 Hello and welcome, my name is Shannon Kemp and I'm the Chief Digital Manager of DataVercity. We would like to thank you for joining this DataVercity webinar, which today is Speed Matters Intelligent Strategies to Accelerate Data-Driven Decisions, sponsored today by CLIC. 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 highlights or questions via Twitter using hashtag DataVercity. And if you'd like to chat with us or with each other, we certainly encourage you to do so. To find the Q&A and the chat panels, you can click those icons in the bottom middle of your screen to activate those features. And just to note, Zoom defaults the chat to section to send it just the panelists, but you may absolutely change it to chat with everybody. As always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now let me introduce to you our team of speakers for today, Michael Dissler and Matt Aslet. Michael is a senior director of product marketing at CLIC responsible for messaging and go-to-market strategies related to big data, IOT, GDPR, CLIC data catalyst, CLIC Associative Big Data Index, and CLIC's architecture and technology. Matt is a research director with responsibility for data, AI and analytics, and AI and analytics channel at 451 Research, a part of S&P Global Market Intelligence. Matt's primary area of focus currently includes data abstraction, virtualization and analytics acceleration, data culture and data literacy, data streaming and streaming data integration as well as hybrid cloud data processing. And with that, I will turn the webinar over to Michael and Matt to get us started. Hello and welcome. Thank you very much and hello everybody. Thanks for joining. So, yeah, as we just talked about, I'm Michael Dissler and Matt, I'm very excited for Matt to join me today. So, Matt's going to, we're going to start off with Matt talking about what he's seen in the market today regarding data integration analytics, which I think are important factors in this idea of speed matters, the importance of making data driven decisions very quickly. Now I should point out that some of the things he's going to point out today are in a report that he recently developed, and everybody attending is going to get a copy of this report. So after Matt speaks, I'll follow with a brief discussion about how CLIC can also contribute to this need for speed. And with that, I'm going to turn it over to Matt. Thanks, Michael. And yeah, thanks everyone for joining this this webinar. Thanks to CLIC for having us on. So yeah, I'm going to kick things off here just talking about some of the key trends we see in a driving change in this space and the importance and benefits of accelerating the path from data to decision. So, if we have the next slide, I think you know the first thing to point out really in relation to that is the growing recognition of the importance of data. Of course, you know people practitioners working with data, data analysts, data management experts, you know, have long understood the importance of data and of course, you know a lot of business decision makers have too. But what we've seen particularly in recent years is the growing understanding, particularly at that, you know, senior decision maker level of the importance of data and the fundamental importance of data as potentially a competitive differentiator. And there's, you know, two, two charts here on this slide which kind of illustrate that, both taken from our Voice the Enterprise data platforms analytics survey. And the, you know, the first on the left hand side here you can see the importance of data 12 months from now we always ask organizations or respondents, you know, to what extent do you think data will be more important to your organization a year from now and as you start saying it will be more important. This is since we started doing these surveys has always been there or thereabouts but it's been sneaking up, you know, over that time so not only is data already important it is growing and importance. As I said, particularly anecdotally we see that amongst senior decision makers. And the reasons for that, you know, are pretty obvious when you look at the chart on the right hand side here, which is the benefits organization see from being more data driven for having more more data driven decision making, you know, these are really fundamental sort of business priorities as you can see things like increasing sales, enhancing customer service and engagement, improving business agility, improving existing or developing new products and services. And then the next slide, you know, what we've seen, particularly in the last 18 months is, you know, amid COVID obviously you know and the pandemic and all the social economic changes that's resulted from that, you know, clearly that has been, you know, has the significant impact on all organizations. And a lot of people, you know, in a very bad way but interestingly, if you think about, you know, the way enterprises have responded to that. You can actually argue there's been some sort of benefits of this in terms of accelerating change and we've seen a proportion of enterprises have actually taken this as an opportunity to develop new products and services develop new ways of engaging with enterprises. You know, sometimes during necessity, obviously we've seen fundamental changes from a retail perspective, people for a long time not able to go into literally into stores in the way they would so shift and acceleration perhaps of the shift towards sort of digital commerce, but also, you know, enterprises just seeing the opportunities in some cases to evolve the way they did things we've even seen this in in industries that have been severely impacted by COVID such as travel and transport, where actually, you know, the companies have taken the opportunity to invest in new initiatives, airlines investing in new baggage handling systems, for example, actually given the opportunity to do that because they have less customers to deal with. So what we've seen in particular in relation to data and analytics and what this chart actually illustrates is the fact that, you know, this sort of acceleration of projects is not necessarily evenly distributed. But actually it's the companies that have been the most successful with data and analytics to date that stand to gain the most from the investments they've made over the past 18 months. So we ask companies about one, you know, the next their success rate with data analytics initiatives in the last couple of years, and then also about, you know, whether they've increased spending on data management analytics, products and services, and also if they've increased the number of scope of active analytics projects as a result of COVID-19. And as you can see, amongst the most successful