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041 - Data as a Product: How to Drive Real Business Value
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In this episode of the Stacked Data Podcast, we explore how modern data teams can drive real business impact by combining commercial thinking with a product mindset.
I’m joined by Ryan, a data leader at Viasat, who has built the company’s only commercially-driven data roadmap across six data teams. With experience spanning analytics engineering, data product management, and team leadership, Ryan brings a practical perspective on how to bridge the gap between technical excellence and business value.
We discuss what it really means to build a commercially-driven roadmap, why many data teams struggle to connect their work to outcomes, and the mindset shift required to become a truly commercial data professional.
We also dive into the concept of “data as a product” — what it looks like in practice, how to prioritise effectively, and how to measure success through adoption, satisfaction, and ROI. Ryan shares a real example of consolidating 200 dashboards into 40, and what that taught him about product thinking, stakeholder alignment, and delivering meaningful impact.
If you’re looking to move beyond reactive reporting and towards proactive, product-led data work, this episode is packed with practical insights.
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Transcript
Importance of Decision Tools
00:00:00
Speaker
The focus, at least for me and and my team, is not to ship dashboards, but to ship decision tools. Things that change the dial, things that change behaviour. A phrase that I'll often get caught saying is is, if we can't track how our tool or our dashboard or our data implementation isn't affecting margin or revenue or retention, we probably haven't quite hit the mark.
00:00:21
Speaker
But if we can't tight those things, then what we have is an automation of some kind of process that not actually a commercial outcome we're seeking. So the technology's changed. For me, five years ago, i super over-indexed on tools. and And these were important for building my career, absolutely. But not at the cost of relationships and understanding the impact. you know And you can only really do that with the relationship. right The data team isn't ever going to, the dashboard is never going to put a dollar or a pound on the P&L directly. But so the people that are using it do. And you need to be really indexed in that. and ah If I could go back, i would just say keep going on the technical path, absolutely. but not at the cost of your relationships and not the cost of being involved in the messy part the value chain.
00:01:01
Speaker
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00:01:27
Speaker
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00:01:41
Speaker
Now, back to the show.
Introduction to Commercial Thinking in Data Teams
00:01:43
Speaker
Hello everyone, welcome to another episode of the Stacked Data podcast, the show for modern data professionals who want to drive real impact.
00:01:52
Speaker
Today we're talking about something I think separates good data teams from great ones, commercial thinking. It's really easy to build dashboards, define KPIs and deliver reporting.
00:02:04
Speaker
But if it doesn't actually change behavior, connect to revenue, retention or margins, then it's not really moving the business forward. Today I'm joined by Ryan, who's incredibly focused in on building commercially driven data roadmaps, teams, and his mindset of treating data as a product with clear ownership, real user reliability, and measurable outcomes.
00:02:31
Speaker
We'll cover a load of challenges in this space and dive into why Ryan thinks this is the right approach and his strategies to unlocking value from data. Let's get into the show.
00:02:43
Speaker
Ryan, it's great to have you on. Been really looking forward to to this episode. As yes, it's definitely is close to my heart and what we see in the industry is a pain point. um So yeah, we're gonna dive into sort of commercial data thinking and why it matters. But before we do that, for the audience, Ryan, it'd be great to to get a bit of an intro on yourself, who you are what your background is, and i suppose where you are are now.
00:03:10
Speaker
yeah Yeah, absolutely. It First, Harry, thank you for having me. i'm Excited to be here. It's an area that i talk about a lot. um I couldn't believe when you said that other people might be interested. This is a good conversation. I'd love to have it. um yeah Let me give some intro.
Ryan White's Career Path and Current Role
00:03:25
Speaker
so My name is Ryan White. I've been in data now for over a decade and like a lot of people, I've kind of zigzagged around a lot of the different data disciplines in that area. and i originally started off straight.
00:03:38
Speaker
I finished university, I did economics and and I ended up in pricing analyst roles. and I was doing a lot of work on the kind of very front edge of RevOps, commercial ops type teams.
00:03:49
Speaker
um sort built my i built my reputation there really just, and that's where the commercial thinking started. I sort of built my reputation and then I zigzagged around for many years and that ended up putting me into actually some product roles and I started doing a lot more work around tooling and also how you deliver data to commercial people or sales people or any kind of a function of a business in a more product form. So trying to come away from a kind of a dashboard factory and and into the product space, worked with a bunch of software engineers on that, found that really interesting. And it all kind of came together for me then in my kind of most recent move to the company I'm at now, which is a company called Viasat.
00:04:28
Speaker
And here I've really had the opportunity to bring those two things together. So it's it's the kind of, we sit in ah in a very in a very unique position. The team is called Growth Analytics, but our remit is very much supporting the commercial success of the business.
00:04:42
Speaker
But it's doing it through the the provision of tools, which which we would call data products. It's like a virtuous cycle is the easy way to describe it. we We help the business understand their commercial performance.
00:04:53
Speaker
And then we we build tools that help them improve their commercial performance. and the two create this circle. so that's That's where I'm at. That's my history and background. Excellent. so I suppose, look, let's dive in, Ryan.
