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022 - The Data Ecosystem: Where do you even start?
213 Plays1 year ago
The data industry is evolving at an exponential rate; what is possible today was a dream just 10 years ago. As data teams become more complex and roles increasingly specialised, the risk of losing a holistic view of the systems and elements that make up a successful data team grows.
This week, I’m joined by data strategist Dylan Anderson from Rekite to discuss the importance of understanding the entire data ecosystem, not just your own "fragment."
Data professionals are becoming more specialised in their respective areas, adopting an inch-wide, mile-deep approach as data roles demand ever-increasing expertise. While specialisation is necessary, are we overlooking the benefits of a broader perspective?
Dylan puts it well: "It’s like appreciating a single piece of a puzzle without acknowledging the entire picture."
In This Episode, We Discuss:
- How to Navigate the Data Ecosystem: Gain insights into understanding the interconnected elements of data roles.
- Avoiding Siloes in Teams and Skill Sets: Learn why it's crucial not to isolate your team or your personal skill set.
- Consequences of Poor Communication: Discover the impact of inadequate communication within data teams.
- The Importance of a Broad Understanding: Understand why having a wide-ranging knowledge of the data ecosystem is beneficial.
Dylan is a globally recognised data strategist who regularly shares his insights, thoughts, and lessons on LinkedIn and through his new newsletter. As a long-time follower of Dylan and his content, it was a privilege to have him on the podcast.
If you don’t already follow Dylan or subscribe to his newsletter, I highly recommend doing so!
Dylan newsletter: Issue #2 - The Data Ecosystem: Where do you even start? (substack.com)
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Transcript
Introduction to Stacked Podcast
00:00:02
Speaker
Hello and welcome to the Stacked podcast brought to you by Cognify, the recruitment partner for modern data teams hosted by me, Harry Golop. Stacked with incredible content from the most influential and successful data teams, interviewing industry experts who share their invaluable journeys, groundbreaking projects, and most importantly, their key learnings.
Featuring Dylan Anderson from RedKai
00:00:26
Speaker
So get ready to join us as we uncover the dynamic world of modern data.
00:00:34
Speaker
Hello, everyone. Welcome to another episode of the Stacked Data podcast. Today, I'm joined by Dylan Anderson from RedKai.
Challenges in Data Industry: Silos & Specialization
00:00:43
Speaker
The data industry is developing at an exponential rate. What is possible today was just a dream 10 years ago. Data teams are becoming more complex, and we are seeing increase in specialization of roles within a narrow field.
00:01:00
Speaker
Can this lead to fewer professionals being able to have holistic view on so many elements that build up a system to make a team successful? Dylan, it's a pleasure to have you on. As a data strategist, I'm keen to dive into this topic. And yeah, I know it's something that you're looking to blog about as well in an upcoming newsletter, if I'm correct.
00:01:21
Speaker
Yeah, so I covered a lot there, but just by introduction, Dylan Anderson lead data strategist at Ridekite and where I focus is on bridging that gap between data and strategy. And to your point, Harry.
00:01:34
Speaker
data is such a complex industry right now. And it has grown so fast over the past 15 years that people don't even know where to start. And people aren't thinking about it in that holistic nature anymore because they're stuck in their individual domains. If you're a data engineer, you go into data engineering, you learn about all the different tools that are available, the coding, the technology.
00:01:58
Speaker
But you don't necessarily have a data governance or data analytics and there's no crossover of domain. So what you're getting is this industry that is growing exponentially but growing in individual silos. And I mean, in my opinion, data and analytics is already complex and.
00:02:16
Speaker
I mean, you talk to your parents or you talk to somebody who's not in the industry and have no idea what you're talking about. So adding that complexity of how do you navigate within it is another big, big part. And as a data strategist working with some of these giant organizations, investing millions in their data, that's where I'm starting to see a lot of cracks and a lot of problems is them being able to understand the complexity and work with it and improve on it and such.
