WEBVTT 1 00:00:00.040 --> 00:00:05.120 Host of this exciting new era learn more and Inside analysis dot com, Inside 2 00:00:05.160 --> 00:00:14.519 Analysis dot Com and now here's your host, Eric Kavanaugh. Ladies and gentlemen, 3 00:00:14.560 --> 00:00:18.000 Hello, and welcome back once again to the only coast to coast radio 4 00:00:18.039 --> 00:00:22.320 show all about the information economy. It's called Inside Analysis near truly. Eric 5 00:00:22.359 --> 00:00:26.960 Kavanaugh is here and folks, we have a fantastic topic lined up for you 6 00:00:27.079 --> 00:00:30.000 today. A good friend of mine, Ellen Rubin, who a company called 7 00:00:30.079 --> 00:00:33.079 Cousley, is on the show. She's been in the business for a while. 8 00:00:33.119 --> 00:00:36.119 She was at in Natisa for those of you who mentioned who remember in 9 00:00:36.159 --> 00:00:41.200 Natisa, which took the market by storm years ago data warehousing appliance, and 10 00:00:41.240 --> 00:00:44.799 then of course it got bought by IBM for like one point two billion dollars 11 00:00:44.920 --> 00:00:48.119 or something amazing like that. And I was like, wow, how did 12 00:00:48.159 --> 00:00:51.399 that happen? I asked my partner, doctor Robin Blore. I said, 13 00:00:51.399 --> 00:00:55.399 why would IBM going behind Natisa? They have their own dB two And I 14 00:00:55.439 --> 00:00:58.600 said, well, when you can borrow money at zero percent, why not? 15 00:01:00.399 --> 00:01:03.920 That sounds like rob that's a good point. That's a good point. 16 00:01:03.000 --> 00:01:06.480 So anyway, yes, Ellen Rubin, welcome to the show. Thank you 17 00:01:06.519 --> 00:01:10.519 for your time today. Thank you, pleasure to be here and nice to 18 00:01:10.719 --> 00:01:15.239 remember remember old times from that time. But you know a lot of technology 19 00:01:15.239 --> 00:01:19.359 has happened since then, for sure. Yes, that's exactly right. I 20 00:01:19.400 --> 00:01:22.359 gave a presentation, in fact, I moderated a panel in New York last 21 00:01:22.400 --> 00:01:26.719 week Forever in Your Technical Debts, which was all about that, like dealing 22 00:01:26.719 --> 00:01:29.760 with technical debt, and it's out there and it's everywhere. I mean, 23 00:01:29.959 --> 00:01:33.840 one of the Finaliert quotes I've heard over the years was someone who defined a 24 00:01:33.920 --> 00:01:40.040 legacy system as any system in production. It's like, all right, I 25 00:01:40.120 --> 00:01:42.719 get that right, Yeah, true. But you folks, we'll talk today 26 00:01:42.840 --> 00:01:47.040 on the show about cause I and what causal AI is, so Ellen, 27 00:01:47.079 --> 00:01:49.200 I'll give my take on things and just kind of see where you fit in 28 00:01:49.280 --> 00:01:53.159 this whole conversation. But you know, when I look at observability, which 29 00:01:53.200 --> 00:01:57.719 is now everywhere, I mean observability, I think it skyrocketed faster than big 30 00:01:57.799 --> 00:02:02.239 data, which went really fast, and of course llms now have skyrocketed faster 31 00:02:02.359 --> 00:02:07.439 than anything, but observability three years ago, I don't even know if it 32 00:02:07.480 --> 00:02:10.159 was a term. And now there are like fifty observability vendors, and I 33 00:02:10.199 --> 00:02:14.599 think part of the reason for that is because of Gubernetes out of Google. 34 00:02:15.039 --> 00:02:22.159 Because this new distributed way of container orchestration is so different and so dynamic that 35 00:02:22.400 --> 00:02:23.919 all of a sudden, we have all these new sources of information that we 36 00:02:23.960 --> 00:02:30.479 could pull together, which is great for developers and site reliability engineers and the 37 00:02:30.599 --> 00:02:35.520 application teams that people actually build the apps and maintain the apps that run business 38 00:02:35.560 --> 00:02:38.280 these days. It's good for them, but also it's a blessing and a 39 00:02:38.319 --> 00:02:42.680 curse because suddenly we have five, ten, twenty different sources of data and 40 00:02:42.759 --> 00:02:45.960 it's up to the individual to parsel all that stuff and to kind of pull 41 00:02:46.039 --> 00:02:50.639 together. And I've noticed this, for gosh, in the first year of 42 00:02:50.759 --> 00:02:54.599 DM Radio, I interviewed a guy from a company called Precisely Zohar Gila. 43 00:02:54.639 --> 00:02:59.800 I'll never forget his name, Zohar and Precisely, I'm sorry. It was 44 00:02:59.800 --> 00:03:04.719 a It was called Precise and it was basically troubleshooting, and so you would 45 00:03:04.759 --> 00:03:09.560 look at CPU usage and network behavior and app performance and all these different things 46 00:03:09.800 --> 00:03:13.800 and you get histograms. But the point is the developer had to piece this 47 00:03:13.840 --> 00:03:15.840 all together in their head, which is hard to do. It requires a 48 00:03:15.919 --> 00:03:21.800 lot of experience, a lot of hard knowledge about how things work and how 49 00:03:21.800 --> 00:03:25.280 things interrelate. And of course these days companies create run books and all kinds 50 00:03:25.319 --> 00:03:29.919 of different things to help the srres and the others figure out, Okay, 51 00:03:30.000 --> 00:03:32.439 if this goes down and that happens, and this happens, then do X. 52 00:03:34.000 --> 00:03:36.479 And that's good for as far as it goes. But wouldn't it be 53 00:03:36.599 --> 00:03:42.719 nice if something came along to dynamically absorb these different data feeds correlate them. 54 00:03:42.759 --> 00:03:45.400 And that's the key. When this goes up, that goes down, which 55 00:03:45.439 --> 00:03:46.479 means one thing. When that goes down, this goes up, that means 56 00:03:46.479 --> 00:03:51.360 something else. To be able to correlate that and understand that in Parson, 57 00:03:52.000 --> 00:03:55.240 that's getting pretty powerful. It's my understanding that's what causal ais is, at 58 00:03:55.319 --> 00:04:00.439 least in this context. Is that right? Right? So, you know, 59 00:04:00.520 --> 00:04:02.960 kind of going and taking the step back that you started with around what's 60 00:04:03.000 --> 00:04:06.479 going on and why are the problems in the industry getting so much worse than 61 00:04:06.479 --> 00:04:11.599 they used to be. You know, obviously monitoring APM, you know, 62 00:04:11.639 --> 00:04:15.759 ability to measure and have metrics on what's going on. These are not new 63 00:04:15.800 --> 00:04:18.720 concepts. Anyone who's been in the IT operational space for a long time and 64 00:04:18.759 --> 00:04:24.720 has had to run and scale and own large applications and infrastructure. You know, 65 00:04:24.759 --> 00:04:27.240 like these are a lot of these problems or old problems, like stuff 66 00:04:27.240 --> 00:04:30.439 breaks, things slow down, things go wrong. You know, you don't 67 00:04:30.519 --> 00:04:33.240 understand what happened. So you know, that's many, many decades of problems. 68 00:04:33.720 --> 00:04:35.920 But the point that you made, which I think is right, is 69 00:04:35.959 --> 00:04:41.160 that in some ways things have accelerated in the past ten years, not only 70 00:04:41.160 --> 00:04:45.040 with the adoption of micro services and you know Kubernetes as a good example of 71 00:04:45.079 --> 00:04:47.720 that, but also just the sheer pace of people using DevOps and being so 72 00:04:47.839 --> 00:04:53.079 agile and rolling things out all the time that you really have a lot of 73 00:04:54.199 --> 00:04:59.519 room for confusion and room for complexity that is in some ways exponential compared to 74 00:04:59.519 --> 00:05:00.800 what you used to do the case. You know, certainly when you had 75 00:05:00.800 --> 00:05:05.600 more of a monolithic environment and people had more of a somewhat less dynamic set 76 00:05:05.639 --> 00:05:10.120 of changes that happened. You know, in a less you know, agile 77 00:05:10.199 --> 00:05:14.759 kind of a rollout approach, you could kind of know more about what was 78 00:05:14.800 --> 00:05:17.000 there or what was in the stack and what changes were being made. And 79 00:05:17.079 --> 00:05:21.839 now you know, there's so much more frequency of changes, and there are 80 00:05:21.839 --> 00:05:27.680 so many things that can happen dynamically where people are using your application and their 81 00:05:27.800 --> 00:05:30.199 you know, their load patterns are changing, or the way in which the 82 00:05:30.199 --> 00:05:33.480 application gets rolled out and scaled out can be constantly causing new problems that you're 83 00:05:33.519 --> 00:05:36.920 not aware of, and no one person has a view of what's actually going 84 00:05:36.959 --> 00:05:43.879 on, not to mention that there are all these interdependencies between services in that 85 00:05:43.959 --> 00:05:47.240 type of an environment, and between the infrastructure and the application layer of things 86 00:05:47.279 --> 00:05:50.040 that frankly, a lot of people have no clue what's going on at the 87 00:05:50.040 --> 00:05:54.079 infrastructure layer anymore, right, Like it used to be a little bit easier 88 00:05:54.120 --> 00:05:56.680 to get to the to the bottom of what was going on there. Now 89 00:05:56.720 --> 00:06:00.160 it's all the inter relationships and the interdependencies. So if you look at that 90 00:06:00.279 --> 00:06:03.439 kind of a world, what you can see is that observability, like data 91 00:06:03.480 --> 00:06:06.480 Dog is a great example, took off with the rise of cloud and this 92 00:06:06.600 --> 00:06:11.120 type of you know, kind of shift into a much more agile world, 93 00:06:11.639 --> 00:06:14.079 and you know, that has driven tremendous growth. And then of course there 94 00:06:14.079 --> 00:06:15.319 are a lot of companies that are sort of the you know, the next 95 00:06:15.360 --> 00:06:19.920 generation of observability but a lot of them. And I would I would make 96 00:06:19.920 --> 00:06:24.240 the case that you know, kind of like the whole industry is still focused 97 00:06:24.240 --> 00:06:28.680 on saying, well, if we just monitor everything, we just have a 98 00:06:28.759 --> 00:06:31.360 metric on everything, every piece of everything, and now there's so many more 99 00:06:31.360 --> 00:06:33.839 any everything is that now we need to store all of that data and we 100 00:06:33.879 --> 00:06:38.319 need to hold on to it for you know, longer term trending and compliance 101 00:06:38.360 --> 00:06:41.160 and all that kind of stuff. And so what they've created is a little 102 00:06:41.160 --> 00:06:44.759 bit of a you know, like a uh, you know, the gift 103 00:06:44.759 --> 00:06:46.839 that keeps on giving, right, Like the more things that you're looking at 104 00:06:46.879 --> 00:06:49.959 and tracking and measuring, the more room there is for people to say, 105 00:06:50.399 --> 00:06:55.000 well, something looks wrong here, what's actually going wrong? Oh, now 106 00:06:55.000 --> 00:06:58.279 we've actually had something more service impacting. Now what do we do? And 107 00:06:58.360 --> 00:07:02.079 often the interdependence of those things are not clear. And also the people who 108 00:07:02.079 --> 00:07:09.079 are involved might actually sit in multiple organizational teams or functions and try to pull 109 00:07:09.079 --> 00:07:12.519 it all together. And the poor you know, SR or incident response person 110 00:07:12.560 --> 00:07:15.240 who gets hit with this is like, Okay, where do I start, 111 00:07:15.879 --> 00:07:17.319 you know, And in particular, if they don't have a lot of knowledge 112 00:07:17.360 --> 00:07:21.360 and expertise of the particular area where the problem is. So these are all 113 00:07:21.360 --> 00:07:25.439 things that we see all the time, and so I think your your initial 114 00:07:25.439 --> 00:07:30.480 point is the right one, which is to some extent, more observability tries 115 00:07:30.519 --> 00:07:32.600 to solve the problem in the industry. In some ways, the more problems 116 00:07:32.639 --> 00:07:36.759 get created because how many hundreds of alerts can you look at? Right? 