At the moment, the information economy as a rod. The world is teeming with innovation as new business models reinvent everyvery industry. Inside Analysis is your source of information and insights about how to make the most of this exciting new era. Learn more and inside analysis dot Comside Analysis dot com. And now here's your host through Eric Kavanaugh. Yes, indeed, folks, welcome to the future. To the future, right. So here we are in two thousand
and twenty three, hurtling forward. I think the quickening has already happened because things are going really, really fast these days. What do they say, The days are long, but the weeks are short, and we're already knocking on the door of halfway through twenty twenty three. I'm not sure I believe it, but here we are, and folks, we have an all star cast for you today thanks to our good friend David Sweener, who decided to
rally some troops and get a whole group of evangelists together. So we've got David Sweener, Sean Rogers, and Nick Jewell an all star cast at analytics and AI experts. We're going to talk about the business value, defining the business value of analytics and AI. Well, it's a much different story now than it was five and in ten years ago. Pretty much everyone understands in
the business world that analytics is really important. How you get there, what you do with it, how quickly you leverage the power of analysis, that's a different story. A lot of companies are doing real time optimizations now using analytics and AI. We're going to talk about artificial intelligence as well, which
is an interesting sidetrack. In many cases, it tries to do the same things that analytics does, and ideally you want to kind of blend these two worlds and get the value of AI for pattern recognition, pattern matching, for understanding what's going on in complex scenarios, and of course analytics run into numbers under tending why that's happening. Really, if you get these two together, you can get a really clear picture of what's going on in your business,
and that's going to help you regardless. So it's it's very good to have these technologies. Exactly which ones you choose will depend upon your budget, of course, and your use case. But these folks know a lot about all that stuff. So David Swayner, since it was your idea, I'll hand it off to you to kick us off here, what do you think the business value is of analytics and AI and what do you do to evangelize all that? Well, Eric, hey, thank you for having us on.
Totally appreciate it. And you know, I thought of this topic. You know, a lot of the clients and prospects we talked to organizations have made a significant investment in data, and so we've we've done a great job of ingesting, collecting, and storing data. And you spent you know, millions and millions of dollars on these things. You know companies have, but when you get to the analytics side of it, it doesn't have the rigor The
number one question that I get is how do we measure that value? How do we measure the ROI most of the clients and customers that I speak with anyways, they don't know how to articulate that beyond personal productivity, so we saved some time. They don't know how to tie it back to specific business KPIs, and so you know, I thought it would be great to have Nick Jewel and Sean Rogers here and to discuss this topic. It's a it's a it's at the top of minds for all the clients I speak with.
Yeah, and Nick Jewel, I think we'll bring you in next. You've been doing this for a long time, and you have your own company as well. And we should point out that David has a whole book series, Tiny Guides. I think he calls them. We'll talk about that in a minute too, But do you also have your own enterprise where you're trying to educate people about the value of analytics? Right? Go ahead, and Nick, Yeah, absolutely, thanks, y, thanks for having me on the
show. Yeah, I get to moonlight as the co founder of a really cool little analytics upskilling startup that we call Data Curious dot AI. We would teaching thousands of students across the world the benefits of analytics, automation and really data literacy to reach their goals. And we'll dive into this more, I'm
sure during the conversation today. I just wanted to throw out there to start with, one of my highlights of my career was getting to interview Billy Bean, who was the general manager over at the Oakland A's, the baseball team made famous by not being Brad Pitt obviously in the movie money Ball, and the idea of value in that film was all about trying to find cheap baseball players that could get on base really easily. If you got on base,
you scored runs, you had a good chance to win the game. So I think when we talk about finding value in analytics and AI, we can turn back to those ideas of big data maybe from a decade ago, the big data vs. You're not going to find value in volume anymore. I think the cloud service providers have probably got that one sewn up. You're probably
not going to find it in variety necessarily anymore. A lot of work that I've done over the last couple of years with one of the more niche players in the analytic space, a company called in Quarter, is really starting to build out the concept of a data lakehouse that gives us so much more flexibility than maybe we saw from traditional data warehouses. But later on in the conversation, I'd love to dive into the topic of that other V, the idea
of velocity. I think there's a lot of room for differentiation in that area. Velocity, volume, variety. These are the three vs that they talked about, and voracity is a fourth V that came into the equation that's not something that chat GPT is too good at Sean Rogers will bring you in on that. It's good a lot of things, but the voracity, yeah, not so much. I wanted to be the first person to say Jack chat GPT. I thought there was a prize involved, you know. I think
