Unlocking the Power of Data in Product Management
Unlock the power of data with Graham Reed, a seasoned product management professional, as he reveals how data can transform your product management strategies. Can you imagine how understanding data can help to build better products and make effective decisions? Graham, who leads product operations at Cobalt, shares interesting insights from his 12-year journey in the world of product management, a trip that has been filled with data-informed decisions, challenges, and triumphs.
We also tackle the hot topic of AI, its potentials and limitations. Are we overlooking the ethical considerations of AI? Graham urges us not to rush into AI without fully understanding its implications, especially in relation to the lack of transparency with AI data sets. This conversation is not just about data, it's about the approach to data as a tool for product management. Join us on this intriguing journey with Graham Reed at the helm, and equip yourself with a new perspective on data's role in product management.
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Intro (the incomparable Sandra Segrest): 00:03
Welcome to Product voices, a podcast where we share valuable insights and useful resources. To help us all be great in product management. Visit the show's website to access the resources discussed on the show, find more information on our fabulous guests, or to submit your product management question to be answered in our special q&a episodes. That's all at product voices.com. And be sure to subscribe to the podcast on your favorite platform. Now, here's our host, JJ Rorie, CEO of great product management.
Hello, and welcome to product voices. Today we're gonna be talking about data in product management, how it can help us what data means it product management, what it looks like, what it feels like, and how it can help us be better product people and build better products. This is one of a series of data product management episode. So I'm excited about it, I've got a great guest here to share his experience all about data and using data and product. So happy and excited for this conversation. I've got Graham Reed with me, he's a 12 year product management veteran, he's now leading product operations at cobalt, who are leaders in pentesting as a service, he's been a product management mentor. In fact, he founded the product mind community, which is a free community, focusing on mental health and well being a product professional. So if you are not already a member of product, mind, please join. It's amazing. I love it. He's a great mentor. He's a great product leader. And I'm really, really excited to have him here. Graham, thanks so much for joining me.
You're very welcome, though happy to be happy to be happy to be back.
Yes, that's right, you're a returning guest
the second time around.
That's right. That's right. So love, that means you're great, it means you're great. So not everybody gets invited back. So just kidding, just kidding. But you are great. So I'm excited. Excited about this. So just tell me a little bit about your background in data and how your career, you know, has been shaped by data, how you you've used data, what what your background in data, either building data products, or and or using data to help build your products.
Yeah, certainly. So you know, I'm a very, I've always been a very logical person, somebody that really likes, you know, reasons behind things to kind of really, you know, have good justification about why we do things really to kind of take emotion out of kind of discerning decision making, and actually look at, you know, the core hard facts of what we do. So, you know, in the product management world, you know, this is this is easy, and also very difficult to, you know, to, to achieve, we have vast amounts of data at our disposal, but then actually doing something with it is is, you know, is important for me, you know, I've been a product manager said for for 1012 years. And when I first started out, you know, this wasn't really a massive thing. You know, and I hate to say, you know, it took me a good number of years to really start to look at data that we had available, and then actually even longer than that, since then start to make make good use of that, you know, in this context, we're talking about things like, you know, how much is our product being used, you know, what elements of it are being used, you know, one thing it took me such a long time to really kind of get my head around, and also the business I was working for, at the time, is, you know, using that data to not only influence the things that we should be looking at to improve, but also looking at things actually, look, nobody's using this product anymore, nobody's using this part of the product anymore, we really should consider sunsetting is or doing something with me, you know, making some decisions. So, you know, any any really kind of grew from there, they, you know, is more midpoint of my kind of product management career that I really finally started to appreciate what it could do for us. And to be fairly ease, you know, the easy accessibility, you know, a lot of this data data was. And so, you know, as I've gone through my career on works on lots of different products, I've really tried to, you know, have a good data strategy for what we're doing, and how do we embed that into what I've done myself or my product teams, you know, use that to inform what, you know, what it is they're doing, and make it an embedded part of their whole, you know, Discovery Process alongside, you know, the more contacts or qualitative, you know, discussions that they're having with customers, you know, and this is, you know, this is a big important part that we'll come on to, I'm sure in this conversation.
