Racing to Data Mastery

Get ready for an exciting episode of AI50 as we dive into "Racing to Data Mastery" with the incredible Matt Gordon, a Microsoft Data Platform MVP and cloud data architect!

Together, we'll explore leveraging data and AI while balancing security, privacy, and ethics, uncovering valuable lessons from Matt's experiences, and discussing fostering collaboration and continuous improvement within his team.

Prepare to be amazed as we discover how Matt successfully drove advantages for customers and partners, and learn how he stays informed in a rapidly evolving landscape.

With Matt's unparalleled expertise in data and AI, you'll be treated to insights on how his passion for racing has influenced his approach to work, leaving you inspired and hungry for more.

By the end of this episode, you'll walk away with game-changing takeaways:
1. Navigating the intersection of data and AI
2. Adapting to trends shaping various industries
3. Maintaining well-being in a fast-paced environment

Don't miss out on this transformative journey! Tune in now, subscribe, and join the conversation. Together, let's harness the power of data and AI to drive innovation!

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Transcript

Matt: 00:00:06
Work is not always fun. And I try to explain that to my kids all the time. They're like, Oh, today you seem bored. Well, yeah, today what I'm doing is boring. Um, but man have, this stuff's really cool. If there's any part of you that's nerdy and there's probably a part of all of us that do this, that is. Have some fun with it.

Hanh: 00:00:27
Hello, welcome to AI50, where your data meets innovation. It's a podcast that explores the thrilling intersections of data, AI, and the real world impact. I'm Hanh Brown, your host, and today we have the privilege of learning from a true trailblazer in the field. Matt Gordon. So in this episode, racing to data mastery, we'll discover how Matt's passion for innovation and racing has fueled his approach to leveraging data and AI for innovation. Pushing boundaries while navigating

Hanh: 00:01:02
the delicate balance of data security, privacy, and ethics. As a Microsoft Data Platform MVP and Accomplished Cloud Data Architect, Matt has been at the forefront of driving significant advantages for customers and partners. So throughout our conversation, Matt will share invaluable insights from his experiences, and He will discuss fostering a culture of collaboration and continuous improvement within his team, as well as the exciting trends shaping the future of various industries. Well, we won't stop there.

Hanh: 00:01:44
We'll also dive into the personal side of Matt's journey, exploring how he maintains well being and resilience in a fast paced environment and the valuable lessons he's learned along the way. So get ready for an engaging and insightful ride as we uncover the secrets to data mastery. And explore the essential skills and qualities needed for professionals in this field. So join me, um, on this adventure to learn how you can harness the power of data and AI to drive innovation and make a lasting impact.

Hanh: 00:02:23
So let's get started. Matt, welcome to the show.

Matt: 00:02:32
Thanks for having me.

Hanh: 00:02:34
Yeah. Well, hey, to kick things off, I'm curious to know more about the experiences and moments that have shaped your journey. Could you share an interesting fact or a story about yourself that many people might not know?

Matt: 00:02:48
Ooh, that many people might not know. Um, see, I, uh, so this seems appropriate since I'm a racing fan, which most people who connect with me online probably know, uh, the Indianapolis 500 is this week, which is arguably the biggest race in, in the world. While I've never been in that, um, I have raced at that track on, on the road course set up, not the oval set up where they run the 500 and I finished third. So I have a trophy from Indie that's out there, uh, in a different room. And probably a lot of people don't know

Matt: 00:03:25
that that was, that was a few years ago. And I tend not to show all that stuff online. So that kind of fits this week.

Hanh: 00:03:35
Yeah. Well, congratulations. So now how has your passion for racing influence your approach to your data or to your work, uh, as a cloud data and architect?

Matt: 00:03:47
Um, so I would say, so I think the thing, there's a lot of things I like about racing, you know, the, the noise, the speed, all that stuff, but the thing that I've always, since I got into driving, which. Is 19 years ago, which I'm very thankful for, very lucky. Um, the thing kind of keeps drawing me back is there is always a way to get better. There's no, there's no such thing as a perfect lap. You can, you know, Oh, I, I made a small mistake here.

Matt: 00:04:18
It cost myself a 10th of a second or even less. Um, and that's, you know, for better or worse, I bring that same attitude to work as well. And yeah, there's always a better way to do something. And it's, it's, it's kind of dovetailed really well with the, where I've ended up, you know, kind of data centric during the day, but AI has always been a big part of that. It used to be kind of a side thing for me, but that that's no longer the case at all. Cause it's all tied like this.

Matt: 00:04:46
Um, now these days, there almost are always better ways to do things, not just improvements to the processes and stuff that you're doing, but actually improvements to the tools that maybe a week or two ago. Had some gaps and now have less gaps.

Hanh: 00:04:59
Yeah. Yeah. Well, that's awesome. What you love for racing is evident and it's fascinating to see how it's shaped your professional life. So I'm curious, what are the most valuable lessons that you've learned in navigating the intersection of data and AI?

Matt: 00:05:22
So, you know, I, that phrase is interesting and I, I use it a lot too, because it is an intersection. I think I actually used it in one of my posts talking about this, but intersection may not actually be the right word. Um, the data is really, data is your foundation and we have a lot of customers that are approaching, uh, saying we need AI and in some cases it stops there. They don't, they don't know what they're asking for. They're, they're coming to Centric. Coming to me and why wanting to learn what that means.

