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035 - How to Be a Strategic Driver of the Business - Senior Director of Wise
194 Plays18 days ago
Welcome back to the Stacked Data Podcast — where we explore what it really takes to build impactful data teams in the modern world.
This week’s episode is all about stepping out of the ticket queue and into the strategic driver seat.
I sat down with Adam Cassar, Director of Analytics at Wise, to explore how analytics teams can break free from the reactive reporting cycle and become genuine business partners. This conversation is packed with real-life examples, practical strategies, and hard-earned lessons from Adam’s experience leading high-performing teams.
We cover:
✅Why many analytics teams end up in a service-provider role — and how to shift that perception
✅The biggest barriers to becoming more strategic (and how to overcome them)
✅How to proactively influence business decisions (not just report on them)
✅What skills, mindsets, and relationships actually matter if you want your team to have impact
Whether you're an IC or leading a data team, this episode is for anyone who wants to stop being a dashboard factory — and start driving real change in the business.
🔁Share it with someone who’s ready to level up their data career
#StackedDataPodcast #AnalyticsEngineering #ModernDataTeam #DataLeadership #Wise #DataStrategy
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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 Gollup.
00:00:13
Speaker
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.
00:00:25
Speaker
So get ready to join us as we uncover the dynamic world of modern data.
Transitioning Data Teams to Business Drivers
00:00:34
Speaker
Hello everyone and welcome back to another episode of the Stacked Data Podcast, the show where we dive into what it really takes to build impactful data teams in the the modern world.
00:00:46
Speaker
Today's episode is all about stepping out of the ticket queue and into the strategic driver problem solver. Delighted to be joined by Adam Kasser, the Director of Analytics at WISE to explore how the data teams can become true strategic drivers of the the business. We're to kick off by unpacking what that really means.
00:01:09
Speaker
Move on to some great examples of the that Adam's got in his history and really providing a really clear roadmap or framework of how to transition yourself into this more influential position. So yeah, whether you're an individual contributor or a leader aiming to elevate your team, then I'm sure there's going to be a lot to unpack in this episode. So let's jump into it.
00:01:32
Speaker
Adam, welcome to the show. Pleasure to have here. Thank you very much for having me here. And good to finally be on the podcast. Yes. and Look, I was scared of bit an intro to yourself, but a very high level director of analytics at
Adam Kasser's Background
00:01:46
Speaker
Wise. So for the audience, it'd be great to understand a bit more about your career journey and I suppose yourself in general.
00:01:52
Speaker
ah Sure. Cool. So a little bit about me. So I'm originally from Malta, which is not something but people would normally guess from the accent. So dad's Canadian, mom's Australian. So I learned to speak English in a house where no one sounded like the locals, if you like.
00:02:08
Speaker
I've been in the UK for the last roughly 20 years and in some kind of role within data for the last 14, 15, depending on how you count that you know first job.
00:02:22
Speaker
So I've done a lot of different things. I've done quite a lot of what we would these days call data engineering or analytics engineering, as it's more recently to come to be known.
00:02:32
Speaker
Done a decent amount of data science, so like slightly more sophisticated statistical modeling, lots and lots of analytics. And I've been managing data teams and building data functions.
00:02:44
Speaker
Usually scale up lending companies, so stuff that's growing pretty quickly and needs to go from nothing works to this has to work at a meaningful scale for the last maybe eight, nine years now. And so, yeah, that.
00:02:56
Speaker
I've been at Wise for nearly four years now. i'm coming out to my four-year sabbatical, and I've moved around quite a bit. I like to joke that my boss's basic approach with me is to throw me at whatever the biggest fire is and the basis that I seem to be okay at fixing those and helping those teams turn around and do useful stuff with the data they've got.
00:03:16
Speaker
So yeah, that's me. Great intro and I've said it before on the podcast, I think, yeah, the The true position of ah of data professionals is is to be a problem solver. And it sounds like you're not a problem solver and a firefighter at the same point.
00:03:32
Speaker
So look, great to to dive into this. I know it's a topic that you're really passionate about, Adam.
Analytics Teams as Strategic Partners
00:03:39
Speaker
And it's about being that strategic driver. So why is it important for for analytics teams to act as a strategic partner rather than just a service provider?
00:03:50
Speaker
So, you know, I was actually thinking about that problem the other day, and my initial reaction was like, you know, yeah to me, that sounds like you're really self-evident, right? It's like, you know, in a modern company, the way you make sense of what's going on, so like what the customers are doing, what's happening internally, um you know, what it's like if you like the reality you're actually in is very much determined by, know,
00:04:14
Speaker
or shown to you by the data. right So it is the basic reporting. It is the analysis that actually gives you a decent grasp on what's going on and what you should be doing about it, or at least informs what you should be doing about it.
