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 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.  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 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.  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.


Come on, put it together for the college Republican, Chris Buster! Down in front of the banner. Down here. Ladies and gentlemen, Demo Plastic Renewal Service, coming to save America on November 6th, and we need your help to do it. Here, just to read you, that the White House may someday be repossessed by Democrats. No, we don't want that to happen. You see, we're afraid of fact-experience, and we think you are, too. We're here today, on our crusade across the country, to save this country from those Demo Plastic Mysteries. Before we start today, we have something to announce, that I know that they don't like it, and we always have to do it before performance. You'll see this now when we clear the air of all the Demo Plastic Mysteries. Our trainees and mission staff is planning to be 24 hours a day, and solve all your so-natural mental problems. Ladies and gentlemen, we need you. And we need your sickness, and all the professionals, and the President of the President. Who do you think you are? The President of the United States. All the President of the United States. Even if you're going to be the President of the United States, I'm sure you're going to be the President of the United States today. But excuse me now, if you're going to hit the road, we're going to put someone else on the bridge. Right in Miami, Florida, in December. This year's staff includes Program Coordinator David Harvey, Assistant Banderet, David Onamalatter. Thank you, David. Ladies and gentlemen, the President of the United States. That's the spirit, everybody. Beautiful. That's the spirit. Thank you. The spirit, the enthusiasm, and you brought it here today. And by golly, we're just going to enjoy spending the time with you. Thank you again for all of you that came in early to be with us. And on behalf of WMBF Radio 1290 and myself, Bill Flynn, we thank all of you for participating today and making this the big success. I'd like you, folks, right now to kindly bring you to the community. Since we are in Ty Cobb Stadium in our home village of Endicott, New York, what better way to start our gentlemen, the mayor of the village of Endicott, Marion Carina. The people of the community represent the melting pot concept, which is the foundation of our great... Mr. President, distinguished dignitaries, mayor, once again. Our President has been so effective is that he has never forgotten, not even for a moment, to present to you the President of the United States. Thank you, Senator and Madam Mayor, the others here in the day, as in all of you ladies and gentlemen, thank you for a most heartwarming reception.  And I told him, I told him just to radio down and ask a simple question. Which way, E.J.? It's good, it's good to be here in Endicott, at the very center of your beautiful valley of opportunity. Warm greetings to your outstanding Senator Alfons D'Amato, your County Executive Carl Young, and your Mayor Marion Carino, and to a superb candidate for the Congress, Connie Cook. Connie, America needs more Republicans in the House of Representatives, and I can't tell you what a pleasure it'll be to have you there on our team. And of course, as I said already, a special greeting to the Majority Leader and President Pro Tem of the New York State Senate, Warren Anderson. Andy, for more than three decades, you've served in the New York State Senate with skill and devotion. You've dedicated yourself to justice, liberty, and economic growth. And because of your efforts, people in this valley and throughout New York State lead fuller, freer lives. On all behalf of all Americans, I thank you. Traveling today, we've flown over a good part of the Empire State. New York is lovely this time of year, and I thought, looking down from that altitude, that I detected just a touch of color beginning to appear in the trees. There was great rivers like the Susquehanna, and a threading their way across the land, and I couldn't help thinking of those majestic towers of Manhattan, the hard-working patriotic neighborhoods of the boroughs and the thriving cities and towns that dot your upstate. Places like Rochester, Buffalo, Syracuse, and yes, Bigaminton, Vestal, Johnson City, and Endicott. You know, it occurred to me that maybe the other side thinks that we'll just concede this great state of New York. Well, they're in for a little surprise. We're not conceding anything to anybody. We're in New York to win. I know that this valley holds a special story. One of hardship overcome, of determination, hard work, family, and faith. And in many ways, your story is America's story. It began almost a century ago when one of the men legendary in the history of this valley, George F. Johnson, came here and established a shoe factory, the Endicott Johnson Corporation. Soon the factory prospered, word spread all the way to Europe, and when immigrants from Poland, Russia, Czechoslovakia, Italy, and a dozen other countries reached America's shores, thousands of them are said to have asked the way to this valley of opportunity in the only English they knew which way E.J. They came here with few possessions, many with nothing but the clothes on their backs, and they asked only the chance to work and work they did. Long hard hours, tanning hides, cutting leather, stitching together the finished shoes. And as family helped family and neighbor helped neighbor, schools were built, houses were constructed, churches and synagogues were