00:05:06
Speaker
Commercial data-driven roadmaps in data, what what does that actually mean in practice? yeah It's a really good
Tracking Impact on Revenue and Retention
00:05:15
Speaker
question. so For me, i can summarize it by saying that the focus, at least for me and and my team, is that is not to ship dashboards but to ship decision tools things that change the dial things that change behavior. so a kind of a phrase that I'll often get caught saying is is if we can't track how our tool or our dashboard or our data implementation isn't affecting margin or revenue or retention, we probably haven't quite hit the mark. It's not that we haven't built something valuable, we probably have. it's got some you know It'll have some use to the business in some way. there's no reason That's the reason we would have done it. but If we can't tight those things, then what we have is
00:05:53
Speaker
It's really ah an automation of some kind of process and it's not actually a commercial outcome we're seeking. I think one of the, it's really interesting because think one of the things that I get, I see so much out of the industry is like data teams, but yeah individuals unable to sort connect to them business outcomes, from yeah what's what's been your biggest impact. It's really hard to articulate. i think we've seen from the recruiting side and and and what data leaders are looking for is this this ability to connect to to the impact. so why Why do you think so many data professionals and teams do struggle with this in the industry?
00:06:31
Speaker
Yeah, yeah yeah um I'll speak from experience here. I think this is a very open area and then there'll be different businesses, different teams, very challenging. and but If I had to summarize an area that I found myself in and an area that I think I speak to when have peers and colleagues in the meetups, you know kind of stuff you guys are hosting and other people are hosting, I tend to find that the teams that suffer with this the most are the teams that have become hardwired around outputs and they've kind of dissipated from the outcome side of things. so and they're They're rewarded and their impetus and their focus as a team is normally getting requests turned around as quickly as possible, things like time to insight metrics. These are valuable by the way, that this isn't bad-mouthing these. I think they're important things we should measure, but it shouldn't be the primary focus of the group um because it it creates this this this cycle where,
00:07:21
Speaker
and you're focused you're you know You're focused on delivering the request as quickly as possible, and then you sort of improve your processes and you get better at doing this. and Then of course, that almost inevitably responds to two more requests. and so you you You go back on the conveyor belt and you deliver those two requests. and Over time, you become this sort of your your your reward cycle becomes how quickly do I ship the request I've been asked for, and you start to lose the ability to connect it to the original problem.
00:07:49
Speaker
and that's That's why it's ah it's not something that happens overnight. I don't think anyone sets up to do this. I think it's something that happens over time under increasing amounts of pressure to deliver and you you just see the two start to come apart.
00:08:02
Speaker
I i can couldn't agree more. and I suppose what does that happen when what happens when for the relationship when this does start to come apart more? and like how I think the problems are how you're perceived internally if you are seen as a support function. so like why why would there Why is there that problem and how can you help navigate that?
Challenges for Data Professionals in Business Outcomes
00:08:23
Speaker
yeah the perception so i know we've spoken about this a lot because perception is so um important. you know If you're joining a business, a lot i mean I recruit and obviously I speak to other people that are looking to build teams and and perception is one of those things that comes up very early in the conversation for data people now. I often get asked the question upfront, if I'm looking to bring someone on board, they'll say to me, hows how is the team how do you work with your colleagues in the business? How how does the team get perceived in the business?
00:08:50
Speaker
um and so it's ah I think it's ah it's a topic that's particularly for people more in their more junior roles looking for impact more so in their career. It's a big thing. and so and if As these things come apart, what what tends to happen, I think, in my experience, I'll give you some examples. so you Over time, you get very good at delivering on the exact request, but what tends to happen is it creates a bit of a serve and request.
00:09:15
Speaker
process. So the business will submit the stuff to you. You'll go away, you'll work on it, you'll come back, but you're no longer necessarily in the room or connected to the problem. That's the start. And then over time, you kind of lose your ability to ah to sort of influence the the thinking about what it is they actually need. So as a data expert, you probably understand what's available. That's probably the very key thing you can bring to that conversation that isn't always obvious when you're being asked for a request. so you know whether it's even possible to start with. Or if it's not possible, you know potentially how to achieve it in a different way or a different outcome. If if if those perception if your perception is that you're the person to get things done but not the person to support solving the problem, you'll often have to have to spend most of your time just connecting those two things in that request backlog. This is like a thing.
00:10:02
Speaker
and Unfortunately, for for me, I've found this in the past is um this this gets worse over time. so the more the The better you get at kind of responding to requests quickly, the more you tend to start introducing safeguards to protect yourself from ambiguous and messy requests because you realize that they're the ones that are slowing you down.
00:10:21
Speaker
and Of course, it's the messy bit is where all the commercial decision making is made. and so Over time, it gets worse and worse actually. You start to find it really difficult. you know Your stakeholders no longer see you as a partner in that relationship. They start to see you as the person the the the techie person.
00:10:36
Speaker
And of course, it gets very comfortable to sit there and a lot of teams do. And and and that that's where the perception then, once it gets to that point, it's kind of a lagging indicator. The process, the funnel is is broken a little bit and you need to start working out. And certainly for me in the past, I've had to then start to say, well,
00:10:52
Speaker
you know In which areas do I think I can have more influence? and and How do i convince these people that what I can bring to the table earlier is actually worth more than what i'm capable of doing with data afterwards? and that's That's the relationship change I'm seeking to to create when I'm looking to change perception in the in the group group that I'm in or or in a team that I'm working with over time.
Treating Data as a Product
00:11:14
Speaker
is it's It's not a one and one und done answer. it's It's a slow one thing.