117 00:07:38.519 --> 00:07:41.839 People simply can't keep track of it and they don't understand it. And sometimes 118 00:07:41.879 --> 00:07:46.480 the alerts that you're getting are the symptoms and not the actual cause. So 119 00:07:46.519 --> 00:07:48.680 that's you know, that's that's the problem space that we're coming into as Cousley, 120 00:07:48.720 --> 00:07:51.319 and you know, bringing a lot of knowledge and expertise from the founding 121 00:07:51.319 --> 00:07:55.839 team from this particular set of problems. Well, I mean you've hit the 122 00:07:55.920 --> 00:07:59.040 nail on the head right there with the fact that many times what you see 123 00:07:59.079 --> 00:08:03.519 are symptoms, it's not the problem. And root cause analysis has always been 124 00:08:03.600 --> 00:08:07.680 difficult. It's always hard. But I will say that the power of machine 125 00:08:07.759 --> 00:08:13.279 learning is very compelling here because machines can scan vast amounts of information log files, 126 00:08:13.319 --> 00:08:18.959 for example, usage patterns, and the key obviously is to capture that 127 00:08:18.480 --> 00:08:24.279 and to identify the correlation, help the end user find the causation or even 128 00:08:24.319 --> 00:08:28.319 find it for them and then catalog that and know for next time when this 129 00:08:28.439 --> 00:08:33.159 set of circumstances comes. Aha, it's probably that problem we had before. 130 00:08:33.559 --> 00:08:35.720 Maybe not. There can always be some new piece of information coming in. 131 00:08:37.000 --> 00:08:43.200 But to be able to dynamically ascertain the correlation between feeds and thus get too 132 00:08:43.200 --> 00:08:48.360 closer to the causation, that's a huge deal because one of my big themes 133 00:08:48.399 --> 00:08:54.200 here is morale and morale of your teams and your sres. Your app teams 134 00:08:54.279 --> 00:08:56.600 are going to have very low morale if they can't get to the bottom of 135 00:08:56.720 --> 00:09:01.120 things. If stuff breaks and they can't even figure out how to turn it 136 00:09:01.159 --> 00:09:03.240 back on again, that is not good for the home team, it's not 137 00:09:03.320 --> 00:09:07.240 good for me, for anyone involved. And so what I love about this 138 00:09:07.360 --> 00:09:11.440 causal AI is that it's greasing the tracks to the correct answers. Right, 139 00:09:13.039 --> 00:09:18.039 Okay, So I'm going to shake your world forget about correlation. Oh, 140 00:09:18.200 --> 00:09:24.360 correlation is history. The way people think about correlation is stuff's going wrong. 141 00:09:24.759 --> 00:09:26.759 And there are a whole bunch of things I got alerted on, and I'm 142 00:09:26.799 --> 00:09:31.919 looking at the picture that I have of my topology and the incidents that I'm 143 00:09:31.960 --> 00:09:35.559 seeing you know that, you know, all my tools are telling me about 144 00:09:35.080 --> 00:09:39.720 and I'm a human being and I'm trying to figure out how those relate to 145 00:09:39.759 --> 00:09:43.919 each other. And mostly what I'm looking at is anomaly detection together with correlation. 146 00:09:45.159 --> 00:09:48.639 That is the state of the world right now. And what we're saying 147 00:09:48.679 --> 00:09:50.600 at causally and what you know, we as the founding team really believe in 148 00:09:50.720 --> 00:09:56.840 very strongly, is in the end that comes down to providing human beings with 149 00:09:56.960 --> 00:10:01.879 pieces of information and updated you know, pictures of their environments and reality that 150 00:10:03.039 --> 00:10:05.200 still they have to piece together what's going on. You know, it's like 151 00:10:05.240 --> 00:10:11.240 the joke right about correlation is not causation. So let's let's just pause on 152 00:10:11.279 --> 00:10:13.919 that for one second. Like, the most the funny one that people always 153 00:10:13.960 --> 00:10:18.120 talk about is how like ice cream sales in the summer and sharks correlate. 154 00:10:18.200 --> 00:10:20.480 Like that's the most obvious one. So like if you think about it, 155 00:10:20.480 --> 00:10:24.480 you realize how ridiculous it is, which is, of course those things correlate 156 00:10:24.519 --> 00:10:28.000 with each other. But the root cause is like that it's the summer, 157 00:10:28.080 --> 00:10:30.639 right, It's a season, and you know, it's hot and people are 158 00:10:30.919 --> 00:10:35.279 outside and sharks are outside too, so like, there's there's something about that 159 00:10:35.279 --> 00:10:41.519 that I think the industry has not kind of accepted or believed that it's possible 160 00:10:41.559 --> 00:10:46.320 to do more than that, and that as a result capturing all of this 161 00:10:46.440 --> 00:10:48.399 data or having to do more of a bottoms up approach in which you try 162 00:10:48.399 --> 00:10:52.279 to like build models that you could automate, you know to your point, 163 00:10:52.320 --> 00:10:54.960 you know, run books and all that. But in the end, all 164 00:10:54.960 --> 00:10:58.799 of that comes down to that some human beings with some expertise and knowledge took 165 00:10:58.799 --> 00:11:01.159 a look at things and said, Okay, that's actually really the thing, 166 00:11:01.399 --> 00:11:03.120 because you know, if you start to fix something and you're not fixing the 167 00:11:03.200 --> 00:11:07.399 root cause, all you're doing is pushing the problem around or just it's going 168 00:11:07.480 --> 00:11:11.799 to come back later. So the philosophy that we have a causely is that 169 00:11:13.360 --> 00:11:18.559 really what you want is you want to have the knowledge of causal relationships of 170 00:11:18.639 --> 00:11:24.799 cause and effect, not correlation, but causal effects captured in software and automation 171 00:11:26.200 --> 00:11:30.039 as opposed to relying on human beings. And that's a really hard problem. 172 00:11:30.159 --> 00:11:33.720 It's not something that a lot of I think people in the operational space and 173 00:11:35.200 --> 00:11:37.799 you know, in AIOps and other areas that are are kind of part of 174 00:11:37.840 --> 00:11:43.159 the industry have really wanted to take on, and so they're focusing more on 175 00:11:43.159 --> 00:11:46.919 how do you automate the human capabilities that are out there and make those more 176 00:11:46.960 --> 00:11:50.279 repeatable. And what we're saying is, let's go to the heart of this 177 00:11:50.480 --> 00:11:56.440 and capture the actual causal knowledge and ability to say what these causal relationships are. 178 00:11:56.879 --> 00:12:00.720 And we do that in a very particular way that's based on a specific 179 00:12:01.120 --> 00:12:03.279 approach that you know, my co founder and the founding team bring to the 180 00:12:03.279 --> 00:12:07.840 table. But really what we're trying to say here is that what you would 181 00:12:07.840 --> 00:12:11.960 want to see in your application environment and in the way that you build and 182 00:12:11.039 --> 00:12:16.639 run applications is that you are able to maintain and run these applications reliably. 183 00:12:16.879 --> 00:12:22.360 You're able to ship code without breaking things, You're able to have new changes 184 00:12:22.399 --> 00:12:26.679 that are constantly happening customers using your application in different ways, and that all 185 00:12:26.720 --> 00:12:31.200 of that can be maintained in a good and healthy state where you're able to 186 00:12:31.240 --> 00:12:35.120 meet your business objectives, you're able to meet your service level objectives. And 187 00:12:35.480 --> 00:12:39.039 at the heart of that is the fact that there is this causal knowledge that's 188 00:12:39.080 --> 00:12:43.679 being captured, because without that, you're just you're playing wackable, right, 189 00:12:43.679 --> 00:12:46.399 you're running around your environment trying to fix things and tweak things and improve them. 190 00:12:46.759 --> 00:12:50.279 So that's the approach that we take to the industry, and we happen 191 00:12:50.320 --> 00:12:54.679 to be using an approach that is a causal AI platform that we built, 192 00:12:54.879 --> 00:13:01.919 and that's technology that we are running our first that is available now for people 193 00:13:01.919 --> 00:13:05.960 to be testing with. So that's that's kind of the approach that we've taken. 194 00:13:07.039 --> 00:13:11.720 And I think that a lot of companies that are out there are hoping 195 00:13:11.759 --> 00:13:16.679 that they will automate things enough, you know, with enough data that eventually 196 00:13:16.720 --> 00:13:18.639 they'll get a picture of reality. But of course then things change ten seconds 197 00:13:18.720 --> 00:13:22.480 later, and one customer's environment is not like another customer's environment. So these 198 00:13:22.480 --> 00:13:26.320 are all things that you know from our approach. The bottom is up is 199 00:13:26.360 --> 00:13:28.879 not the right way, The top down is the right way to think about 200 00:13:28.879 --> 00:13:33.559 it, and that's that's part of the Causal platform. Yeah, that's very 201 00:13:33.559 --> 00:13:37.440 interesting. So in other words, the technology, the AI that you're using 202 00:13:37.559 --> 00:13:43.120 is designed to focus on understanding root cause, which, as we've discussed, 203 00:13:43.240 --> 00:13:46.960 is very difficult typically to do, but it's not terribly difficult depending upon the 204 00:13:48.000 --> 00:13:52.960 algorithms that you're deploying because you can run what if scenarios very quickly, you 205 00:13:52.000 --> 00:13:56.799 can do all sorts of Monty Carlo assessments essentially on the data, and then 206 00:13:56.799 --> 00:14:01.000 I'm guessing that when you find certain things you are cataloging, that is that 207 00:14:01.039 --> 00:14:05.080 correct. Well, we're actually using a very different approach than that. So 208 00:14:05.120 --> 00:14:09.559 some of the things people are familiar with using, you know, machine learning 209 00:14:09.720 --> 00:14:13.200 and you know, more traditional types of AI approaches that are out there, 210 00:14:13.240 --> 00:14:20.120 are approaches that other companies use. We are coming at it from the perspective 211 00:14:20.200 --> 00:14:24.200 that you need to start with what we call structured causal models, and they're 212 00:14:24.240 --> 00:14:28.639 what we're able to do is we're able to capture the relationship between the root 213 00:14:28.679 --> 00:14:35.759 cause and its symptoms in a very generalizable and very abstracted way. And we've 214 00:14:35.799 --> 00:14:39.320 created these and we've captured them in software and that's a key part of what 215 00:14:39.360 --> 00:14:41.799 we've built, and that just out of the box. You know, it's 216 00:14:41.840 --> 00:14:45.440 not like, oh, we have to come in and understand something unique about 217 00:14:45.480 --> 00:14:48.600 a customer's environment or what their