HOST: David Broussard
GUESTS: Austin Tidmore, Senior Director – Data Analytics and AI/ML; Amanda Inman, Managing Director – Agile; Zac Guidone, Director – Data and Analytics;
SUMMARY: Three of Apex Systems' Practice Directors bring us to the intersection of Data and Analytics and Agile, where we find the modern data team.
Modern Data Team Podcast
Adelina Kainer 00:00
Welcome to Digital Reimagined, a podcast packed with insights from Apex Systems, a world class technology services leader working to reimagine value for our clients.
David Broussard 00:11
We'll bring you the voices of industry experts to showcase our proven solutions that span across digital innovation, modern enterprise, and workforce mobilization.
David Broussard 00:21
Hi everyone, welcome to another episode of Digital Reimagined. My name is David Broussard, and I'll be your host. today, I am joined by three of Apex Systems' Practice Directors today: Amanda Inman, our Practice Director for Agile, Zac Guidone, our Practice Director for Enterprise Data and Analytic Strategy, and Austin Tidmore, our Practice Director for all things Analytics and AI. Zac, Amanda, Austin, welcome! Excited to have you.
Austin Tidmore 00:48
Thanks for having us.
Amanda Inman 00:49
David Broussard 00:51
Absolutely. And we're talking about the "modern data team". And as we think about so many organizations going through these digital transformations and these journeys that they've been on, we're going to discuss today how so many companies think about digital transformation, Data and Analytics technology transformation, meets at the intersection of the organizational cultural changes of developing Agile practices and how those two things work together. So, really excited for this conversation, crew. To get started, what is the motivation behind this "modern data team".
Zac Guidone 01:30
So the motivation behind the "modern data team", for me, it really comes down to this concept of digital transformation. And it's a journey that I think many of our customers have been on for quite some time. And it's usually spurred by some of the pressures, the economic environment or the competitive environment, kind of bring to the table. Whether, recently, we've kind of seen the impact of inflation, or even before that, the reduction in barriers to entry that technology and cloud technology specifically, has really caused. I think we've all heard the saying, you know, "your margin is my opportunity," And as companies kind of reflect on that, and strive to be more Agile, we really find ourselves talking quite a bit about this concept of a "modern data team".
Amanda Inman 02:17
Yeah, and I would say, from an Agile perspective, you know, along with the digital transformation, Agile transformation, it's very close-knit to that concept, and really enables that change, You know, especially trends that we're seeing in the Agile space, you know, clients are moving, especially to product-centric delivery models, and it's really requiring business and IT to partner. Having two seats at the table and really talking through, you know, how are we going to make this work? What is value that we're delivering?
Austin Tidmore 02:47
I think if there was a word that I use almost as much as "transformation" in the last year or so, with our clients, that's idea of "modernization", right, or just the idea of having a modern Data and Analytics team, environment, and you know, it really became apparent as we sought to modernize our clients' technology or, you know, their technical platforms, the way that their teams were delivering data, delivering analytics throughout the organization was getting modernized too, and that kind of spurred this white paper series that we've been doing around modernization. Certainly has spurred a lot of great conversation between our Data and Agile practices, as we think of you know, the ways that data teams can not only use modern tools to deliver what they do, but also modern and more Agile ways of delivering what they do. And we found there's just a ton of connection points, a ton of synergy between these two capability areas.
David Broussard 03:40
That's great. And hearing in the last couple of minutes, just so many hot button topics that we know are being discussed across enterprises- Data and Analytics, Agile Transformation, Digital Transformation, that's what everybody's talking about right now. So when we incorporate all these pieces together and talk about this "modern data team", what do you think some of the traits are? I'd love to hear from all your different perspectives on this, of what a successful modern data team might look like in terms of the characteristics and maybe even more importantly, what are some of the shortcomings that we see for a modern data team that isn't successful?
Austin Tidmore 04:17
Well, yeah, and like I was just mentioning too, in terms of modernizing, not only, you know, what a Data and Analytics team does, but how they do it, it really comes down to responsiveness. The traditional view of, you know, IT-centric data teams being really monolithic, really process-oriented in the way that data gets delivered, and it really hasn't traditionally been a good playing ground for more Agile or iterative ways of delivering, and so because, you know, organizations are always being pressured to do more with less, that idea of responsiveness or agility, with Data and Analytics teams really has to be brought to the forefront.