right now we're at r at an interesting inflection point. Right now it feels to me like it did when the Internet arrived for the public, for the consumers, for mass adoption, and suddenly things like or of AI are in the public eye. And was I mentioned in the pre call. My family had a wedding this weekend. I had two people asked me about chat GBT. They were not the normal persona I expected to hear from on that topic. And it's always interesting to see what we all know collectively as not a
brand new technology. AI has been around for decades, but it is really, you know, gaining some interesting traction. The public stuff is great. I think the more interesting things are happening in the enterprise. And I agree with you know what Nick just said a moment ago about velocity. How fasking you make a decision? How accurate is the decision you're making? Can you
automate that decision, and then there's architectural challenges. Can you move that decision outside of your IT data center, and can you get it on a device to make a decision, can you get it out to the edge of your business world, and so on. So there's a countless topics for us to discuss here, but it is kind of fun and it has that feeling that definitely reminds me of all of the disruption when the world discovered the Internet, which had been around for a while as we all know, but it was
this brand new thing in the nineties where everybody thought this was this classic, brand new toy. I just feel like I see the same thing with AI and especially large language models and generative and so on. Some Yeah, it will be a fun topic to explore. Yeah, and the costs have really come down now. That's partially because of open source technology. It's partially because
of the cloud. It's partially because of a company called Amazon that really baked into their corporate DNA this trajectory of bringing costs down over time that was never a thing before that, I recall. I mean people would lower the costs of things as commodities became more prevalent, but the fact that they really baked
that into their approach and their mission. I'll throw out to David between a first, I think that's one of the reasons why costs have really come down and the cloud has become such a prevalent force in our lives these days. What do you think, David, Yeah, you know, I think that
costs um you know pieces is very interesting to me. And you know, to mention something that you know, Sean had said, you know this this this waste that's in the analytics process, and I think this is where companies can tie this back to our I if I look at an analytics process that starts you have some sort of business event, you need to prepare the data to get analytics ready, you have to complete the analysis, you need to
make a business decision, and then you need to take business action. And if you look across that spectrum, there's data latency, there's analytics latency, there's decision latency, and there's action latency. I think these things, you know that if you can compress this cycle, that's where companies are going to get that that value. So that's that I think there's there's a lot to be learned that if you just take sort of a process approach to this.
It's not just doing analytics for the sake of analytics. You have to take that business event and tie it to business action. No, I agree with that, And you know, we'd talk about big events. We've had lots
of disruptions in the last few years. We had. It really started in the previous administration here in the States with tariffs on China, which no one had ever done before like that, and that threw the supply chains off, and then of course COVID comes around as a huge disruption, and that really made us focus on, I think the power of analytics, because we also had to really focus on workflows and processes and how to get to data and how to do things. And a lot of people figured out, hey,
man, we have to change how we're doing things. We have to take sort of an API first strategy maybe whether it's security or just network connectivity or remote working. All that stuff kind of just through the apple cart sideways.
And I think we learned a lot from that, Nick Jewel, What do you think, Well, absolutely, I mean I'm not sure how it affected you folks over in the US, but that big tanker that bloked up the sewers canal made everybody panic over here, certainly in Europe because the supply chains
broke. We realized we didn't have that resilience across the board. So putting in data programs, analytics programs, getting to AI programs so that you could start to suggest alternate roots, alternate suppliers, ask more of those what if questions, more sophisticated analytics became priority number one. So I think over the last few years, obviously the large shock of COVID has made data and analytics forefront in the minds of the public, the media, governments around the world.
But I think that real time element, when things like supply chains get threatened, is such an opportunity for more advanced analytics to enter our lives in a beneficial way. Yeah, and I think the AI really comes in machine only comes in handy with things like supply chain because a traditional relational database you can get some analysis of product amounts and locations and things, but you really kind of need more of a graph technology and you need some AI to be
able to run all too different simulations. Right, what do we do this? What if we do that? What are the costs going to be? You know, in the old days twenty years ago, you would just kind of think about it and take your best shot. I mean, you might have some decent software you're using. SaaS, of course, has been doing cool stuff for a long time. Sas out of a Kerry, North Carolina.