relationships are there. But we've done it at 218 00:14:48.600 --> 00:14:54.159 a higher level where we're looking at problems that are things like noisy neighbor and 219 00:14:54.320 --> 00:15:01.799 database slowdowns and congestion across data pipelines, and you know, message broker problems 220 00:15:01.840 --> 00:15:05.840 where often it's the inter relationships between a lot of the different parts of the 221 00:15:05.960 --> 00:15:13.120 environment and uh, things that could be very multi variate, right in terms 222 00:15:13.120 --> 00:15:16.279 of how many different things are interacting with each other in real time make it 223 00:15:16.320 --> 00:15:22.159 really hard for human beings to troubleshoot. And so we've captured those in our 224 00:15:22.240 --> 00:15:26.200 structured causal models, and then we're applying the causal inference engine that we've built 225 00:15:26.559 --> 00:15:31.159 to these problems. And all of that is happening with live and dynamic real 226 00:15:31.200 --> 00:15:35.399 time customer environment right where we're sitting in their environment, and we're able to 227 00:15:35.440 --> 00:15:39.200 see a lot of the you know, kind of like the data sources that 228 00:15:39.320 --> 00:15:43.080 exist about their you know, what's happening in the kumunetes cluster, and then 229 00:15:43.080 --> 00:15:46.399 their cloud services, and what's happening, you know, across the environment with 230 00:15:46.480 --> 00:15:50.080 databases and networks and you know, and message brokers and networks and stuff like 231 00:15:50.080 --> 00:15:54.200 that. So we're we're essentially taking all of that in as live, real 232 00:15:54.240 --> 00:15:58.159 time feeds, and we're able to then apply that to these causal models and 233 00:15:58.519 --> 00:16:02.879 use the cause imprints to say, hey, at this moment in this particular 234 00:16:02.919 --> 00:16:07.360 customer's application, you know this particular set of scenarios. These are the things 235 00:16:07.360 --> 00:16:11.759 that are problems that are either happening right now and you need to do something 236 00:16:11.759 --> 00:16:14.639 about them immediately, and here's how you should take action, or these are 237 00:16:14.679 --> 00:16:18.120 emerging problems, things that are potential things that could happen in the future that 238 00:16:18.159 --> 00:16:19.639 you would want to be aware of and you may want to take action on 239 00:16:19.799 --> 00:16:25.200 in a more preventative way. And that's the causal relationships really help us to 240 00:16:25.200 --> 00:16:29.679 do all of that. That's very interesting. So you built these models, 241 00:16:29.720 --> 00:16:32.240 and we'll pick this up in the second segment here in just a minute, 242 00:16:32.279 --> 00:16:38.039 but it sounds to me like a foundational model for causal AI for troubleshooting and 243 00:16:38.080 --> 00:16:41.759 finding root cause analysis. And just to explain to our audience out there, 244 00:16:42.120 --> 00:16:47.279 foundational models, well, like the large language models are I think going to 245 00:16:47.320 --> 00:16:52.320 reinvent enterprise software, but they have to be very focused. The large language 246 00:16:52.320 --> 00:16:56.080 models are very, very big, They have billions of parameters. They still 247 00:16:56.120 --> 00:17:00.799 work. One of my good friends heads up actually at Northeast in Massachusetts, 248 00:17:02.080 --> 00:17:04.559 the Institute for Experiential AI, and he was joking when I talked to him 249 00:17:04.559 --> 00:17:07.400 on the show earlier this year. He's like, these models they're too big, 250 00:17:07.559 --> 00:17:11.759 they're not supposed to work. Nobody knows why they work. I'm okay, 251 00:17:11.759 --> 00:17:15.200 you're running the whole institute. Sometimes I does have that feeling, right 252 00:17:15.200 --> 00:17:19.559 like nobody that's kind of scary. But so we'll pick this that out of 253 00:17:19.599 --> 00:17:22.519 the break. But it sounds to me like you have built a foundational model 254 00:17:22.640 --> 00:17:26.720 in this space and it's very very interesting stuff. Don't don't touch that down, 255 00:17:26.720 --> 00:17:29.400 folks. And we were right back. You're listening to Inside Analysis. 256 00:17:30.279 --> 00:17:42.720 Expected welcome back to Inside Analysis. Here's your host, Eric Tavanaugh. All 257 00:17:42.799 --> 00:17:47.880 right, folks, back here on Inside Analysis talking to Ellen Rubin of Causey, 258 00:17:48.319 --> 00:17:52.119 which is a company. Guess what, folks on cause AI, which 259 00:17:52.160 --> 00:17:56.319 is really interesting stuff. So this is not large language models. This is 260 00:17:56.359 --> 00:18:00.599 not some of the other foundational models you've heard about. Specifically, what the 261 00:18:00.599 --> 00:18:04.799 folks did here is they focused on these structured causal models and they look for 262 00:18:04.960 --> 00:18:10.839 very specific types of symptoms and can help you use an inference engine to know 263 00:18:10.960 --> 00:18:15.279 when this variable changes, that variable is going to change. So they're basically 264 00:18:15.319 --> 00:18:18.119 looking at the inner workings of a system to understand how it works, and 265 00:18:18.200 --> 00:18:22.759 that's why they can get to the root cause of some very complex problems very 266 00:18:22.799 --> 00:18:26.000 quickly. Is that about right on? Yeah, Yeah, that's definitely in 267 00:18:26.000 --> 00:18:30.079 the right direction. So, you know, for us, the whole point 268 00:18:30.079 --> 00:18:36.319 of this is to remove human effort and you know, challenges that require sometimes 269 00:18:36.440 --> 00:18:38.240 days worth of like what the heck is going on in my environment? And 270 00:18:38.279 --> 00:18:44.440 so there is no there's no effort or lift from people who would be using 271 00:18:44.680 --> 00:18:48.319 causely to have to you know, use our inference engine, or to have 272 00:18:48.359 --> 00:18:52.039 to create any of the models or apply the models or whatever. That's just 273 00:18:52.079 --> 00:18:56.599 completely automated and completely out of the box. And that's something that we think 274 00:18:56.640 --> 00:19:00.400 is really critical here, which is if we're going to automate this in a 275 00:19:00.440 --> 00:19:03.519 way that allows us to go all the way through to what do I need 276 00:19:03.559 --> 00:19:07.039 to do to fix this or who do I need to inform about this information 277 00:19:07.079 --> 00:19:08.400 so that they can make a change in their code or do whatever it is 278 00:19:08.400 --> 00:19:11.359 that needs to get done. You need to take them all the way through 279 00:19:11.480 --> 00:19:17.759 right to the conclusion. And so it's really about understanding the root cause of 280 00:19:17.759 --> 00:19:21.720 what is happening in the environment, and even better to help people see things 281 00:19:21.720 --> 00:19:25.319 that could be emerging problems and potential things that might be happening in the future, 282 00:19:25.400 --> 00:19:27.599 because you know, it's such a dynamic environment that they're living in. 283 00:19:29.000 --> 00:19:33.359 So that's really important. And I think what you were saying earlier is key, 284 00:19:33.400 --> 00:19:38.279 which is we're really looking for the symptoms of certain types of problems that 285 00:19:38.319 --> 00:19:41.839 are these you know, often challenging, very complex problems that people deal with 286 00:19:41.880 --> 00:19:47.440 all the time. And by doing that, we are more efficient and I 287 00:19:47.440 --> 00:19:51.839 would say almost more like laser focused on. Based on seeing these particular types 288 00:19:51.880 --> 00:19:53.680 of symptoms, we can tell you what the root cause is, and we 289 00:19:53.759 --> 00:19:57.079 can show that to you automatically in a completely, you know, real time 290 00:19:57.119 --> 00:20:02.480 way, and it doesn't require as the human being to then stiff through a 291 00:20:02.480 --> 00:20:04.599 lot of other pieces of data and try to say, oh, okay, 292 00:20:04.599 --> 00:20:07.160 that looks like it. Now, let's go try to fix that and see 293 00:20:07.160 --> 00:20:11.759 if that actually was the root cause. We're telling you automatically. So we 294 00:20:11.799 --> 00:20:15.119 think that that is that's just the way the world needs to work in the 295 00:20:15.160 --> 00:20:18.240 future, and it's been very very hard for people to do that up until 296 00:20:18.240 --> 00:20:22.640 now. The fact that there are lllms and the fact that there's machine learning. 297 00:20:23.319 --> 00:20:26.480 You know, we'll take advantage of all of these techniques that are out 298 00:20:26.480 --> 00:20:30.400 there. These are known techniques, but what we're bringing uniquely to the table 299 00:20:30.200 --> 00:20:33.759 is the ability and the expertise to have built a platform that is this really 300 00:20:33.799 --> 00:20:38.079 really scalable, really really you know, automated platform for capturing cause and effect 301 00:20:38.119 --> 00:20:42.680 relationships, which you know nobody else has done so far. Yeah, and 302 00:20:42.720 --> 00:20:47.759 it gets into graphs too, right eight, like directing graphs and acyclic graphs 303 00:20:47.759 --> 00:20:51.680 and things of this nature where x leads to hy, which leads to z, 304 00:20:51.880 --> 00:20:56.200 et cetera. So you identify these patterns and then you look for when 305 00:20:56.279 --> 00:20:59.720 it applies in a particular scenario. Now I'll throw out sort of a random 306 00:21:00.119 --> 00:21:04.319 anomaly here or analogy. I should say. What I remember from taking calculus 307 00:21:04.440 --> 00:21:07.240 years ago is that if you skip a day class, first of all, 308 00:21:07.279 --> 00:21:11.920 your toast so do not skip. Okay, but you kind of do that 309 00:21:11.960 --> 00:21:14.720 back and then too, right, that's right, that you probably shouldn't skip 310 00:21:14.720 --> 00:21:17.359 a day. But I remember what I remembered by it that it was very 311 00:21:17.400 --> 00:21:21.400 interesting, is that their x number of patterns and you have to sort of 312 00:21:21.880 --> 00:21:26.440 you have to infer what the nature of the problem is to apply the right 313 00:21:26.519 --> 00:21:30.680 formula to figure out the problem, and it sounds like these structured CASEM models 314 00:21:32.000 --> 00:21:36.319 are the foundation for that. So they have a certain number of these these 315 00:21:36.400 --> 00:21:40.799 views of the world essentially baked in from experience, and then you apply it 316 00:21:40.839 --> 00:21:44.039 to whatever the situation is. And then the way it works is it looks 317 00:21:44.079 --> 00:21:48.079 for certain characteristics, certain symptoms as you mentioned, and when it finds that, 318 00:21:48.119 --> 00:21:51.839 it goes aha, we recognize this pattern, we think this is what's 319 00:21:51.880 --> 00:21:55.440 going on. That's that's basically how it works, right right, Although it's 320 00:21:55.480 --> 00:21:57.319 you know, it's less about we think and more that we feel confident and 321 00:21:57.359 --> 00:22:03.799 that you know, a lot of it is applying it. Applying this causal 322 00:22:03.920 --> 00:22:10.920 ai approach to the cloud native and operational world is what is unique here, 323 00:22:11.119 --> 00:22:14.640 right, Like the field of causal ai, you know, we were chatting 324 00:22:14.640 --> 00:22:15.960 about this previously. You