those that say that, you know, 81% or more of their projects have been successful, they're twice as likely as the least successful, more than twice as likely as the least successful to have increased spending on data management and analytics products and services, specifically as a result of COVID-19. So as I said, so I'm using that as a trigger point to actually, you know, put the foot to the floor if I can accelerate ahead. And not quite as the same margin but almost twice as likely also to have increased the number and scope of active analytics projects. Again, some of that was through necessity obviously we saw a lot of organizations suddenly found, you know, the data and the projects they previously were based upon perhaps, you know, just didn't make sense in the current scenario, but also, you know, some of that through choice and actually actively investing in data analytics projects, you know, in order to try and predict obviously in some cases like, you know, what's going to happen when people return to stores and people return to traveling. And so that, you know, the next slide, we can see, you know, clearly, what's there are a lot of benefits to being data driven as we talked about before, there are obviously, you know, some challenges as well and we can can look at this through the lens of the most data driven companies versus this or the average company, if you like, or, you know, all respondents to our surveys. And this is kind of interesting because it highlights that, you know, one that being data driven is a lot more, you know, easier said than done there are some real challenges that come along with that. And secondly, that, you know, even amongst the most data driven companies, there are some still some barriers and challenges they face. The first thing is that in some cases, those are different barriers. So for example, we've highlighted here a few areas where, you know, all respondents were more concerned or more challenged by some barriers than the most data driven companies, and perhaps not surprisingly, you know, it's things like budget constraints or skilled resources or lack of support involvement from senior leadership. The key is that, you know, amongst those more data driven companies companies that make a significantly higher proportion of their business decisions driven by data, they have that support from senior leadership, which obviously, you know, would say you would understand translates into less budget limitations, although not complete lack of them, and also less challenges in terms of skill resources, although again, not a complete lack of them. Interestingly, though, if we go to the next slide here, we see that there are some areas where the more data driven companies are actually more challenged or perhaps you could be saying more concerned about some barriers than, you know, the average respondent, if you like. So some of these areas illustrate where the more you try to do with data, actually, you know, other barriers, you know, arise or become more, more evident. So, you know, one of those we see, we've seen this in numerous surveys of the is is data security. It's not a course that data security is, is not a challenge for in the early stages of a project. But certainly I think more ambitious you are as an organization, you know, with data, the more you try to do with data, the more security concerns, you know, come to the fore and privacy, obviously is another area which is similar to that. And then also, building and maintaining the data infrastructure that can support these analytics projects. Again, it's not that, you know, so in the early stages, these things are easy, but obviously, if we're perhaps a small tactical initiative, you don't get the complexity of challenges, you perhaps get as again you try to be more ambitious as a company and having more strategic approach to being data driven. We go to the next slide, you know, one of the key challenges we see that affects organizations of, you know, of all type, you know, regardless of how sort of ambitious they are in relation to data is the time taken to actually generate insight from data. And as you can see here, you know, this is looking at all respondents, you know, 60% of respondents saying it takes them more than three days to generate insight from raw data. And this, as I say, is an ongoing problem and it's a significant problem because as we look at the next slide, we can see there are some really significant business benefits to be had from, you know, from reducing that time and reducing those delays. You know, these are fundamental business priorities, things like increasing sales, improving business agility, enhancing customer service and engagement, and also things like empowering and aligning internal decision makers across different departments of the organization. There are some really significant benefits to be had and as we saw earlier, it's the most data driven companies is the most successful to date with data analytics that stand to gain the most from those as they continue their investment in data products and services. You know, if we go to the next slide, you know, one of the ways we talk about this is about accelerating that path from data to decision. And what we see is that particularly, you know, modern data projects require approaches that are burdened by traditional assumptions about the path from data to decision being served by standalone production services. Now, you know, this is obviously this graphic here is a simplification brief you think about, you know, the process is involved from going from from data to some kind of business decision, they can more or less be divided into three groups there's ingestion of data and integration of data. There's then the storage and processing of data, and then obviously there's a visualization and analysis of that data. What we've seen is historically, you know, these were three distinct steps in a process that was served by three distinct product and technologies, historically delivered by three different vendors now you know through both margin acquisition and research and development. Clearly, I think you know most most data analytics providers today can can address all of these steps, but still often with three different products and all with functionality that same that three different groups of users. And what we see is that overall there are really potential opportunities to deliver enhanced efficiency and reduce data friction between data consumers and the people who are responsible for, you know, the operating and maintaining the data architecture through the use of unified data platforms that actually consolidate the functionality required to accelerate the path from data to decision. Now of course, if we go to the next slide, you