established, and this valley became home to some of the proudest communities in our nation. Towns that had seen firsthand all that free men and women can accomplish. In time, however, the shoe business changed. The factories in town began to offer fewer jobs, and some feared that prosperity would leave this good valley forever. Yet one group of men and women had a great vision, a vision to bring this valley prosperity it had never before dreamed possible, a vision to launch a revolution that would change the world. Their leader was Thomas Watson Sr. He had grown up in a small town called Painted Post, down the road from here, where he learned how to stick with a job until it's finished. Watson started with a company whose mainstays were punch card machines and time clocks. And in 1953, a long time ago for some of you, but just the other day for some of us, the company that Watson had renamed IBM began making the first mass-produced commercial computer in history, the 650. Less than a half a mile from this spot. Scores of the IBM workers were sons and daughters of immigrants who had worked in the shoe factories. When they began, the best market research had predicted that fewer than 1,000 computers would be sold in the entire 20th century. Well, IBM's first model sold almost twice that number in just five years. And now there are IBM plants in Endicott and around the world. And the computer revolution that so many of you helped to start promises to change life on Earth more profoundly than the industrial revolution of a century ago. Already, computers have made possible dazzling medical breakthroughs that will enable us all to live longer, healthier, and fuller lives. Computers are helping to make our basic industries like steel and autos more efficient, better able to compete in the world market. And computers manufactured at IBM, how we go, where some of you work, they guide our space shuttles on their historic missions. You are the people who are making America a rocket of hope shooting to the stars. Today, firms in this valley make not only computers, but flight simulators, aircraft parts, and a host of other sophisticated products. The shoe business has adjusted to the economic conditions and is working again. Your schools are better than ever. Your neighborhoods are strong. You still have a vigorous sense of ethnic pride. You can't talk to Mayor Carino for long without feeling how proud she is to be an Italian American. And here you carry a sense of civic loyalty that shows up in organizations like the Tri-Cities Opera and the Roberson Center. I know how proud you all must be to live here. You've shown me that. And I just have to believe that the lesson of this valley is a lesson for our entire nation. With opportunity, there's no limit to what Americans can achieve. Opportunity, opportunity that chance to work hard and make our dreams come true, this is just what our administration is laboring to provide. You know, in five years, taxes doubled, average monthly mortgage payments more than doubled, and the real after-tax income of the average American actually began to decline. Our opponents preach a great deal about fairness. Well, it's true their policies were fair in one respect. They didn't discriminate. They made everybody miserable. Now we could go on, but we don't want to talk about their failures, do we? I can assure you they don't. Well, then let's talk about how, by working together, we're achieving great successes today and we'll go on to build an even greater nation tomorrow. On the economic front, we've knocked inflation down from 12.4 to 4.1 percent. And today, from Maine to New York to California, a vast economic expansion is surging ahead. And now, see, I think I see at least one or two students in the audience. It's test time. I'd like to ask you some questions about a certain country. Now, I don't want to give away the answer by naming the country. I'll give you just a little hint. It has three initials and its first two are U.S. Now, of all the great industrialized nations in the world, which has shown by far the strongest, most widespread, and most sustained economic growth. I've got more questions. What country had a record 600,000 new business incorporations last year alone? What nation is showing the fastest rate of business investment in four decades? And what country can say that its productivity is up, its consumer spending is up, and its take-home pay is up? And during the past 20 months, what country created 6 million new jobs? And what nation created, on the average, more new jobs each month during the last 12 months than all the countries of Western Europe together created over the past 10 years? You score 100, that's right, USA.  For the first time since the administration of President Kennedy, the share of earnings flowing to the government is not increasing. Today, more of your earnings are staying with your families in your neighborhoods, in your state, right where they belong. To all those Democrats who have been loyal to the party of FDR, Harry Truman and JFK, but who believe that its current leaders have changed that party, that they no longer stand firmly for America's responsibilities in the world, that they no longer protect the working people of this country, we say to them, join us, come walk with us down the new path of hope and opportunity. I can speak to that because I did that already. I was a Democrat, and I changed when I found I could no longer follow the course of the leadership of that party. With your support during the next four years, we'll keep going forward. We'll start by keeping government under control, by enacting an eye-line item veto and a constitutional amendment mandating