But Sean Rogers the throat over to you. I think what Nick is talking about, these disruptions really led to a keen focus on different ways of solving problems, and I think that led to a boom and AI machine learning. What do you think, Well, I think it was certainly a cause of it. But we've seen this a couple of times when these big disruptions come along. For those we've all been in this business a long time.
The financial dip that we saw in a seven OA disrupted data change data for companies, and then of course the chain reaction on up changed the analytics, the big ones that we're talking about now. I have asked numerous customers, you know, especially in the executive ranks, how dramatically did your data change in March of twenty twenty And you don't get a clear answer because everyone was so busy trying to fight the higher level issues of how do we shift our
marketing messages, what words don't we use? Do we want to invest here or there? A lot of companies didn't notice how dramatically things were evolving and changing quickly, and supply chains a great example. E commerce is another one. The big companies that quote unquote did so well during COVID were not as prepared as they probably should have been. You guys remember early days, Zoom
struggled as they were growing. There was growing pains with other companies that were filling these gaps, and so understanding the dynamics of your data and how you're applying AI to it. Models that worked on March first did not work on March thirty first. They were they were not performing at the level they needed
to. And so that takes you to some of these other big topics that I'm hoping we'll touch on, like model ops and data ops and how do you bring it up to that next level and provide the scale that's required but also the operational scale that's required so that you can be nimble and so on. So yeah, I you know, these big disruptors even you know, natural things, right, natural catastrophes and so on, shift data all over the place. And then affect models and affect the interaction of those models.
So yeah, and we really will. We'll talk about automation too, And you know, automation is something that is always helpful if you do it correctly. You have to be careful about what you automate because you can automate a crap process and then get a really crappy process and get very unhappy customers. I mean, I've seen this stuff happen. And marketing automation is hard.
I was talking to a guy who's been in the industry for a long time and he told me, because we were talking about HubSpot, I think and I just said, from my experience, it is really hard to map out multi touch strategies using even something like HubSpot because of so many exceptions. There are so many is where someone's going to go outside that perceived decision tree and the automation is going to fail when that happens. So you have to think
through all these little bits and pieces and it's hard. And he said, yeah, it's taking us about a year. So you've got a team together, it takes a year. I mean las days like takes a year. What are you building a data warehouse there? That told me don't long used to take to build data warehouses. But the point is that it's hard, but you still have to do it. I mean, automation is going to be a crucial success factor, whether that's for data ing, data cleansing,
data provisioning, or whatever, all along that value chain. You want to be automating whatever you can, right David, Yeah, absolutely, I mean I think that's the power. You know, I have this you know theory on AI. It's you know, data plus analytics plus automation. That's my sort of basic definition. Now I think, you know, people will certainly could argue with that, but I think that's what it boils down to. And you know, so automating, to your point, automating a crappy process.
You know, you can just bad decisions faster. So that's that's not the right way. I think people forget to do is reimagine their process. Is there a better way to do something? I think where most organizations fall down is, hey, we got a prediction. I can predict something a week, a day, a minute, you know, whatever you know into the future. But their business isn't agile enough to act upon that insight,
or that they can't they can make a decision. But they can't. The systems aren't just the fragile and this ability to action as result of a prediction, that's where I think a lot of companies fall down. They don't they can't act upon it because their business is relying on things that their manual processes. They're not automated, and companies struggle there. Yeah, well, and there's a speed to insight and a speed to action that matters. Think call
centers, think point of purchase, things of this nature. The companies and like some of the telcods were good about this early in the game, delivering a customer value score to the person on the front on their screen so they can see, Hey, waste your time with this person. He's always in the bills the main sure next week, don't worry about that person so much. Were as a high value customer. But you have to get that insight to the person right at the time when they're on the phone. So that
takes a lot of engineering to make sure you get it right. So you've got to do the analytics, you've got to do the automation, you've got to do the engineering. All these things have to come together for it to
work. Just right, correct, Nick Jewel, Well, absolutely, And I think this is where I have a little bit of a personal be in my bonnet around some of the concepts in the modern data stack, where you know, we're entering data into these large, often legacy systems like ERP systems, big CRM systems, and the whole infrastructure, the whole machinery that's in place to move that data, to ingest it, to clean it, to prepare it, and then to get it into a model so that somebody sitting
there with a customer can make the right decision. That takes too long. There's too many touches. If it was a kitchen, you'd have had fifty fingers on your food at this point, and you just can't get to that data fast enough. So I think there's a bit of a myth that's something like a modern to stack solution solves every problem. We really need to be very careful about how we do transformation of data as we lift it from these
sool systems and get it into the systems of insights. And I think there's a lot of work we can still do to improve that process. Yeah, and that's a that's a really good point. One of the analysts I've had on the show many times, Rick Sherman, he jokes he doesn't like the modern data stack. He says, usually it's glued together with a couple of lines of code somewhere, and it's pretty darned fragile, and fragility is not what you want in a data world where you're trying to maintain the tension,
if you will, from source to target. You don't want to have to be jumping two, three, four or five different systems because guess what, any one of those systems going slowly breaks the whole chain right, and chain is only as strong as its weakest link, and that I think is the achilles heel of the modern data stack as we see it today. They're going to be taking that stuff up. I'm sure over time, but folks don't touch out that we're talking to three experts. I can't believe the second one
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now with a free call. Eight hundred two eight nine o four one three, eight hundred two eight nine O four one three eight hundred two eight nine O four one three. That's eight hundred two eight nine. Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh. Yes, in take us in the futures where chattt doesn't hallucinate. I love that they came up with that term hallucination. That's pretty funny. It will make stuff up, folks.