Matt: 00:05:59
And it's because there's pressure from a board, from a CEO, from shareholders. Well, you know, whatever. And, uh, they, then when we tell them like, okay, well, here's, here's some kind of whiz bang stuff that you can do to kind of show off this stuff's really, really cool. And I go, that's great. How do we do that? Well, you know, given the scale of the data. You're talking about, there's a lot of, there's going to be a lot of data cleansing, data engineering, things like

Matt: 00:06:25
that to make sure that the AI is doing, is making, hopefully it's not making decisions, um, but make sure that it's presenting you with the right information for that, the data has got to be clean. And that they get a little less excited about that because it sounds boring. Um, but it really is necessary that you have the cool AI, like all, all the demos you're seeing this week at build and things like that. are very cool. But if you'll note, a lot of them are recorded, they're curated, and they're built on data

Matt: 00:06:57
sets that are well understood. The ones that those of us that kind of follow Microsoft stuff often see. Um, that's not necessarily the point we have to get to, uh, with customers and users, but we have to get a lot closer to that point than a lot of, you know, Customers are. So really, even though intersection makes sense, uh, data is really the foundation and the, I think the quicker that, that we all understand that, um, some of our worries about what AI may or may not do. We can temper those a bit because

Matt: 00:07:31
if we give a good information, it's likely to give good information back.

Hanh: 00:07:38
Very true. And you know, um, I always say when people ask, like, we know we need to integrate AI, what should we do? Well, the first thing I always say is, well, what are your pain points? What is it in your workflow, your organization, that you would just love to do away with? Whether it's repetitive tasks, content creation, um, customer inquiries that's, you know, coming at you, but you need to filter them, email, uh, cleansing, you know, whatever that is. But only you, uh, in your organization,

Hanh: 00:08:12
You need to identify what those pain points are and then prioritize them. Start with something simple, right? Because what you want to do is gain confidence that, uh, AI can enhance, augment these areas. I think once you can, can be proven, Oh wow, it's really helped me in this one or two areas. Let's dial it up. So I think that that way, um, they'll understand that AI is not a silver bullet for everything. Absolutely not. And it doesn't make decisions for you.

Hanh: 00:08:45
I don't think so. Absolutely not. It will uncover insights. Then you make the decision accordingly. And that's the key, though. You know, we talk about data, data visualization through power bi and so forth. Well, these are beautiful, right? They're beautiful to look at. Very impressive. But the end of the day, the business is not into creating charts. They're in create their, they have to make decisions based on those charts.

Hanh: 00:09:11
And that's what I always encourage people is that this isn't just an exercise. Um, it's a learning process, start small, gain confidence, but the end of the day, you got to make decisions.

Matt: 00:09:22
Yeah. Well, and, and we see it, you know, I think what gets lost in this is there's a lot of science for sure, but there's a bit of art and science to getting a co pilot AI assistant or something like that to do what you want it to do. And the example that I like to give in, in the talks I give now is, you know, not that long ago, probably a couple months ago, I was waiting for a friend at a bar. I was kicking around an idea for a demo for a talk. And in 10 minutes, I got co pilot to tell me that I had won the Indy 500 last year.

Matt: 00:09:54
I hadn't, I was in the stands. I saw the guy who did, it wasn't me. And the winner's check never showed up, but I got it to write a little news story about how I had done that. And I was the first Microsoft MVP to have ever won the race. And that was, that was 10 minutes of me. Messing around. And it's kind of funny, um, put that in a business context. And that's why the data you're basing this on the guardrails around that. And all those things are so important because if you, if you make an important

Matt: 00:10:25
executive call based on something that a virtual assistant told you, and somebody who was up to no good, like me, um, You know, fed it some bad information that the consequences of that can be really bad. Yeah. We've seen some in, in the news where I think air Canada lost a court case because somebody basically had messed around with their virtual assistant and got it to sell a plane ticket for a dollar and a court ruled that. Yes, it's not a real person, but it's a representative of the company that entered into an agreement, you know, and

Matt: 00:10:54
that was, I think the, the fair was 700. So air Canada lost 700 bucks. Okay. Whatever magnify that across a huge scale. And these, this is, it sounds silly, but this is all really important stuff to get right.

Hanh: 00:11:09
Yeah, it is. It is. So can you share, um, where you successfully leveraged data and AI to drive significant advantages for a customer or partner?

Matt: 00:11:26
Yeah. So obviously can't use names, um, and it would have to be very vague about this. Cause if I even mentioned like location or industry, everybody's like, Oh, okay. That's one of these three. Um, but yeah, there, there was, and it's actually kind of emblematic of, of. all of what we've talked about so far. So they were very interested in, uh, because they didn't have strong Power BI folks. That was kind of a growing part of their shop. But you know, they, they didn't

Matt: 00:11:53
have mastery of that, let's say. So they were interested in seeing the, um, kind of AI powered, you know, Capabilities of what we could do there. So, like, uh, Q and A has been in Power BI for almost four years. Um, so that's doing natural language processing under the covers. You know, you could argue that was a copilot before we called it that. But you could say, you know, what were my sales of this? On this month, uh, in this year, and it will give you kind of a basic chart and you can do all that.

Matt: 00:12:28
So we, we took that a couple of steps further with some of the knowledge that we had and we're showing them on the fly, you know, having kind of that copilot type experience, create pretty nice charts over things that was all well and good. So we showed kind of the first few and they're like, wow, this is amazing. You know, tell it to change this. So we go and do that. Oh, that's, that's awesome. Well, we didn't get too far into it before somebody said, well, you know, ask it to do this, this, and this we did, and it comes up.