Yeah, I love that. And I think it's, it's a great way to frame frame it because data is available for most of us, even even if not easily accessible because it can reside in lots of different or parts of the organization and not not always come to us in the perfect form, but it is for the most part, I think for most people that work out there, today, there is data available to them, how we use it, and how we access it. And then how we allow it to inform our decisions is, you know, that's the step that that divides us, right, like some of us do it well, and some of us don't quite do it well, and you actually used that term data informed or inform decisions, right. And so I want to dig a little bit there. So, you know, we hear data driven a lot, right? We're a data driven company, we're a data driven product team. And then the term data informed. You know, tell me if you think there's a difference there. Do you see one or the other? As the kind of correct direction data driven data informed? Do you see him as synonymous? Or do you see a difference there?
Or do you see a difference? You know, and again, I'm, I am somebody that does like to kind of look back on my own career and see where, you know, I've done things actually, I now look back on and I go, I can't believe I actually did that is you words that way. And again, you know, kind of midpoint to the kind of more recently in my career, you know, really kind of said, you know, yeah, we're data informed, we, you know, we take the data, we make our decisions based upon the data. And then again, only only a few years ago, I looked back on this, and I'm like, firstly, I don't, we didn't, we didn't do this, we didn't just ignore everything, you know, what customers were telling us, what our sales teams were tenants, what our support teams were telling us what the market was telling us. We didn't ignore all of that, and just go, right, nobody's using this part of the platform. So we're gonna ditch and everybody's using this part of the platform, so we're going to spend, you know, we're going to really heavily invest in this. We didn't do that at all, we were naturally been helped along and been, you know, informed by the data. And the problem was, I kind of have with this modern term, you know, everybody's really jumping on the data driven, you know, we look at the data we really focus on that we really be led by that is, okay, I don't think you are, but if you are, you're missing a huge amount of information, to be informed by the data to cue kind of gives you a pointer. It says, Look, this is what our customers or our visitors or anything, anybody that's using part of our platform, you know, is is using, how much are they using it? When are they using it? What are they doing with the platform, and we can combine that with lots of different aspects. But we actually really shouldn't be just looking at because we want to be hearing what our customers think they might be using it a lot. But there are reasons for that. And this is the this is the really interesting part that I find is a very surface level, you know, we want to be looking at usage analytics, let's just use that for the moment is, you know, if a if a page on your platform is being used heavily, okay, in many, in many situations, that'd be great. If they're spending a long time on a page in your platform, again, in many situations, that's probably great. But then Where's where's the context with that, if they actually spend a long time on your login page, for example, that's not a good thing. You know, this is a piece of information that we want to use to say, Hang on a second, we need to really improve this process. Why are people spending minutes let alone seconds on, you know, our login page, that should be a one and done off you go? We we should be seeing very small amount of time here. So the trends that we're looking for on the pages and our usage of our pages and the data that we have, you know, has context with it. And that's not data. That's not excuse me. quantitive, you know, cold, hard facts. This is context. This is, what do we want people to be using? And we need to be kind of identifying that, again, alongside the quality of information. So we need to be finding out from our users. Okay, well, why are we spending so much time on that login page? Why are we not spending lots of time on our E commerce shopping pages and things like that? Why are we not using our search bars? But we are seeing lots of scrolling up and down? Okay, well, you know, very simply as that scroll bar not obvious enough, is not helpful enough, is there a problem with things that we that data is not telling us? So ultimately, a lot of this and I know I'm being very simplistic with a lot of this but you know, there is qualitative context that goes with all of this and so it's important to be informed by the data just as much as you're informed by your customers your salespeople, your gut feeling you know, we society in product management society really seems to shied away from okay we know we don't go on gut feelings we go on this we go be very data driven, but you know, gut feeling is that is experience you know, in another word is experience and experiences really Important have we've seen these sorts of things before? What should we be looking at to help us, you know, identify these patterns to extrapolate data, or information from this war data. And this is what we need to be doing, we need to be taking this real, holistic, cross cross functional approach to data, not just saying cold, hard facts, and that's what we're going to be going. So absolutely, there is a real difference.
