about model explainability is also really interesting and I suspect that a trend for 2024 will be the rise of model explainability. And I say that because if I ask anyone on this call a question and then have you explained to me why it is you gave me the answer you gave the explanation is likely to have as much bias as the answer. And similarly, asking a model, any model trained on a large volume of data is, you know, the challenge of trying to make something explainable is is arguably harder than having to give you the answer in the first place. And you can by the way you can test this by going to chat GPT and asking it why it gave the answer it gave. It does an atrocious job of answering that question, as you would expect. So I think it's a, it's an extremely important problem that that, you know, we're, you know, only beginning to touch the surface of solving. Well again thank you both so much for this great presentation and time but I'm afraid that is all the time that we have scheduled for this webinar again just a reminder to all registrants I will send a follow up email by entity Monday with links and slides and links to the recording. Thanks to all our attendees who have been so engaged in everything that we do thanks to teamer for sponsoring today and hoping to make these webinars happen. I hope you all have a great day. Thanks William thanks Anthony. Thank you.