00:02:45
Speaker
So everything that you spoke to to start a software is completely accurate. And this is kind of an area that I'm looking more and more to diving into because I see it as a huge, huge emerging problem within organizations, within the data industry as a whole.
00:03:01
Speaker
It's interesting that you mentioned the word silos. I mean, obviously in larger organizations, particularly silos is what everyone is trying to move away from. That's, I think, where we can all agree that there's no single source of truth. There's no modern data stack where there is one point of trust for the organization. So it's interesting that we have teams that are silos in organizations, but now we're
00:03:23
Speaker
seeing that within these roles, which are siloing. So could you set the scene, I suppose, to where did this real passion come from as to trying to address this in a bit more depth? You've obviously seen some of these problems, but yeah, help elaborate that for the audience as to why you think this is a problem within the industry.
00:03:42
Speaker
Sure.
Client Experiences with Data Silos
00:03:43
Speaker
So I mean, I've been working in data strategy for quite a few years now. And every one of my projects and for context, typically I work with a client for one to two months. So within a year or two, I end up working with about 20 to 25 different clients. And a lot of these clients are leading providers. For example, one of the biggest clients is a leading distributor of alcohol products in the world. So they are big teams and big organizations.
00:04:09
Speaker
And what I seen working with all these clients, a common thread is.
00:04:15
Speaker
The silos, as you've mentioned, is kind of that underpinning org structure and operating model. It doesn't quite allow data to make an impact. And I think everyone talks about how the data team is siloed with the organization. They're vertical where they should be horizontal because they should be supporting all the different domains. You should have a data team to support marketing, to support finance.
00:04:40
Speaker
But in its own, in a lot of organizations, you just have a data team on the side and it's sometimes talking to marketing, giving them products and marketing's like, I don't want that product anymore. So that's the first big issue of silos. And I think organizations are starting to come along to that and to figure out, Oh, actually our problem isn't that we have the wrong technology. It's that we haven't structured our organization to, to allow for the data insights to make a real difference in our business.
00:05:10
Speaker
And so I think organizations are starting to come around that. And that's one of the biggest problems I've seen working with a lot of my clients. The second one, which you alluded to as well, is that additional silo. So there's a next level of silo. Let's call it silo v2. And that's within the data team. So as these data teams are growing, you've got different capabilities within a data team. So you've got the engineering, the architects, you've got the analysts, the data scientists, the governance team, the platform team, et cetera.
00:05:38
Speaker
And within them, they're not talking to each other. So, for example, the platform team is signing up for some technology, but that doesn't quite integrate with the analytics team. That definitely doesn't integrate with the marketing team. And that's because there's that lack of communication as these teams grow bigger.
00:05:56
Speaker
It's the classic nature of an organization as it gets bigger, it gets more complex, and it gets harder to manage. And that's, I think, where data is coming into.
Machine Learning Landscape & Solutions
00:06:05
Speaker
The last thing I'll mention on this is just, I think, last week Matt Turk released his annual MAD landscape, which stands for machine learning, AI, and data of all the different organizations that cover
00:06:20
Speaker
different parts of the data ecosystem. And I think now that he has a one single image with 2000 logos on it of different companies who are all trying to say, yes, we can solve your problem. Don't worry. We can make you have clean data or we can make your things work faster. We can get you the insights you need. And it's just becoming
00:06:42
Speaker
unimaginable, like how do you grasp it as a person with just one brain? And as an organization who's trying to do so much, how do you kind of pull that together? So that's really what I'm seeing. And there's no solution out there. I don't see people really actively trying to solve this other than just continuing to do what they continuously do.
Improving Collaboration in Data Teams
00:07:03
Speaker
I can definitely relate with the challenge you mentioned about poor communication between these teams. It's so often that you hear the product analytics team aren't guessing. I haven't got the right source of data or whatever the challenge is from the engineering team. And I think it's the fact that they're not
00:07:21
Speaker
you know, looking over the fence, they don't have empathy to what the other team is doing. And I think if there was to increase that sort of line of communication between the teams, you have a much better flow between both the people within the team, but the actual data as well and the systems that you're building. So I guess keen to see if is this sort of similar problems that you've seen and what problems does this cause for organizations and data leaders?