that government stop spending more than it takes in. We'll fight for enterprise zones to help Americans in disadvantaged areas get off unemployment and welfare and start climbing the economic ladder. Others, others would raise your taxes and the taxes of working families all across America. Well, we're not going to let them enact their tax plan, not on your life. Our pledge is for tax simplification to make the system more fair and easier to understand so we can bring yours and everybody's income tax rates further down, not up. The American people aren't under-taxed. The government in Washington is overfed. You know, I sometimes think that the main difference between ourselves and the other side is we see in America where every day is the 4th of July and they see in America where every day is April 15th. Now, as our economy grows, we'll need to go forward with the values of faith, family, neighborhood, and good hard work. And together we're already making an impressive start. In the past four years, we've helped lead a grassroots revolution to recommit our schools to an agenda for excellence that will reach every child in this land. Teachers, school principals, and school boards are joining with parents to bring back discipline and higher standards. And what do you know? After 20 years of decline, achievement by all the records is up. And today's schools like Union Endicut have begun using computers to give our sons and daughters better education and to prepare them for an exciting world where the great challenges will lie not just here at home but in the limitless frontiers of science, technology, and space. We're cracking down on crime. We say with no hesitation, yes, there are such things as right and wrong and yes, for hardened criminals preying on our society, punishment must be sure and swift. Beautiful North country, 165 miles new north lies the Army's Fort Drum, one of the finest all-weather land training installations anywhere in the United States. As the Secretary of the Army announced yesterday, we have chosen Fort Drum to be the home base for one of the Army's new light infantry divisions. Troops stationed at Fort Drum will be able to reach quickly any of the world's potential trouble spots and they'll have the benefit of some of the finest year-round training available. And let me make one thing plain. We're not out for any territorial gain or to impose ourselves on anyone. But believe me, America must never again let its guard down. And since 1980, not a single nation has fallen to communist aggression and the people of one nation, Grenada, have been set free. Now, you may remember that some on the other side compared the American mission in Grenada with the Soviet invasion of Afghanistan. When I first heard that, it sort of touched my temperature control. The people of Afghanistan risked their lives to combat the Russian troops. Our men, by contrast, entered Grenada at the request of six Caribbean democracies and received from the Grenadian people themselves an emotional demonstration of gratitude. And today, less than a year after that mission, when the job was done, every American combat soldier has come home. Everyone. We'll let our opponents ponder Soviet motives in world affairs all they want. We intend to concentrate on America's goal, which is human liberty. When those immigrants came to our shores and said, which way, E.J., they were asking which way opportunity, which way peace, which way freedom. My dream for America, and I know it's one you share, is to see the kind of success stories in this valley multiply a million times over. And with you by our side, I just know we're going to make history again. Our victory will be a victory for America's future and the land that President Lincoln called the last best hope of Earth. And that nation will rise to meet her greatest days. I can't tell you how grateful I am and how you've warmed my heart, but I thank you and God bless you all. Thank you, ladies and gentlemen. This moment comes to us only once in our lives, and we're going to take just a few more moments to share it together. We have a couple special prizes for our honored guests, the President of the United States. I'd like to direct your attention, ladies and gentlemen, to the left of our band, stand to your right as we present the chorus of Mary Beth Gesher from Vestal High School, Craig Hawkins from Vestal High School, Webster Bowden from Johnson City, Kim Cox of Johnson City, Jeffrey Baker of Union Endicott, and Laura Huntington of Union Endicott, along with the rubber band as they join us in singing God Bless, the USA, accompanied by the rubber band. Thank you very much, the chorus and the rubber band. And God bless the USA. Since this, Mr. President, is Union Endicott High School in Tiger Country. Just a few more moments to have a special, deserved presentation by the Union Endicott High School U.E. Tiger mascot Lisa Tomic. And also joining us, the Union Endicott under the direction of Fran Angelang from the Union Endicott's award-winning number one football team. Captain... Captain Maurice Lafuse and Lisa Tomic. Thank you, Bill. Mr. President, honored guests, the U.E. football team would like to present this jersey to Mr. Reagan to mark this special occasion. They tell me that I got to get back in the airplane for those who couldn't see this football jersey says on the back, the gipper. But I just have to tell you, this has to be the world's greatest coincidence when you played your fight song, your football high school fight song, that happens to be the same tune of the fight song of Dixon, Illinois where I played high school football. Ladies and gentlemen, the President of the United States.