00:04:26
Speaker
right And to that extent, for companies or teams to not want to take advantage of that and really make strategic use of their data, as in inform what they're doing and why,
00:04:39
Speaker
It's a little bit like saying, like you know well, I want to go do this MMA fight, but I want to be blindfolded while the other guy's not. right It's like you're just fighting at a huge disadvantage, and it just doesn't make any sense. And so there's like tons of value there, and you should want to be involved in that.
00:04:54
Speaker
Similar from the perspective of the data team, I think most data teams are made out of people who are much happier and knowing they're at the center of the decision making. So just by disposition, analysts tend to be like this, right?
00:05:07
Speaker
And I think it's really important that they behave and act in such a way that is going to put them in the driver's seat by a lot of decision making and is going to help them achieve their full potential and actually having a big impact on what the company does, right?
00:05:24
Speaker
So I think I'd like to even maybe take a step back as well from from there, Adam. what we what What do we mean when we say a strategic partner and a strategic driver? It's all very fancy, but what what are we actually talking about here?
00:05:38
Speaker
So I also think about that problem a lot. And i think there's basically two senses in which we mean strategic when we're talking about it like this. One is helping to figure out what it is our overall goal should be.
00:05:53
Speaker
So the really high level strategic part, like, so how should we compete? right How should we conduct ourselves to actually win and this competitive marketplace we're in, whatever that is for your particular company?
00:06:04
Speaker
right and The other sense in which I think we mean it is much more about like, okay, so how should we go about that? Right? So like, say, ah wise, our job is to or our strategic vision is that we want to be able to enable customers to send money anywhere in the world for as cheap as we possibly can, right?
00:06:27
Speaker
But there's a big gap between saying that and building something that actually achieves it. right So there's this important element here, what you want to fill in with loads of different things, so like UX insights, user interviews, and so forth. But a huge chunk of that is data. right So like we want to use the data to understand the dynamics of this marketplace, how users are responding to certain things, and therefore what the smartest next move for us is. right So if you like, I guess the short answer to your question is how can we make decisions about what we should be doing rather than merely going through the mechanical process of surfacing data and then that's it, right? So we're not really part of the decision. We're just enabling other people to make decisions.
00:07:11
Speaker
That's an excellent answer. It's so about positioning yourself to be able to be the one driving the decisions and making the recommendations rather than, I suppose, just looking backwards and and speaking about what's happened necessarily in retrospect. So could you could you share an example, Adam, throughout your career where yeah Data has has made that direct impact on the business decision. And i imagine this might have been stuff which, yeah you why your boss sees you as this this firefighter.
00:07:43
Speaker
There's a lot of stuff we're doing right now at Wise actually that I can't talk about for commercial reasons, but it's pretty cool and driving a lot of strategic decisions to take my words for it. Let me talk about some older examples that are safer to talk
Improving Customer Service Efficiency at Wise
00:07:55
Speaker
about though. So for example, when I first started here, I started out by leading the data team that was handling our customer service data, so our customer service function, which is like huge. it's late I think it's currently like two and a half thousand people kind of thing, right? So it's a big obligation, right?
00:08:10
Speaker
So there there was a lot about that team that you could definitely improve when I started. So the data quality was like ah poor. The tooling wasn't great. The way they worked with stakeholders wasn't brilliant.
00:08:22
Speaker
But we kind of did do a lot of work to solve that, which was great. But at the end of it, we still had this really big problem. which was that CS at Wise was kind of like this passive victim of whatever product did upstream, right? So a product might launch something, and you might get 3,000 new contacts.
00:08:40
Speaker
And CS not only wouldn't necessarily know it was coming, but there was nothing they could really do about it, right? And you know multiply that by all the products we have and all the markets we operate, and like you know you're forever subject to this kind of like almost random-seeming deluge coming at any time, right?
00:08:58
Speaker
So this wasn't great, and but the problem we had was there was no really good way to do anything about it because there was no connection for the product teams between their actions and all the contacts they were creating.
00:09:08
Speaker
right So they couldn't really do anything about it. So one of the things we did like right after we... So we went through a period of like maybe nine months of actually getting all the basics fixed for CS in terms of their data.