And basically chat GPT is large language model. It's a predictive engine. It's designed to predict the words that it thinks you want to hear based upon the prompt you give it. But I wanted to get back to this whole concept of speed and what has to be real time and what doesn't have to be real time And someone who knows a lot about that is Sean Rogers. So Sean from Bark these days Bark Research tell us about this collision of value. Well, you know, it's this idea of you know, not everything
has to be real time or right time. I think all too often we get distracted by our ability to do cool things, and then we think that it has to be applied everything we're doing it. And I think, like the big data phenomenon kind of taught us that we started off with everybody has to have My hadup has to be bigger than you're a hadup. And I would like a purple hadup because my competitors have a blue one, and they tried to do everything in it, and then they quickly found out that it's
applicable to certain workloads. And I think the same thing happens here around the value of AI and the value of automation. You've got to apply it to the right circumstance, to the correct workloads. A friend of mine, doctor Richard Hackthorne, has published an awful lot of work on this for many years, about the intersection of where the value is and the timing of a decision.
That if the timing is too long, you miss that value into and you give a great example of that a minute ago, call center interactivity and thing of that, things of that nature. It's all about bringing the decision and the time it takes to make that decision to the highest value point. And it's not for everything you do down there's new strategies moving your analytics to
the data instead of the data to the analytics. Those types of finesse sophistication strategies are allowing companies to meet that mark, and it's especially important around you know, like modern data stacks that just you know, we've a greater They can be kind of fragile, so you can't try to do it for everything. Do it for the right stuff, and then make sure you're hitting that target of I'm in value. Yeah, well that's a great point, right,
make sure you're hitting the target. You always want to be focused on the business value and the use cases really matter, and there are lots of use cases for AI these days. You know, healthcare is a really big one, and population health for example, can be really aided by being able to analyze data at scale. I'm actually talking to the chief analytics officer from
CBS. She's going to speak at an event with me for Reuters in November, and she was talking about how they're using AI and analytics to be to do sentiment analysis across thousands and tens of thousands and hundreds of thousands of comments on YouTube and Instagram and Twitter and places where people do throw other opinions into the pool. Well, I mean they're fun to read sometimes, but to get meaningful value out of them, that takes some pretty serious text analytics work
to identify the word clouds, identify the cohordes. Who are these different people? What are the demographics we care about? But she's telling me that they are intently focused on that day after day after day. And that's a pretty interesting use case in and of itself. Nick jul, what do you think
about that or some other good use cases for AI? Well, absolutely, I was just my mind was drawing back to some work I did a couple of years ago with the Path organization out of San Francisco, which does a lot of work with the Gates Foundation to sort of trends, spread as much goodwill as they possibly can with the combination of data and boots on the ground
to actually solve problems like eradicating malaria. Another really simple example in this area was during COVID governments were trying to give to the most needy people in those countries, and one great example is a program called NOVESI, which was out of Togo, very small country over an Oceania, and basically they wanted to give out aid. They really struggled to work out who needed the aid the
most. They didn't have a really good socioeconomic registry across the country. So, as we were talking about earlier, they took really interesting sources of data
IoT data. They took satellite images, they took mobile phone metadata the information about the calls rather than the calls themselves, to actually identify the poorest geographic regions, the poorest mobile subscribers based on their behavior, and could actually target the groups that were most at risk, and they actually managed to hit the twenty nine percent most poorest economic groups in that country as a result of using