Well, listen, it is fantastic to be back in Belenapoli. Buongiorno tutti. Chief of Naval Operations, Admiral John Richardson met with sailors and DoD personnel at NSA Naples to answer questions regarding the role of Sixth Fleet in current and future naval operations. It's the thing I wanted to do most urgently and most importantly is just to have a chance to connect with all of you. It's what I came here to do anyway is to hear what's on your mind and so we'll open it up for questions now. I wanted to know if you feel the budget in the future is going to change operations here in our AOR? It's going to continue to be a challenge. I just have to be honest with you. But consistent with this question that we just had over here, the forward forces will always get priority. I'm just always lifted up and inspired by the creativity and the energy and the positivity of all of our sailors around the world. I feel very deeply that our people are front and center. They're the heart and soul of what we do. I just feel so privileged to be their CNO and I look forward to supporting them in every way I can. During his visit to Naples, Admiral Richardson met up with Navy spouses at NSA Naples support site to discuss issues concerning military families. Our families are an integral part of the Navy team. They do so much to allow us to accomplish the mission. And so I think it's very important to spend some time with our families and understand what their challenges are. This is Admiral Richardson's last stop on his round the world tour visiting sailors. Petty officer on the western end is Naples, Italy.


Welcome to this episode of Door Hardware Nerds. I'm your host, Mia Merrill. Today, I'm joined by Kevin Waring. Kevin, welcome back and please introduce yourself. Well, hey Mia, good afternoon. It's great to be back on another episode here of the Door Hardware Nerds. So thank you for having me. So to everybody out there, I'm Kevin Waring, Director of Business Development for the Yale Commercial Team here at Osa Abloy and thrilled to take a couple minutes of your day today. Yeah. So Kevin is a long time listener and a many time caller. No, we've had him on a couple of episodes and you can go back and find those. But Kevin, since last time we talked, where are you finding successes? Yeah. So certainly the markets are still running strong. We have a lot of new construction starts going on and businesses is going very well. But one of the areas that we've been hitting hard and doing well at is the upgrade market. And what we're finding is a lot of people out there are looking to upgrade their openings, looking to upgrade their facilities and for solutions that we might be able to offer them. Yeah. So I think one of the products that probably fits well in here is we launched a new cylindrical indicator in January. How is that going? So it's been a great launch, a great success. But not only is the cylindrical indicator a lot by itself a great success, but the whole family suite of products that it offers, it couples well with a deadbolt indicator. We have mortise indicators and of course a cylindrical indicator that's been highly successful. So it's been a really good campaign for us, great product to the market. And as I mentioned in the previous episode, it's not just for bathrooms. We've seen a lot of success for other facilities or other needs. Could be band or music rooms within universities. It could be board rooms, it could be conference rooms. But certainly the main driver there is in the restroom space. So when people are looking to upgrade their facilities, it's one of the first thing that comes to mind is the indicator occupancy products that we offer. Yeah, I think we're probably just seeing the beginning of really where an indicator is going to be used. And also this week Aero has given us an APL to give away. So make sure that you go back to my Monday's post and enter in that because we'll be announcing winners on Friday. So excited to give that away. Hopefully someone very special wins it. I think that's great the more that we can get product awareness out there, the better it is. But as we look at the upgrade opportunities, certainly the indicators are a great section. But other spaces that we see, facilities are always saying, how can I upgrade and not replace everything with brand new hardware? And one of the opportunities that we have is in our exit device platforms where we can add an MELR, Electrical Attraction Kit to an existing device and to upgrade to a more automated solution that way. And then of course, we round out and we offer a lot of standalone access control products