00:11:19
Speaker
No, it's definitely not. I think the the thing you said is like that, just building up that trust and through communication, understanding what the pain points are, being able to take them through how you could, can help them is is I think probably strategies of how you can rebuild that trust if that gap has started to split. It might be a good sort segue to talk about, know you've mentioned when we've spoken past about how sort of software engineering and product is probably a few years ahead of where data is in its maturity and development is a as a department. like What lessons you think we could take from
00:11:56
Speaker
software and product teams. I suppose they come under different pillars because software is more of a ticket team, but how maybe how they work with with product. yeah what What lessons do you reckon we can bring from them?
00:12:08
Speaker
yeah is it yeah i'm yeah They're very different disciplines, but they work together. right I think most most companies these days kind of have this sort of looping process where the product team and the engineering team are kind of seen as the as the front door for for feature deliver development on on any kind of thing you're building.
00:12:25
Speaker
and Before we jump, i was going to say actually um on on the perception thing, because it leads nicely into product, is is as your perception starts to slide, and I think a a symptom I've noticed over over the years is if your perception starts to lag too much, um the business will create the capability that you're looking to fill. that kind of they they They will create it around you. so um The example i'll give is, is um and it leads into why the product thinking is so important, is we've started to see, or we did start to see, this is kind of in a reverse trajectory now, thankfully but you know finance teams will spin up analytics departments or they'll spin up RevOps departments.
00:13:02
Speaker
and and The challenge is is that what we've done over time is that we've lost sight of the of what the the dial turner is for those teams. and so We've been very effective at creating a mechanism for them to get data and we've been very effective at providing kind of automations but potentially, but we've maybe not actually solved their problem. and um We can deep dive into a finance use case later, but um they're often the the culprits of this because they're pretty savvy, they understand data very well, and they're right on the pulse of commercials of the business. I tend to find they're the department you want to work with.
00:13:34
Speaker
And then that that leads into product, right? So so when you're trying to reverse that flow, I think that's the thing, I always say, the thing that product has been so influential for me in my career, the way to think about things, is is that you can actually boil down product. And I'm kind of ignoring the, there' ah you know, you can get talking about agile at this point. Let's park all of the methodologies for now and just talk simply about how do we organize our thinking about what to build for who, for what purpose. And that's really what I think about when i think about product.
00:14:02
Speaker
ah You can really rationalize it and i can I'm going to oversimplify here and probably upset some people, but you can really oversimplify it to i really clear ownership, really clear life cycle, and like an always on reliable mantra. so so I'll go into a bit more detail.
00:14:18
Speaker
Clear ownership is not just um who owns the dashboard, but who Who's going to use that dashboard to make a decision? they own They own that product as much as you do in this cycle. right we're At some point, we would like the decision, we'd like the business to alter course or potentially not alter course, but we'd like the decision we'd like the business to make a decision. and The chances are the data person is be the person to actually make that decision, but that means that the ownership of this kind of product you're building needs to have some accountability in the business. You need to have someone who's there saying,
00:14:48
Speaker
you know if I had this information, this is what I would do with it. and so That's what I mean by clear ownership in this space. I think product has had this for for a while. they've kind of had the the The product owner is often the proxy for a customer. and so In our sense, we might not have a product owner, but we'll still have products. and The product owner in this sense would be someone in the business who's making a decision.
00:15:09
Speaker
um you know Contracts, life cycles is a big one. and One of the things, Harry, you've asked me about a number of times, I think we're going to talk about it a little bit later, but we did a big rationalization exercise of dashboards in the organization a few years ago, still ongoing to be honest.
00:15:25
Speaker
um and I think life cycle is one of those things in product that's super hot. and it's you know A good product manager will always be looking at not just what features to build, but also what's not being used and what do they need to get rid of.
00:15:37
Speaker
and I think that's ah that's a really strong, that's a it's um it's not natural for data people. We spend so long curating and thinking and getting really complex logic built.
00:15:49
Speaker
And sometimes it starts to fall flat and we need to be so much better at just saying, no, it's it's kind of it's gone, it's finished, archive it move it away, consolidate it. it's Over time, it's it's one of these problems. It's a perception problem because it creates fractions in the data warehouse or in in in your BI landscape, people have two ways to solve the same problem and then you end up with conflicting information and it erodes the trust. so so Actually killing stuff off as much as it can be painful is actually a really important part of product thinking i think we need to bring across.
00:16:20
Speaker
Always on, I think yeah you know kind of products are that they're not kind of people reliant. So um the idea of a data product is it's ah that stakeholder might be using it, they might be around the side of the world and it should be performing for them as it always has done, even without human intervention.
00:16:37
Speaker
I actually think we my most teams I've been involved in, worked in, have been pretty good at this. I think we've, as it's as ah as an industry, we've been pretty good at realizing that we need to remove ou ourselves from the decision making loop um in many cases.
00:16:52
Speaker
I think it's actually a problem in some cases, but in in many cases, if you've got an operational pattern of the business, you want to remove yourself from the loop as much as possible. That's what think about the product leadership there. so that makes sense to you know Does that make sense?