Amanda Inman 04:55
Yeah, and just to add to that point, I would say, when we talk about, you know, teams that we want to build that are going to be cross-functional. And if you think about it from an Agile perspective, we really want to build vertical teams that are capable of full-on autonomy and ability to deliver features. It's kind of bringing, you know, that responsiveness in, but it's also bringing in the ability to reduce dependencies outside of your team. And we're seeing lots of different types of pilots happening with that. So like, for instance, "Hey, let's put data engineers directly into an Agile development team." Let them have complete access to the full stack, so that they're able to immediately change data all the way through the layers, and they're able to see that outcome in the UI, so that they know this is working, and it's not going to require a ton of outside testing.
Zac Guidone 05:45
Yeah, I think I would just double down on this concept of "collaborative" and "autonomy", those two things. You know, with my own career, I've kind of dabbled back and forth between business roles or technical roles. And it's really given me an effective perspective in a lot of these situations, when we're talking about trying to get a feature to market quickly and efficiently, and trying to navigate some of those business concepts and marry them up with the technical contex that's really important. And I think that trickles down into this concept of the "modern data team" and having really diverse skills. So I think in the past, we've kind of seen really deep and narrow skill sets, or really broad and shallow skill sets. And on this modern data team, folks have maybe one two or three specialties, they can play one or two of the specialized Data and Analytics roles. And it really sets the team up for success to consistently deliver and to be able to pivot as unexpected obstacles come up.
Austin Tidmore 06:49
Yeah, it's providing a lot of great opportunities for, especially Data and Analytics experts to become "E-shaped", right, or definitely "T-shaped", as we think about it previously. But traditionally, we always talked about even the imagery of like a data pipeline, right? There's like one way in, and there's one way out. And traditionally, there's just not a whole lot of visibility to what's kind of happening in the middle. But now that data engineering is looking more like software engineering, and data platforms are looking more like, you know, API's, and services, and infrastructure automation, it's providing people who might have just kind of considered themselves an ETL developer in the past, or a BI designer, dashboard designer, you know it's really given them a lot of great opportunities to find a lot of ways to expand their skill sets, and I would definitely say then kind of contribute to an overall more responsive data delivery process, you know, for the organization.
David Broussard 07:44
So all these things I'm hearing right now, more collaboration, different roles being played on teams... it's a lot of change. People fear change, companies fear change, as exciting as it sounds. What challenges do you find companies are experiencing most when it comes to these modern data teams and building them?
Austin Tidmore 08:06
Well, I think, you know, a challenge, and Zac, I'd love to hear your perspective on this. As you know, data is just so linear in terms of how it traditionally has gotten developed. And so a challenge sometimes is to really help data teams think about their work in more modular ways. You know, it's not just somebody who is creating an element on a dashboard that is delivering a data product. Whether you're modeling data, whether you're integrating it, whether you're putting it on the front end of a dashboard, you are building data products along the way, and serving a customer along the way. It's definitely a paradigm shift and there's lots of challenges in helping data organizations see their work as modular and therefore organize it and deliver it accordingly, not some monolithic way. But Zac, what have you seen there?
Zac Guidone 08:52
First and foremost, I like the analogy of LEGOs in this situation, because whether we're talking to some of the business stakeholders, executive stakeholders, or the data team, that tends to resonate. So in a given dashboard, in a given pipeline, in a given environment, there's different assets and series of LEGOs. And over time, if the whole team takes that mindset, that product mindset, that complete unit of value that you kind of get through the Agile framework, you develop, you know, a nice library of LEGOs that you can pull from. And not only does it provide space and capacity back to the modern data team, it really sets up the entire organization for success. Because I think the second point would be ultimately, at the end of the day, this team, the role that they're playing is to really bring that change to the rest of the organization in the enterprise. The modern data team and this concept of taking a modular approach and building these assets over time really sets up the entire enterprise to reach maturity at one point where you can start to offer these things in a self-service, end user capability at scale. There is a lot of change, I think the modern data team is at the center of it. But I personally am excited for the role that I see this team playing for our clients today and for tomorrow.