Yeah, I actually love that perspective. And I agree with you 100%. I think data driven is a term that's used a lot these days. And I think it misses a lot of the point, I'm not sure there's one definition that may be part of the problem is that we're using different terms, but I, I love your, your perspective on data informed, and the fact that lots of different inputs can come into our decisions and should come into our decisions. And, and what you said about gut being basically experience it, at least in this context is spot on. I couldn't agree more. And I think, sure, you know, just my gut reaction is x, well, your gut reaction is x, because of the experiences you've gone through, right? You don't want to make every decision on gut, you don't want to make every decision on the other end of that spectrum, which I think some folks or some organizations are moving to, which is Nope, I don't have quantifiable data, I'm not making the decision on it. And to your point, I think it's really, really dangerous to do that. I think, I think we can still make some really good decisions, you know, qualitative and quantitative couple together.
And I think, you know, just to kind of expand a little bit, you know, we, we've talked a lot, a lot already about kind of, you know, getting that data and information from, you know, our usage of the platform, what are they doing within our platforms, we have, what's the market telling us? What are the trends there, and things like that. But as you strip that away, for the moment, you know, let's look at from a real product managers perspective, you know, we'll use a lot of a lot of facts, statistics and data, you know, in our prioritization, so we'll use it on many different frameworks, you know, whether it be rice or whatever, that, you know, have lots of numbers that are associated with them to help us prioritize, and, you know, there's, there's detractors, and positive people about, you know, all of those different frameworks and things like that. But the fact is, you know, a lot of this helps us to prioritize what we think we should do next. You know, when we have our backlogs, we can't sit there constantly, you know, and reprioritize repository, prioritize, we need the data to help us do 80% of the work things at the top of the list, okay, lots of data is telling us, we probably should do those things first, okay. That's all great. But at the end of the day, we still look at those lists. And we say that, like the data is telling us this. But actually, we know there's some intangible piece here, we know that a big a big competitors working on this, and we have to get there first a big customers a belt to hit, but we don't have the data on this to really back this up. There's a gut feeling behind it. Similarly, as well, what you do have competing priorities, if you just work on the stats, you know, you're you're deadlock. So actually, you need that qualitative, contextual information to help you make those decisions as well. So it's exactly the same pieces, we need to experience, we need the outside information to help us and it's just as important as that quantitative, factual data.
Absolutely, absolutely. So when you're when you're using these various types of information, types of inputs, and obviously, you're most likely doing it in a cross functional way, as as we always do. in product management, you've got, you've got various people with, you know, different roles, they're playing and perspectives, and maybe even incentives. So you've got a lot of moving parts basically, and for us, as product leaders, and then product managers to ensure that we're using kind of the right balance of quantitative, qualitative, etc, etc. We need everybody to basically be on the same page. Right? And so those are expectations, I guess, you could say, so, you know, how have you succeeded in, you know, helping the organization, helping the larger team, understand what your expectations were in terms of what data to use, how to use them, you know, expectations of the data itself, you know, that sort of thing, and any, any insights to share on expectations across the team in how data can best be used.