know, like the field of causal ai is a 325 00:22:17.000 --> 00:22:18.359 known field. This is that people have been at this for a very very 326 00:22:18.359 --> 00:22:22.440 long time. There are people who are famous in this field, like Giba 327 00:22:22.480 --> 00:22:26.359 Pearl and others who have worked on this. You know, many universities and 328 00:22:26.480 --> 00:22:36.079 it's frequently applied in things like healthcare and marketing and financial markets and stuff like 329 00:22:36.079 --> 00:22:38.319 that, where the need to know about causality has been strong, and people 330 00:22:38.319 --> 00:22:42.480 have applied it into those situations, but usually it's done in a very project 331 00:22:42.519 --> 00:22:45.200 based type of approach, like here, we're going to build something with this 332 00:22:45.240 --> 00:22:49.920 particular organization or this particular team that solves these particular problems. And in a 333 00:22:49.920 --> 00:22:52.440 lot of ways, it's kind of a closed system, right, it's meant 334 00:22:52.480 --> 00:22:56.200 for that particular you know, it's it's kind of a proprietary thing that that 335 00:22:56.240 --> 00:23:00.000 people work on within the analytics or the data teams. What we're saying is 336 00:23:00.079 --> 00:23:03.599 we've now created this in a really generalized way, in an abstracted way that 337 00:23:03.640 --> 00:23:10.799 can be applied to the overall cloud native and IT operational world. And you 338 00:23:10.799 --> 00:23:12.480 know, we happen to have picked a certain set of initial problems that we're 339 00:23:12.480 --> 00:23:15.720 focused on, and we're doing official you know, work with companies that are 340 00:23:15.799 --> 00:23:21.119 using more micro services based environments, but it's more generally applicable than that. 341 00:23:22.400 --> 00:23:25.920 No, that's interesting. So you have a very focused approach and that has 342 00:23:25.960 --> 00:23:30.200 allowed you to leverage this these models and let's face it, I mean the 343 00:23:30.319 --> 00:23:34.720 challenge now for developer teams, for application teams, to your point earlier, 344 00:23:34.799 --> 00:23:38.519 is that the environment is incredibly complex and it's getting more complex by the day, 345 00:23:38.839 --> 00:23:41.839 and I don't think that's going to change. So what can you do 346 00:23:41.880 --> 00:23:45.759 differently? And that's where this approach comes in, right, is to leverage 347 00:23:47.200 --> 00:23:52.359 the known science of causal AI and to map that against the symptoms and all 348 00:23:52.359 --> 00:23:56.359 the data that's coming from these various environments. To keep it simple and look 349 00:23:56.400 --> 00:24:02.720 for the patterns right right, and make it an out of the box experience. 350 00:24:02.759 --> 00:24:07.480 Like I think about the people who are responsible for building and operating large 351 00:24:07.519 --> 00:24:11.200 scale, very very you know, kind of complex application environments like you know, 352 00:24:11.279 --> 00:24:15.960 huge huge companies that have these applications where they have like thousands of applications, 353 00:24:15.039 --> 00:24:18.720 is also true, And think about the nature of the job for the 354 00:24:18.759 --> 00:24:23.640 people who are the DevOps people, the srees, the people who are handling 355 00:24:23.680 --> 00:24:26.240 incident response, the people who are on call, you know, like all 356 00:24:26.279 --> 00:24:30.400 of those types of people, you know, let alone the people who are 357 00:24:30.000 --> 00:24:33.240 in the mode of like I own the you know, the the KPIs and 358 00:24:33.319 --> 00:24:37.279 the you know, the metrics that are you know, the business and technical 359 00:24:37.319 --> 00:24:41.119 metrics for for how this application is behaving. Like you don't want things to 360 00:24:41.200 --> 00:24:45.519 break all the time. You want to ship code faster, and you want 361 00:24:45.559 --> 00:24:48.839 to be able to adapt to dynamic and changing customer needs, and you know 362 00:24:49.400 --> 00:24:53.759 things that happen at peak periods, and you know new things that get thrown 363 00:24:53.799 --> 00:24:59.240 in because the cloud infrastructure underneath you keeps changing. You're living in that world, 364 00:24:59.319 --> 00:25:02.359 and a lot of time what you're thinking is, oh my god, 365 00:25:02.559 --> 00:25:06.400 like if I just don't have every piece of information and every single thing that's 366 00:25:06.440 --> 00:25:10.039 in my environment, I might you know, something terrible might happen, right 367 00:25:10.119 --> 00:25:12.839 or I might not achieve the goals that the business needs. And you know, 368 00:25:12.880 --> 00:25:18.680 from us, the philosophy is, what can we take away a lot 369 00:25:18.720 --> 00:25:22.559 of that confusion and difficulty and pressure. And even if you're not somebody who's 370 00:25:22.559 --> 00:25:25.640 been on the team for very long, or even if you only know about 371 00:25:25.640 --> 00:25:29.119 a few services that are the services you're responsible for, how do we give 372 00:25:29.160 --> 00:25:33.240 you this kind of out of the box and really fast experience that allows you 373 00:25:33.319 --> 00:25:34.400 to then say, okay, well I don't have to worry about that anymore. 374 00:25:34.559 --> 00:25:37.720 Rightly, the environment is going to be healthy and it's going to be 375 00:25:37.799 --> 00:25:38.759 resilient, and we're not going to be constantly, you know, kind of 376 00:25:38.759 --> 00:25:42.480 playing the whack a mole game and really we can focus on the things we 377 00:25:42.519 --> 00:25:45.480 care about, which usually have to do with I have new features to roll 378 00:25:45.519 --> 00:25:48.839 out. I want to make sure that I'm you know, kind of architecting 379 00:25:48.880 --> 00:25:52.880 things for a better experience for the customer or for bigger scale or whatever it 380 00:25:52.960 --> 00:25:56.559 is, and that's where the energy should be going. Yeah, well, 381 00:25:56.599 --> 00:26:00.319 I mean I happen to know from experience to talk about whack them. You 382 00:26:00.319 --> 00:26:03.519 can spend the rest of your life trying to track down problems and never even 383 00:26:03.559 --> 00:26:06.359 get there if you don't have the right perspective, if you don't have the 384 00:26:06.440 --> 00:26:11.079 right information at your fingertips. And what you're talking about is providing this sort 385 00:26:11.079 --> 00:26:17.039 of foundational component to solve those problems out of the box. I guess one 386 00:26:17.119 --> 00:26:19.680 question I have is how do you deploy Like when someone actually buys the software, 387 00:26:19.759 --> 00:26:22.000 where do they deployed? How do they deployed? Is it in the 388 00:26:22.000 --> 00:26:27.559 instance in the cloud or how does that work? So the initial product, 389 00:26:29.160 --> 00:26:34.400 initial costly product is it deploys into a Kubernetes cluster. So we kind of 390 00:26:34.440 --> 00:26:37.240 are you know, using that as an initial As I mentioned, that's kind 391 00:26:37.240 --> 00:26:40.920 of our initial decision about you know, kind of starting with those types of 392 00:26:41.039 --> 00:26:45.039 environments that can be used more generally than that, but for the initial product, 393 00:26:45.079 --> 00:26:48.319 people would deploy us and it's like a couple of minutes, you know, 394 00:26:48.400 --> 00:26:52.319 to be up and running within in their Kubernetes environment. And what we 395 00:26:52.480 --> 00:26:56.000 are able to do though, is we're able to see things that are you 396 00:26:56.039 --> 00:26:59.400 know, well beyond what's happening in Kubernetes. So all the different cloud services 397 00:26:59.440 --> 00:27:03.440 and the you know, like all all those different inter relationships that I was 398 00:27:03.480 --> 00:27:07.759 describing, we're capturing those automatically and out of the box, using a lot 399 00:27:07.799 --> 00:27:12.319 of different data sources. So you know, uh APIs from the Kubernetes and 400 00:27:12.319 --> 00:27:18.640 cloud environments, using uh accessing data from Prometheus, leveraging open telemetry, e 401 00:27:18.720 --> 00:27:21.640 B, p F st O, all of you know, a lot of 402 00:27:21.680 --> 00:27:26.559 open source tools, and then being able to take alerts and other information from 403 00:27:26.680 --> 00:27:29.759 products that a lot of the customers would be using, like data Dog and 404 00:27:29.839 --> 00:27:33.920 similar observability products. So all of that is kind of input to us, 405 00:27:33.200 --> 00:27:37.440 but that just happens automatically. The customer doesn't do anything, you know, 406 00:27:37.519 --> 00:27:41.240 to to make that happen. And then uh, you know, the idea 407 00:27:41.319 --> 00:27:45.079 for these customers is that they would be able to immediately see like it's not 408 00:27:45.160 --> 00:27:48.599 They don't have to run it for a while and baseline anything like they can 409 00:27:48.640 --> 00:27:52.400 immediately out of the box start to see what's happening in their environment. Not 410 00:27:52.440 --> 00:27:55.559 only you know, of course, the topology and you know what their environment 411 00:27:55.559 --> 00:27:59.799 looks like, but the causal relationships that are in place across that environment. 412 00:28:00.200 --> 00:28:03.160 And we think that that's really exciting and compelling that when people can see that 413 00:28:03.160 --> 00:28:04.119 all of a sudden, they're like, oh wow, Like we have this 414 00:28:04.240 --> 00:28:11.039 idea of a causality tree that basically shows the root cause with all of the 415 00:28:11.039 --> 00:28:15.480 different symptoms, and that's something that is a view within the product that we're 416 00:28:15.519 --> 00:28:18.799 able to say, here, look for this particular issue that's happening. You 417 00:28:18.839 --> 00:28:22.519 know, it's either an existing problem or it's a problem that could happen in 418 00:28:22.519 --> 00:28:26.799 the future. Here are all the causal relationships, and we can tell you 419 00:28:26.880 --> 00:28:29.680 a lot of the information around them, and you can dig deeper on them, 420 00:28:29.759 --> 00:28:33.279 you know, with as much you know, more detail and metrics and 421 00:28:33.359 --> 00:28:37.119 data. But really the thing that you want to see is in my environment, 422 00:28:37.319 --> 00:28:41.680 this particular issue is happening or it's about to happen, and here are 423 00:28:41.720 --> 00:28:45.680 all the causal relationships that are captured visually, and so we think that's that's 424 00:28:45.680 --> 00:28:48.680 really fun and that people you know, will be excited to have that, 425 00:28:48.880 --> 00:28:52.119 and you know, kind of early working with early customers, a lot of 426 00:28:52.160 --> 00:28:56.359 times what they say is, this is the first time we have this view 427 00:28:56.480 --> 00:29:00.759 of what's happening in our environment as opposed to a more general cology view which 428 00:29:00.799 --> 00:29:04.599 I think they're used to seeing. Well, I mean, understanding causation across 429 00:29:04.640 --> 00:29:11.079 these environments. That's the key, because one problem here can propagate over here 430 00:29:11.079 --> 00:29:14.160 and over there, and that's those are the symptoms that you're seeing. But 431 00:29:14.279 --> 00:29:18.240 to your point, with this structured causal model, you're able to