Matt: 00:12:59
It looks great. Pretty fast that the data was well modeled, well understood. All, all the important stuff was done. But they're like, well, that's wrong. Well, that kind of beg, you know, we wanted to understand, like, how do you know that so fast? Like the chart came up in five seconds and somebody in the room, that's wrong. I said, well, I know that the system, we get these particular data points from upstream always inflates this particular metric because of a flaw in itself. Um, and so we, you know, we

Matt: 00:13:29
generally, before we produce the charts as humans, we clean that up. We know that it's inflated by. X amount, and we adjust for that, but the AI doesn't know that. And, you know, there were, there were people up the executive chain from these very experienced folks talking in all these kind of hype filled terms. Like, well, I'm not going to need a team in X years because the robots will do it all, right? The AI doesn't know that. And that's a very, that's a very particular use case.

Matt: 00:14:04
But that's almost every client I've ever had has something like that. There's that human experience that's like, well, we know that's wrong. Or we know that actually lags from what it says or whatever. That's very difficult to teach any sort of a virtual assistant to say, well, you need to adjust for this because. Um, so that's, you know, something like that is, is kind of the most recent example, but I like it because it also, you know, the room kind of went from, wow, this is amazing. And probably people are sitting

Matt: 00:14:32
there saying, like, well, gee, I build these, this is my day job. If it could do it in five seconds, like, what am I doing? And then you get kind of the record scratch moment where it's like, well, this is wrong, but there's really no way to tell a computer that it's wrong. It's not now. Um, And so I, it was a really interesting meeting because I think everybody started out going one way and at the end said, well, this is, these are really powerful tools, but, you know, maybe the name co pilot is

Matt: 00:15:01
good because you still need a pilot.

Hanh: 00:15:03
Exactly. You need human judgment.

Matt: 00:15:07
Exactly.

Hanh: 00:15:08
And, um, I always think AI, you know, co pilot is like a GPS. Well, you need a person to direct where it's going, right? It's not a silver bullet solution, but it certainly uncover many insights, but it needs human expertise, human judgment, uh, human experience and so forth. Yeah. So, so true. Well, those are powerful lessons and I'd like to hear more about how you've applied them, let's say into real world situations. So how do you balance pushing the

Hanh: 00:15:40
boundaries of technology, with ensuring data security, privacy, and ethics?

Matt: 00:15:50
It's um, so that's, it's always been an important part of this. So I was, um, I coauthored a book on what I like to say was Azure AI before it was cool. So, uh, I worked with some folks to author a book on cognitive services that came out, um, basically, uh, late in 2021. So there are some really good foundational things, but the world has passed probably five of those eight chapters by, um, just things have changed so much, but we felt really strongly. That we needed to include a chapter on ethics in that book, because it's so

Matt: 00:16:26
easy to get caught up in the, in the coolness of all this stuff and nerd out. And then when you take a breath, it's like, well, have we acquired that data the right way? Are we safeguarding it the right way? Did people consent for that data to be involved in this stuff? And, you know, our, our book was very focused on what the cognitive services offerings were then. So LLMs were not a part of that. And that introduces a whole other set of points to think about, let's say on, you know, we're training

Matt: 00:16:57
it on data, especially some of the, like, we're training it on data that other people created that they own. Um, and that's, I'm, I'm not a lawyer, so we probably shouldn't get too much farther into that, but you know, every, it's never the most fun part of any project that kind of dovetails these two, but it's where I said earlier, where data is the foundation of. Good AI, basically, um, security is what keeps it from going off off the rails and what keeps people out of trouble. Honestly, um, you know, some of what we work on.

Matt: 00:17:38
It's an internal system. If it gets some data, it's not supposed to. Very few people know, not a big deal. Some of what I've been able to work on over the years was very customer facing to very large groups. You get that wrong, you end up on the news or worse. And so, but it's, you know. It's all important and it's kind of, it's, it's crucial to talk through this stuff. And that's where going back to kind of the art and science a bit, those are tricky conversations to have because

Matt: 00:18:10
there are customers right now, you know, I talked to them as part of my day job. I talked to them as part of speaking at events where it's like, well, you know, we've been challenged to, to do and. AI thing this year, and our budget is X, okay, well, we can do the AI thing for that, but we can't secure the data the way we need to, we can't cleanse the data, and they're like, well, our budget is X, and those are difficult conversations to have, because sometimes, unfortunately, even though the work itself might be exciting, if it's not going to be done the proper way, you know, you kind of,

Matt: 00:18:49
kind of have to walk away from it, say, well, I, I wish you the best of luck, but in good conscience, I can't really be. I, you know, I shouldn't be a part of that because I know there's things that we should be doing that the budget doesn't allow for, and I get it. Money does not grow on trees and you have what you have. Um, but yeah, sometimes it's just not, not appropriate to carry that forward. Unfortunately, there's almost always somebody that will. And so I, I think, uh, we're going to keep seeing new stories about things like these

Matt: 00:19:23
going off, off the rails, because yeah, there's always somebody that'll cut it. Take that check to do the thing, and even when the thing isn't maybe not the most appropriate.

Hanh: 00:19:34
Well, I think that's a wonderful thing about the Microsoft AI ecosystem that it really streamlines and it's truly an ecosystem. It's not like a one off large language models, right? It's an entire ecosystem. So, and it's low code. So I think most of it, most of it, most of it. I mean, you, you know, people say it's low code as if it's like you wash your hands and you're done. Well, absolutely not.