these modern data sources with advanced machine learning models in ways they just couldn't have dreamed of just a few years before. Wow, and you know, you brought up something that I think is absolutely fascinating, and maybe bring David to comment on it, and then seun is this wealth of information sources that we
have these days. IoT in particular is so powerful because you can track things and you can understand the movement of people of objects in the factory, for example, even football to be expected at shopping malls based upon the traffic and
coming into the city that morning. These are real use cases that some very forward thinking companies have now put into play and that can be very very Now we're leveraging what I call real world data at scale, and I think we're still in the period of time of trying to reconcile what we thought was the case with what we're now seeing as the case. But David Sweinter, what do you think? Yeah? No, I think you know we're all walking
IoT devices these days. Yeah, that's right, all phones and they're in our car. You know. It brings up an interesting question to me, though, is you know, who owns this data? Are there concerns about privacy? See insecurity? And you know, what is that limit? You know, we we've certainly seen that big tech companies they're not going to police
themselves. I think you know, even you know, Google had written at in her original research paper, if you're motivated by by profit, that sort of always wins, right, And so now we got all these ads. So I appreciate the value of having all this data. I have a little concerned on sort of who owns it, who controls it? You know in that classic who Watches the Watchman? Yeah, how do you how do you
you know? I would love to hear hear thoughts on that. Maybe Sean Rogers quis custodiat ipsos custodes who watches the watch David knows this and uh. And the last book I wrote, I have a chapter called the Innovative or Ikey, and it was an entire chapter about how far is too far? How far you know? When does insight and helpfulness or automated AI start to make you feel a little uncomfortable? And the weird thing is or maybe not
weird. It's a different threshold for everybody. All of us feel differently. I have expectations, and I want AI to help me do things. I want Amazon to tell me what's shirt to buy with whatever slacks I just got. I want to know what other people ordered when they went to that restaurant. I want all of those things. Some people don't care for that. And so I think one of the balances to David's point about privacy and so on, is going to be how we're going to find what works for the
majority and giving controls to the others to limit their interaction. But I think just as the Internet disrupted many years ago, or the Worldwide Web, as we were talking during the commercial, it exposed this great technologies existed a long time ago. So I think there's going to be acceptance and rejection on these types of interactions, And it will also depend upon the the aider you're in.
Right. If I'm in a business environment, I have an expectation that a bi tool is going to use AI to augment the view of the things that I'm doing. And I may not have that same level of expectation with my sports favorite sports website. So yeah, I don't. I don't know, Guys, does that make sense to me? Do you see this collision
of innovation, innovative and IKEY coming? Yeah? Absolutely, Suan, I think I think for me, this is something I've been watching for a couple of years, as I watched Jeff Bezos get closer and closer to William Shatner's orbit, you know, sending him into space doing all that cool stuff. William Shatner has been sitting down with Amazon and basically pouring his heart and soul into a model right now, so that when he dies, you'll be able
to talk to William Shatner. How close do we get to IKEY When you can do that with your mother, grandmother, a significant other, that won't be that far away, Right, It'll be David Sweener as a service, and we'll be able to call and tap in on his expertise. But seriously, for this we are talking about, I think the need for privacy and
ethics, but also synthetic data. I think when it comes to these niche very sensitive areas where we do have privacy issues, healthcare data, finance data, we need to be investing more in topics like synthetic data production to be able to train these models on domain areas rather than necessarily just harvesting everyone's data for that purpose. So I got an expansion for you on this one, Nick, I'm not sure you would have seen it on TV where you live.