say with 432 00:29:18.359 --> 00:29:22.319 great certitude, when certain conditions are met, Okay, we know what this 433 00:29:22.480 --> 00:29:27.240 is, right, it's a pattern matching scenario. But it gets you to 434 00:29:27.319 --> 00:29:30.119 the root cause very very quickly, like, okay, this is it now 435 00:29:30.160 --> 00:29:34.759 we know basically it's not even probabilistic. We know that when these conditions are 436 00:29:34.759 --> 00:29:38.160 in place, X is happening, or we know in those conditions are the 437 00:29:38.200 --> 00:29:41.559 place, why is happening? And here's what you do? And then you 438 00:29:41.599 --> 00:29:45.240 actually go the next step is that right? Do you actually then remediate with 439 00:29:45.279 --> 00:29:52.519 automation to solve some of these problems. So you're a experienced person in the 440 00:29:52.519 --> 00:29:56.480 industry, so the remediation conversation is always a conversation, right, So basically 441 00:29:56.519 --> 00:30:00.960 we can remediate all the way and take action. That is definitely something that 442 00:30:00.000 --> 00:30:03.039 we do, and we can do it, and in some cases customers have 443 00:30:03.079 --> 00:30:07.039 told us please do right, like if you can actually just go ahead and 444 00:30:07.359 --> 00:30:11.920 you know, kind of do certain types of actions, please fix it, 445 00:30:11.119 --> 00:30:15.279 Yeah, take it, take it away. But often in some of the 446 00:30:15.319 --> 00:30:18.880 larger customers we're working with, there's a desire for it to become a part 447 00:30:18.960 --> 00:30:23.839 of a set of change management pipe you know, CICV pipeline that's already in 448 00:30:23.880 --> 00:30:27.359 place, and for obvious reasons, right, like, you can't go around 449 00:30:27.359 --> 00:30:30.400 those systems, you have to live within them. And so a lot of 450 00:30:30.440 --> 00:30:33.279 the work that we're doing is to just make sure that we tie very seamlessly 451 00:30:33.319 --> 00:30:37.079 into that, Like how do we make sure that if the way that they 452 00:30:37.240 --> 00:30:40.599 take care of certain types of problems is by a set of things that they've 453 00:30:40.599 --> 00:30:42.279 built in their infrastructure as code, well, then we should just tie into 454 00:30:42.279 --> 00:30:45.119 that. Right, We're not going to reinvent the wheel for them. On 455 00:30:45.440 --> 00:30:48.640 any of those things, and some of it, you know, as to 456 00:30:48.680 --> 00:30:52.160 some of the things that you were saying a little earlier, there's like the 457 00:30:52.160 --> 00:30:57.359 if the right person gets the information they need and they're able to have that 458 00:30:57.440 --> 00:31:03.200 information immediately, then sometimes the action that needs to be taken is a human 459 00:31:03.240 --> 00:31:06.039 action, right, It isn't really an automated action, like could you re 460 00:31:06.200 --> 00:31:08.720 architect this so that it doesn't have this choke point? Kay? That may 461 00:31:08.759 --> 00:31:14.680 take a little bit. See. This is very very interesting though, because 462 00:31:15.039 --> 00:31:18.519 I understand that, and I think a lot of people on the outside of 463 00:31:18.720 --> 00:31:22.720 enterprise applications don't fully appreciate this, and I think they will more and more 464 00:31:22.799 --> 00:31:26.960 so as time goes by. But every environment is different, Every environment is 465 00:31:27.000 --> 00:31:30.880 bespoke, especially when you have these hybrid cloud environments. I mean, yes, 466 00:31:30.960 --> 00:31:36.880 the cloud is sort of garden variety if you will X number of services 467 00:31:36.920 --> 00:31:40.160 that we can all use and cobble together. The second you touch some on 468 00:31:40.240 --> 00:31:44.960 prem environment, you have a whole other set of challenges that are very complex 469 00:31:45.200 --> 00:31:48.319 and that are very bespoke, and that will need special attention, which is 470 00:31:48.319 --> 00:31:53.119 why off the shelf stuff sometimes requires you to take some extra act added steps 471 00:31:53.279 --> 00:31:56.799 to connect it all and to get it through and to understand with the client 472 00:31:57.200 --> 00:32:00.799 what environment they want, what do they want want to get out of this. 473 00:32:00.880 --> 00:32:02.920 So I'd love that it solves a lot of these problems in terms of 474 00:32:02.920 --> 00:32:07.160 getting to root cause analysis, but you have some opportunity for the client to 475 00:32:07.200 --> 00:32:09.799 say, well, we'll do this or do that when it comes through, 476 00:32:09.920 --> 00:32:15.759 because they're just different environments with different responsibilities in different people. Right. Having 477 00:32:15.799 --> 00:32:19.559 spent my entire career working with enterprise customers, you have to meet them where 478 00:32:19.559 --> 00:32:22.759 they are on some level. So they're on a journey, right. They're 479 00:32:22.759 --> 00:32:25.839 on a journey in cloud adoption, they're on a journey in Kubernetes adoption. 480 00:32:27.079 --> 00:32:30.440 They're using different observability and they're adopting more like a lot of them are on 481 00:32:30.440 --> 00:32:32.920 a journey with open telemetry, right, and trying to figure out how to 482 00:32:32.960 --> 00:32:36.920 bring that in a more standard way across their environment, because they know that 483 00:32:36.920 --> 00:32:39.759 that's a direction the industry is moving in. All of that's happening in the 484 00:32:39.759 --> 00:32:44.240 background, and you know, our job is to make sure that it doesn't 485 00:32:44.240 --> 00:32:46.799 really matter that where the customer is in that journey, we're able to kind 486 00:32:46.839 --> 00:32:50.480 of meet them there and provide them with the things that they need. And 487 00:32:50.759 --> 00:32:52.839 as much as we can build things into the product, you know, automatically, 488 00:32:53.240 --> 00:32:57.839 the better. And also to a certain extent, sometimes it's aspirational, 489 00:32:57.920 --> 00:33:00.240 Right, you have these customers who are like, we really want to move 490 00:33:00.279 --> 00:33:04.119 in this direction, and this actually is going to help pull us forward, 491 00:33:04.400 --> 00:33:06.920 right, Like the fact that we're going to be able to see all of 492 00:33:06.960 --> 00:33:09.759 these different service dependencies and all of these inter relationships is something that we know 493 00:33:09.839 --> 00:33:13.640 we want to do and that this is like, here's a great use case 494 00:33:13.680 --> 00:33:15.359 for what you're going to do with having that information is you're going to be 495 00:33:15.359 --> 00:33:20.440 able to see more causal relationships across all of that. And that's it. 496 00:33:20.519 --> 00:33:23.880 That's exactly not it's useful data. That's exactly right. Folks, don't touch 497 00:33:23.880 --> 00:33:27.759 out. That will be right back with Ellen Rubert of causely Ai. You're 498 00:33:27.799 --> 00:33:38.640 listening the Inside Analysis. Welcome back to Inside Analysis. Here's your host, 499 00:33:39.160 --> 00:33:45.319 Eric Tabanac. All right, folks back here on Inside Analysis talking all about 500 00:33:45.440 --> 00:33:52.640 causeal Ai with Couseley dot Io and Ellen Rubin and Ellen, we were talking 501 00:33:52.640 --> 00:33:54.200 about the people, right, every company is people. A lot of people 502 00:33:54.240 --> 00:33:57.640 say it's the technology, it's the hardware, it's the software, all this 503 00:33:57.680 --> 00:34:00.519 stuff. That's true, but really a company is built around the people. 504 00:34:00.880 --> 00:34:04.680 And one of the coolest things I've noticed being in this industry now for you 505 00:34:04.680 --> 00:34:07.400 know, twenty five almost thirty years or so, is that you can see 506 00:34:07.440 --> 00:34:12.880 the progression of individual professionals from one company to another. They learn things, 507 00:34:12.920 --> 00:34:15.480 they learned about this situation, that situation, and that becomes part of the 508 00:34:15.480 --> 00:34:19.920 fabric of their view of the world. And that's how you can learn how 509 00:34:19.920 --> 00:34:22.679 to bring things together and create whole new projects. And I think what you've 510 00:34:22.679 --> 00:34:27.360 done here is very interesting because you came at it from a different direction. 511 00:34:27.559 --> 00:34:30.639 Like a lot of this stuff is inside out almost you refer to it as 512 00:34:30.679 --> 00:34:34.199 bottom up. I'm always going to say it's more outside in because you're like 513 00:34:34.599 --> 00:34:37.360 or maybe it's the maybe it's the inverse. I'm not sure. But the 514 00:34:37.400 --> 00:34:39.840 point is you figured out a different way to address this issue. And the 515 00:34:39.840 --> 00:34:43.800 people are the ones who came up with that. So who who got together 516 00:34:43.880 --> 00:34:46.559 to create this besides you? Thanks? Yeah, And I agree with your 517 00:34:46.559 --> 00:34:50.440 analogy of it being you know that it's outside in instead of inside out. 518 00:34:50.440 --> 00:34:54.639 So I like that. I think the people in this case, you know, 519 00:34:54.679 --> 00:34:58.159 I mean, look the founding team is always critical. And the reason 520 00:34:58.199 --> 00:35:00.960 founding teams start something is because they had a problem that they saw, they 521 00:35:00.000 --> 00:35:02.880 wanted to solve it, and they think they have something interesting to say, 522 00:35:02.920 --> 00:35:07.519 you know, to bring to the table. So in my experience having started 523 00:35:07.559 --> 00:35:09.599 several companies and you know, as you mentioned, being part of some other 524 00:35:09.800 --> 00:35:15.480 you know, startups, from from the very beginning, this team is it's 525 00:35:15.559 --> 00:35:20.280 pretty spectacular. So we have my co founder, schmul Kleeger, who was 526 00:35:20.360 --> 00:35:24.239 the founder of most recently of a company called Turbonomic that was acquired by also 527 00:35:24.280 --> 00:35:28.800 biog. Yeah, so they were you know, they had this fantastic, 528 00:35:28.880 --> 00:35:31.880 you know, two billion dollar outcome with with IBM, but before that they 529 00:35:31.920 --> 00:35:37.719 had taken on a lot of issues about IT operation and how you are able 530 00:35:37.760 --> 00:35:42.079 to automate initially in a VMware environment, and then they kind of moved out 531 00:35:42.079 --> 00:35:45.159 and you know, made it a broader problem set and so a lot of 532 00:35:45.159 --> 00:35:51.800 this idea of automating IT environments and making sure that they're working really well. 533 00:35:51.920 --> 00:35:53.119 They solved a lot of it, and it was kind of more focused at 534 00:35:53.119 --> 00:35:55.760 the infrastructure layer, right, That's where a lot of the work was done 535 00:35:57.599 --> 00:36:00.760 prior to that Schmuel was the under of a company called smart Son if you 536 00:36:00.920 --> 00:36:07.639 remember that company. They were in the root cause analysis for network management and 537 00:36:07.679 --> 00:36:09.639 they were acquired by e MC and that was also a really successful outcome. 