Matt: 00:19:59
I wish. Yeah.

Hanh: 00:20:01
But, but here's the thing though, I believe it's still very doable. I don't know what the X budget entails. Um, and that's why I always say start small. Maybe you could work it within your X budget. And then as you gain confidence, you may have to X whatever exponential or times two as you gain confidence, because when you're fixed on that X, you're also going to be fixed on the. Um, the reaping the benefits of AI.

Matt: 00:20:33
Projects like that, I think, lend themselves best to a P. O. C. Your point start small, especially as quickly as the technology's evolving. Um, you know, I know, like from the announcements that build yesterday, there's demos that I that newly worked six or seven weeks ago in talks of mind that I now have to change because the way those have to work have. Shifted entirely. It won't continue at that pace, but it's good to have kind of a POC window

Matt: 00:21:00
where you can keep the budget small. You can pivot to some different tool sets or models or whatever, depending on what you're trying to do. And then at a certain point you do need to commit to what you've got and scale it. But it also helps, you know, one thing I found about these is keeping it small helps you win budget approval for the big stuff because so much of this stuff, the demo is cool. And eventually, especially when you're talking about the people that are going to kind of sign that check to let you do the big thing, they're largely not technical.

Matt: 00:21:32
So you show them something that looks like magic, even to you and I, um, to them, it looks amazing. And they're like, Oh, we can do that. And we can do that at scale for all, all our users. Yeah, we'll find money for it because that. You know, that sets us apart from competitor X that doesn't know how to, how to do any of this stuff because they didn't hire you. Um, that's, those are fun to be a part of, but yeah, it's always, it's, it's a bit of a trick sometimes to

Matt: 00:21:58
get a client to narrow their scope and say, no, actually we want less money for this because we want to take better care of you in, in the longterm. Like, we know you've come to us and said, I have this pile of cash for you to do this thing. And we're like, you know what, we don't want all that yet. Cause we're going to do it smaller, uh, but we're going to do it right. Yeah. And then you can go back, you can save part of that pile. We can go back to the people that

Matt: 00:22:19
can grant us a much larger pile and say, look at this cool thing we've done. That's going to be helpful that, that puts you in a space that, you know, other firms aren't. And by the way, we'd like to larger pile of cash to finish this work.

Hanh: 00:22:34
Yeah. No, absolutely. And the thing is, you know, we want to be integral to the whole idea of democratizing AI, uh, to all levels, small, medium, large size businesses. Uh, again, nowadays it's very doable. It's to what scale and what is the size of your pain points.

Matt: 00:22:52
Right?

Hanh: 00:22:58
So well, as you discuss great power comes with great responsibility. So can you discuss a challenging situation that you face at work? Um, and how you approach it while maintaining well being and, um, I guess, uh, a lesson that you can share with the audience.

Matt: 00:23:16
Yeah. And I don't always follow my advice as much as I should, but it's, you know, I, I've been fortunate enough to speak on these topics at a few conferences, um, some tech conferences kind of shy away from whether you call it professional development or mental health or wellness or well being or. Um, some tech conferences will not include those topics. Some do, and I'm very thankful that they do because I, I think we all need that. Um, in terms of a particular situation, there have been several times, and it's

Matt: 00:23:51
not always a lesson that I take well. Um, but I was reminded of this recently with one of my friends saying. No is a complete sentence sometimes, um, especially like we've talked about you're in this data and AI world that is so fast paced that things are changing so much. You feel like, oh man, if I take a week off, I've missed an announcement on this or I'm going to be behind on that. Um, it's, it's really important to sometimes turn down opportunities. Um, you know, whether it's, whether it's. Doing a talk, uh, whether it's doing a podcast, um, whether it's a job that

Matt: 00:24:37
you're like, well, the opportunities here could be really exciting, but I know, you know, they're in kind of a startup mentality and that's maybe not where my life is now. Those are all important things to think about. Um, and just like I said, it's weird to tell a customer, no, we don't want all of your money yet because we're going to do this right. Very similar to this. Sometimes you need to tell somebody that wants you to help with something or they want to feature you as something.

Matt: 00:25:05
Uh, no, because it just, it doesn't, it doesn't fit with where you're at and, and where you're going and maybe the right way to do certain things.

Hanh: 00:25:20
Very true. I am. It's good to ensure that your values, your goals. Expectations are aligned, right? And that's not something that's readily fall into place, uh, everywhere you go. So I think it's important. That it's mutual, um, you, your values are in alignment with what you're trying to do, especially when it comes to AI. So that's really important.

Matt: 00:25:44
Yeah. Well, and yeah, it's also good, and this is not a lesson I learned straight out of school. That's for sure. Um, networking is important. Because a lot of, especially if you're thinking about like a professional opportunity, like you're going to leave a job to go to the next job. Companies are very good at selling what they are or what they want to be. A lot of times they're selling what they want to be or what they think they are versus what

Matt: 00:26:09
they may actually be day to day. Um, if, if you have a large enough network, You can generally find out some details about the day to day that they are unwilling to share. You know, you find out if you'll say, Hey, I'm talking about going to work at XYZ. What do you know? Oh, well, I had a friend of mine that worked there for two years. And so it's like, Oh, well, they talk about work life balance and all that. It's like, well, yeah, but, but my friend that worked there said, here's actually what they did.