00:07:49
Speaker
Yeah, I mean, I really like the word that you use, empathy. I think that's great because I think, well, we all know the common nature of data professionals like to avoid the soft skills until it hits them the face and they have to learn the soft skills to do well at their job. And I think empathy is one of them because that is totally a soft skill and that's not something you can learn on data camp. And I think learning to work with others is completely, and others outside your domain as well, is
00:08:16
Speaker
such an essential part of really succeeding as an overall team. But the other thing too is we're not really incentivized as data professionals. We're not incentivized to do that because right now your KPIs, your metrics, your success is measured by what you do within your team. And no one's actually looking at it in a holistic fashion and being like, oh, wait, wait, there's dependencies between teams. But
00:08:39
Speaker
but they're not coordinating with each other. How can we fix that? And I think that's kind of the next step that a lot of these organizations need to take. And if they don't take it, they're going to continue having these problems. So you mentioned what kind of problems and issues am I talking about? I mean, it's everything, like everything that I think on LinkedIn, and I use LinkedIn quite a bit. Everyone posts about data quality. I see at least like three to four posts about data quality and how big a problem it is on a daily basis.
00:09:09
Speaker
And the reasons that they put for those problems with data quality come from so many different sources. It's not one you can't have a data governance program that will fix data quality because it could be the engineering problem or it could be the business isn't feeding the right data in or it could be the data is being used incorrectly and then therefore creating outputs with poor quality. So there's so many different sources of this one problem.
00:09:33
Speaker
And if you don't think about it in a holistic manner, how are you going to actually solve it? What you're going to be doing is you're just going to be putting band-aids over a huge issue.
Holistic Approach to Data Strategy
00:09:44
Speaker
And those band-aids might solve things in the short term, or they might solve it for one particular instance, but you're not actually solving the root causes and the root issues that underpin everything. And I think
00:09:56
Speaker
What you have to do to solve that is you have to think holistically, you have to think more strategically, and you have to understand how things mesh together and how it should be done to get those optimal results that you want in the long term. And that takes a lot of work.
00:10:12
Speaker
And that's another key reason why this is happening is because people don't like to put a lot of work, if there's a quick fix, they'll take it. And a lot of organizations are taking these quick fixes concurrently and it leads to a huge issue down the line. And that's what you get with like legacy tech debt and legacy software.
00:10:32
Speaker
I don't think it's just the tech debt. It's not just the technical though we touched on it a bit earlier. It's the process and the mindset of the data team and how that is going to influence decision. If that's not being done and it's not being actioned in the right way, then that is, I suppose it's a form of debt, but not something that you can necessarily see, right?
Leaders' Broader Business Perspective
00:10:56
Speaker
Oh, totally. I mean, yeah, it comes down to your people, right? And I mean, you know this working with how many people trying to place them in the right places. And if they're not of the right mindset, if they're not led in the right direction, they're not going to help solve the problem in the long run. And I think that is that is a big part of it, our leaders and data.
00:11:21
Speaker
Most of them are really good, but some of them might think more from their domain perspective rather than from their entire business perspective, especially if they've been brought up as an individual contributor in a technical realm. It's harder to get that wider perspective when you're fixing daily engineering problems.
00:11:44
Speaker
And not to say like you need a data engineer or a platform engineer at a leadership level to help direct that path to the platform, but it's a totally different job than it was at the beginning of their career or even in the middle of their career. So how do you, as an organization, how do you educate leaders and help them get to that point where they're thinking more holistically about the business and they understand
00:12:07
Speaker
what you do as an engineering fix here will create lasting effects across over here in your analytics side or your data science side. So I think that we can agree that this over specialization can be causing a problem and the solution to it is having this holistic bigger picture view on all of these different areas within data gets you to be able to see everything as a whole.