00:09:21
Speaker
And once we did that, one of the first things we actually worked on and I really pushed for was for us to build a contact attribution model. right So what that does is it basically takes all the contacts we get, and you know which customer, how they got touch, like you know all this kind of stuff about them, and then What we did was we actually connected that to a lot of other data we had about customer transactions. we have a lot of very detailed information about the step of a transaction you were in at any given time. So we have all that timestamp and all that.
00:09:53
Speaker
And through a little bit of SQL joinery and some like giant case when statements, what we were able to achieve was basically say, OK, at the time this contact happened, this was the last thing the customer was doing when they got stuck or when something bad happened to them. So like you know maybe the transaction was interrupted with something.
00:10:11
Speaker
And using that and like basically then some classification logic on top, so if it was this, then it was probably this product they were using in this market. We were able to then basically build a rule set that said, OK, so this contact very likely belongs to this team, right which we were able to mostly get right. There's a little bit of an accuracy around it, but it it works pretty well. right And this was pretty revolutionary, actually, right because all of a sudden, all the product teams could suddenly understand how many contacts they were creating and why they were creating because we had really granular information about what they were doing up to that point, right or when the problem happened. right
00:10:48
Speaker
And that was a really big deal. So like over like the subsequent two years, the global contact rate for has dropped by 20%, which was like a huge deal. But it also meant that you know we had like whole squads of people who were kind of mobilized to suddenly solve these problems once they could suddenly realized this was happening. right um So that was a really cool one. That was a lot of fun. And it really helped, I think, change the way those teams thought about their actions and the way that they actually measured their own success. right So it's kind of become a failure criteria almost. like If you generate too many contacts with this, something went wrong in this launch.
00:11:22
Speaker
right So do something about it for next time. right That's excellent. Another thing that stood out for me though is you you taking a step back and looking on you across the organization. I think yeah at every organization is guilty of having its silos.
00:11:36
Speaker
and I think that's the the magical part about data right? and you You touch every facet of the the business. and That's a problem where youve you' you've used the data, zoomed out, and then just by but sharing that and showing yeah the the impact, it's been able to to cause d direct change, which is great to hear, a great example. Thanks for sharing, Adam. so You the part of the podcast is all about sharing strategies, understanding why there are some of the biggest challenges that exist. So for data professionals, for leaders, what are the biggest barriers that are preventing analytics teams and individuals from taking a more strategic role in your opinion? yeah
00:12:15
Speaker
So I think there's kind of, so I think that there are probably three major ways I think about it. So like i I think effectively, if you can kind of nail these three following things, like you'll probably do a really good job with the strategically changing things, with some exceptions.
00:12:35
Speaker
So I think the first really important thing to do is to actually just, as a data team, get your fundamentals right. right So very often, or the classic scenario I run into, is that the data team is not being run in such a way that they can do the basics of their job well enough that they really have the time or capacity to think about these larger problems. right So what am I talking about? right So like you have data that's really hard to use. You have not done your modeling, again, these days, what's getting called semantic layers and all this. But you know just nice data model that makes all querying really easy. like You haven't done that.
00:13:12
Speaker
It takes like 12 joins to answer basic queries. And it takes ages to run because it's such a complicated query. right they if you if If this is like one of your situations, you're going to really struggle to be spending time thinking about these larger issues. right Similar, in addition to the data not working, you haven't necessarily surfaced that in a good way. So another classic problem is the data team kind of has the data under control, but their dashboards are really hard to interpret and no one knows what's going on.
00:13:40
Speaker
So they're just constantly getting pinged with like you know all these requests for data that exists elsewhere anyway. right And that's a real problem. So a lot of the time, I see data teams that are just doing very repetitive work around that when really they should just work on like you know improving their dashboard presentation. right yeah I recommend storytelling with data, for example, to people. like A lot of the first thing I do is go like go read storytelling with data because I can't read anything in this dashboard. right And solving that kind of thing both really helps to free up time, but also really helps to create data-rich environment.
00:14:15
Speaker
right So another important point to make here is that in addition to just having enough capacity to be able to work on these more complex problems while like you know all the operational stuff is kind of automated,
00:14:26
Speaker
right It's really important to create a data rich environment because 90% of how you have an impact is just by making sure people have the right numbers to make decisions with.
00:14:38
Speaker
And it's unambiguous and it's clear, right? One of the most important things we do is we basically end arguments by going, nope, this is the number and everyone trusts this number, right? So I think that's kind of like the the first key thing is just get the fundamentals right, get your data right, surface it well, and then you can spend all of your time focusing on the right problems. And if you can do all of those things, 90% of it is done, basically. You don't have to, you can kind of stop there.