Matt: 00:26:41
Um, so things like that are really important. Um, it's, it's a struggle, you know, especially for me, like sometimes at an event, like you don't want to come out of your shell, you're just there to learn stuff and, and you don't want to talk to people, but that's really where your career starts to take the next step. And it gets you into better opportunities and also keeps you out of ones that you shouldn't be in, because you, you may know somebody that. It's going to give you that bit of knowledge where you're like, okay,

Matt: 00:27:10
actually, I, I should say no here, where on his face, it doesn't seem like it.

Hanh: 00:27:16
So true. Well, what exciting trends in data and AI do you think will shape the future of various industries?

Matt: 00:27:31
All of them. Uh.

Hanh: 00:27:33
Yeah. Is there one that's not affected?

Matt: 00:27:37
That's the thing, you know, the, the kind of the overarching AI session that I do right now is more focused on kind of data professionals, the tools they use and the environments they're in, and where AI can help them and where maybe somebody is going to say, Oh, you can do this this way, but it's not the best way, whether it's AI powered or whatever the buzzword is, wait, because it's not there. Um, wait, yeah. In terms of trends, um, This stuff is going to change for anybody in technology, especially data pros. It is going to change

Matt: 00:28:14
the way that you work. It's already changed the way I work. Um, it's, it's probably for the worst change the way people attend meetings. I've been part of more than one meeting this year where you ask somebody something and they're like, Oh, I'm sorry. I was multitasking over here. Uh, cause I know I'm going to get the summary from co pilot after. After we're done and it's like, well, okay. Uh, but being present in the meeting makes it much better meeting for, for the rest of us.

Matt: 00:28:44
Um, yeah, it's all going to change the way we work. I think for me, probably the trend to keep an eye on for data professionals like me and for AI practitioners to understand is that data engineering is critically important. Um, that's, that's the skill, not that makes all this go. Cool. But you really need skilled engineers to get the data cleanse, to get it moved around position the way it needs to be for the AI coolness to take it from there and, and do it's it's stuff.

Matt: 00:29:21
So I don't know if that's a career trend or, or what, but yeah, not that data engineering, it's always been important, but I think for this stuff, it's really, really important. And if it's, you know, if I'm somebody just getting into data work, I'm trying to figure out like, do I want to be a. Um, DBA, do I want to be a report dev or something like that? Um, all those are still valid and will be for a very, very, very long time. But I see data engineering, maybe those types of rules almost being the first ones to kind of, Come along for the ride

Matt: 00:29:52
with, with all your prompt engineers and ML folks and, and all that, um, because none of that can do what it does well enough without good data.

Hanh: 00:30:06
So true. So how do you stay informed and adapt to this rapidly evolving landscape? I mean, we talked a couple of the same. Yeah, it's hard. Within one week or. Speed of advancement. So how do you stay on top of all that? And how do you learn and execute and gain trust and share that with your partners?

Matt: 00:30:32
Yeah, it's uh, it's really difficult. You know, I'm I'm very fortunate. Um, you know, I've I've been a Microsoft MVP for six years. So with that comes a network where a lot of times I know what's coming. And I can not only prepare myself for it and build content, things like that. I'll be doing that later today. Actually some, some blogs that I know stuff will be announced at bill later this week. And I'm kind of putting, you know, finishing things on, on that.

Matt: 00:31:03
So, um, people can read it. And so I don't break my NDA by putting it out before I'm supposed to. Um, so I'm a little bit ahead of the game in that way. And I'm very fortunate to be there, but what I would say is the best way to keep abreast of this stuff. Is, find the community that's interested in what you're interested in. Mm-Hmm. . Um, you know, for me being, being a SQL server, DBA is my first data job. You know, the, the Microsoft ecosystem has always been what I'm a part of.

Matt: 00:31:38
Now, I've broadened beyond that as I've gotten older. Um, but it's still like, that's the community that I call home. And I think particularly in kind of the AI arms race, I've been very fortunate to be there because Microsoft's made a lot of smart investments and maybe are a bit ahead of the curve and certainly I can argue that, um, but it's been a good place to be, but a lot of that knowledge just comes from connecting with people online, whether it's people I know in real life or not. And reading what they're talking

Matt: 00:32:10
about, reading the blogs that they're reading, uh, reading the blogs that they're writing. Um, that's kind of the best way, but there is no silver bullet. Like I, I wish I could say, Oh, there's an account you can follow on X that just, they post all the best stuff and you just go read them and you'll be up to speed. Um, I have yet to find one. It would be great. Um, but yeah, it's really just plugging in, you know, fighting your, Local user groups, like a lot of local user groups that kind of are

Matt: 00:32:43
still foundationally Microsoft data. We do an AI session every month and a lot of the other groups are starting to do that because stuff isn't data over here and AI here, it's like that. Um, and, and the interest is twofold too. So even the people that are like, well, I'm a, I'm a DBA, but I still want to know about this stuff. So I'm going to come, come to the meeting and maybe once or twice a year, you'll have a topic for me on. On the data side, but I'm always learning something on the AI side. So it's, you know, find

Matt: 00:33:13
your local user group. Um, I know in the Microsoft world, if you Google or Bing search, uh, I think Azure tech groups, uh, you'll find my group, you'll find hundreds of others all over the world. Um, I, so that's, that's where I'd start. And then it's, it's almost, you know, find the people that lead those groups, the people that speak at those groups and connect with them on LinkedIn. Uh, maybe Twitter X, it's not what it was. It can still be useful for stuff like this. And then once you're linked to them,

Matt: 00:33:46
look at the people that they follow, look at the people that they retweet and repost and all that stuff. Connect with them. And that's, then you kind of start seeing what's flying around because that's really what it is. There's so information, so much information flying around that just kind of get in, get in a good space with good people and try to consume what you can. But, Try to limit your screen time too. Cause it's not good for us. And I say that as somebody who's really, really bad at that, but, um, yeah, you

Matt: 00:34:18
can, you can scroll for hours, just don't.