Austin Tidmore 10:15
I think too, I've definitely seen data teams really just kind of shift their project management to-do list into a tool like Jira, or Azure Data, or Azure DevOps and say, "Well, great, now we're managing our work in an Agile way." But, you know, they're not doing anything about what Zac just said, in terms of, you know, there's always something on fire in the data world I guess, there's always limited bandwidth to kind of think critically and take on new things. So have you seen anything particular that you would call out in terms of, you know, things to do or things not to do when you're, kind of, really trying to make some tactical progress and adapting more Agile ways of delivering data.
Amanda Inman 10:52
Yeah, I would definitely say Minimal Viable Product. And it's core to learning how to break things down. And I know, it's easy for folks to say, "No, you can't, it's impossible, we have to deliver this entire thing," right? But you don't, right, there are ways to break it down. And I think, to be honest, it takes critical thinking. It's a practice that has to be learned and taught. And I would say that what we tend to see that provides the most success is training and coaching. You know, thinking of all the transformations we've supported, and this is the same with engineering practices in general, right? So it's, "Hey, how do I take this big requirement, and I look at it differently?", right? And I would say, tasking really helps with that. If you really start to think about how long it's going to take you to deliver something, and you start to task out things, and you have some guidelines around tasking such as, "Hey, I can only create a task that's maybe a few hours long or a couple of hours," then you start to realize, "Oh, this is a big ask, this thing's going to take weeks to deliver." Does it really have to be an Agile, you know, story on its own? Or it sounds like a feature? Sounds pretty big. Right? So how do you start to look at that a little bit differently. So the point of that is really there's techniques that we leverage to help folks start to change and look at things a little bit differently. All that to say, training, coaching are extremely important. And having experienced resources that are used to these methods in the team is always a plus.
David Broussard 12:29
This has just been such wealth of awesome information and thoughts. It's hard to think of how to summarize this and end it, but I'll try with: I'd love each of your perspectives on if a company is thinking, "Why now?" or resisting the concept of this modern data team, if it's applicable, what would you say to them? Why now? Why do it now?
Zac Guidone 12:54
We talked about a lot of skills, I think, today, spanning Agile and spanning various elements on the data side and the technical side, and actually, in between. All of those skills are going to take time, they're new muscles to develop. And the business case for Digital Transformation has already been made. There's no better time. You can't afford to wait to start that journey, and get those skills, and get that muscle memory in motion. I think that's how I would answer that question.
Amanda Inman 13:22
Yeah, because guess what? There's already others ahead of you. Right? They've already started. So now you're behind. And yeah, to your point, it is a learning curve, that needs quality time and focus. It doesn't mean things have to stop. But I would agree with that. I think that being able to pivot, establishing architecture in a way that allows your data to pivot, it's a no brainer. So I would say if you're not accounting for that now, you will suffer at some point. Your market share, you know, ultimately, you're not going to get something out to the market as quickly as the next guy.
Austin Tidmore 14:04
Yeah, I think I've heard that phrase "do more with less", I think, more times than I care to admit and regardless of what's really motivating that, I know that just the way that business and enterprise is trending is we have to find really smart ways to challenge the status quo, especially in an ever-changing field like technology. And because what we're talking about here is so outcome-focused, it's so customer- and people-centric or -driven. I think, "Why now?" Well, I think there's so many good reasons wrapped up in this topic, to kind of challenge the status quo. If you have to look at the way that data and analytics is being delivered within the organization and to think about how it could be better, how it could be made more efficient, but with like this relentless pursuit of the end user, of the customer, of the person, there's really not too much downside to that. You know, even if it takes a little longer to develop that muscle, even if, you know, there's some change or so there's some disruption to manage in the short term, that kind of values-driven way to approach technology, to approach your business process, I think is- like I said, there's too much upside.
David Broussard 15:20
Well, Austin, Zac, Amanda, this was a fantastic conversation, all of your insight was so valuable, and I really appreciate you taking the time to chat with me today.
Amanda Inman 15:29
Thanks for having us.
Zac Guidone 15:30
Austin Tidmore 15:30
Yeah, I really appreciate you bringing us on, thanks.
David Broussard 15:36
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Adelina Kainer 15:41
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David Broussard 15:54
The music you heard was Do Ba Do, by Otis Galloway.