00:15:04
Speaker
Although I think there's more you can do if you want to be a bit more of a strategic partner. How do you work on the right problems though there, Adam? Because I think i think people do, and you know a lot of the people I speak to are are trying to build their foundations and get themselves into a position where they ah can be like this. But say you're you're there, I think that's one thing that I see as missing is how how do you know what the right problem is to work on?
00:15:28
Speaker
I think that's a really good point. So let's think about that. I think that in general, there's two ways I think about this, right? One is that if you are doing a good, basic, meaningful analysis and exploratory data analysis of your space, so say, for example, like you know you are in charge of some landing page or checkout page or something like that.
00:15:53
Speaker
One of the common techniques that I will use or that I think is quite important is to spend time actually understanding the dynamics of how people are moving through that. So, for example, have you actually checked all the funnel processes associated with this? Right.
00:16:06
Speaker
Have you split this by particular markets or user groups or things like that? And have you actually done all the and do do you understand the data in this area well enough to be able to say, OK, this is where the really big opportunities for drop offs are.
00:16:16
Speaker
Right. So that's one way of thinking about it. I think another pretty important way to think about this is actually just getting this from business context.
00:16:27
Speaker
right So talk to stakeholders, understand a little bit what the overall product is trying to achieve. And that will often give you really good insights into kind of what's missing. So like, what are these guys missing in terms of the data they're using and how they want to use it? So maybe I can give an example of that, actually, as like a good way of illustrating what I mean. Yeah, that'd be great.
Enhancing Babylon Health's Chatbot Accuracy
00:16:48
Speaker
So before Wise, I worked at a company called Babylon Health, who were like, they were a health tech company, basically. And one of their flagship products was the medical chatbot that purported to be able to diagnose what was wrong with you, as well as a GP could, right?
00:17:04
Speaker
So what would happen is you talk to it and say, my ankle is hurt and it would ask you a bunch of questions. then At the end, it would give you a diagnosis and off you went. Right. So there was kind of like a really big problem with how this team was working, which was that none of them were focused on trying to make that chatbot more accurate.
00:17:22
Speaker
Right. And that just like seemed weird. I was like, why why is this happening? how How could this not be like item number one? And it turns out talking to this team that it's not that they didn't believe that was an important question. It was just that they believed that there was no way to measure that.
00:17:36
Speaker
Right. Which kind of makes sense. Right. Because like you spit out a diagnosis to some users logging into the app and then off they go and you don't really know if they have viral bronchitis or something like that. Right.
00:17:48
Speaker
And so it became that because there was no effective way to measure the output of the chatbot, no one bothered trying to optimize it. So you see, going back to what i was telling you about, like, just so what's the right problem here? Because, you know, this team suddenly starts focusing on, oh, maybe if we deliver a diagnosis faster, that's the right thing.
00:18:02
Speaker
But, you know, intuitively, you just kind of know, like, that's that's not really what we need to care about with this. think right Yeah, you you want your chatbot, you want you own your doctor to tell you what's wrong. You don't want them to give you a lick their finger and pick the closest thing.
00:18:15
Speaker
Yeah, I want you to be accurate and not give me an answer three seconds faster. Right? Yeah. Because intuitively that makes a lot of sense, right? And so having like identified that as like a big problem there in terms of like what they could couldn't measure, me and my team actually came up with a way to solve that problem.
00:18:33
Speaker
And if you're curious, the answer to that was that of about 10% of the patients who were using that chatbot, the 10% would go on to you have an appointment with an actual GP after that.
00:18:48
Speaker
And we would have the records from that GP about what their diagnosis was. So basically, what we were able to do, it wasn't completely straightforward, what we were able to do was join the diagnosis from the GP onto the diagnosis from the chatbot.
00:19:01
Speaker
And because of an interesting quirk about how medical diagnoses are organized, they're organized in these kind of hierarchical trees, which also means that you can traverse the tree to see how far away or how many nodes away you were from the GP's diagnosis. right so you could say, OK, so you were like a little bit close. You were moderately close. You were like miles away. this right and As soon as we were able to do that, all of a sudden, it totally changed the way that team operated because suddenly it became about, oh, hey, like we're really inaccurate on these flows or we're completely misdiagnosing this thing. Why is this? and Once you open up the model with this knowledge of where it's going wrong, it was actually pretty straightforward in most of those cases to figure out how to fix it or improve it pretty radically for fairly little input. right
00:19:46
Speaker
ah But so that's what I mean. Like, you know, very often, like, you'll find people are asking the wrong questions just because they don't necessarily have a great way to ask the right ones, or they think that it's impossible to answer the right ones.