Hanh: 00:34:20
And, and you know, how I look at that is my gosh. Um, find a reliable source of information and you know, stick with that and engage and learn and also contribute, but you're right about limiting your, Up to date with everything. Cause they can, it's very unhealthy at times.

Matt: 00:34:40
It's not possible.

Hanh: 00:34:41
Yeah, it's not. And you always feel like you're behind if you're trying to do that daily.

Matt: 00:34:46
Yeah. And I feel like as much good information as LinkedIn has right now, and it does, I've seen a lot of the technical information that used to be on Twitter, especially for like Microsoft data and things like that is now on both. Like it hasn't left Twitter entirely, but LinkedIn is a, is a better source. But LinkedIn, you also see the kind of. Uh, inflation of titles and things like that. And you'll see people that will say, like, AI, you know, yeah, everybody's a guru, you know?

Matt: 00:35:14
Oh, yeah. It's like, I know AI A to Z. It's like, well, maybe, maybe you did for six hours a week ago, but now you don't.

Hanh: 00:35:22
True. True. And well, here's an example of networking event. We met where did we meet? At the Fabric, right?

Matt: 00:35:34
Yeah. Yeah. Fabric Conference, uh, Vegas, and they're doing one in Europe this fall. So for, Folks that might be listening to this saying, well, Vegas sounds great, but it's several thousand miles for me. There will be one over there. I think it's late September in Stockholm. Um, yeah. And that's, that's a funny one because I wasn't talking about Fabric at all. Um, I did kind of at the Microsoft booth and some of the, some of my non presentation things, but I was

Matt: 00:36:00
there talking about AI And all that. So, um, if you've looked at a conference that has fabric in the name and you're like, well, I don't work with that. There's tons of other good sessions there. Um, it's not just fabric, even though they've called it that.

Hanh: 00:36:19
You know, um, in the realm of data, ingesting data, uh, privacy, ethics, transparency, that is a great start purview purview and fabric. You, you gotta start there so that you can gain confidence before you Utilize AI, right? So to me, that's just huge. And that's like a different episode.

Matt: 00:36:43
Yeah, it is. Um, but yeah, going back to what you talked about, where it's kind of low code, no code. Um, it's been really fascinating to see Microsoft's investment in that. You know, I know, like I started talking about AI topics back seven, almost exactly seven years ago. And then even like I was using logic apps to call a cognitive services API, but there was no code involved. And, um, yeah, I wish I could have seen the future then because I was just like, well, this is cool.

Matt: 00:37:12
And this is obviously not out of the goodness of their heart because they want you to consume and spend money and all that. But it's like, well, this is really interesting that you've got it next to the documentation on how to write. Grownup code and call this stuff. You've got a low code experience to do that, to see where it's gone, um, is, is pretty fascinating, honestly, and you can still do all the, all the grownup code stuff and you have to at certain levels, but it's much more accessible. It's kind of, it's that drum

Matt: 00:37:41
that I've beat for seven years. I I've given a lot of sessions about, Hey, I want you to learn that this stuff is out there. That it's not hard to use in part. Um, and it's been cool to see Microsoft come along for that ride. And, and yeah, some of the stuff you can do is obviously way beyond what I was doing seven years ago. Um, but yeah, it's, it's really neat to see because it does allow people maybe that are technical, but aren't devs. They can, they can do this stuff. You don't have to go learn C

Matt: 00:38:14
sharp or Python or whatever. Arguably you should learn Python for other reasons. Going back to what I was saying about data engineers, but, um, you don't have to, to play with this stuff and you don't have to, to. Operationalize this stuff too.

Hanh: 00:38:30
Yeah, I agree. I agree. You know, I heard from Jensen, um, in one of the interviews, he said the, the most popular language isn't, you know, SQL or C plus plus or, or Python. It's English.

Matt: 00:38:44
Yeah.

Hanh: 00:38:45
Yeah. And there's a lot of truth to that. Yeah.

Matt: 00:38:48
Yup. Yup.

Hanh: 00:38:49
Yeah. So now how do you approach working with diverse clients and ensure that those solutions are tailored to their needs?

Matt: 00:39:04
That, that's an interesting question. Um, I think the best way, the best way that I can explain that an old boss of mine, um, who was fantastic, uh, used to use a phrase called listening between the lines because, and I'm not sure how you mean the question, but for me. Diversity of client could be things you see, it could be things you can't. It could be things in their, in their background, in their experiences, whatever. Um, but especially for a consultant, like your technical knowledge matters.

Matt: 00:39:41
Your ability to listen and converse and understand matters more. Um, the, the tech changes, which is not to say you don't have to be a technical expert to be a good data consultant. I, I'd like to think you do. Um, you have to, you have to be able to put yourself in people's shoes and going back to what you've said a few times, like understanding the pain points and sometimes what they're saying is the pain point isn't, it's actually something else and it's kind of your job to guide that conversation from. Okay.