know, data acceleration of data initiatives isn't just about products and services and what it's you know make that absolutely clear. You know we see so this is a chart here shows this up the top three steps that survey respondents tell us they have taken to improve data culture in their organization and you know on the on the far right there we see that increasing and improving employee data literacy and skills is, you know, is a significant response 35% of respondents selecting that. But that said, we actually do see that in many organizations they still do take let's say like a product first approach to improving data culture and being more data driven. That's the ability that starts with the products. And you know, the top two answers here were then investing in new analytics products and services 40% of respondents selected that. And then interestingly, ahead of that, investing in new data management products and services with 44% and that actually reflects something we've been talking about and writing about no research, quite a bit actually the last couple of years in that data is an enabler for an enhanced analytics and particularly self service analytics, you need, you know, the fundamental data management and data governance capabilities to actually, you know, to be in place to enable organizations to go faster. And as I was just saying, this really highlights the fact that for many organizations, they're still taking a product centric approach to improving data culture. Now, if we go to the next slide Michael mentioned earlier, you know, this this report we published last late last year, talking about enterprise intelligence platforms, and the role that they have to play in accelerating data to decision. And to say, you know, this, this wasn't a sort of a one off report, this is part of it, you know, a series actually we've done, and this concept of the enterprise intelligence platform has been part of our research for the last couple of years. And the reason we picked out a couple of key sentences, you know, from the report because they really highlight, you know, what we see is happening and the reasons for some changing attitudes in this space. And this is, you know, despite obviously enormous innovation across the data and analytics sector in recent years. We see that there is for many organizations a gap between what's theoretically possible if you look at sort of the state of the art of data analytics, and what they're actually able to deliver and the extent to which their investments can make a practical meaningful impact on business decision making. And there are multiple reasons for that, of course, but one of them that we see is that in many companies. Projects remain built around the idea of pipelines and analytics processes that assume those, you know, three steps that we talked about previously. They are provided for with standalone products for data integration, data processing, and analytics, and there is a fundamental requirement on three different technologies for people with three different roles on that path from day to do decision. We, as I said, have described sort of what we see as the alternative as an enterprise intelligence platform. If we go to the next slide, we can see the extent to which we see that demand is evolving for products that actually deliver a consolidated platform that addresses all those three steps in this in a single environment, obviously with functionality still aimed at different users, but with a single environment, and we actually saw that, you know, nearly half 46% of respondents said, you know, for a greenfield analytics initiative and assuming, you know, all things being equal function upon a front of functionality basis that they would actually have a preference for a consolidated platform. Clearly, that's, you know, that's less than half 54% said they'd have a preference for multiple standalone offerings but if you think that, you know, multiple standalone offerings was the default for so long. So I think that, you know, we saw this is actually showing that there's a significant level of, of interest and not just interest but a preference actually for a some sort of consolidated environment. If you go to the next slide, you know, we look at, you know, what we see as some of the requirements that we would, we would refer to as an enterprise intelligence platform and that there's a couple here and I'll describe these in a little bit more detail So load data first and ask questions later. We've seen, you know, the way in which companies think about the loading data into what was, you know, traditionally a data warehouse environment has evolved and increasingly organizations are looking to use the processing power of the data storage and processing platform to apply schema query time rather than then pre defining that that schema as they load data in. So that's been a fundamental shift that's been going on over multiple years and is driving adoption of emerging technologies and emerging data platforms. In a second, we talk about all the data all the time, you know, companies clearly are trying to make users as much data as is, as is, you know, possible economically possible to perform their analytics queries in order to get, you know, the highest value. You know, we see that, you know, one of the ways of doing that is to, is to sort of use metadata to actually automate your identification and tagging of data as it's ingested into an environment. And then as I said that helps to, to, then enable sort of the application of schema query time. We also see that in addition to enabling enterprises to process data that's persisted natively, you know, modern enterprise intelligence data platform should also enable users to incorporate the results of queries performed in data in other locations as required so clearly, especially as we see data is increasingly spread across multiple clouds, as well as on premises data centers, you know, as well as services service applications, there is an increasing need to be able to query data in multiple locations in multiple services to have a complete view of that data rather than the traditional approach of pulling that all into a single sort of consolidated data warehouse. Thirdly, take the query to the data. I mentioned, you know, it was an increasing volume of data is now in cloud storage services NAS that grows, it's becoming increasingly impractical to move that data around. We see many enterprises investing in taking the query to the data, taking advantage of things like query data, query federation data virtualization capabilities to analyze data in and across multiple locations without having to first move and transform it. So reduced data friction is another key element in a modern data platforms we think should