and could be our next touch products. They could be a Z-Wave product or a standalone without a radio that you program right there in the door. So again, all different avenues that have been really good for upgrading people's openings. Yeah, I often think we still have, I know we talk about EAC all the time, but there's so many facilities that are just a mechanical base and they want to bring themselves in the 21st century and there's a lot of opportunities for that. Yeah, certainly to get off of the mechanical QAs and to upgrade and look at the next step without going into a full blown solution is that mid-tier standalone solution. And again, a lot of products that we can offer to help those facilities. Yeah, so Kevin, are there any exciting product updates that might be coming for the upgrade market? Well, I feel like you're trying to get a peek behind the curtain there, but what I can say is I also always very focused on the upgrade solutions. And as we look through the remainder of 2023 and coming into 2024, expect a lot of great products that people have been asking for for a long time, but just keep an eye out. I don't want to tip my hat too much, but there's definitely a lot of products coming later this year and into the beginning of next year that'll certainly fit well for this upgrade market. All right, well, any final thoughts you want to share with us today? No, just appreciate the opportunity to be here and thanks for spreading the word and helping share the message. Yeah, well, thank you, Kevin. And for everybody out there, thanks for watching. I will drop some links in the comments below. And if you have any questions or comments, leave them below as well. Have a good one. Thanks, everybody.


Black Motion dumps Myrta Bongs. In 2020, Black Motion celebrated its 10-year anniversary as one of the leading Afrohouse DJing duo consisting of members Thabo Smol Mabongwe and Bongani Myrta Bongs Mahosana. At the time, the duo released their third studio album which was titled The Healers, The Last Chapter. It had seemed at the time that the album was a celebration of the legacy and impact the duo had made since breaking into the mainstream music scene. However, in the two years since the decade celebration and the release of The Last Chapter, it seems that the album was a self-fulfilling prophecy as it seems that despite their previous assertions, Black Motion is not the Black Motion we have grown to know and love as Myrta Bongs has been replaced. The first time it was announced that Black Motion had split was during the reign of Musa Kaula and his tyranny on our favorite Mzansi celebs. At the time, the speculation was fueled by the fact that Thabo Smol better known as the Kajili Kajili man after his encounters with Zodway Wabantu, was performing solo under the name Black Motion. On the other hand, Myrta Bongs was falling in love, becoming a second-time father and getting married to DJ Zinl. Changes which brought on the official name change for Myrta Bongs now referred to as Morda. But at the time, not only did Morda deny the allegations, but Black Motion went on to release a statement that refuted the allegations by sharing that. Spirit Motion would like to state in a record that Black Motion members Thabo Mabongwe aka Smol and Bongani Mahosana aka Myrta Bongs have not parted ways. Black Motion still exists and has plans for more years to come. To squash the allegations, the duo would then release their most recent, and possibly last, album under their original formation titled Talking to the Drums. When Morda released his debut solo album, Asanti, titled after his second-born child, the one he has with DJ Zinl and not his first born, we should have known that something is off. This is as Black Motion is part of the Tastig Heritage Festival. As such, they were partaking in promotional interviews ahead of the festival. However, instead of there being Thabo Smol and Morda, there was Thabo Smol and new member Kabalo Koma, who in the titles is confirmed as the member of Black Motion. It also seems that part of the agreement of Thabo Smol conducting the interview was that he was not asked about the matter of the status of his relationship with Morda as it was not even asked. More than that, part of the indication that there might be more to the matter at hand is the fact that Black Motion also promised new music with an emphasis on drums, which is Smol's specialty when it was just him and Morda. Not much is known about Kabalo Koma or what he will bring to the group. But fans attending the festival are in for a surprise when they see that Morda is not present for the performance.