00:17:06
Speaker
I remember this talk from someone in the industry, yeah actually owns Looker at Deliveroo and their BI instance. and he He brought up that point and always stuck with me is that data professionals do love to build, but they hate to they hate to delete and they hate to to get rid of. and um yeah self-serve bi was seen as this you know the holy grail of of of business intelligence and analytics um Yeah, I've got plenty of horror stories, some of which we covered on this podcast about bloat and the the challenges of discoverability, et cetera. And yeah, I think the the point that you made around ownership is is really key, both who's the owner on the data side, but i yeah, I haven't maybe heard it framed like that with the owner of the the customer, whether that's an internal customer or whoever, and and the making sure that they're they're accountable to actually using it And I suppose that's where you need to be that that business partner.
00:18:04
Speaker
you're done Yeah. yeah and i'll give you I'll give you some clarity on that. like ah I'll give you a practical example. right I think that's always helpful.
Effective Data Tool Utilization by Finance VP
00:18:10
Speaker
so we um we built a bunch of we've Over the years, we've built countless numbers of solutions to different people. What we found though is some sort of about six to months ago, there was ah an operational process in the company that wasn't perhaps running as smoothly as it could. and There was a lot of requests and everyone would have heard these requests. We need better metrics. We want a single dashboard. These are kind of common common things. and so sort of but you know followed my my own advice on this and me and a couple of members of the team, we really sought to understand what the kinds of decisions were being made in that kind of thing.
00:18:41
Speaker
um And we delivered an iteration of this dashboard. It was pretty good, I thought. It kind of covered their bases. and What we found with that is it was sort of being exported into PowerPoint, being sent out as a pre-read, questionable amount of punch on that. It's not, I'm not 100% sure whether it's actually being used, but at least we had some consistency.
00:19:00
Speaker
the the The thing that changed the game wasn't the redesign of the interface, wasn't the tool, it wasn't the metric design. The thing that actually changed the game was that the the the the finance VP said, you know what, I'm going to bring this up. You're going to come into the room with me. so you know You're going to be there if anything goes wrong or anything breaks. but I'm going to bring this up and we're going to go through it line by line, metric by metric, on the dashboard, in the meeting. and he basically said, you're all now part of but you you're part of my team um and my role in this is to make sure that we're holding people accountable for any changes or any actions that need to happen. This tool needs to make that process work. and so By him taking ownership of that decision-making process, he he effectively became the owner
00:19:42
Speaker
from the business's standpoint. and that you know that wasn't We didn't have to do anything clever in the data. We had everything, we had all the raw ingredients in place. All we changed was the framing of how to deploy this by giving it clear ownership in the business with a person.
00:19:56
Speaker
really interesting. Great of that, I suppose, the finance professionals come in sort of help enable that as well. um I suppose that light lamp sort of moves us on quite nicely to, suppose, i like a tangible lesson that people could apply. I think, you know, it's one of the points of the podcast. So, you know, what would be a ah mindset shift that you you'd recommend data professionals taking to think more commercially about and the the problems that that they're having and like, yeah, can you give us an example of how how adopting that mindset shift um actually changes how how how how you've worked or so you've seen your teamwork?
00:20:40
Speaker
but yeah yeah Yeah, absolutely.
00:20:45
Speaker
Yeah, i am I will borrow something from a colleague, and a previous manager of mine. he He sort of talks about people in the sense of being operators, um which I always found a little bit ambiguous, but I think it makes a lot of sense in the context of this question that you just asked because your your your commercial data people are technical enough to do the data job, absolutely, but they're they're actually embedded, they're operators. they they The bit they get the kick out of, the bit they like, the bit that they see as most valuable is when they're tapping they're tapping the sales director on the shoulder with a piece of information that they probably didn't know from an investigation they're doing over here, or that they're facilitating some kind of interlink between the customer support team and a regional sales team on a particular issue that's going on. they' they're not They don't see their role as purely getting data to the front door. They see the role as getting data into the hands of the person or the or the thing that's going to make a difference. and That last mile of work is purely operator work. right it's not it's um
00:21:53
Speaker
You could argue you don't even really need to build the data to do it. In fact, some of the more recent developments in my kind of domain have been having people that are far less technical we've ever had, but much more data savvy,
00:22:05
Speaker
and that's because we've really sort of embraced this idea that you want these people, in the truest sense of the word analyst, to be taking stuff into that into that into that operational flow of the business, not just delivering it to the front door.
00:22:21
Speaker
I think that's, so for me, if I bring it right back to the thing, which is what would you do, would I recommend or what would I do differently? I would say try and open up that last piece. Get data to the front door, get it to the dashboard, get it to the point where it is, and then say, what could I do after this? Let me go and read the dashboard. It's another thing I personally don't do enough of, and I think my team probably don't do enough of is,
00:22:45
Speaker
Go and look at the right dashboards we actually built ourselves. and Then let's say, well, what would we do? and then and Then let's go and ask someone else. you know Let's this's take that insight to them. Let's see how they feel about that and let's see why they're not actioning that. Did they not know about it or they did know about it? Is it not quite right?
00:23:00
Speaker
let's Let's build a network around these things and become a bit more of an operator in that space. um Great advice, especially that that last mile. it's i yeah Another way you can frame it is it's the the so what. um know to To what you've built, whether it's an insight, a dashboard, and exactly what you said. What's the next stage? What decision would you make off the back the back of that? and I think if you're having a chance to run that and actually tell and and make recommendations to the stakeholders, they you get that better feedback loop of,
00:23:35
Speaker
oh we We don't like that because of X. and Then maybe the the data is is is, you can change their opinion because actually they haven't seen something. so yeah That's really, really, really helpful. lunch I think another thing that you're super passionate about, Ryan, is sort of treating data as a product. We've obviously mentioned stealing some stuff from the product space and you've dropped in about building data products already.