The CEO of Alben Ai was in front of our Congress a week or so ago trying to teach all the seventy five year old senators what AI is, which should have been built as a comedy show. But they did use a piece in that where the setup for part of the conversation was supposed to be one of the senators speaking and his lips weren't moving and the words were coming and it was a very eloquent paragraph of text and it was AI copying,
and so the AI was speaking in the senator's voice. Wow, And it went into the record here in Congress, says AI gave testimony, kind of made a statement, so, you know, wow, where does that go? I think the senator did it because Ai was a lot more eloquent about AI than he was going to be. But but you know, it's it's the synthetic idea of synthetic data and aim, you know, coppying people's voices. I mean, there's gonna there's gonna have to be some guardrails here
pretty soon. So this brings up an interesting question those shot. So you know, the US has always sort of been behind, you know where nickolabs in the world in terms of regulations on AI and privacy. Now, if we stepped back, we say, okay, Europe has this sort of regulations, the US has this sort of regulations. China, who knows what China is going to do, They have their own regulations. So is there like, like, what does that balance like if we're really strict over here,
not strict here and doing something totally weird over here. You know, I'm curious about you know, your thoughts on that. You highlighted the balance, right, it's the extremes of both are going to be curtailing and the inside lane with whatever that is, I had the privilege David, when you and
I worked together years ago. I was at the EU and I got to have a conversation, gave a talk there about would would the strict governance over AI and big data limit the ability for the EU to actually keep pace with crazy countries like the US who were not really doing any guardrails at that point. And it was an interesting debate. And I can tell you I had that conversation in twenty sixteen. I wrote the chapter in my book about innovative
or ikey in two thousand and seventeen. And we're not there yet. And then when something like chat GBT shows up in the public market and is being adopted by college kids to do their homework and everybody else in kind of odd ways, it's just opening up. I think it's going to be a forcing factor David. Right, it's gonna it's gonna make us find the guard rails, and it's gonna make us identify that inner spot because both ends of it
probably aren't the way to go. Yeah. No, these are really interesting points too. And Nick I was talking to a guy from England. He runs an organization called the Data City and they're doing some really cool stuff around transparency. And I'm pretty sure Sean knows about my evangelism in the era of transparency in this for transparency in federal spending, but also in corporate transparency.
And he was telling me that in England, if you have over million pounds a year of revenue, you have to open your books, whether you're a public company or not. And I was like, that's pretty cool. I mean, I really like this focus on especially again as AI start taking off and getting a life of its own and being able to imitate people extremely well, we need transparency and we need trusted voices, trusted channels to be able to deliver trusted information. What do you think, Nick, Well, absolutely
so. I've done some work with a data science charity in the UK called Data Kind and we did a big hackathon all around exactly what you said. A company called Open Corporates opened the books and all the different companies that were based out of the UK, based out of the various communities in the islands around the UK, Jersey as a tax haven for example, Bermuda and other areas like that, and we crawled that data looking for patterns. But without
that transparency, none of that machine learning would have been possible. We built an amazing graph database that allowed us to capture who was opening serial companies day after day, why were they doing that. We had a whole team of data journalists just to look into the things that the model produced. So transparency leads to some amazing benefits. But if I may one last point around just
the general concept of bias inside these models. Myself and my co founder a couple of months ago, just as GPT was really hitting the hype cycle, we did some frivolous tests and we were like, tell me a joke about the Irish. GPT would give you a joke, Tell me a joke about the Polish, no problem at all. Give me a joke about the Chinese. I'm sorry, I'm an AI model. I can't do that. And there were certain countries where it was forbidden, and it was really interesting to
see. Guardrails are obviously in place, but they're not applied consistently, you
know. And I had this thought because there is this big question, and I predict there are going to be some heavy duty legal battles going on in the very near future going up against open AI and any of these large language models because it seems to me you could create a machine learning algorithm to request text created by chat GBT and then do a handful of exact phrase searches from that document to the interwebs to see because it's getting this information from somewhere,
So is it violating copyright? I have to believe that it is at some point, right, it can't. There are only so many ways to say things, which is why the chat GPT is even possible. But to me,
that's a very interesting shoe yet to fall. What do you think, Sean real quick, Well, I think it's I think some are doing it better than others, being for instance, if you go there and use their system, they will give you footnotes at the bottom of the response and they'll say I pulled this segment from a report that was written and on one of the searches I did recently, it took me back to a report at TDWY, a renowned source or respected source. I was happy to see that I
knew the man who it was, an older piece of research. Philip wrote it, and and so you know, I felt good about that. So I think some of that transparency here, you're going to start having to see that like a Sean wrote two books and here's the titles. And I would have liked to have known why they why why chat got one of my titles wrong and attributed me to a book I didn't write. So it's coming.