Matt: 00:40:23
Yep. That's definitely a pain point, but what's that rooted from, you know, where, where downstream, is that an issue or is that something that they're bringing to the table? Something from their background and they're kind of hyper focused on something that really isn't the pain point for the business. It's a point of annoyance for them, which doesn't make it any less important, but it's our job to kind of pull all that out. Right. And understand like, well, this is very

Matt: 00:40:46
frustrating for this team or this person for the business that's down here. And actually, the bigger issue is a business wide issue. And that's up here. And then walking away from that and having everybody think that they were listened to because they were and that their issue will be taken care of. Maybe not in the priority order that they wanted. Um, but you don't, you know, having been on both sides of this, It's very frustrating to talk to a consultant and walk away and feel

Matt: 00:41:20
like they didn't hear what I said. They, they came in here to sell me something. Um, and I think, you know, especially in, in the Microsoft world right now, it's fabric, all the things, right. And fabric's really, really cool. But you do run across some people that are just, that fabrics, the hammer for every nail and they don't take the time to say, well, maybe that's not a nail at all. Maybe it's a screw. I actually need a screwdriver. Like the hammer will, it'll nail a screwdriver in there

Matt: 00:41:50
eventually, but it'll mangle it. You won't be able to, you won't be able to do anything else with it. Um, so it's just, for me, it all comes back to listening and I was thankful to have a couple of really good bosses and mentors that brought that home because, you know, when I was younger, you got a college, you think, you know, everything, um, as I, you know, you start to get into this world, you're just kind of telling, it's like, well, what you did is dumb and you need to do this and you may be right, but you have to be right the right way, um, just cause you won the argument.

Matt: 00:42:23
They may say, well, that guy's really smart, but he's a jerk and he doesn't listen to us at all, so we're not going to continue working with him. Um, so, so you, you won the battle, but lost the war, which is never good.

Hanh: 00:42:35
Yeah, that's true. And you know, I gotta tell you, I'm kind of surprised when I talk to, um, prospective clients when asking about what are your pain points, what problem is you're trying to solve? I'm really surprised how long it takes them to answer. And they struggle with that. Well, here's the thing. I mean, how I look at it, if, you know, if you've got a business, obviously you're, you've got goals to achieve and along the way, um, you have your workflow, your process,

Hanh: 00:43:00
how you achieve your goals, right? And what are some of those oppositions, which in my mind are your pain points. So let's, you know, lay it out, prioritize low, medium, high, and focus on the low ones, perhaps that might be more achievable to assume that AI is a solution. Well, it isn't. Certainly not when you don't know what your, where your heartache is, that you're trying to, you know, get better or increase productivity and so forth. So those are like internal questions that the company has to answer prior to

Hanh: 00:43:43
even exploring how AI is going to help. Again, it's not a silver bullet solution. So, um, You know, but those conversations I got to tell you is it's a paradigm shift for folks. It's a huge paradigm shift and you want to be respectful. You want to guide them along paradigm shift. Um, how do, how do I say this? You can try to influence that, but nobody's got time to be waiting for people to change their paradigm shift. You know what I mean?

Matt: 00:44:10
No, no. And right. That's the, yeah, that it, how many more use cases.

Hanh: 00:44:19
How many more use cases do you need to see that AI can impact your day to day life and your work life and so forth? So, so it's a fine balance.

Matt: 00:44:31
It is, and it's really, it's funny for, you know, for like person to person interactions. Sometimes it is about driving interest in this stuff for all my corporate and kind of customer or prospect interactions, it's trying to get the enthusiasm under control of it, right? There's all this external pressure. Well, this firm that we compete with says they have AI that does whatever. Well, we need that too. It's like, well, actually what they're saying, they're full of it. That's not, that's not

Matt: 00:45:01
at all what it's doing. It's doing, you know, this, this, and this wrong. Let's try to get this to go right for you so you can kind of rightly claim, uh, superiority over what their solution was, but sometimes people don't want to hear it, right? Cause it's like it moves fast and you get executives under pressure and like, you know, I can think obviously won't name names, but I can think of of a customer that we were having kind of a. We were kind of laying out a project schedule, uh, for moving some data

Matt: 00:45:35
assets of theirs to the cloud and somebody piped in and said, well, at the recent board meeting, they said, we need to be, we need to have an AI solution implemented by December. They are on very old, largely out of support, um, Windows and SQL and all that. It's a big jump. So it's not just a paradigm shift kind of for us. It's a culture shift for places sometimes and a skill shift.

Hanh: 00:46:06
Yeah,

Matt: 00:46:06
You're trying to get all of it, right? They're very comfortable in, in this on prem world with versions of SQL that are, that have some years on them. And, um, you know, the jump to cloud based stuff of that, and then putting AI things over it, the terms you have to learn, the things you have to understand. It's massive. Um, you know, can we get it all done in six months? Sure. But, um, there's got to be a tremendous organizational commitment to that.

Matt: 00:46:33
And for some organizations, it's a, it's a, it's a struggle to understand that because they think we're slowing them down just cause and really we're slowing them down. It's like, you're not ready. You're going to jump into this, uh, you know, the wrong way, have spent a lot of money and not really not gotten value out of it. Um, it's not, it may feel like a race, but it's, it isn't as much as I think companies, especially small and midsize are feeling. There's time to do it right.

Matt: 00:47:02
Still, we, we need to move quickly, but there's time to do it right.