00:24:02
Speaker
The term's growing a lot in the industry, but let's like start the basics. like what What is the term sort data as a product and what does that mean in practice?
00:24:13
Speaker
Yeah. um Again, lot this is one of those questions, culture is a big one. Another one is I get asked a lot is around data as a product. um i I haven't really landed on the perfect analogy, but I would say a good place to start would be to treat data. Think of it like an internal SaaS tool.
00:24:32
Speaker
I think that's that's the simplest way to start the thinking process. Now, not everything fits this perfectly, but um but I think this is a really great great place to start. We'll build on some of the themes earlier. so With clear ownership in a SaaS tool, you know the when Salesforce come in and they sell a piece of software to a company, there's an owner on the internal side as well. right You'll have someone who's implementing Salesforce. They're ensuring that people are onboarded to Salesforce.
00:24:59
Speaker
You need to think about your data products the same way. you know Who have you got that's on the on the inside of that process who's going to bring that stuff through? and you know sort of Define focus focus on the life cycle, making sure you're taking you're turning things off.
00:25:14
Speaker
and i don't I don't think it needs to be as as well-defined as some of the products kind of doesn't need to be a full implementation of product mindset, but i think taking some of these high-level categorizations is is ah is an important thing.
00:25:29
Speaker
um i am i read ah I read many, many years ago when I was sort of dab dabbling in a product role, I read a book called Inspired by a guy called Marty Kagan. and I would recommend that book to anyone. i don't At ah no point does he talk about how to build really effective dashboards or or how to get data products stood up, but he he sort of takes this sort of stand back approach and he says, you know you're probably a consumer of data products and you probably know the ways that you like to work.
00:25:57
Speaker
ah Let's just kind of work out how to systemize that. and and I found that very approachable and applicable to data. Excellent. well We can definitely put a link to that in the in the show show notes. and yeah I think we can definitely borrow a load of stuff from outside of the ah from the industry.
00:26:16
Speaker
um Maybe it's good time. You mentioned obviously about this this project earlier, and it's something I know we've spoken about a lot. um Obviously, you build great data products.
00:26:28
Speaker
well As you said, dashboards can be massively inflated. um think you've got a really good story, I think, from a how many dashboards you had to to to where you should be. I think that's probably a good starting point to understand how we should build data products moving forward.
00:26:43
Speaker
Yeah, I agree. It's a good good place to think about it. so I'll start with something potentially controversial. right so um I think almost all data teams have too many dashboards. and The reality is the dashboard, we don't have this many dashboards because people love dashboards.
00:26:57
Speaker
We have this many dashboards because people like very specific little things and they like people making like they're like having full autonomy and ownership. and What you end up with is ah is a shotgun of like half-baked or semi-proven dashboards.
00:27:14
Speaker
So when we think about dashboards, if i know and this may not apply to everyone, but I've certainly found that i almost every team data team has too many dashboards trying to solve the same subset of problems.
00:27:27
Speaker
So yeah, let me talk about it. so um About or three years ago, we so the the classic thing that happens to all data teams is you get hit with a big
Consolidating Dashboards for Better Engagement
00:27:39
Speaker
migration. right um They want to change technology stacks, they want to reinvent things, and and I get it. so so That comes around and they're doing a pretty significant shift of of not just the data warehouse technology, but they're also shifting a lot of the underlying applications and the pipelines.
00:27:54
Speaker
and The decision really at the time was, you know we're going to have to we're goingnna have to kind of re-architect a bit here. There's no way we're going be able to just take what we have today and and produce it there. Migrations, they what they are. so As part of it kickstarts the process that, well, what do we do about this? and so there's ah there's that That creates the momentum to start thinking about how to get rid of them.
00:28:16
Speaker
I think the thing that's really interesting is the way we approach this for product mindset. so If you take the 200, typically what you can do is you can go and look at kind of engagement metrics. you know Most BI tools will produce these. Who's logging in? How much are they using them?
00:28:30
Speaker
When's it last updated? You can kind of look at the very high level health of these things. and That will often help you get rid of some some bits and pieces. and That's kind of feeding into this deprecation part of the product mindset.
00:28:41
Speaker
um but i i think the other part of it is then and what we we we tried to do was then to say, well, hold a minute. we're building very similar dashboards as well. So we started to kind of, we had this big inventory list and we said, well, let's organize it by yeah kind of definitely used, probably used, aren't really used. they're probably used or definitely used, and let's organize them by common theme.
00:29:07
Speaker
And that is, and and the themes we kind of landed on were, what are we trying to, who are we trying to get to do what? Which is a very ambiguous way of saying, are we trying to get salespeople to more proactively target their customers? Or are we trying to get finance to consolidate onto a single forecasting tool?
00:29:25
Speaker
What are we trying to do? And what we found is that you've got, Many tools that are doing part, if you if you think about it in that really kind of high level, you've got lots of these dashboards ah ah kind of aiming at the same thing.