enable multiple users to have access to and make use of the same data for multiple purposes. Clearly that's easier said than done but you know we see that is something that a lot of enterprises are focused on, particularly in terms of reducing the friction between data consumers. There's analysts developers senior decision makers and data operators of data management and it professionals and you know reduce the friction between those two groups and and that being a key way of accelerating in a data driven decision making. And then finally support obviously analytics innovation so you know we see that you know providing access to data for multiple purposes is an enabler of analytics innovation because it gives users the ability to explore data and rapidly iterate analytics projects. Innovation is also enabled by support for more advanced analytics functionality or see data science machine learning predictive analytics capabilities as part of the overall sort of and data analytics portfolio. So until the next slide, we're just sort of wrapping up here really in terms of the key points in terms of the, you know, just been a sort of a rapid look at some of the key trends we see shaping this this space. We see that you know in order to remain competitive, especially obviously given the, you know this fast changing socioeconomic conditions that we've seen in the last year and a half enterprises need to invest in data analytics product services and functionality that support business agility. And as we said, you know we've seen it especially the most data driven the most successful with data analytics are the ones that have actually, you know accelerated ahead with doing that during the pandemic and therefore accelerating that path from data to decision. We've got the three steps on the path from data to decision data ingestion integration data storage data processing data visualization and analysis and clearly at each of those steps there are opportunities to deliver improved efficiency and reduce data friction, but also we see that you know the combined enterprise intelligence platform can potentially provide a single environment that can can also, you know, improve efficiency and accelerate that path from data to decision. Clearly the acceleration of business insight relies on a combination of capabilities that deliver more agile approaches to data ingestion and data integration. So you know we talked about the role that data literacy has to play it's not all about products and services, as I said, but together you know these capabilities can drive the acceleration of multiple business intelligence and analytics tools by multiple users across an organization for multiple purposes. Thank you for that. I'll thank you for your time and I think maybe there's been questions already come in while I've been talking to will come to those in due course but for now I'll hand you back to Michael and continue with their presentation here. Thank you very much Matt that was great some great insights and yeah we've already seen a number of great questions come in and we'll be answering them. I wanted to spend a little bit of time just talking about how click fits in with some of these trends and recommendations that Matt just talked about. You know he put up this I think a nice little visualization of kind of the what I call it's like the flow of data going from kind of raw data to making a decision about it and actually click has a fairly similar type of little diagram. The same kind of thing where you start with raw data we call informed actions as opposed to decisions and we divided a very kind of very simple point of view to these these four steps first to free it you know freeing the data from various raw data repositories that it may be very hard to get to finding it basically free the data but how do we make sure everybody in the organization can find the data they're looking for understand it is of course more on the analysis. Generating some analysis and hopefully gaining some insights in the data and then finally action it not just to look at information and do some analysis but to do something with that information. And we have kind of put it together with terms of our products if you haven't looked at click in a while you'll find that we're no longer just data visualization that certainly is our heritage. But we really expanded our portfolio since then and we're really more of an end to end solution spanning this whole phase from raw data to informed actions. And just want to spend a little bit of time going over kind of the three main components of our overall portfolio which is around data integration data analytics and then what we call data literacy as a service which is really more around customer success. So data integration so that's really focused on the kind of points in the flow of freeing the data free it and finding the data find it. So first important point I think for click is what we call our change data capture technology. So this is the whole idea about being able to support the movement of data in real time some people call real time data streaming and we do it in a very highly efficient a high performance way. And fortunately I think and I think Matt show this a bit you know the the days of having nightly updates to your data is really not sufficient as more and more people want to get that information they want to get in real time. But they want to be able to do this get this information get it real time without having a big impact to that source database you know DBAs are very protective of their databases and if you start hammering it and really affecting the performance to pull the data out and they don't like that. And so we have a method that really is a very low impact way can very scalable and we detect the change in the data itself or a change in the model. And we immediately update that target environment so it's a very highly efficient and somewhat unique approach to the movement of data. Second is around data automation and match touch upon this to the idea of going to more cloud based data warehouses and certainly we're seeing that with a huge number of customers they want to go to things like snowflake and data bricks and stuff like that. But how do they do that at scale, without a lot of time and resources, because I think the classic way to do this is you provide you have some very highly skilled technical resources, developing code developing ETL code to pull the data out coming up with a model how it's going to be in the new data warehouse. That's a lot of time, a lot of effort, and quite frankly, a lot of risk because you're manually putting together this whole process. So we offer automated capabilities and a very good using a configuration type tools to really model what that cloud