Hanh: 00:47:05
No, I agree. I agree. So now how do you, um, how do you approach working with diverse clients and ensure that their needs and your expectations are met? Um, and stay within the boundaries of ethical. So we're at the end of our call. Thank you so much for your time. So is there anything else that you would like to share with the folks who are interested in getting into AI and data? And I guess, what is the most impact you think that AI and, um,

Hanh: 00:47:46
and data can serve businesses? And what attitude should be people take on?

Matt: 00:47:52
Don't be scared of it. It's, it's, it's how I like to summarize the session that I've got currently. Don't be scared of it. Engage with it. Find the way that you want to do that. It is, like we said earlier, it's not possible to be an expert on all this stuff. Um, and I think especially if you're, if you're like me, if you're a. Data person, find the ways that it can kind of enhance your job or, or enhance your skill sets. So you can go to that next interview

Matt: 00:48:17
and say, not only am I a data X, um, I also understand vectors and, and things like that, or whatever technical or not technical part of AI, you kind of want to add on to your skill sets. You know, I guess as far as advice, I'd separate that into business and personal. For businesses, concentrate on finding the pain point. Uh, don't necessarily create in AI something to check a box. That's not going to be probably a solution that grows well. It's probably not going to be a solution that ages well.

Matt: 00:48:56
You're probably not going to get a lot of value out of whatever you've built. Um, no matter the quality of the people that have built it, because you haven't really thought through what you want, you've just tried to tick a box. Now, having said that, I understand that sometimes for incentives, bonuses, whatever the box has to be ticked and that's okay. And if that's the spot you're in build. The best thing you can tick that box, but then don't stop thinking about what, what it should actually be, because there's a lot of maturation

Matt: 00:49:29
that needs to go on around this. And that's where I'd like to think people like me can help. Cause you're coming in from external, you're bringing experiences from other places and you say, well, I, I hear you. Let's talk about what I saw over here and maybe this fits you better for personal. You know, I would say, look at Try to engage with it in a way that's interesting to you and interesting opportunities will come out of that. I mean, I started working with this stuff cause I wanted to be talked about on a soccer podcast.

Matt: 00:50:00
It worked, but that, you know, I had no idea that would lead to the opportunities that I've had. So, you know, start there. And if you're still, if you're still like, well, listen, I need to, I need to work with this stuff. It's interesting. I don't know where to start. Try to think about something that helps people. You know, we were talking pre show about a friend of mine, you know, who's, who's been dealing with ALS for two years. And uh, you'll see more on my

Matt: 00:50:25
social media, um, over the next weeks and months about fundraising and ways we can help him. But without people thinking about how AI can benefit accessibility for people who have challenges with a wide variety of things, whether it's movement, vision, speech, all those things. Um, if you're having trouble kind of plugging in, like. Well, I need something to inspire me to really get into this stuff. Try to help people. Um, I that's, that's the most, most inspirational thing I can think of.

Matt: 00:50:57
And for all the cool things we can do for businesses here that are important, most important is impacting somebody's life like that.

Hanh: 00:51:07
So true. And it is, um, very possible nowadays to use AI to help folks, like you said, ALS. Um, my, my demographic is 50 plus, for instance, it could be mobility. It could be, be my eye, sort of speak right now that, uh, the AI can help, uh, read the environment of the user and explain where you, where your abouts are and cognitive decline. People that may have limited ability to speak. Or even speak different languages all together. So there's so many opportunities to

Hanh: 00:51:39
solve problems and help enhance lives. So, yeah, I think that's a great way to end it. Use AI to solve problems. And proceed with enthusiasm, but with caution. Well, thank you. Thank you so much, Matt, for sharing your wealth of knowledge, experiences. So many insights with us, uh, from leveraging data, uh, and AI to drive, uh, significant advantages, and also fostering a culture of innovation, uh, so you exemplify what it means to be a trailblazer in this field,

Hanh: 00:52:15
and your perspective is, um, uh, on maintaining your well being, resilience, and importance of, uh, continuous learning is a reminder to all of us that when, uh, as we reflect on meaningful collaborations and difficult situations and our accomplishments, um, use AI as an inspiration.

Matt: 00:52:40
Right. And, and have fun. Yeah. Work is not always fun. And I try to explain that to my kids all the time. They're like, Oh, today you seem bored. Well, yeah, today what I'm doing Is boring. Um, but man have, this stuff's really cool. If there's any part of you that's nerdy. And there's probably a part of all of us that do this. That is have some fun with it.

Hanh: 00:53:02
Yeah, that's so true. And the road to data mastery is an evolution is continuous opportunities, growth, opposition, adaptation. But the end of the day, it makes real impact. Oh, yeah, that is very exciting. So the folks that are listening, stay curious, be resilient and. Don't stop learning. You got to foster a lifelong learning attitude. I'm approaching 60 and I'm still learning and I'm encouraging my older siblings to learn how to use

Hanh: 00:53:33
AI to enhance their financial, their Medicare understanding and so forth. And of course dealing with health, uh, you know, healthy aging. So, um, so all those are, are very good. We'd like to hear your thoughts, the audience about this episode. So continue with the conversation, give us feedback, questions, share some personal experience, reach out to Matt or I on social media, uh, on Spotify, YouTube channel, LinkedIn, join our online community. Uh, and connect with like minded AI enthusiasts.

Hanh: 00:54:13
And then until next time, I'm Hanh Brown signing off from AI50, where your data meets innovation. Keep innovating, keep growing, and we'll see you on the next episode.