Live from the Mandalay Convention Center in Las Vegas, Nevada, it's The Cube at IBM Insight 2014. Here are your hosts, John Furrier and Dave Vellante. Welcome back, we're here live, I was always mixing it up here in The Cube, live in Las Vegas for IBM Insight. This is the show about big day, this word all comes together around insights, analytics, and I'm John Furrier, we're with Dave Vellante. I'm John Furrier, we're with Dave Vellante and Tim Crawford, out in the field, getting all the data, appreciate the comments on CrowdChat. I want to do a quick segment and then we're going to bring in Bob Patiano to come in and talk with us because we've got some short time. Give us a quick take on keynotes and observations, I mean it's really got some energy here. It does. This is a big change from past IBM conferences, there's definitely a lot more energy as well as the conversation has evolved too. It seems to be kind of moving up the stack a bit, we're looking more at kind of the culmination of where data cloud, but there is definitely more of an emphasis now on the customer engagement piece, which is why we're doing all this in the first place. So everybody talks about how their company is data driven, they want to be data driven. When you look at the CIOs that you work with, what's the bell curve look like? Are they really data driven? What do they need to be to become data driven? Should they become data driven? Yeah, great question. They absolutely should become data driven. The problem is we're still in the early days, we're still in the early infancy of some of this move along the continuum of adopting data within IT. So IT getting more involved as opposed to marketing or other line of business groups. So I do think that we're going to start to see that shift and we are starting to see that shift, but it's not the masses yet. There's a lot of foundational work around cloud, around the infrastructure that needs to take place first to be able to capitalize on some of these other pieces. What are some of the other big mega trends that you're seeing within the CIO community that are relevant to sort of what's going on here at Insight? Yeah, the first piece is around cloud. How do we start to put together a holistic cloud strategy that helps us kind of up level the conversation but also gives us ready to adopt some of these new analytics and data strategies? You can't take your traditional infrastructure, your traditional methodologies and apply some of the things that are being discussed here at Insight and expect them just to work. It's a whole different methodology, it's a whole different process and that's part of the conversation in terms of how it's starting to evolve and how it's changing. So Tim, one of the things we've noticed is that when you look at these new emerging companies that whether they're data driven or they're born in the cloud, whatever buzzword you want to use, and I'm thinking about companies like Uber or New Innovations like Fitbit or Airbnb and the like they seem to be leveraging technologies but not worrying about owning them. And if you think about historically industries have sort of built their own vertical stack, whether it's design, production, manufacturing, distribution, sales, partnerships and everything's sort of hardened within that stack. It seems like these new organizations are riding on top of this digital matrix, if you will. So my question is can the traditional Fortune 1000 ride on top of that or is their stack too hardened? Are they open? Are they receptive to that? Should they be receptive to that? Yeah, I think great questions and I think this is where it kind of hits home especially for the traditional enterprise CIO. And we heard a bit about this in the keynotes this morning, right? There's a lot of discussion around the airline industry and I think that's a great example of what's an industry that I've worked with. At the end of the day you still have those legacy systems as a CIO. You still have those legacy systems you have to contend with, right? You're not an Airbnb, you're not an Uber, you can't just build from the ground up to start out with. You have to consider that you have those new aspects that you want to take advantage of, but at the same time you have this huge portfolio that you may not have the right economics to be able to make that shift yet. And so it's a balancing act for the CIO to start to evolve and move through this. I think the challenge is how quickly can they move through that progression? It's a time question as opposed to an interest or a move of whether they should or shouldn't. Tim, I got to ask you about the social engagement. Your comment on CrowdChat got a lot of votes social engagement, outpacing other more common sources of data. I really want to drill down on this because we're going to have Bob Pciano come on. I want to get his executive opinion but you're very involved in social media. You're involved in our beta with CrowdChat and you're out there as a probe in the social sphere. We're seeing all the dynamics. Engagement has always been like a business way of data warehousing. Systems of engagement.  