based data warehouse or day like is going to look like. And then we automatically generate the ETL code kind of behind the scenes that that hundreds or thousands of lines of ETL code is generated automatically. So that's a huge improvement it means very of course it could be done a lot quicker than doing the manual effort, a lot less cost, in terms of the time and resources, and also improves the the lower lowers the risk, because you're it's a, it's a repeatable automated process. Last thing it's very important is that it improves your flexibility. So I think there's some people think we finally got everything on to perhaps this particular cloud warehouse, but all of a sudden a new CIO comes in he says he wants to change to another, another warehouse. That could be a lot of effort in a more manual mode, but with our tools a click, you're simply telling it, we're now pointing this type of data warehouse, and it regenerates all that ETL code behind the scenes again so really helps you being flexible. So this is around data catalog, and this is really a more around the defined area, you know it's great that we can free the data but how do I know exactly what did I have great I've moved all this data to that cloud data warehouse. What do I have exactly, and how do I let everybody know what this is. And that's really where a catalog comes in play enriching preparing and even help delivering this trusted govern data. So on the data integration side on the data analytics side. Again, if you've heard a click in the past, one of our key differentiators is what we call the associate engine or the associate difference. And that's still a huge important factor for us. This basically I like the the term peripheral vision. It gives your users to see kind of what we call the whole story of their data. Let's just take a really simple example in your in your, you want to see all the sales in the Northeast, and your typical SQL based solution, when it's going to drilling down, it's only going to bring back what sold in the Northeast. But click is kind of maintain that data in context and it's not excluding other data it's bringing all the related data back. And so, for example, one thing you may see with click is not only what you sold in the Northeast, but what did not sell in the Northeast means you're expecting to be sold, but didn't happen. Or perhaps you're seeing that this product didn't sell very well. I want to see immediately what the how this product sold in other parts of the country. And a lot of SQL type based tools that would be another query another way to the get that data out that all those links are automatically put together within our associate engine you can immediately change course, and now look instead of looking at sales in the Northeast, looking at how a particular product sold across the country. So it's that type of context giving you that kind of idea of peripheral vision versus more really have a tunnel vision with other solutions that really can enrich the analytic experience for everybody. A next important point or analytics for us is our what we call our augmented analytics. This is really building upon the associate engine and adding a unique kind of cognitive engine ability so they things like machine learning to really enrich the analytic experience of all users. Now I say enrich. I think some people when they hear about AI or machine learning that it's really kind of more of a black box approach where there's the human isn't really involved in the process. We don't really see that we really see that this can as we say augment the user. So really taking that data, applying some some technology to it and generating some more insights and really help the user and power the user and rich the user. And what type of insights they can find within the data. Lastly around data analytics is what we call kind of embedded embedded the point of decision. And really this is around the concept of that instead of the user having to go to analytics, bringing the analytics to the user. So that could be in the form of perhaps within their their portal there may be the various enterprise applications that your users are using across the organization your Internet's extra nets. The idea that within that overall view that they're used to now click can be part of that that view, or perhaps you want to have quick kind of more working the background have all they kind of be the engine to generate those insights but from the user's point of view. They're looking at a custom UI that they're used to. And we have a full set of open API is and that allows users to do that so that the user doesn't even know that click is behind the scenes and generating those insights. So the last part of our flow is what we call data literacy as a service. This is really focused on our customer success. First, not surprisingly, we offer enterprise support. We have global companies around the world we have over 50,000 customers around the world, many of them are large enterprise customers may need that around the clock support and of course we provide that a signature So this really is what we're saying here is the idea that we're going to basically tailor what our customer needs in terms of consulting education it's not kind of a generic one size fits all. We're going to tailor exactly what education, what consulting need for your particular purchase for your particular environment. And lastly around data literacy and this is something that Matt touched upon, which we also think is very important the idea of data literacy. As he pointed out, people are thinking that buying the technology, the analytics or the data integration technology is important. But if your users don't understand the data that they're looking at all the technology in the world isn't going to help. This has been a really a strong push for click we've been really kind of been pushing on this and investing on the last few years. We have consulting education certification assessments all around the concept of data literacy. And very importantly, all these programs are product agnostic. It's really not showing the the click products. It's really talking about the concept of data literacy. By the way, if you're interested if you want to see for yourself, maybe how data littered you are there's a free assessment on our site again it has nothing to do with the product. So if you want to check it out and see how data littered you are, feel free to check that out. So again, that's kind of the three main concepts that click now offers in terms of portfolio, the data integration, the data analytics and data literacy. Let's talk about an example about how customers making use of that. The first one is a company called I a American warranty, as the name sounds they're in the warranty