00:19:59
Speaker
So for me, it was again about that sort of stepping back and looking at what what we actually trying to achieve here. And then from there asking that. That right question. Excellent advice.
00:20:10
Speaker
One of the other things I'm keen to to ask you about, Adam, is um what yeah when I'm interviewing people further for Cognify's clients, um one of the biggest things I ask, you know, tell me about an impactful project that you worked on. And, you know, it's surprising about how often you get told about a ticket that someone's answered.
00:20:29
Speaker
And whilst tickets I know are important, are always going to be a part of data teams lives. How do you go beyond just being a ticket servicer? And yeah is the business guilty and is the cult cultures of some some organizations guilty of of treating data teams as a ticket desk engine? engine and and And how as professional or a leader do you help change and and and change the script on that and become more of that business partner and and strategic thinker?
Automation and Context in Data Teams
00:20:58
Speaker
So let me answer a few of the points there in slightly different order. So I guess on the one of your points about how do you change the culture maybe within the data team,
00:21:10
Speaker
One of the things I'm very big on, or I think I press my teams on pretty hard, is I am very explicit with them that I expect them to be automating a lot of the low level work. Like if you are still using complex dashboards or going through a lot of hoops to answer basic questions after two months, um something is not right and we need to fix it. So let's sit down and figure out what's going wrong. Right.
00:21:32
Speaker
You might tell from my tone of voice, I don't have a lot of patience about this stuff. And the second thing that I really press them on is I want to see that once you have got these basics in place, so you have automated a lot of the basic data surfacing and you've made the data reliable at like a DAG level, if you like.
00:21:52
Speaker
then I want to see that you are actively going out of your way to figure out the team's context, what they need, and suggest ways to grow. right So I'm kind of explicitly setting the expectation with them that their job is not to merely churn the number. Their job is to contribute to the growth of the product or the team or whatever else they're doing.
00:22:10
Speaker
right And so like that's how you that's how I think we handle it on the analytics side. And that tends to work pretty well. I think with stakeholders, it's actually a bit more complex because stakeholders come in a lot of flavors.
00:22:24
Speaker
right And there's lots of ways that stakeholder culture can really dampen the impact of analytics teams. And there's various different things you do to find your response to that.
00:22:35
Speaker
right So maybe to talk about it a little bit. So let's say there's maybe four or five different stakeholder issues or stakeholder cultures that create these problems. And i think, for example, in a lot of cases, the reason you might get pushed back from stakeholders or they might have very low expectations of the analytics team is that they may just be like unaware of what you can actually achieve with data. right So I did some consulting six, seven years ago for a couple of years.
00:23:03
Speaker
And I do remember dealing with a lot of like fairly naive stakeholders who didn't have a lot of digital exposure. And honestly, i was stunned at the time at how Sometimes he would just show up with like a thoughtful series of graphs and analyses about their business and their website that they had never seen before.
00:23:20
Speaker
And the level of amazement they had that you could do this with the data was actually kind of moving. It was kind of sweet to see like how impressed they were by like a waterfall chart or a scatterplot dividing their data in some meaningful way.
00:23:35
Speaker
And so sometimes it's just that. Sometimes it's just showing them what's possible by getting your fundamentals right and doing a good job with their data. right Other times or like things that are a bit harder to deal with but still possible to deal with are when you, for example, have stakeholders who are little bit chaotic.
00:23:53
Speaker
right So ah another really, really common scenario at a fast growing company is that the data teams are actually made out of good people. But the thing is they're being jerked around by stakeholder priorities so fast that they don't have time to deliver anything meaningful. So it's all very shallow work.
00:24:09
Speaker
And then what ends up happening is stakeholders only ever see them produce shallow work. So they assume the analyst must be bad. That's obviously how that works. right And so that can be another really difficult scenario. But it's solvable.
00:24:21
Speaker
Those are kind of those scenarios where I might, like as a lead for that team, take a bit more control, go like, right, that's it. All requests come through me. I'm going to prioritize this. And then over the next weeks and months, work with stakeholders to get back to a calmer program of work. right So we're going to deliver this in this order. or I want to take some time to just invest in data yeah fixing data pipelines because it's causing headaches and so forth.
00:24:44
Speaker
Another one that can be quite difficult to deal with, actually, is and more common than you think, is kind of when the stakeholders think they can do stuff better than analysts. right And that can happen for a couple of reasons. like I've been in a few places where initially the analytics team's performance has been so poor that the stakeholders just don't have any faith that the analysts really have anything to contribute.