00:12:36
Speaker
Could you outline some of these different areas within Data Then? I think there's the obvious ones, BI, data engineering. But yeah, help for the audience explain and just, I suppose, demonstrate the scale of these different areas and why they're so important of understanding each one of these elements. So how long do we have, Harry?
00:12:56
Speaker
So I think it's, it's massive. So I mean, anything at the top of the episode, you mentioned that was writing a newsletter, which I'm going to be launching. Well, when this podcast comes out, it's probably already launched, but it's called navigating the data ecosystem. It's called, I think it's called the data ecosystem. And that
00:13:14
Speaker
in essence, is me trying to cover each one of those areas. And right now, what I've done in my own time is I've created about 15 high-level areas, each one with about four or five sub-level areas that you need to know about. And so that just gives you an understanding of the scope of it.
00:13:34
Speaker
For example, I mean, within this, you have the business. So understanding what the business is, how data will actually drive value for the business, what that means. Another part is kind of the data platform, the enterprise data platform. So that includes everything from the architecture that goes into it, how the data is modeled, what technology you use, the
00:13:56
Speaker
the engineering that kind of facilitates the data movement. Another big piece, of course, that you mentioned is the analytics. So how is data consumed? How do you get the insights you need from the data? And that could be machine learning, AI, advanced analytics. We know there's many different parts of that dashboard is reporting. So all of that realm. And then within that, there's a ton of other things. So as I mentioned before, governance,
00:14:20
Speaker
important data quality fits within governance and observability as well as data management so i mean i can keep going but there's just so many different pieces and i think everyone knows. Everyone will recognize everything i just they'll recognize each topic in each area and they'll be like oh yeah i can explain that i understand what that is.
00:14:41
Speaker
But what I find is that people don't necessarily think about those different areas when they're doing their day to day and when they're kind of creating their own solutions in their own domain. What does it mean to take into consideration the data strategy or what does it mean to take into consideration the actual output on the consumption side?
00:15:03
Speaker
And I think that's where it really helps to have this underlying view of how things mesh together. And one reason I'm taking this on from the newsletter perspective, and hopefully it'll turn into a book at some point, is because if you look out there,
00:15:20
Speaker
at all the literature about data, all the things in data. There's nothing that quite brings it together from an educational perspective. There's a lot of individual domain stuff that experts have written and they've written really good stuff on it. And it does touch on some of the secondary elements of that data domain, but nothing really pulls it back and pulls it and links everything together.
Addressing Gaps in Data Literature
00:15:42
Speaker
So that's really what I think is missing in understanding the data ecosystem.
00:15:49
Speaker
So you're building the taste of Bible. I mean.
00:15:53
Speaker
Yeah, is it the St. James version of the Bible? I don't know. But yeah, I like to say it as I think in Canada, because I'm Canadian, it's called Cole's Notes. In the US, it's called Cliff's Notes. But basically, what's that data for dummies type thing? Yeah. But more than just like an explanation of what each thing is, like you can Wikipedia that or you can chat to GPT and it will cost you zero dollars and it'll be super easy. But it's more about like, why does this matter? So what is it?
00:16:22
Speaker
Why does it matter? How does it link back and add value to a business? And what are some frameworks to wrap your head around it and how it links up with some of the other domains? And I think that's important because you can't really understand that until you've seen it happen in an organization and in a business. And I think one reason why this kind of topic hasn't been broached much is because while people have experienced that in a business,
00:16:50
Speaker
They're often within one domain. So like head of data engineering with 20 years of experience has experienced everything in engineering. They know of platforms. They know what happens. They know what it's doing. But they might not really understand kind of how governance links to the data strategy.
00:17:07
Speaker
even though that might impact them, they just kind of get the outputs from that. And I think that perspective is really hard to gain. And even I would say CDOs don't gain that because they spent half their time trying to get more money from the C-suite or just trying to solve the problems at an executive level. So it's a really hard thing to kind of contextualize, especially given that singular view of most people's roles.
00:17:35
Speaker
I mean, I came to understand and hear if you've got any strategies to people gaining this kind of knowledge.