business around warranties for cars in North America. And they have both our data integration and data analytics products within their their organization, and some huge and see under the value column there, some huge gains in terms of you know we talk about the need for speed, huge gains in terms of speed, getting the data ready 10x improvement there, developer productivity of 5x improvement. Really were able to rapidly get up to speed and get deployed very quickly they were seeing some great insights after three months. I like that quote on the right here turns out a lot of their assumptions were wrong. Click has changed how we see our customers in business. That is, that is really something that we see with a lot of our customers they say they that they find that not only this click provide a lot of value in showing what happened, but also showing what did not happen. Again, my simple example before about what were sales in the Northeast, being able to show what did not happen what did not sell in the Northeast is probably more same or more valuable than showing what did happen. Talked about the idea of data literacy, you know they had an 80% adoption rate after three months so it was a really, a really great example here of a customer making use of the full breadth of our tools and showing some great ROI so great, we will talk about that. So, again, we talked about this idea of this flow this free it find it understand it and action it, but we really don't want to stop there we were taking our vision kind of the next level and building upon all these building blocks into something that we call active intelligence and active intelligence what do we mean by that really kind of simply as the word implies it's being more active it's being more agile. If you think about more traditional bi traditional bi is around that you have this particular kind of curate data set here's your data. Here's your particular dashboard. This is what you get. No changes are allowed. Not the real world these days it has to be very you have to be very agile very active you have to react in real time. Certainly things like coven show the need to react very quickly to the current situation. And so active intelligence means using real time data using up to the date information. So looking back at traditional bi the the idea of not having a government and data pipeline, I think is has been seen. I even see many people are still using Excel as their quote unquote data pipeline to move data around that's that's really not a very good government repeatable method. Another part of active intelligence is having that intelligent analytics analytics data pipeline, the idea that it's repeatable that it's automated that it can automatically intelligently profile the information transform the data. That's very important to. And the last, just by the term active intelligence, the traditional is more designed to inform not compel action, and really active intelligence important point is to do something with the data not just look at it and discover insights, but actually do something trigger that immediate actions. So if some of this maybe sound a little familiar that that's I agree it should because really I think there's those very well with what Matt was just talking about in terms of the enterprise intelligent platforms, really active intelligence I think is a very much fits with that concept of the, the enterprise intelligent platforms. So just to wrap up just talk about two more customer examples of here of customers that have really made use of our active intelligence tools. And then is a JB hunt. So JB hunt is a shipper and logistics supplier in North America, you've been on the highways actually just saw one of their trucks on the road this morning. The main issue was they had a lot of different data sources, but they didn't have many people to manage those data resources and so what they were doing were manually loading the data, once a day to get some reports about what was going on. And certainly when you have trucks on the road all over the country. Once a day getting updates isn't very, very timely information. So they had all these different data sources. How do they just go very quickly. They also had a lot of different kinds of data. They had blog data that structured data, all different kinds of data. So they needed some type of automated process, they need this thing to run 24 seven and click really help them made it very efficiently, maybe for their team, pull all that data together and give basically for them real time was one to two minutes So now when they get a call from a shippers and carriers where is my truckload of goods right now, they can tell them that they can give them real time insight into where their goods are so really a huge amount of value add for them, but they didn't have to add a lot of resources they didn't have to add a lot of infrastructure, made use of the people that they had already and become a lot more proficient and productive. And last, you know, we talked about COVID so here is that actually an example of COVID this is a regional hospital in the UK. And they, not surprisingly had to adapt very quickly due to COVID. Fortunately, they had already had click in installed, and we're using already and implemented it. So they were fortunately knowledgeable and had begun their, their, basically their analytics journey, but they were really focused before COVID hit more on long term improvement efforts. And so they quickly had to change and it turned in quite frankly in a survival tool to handle the rapidly changing events of COVID. And they adapted various dashboards. So nurses could know where patients were in their COVID status, you know, previously to before COVID it didn't matter what type of ailment were patient when one room to the patient in the next room. And of course you wanted to have all your COVID folks in one wing or one hallway of the hospital and make sure they were isolated from the rest of the patients in the hospital and click allowed the nurses to get very much of the data information about what was going on, where all the patients were going to coming into the ward. Also, help them with quickly capturing the pandemic related matters, such as how much oxygen they had, which of course is a very important tool in treating people with COVID and making sure that they they're running low they could plan ahead and order more oxygen in time. Sadly, one of the things they kept track of was the mortuary capacity, making sure there was enough enough space there if need be. This hospital chain actually hired a data scientist before COVID started and that data scientist was actually doing some predictive modeling. But that predictive modeling was based on eight week averages. Well naturally in COVID a