00:25:07
Speaker
right And that's just a case of like you have to gradually over time demonstrate value and demonstrate you're doing useful stuff. I think there's other places where just, and ironically, at very successful companies that have done really well, you'll often have stakeholders who think they know better.
00:25:22
Speaker
you know I won't name any names, but like I definitely know a few high-level product people who think they could write a Python notebook that shows useful stuff, and they really cannot.
00:25:32
Speaker
ah Or like you figure that you know if you can't do it in Excel, I can do stuff in Excel, therefore, like you know why do I need you? right And again, i think like with those types, it's quite important to be a bit more delicate and a bit like, you know, you don't necessarily call out that there's some Dunning-Kruger going on here, right? Yeah, you're going to get into a head-busting situation there. Yeah, like don't do that. Like you friend them, understand context, listen to their ideas, be super interested, and then, you know, gradually show them impressive and cool stuff. Because once they like you and you show them impressive stuff, they're not as hostile or as resistant that thing.
00:26:11
Speaker
So those the major types. I think like there's like one last type that's also really difficult to deal with, which is the not motivated type. So you know ah there are certain organizations where there's just no motive on any of these teams to do anything better.
00:26:25
Speaker
And that's the i think that's actually the hardest one to deal with, right? Because they they don't care. They're not going to change anything because they're comfortable with how they are. They're not going to lose their job. And therefore, they have no need to change what they're doing. So you can have all the ideas you want, but they're not going to do anything with it.
00:26:41
Speaker
I think those are actually the hardest ones to deal with. Frankly, my advice, if you find yourself in that situation, is quit. Because in those kind of in my experience in those organizations, if you are unless you are pretty near the top, it's going to be super hard to change that culture.
00:26:56
Speaker
And therefore you- It's already set it right in there. Yeah. the That is the teams for for people to go to where they're not looking to to progress. They're not looking to drive. They're not looking to to be be the best, I think. And that's fine. Some organizations don't have that mentality, but it's not going to help you in your career to to develop and to build great things. Yeah. Like unless you really want a job where you just kind of show up and push a button and that's actually what you want, then that's probably not for you.
00:27:24
Speaker
Right. If it is for you, great. all about to but you, but it's not my flavor. Yeah, yeah i think that's excellent um advice. And I i think the piece I took away there is obviously empathy towards your stakeholder and having taking a moment to really understand who you're dealing with, get to know them, get to know their personalities, get to know how to be position yourself, and then you can then yeah align yourself to be able to influence them and in the right way, because there just yeah there's no ah no two people are the same, but I like how you've broken it down into them.
00:27:57
Speaker
into them types, Adam. So moving on to, I suppose, some i suppose advice and strategies on how to really become this ah ah trusted business partner. So yeah how how can teams shift from being reactive and answering requests to being ah yeah more more proactive in in their approach?
00:28:20
Speaker
So I think we covered like a little bit of that with the previous answer about you know setting the expectations for the team. yeah So like I expect you to help drive that growth.
00:28:31
Speaker
But maybe to open that up a little bit, I think that there's a whole thing about credibility, right? And so one of the things that I'm extremely touchy about is any of my putting out misleading more mislabeled or confusing numbers.
00:28:51
Speaker
And the reason for this is that the credibility of the analyst is everything, right? So if, again, you're kind of, so I, again, I've inherited teams where like the chief complaints from the stakeholders who will take me aside and say like, listen, these people give me like two different numbers about the same thing, depending on the day I ask.
00:29:08
Speaker
And I just don't know what's going on, right? That sort of thing is fatal. You need to work really hard to make sure that does not happen and run a pretty tight ship. That's why I care so much about the DAGs. That's why I care about ah automated testing, clear DAG courts, all that stuff.
00:29:23
Speaker
And that goes back to what you're saying about yeah your fundamental data foundations. Exactly, right? But the nice thing about that, though, is that once you have that, so once you've done a good job of that, funnily enough, the credibility of the analyst goes to the roof, right? So i was working with... um So like a few years ago, i was working with one of my stakeholders here. He's now a friend of mine. He's a cool dude.
00:29:47
Speaker
And we had basically just gone through this whole process of getting that team's data in good shape and making sure we had reliable numbers and you know All that we had really done was we produced dashboards going, okay, these are the daily KPIs, and they were correct, and everyone agreed they were correct, they were clear, and it was obvious pretty much from the way we'd broken down the data what might have gone wrong on a particular day, right?