Gaining Broader Data Knowledge
00:17:44
Speaker
The one example that I think I've mentioned to you in the past that I think really rings true, I heard it on another podcast, and it's from Formula One as a sport, and Adrian Newey is the godfather of engineering within the space. Many of the technical directors and CTOs within Formula One are from an era where
00:18:05
Speaker
they were generalists. They were from the teams of 40 people, and they had to cover everything across that car. Now, these teams are 1,000 people large, and they have teams of people focusing on just one element. So as these technical people progress within their careers, they don't understand how the aerodynamics filters into the power unit, for example. And it seems that this is a very similar
00:18:31
Speaker
similar challenge that we're starting to face at Data because the scale is growing now. I think some strategies to employ are these sorts of condiments to different teams, but yeah, keen to hear any solutions and strategies you've got for the audience.
00:18:48
Speaker
Yeah i mean i don't think it's a it's a non easy fix for sure i think to your point. That kind of model is very similar to what the data world is experiencing and i think a lot of other industries go through the similar thing i think as you get more senior you tend to have.
00:19:07
Speaker
A larger responsibility and that's where you gain that knowledge of different things and how they interact. And I think that is still the best way to gain that experience. But on top of that, I think it's around organizations doing more to.
00:19:24
Speaker
understand as they hire and as they promote individuals within the organization that that person is going to be doing a different role than what they were before. And to do that different role, they might need additional training, context, secondment is a great idea, and being able to experience the other side so that they can make better decisions within their current role.
00:19:50
Speaker
And I think even simple things like forums between teams or shared channels on Slack and shared best practice via lunch and learns, those are really, really powerful for helping train and teach people.
00:20:07
Speaker
The other big thing, and it goes back to the operating model stuff we touched on at the beginning of the show, is accountabilities. And I think as data has evolved so quickly, the accountabilities are constantly shifting within teams. And teams don't really know what they are responsible for. So I'll give an example. I have one client where engineers do
00:20:32
Speaker
pretty much everything on the platform on the back end and like their role is literally like four different roles in one and people think of engineers and they're like oh they're an engineer they can just do it all. And then I've got another client where engineers are very specialized and they're focused on building data pipelines because they also have architects who help model the data and they've also got like solution architects who then are that link between the data consumption and the engineering.
00:20:55
Speaker
So how we, I mean, it's the lexicon thing, which is a huge issue in data, but how we articulate what the accountability is of that person is important for them understanding what they need to be responsible for and what they need to learn. So going back to your F1 example, because data has become this behemoth of an industry, just like F1 has become this behemoth of the sport,
00:21:19
Speaker
there has to be a level of understanding of how you progress so that you have people who you need to be specialized. They're super specialized and you train them as super specialized aerodynamics engineer, for example, in F1. But when they move to that, okay, we're the wing engineer now, not just aerodynamics engineer, I need to understand how everything impacts the wing, not just the aerodynamics of it, but also the, I don't know, the technology. Yeah.
00:21:47
Speaker
I'm not an F1 engineer but that kind of realization that it's not as simple as you'll learn the next role by doing a bit more. It's more of a this is a different beast and you have to grow and understand a bit more beyond what you have done before in order to succeed.
High-Level Thinkers in Data Strategy
00:22:08
Speaker
One thing that I've seen an increase in the industry is the increase of level of staff, level of engineers, these principle level roles. That kind of support I think is essential for a leader in this sort of context because they tend, them types of people tend to be these bigger thinkers, more technical focus within their given domain, whether that's an analyst or a
00:22:29
Speaker
a full-on full-fledged data engineer. But I think that could be maybe a strategy for helping data leaders upskill, having these high-level thinkers who have breadth in skill sets. I don't know what your thoughts are on that as a strategy. Yes, or having them high-level thinkers with you, but also understand what's going on on the ground.
00:22:51
Speaker
Yeah, no, I think it's important. It's essential and it's essential to get their voices in to the right people so that the people at the higher up level, CDO, head of data, understand these things. I call it, it's not so much a, how I think of it is I think people think a lot CDO, main boss, it's a one kind of tiered leadership. They have a few people, but really what data leadership needs to be is a kind of collaborative group of experts.