lot can change in eight weeks. So that was pretty much irrelevant. But they were able to adapt that and makes basically use the results of the last few days to do modeling on it and they were able to really make some very accurate predictions based on those current trends. This was very important because this hospital chain had actually the fewest number of critical care or what's a biblical ICU intensive care units in their overall part of the country. And so it was really critical for them to understand exactly predicting how many beds they're going to need how many accurate to be critical care intensive care beds they're going to need in the in the future. So this has been a huge update to them as I said, able to get a live updates on what's going on was extremely important for them. Okay, so just to wrap things up and again I see the questions coming in which is great. You know I talked about this very simple flow of free it find it understand it and action it. Now if you put kind of layer over at various types of analysis. I think from the left you think about free it and you just kind of them. Well that's great you can now discover, you know some data and just do some maybe analysis just discovering what you have. You follow that all the way to the right, and you have with action you have alerting type of analysis. So, really if I kind of lay over a time scale for this really I like to say it's kind of going from slow to fast. You know what the time to answer scale is kind of going from slow to fast. And that I think is really kind of a theme hopefully you've seen in this webinar today that really what we're talking about is how can we streamline the time to answer. How can we make it quicker to get the data you need to get the insights you need to take the action that you need to take. That's I think really important point hopefully you've gained a couple of important points and maybe some some good insight on this based on this webinar. I just wanted to remind everybody, but thank you for attending this webinar, everybody attending is going to get a copy of this enterprise intelligent platform from 451 research you'll be getting that afterwards. If you're interested in learning more about active intelligence this term I just talked about click, feel free to go to click calm slash active intelligence. And with that, I believe we are open to have questions. Michael and Matt thank you so much for these great presentation we do have a lot of questions coming in to answer the most commonly asked questions just a reminder I will send a follow up email to all registrants by end of day Thursday for this webinar. The links to the slides and the recording in the report that was just mentioned as well. And if you have questions for Michael Matt feel free to submit them in the q amp a portion of your screen. So diving in here. How do you get around building a data pipeline that puts data in a systematic layer that uses users can understand versus just providing raw data, building that pipeline is the most time consuming part of providing data for self service scenes. Well, I'll take a stab that first I mean I think that that's part of, you know, what we, when I talked about it in terms of that I think the classic situation in building that pipeline as you say is very time consuming. I think because you're the traditional ways using kind of a manual effort to put together that pipeline. You know I think by by having an automated way to define that pipeline, and keep an eye on that pipeline and make sure it runs the same way every time. I think that's, that's an important aspect of streamlining that that process. You know a question came in earlier that's kind of along those lines into the chat section, you know what's the likely point of failure people process or tech solution, and I bring that both you guys. Yeah, good question I'll take that first and then maybe maybe you can give your bill I, I, I think, you know, I think it's, to me, likely point of fact is what I call more culture, which I think is sort of a combination of people and processes. You know, as I talked about earlier you know technology yes certainly you do need the right technology, but if the people aren't on board if you don't have the process in place to support that if the, the executive staff is not on board. You know that's just not the culture. That's my experience has been more than a likely point of failure. Yeah, I think I'd agree with that I was just thinking about this question while Michael was talking then I sort of go back to one of the slides I said about, you know, one of the reasons I think we see that there is perhaps a gap between what is technically possible with the products that are available today, what companies are achieving is, you know, it comes down to it to potentially sort of the culture also sort of assumptions about the way things should be done, but based on the way the head is historically being done. And obviously that is a combination of people and and process but yeah ultimately it comes down to perhaps some of the cultural aspect. So many great questions coming in so what are things you can do to remove data friction. Yeah, I can I can I can start with that and I think there was I saw there was some chat and people talking about you know perhaps you know the fact that, as I said on the slide many projects seem to be sort of product driven initiative seem to product driven but clearly, you know data literacy and skills was was third on the list of the steps organizations are taking to improve data culture so with that I think that does show that companies are trying to address that. Obviously I only talked about the top three in that particular slide and some of the other things, you know that we've seen companies doing is is trying to solve, you know, particularly in relation to reducing data friction is about organizational cultural change. So sort of fostering collaboration between, you know those data owners that providers and consumers and operators. You know, I think if where organizations can create a culture of being data driven that it often comes from above whether it's the CEO. That's why I was increasingly CDOs or someone of an equivalent title, you know that they can drive change that they can get people in literally in the same room well, maybe the last 18 months but on the same zoom call, or what have you, and get people talking and encourage people to talk and I think another part of it is actually talking about success stories. It sounds really simple but we've seen I've seen organizations this can be really successful actually talking about not just the outcomes but but the data and analytics processes and collaboration that enabled those outcomes and just get that. Again, it's it's part of that