Yeah, that's that's a very very good point. These folks, these organizations are really moving quickly to I mean, really interesting things are happening right now. They're addressing about these concerns. So it's going to get better faster. We'll be right back. You are listening to inside Analysis. Do you own an annuity, either fixed, rate, indexed or variable? Are you paying
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Has the Irs and you a letter demanding payment? You may not owe what they claim. Make this free call to the tax doctor now let them negotiate with the IRS on your behalf. Eight hundred four eight five four eight O three eight hundred four eight five four eight O three. That's eight hundred five eight Welcome back to Inside Analysis. Here's your host, Eric Kabanaugh. All right, folks back here on Inside Analysis talking to an all star cast of
analytics and AI experts. Are good buddy, Nick Jeweles dining in from across the pond, David Sweener from up in New Hampshire, I believe, north of here, and Sean Rogers way out west in the Colorado regions. We're talking about the business value and we should get back to that ROI. So I'll throw it over to David. I mean, clearly, you can save time, you can do much more effective research, and it's also changing the mindsets about how you do things. But being able to define the specific value
that's always going to be hard. But if you can tie a decision to an analytic, for example, and then show hey, this is why we sold that deal that million dollar project, well that's the best kind of roy where you can actually tie it to moneycutting in the door or what do you think? David? Yeah? Absolutely, And I wrote a blog that it's getting quite a bit attraction on this, and I call it the art of the AI KPI. So you know, I really think of this in sort
of five five different dimensions. So there's the business impacts of data, analytics and AI, so we can tie you know, where we're getting more customers, We're going to market faster. That's great. I think the second one is time from you know, data creation to insight and action. So we talked about that a little bit earlier. How can we compress that cycle so we can make decisions faster. I think the third dimension people can measure is
data quality. How is your data quality? That a crappy data quality, you know you're not going to get great decisions. You can look at the impact on your organization, so data literacy levels within your organization, the more people that can participate in the data and analytics process within your organization, those companies outperform companies that have a lower percentage population. And the last one is really risk. You can measure your risk of your AI application. So those
are sort of the five sort of dimensions. I tend to think about how companies can constart to measure the value of their analytics investments. Yeah. I like the data quality example, Nick Joel, I'll throw it over to you because again, AI and machine learning, these are very good at tackling tedious tasks at scale, very simple things that a human can usually do, but
very very very slowly. And folks, anyone who out there in the enterprise world who thinks that your data quality is very very good, all you got to do is ask someone to show you one database. Just pick any database at all and open it up to any page, and you're going to see problems or it's depressing quite frankly. But these little algorithms they can just kind of churn through this stuff and what they can do is flag for you all the things that you can do to clean up. And you're starting to see
this, like in Google Sheets for example, Demo is doing this. A bunch of vendors are now all offering these sort of recommendation engines. Hey do you want to clean up your state characteristics in this com Do you want to clean this up? Clean that up? And a lot of times they're right, And I'm like, that's pretty good, Nick Jewel, what do you think? Well, absolutely, I think this actually touches on two of David's
points, So I think you've got the essence of automation. So once you've identified a problem, how quickly can you get it out of a human's hands into the playbook that you run every single night, every single load. But also actually it goes after bottom line use cases, so cost savings. It could be you know, you improve your supply chain management because you fix some of these look up codes that are always causing the system to fail. Bottom
line stuff's all about going after friction within a business process. If you can oil that machine so it runs faster, you are going to make the case for analytic investment. That's a That's a great example, Sean Rogers, I'll throw it over to you. There are lots of ways that you can improve efficiency, like Nick just said, just finding the look up codes that are causing you trouble prioritization. This is something I came up with last year.
I was trying to figure out what's a good methodology I can put in place to explain the importance of knowing what to do and knowing what to do at any given moment. Kind of a big deal. I mean, if you're a firefighter, it's pretty obvious what you do you put the fire out. But for lots of other jobs, especially running small businesses man that you could be doing any number of things at any point in time, where will you get the most value from it? I think AI can help in that category.