538 00:36:09.679 --> 00:36:14.840 So it was kind of like the people who are the founding team. There's 539 00:36:14.880 --> 00:36:20.280 a huge amount of DNA from Turbonomic and also from the previous company Smarts with 540 00:36:20.360 --> 00:36:23.360 this root cause analysis expertise and knowledge that was applied back in the day to 541 00:36:23.480 --> 00:36:27.440 you know, a completely different world, you know, much more monolithic on 542 00:36:27.519 --> 00:36:30.320 prem static, you know, all these things that were going on, but 543 00:36:30.360 --> 00:36:35.000 the building blocks were the same as the things that have been brought to Cousely 544 00:36:35.360 --> 00:36:39.800 and then large scale, enormous IT environments you know at Turbonomic that needed to 545 00:36:39.800 --> 00:36:44.199 be managed and run in a way that you know, took away a lot 546 00:36:44.199 --> 00:36:46.239 of the pressure from the people who were having to do these things as you 547 00:36:46.239 --> 00:36:51.239 know, as initially VMware and then Cloud became you know, hugely deployed. 548 00:36:51.800 --> 00:36:55.119 So that's that's a good chunk of the team. And then for myself, 549 00:36:55.159 --> 00:36:58.519 you know, you mentioned Natisa, which is you know kind of back in 550 00:36:58.559 --> 00:37:01.480 the back in the day and data where housing, but I also after that 551 00:37:01.559 --> 00:37:06.159 started a couple of my own companies that were very focused on hybrid cloud and 552 00:37:06.239 --> 00:37:09.559 being at cloud infrastructure layer and understanding, you know, how do you create 553 00:37:09.639 --> 00:37:14.079 environments that span on prem into the cloud or multiple clouds, and how do 554 00:37:14.079 --> 00:37:17.519 you manage that as they grow? And it kind of culminated for me as 555 00:37:17.559 --> 00:37:22.599 a general manager. My previous company was acquired by AWS and so I found 556 00:37:22.599 --> 00:37:27.360 myself, you know, within the hyperscaler world, you know, as opposed 557 00:37:27.360 --> 00:37:32.039 to competing with or partnering with or whatever, I was actually inside running very 558 00:37:32.119 --> 00:37:37.800 very large hybrid cloud storage services. And so the experiences that I had there 559 00:37:37.880 --> 00:37:42.280 were the ones that are pretty formative for me for wanting to start causely, 560 00:37:42.360 --> 00:37:45.800 which is what it feels like to be that owner of the service, you 561 00:37:45.800 --> 00:37:49.679 know, as the Amazon model is, you know, very much focused on 562 00:37:50.400 --> 00:37:52.639 service owners and small teams and you know, like you know, really like 563 00:37:52.679 --> 00:37:54.639 you build it, you run it, you own it, all that kind 564 00:37:54.679 --> 00:38:00.800 of stuff. So doing that at global scale and having things break all the 565 00:38:00.840 --> 00:38:05.800 time constantly, and it wasn't even just necessarily your services, like you made 566 00:38:05.800 --> 00:38:08.400 some changes, something happened, but then like three other services had other things 567 00:38:08.400 --> 00:38:12.800 going on and they impacted you and you were dealing with the impact of it, 568 00:38:13.360 --> 00:38:16.079 and so you know, on call, being paiged, trying to pull 569 00:38:16.079 --> 00:38:20.320 people together from different teams with different expertise. You know, if the right 570 00:38:20.320 --> 00:38:22.079 person wasn't available, you could get yourself into a you know, a difficult 571 00:38:22.119 --> 00:38:24.639 place to you know, to get to the root cause. So I like 572 00:38:24.679 --> 00:38:27.800 to say, like I live that, so I know, I know how 573 00:38:27.800 --> 00:38:30.360 that feels. And then the people who are the founding team you know school 574 00:38:30.440 --> 00:38:36.920 and the founding engineering team that that that has have built this coretu causally ey 575 00:38:37.000 --> 00:38:39.440 core platform and also you know, the the products that we're building around it, 576 00:38:40.159 --> 00:38:43.480 you know, like they they have what I would say is like unfair 577 00:38:43.519 --> 00:38:47.400 advantage in understanding how you build this stuff and how you do it leveraging causality. 578 00:38:49.000 --> 00:38:52.400 So you know, so it's exciting and it's very cool. And you 579 00:38:52.440 --> 00:38:54.239 know we're doing it as a remote company, so that's that's new too. 580 00:38:54.400 --> 00:38:58.719 We're like, you know, we're remote from birth and you know, doing 581 00:38:58.719 --> 00:39:02.119 it you know, around across the US and hold internationally and stuff like that. 582 00:39:02.199 --> 00:39:07.079 So it's I think it's say, it's never dull, never dull well 583 00:39:07.119 --> 00:39:12.880 And you speak to being inside a w WES and of course owning the product, 584 00:39:12.920 --> 00:39:15.239 owning the service. So when something goes wrong, well, it's it's 585 00:39:15.360 --> 00:39:17.920 up to you to figure that stuff out. And I see this time and 586 00:39:17.960 --> 00:39:24.079 time again in the enterprise software world where someone experienced a very hectic situation or 587 00:39:24.079 --> 00:39:28.519 a very challenging problem in a past life and they go, aha, I 588 00:39:28.559 --> 00:39:30.599 know how to solve this. And oftentimes they try to solve it within the 589 00:39:30.679 --> 00:39:34.760 organization. They maybe make the pitch to the executives, they say, hey, 590 00:39:35.000 --> 00:39:37.639 we want to do X, y Z. Sometimes it happens a lot 591 00:39:37.639 --> 00:39:38.800 of times it doesn't, and so what do they do. They spunter off 592 00:39:38.840 --> 00:39:44.239 and start something new. And I know for sure in the software world the 593 00:39:44.360 --> 00:39:49.159 kernel is so important to understanding what you want to build, and building a 594 00:39:49.199 --> 00:39:52.599 foundation like the kernel, which you've done here with these structured causal models, 595 00:39:53.400 --> 00:39:57.000 is very important. Well, I'm a big fan of cliches. One of 596 00:39:57.039 --> 00:40:00.960 my favorite cliches is, well, begun is half done. Right. If 597 00:40:00.960 --> 00:40:04.039 you get off on a good start, you're halfway there. But if you 598 00:40:04.119 --> 00:40:07.840 don't, you could be spinning your wheels for months, weeks, years. 599 00:40:07.880 --> 00:40:10.760 It happens. I mean, I'm sure you've seen projects at big corporations where 600 00:40:10.800 --> 00:40:14.400 you know, they launch it, they have all these great plans and it 601 00:40:14.519 --> 00:40:17.559 just goes sideways. They never get there. Millions of dollars wasted. That's 602 00:40:17.719 --> 00:40:22.360 painful, painful to see and just and not good for morale. So what 603 00:40:22.400 --> 00:40:27.199 I like the most about your approach here at causeley dot io if you want 604 00:40:27.239 --> 00:40:30.559 to look it up online, folks, is that you've you've focused on causality 605 00:40:30.639 --> 00:40:35.559 as the core of the problem, because that is what gets you to your 606 00:40:35.599 --> 00:40:38.119 answers. If you know the cause and you can see the effects, because 607 00:40:38.119 --> 00:40:40.159 that's what we're talking about, right, you see the effects, you have 608 00:40:40.199 --> 00:40:45.440 a model that can then understand, oh, though that array of effects means 609 00:40:45.760 --> 00:40:49.960 X, and that X is the cause, right, definitely, Yeah, 610 00:40:49.960 --> 00:40:57.960 And you know a lot of it is helping customers see the difference between when 611 00:40:58.000 --> 00:41:02.159 you know the causal relationship ships and when you do right, Like that's that's 612 00:41:02.199 --> 00:41:07.760 a key thing. And so intellectually people understand that this is something that is 613 00:41:07.800 --> 00:41:09.039 different or it's hard, or it's new and all that kind of thing. 614 00:41:09.079 --> 00:41:13.000 But to a Llergic extent, what they're sort of saying is, well, 615 00:41:13.039 --> 00:41:15.199 I've built a lot of stuff, and I have a lot of tools, 616 00:41:15.280 --> 00:41:16.679 and I collect all this data, and I have all these people that I've 617 00:41:16.719 --> 00:41:20.039 trained, and I have these run books and you know, like it's it's 618 00:41:20.119 --> 00:41:22.639 layers and layers of technology and process you know that people have had to put. 619 00:41:22.639 --> 00:41:27.920 You can't run large scale applications without that today, it's just impossible. 620 00:41:28.320 --> 00:41:30.800 You would have to have a lot of things in place. And so what 621 00:41:30.920 --> 00:41:35.880 we're essentially trying to do is sort of break the paradigm and say, Okay, 622 00:41:35.920 --> 00:41:37.360 that's the way you do it now, and it you know, I 623 00:41:37.440 --> 00:41:39.960 mean, it works to the extent that you keep things running, but it 624 00:41:40.519 --> 00:41:47.079 brings these things into such a physical, manual like human place on a daily 625 00:41:47.119 --> 00:41:52.199 basis. And what if the answer was that these things were just happening automatically 626 00:41:52.400 --> 00:41:53.880 and you didn't have to think about it anymore. And how would that feel? 627 00:41:53.920 --> 00:41:59.360 And that's a very like, it's a very stark Comparison's our that's our 628 00:41:59.440 --> 00:42:02.280 job, right is to make that really visible and clear well, and it 629 00:42:02.280 --> 00:42:06.480 gets you on the path to productivity, right, because the kinds of things 630 00:42:06.559 --> 00:42:13.159 you're solving for are nuisances. Quite frankly, they are not on the critical 631 00:42:13.199 --> 00:42:16.679 success path of designing something new. They are hurdles that you encounter along the 632 00:42:16.760 --> 00:42:22.000 way, Hurdles that result from someone else's code, sometimes it's from your own 633 00:42:22.039 --> 00:42:25.480 code, sometimes it's just the environment. But basically, when you're fixing things, 634 00:42:25.519 --> 00:42:30.079 you're not building something new. You're just trying to put out fires all 635 00:42:30.159 --> 00:42:32.920 day and back to morale. That's not good for morale, it's not good 636 00:42:34.000 --> 00:42:38.079 for anyone. But I do understand and appreciate the mindset change that needs to 637 00:42:38.119 --> 00:42:42.000 be embraced. You know, I've heard lots of different ways to describe this, 638 00:42:42.159 --> 00:42:45.159 but you know, in terms of following an algorithm and just believing what 639 00:42:45.159 --> 00:42:47.599 it tells you, we do that all day with our smartphones. In the 640 00:42:47.639 --> 00:42:52.079 maps, you're just following along with what it tells you to do. And 641 00:42:52.159 --> 00:42:55.280 I've learned that it's a trust experience, right, Like at the first time, 642 00:42:55.360 --> 00:42:58.599 they're like, wait, where are we going? Right? You know? 643 00:42:58.639 --> 00:43:02.119 And then of course right. Although I will say that I've learned with 644 00:43:02.159 --> 00:43:07.840 these maps they have difficulty in very short spaces, like if you have to 645 00:43:07.840 --> 00:43:10.239 get around somewhere, they're not able to figure out, hey, just pulling 646 00:43:10.280 --> 00:43:15.119 a parking lot to turn around, like I've done this several times, Like 647 00:43:15.159 --> 00:43:16.400 okay, now he wants me to go up on the highway and around like 648 00:43:16.440 --> 00:43:20.079 this. Now I'm just going to turn around and go back, so you'd 649 00:43:20.159 --> 00:43:22.119 have to kind of watch out for That's this bit of an off kilter analogy, 650 00:43:22.159 --> 00:43:27.360 but point being, we're following what they tell us. We're listening to 651 00:43:27.400 --> 00:43:30.159 the bots as we walk down the street. Okay, turn this, turn 652 00:43:30.280 --> 00:43:32.760 left, turn right, So you're solving for that at a foundational level, 653 00:43:34.079 --> 00:43:37.440 for the cause of these things and these causes. Man, the other way 654 00:43:37.440 --> 00:43:42.679 of doing things, the traditional correlation way, it can't get done. It 655 00:43:42.719 --> 00:43:45.760 does get done all the time, but it takes so much time and so 656 00:43:45.880 --> 00:43:49.960 much effort and so much lost time in production. Right. So well, 657 00:43:50.000 --> 00:43:52.440 folks, we got podcast bonus segments coming up here next. Send me an 658 00:43:52.440 --> 00:43:55.639 email if I want to be on the show. Info at insideanalysis dot com. 659 00:43:55.639 --> 00:44:00.480 We've been talking to Ellen Rubin Ofcousely costly dot io. Be right back 660 00:44:00.519 --> 00:44:07.079 for the podcast bonus Boks. Time for the podcast bonus segment here and a 661 00:44:07.119 --> 00:44:13.719 fantastic show solving the observability conundrum cause and effect. We're talking to Ellen Rubin 662 00:44:14.000 --> 00:44:17.039 of Coslely. It's coosely dot io on your internet browser. If you want 663 00:44:17.039 --> 00:44:20.400 to learn more, and I wanted to talk to you Ellen about one of 664 00:44:20.440 --> 00:44:23.079 the coolest things I've seen happening in this industry, and that is open telemetry. 