00:30:09
Speaker
So we did that, and then we did a few basic pieces of analysis where we said stuff like, you know hey, you know when it takes us more than three minutes to answer the phone call from the customer, their probability of churning jumps 50%.
00:30:24
Speaker
or you know just stuff like that, right? Or like, hey, like you know when we pass the customers around more than three times between agents on the same problem, like you know they tend to drop off and like we have lower lifetime values because of them, right?
00:30:37
Speaker
And none of that was like really complicated analysis. It was all stuff we did in an afternoon and it was like really easy. So for our perspective, it was simple. But because we started saying that sort of thing and people started going, oh, okay, cool, this is a really interesting perspective on the world. Hang on, let me bring these guys into conversations.
00:30:52
Speaker
And where does this go? like Basically, I'm having a Slack conversation with a bunch of stakeholders at once, and this stakeholder of mine like is proposing an idea, something he wants to do. And like I just didn't agree with it. Not like I had like really strong evidence. It just didn't sound like a good idea to me.
00:31:08
Speaker
and i'm like And I'm saying, so like I just don't think this is a great idea on the public Slack thread. And he instantly DMs me. And he goes, like dude, what are you doing? like you know like Can we not talk about this privately? And I'm like, yeah. But yeah I'm just like sort of voicing that I don't think it's a great idea. We're having discussion. right And he's like, yeah, but when you say it, it really matters. And I'm like, what do you mean? And he's like, well, you're a data guy with credibility. If you tell them the sky is yellow, they believe the sky is yellow.
00:31:34
Speaker
Right. And I like that story because it kind of illustrates the point. and Once you get these fundamentals right, once you start making pretty straightforward observations, data is in like a really privileged position. Actually, you become the conciliary of like all these other teams who want to make decisions. Right.
00:31:47
Speaker
So I think, and I can't stress enough, how doing a lot of those fundamentals and just doing a basically good job of them already puts you in a really kind of prestigious position, if you'd like, right? I guess maybe moving on from that a little bit, so apart from just get the fundamentals right, I was having to think about how it is you can like you know actually have a bit more of an impact or look for these kinds of opportunities.
00:32:10
Speaker
And the best answer I have is actually read a lot of sales books, right? Which sounds like a weird piece of recommendation to give out on a data podcast, right? But to unpack that a little bit, my dad got me reading a lot of sales books when I was in my early 20s on the basis that it would be a useful skill for me to have.
00:32:26
Speaker
And fundamentally, what that actually breaks down to is not like pitching in a certain way. It's actually about listening to what people's problems are and finding ways to help them solve it and explaining those solutions in terms that they will understand. right And if I'm being totally honest, every single time that I have been able to generate a useful insight that goes beyond just barely reporting basics,
00:32:53
Speaker
the basics right has been because I listen very carefully to stakeholders and I talk to them a lot and try to understand the world from their perspective and the struggles they're dealing with and just be alert for ways in which you can use the data to help them solve that problem.
00:33:08
Speaker
right And obviously there's few things to go with that, like you need to be attentive to their problems, you need to want to spend time with them, gain a lot of their context and kind of be alert for this stuff.
00:33:20
Speaker
But I think that's a pretty good way of thinking about the problem and that that is really and effective way to become a bit of a strategic partner. I couldn't agree more. I but i say it quite regularly, actually, that, you know, especially sales is is influencing and how you influence is by understanding what a problem is and then positioning the right solution there. So, you know, I've read a lot of sales books myself and i see a lot of that ringing true because, you know, it's about being a consultant. it's about being consultative and that's the the key. And obviously, I think the other thing to go one step further there is you find some
00:33:57
Speaker
yeah really interesting insight, which is going to transform the an area of the business. But if there's that hesitation in the business, business has been doing something yeah for a certain way, you've got to influence these people to convince them that yeah to to trust the data, to to follow yourself. And yeah that is that is you you are selling your you know your your proposition, your your insight, effectively. so So I really agree with that. And then the really great piece of the advice to to here so how do we measure this think that's the you know the final bit you're a leader you're you're a practitioner you're you're trying to apply some of the stuff that we've spoken about today how do you see if it's really working how do you see yeah if you're becoming more of that that business partner
00:34:44
Speaker
So that's all that that that's not a super easy question to answer.
Measuring Success with Data Insights
00:34:48
Speaker
i know. Yes. Maybe i could tell some stories about times where I was persuaded that I had done a good job, right? So for example, at a previous company, Nogwise, when I started there, the data team's reputation was kind of in the toilet a little bit because they just weren't providing anything. The answers were obviously wrong. They were taking days. And so people got no value out of it.