Two, what do you think, Sean, Well, yeah, I think it's going to get us to a finish line a whole lot quicker than we used to be able to. In my role, I get to be with a lot of different software companies and get these kind of behind the scene briefings, And I spent a couple of days in Las Vegas a week or
two ago with Informatica, for instance. They're automating an awful lot of the things that are around being able to be literate and understand your data, being able to have better data quality, and being able to leverage the metadata that exists in your environment in a way that you couldn't before, kind of pulling everything from data catalogs all the way up to make sure that there's continuity between
the systems and the stack that you're operating. And so it's I think it's one of the most disruptive automated aspects of data management then that we've ever seen. And I think we're just going to keep seeing it get better and better and better over the coming year or so. And I think it's going to
be extremely disruptive, but in a really positive way. So I talk about this idea of everything old is new again quite often, and by that I mean MBM has been around a long time, data quality a long time, and projects get started and projects were abandoned, and a lot of times they were abandoned because someone saw another bright, shiny toy of technology that they wanted to go chase and they shifted budget. It's the new opportunities to AI brings
to data management as a foundational layer to better analytics and automated AI. It's incredible and it's really exciting. So I think we're going to see a lot there. And to David's first comment is that where the value is that where the return on investment is out absolutely well. And also, like one last question I'll throw out to the team here and David maybe over to you first
about where these things are headed. And in my opinion, one of the most important things to note is that the value of AI, I think is largely going to manifest in the form of suggestions, Hey, why don't you do this, why don't you do that? And when that suggestion comes in some sort of enterprise portal where you're doing your work every day, well, guess what the machine can track when you said yes, when you said no, when you went with certain suggestions, and then if you have if you're
selling goods, for example, you should be able to track that. I think you should raise the price fifty cents. Okay, let's do it a month later. Look, we made more profit, so we'll be able to calculate that roim much more accurately in the future because of AI, because everything is captured in the cloud. David, what do you think? Yeah,
I certainly think rapid iteration is definitely something to keep an eye on. You know where if I just was to look in the future, I think right now we're talking about mainly software and using it to predictions and dashboards and things like that. I think where this will be heading is there's gonna be more robotics out there, and you know, I'm gonna just take an example.
If you look at Japan, thirty percent of the population in Japan is over sixty five, so it has the oldest population on Earth with the exception of Monaco. The median age is about forty eight point seven and it's the world's median age is thirty point two. So there's a lot of elderly people that need help they need help getting around, they need help getting things from the
store, they need help with healthcare, they need companionship. And so I think we're gonna see a lot of this really embedded in robotics that's gonna help people for the better. And the other I think big area is physics based AI. So right now, these algorithms spit out things without context to the environment. And if you can take a little bit about the environment, here's
the guardrails, here's how this environ it behaves and works. And if you think about process steps, these things are sort of connected process step one, two three. I think this is where we're going to see AI. We're going to infuse it with physical system, the physics of it, and we're going to get better predictions there. And that's sort of where I think AI is headed. I like that better predictions. Nick Jewel, closing thoughts from
you where these things are heading? Oh fantastic. Well, Dave Sweener went down the rout of physics. I'm going to go back to math all the way back to the beginnings. So I've been looking really closely into a thing called causal AI recently. So Eric, to your point about making better recommendations. The kind of classical statistics that we've used in advanced analytics for years centuries
really are actually quite limited by what they can do. So you want to make a prediction, what happens when I raise the price by three times? Will people still buy it? It's actually incredibly hard for classical models to answer. So causal AI is all about really getting into this hard space of cause and effect. What would happen if I changed the price by three times? Would people still buy it? What happens is to find a relationship, you
know, between smoking and death. That was a question that was incredibly hard for traditional statistics to answer. There are some amazing startups in this space. There's a lot of work going on bringing in graph databases to map these causal models. I think we're going to see you disruptive, staggering potential in this space. Yeah, I like that staggering potential. Buckle up, Sean Rodgers,
what do you think? One minute? Last thing I will add is I think going back to David's literacy idea of understanding the information that we're getting, I'm seeing a lot of the VII vendors bringing to market things like key drivers, right, So you can do the query what did we sell on the western region, and you can ask it a question of why sales are down and now the systems are servicing here's the four things and the number one
key reason that this occurred, and it drives that literacy idea so that you can do more and get greater insights. And that's that's been coming for a while, but I think the new things that we're seeing with AI and mL
are certainly going to drive it. Yeah, I think so too. And these large language models going single tenants right where you can basically have one and connect it to your information system, so you're not just plugging it into the greater large language model, which of course is a leaky way to give your trade secrets to everyone in the world who puts the right prompt Q in there. That is going to change things as well that I'm hearing that's coming down
the pike right now. But the cool thing is that the speed at which you can get answers these days is going to do wonders for the analytics industry because we all know we're in this business. We all know how exciting it is to figure something out, make a decision and watch it work. It's amazing stuff, and many, many, many more people are going to do that because it's going to work a lot faster. Look these guys up on LinkedIn. We're talking next. A journey of a thousand words begins here kc
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