That's like a data thing. But now engagement is a completely different animal when you've got mobile infrastructure. Internet of things is humans, aspect of it. You're actually touching customers. You're seeing this trend with IBM is pretty solid. Social business really is well positioned to be that e-business for this generation. What's your take? Where are they? Are they delivering? Are they in a good position? What's your analysis? Well, I think the first spot that you have to look at is are they having the right conversation? And I think they are having the right conversation in terms of connecting the dots. You have to think about how people are engaging, how customers are engaging with different services and different products. And it all starts with one of these. It starts with a mobile phone. I mean, everyone's got a mobile phone. I tweeted something, a quote that came out of the keynote, talking about that the mobile phone in your pocket is about a hundred times more powerful than the average satellite spinning around the earth today. I mean, that's a huge change. But if you look at how humans can go up like the Google executive in a parachute and break the record for Bumgart and it's like red bull what? So if I take my smart phone up with that, I mean, what impact does that have? But I think the first step is you've got to say the right words. And what I am starting to see with IBM is they're saying the right words. They're connecting the dots of why we're talking about technology, why we're talking about cloud, why we're talking about data is to get us better at customer engagement. And so that's the first step. The second step is actually being able to connect the dots technologically so it doesn't look like just silos of expertise but it looks like an actual cohesive portfolio. And that's the piece we have to dig a little further into. I think IBM is doing a great job at hanging the story together but I think they're really missing the boat on social engagement and it relates to long tail distribution. Let me give you my reason on this. Get your take on it. The long tail concept is not known to, it's not a foreign concept to internet companies, internet infrastructure, search engines and content, popular content, high traffic and then targeted content. We talked to Kirk who's the physicist, computational analysis, the values in the long tail, right? So the long tails where these data points were that were traditionally thrown away by software packages as skewed standard deviation off the medium, throw it away when in reality whether it's a conversation or data point, these key long tail data points are really awesome, valuable content pieces. They are and that gets back to the personalization and intimacy of the engagement itself. So now it's no longer about trying to figure out where is the mass and adopting to address the masses but it's trying to figure out how does John want his pieces and how does Tim want his pieces and how does Dave want his pieces. And that becomes your long tail. So you still have to address the masses and I don't think that goes away but that long tail becomes immensely important especially as we start thinking about one-offs. Of course that's our religion here at the Q. We love broadcasting to the long tail, using the crowd chat, having all the tools, the social engagement with things like the social lounge here which is an awesome event. The VIP got my little badge on here, VIP influencer. IBM is doing an amazing job with influencers outreach, bringing that social collaboration. I want to end it there and I want you to just tell us what you got on the agenda, what you got planned for this week, what events you're going to, what meetups, what kind of, where's the parties, give us a quick overview. Yeah I think it's jumping between the different discussions. So trying to cover not just what's happening here at Impact in terms of the sessions and the keynotes but also the one-off conversation. So we talk about the long tail but it's the conversations that are happening here in the lounge it's also the conversations that are happening in the hallway. But also look at the expo and I want to see how the ecosystem is starting to evolve too because now as we start to think about how people are buying these products and services it's not just about how one company is buying a product or service but it's about the ecosystem and so we're buying into ecosystems and that's another piece I'm going to be looking at. Well we love it. Dave and I love working with our crowd chat venture. We're introducing this new concept called Relationship Craft which is bad not the social interest craft which is exploding right now and I think the relationships in social is key. We love it. IBM is really a great opposition. I love their social business and I got to say folks out there if you remember the web e-business was a concept IBM pioneered. You don't talk about that anymore because everyone does e-business called e-commerce. I think social business is that same kind of spot where it's early and it's just going to be really really relevant. So great to have you involved in all of our collaboration. Tim, Tim, Crawford Influencer and a friend of the Cube. We'll be right back with our next guest for this short break.