00:29:36
Speaker
um They're all trying to do the same thing. so They're all filling different parts and they've got slightly different use cases. and It's not as easy as just being able to say, well, look, everything that's in a similar thing, just turn it into one. It's not as easy as that, but it does then start to create some momentum. so We call them decision moments. We had a set of decision moments in a set of teams that we wanted to really target with this.
00:29:59
Speaker
We brought the dashboards into it, and then we basically sat down as a group and we said, well, you know I think these three things are almost identically they're almost identical and we could definitely could just combine those. and Actually, we think this is a different thing, but what we should do is make sure those two things are part of the same user experience journey rather than having two separate dashboards for it. so in know In a practical sense like Tableau, that might mean they appear as linked dashboards to the user. They don't know any different. They have they always enter through one dashboard and it links to another.
00:30:30
Speaker
But for them, they've got one dashboard and it all kind of works. For us, we might maintain them separately, but it allows us to start bringing those purposes together. um and You don't do them all at once. You don't try and reinvent them all. But for us, this was an extensive migration. and so We sort of just worked through decision moment by decision moment. nices That's kind of how we did it. and that to To kind of bring it all to a conclusion that the impact this has had.
00:30:54
Speaker
you know We were looking at about 200, 250 dashboards and we had monthly active users in Tableau at the time. which means that every user had more than one dashboard to them you know on average.
00:31:09
Speaker
and When you say in those terms, it feels nuts because these people are logging in once a month and they were all basically logging into the same five or six or seven or eight tools roughly, depending on their team. so so we It was very obvious we had a we had a challenge here. so By doing this exercise, we're at about 40 now.
00:31:26
Speaker
forty now um and I would say- It's a reduction. Yes, it's quite a significant reduction. and The plan is to go further with this. um you know we'd love i'd love to go We'd love to go further with this, because it's's and and but the the challenge is now is that um you know you've really got to start thinking about removing dashboards entirely at this point and start and thinking about other ways of engaging users in data. This where get that last mile piece. industry favorite at the moment. That's when to think about things like AI. But but right now, 40 feels very manageable. It's a good amount. They're quite robust.
00:32:00
Speaker
um Yeah, it's it's it's ah it's a significant improvement for us. um It's gone well. I'm pleased with it. I think we're all quite pleased with it. Yeah, it sounds like great, obviously, it's huge den initiative it to go from 200 to 40 in terms of maintenance alone, ease of discoverability. I think you're not alone in that problem. I hear many teams and companies with with exactly the the the same problem. I like your strategy of how you broke it down. One, just general health. What are people actually using?
00:32:33
Speaker
and Then sounds like you're almost sort of categorizing who's using what and why. and Then is there, can you spot any sort multi-use case? Can we compine combine some of these yeah um to then have a really clear clear sort of roadmaps and and clear products of what they what they do um yeah and i would just ah You're absolutely spot on. um yeah that That categorization, I would even add to that, right? The the one thing, probably didn't mention it, but I think it's really important is it's part the inventory.
00:33:03
Speaker
and When you group by decision moments, and we're talking about commercial mindset earlier, you often find that the metric the commercial part of that bag of that product is much clearer once you've done this exercise. so You've got three dashboards that might be showing the customer base from slightly different views.
00:33:20
Speaker
When you start to group them together, the decision moment for that might be, we want to make sure we're proactively renewing certain customer groups or that we're proactively targeting certain areas with campaigns.
00:33:32
Speaker
And you know the people that are going to enforce this and you know that they're doing it to try and either reduce a churn rate or to you know drive proactive upgrading contracts or to even as simply as just move revenue from more risky pots to less risky pots.
00:33:47
Speaker
um suddenly you're talking about revenue. right yeah that that's That's where you're now, that now you're you're engaging this person on, and it's no longer about a dashboard that has to be you know delivered on a Tuesday morning, this quality with this, this. You're now talking about, well, um if you want to protect this, you knowre we're trying to protect these customers. and I think the best way to do that is to provide this kind of mechanism and you work backwards from that. and that That creates the cut through that I think a lot of teams, you know we talk about the reactive stuff. If you want to get out of the reactive space, um start having those conversations about revenue and start start building the trust that you know I'm here to support to support your goal. I'm not i'm not here to to bring you to the shop front and give you a tool. I'm here to support your goal and I just happen to be an expert in building data to to do that.
00:34:32
Speaker
I think it's a great mindset. and you're You're a problem solver and data is is one of your tools in your in in your box. I think that's ultimately how you should be trying to perceive yourself and want want to be perceived.
00:34:45
Speaker
the perceived um it's This sounds great. and like Now you've built out, and you might be so just building out a new data product, a dashboard, you've obviously consolidated. how How do you measure the success
Tying Data Products to Business Success
00:34:59
Speaker
of it? How do you know that what you've done is is is driving the right bright areas, whether that's adoption, satisfaction, ah ROI? what what What are your sort of success success metrics?
00:35:11
Speaker
yeah yeah um yeah it's it's it's It obviously varies, um but I would say generally speaking, and I'll i'll reinforce what I said right at the beginning of all this, which is if we can't tie it somehow to a revenue-based or a cost reduction-based initiative, then we probably haven't quite got the product's direction right yet. That doesn't mean we don't build stuff that isn't tied to it. We do. we have obviously commitments to support the operations of the business you know just all left moving stuff from left to right, that does exist. and but When we're developing products, we we think very hard about that. so I would say um you always want to try and make sure you're aligning to to that piece. and so We're building tools at the moment where we're building a very very robust tool at the moment where we're trying to support the prospecting part of the funnel in sales. so we've got We've got Salesforce, we have extensive marketing you know activity going on.