665 00:44:23.599 --> 00:44:27.239 This is just a couple of years ago that it really took off. 666 00:44:27.639 --> 00:44:30.519 But I love this movement. I'm a huge fan of open source software. 667 00:44:30.559 --> 00:44:36.000 First and foremost hats off to Alina Stour of AOLS with Linux. But now 668 00:44:36.039 --> 00:44:37.320 there's a whole stack. I mean, you and I have lived through this, 669 00:44:37.440 --> 00:44:42.719 the had dupe movement, the open source movement really climbed up the data 670 00:44:42.760 --> 00:44:46.159 stack, and this whole spirit of openness is I think very powerful and very 671 00:44:46.159 --> 00:44:52.760 good, especially for highly spread out teams of people working on stuff. If 672 00:44:52.800 --> 00:44:57.039 there's transparency, there's trust. When there's lack of transparency, lack of trust 673 00:44:57.119 --> 00:45:00.239 kind of creeps up. But this opens lemas to stuff. I think it 674 00:45:00.280 --> 00:45:05.320 is fantastic because it's a standard and this and everyone is now moving towards this 675 00:45:05.400 --> 00:45:07.400 standard, so we can all see what everyone else is working on it we 676 00:45:07.440 --> 00:45:10.280 can at least benefit from this data source. What do you think about all 677 00:45:10.280 --> 00:45:14.599 that? I think it's huge, and you know, it doesn't take me 678 00:45:14.679 --> 00:45:15.480 to say that it's catching on. It in a big way. You know, 679 00:45:15.519 --> 00:45:17.639 you can see the statistics, you know, in terms of how much 680 00:45:17.639 --> 00:45:21.880 interest there is and how many people are participating in a lot of big companies 681 00:45:21.880 --> 00:45:24.880 that have gotten involved with it. And in the observability world, you definitely 682 00:45:24.920 --> 00:45:30.360 see that the kind of the large observability vendors have now adopted it because typically 683 00:45:30.360 --> 00:45:35.519 in the past they had some sort of a more proprietary way of capturing data 684 00:45:35.559 --> 00:45:37.599 and stuff like that. And it's not new, right, you know, 685 00:45:37.639 --> 00:45:39.719 the APM tools have done that for a while, but they've all realized that 686 00:45:39.760 --> 00:45:44.360 this is the future and that the customers are asking them to adopt this more 687 00:45:44.480 --> 00:45:46.440 you know, kind of open standard that then can become the kind of the 688 00:45:46.480 --> 00:45:50.119 source of a lot of that information. And so that's great. I think 689 00:45:50.119 --> 00:45:52.960 it's really good for customers, it's really good for the industry, and it 690 00:45:53.079 --> 00:45:58.679 provides that visibility that's been super hard to have, right in terms of all 691 00:45:58.719 --> 00:46:01.880 of these relationships us in an environment that tell you, you know, sort 692 00:46:01.920 --> 00:46:05.800 of what's connected to what and why, you know, like they're there are 693 00:46:05.840 --> 00:46:09.480 those types of things that are just not visible from the more traditional observability tools. 694 00:46:10.239 --> 00:46:15.679 The thing that we've found that's been very interesting is that in spite of 695 00:46:15.719 --> 00:46:20.880 all of that excitement and interest, the adoption of open telemetry has some challenges 696 00:46:20.920 --> 00:46:22.079 to it, right, you know, in terms of there are changes you 697 00:46:22.119 --> 00:46:24.880 have to make to your application and things that you need to do that take 698 00:46:24.960 --> 00:46:28.559 time, and you know, people are I guess what I would say is 699 00:46:28.559 --> 00:46:30.360 people are taking this approach again in which they feel that then they need to 700 00:46:30.400 --> 00:46:36.079 apply the open telemetry you know, to everything, like it's like a banket 701 00:46:36.119 --> 00:46:37.760 thing that they need to do, and then they go, oh my god, 702 00:46:37.760 --> 00:46:39.360 that's a huge amount of work and also, oh my god, the 703 00:46:39.400 --> 00:46:42.920 amount of data that's going to be generated, what are we going to do 704 00:46:42.960 --> 00:46:44.920 with it? Then we have to store it and we have to figure out 705 00:46:44.920 --> 00:46:46.239 how to access it, and you know, it becomes it becomes a data 706 00:46:46.280 --> 00:46:52.480 management problem on top of everything else. So there's often pushback in terms of 707 00:46:52.519 --> 00:46:54.400 how quickly and we see this in some of the larger customers we're working with 708 00:46:54.639 --> 00:46:58.440 of the adoption path, Like we know, we want to move in this 709 00:46:58.480 --> 00:47:01.000 direction, and we're going to pick certain applications where the benefit of doing it 710 00:47:01.039 --> 00:47:05.239 is very high, and we're going to start there and then essentially we're going 711 00:47:05.320 --> 00:47:08.800 to like take on the challenges of making that work for these particular applications, 712 00:47:09.239 --> 00:47:12.239 and we're going to try to figure out how to do it in a more 713 00:47:12.559 --> 00:47:15.039 cost effective and optimize type of a way. But that isn't like a quick 714 00:47:15.199 --> 00:47:19.960 like you know, press a button and everything's all set right, So see 715 00:47:20.000 --> 00:47:22.920 that there are these other open source projects that are meant to help with that. 716 00:47:22.079 --> 00:47:25.639 So, you know, eBPF is obviously something people are taking advantage of, 717 00:47:25.719 --> 00:47:29.760 you know, to try to see things. You know, people are 718 00:47:29.840 --> 00:47:32.920 using. Odegos is a project that we're familiar with where it kind of automates 719 00:47:32.960 --> 00:47:37.800 more of the open telemetry process. So there are different types of projects and 720 00:47:37.840 --> 00:47:40.920 tools that are out there to help make it faster and easier and less you 721 00:47:40.960 --> 00:47:45.079 know, kind of physical labor to be involved. But I guess I would 722 00:47:45.159 --> 00:47:52.679 say that one of the things that we believe is that, just as with 723 00:47:52.719 --> 00:47:58.400 all these other things that people have put in place, throwing tons more data 724 00:47:59.159 --> 00:48:02.079 at people to then try to analyze is again not the answer. It's like 725 00:48:02.519 --> 00:48:05.519 the thing. And one of the things that I was reading recently in an 726 00:48:05.599 --> 00:48:10.320 article about open telemetry was you should know what data you're looking for, and 727 00:48:10.360 --> 00:48:15.719 then you should optimize for capturing that data so that then you know what you're 728 00:48:15.760 --> 00:48:20.239 actually going to be learning and seeing, as opposed to if I just can 729 00:48:20.280 --> 00:48:22.239 see everything, then one of my what will it tell me? What will 730 00:48:22.239 --> 00:48:25.159 it teach me? Like like how many times do we need to do this 731 00:48:25.199 --> 00:48:30.320 before it becomes obvious? But like this much more sort of like you know, 732 00:48:30.400 --> 00:48:32.599 needle in a haystack and looking for very particular things that will tell you 733 00:48:32.639 --> 00:48:37.880 the answers to the questions. It requires some more planning and thinking up front, 734 00:48:37.039 --> 00:48:40.480 and I think that's where the answer to this lies, because otherwise, 735 00:48:40.519 --> 00:48:43.280 you know, you're sort of back in the same thing, which is I 736 00:48:43.320 --> 00:48:45.360 have all this data, nobody ever even looks at it. I don't even 737 00:48:45.360 --> 00:48:47.840 know why I'm storing it anymore, and that that would be a shame for 738 00:48:47.880 --> 00:48:52.239 that to be the case. So we're kind of use it smart, use 739 00:48:52.280 --> 00:48:54.960 it efficiently. You know. That's that's well, I'll tell you what I'll 740 00:48:55.000 --> 00:48:58.760 close on this. I'd be curious to hear your thoughts in this curveball question. 741 00:48:58.920 --> 00:49:00.880 So I love the open of lemon because again, it's transparency. We 742 00:49:00.920 --> 00:49:05.320 can all see what's going on at least eighty percent. Let's say, what's 743 00:49:05.320 --> 00:49:07.760 actually happening, and then I'll choose my twenty percent of that which is meaningful 744 00:49:07.760 --> 00:49:13.599 to my business. I would love to see open standards in the marketing world, 745 00:49:13.639 --> 00:49:16.079 in the digital marketing world in particular, because the numbers are off the 746 00:49:16.159 --> 00:49:22.039 charts. I almost put together a talk for the Data Universe conference called SAT. 747 00:49:22.119 --> 00:49:27.000 It was called lies, damn lies and SaaS statistics because they're just all 748 00:49:27.039 --> 00:49:30.599 over the map. I mean, like with YouTube or Instagram or these various 749 00:49:30.639 --> 00:49:34.760 engines, we throw a little money at it and all the numbers bounce off 750 00:49:34.800 --> 00:49:37.239 the page and like, well, what did I actually just buy there? 751 00:49:37.599 --> 00:49:39.239 Like it's hard to get to the bottom of things and to correlate, you 752 00:49:39.280 --> 00:49:43.880 know. And I've learned how to read through the data that I get from 753 00:49:43.920 --> 00:49:47.199 my email marketing tools, just from usage patterns, so I know, okay, 754 00:49:47.280 --> 00:49:51.320 the clicks I see in the first five seconds of cent of this email 755 00:49:51.639 --> 00:49:55.679 probably spam filters, probably you know, some firewalls kicking into place. But 756 00:49:55.840 --> 00:50:00.199 being able to make sense of the numbers is really important for mark and I 757 00:50:00.239 --> 00:50:04.239 haven't really seen much of a move around that. And it's like, well, 758 00:50:04.320 --> 00:50:07.480 Salesforce could do it, or Marcato or any of these guys, but 759 00:50:07.480 --> 00:50:08.559 no one seems to do that. Do you have any thoughts on that and 760 00:50:08.599 --> 00:50:13.800 what do you think about getting some more transparency into what these numbers actually mean 761 00:50:13.840 --> 00:50:16.119 and how they're generated. I totally agree with you, and you know, 762 00:50:16.679 --> 00:50:21.840 everybody who's in the IT world deals with these issues right about trying to like, 763 00:50:21.960 --> 00:50:23.679 you know, tell your story and get out there. And you know, 764 00:50:23.920 --> 00:50:27.280 we're all using a lot of the same platforms and stuff like that. 765 00:50:27.559 --> 00:50:30.159 But to me, it sounds like an idea for a new company, so 766 00:50:30.280 --> 00:50:35.599 you know, off you go go. I actually know a senior executive who 767 00:50:35.920 --> 00:50:38.000 hinted at starting a new company, so maybe I'll lean on him to get 768 00:50:38.000 --> 00:50:42.199 the ball rowing. He knows a lot about Marcato too, because when you 769 00:50:42.320 --> 00:50:45.480 know better, you do better, right, And I think that's the key 770 00:50:45.639 --> 00:50:49.760 with what Cousley is doing and what this future company I'm thinking about is doing. 