00:23:20
Speaker
You need to have a leader in each capability who, one, can speak at that high level where they understand what's happening in the business. They understand what their capability brings to the business. But two, to your point, is they know they have a pulse on what's happening within their capability to drive those things.
00:23:39
Speaker
And then in they might not be like the best if let's go back to engineering, they might not be the best engineer anymore because they probably haven't coded in a few years because they spend all their day on meetings and and coordinated across teams. But they understand what the engineers are doing to develop the platform to develop the pipelines.
00:24:00
Speaker
And then three, the other thing that is that within that tiered the multi-dimensional leadership perspective is that those individuals need to be able to facilitate the coordination across teams. That might be through them. They might be on some kind of forum, some collaborative groupings and working groups, but it might also be passing it off
00:24:21
Speaker
to the next few senior engineers or senior people on their team so that they can get that experience coordinating with certain individuals. And that's kind of going back to the earlier example, that's how you develop that leadership skill within those individuals. And that's how they then learn to think beyond their original domain.
Data Unicorns & Generalists
00:24:42
Speaker
Dylan, this profile that we're talking about, this sort of data generalist, this person that understands, has great breadth of understanding and a specialism at the same time, we often refer to them as data unicorns. And when a client comes to me asking for a data unicorn, it's usually a startup that is looking to try and compile a load of roles into one. Yeah, obviously, as these organizations grow, these profiles tend to be compartmentalized.
00:25:12
Speaker
I suppose you can't expect everyone to know everything, right? What do you believe is the right level of knowledge that you have to build up across this area as being a generalist? And what's your, I suppose, argument to being a data unicorn and the pros and cons of being a data unicorn? Sure, yeah, data unicorn. It's a wealth thrown around term, isn't it? Everyone's looking for them. No one wants to pay them what they deserve, and there are few and far between.
00:25:41
Speaker
I think the role of, I'll answer your first question first, but the role of like generalism in any data role, I think is as much as that person wants it to be.
00:25:54
Speaker
And I think it's dependent on the individual because usually generalist, everything I'm talking about right now and we're having a conversation on isn't really in the job specs of most roles and most data roles or the data analysts or data engineer or whomever. I think it comes in the further along you get in your career.
00:26:14
Speaker
But if you want to kind of advance quickly, and if you want to be the manager, be a CDO or a head of later on in your career, doing these things and being more generalist by thinking about the business, by thinking about the other domains is critical. And I often speak specifically to
00:26:35
Speaker
about business acumen because it's something that they don't teach that much to data individuals, but it takes you so much further than the next person. And now that chat GPT can code better than probably I can code. And then if you are just a code monkey,
00:26:55
Speaker
then you're not going to be differentiated from Chachi BT or AI in five years. So how can you differentiate yourself? And it's learning and gaining that business activity of like, okay, I'm building this code in a way that is more efficient, that does better things because it's what the business needs, rather than just building the baseline code that probably breaks and then copy and pasting and such like
00:27:20
Speaker
So that's a generalist piece. If a person wants to kind of progress in their career, I think it's important for them to kind of have that generalist foundation in areas that they think are important to their role. The second piece, I think you asked about kind of the unicorn piece of the pros and cons of them.
00:27:40
Speaker
So every first data hire role I see on LinkedIn or I hear about or something like that, it just gives me the shivers. I'm just like, oh, it looks so appealing.
Challenges for First Data Hire in Startups
00:27:52
Speaker
But you just know what that enormous task you're going to come upon of creating this whole platform, justifying the spend on tools, and trying to create some value.