I mean, I've got again, another great kind of learning experience for me, you know, this is almost like I feel like this is your life Graham actually. So, again, you know, I used to really think and I'm into sex, I still do believe a little bit of being very transparent Without data, so, you know, wanting to share everything, hide nothing. Make the information and dashboards, you know, in your Power BI eyes or your Google Data studios or whatever you tableaus and make them available to, to, you know, certainly to have internal staff on the under on the thought that, you know, people would be just as interested as me in the data, that they would want to explore it themselves and want to drill down into, you know, the data. And similarly, as well, kind of saying, you know, what, there's, there's lots of data here, I present one thing, people can go and take what they want from it. And actually, you know, the reality was, is, people either don't understand or just don't have time for it. It's a lovely thought, yeah, product managers, and other, you know, suitably technical people without, you know, without being offensive, you know, we could get there, and others just don't, they don't have the time. And so actually, you know, much more recently, I've been very, very acutely focused on having a strategy on what we want to explore, and also what we want to present to the right people at the right time, in the right format. So this is not about kind of sharing, set, excuse me, seven 810 different dashboards, you know, with everybody, but actually the right dashboard or couple of dashboards, with a sales team. And why couple of dashboards with a marketing team, that actually may be sharing the some of the same information, but potentially from a slightly different lens. And also explaining, you know, what it is we're showing to them as well, because everybody across the business has a job to do. Everybody has, you know, outcomes they need to achieve? So what is this data actually going to do for them? Is it what value is it given to them, we need to be making sure that is, is providing value to them. And you know, and this is, this is my big takeaways, as a product manager, and now product operations is, you know, we need to just be giving people what they need, not necessarily everything, sometimes not even what they think they want, sometimes we need to drill down a little bit and actually provide them what it is they actually need and even kind of question, you know, what it is that they need? And how does that conflict with what they're asking for? But certainly sticking to, you know, they're very quick, short, sharp information that, you know, that teams need. So what are the what are the sales teams need? You know, they need to understand, you know, things like, how many more people are using our platforms, what areas of our platforms are being used a lot? What is not being used? Or how does that correspond to how they are selling our platform? Similarly, with marketing as well, you know, are there some trends that they can hit upon, that have come from the market that, you know, correlate with how people using our platform right now, I'll give you an example. In our platform, right now, we've got a new insights section. And this is, you know, this, this is proving quite popular at the moment, will it stay that way? To be honest, probably not. It's kind of new, and it's interesting, but what does this allow us to do? This allows us right now, to play upon that to to help that as a sales process to help market it, will it stay that way? No. And so we'll change tact again, and that working across the business, you know, using that data as a point, it's just one thing that we can do, we can do very, very well, with that, that supports the business as a whole and each individual team achieve their own goals.
Yeah, that makes a lot of sense. And, you know, again, it's, it's things you've learned along the way, right? You you may have thought about something one way in the beginning or earlier in your career, and you've learned along the way so in that vein, you know, what, what skills do you advise newer product managers to have to build when it comes to data? And again, it's not just, you know, deep data research skills, most likely, based on our earlier conversation, but you know, what skills do you believe that product managers need in this realm these days? You know, how do you mentor and coach others to build up certain skills?
I mean, yeah, it would the one skill I wish I had was was good database skills, you know, typically something like SQL, I can I can dabble you know, I can do select streams, I can do the basics. I would love to be able to competently and confidently be able to do more because even if he's not using that as a as I think the general syntax the general way approaching databases and queries and things like that is largely similar, regardless of whatever it is you're using. So, absolutely, that's a skill that I think has, has made a comeback if more than anything else, I thought it was kind of dying out a little bit. And it seems to have made a real comeback. As data platforms become so much more accessible data analysis and dashboarding platforms become so accessible, so easy to use, you know, these skills, you know, seem to be back back in demand. Again, I think, because those skills are accessible to more people, you know, you don't have to be a developer, or an engineer to be able to write these statements and things like that, as well as data is so important. And it's very much shifted to the product management side of things, as you know, away from engineering, certainly. And interestingly, a trend of of not having necessarily, certainly smaller organizations, not having data science units and data teams, be expecting the out parts of the business to be able to take these on either operational teams like product operations, sales operations, or even smaller than just product teams themselves to be able to get out and supply this information for their own needs. So absolutely, these skills. And, you know, if nothing else, you know, that a desire to kind of think, logically, how do we get at this data? Where is it? What do these data structures look like? That, you know, above all, that's something I mean, even now, I wish I had, you know, the skills to be able to do a lot more than than I can do right now.