00:36:08
Speaker
know We're a global company with lots of different intricate interlinked markets and we have ah you know a combination of complex satellite networks that all overlap. so It's not super obvious as to which customers we should be serving with what kind of product at what kind of price. and that's ah It's a moving target because things are always changing. and so we're building ah We've been building for a while now.
00:36:29
Speaker
really not nothing new in the data space. The data is kind of was existed here, but the product is quite different because it's now a tool that sits above Salesforce in the funnel. And what it's designed to do is rather than rather than have the account manager or the salesperson or the VP come up with, I want to target this customer, which is kind of the traditional way to do this, It goes other way around. It says, well, this is areas of opportunity and people that we've managed to simulate or or we've correlated to that. to to that data so Same data, but I've reversed the flow. So I've just allowed people to bring stuff into the prospect funnel slightly differently. I use the I very liberally there because there's a very smart team of people that are actually building this and then my job is to basically start their way a lot of the time. but yeah this is ah This is a very influential product um and it's completely tied to revenue. right so so We know how many things we put in the hopper and we know how successfully they convert and we know how they successfully convert against a other forms of ah of prospecting.
00:37:26
Speaker
and The feedback's just been phenomenal. um you know it's you know Engagement metrics obviously are great. We we saw this very early on, and but also just the the amount of features that we're being asked to develop is far beyond our ability to to deliver on that. so this was another this is i mean This is a great example of of kind of where we've managed to just about find product fit now, and it's it's all tied to revenue.
00:37:51
Speaker
and but yeah i I digress. Yeah, go on. No, no, no. no that's real i mean I love how you've also talked you've seen ah the ability for a data product. It's actually changed the mindset and the systems and process that have clearly been ingrained probably in sales team for for for a long time.
00:38:09
Speaker
um yeah That type of tool in in our industry, I can imagine being being being really helpful. um But yeah, I suppose moving on, Ryan, i think one thing that that we're talking about, yeah building products, consolidating,
00:38:26
Speaker
There's always a million and one requests from data teams. I haven't seen a single data leader that that has not got, or data professional that's not got a million and one things to do and priorities to to work on. so but What have you found is the best, most effective way for you to identify and prioritise the problems and the work that you should be focusing
Prioritizing High-Impact Data Projects
00:38:48
Speaker
in on? Because think that's something that people can get a lot of lot of value from. what What should I be working on and how um um why should I be working on it?
00:38:56
Speaker
yeah Yeah. it's um yeah it's Again, it's a great question. so ah there's no I've definitely made mistakes here where i've I've felt like I needed to invest or index into something in particular because it was a big problem. I think everyone has. right They've built things that ah very very great they're They're fantastically competent, technical and and brilliant solutions that don't really sort of solve problems.
00:39:20
Speaker
I don't have a clear kind of one like a silver bullet on this, but what have found in my experience is the problems that tend to be ones that are really worth solving are normally the moat what they're normally ambiguous in nature. unlikely to it's unlikely to come to as a fully formed idea Depending on your relationship with stakeholder and their level of data maturity, you may get something that's reasonably mature, but it's unlikely to come fully formed. think that these problems, the ones that are worth solving are normally quite nebulous and challenging to start with.
00:39:52
Speaker
and I would say that they're also normally pretty high stakes. so They're normally ones where the only way that people can articulate the answer or the next step to that problem is very binary.
00:40:04
Speaker
if it's ah If it's an answer they go, well, you know, it could do this, we could do this, and there's lots of ambiguity. They're not clear. The chances are that it's a problem, absolutely, but perhaps it's not something that's keeping them up at night or something that they couldn't reasonably kind of solve with the tools they have to hand. The ones where they come to and that that there is a binary, they say, well, look, we don't do this. I think we could we could lose this customer. If we don't do this, I think this is a market we're just going to fail in.
00:40:29
Speaker
This product is just not going to launch correctly. they may ah They may be right or wrong about this and they may be over-egging it, but they tend to be the ones would focus on. so So nebulous and where they don't have a really good idea as to what the outcome is going to be, but they do know that not doing something is pretty detrimental.
00:40:46
Speaker
If I can get those two things, I'll almost always invest some time figuring out what we can do to help and spending some time with them. Yes, I think it's tying it back to what what's what's the consequence of not doing it what what what or what's going to be the impact if it's either one, and if it's going to be a massive increase in X or by not doing this, we're going to have X happen. I think that's that's the questions that you want to be asking. why should When you're asking, why should I be doing it?
00:41:14
Speaker
It's not why, it's what's going to be the impact. I think from there, you can all probably reverse engineer which which which project's going to have the biggest lever and yeah ultimately can you pat yourself on the back on and take ownership for enabling.
00:41:28
Speaker
Yes, you're absolutely spot and That's a great point as well. yeah e You often have to ask that question explicitly. It's very unlikely you're going to get that. That's going to be forthcoming. It's a little bit uncomfortable. You have got to go, well, look, I could build this for you, but what are you going to do with it? What happens if we don't build it?