00:28:06
Speaker
out of nothing in six months or something and meanwhile you're probably both strategizing building the roadmap but also in the day-to-day coding and stuff like that and it's just not feasible you need it you need to have that first day to hire but i think
00:28:25
Speaker
It's something that I think would probably suffice for someone who has a bit of management experience, still enjoys the technical, but wants to kind of start something new, drive something new and build something cool and doesn't mind spending a lot of time on it. But the pro is you learn a lot and you learn pretty much immediately how
00:28:49
Speaker
tough it is, and how many problems you're going to go through. And then that's when you get thrown into the firefighting as well. So everything we were talking about earlier about the holistic side of it, it starts to go out the window when you don't have hours to think about it. And then you're doing, you're doing, you're doing. 12 months later, you've got a data platform set up, you're delivering some stuff. And this data you've got almost created something awesome. But it's going to create long term problems because it's been done so quickly. It's been done in an ad hoc way.
00:29:17
Speaker
So there's a way of building sustainably and scaling that I think you need to think about when you're hiring a data unicorn or you're looking for that one, let's say you're actually going to get one, or you're hiring a new data team. But yeah, it's an interesting topic. I mean, Harry, you probably know a lot about that having talked to candidates of all ranges in that area, right?
00:29:40
Speaker
Yeah, and I think the biggest one is the client's organizations that's usually there, as you said, their first hire. I think they don't know what the challenge is ahead of them. And quite quickly, it's, oh, we need several people for this. Whilst you might have one person coordinating it, if you want it done quickly, it's too much to
00:30:00
Speaker
buy it off. But I think what you said first around the business acumen is a point that I think needs to be continued to
Business Acumen for Data Professionals
00:30:09
Speaker
drum home. Everyone's focused on entering the industry, being the best coder, being the best programmer, building the best solution. But you can't do any of that and you can't drive value if you're not understanding what the business actually wants to achieve and if what the business actually wants you to
00:30:25
Speaker
to build and I think too many people I hear stories of you know taking something on first thoughts and first sort of a conversation goes away build something comes back and the business has moved on or it's not quite what they wanted so I think that that ability to really manage your stakeholders and have that commercial acumen as you put it I think is one of the the most underrated skills in data and it's not something that people
00:30:50
Speaker
Look to refine and it's going to later when you look to try and make that step up to leadership You wonder why why you can't that that's when people I think start to realize it Yeah, and just to build on I've never used this analogy before but it kind of came to me when you were talking I mean, I'm a consultant and people if they've seen movies and consulting or like a show on consulting they think like oh, yeah, we just
00:31:14
Speaker
take two days, build a really cool idea, then put it in front of the client and just bugger off. But I mean, really, it's kind of the same process. And it's a process that data people need to take with their stakeholders as well, where you engage, you have a kickoff, you have consultations, interviews, you understand what the business needs and what it's about. You understand the business model. Then you kind of apply your thinking and your knowledge of like, OK, how is data going to make a difference? What's that going to be?
00:31:43
Speaker
And you verify that with your stakeholders in the business. And that's kind of really that process. It's tried and true. I mean, consulting is a trillion dollar industry in the world. And that's what it's built on. It's built on that process of consulting with your stakeholders and delivering good solutions. And I think
00:32:02
Speaker
If people think in that way from a data perspective, you're going to get a lot better outputs than just like, I think you'd love this cool NLP tool that we just built with Gen AI. And everyone's like, nah, I'm still trying to automate my reporting. I'm still using Excel.
00:32:19
Speaker
So it's that kind of thinking where you really need someone, you need that consultation, and then you need to kind of bring the stakeholders along. I'm going to refer back to your other question about the companies that's looking for that new data, that data unicorn, and what you mentioned of they don't often know what they're looking for.
00:32:39
Speaker
And so you can't ask them and get a response. You have to work with them to get to where you're gonna be. And that's a constant slog. It's a necessary thing because you need your stakeholders bought in and you need your stakeholders on board and communicating what they need, but you need to also be that expert, be like, okay, you don't need this, but you need this. What do you think? And that's where you get to a really good solution.
00:33:08
Speaker
That topic starts to go into treating data as a product, but I think we could go on for hours on that, Dylan. But maybe another time, I think we've covered a lot of ground, and I think your topic of, yeah, this over-specialization of roles and what you're building in this newsletter sounds incredibly insightful.