00:35:11
Speaker
And when I started, i could not get a meeting. Like mid-level PMs would not bother talking to me. It's like, oh, stay to team, whatever. Like, don't want to talk about it. And it was kind of like, you know, not exactly disrespectful, but like they had been burned so many times by this that they just weren't going to invest in it. And, you know, friendly people down at the pub would explain this to me as like, look, you're just fighting against like, you know,
00:35:32
Speaker
so many disappointing expectations, like, sorry, you're doomed. Like they were kind of like, I'm sure you're going to be able to do it either. Right. So fast forward, like 18 months of like a lot of this diligent, focusing on fundamentals, working with sympathetic people to just build enough of a reputation. Right.
00:35:48
Speaker
And by that point, now PMs were like fighting over analysts, like I would have PMs who would like, or product leads who would just like stick meetings in the calendar with me be like, look, like, I know they've got the analyst for this quarter, but I need that guy for this because what we're doing is way more important, right?
00:36:05
Speaker
And like, now you've suddenly gone from like, no one will take a meeting with you to all of a sudden people are fighting over everything and deliberately laughing at your jokes because like, you know, how to see what you're doing is super valuable, right?
00:36:16
Speaker
So think that's quite important. I think there's other ah good indicators, right? So even here at WISE, like, you know, we've been on a journey of transforming the data from like, you know, maybe not working as well as you could to actually being pretty impactful.
00:36:30
Speaker
And there's good indicators a lot of the time where you can see all of a sudden company strategy hinging on a piece of analysis that your team has done. And it shows up in the, you know, the big company-wide presentation going, look, we've done this, we've learned this about how the product works and we're going to change tack, right?
00:36:46
Speaker
And so I think that those kind of good signals are really interesting ones. I think actually maybe to build on that, Whether or not people are acting on the insights your team are creating is a really, really important litmus test, right?
00:36:59
Speaker
So like, are people visibly doing stuff because of the work you've done? That's that's a huge one, right? I think if you've got that, then you can be- a good sign. that Yeah, you've done a good job there, right? And that's a story to tell, right?
00:37:10
Speaker
I think that's one of the the key things, you know is again, like that I look for when when putting people towards the client is are they able to tell that story of that impact and what needle and what legal was was told?
00:37:22
Speaker
Yes. Excellent. So look, it's been a lovely conversation, super insightful, Adam. um Before I let you go, What would be, I suppose, that the top three things that a data professional should really focus on to really transition to this more business partner strategic role?
Key Takeaways for Data Professionals
00:37:42
Speaker
I know we've probably covered it all, but you know what are the three key takeaways for someone to try and try and work on So what I would really advise them to focus on is, OK, so let's break this down a little bit.
00:37:57
Speaker
Get really good at data modeling, right? So you will save so much time and energy by making sure you're doing that well and then make sure that you can communicate that data, especially things like regular dashboards in an effective way.
00:38:09
Speaker
Right. Because it's unfortunate that some of the most brilliant geniuses in analytics can't communicate what you're talking about well enough that people will do anything with it. So mastering both of that technical proficiency, but also actually the presentation proficiency, if you'd like, is super important, right?
00:38:26
Speaker
Arguably more important in many ways. So definitely do that. I guess maybe the second thing that I would recommend is think of yourself as responsible for the success of the business. right So like one of the most important adages I try to live by is that I have two jobs. right One is to run a good data function, and the other one is to help run the business.
00:38:47
Speaker
And if I neglect the second one, then I'm not going to act like an owner. I'm not going to get involved in what's going on. And I think that takes away from your ability to have influence, but also to help steer things for the better.
00:38:59
Speaker
Lastly, i think the stakeholder relationships are pretty key. Like knowing what's on their minds, knowing what their concerns are, really helps you craft your behaviors around what they need in order to do well and what they're likely to act on as well.
00:39:15
Speaker
So I don't think you can really skimp on that. Once youve got your fundamentals in place, once you're involved in how the business works generally, get inside their heads and understand what you can do for them. Adam, it's been a real pleasure. and I've learned lot. I'm sure the audience has. some Thank you so much for for joining me on the show.
00:39:33
Speaker
Thank you very much for having me. It's been a real pleasure. Excellent. Well, that's it for this week, folks. We will see you in the next episode. Thanks for listening.
00:39:44
Speaker
Well, that's it for this week. Thank you so, so much for tuning in. I really hope you've learned something. ah know I have. The Stacked 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.
00:40:05
Speaker
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00:40:16
Speaker
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00:40:27
Speaker
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