00:41:45
Speaker
That's a little uncomfortable the first few times you ask it, definitely. Yes, I think it's just you know when thinking about your own career, um yeah your own impact, your own career, when you want to go and get that next promotion or you want to look for a new job, you need to be able to tell stories of what why should someone hire you, what impact have you had? so Making sure each and every day that you're focusing in on the stuff that can have the most impact, i think is something that everyone should should obviously to try and try and do and um Wrapping up, Ryan, I suppose the the final thing, there's a lot of teams that are still in reactive mode and want to try and move towards this more proactive product-led type
Transitioning to Proactive Data Practices
00:42:27
Speaker
delivery.
00:42:27
Speaker
um What's your advice for the steps that they can take to go on to start that journey? yeah yeah I would say um
00:42:40
Speaker
In short, just one concept, one team, one at a time. um don't don't it's it's it's This is a hearts and minds change.
00:42:51
Speaker
It takes time to get even people within the team aligned to this. It gets tighter takes time for your stakeholders to accept this and not all of them fully ever will. so it Really, it's it's picking off, you know the example we had is is the finance VP, this was years ago when he first stepped into this role. Since then, his whole team are now kind of acting in this capacity for us.
00:43:14
Speaker
They're sort of evangelists for our product and they're our co-sponsors for a lot of things now. um It started with one, and it's it's gone from there. We've actually started to structure our team to continue this model with more and more people in that way, but it is one at a time. um and I would say, if you if you're particularly if you're kind of looking at their dashboards as well, i mean, for us, and when we look to those big list of dashboards, you you can you can pick some off and you can say,
00:43:42
Speaker
you know this is a really strong product. This is this is is' well engaged, I can tie it to revenue. we have a really strong advocate for it in the business. You probably have most of the ingredients there. and Actually, the only thing that maybe you might be missing is the fact that you feel like you're you're in the order taking position and not in the partner position.
00:43:59
Speaker
um The chances are that stakeholder you've got, that person who's advocating and evangelizing your solution, they're probably well up for you being a partner. I would imagine that they're very open to having that conversation, and talking about, okay, well, what more can we do you know they They obviously love the thing, they want to use it, it's been making a difference for them.
00:44:17
Speaker
so so so Start there, you know one at a time and and see what you've got. I'd be amazed if most teams couldn't could highlight a couple of really strong candidates for a shift in mindset towards more product and commercial with their existing setup.
00:44:31
Speaker
Excellent. Look, Ryan, now I think that's a you've shared some real gems there and some nuggets, I think, in trying to change that mindset shift. And I suppose how you just build a relationship really with the with the stakeholders in the business, I think that's going to be, especially in the world of AI, is going to be your your differentiator as a data professional.
00:44:52
Speaker
technology is becoming easier and easier to use and to build and not to take anything away from the building, but knowing what to build and how to how it's going to impact the business is is just as important, if not more so important as we we move forward. so um Thanks for sharing everything.
Balancing Technical Skills with Relationship Building
00:45:12
Speaker
I suppose before I let you go, um What's the piece of advice that you'd like to give yourself now the um that you could you could give yourself five years ago from what you know now?
00:45:26
Speaker
yeah You've set me up really well for this um with your previous comment. so the The technology's changed, but the philosophy. so I think for me, five years ago, i super over-indexed on tools. i I knew everything about the BI tool. I knew how to optimize. and and and These were important for building my career, absolutely. um but not at the cost of relationships and understanding the impact. You know and you can only really do that with the relationship. right The data team isn't ever going to, a dashboard is never going to put a dollar or or a pound on the P&L directly, very unlikely. but You might say, okay, that's not true. You could do, but generally it doesn't.
00:46:03
Speaker
um But so the people that are using it do. and you need to be really indexed in that. If I if i could go back, I would just say you know keep going on the technical for myself personally, and keep going on the technical path, absolutely. It's important to to know how to do this stuff, ah but not at the cost of your relationships and not at the cost of being involved in the messy part value chain. That's that's the area that's been most profitable for me and the area I wish I'd have gone to and and will continue to go into in the future.
00:46:29
Speaker
Yeah, I think it's great advice. People that can thrive in ambiguity, can make sense of it and and drive value from it, I think are the people that continue to rise in in their careers. So um yeah, thanks so much for joining me Ryan. It's been been great to speak.
00:46:45
Speaker
Yeah, thanks for having me, Harry. Great time. That's it for this week, folks. We'll see you in a couple of weeks. Hi everyone, just a quick one from me. If you've enjoyed today's episode, I'd be so grateful if you could hit that follow button or leave us a rating.
00:46:59
Speaker
Even better, pass the show on to a friend who might also get some insight from it. It really helps us grow the community and continue to share amazing conversations. I also wanted to take a minute to talk to you about Cognify.
00:47:12
Speaker
Those of you that don't know, Cognify is the leading recruitment partner for modern data teams. We help some of the world's best organizations scale data and drive real value from the buyers that they make. If you're thinking about building a team or making a hire and you're struggling with talent or just want some insights on the market, then I'd love to jump on a call with you and tell you a bit more.
00:47:35
Speaker
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00:47:48
Speaker
Finally, big thank you to Omni, this season's sponsor. If you'd like to learn more about the AI analytics that Omni can deliver you, then check out the link in the show notes or come speak to me. I can happily point you in the right direction.
00:48:01
Speaker
Again, thanks for listening and we look forward seeing you a few weeks time.