The Data Ecosystem Newsletter & Career Tips
00:33:28
Speaker
We'll make sure that there'll be a link in the podcast and vice versa for you to go and check it out. And yeah, I hope to be seeing the book in the not too distant future. Yeah, we'll see how long the process to write a book is. But yeah, no, that'd be great. The newsletter is called The Data Ecosystem. And yeah, my LinkedIn, Dylan Anderson, there'll be a link to that there.
00:33:51
Speaker
Yeah, looking basically in the newsletter, I'm looking to cover a subtopic every week, kind of what is it, why is it important, how does it start to link to a lot of the different things that everyone's doing and do that in a five to six minute blurb on a newsletter. So, I mean, on Sunday, so a nice little Sunday morning read with your coffee.
00:34:12
Speaker
and you're hopefully not thinking that much of a data on Sunday, so. Get ready for it. Well, definitely go follow Dylan on LinkedIn. He posts a lot, always incredibly insightful. But yeah, before you go, Dylan, we've just got the quick fire round of questions. So this is exciting. We asked all the guests, and yeah, hopefully helps the audience upskill in certain areas. So the first one, Dylan, is how do you know if a job opportunity is the right one for you?
00:34:42
Speaker
It speaks to what I want in my career. By quick fire, do you want quick answers? Yeah, yeah, yeah, no. So basically, it helps you get to where you want to go.
00:34:54
Speaker
Yeah. It's a job opportunity that will help you learn and develop. It's not about the money. It's not about the brand. It's about what is the experience going to give me and what's the organizational culture around that. Brilliant. Next one, best interview tip. Listen to what people say and respond to what they say and recall back from that. That is for everything presentation-wise because then people will remember and actually be engaged with what you're saying.
00:35:24
Speaker
I think that's a great one that can be taken through into just speaking with stakeholders or clients, isn't it, as part of that requirement gathering. So why not bring it into your interview process? The final one, Dylan, if you could recommend one resource to help people up skill, what would it be?
00:35:44
Speaker
Has to be the newsletter, right? Yeah, well, I mean, the data ecosystem for sure, because it covers everything, right? So it doesn't matter what area you're in. But I mean, on that note, though, I would say, honestly, LinkedIn, find the community, find the people who are in your domain, see what they're writing about, posting about and see what references and resources they link up because
00:36:06
Speaker
depending on your role, you're going to be looking for something different. And, and you have to look to those experts to see where they're pointing. And then, and only then you'll find that the books you need to set you on that trajectory and the newsletter, obviously. Yeah.
00:36:21
Speaker
Yeah, definitely. I think LinkedIn, the community are there to help people love to share what they've done and their lessons. And, you know, share yourself, I think is another good one. You know, people will critique, people will like, people will support. So yeah, do that. But Dylan, thanks ever so much for joining us on the pod. I think my key takeaways from this conversation is have empathy to the other data professionals around you, understand their team and have that holistic approach and make sure you don't forget commercial acumen.
00:36:51
Speaker
Thank you for sharing. Totally. No, those are some good takeaways. Thanks so much, Harry. This has been great. Cheers, Dylan. See everyone next week. Well, that's it for this week. Thank you so, so much for tuning in. I really hope you've learned something. I know I have. The Stack Podcast aims to share real journeys and lessons that empower you and the entire community. Together, we aim to unlock new perspectives and overcome challenges in the ever-evolving landscape of modern data.
00:37:21
Speaker
Today's episode was brought to you by Cognify, the recruitment partner for modern data teams. If you've enjoyed today's episode, hit that follow button to stay updated with our latest releases. More importantly, if you believe this episode could benefit someone you know, please share it with them. We're always on the lookout for new guests who have inspiring stories and valuable lessons to share with our community.
00:37:43
Speaker
If you or someone you know fits that pill, please don't hesitate to reach out. I've been Harry Gollop from Cognify, your host and guide on this data-driven journey. Until next time, over and out.