culture and I suppose it's part of data literacy really is that understanding of of the role that data analytics has to play in in throughout the decision making process. Lovely so I'm surprised that companies do not invest more on people are people not key in the move to data. Yeah, I think you know this question obviously, you know, to some extent so you know it was very much related to the one we just answered and and again you know when we look at the steps people take companies I should say taking to improve data culture. It's a fair point that you know we say, you know, as I highlighted the top two are about products and, and it would seem from that that people are forgotten but I do you know it do see that there are other steps companies are taking you know investing in analytics skills across, you know, not just in in that you know the data and analytics team but across an organization, you know, talked about the role potentially of the chief data officer or chief analytics officer or you know someone of that ilk. And I do see that that companies are investing in that role. You know, actually, potentially, we see some companies reorganizing the way they spread analytics skills. I should say people with analytics skills across a company, you know, perhaps less of a centralized group and actually distribute them within departments and so yeah there's multiple steps and obviously, you know, it depends on from the company, which they take and to be fair, you know, all of those things I've just talked about do appear lower down in the list, particularly well from our surveys at least of steps companies are taking but they are on the list they are things companies are doing. And I think, you know, the fact that data literacy was there in third place is, you know, should be seen as a positive sign. You know, even if, as I mentioned earlier, you know, it still seems that for a lot of companies, you know, their first sort of step is very often still around around choosing a product. But there are other aspects to it. Great. So, in what ways does agility differ between retail applications and more complex applications in healthcare such as clinical decisions support systems. You're also not familiar with clinical decision support systems so yeah I mean just to just to be clear, I gave a, you know, examples we gave war of using of data analytics in particular industries but our solution is not focused on, you know, can be used by many, many different industries so to be honest, I'm not sure we may be a little bit apples and oranges type comparison here. I'll ask that question if they have additional clarifying clarifying comments for that. So, but moving on here so looking down so that just the associative difference get created automatically based on the data or does the person who creates the visualization need to do something to ensure it is available. It happens automatically and it automatically it brings it basically in memory so that's why part of its its speed in terms of when you go in different directions and not having a lag. So it happens automatically and it makes all the relationships automatically get a little techie here it's almost like a thing of it as a complete outer join. The cool thing is that it will do this not just with data from one data source but it can be pulling data from multiple sources you will make all those relationships and connections across all those multiple data sources. So it's a really great way to do some analysis when you're not just looking at one data set but multiple different data sets from different locations and different sources. And I'm assuming throughout these that you all are going to jump in if you have additional things to add so that it's about so yeah. So let me keep cruising on here. Given the need for accelerating data decision making especially post COVID-19 any thoughts on implications with regards to ethics especially when considering diverse cultural perceptions. Yeah well I'll take that first and maybe Matt if you want to add I mean I think this is. First of all I give us back to the idea of culture I think you know that's a very important part of introducing new new systems and stuff like that to keep in mind that the culture. But this also goes to the point as we said we take a look of when you have all these new technology and things like artificial intelligence stuff like that you know we look at as as an augmented not a black box. I think that can you know really get into ethical situations where you have the system make a decision and carry it out for by itself without any people involved and there's been some both humorous and non humorous examples of that. Unfortunately, I may read in the media. So it's a even though you're accelerating data decisions I think it's extremely important to have the human involved in that, not just totally automated. And then secondly, just in terms of, you still have the issues of security and protecting private information, as well as the point out here that different cultural perceptions depending on what region of the world you're working in. Yeah, and I agree with that and I think you know within our research and by our hair I mean not just 451 research but also our parent company s&p global and the market intelligence part of that. Yeah, a very, a really significant shift in the last year you also focus on, you know, environmental social and governance aspects and I think from a data analytics perspective, one of the key areas we see that is around ethics, I think, you know, whether it be the employees or consumers obviously where we're all both of those have a, you know, a much greater awareness of, you know, ethical challenges around the use of data when that's, you know, through a regulatory for regulatory reasons or just understanding, you know, bias inherent in, especially as decisions are potentially increasingly automated so so yeah I think it is something that we see is increasingly top of mind for consumers and employees and therefore obviously decision makers within organizations, and of course, you know, in the, you know, the developers and data scientists as they are creating these models they need to be cognizant of those issues and I think that we do see that increasingly are. I love good timing and that's such perfect timing as we reach the end of the questions here and reach the end of the hour. So just a reminder again to all of our attendees thank you so much for being involved in just to let you know I'll be sending a follow up email by end of day Thursday with links to the slides, the recording and all the information mentioned throughout. And Matt thank you so much for this great presentation it's been fantastic. And again thanks to our attendees hope y'all have a great day. Thanks everyone.