771 00:50:50.119 --> 00:50:52.199 I just want to know what's happening. I want to know what's happening, 772 00:50:52.239 --> 00:50:53.920 and I want to know what do you need to do to fix it? 773 00:50:53.960 --> 00:50:57.639 And even better, you want somebody else to fix it automatically. That 774 00:50:57.639 --> 00:51:00.719 would be great. That's right. Well, Allan, thank you so much 775 00:51:00.719 --> 00:51:04.519 for your time. Look her up online on LinkedIn, Ellenreubin costly dot Io. 776 00:51:04.639 --> 00:51:12.800 You've been listening to Inside Analysis, Marino Valley, Corona and Riverside. 777 00:51:12.840 --> 00:51:23.039 Listen to kaa kaa, the station that leaves no listener behind. Redlands Ranch 778 00:51:23.119 --> 00:51:29.039 Market is a unique, full service international grocery store that specializes in authentic food 779 00:51:29.079 --> 00:51:32.920 items from Mexico, India, and from many Mediterranean and Asian countries, including 780 00:51:32.960 --> 00:51:37.920 popular items from the US. They offer fresh baked items from their in house 781 00:51:37.920 --> 00:51:43.079 bakery, housemade tortillas from their tortill area, a delicious array of prepared Mexican 782 00:51:43.119 --> 00:51:46.000 foods, a terrific fresh food and juice bar, and a large selection of 783 00:51:46.039 --> 00:51:51.639 meats, seafoods and deli sandwiches, salads and hellal meats. 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The complete website is to hebot club 801 00:53:07.360 --> 00:53:12.559 dot com or call us at eight one eight six one zero eight zero eight 802 00:53:12.639 --> 00:53:16.519 eight Monday through Saturday nine am to five pm California time. That's eight one 803 00:53:16.559 --> 00:53:22.360 eight six one zero eight zero eight eight to Hebot Club dot com. If 804 00:53:22.360 --> 00:53:24.480 you're looking for a fuller, part time sales position and you have radio, 805 00:53:24.599 --> 00:53:30.239 TV or print media experience, KCAA has a great opportunity waiting for you that 806 00:53:30.320 --> 00:53:32.760 pays the highest commissions in the market. If you're interested in a sales position 807 00:53:32.800 --> 00:53:40.760 with us, email CEO at KCAA Radio dot Com. Kca with sixty years 808 00:53:40.800 --> 00:53:47.480 of fascinating facts. This is the man from yesterday and back in time. 809 00:53:47.519 --> 00:53:52.039 We go to this time in nineteen eighty two, Apparently Tommy Twoton's new hit 810 00:53:52.119 --> 00:53:58.400 tune eight six seven five three oh nine is ringing up problems for those with 811 00:53:58.519 --> 00:54:01.639 the same phone numbers. In Cleveland, a lady called a radio station to 812 00:54:01.639 --> 00:54:21.360 ask why hundreds of total strangers have been calling her asking for Jenny. And 813 00:54:23.599 --> 00:54:28.719 from this time in nineteen fifty nine, ABCTV says it's going to air a 814 00:54:28.760 --> 00:54:32.639 new cartoon called Rocky and his Friends with Oatwinkle the Moose. It begins this 815 00:54:32.760 --> 00:54:37.760 fall. It'll air late afternoons on ABCTV. We're gonna have a lot of 816 00:54:37.840 --> 00:54:43.440 fun. Come on and join us, juree. There's only room for one 817 00:54:43.480 --> 00:54:46.880 more. And from around this time in nineteen seventy one, Andy Griffiths new 818 00:54:46.960 --> 00:54:52.400 show is a dud. It's only been on CBS since January here in nineteen 819 00:54:52.480 --> 00:54:55.239 seventy one, and it'll be canceled by the end of May. The new 820 00:54:55.400 --> 00:55:07.280 Andy Grisha Show brought to you by with more at Man from Yesterday dot Com. 821 00:55:07.480 --> 00:55:12.519 For over seventy five years, the Marine Toys for Tots program has provided 822 00:55:12.559 --> 00:55:17.840 toys and emotional support to economically disadvantaged children, primarily during the holidays, but 823 00:55:17.960 --> 00:55:22.599 needs are not just seasonal, and now neither is Toys for Tots. They've 824 00:55:22.639 --> 00:55:29.239 expanded their outreach to support families in need all year long with their new programs, 825 00:55:29.320 --> 00:55:32.280 including the Foster Care Initiative, the Native American Program, and the Youth 826 00:55:32.360 --> 00:55:37.639 Ambassador Program. To learn how you can help, visit Toys for Toots dot 827 00:55:37.760 --> 00:55:43.199 org. Hi, I'm Lanni Swarodwow, and I'm back on KCAA ten fifty 828 00:55:43.239 --> 00:55:47.000 AM and Express one oh six point five FM every Tuesday at eight pm. 829 00:55:47.000 --> 00:55:53.159 My show is Beyond Common Sense. It's Lanny Sense featuring me Lanni Swardlowe, 830 00:55:53.239 --> 00:56:00.039 kcaa's resident gay, Jewish liberal potsmoking race mixing, left hand atheists, an 831 00:56:00.119 --> 00:56:07.519 evangelical fundamentalist, Christian nationalist, worst Nightmare with subjects that no one else will 832 00:56:07.559 --> 00:56:13.719 touch in quite the same way. Every Tuesday at APM on Express one oh 833 00:56:13.760 --> 00:56:17.960 six point five FM, the Legacy ten fifty AM, and live streaming on 834 00:56:19.079 --> 00:56:24.039 kcaradio dot com. KCAA Radio has openings for one hour talk shows. If 835 00:56:24.079 --> 00:56:28.639 you want to host a radio show, now is the time. Make KCAA 836 00:56:28.760 --> 00:56:32.159 your flangship station. Our rates are affordable and our services are second to none. 837 00:56:32.239 --> 00:56:37.239 We broadcast to a population of five million people plus. We stream and 838 00:56:37.320 --> 00:56:40.880 podcast on all major online audio and video systems. If you've been thinking about 839 00:56:40.920 --> 00:56:46.519 broadcasting a weekly radio program on real radio plus the internet, contact our CEO 840 00:56:46.760 --> 00:56:52.360 at two eight one five ninety eight hundred two eight one five nine nine ninety 841 00:56:52.360 --> 00:56:55.440 eight hundred. You can skype your show from your home to our Redlands, 842 00:56:55.480 --> 00:57:00.559 California studio, where our live producers and engineers are. I need to work 843 00:57:00.559 --> 00:57:05.360 with you personally. A radio program on KCAA is the perfect work from home 844 00:57:05.440 --> 00:57:09.840 avocation in these stressful times. Just time kcaradio dot com into your browser to 845 00:57:09.920 --> 00:57:14.400 learn more about hosting a show on the best station in the nation, or 846 00:57:14.480 --> 00:57:22.719 call our CEO for details. Two eight one five ninety eight hundred NBC News 847 00:57:22.840 --> 00:57:25.760 Radio. I'm Chris Ragio. Opening statements are expected tomorrow. In former President 848 00:57:25.800 --> 00:57:30.039 Trump's criminal trial in New York, Judge Wan Mr Shan ruled prosecutors do not 849 00:57:30.280 --> 00:57:34.239 have to give advanced notice of the witness list, a Trump's legal team, 850 00:57:34.320 --> 00:57:37.840 saying Trump cannot be trusted not to post about them. Trump has complained about 851 00:57:37.840 --> 00:57:42.880 his gag order, noting prosecution witnesses are allowed to talk about the case, 852 00:57:42.960 --> 00:57:46.239 but he cannot. Trump has accused of falsifying records to cover up hush money 853 00:57:46.280 --> 00:57:50.920 payments to an adult film star before he was elected. In twenty sixteen, 854 00:57:51.199 --> 00:57:54.440 the Senate will vote on a massive foreign aid package that'll provide military support to 855 00:57:54.519 --> 00:57:59.599 Ukraine. Yesterday, the House passed a ninety five billion dollar legislative package that 856 00:58:00.000 --> 00:58:04.360 it would provide security assistance to Ukraine, Israel, and Taiwan over the objections 857 00:58:04.400 --> 00:58:07.280 of some Republicans. The bill now heads to the Senate, where preliminary voting 858 00:58:07.280 --> 00:58:12.119 could begin as soon as Tuesday. More Democrats are speaking out against threats to 859 00:58:12.159 --> 00:58:15.599 remove House Speaker Mike Johnson. In an interview with Fox New Sunday, Florida 860 00:58:15.679 --> 00:58:21.599 Congressman and Jared Moskowitz said removing Johnson would only embolden foreign adversaries, including China, 861 00:58:21.800 --> 00:58:24.960 Russia and Iran. No, Russia and China are trying to destabilize the 862 00:58:25.000 --> 00:58:28.519 country, trying to stabilize the world, and we have to make sure the 863 00:58:28.639 --> 00:58:32.480 United States stands for Western democracy. He slammed Georgia Republican Marjorie Taylor Green, 864 00:58:32.480 --> 00:58:37.400 who introduced a motion to a Kate Johnson's speakership last month over his handling of 865 00:58:37.480 --> 00:58:40.880 Ukraine aid and government spending. It's not clear if Green will force the motion 866 00:58:42.000 --> 00:58:45.000 to vacate to the floor for a vote. Michigan authority said two children were 867 00:58:45.079 --> 00:58:50.480 killed and a dozen others injured when a drunk driver crashed into a children's birthday 868 00:58:50.480 --> 00:58:53.880 party yesterday. The Monroe County Sheriff says the vehicle drove into a building in 869 00:58:53.920 --> 00:58:58.559 the town of Newport, south of Detroit. Police say the sixty six year 870 00:58:58.559 --> 00:59:01.199 old woman behind the wheeld was in tuckicated and is being held in the county 871 00:59:01.280 --> 00:59:06.000 jail. Nine people, including six adults, were taken to hospitals with life 872 00:59:06.000 --> 00:59:08.800 threatening injuries. Police said the two children who died were an eight year old 873 00:59:08.800 --> 00:59:13.880 girl and her five year old brother. According to investigators, the driver's facing 874 00:59:13.880 --> 00:59:20.519 a variety of felony charges. I'm Chris Karagio, NBC News Radio, NBC 875 00:59:20.599 --> 00:59:25.440 News. I'm CACAA Lomalinde sponsored by Teamsters Local nineteen thirty two Protecting the Future 876 00:59:25.480 --> 00:59:35.360 of Working Families, Teamsters nineteen thirty two dot org. You're listening to an 877 00:59:35.519 --> 00:59:45.119 encore presentation of this program KCAA The Inland Talk Express. Thank you for jarning 878 00:59:45.159 --> 00:59:51.360 this for this edition of Justice Watch with Attorneys Zulu Ali. I am Attorney 879 00:59:51.400 --> 00:59:55.760 Zulu Ali with the Justice Watch crew, Rosa Nunyaz, Michael Balao Clark, 880 00:59:57.159 --> 01:00:00.159 and doctor Akhil Basher. Here, like each week or tonight, like each 881 01:00:00.199 --> 01:00:06.440 week, we'll be talking about critical legal and social justice issues affecting our communities. 882 01:00:07.239 --> 01:00:08.440 This week, the topic is