Yeah, yeah, that that's spot on, in my experience, as well. And I, you know, this and other listeners know this, because I mentioned it from time to time, I'm not a technical person, I don't have an engineering or computer science, background or education. And so I've always navigated my career, more from the business side, if you will, not the, you know, haven't had to have some knowledge and literacy. But I too, would, would prefer to have the kind of data analysis skills without being the, you know, deep engineer, if you will. But at the end of the day, it's also just as important to be able to turn that into a story or turn that into something that, you know, like you mentioned earlier resonates with various audiences. And so I think that's a really important balance that I'm seeing more and more product managers need. One thing that I've seen work is, you know, using SQL using whatever is, is, is good, I actually took my first SQL class not too long ago. So I'm like, I'm, like 800 years old, and I'm finally learning SQL. But it's, you know, it's, it's more about, okay, what questions would I asked have the data, right, what if I had the answer to this question, I could manage my product better, I can make a better decision. Right? It's about starting there, as opposed to starting with all the data that we could access. And if you can do that, then you can back into the mechanisms, I think, which, which is interesting and important as well,
I you I think you've said two Absolutely. Key critical things there in Yes, absolutely. You know, the, the, the more the, you know, the more detailed approach to this is to say, you know, what is I want, and then as you start, you know, you start with the end of the story, this is the outcome that I want, and you work back, you don't go well, here's some data. What could I do with this? Although, interestingly, that's actually that is something that I explore from time to time, because, you know, one of the things that, you know, in a data strategy, and funnily enough, I'm just rebuilding Cabo stage strategy right now, is, we there's lots of, there's lots of layers of kind of data analysis that you know, that up incrementally more difficult to do something with, you know, you've got a very surface level, you know, how many people are using this page? And how many people using that page? Lovely, great, fairly simple to get at? Useful. Fantastic. So then actually, how do we then start to go to the next layer of, I want to find out all of the customers that have left us, you know, in terms of kind of churn? What what are they using? And what are they not using? Okay, so now we need to combine this information, find our chain customers. And so what, what features have they used? Let me find some trends of some of our key features that they have used, or they haven't used? Does this start to create a pattern? Are we seeing that because some customers have not used this page? Does this tell us something? We don't know? You know, now we're starting to get into the realms of creating hypotheses, you know, and things to go and answer and funny enough that's a key part of this new strategies. I don't know we don't we don't necessarily know this at the moment what parts of our platform our alternative their customers off, or more importantly, what ones are keeping customers for us. But this isn't next level. It's much more difficult, much more time consuming to get her but infinitely more useful. But it's difficult to start with the end of the story. In that case, I'm not I'm not I'm actually approaching this from the starting point and going well, let's explore let's let's, let's, let's take a data set. And let's see what what is we don't know if there's any patterns. We don't even know. How would you combine this together yet, but let's go down a pathway. Let's explore, let's see, what's there. I think the second point that you said, storytelling, absolutely 100%. You know, you don't need to be able to get at, you know, the data yourself. But when you've got whatever data is, however, you have got at it, storytelling with it, you know, why is this important? Where what is the value? Just today, you know, I presented at our company or hands, you know, I do it every month, what the latest, you know, headlines stats for our platform, we've got some ones that have gone up, we've got some ones that have gone down. Actually, for me today, it was very easy, because actually, the stats were fairly, you know, a percentage up or percentage down, not worth worrying about realistically, just a small dip. But sometimes we've got, you know, some good good ones to talk about, you know, why have we had it, we had an interesting, so we had one in February, at the end of February, we had a fairly significant drop off in our, in our monthly users of the platform. But actually, in a little bit of digging, you know, we we ascertained by this force, because February is three days shorter, three working days shorter than any other month, and certainly the January, and also there was an American holiday in February as well. So we've lost four days out of roughly 20 working days, so 1/5. And funnily enough, our numbers were down by almost exactly 1/5. So actually, what was really important there was digging into that looking for that justification, again, coming back to this quality of this this contextual information to you know, understand, not just going, Oh, my God, we've gone down by a fifth, you know, what I got? Well, we got to do, why is there a technical reason for this? Actually, there's, there's wider world implications as to, you know, why. And the reason was, because, to be honest, everyone was doing something else those days. So you know, that there was an interesting piece to identify and tell a good story with that. And it was, it was a fantastic piece of information frack for our business.
Yeah, I love that story. It's, it's a great example of, you know, the data telling the story or the story behind the data, right. And, and, you know, you see this on a dashboard, and, you know, you freak out or you get too excited, or whatever it is, and, well, the story is this story sometimes is very important. And there's follow up needed, and sometimes the story is just an explanation. So, yeah, that's, that's awesome. So, truth be told, there's no good conversation about data these days without talking about AI. So I'm going to ask you, your thoughts on that. Tell me, tell me your experience in AI and, and, you know, kind of what you're thinking right now, with, with AI being around for a while, but, you know, getting gotten a ton of visibility, good and bad. And, you know, tell me, tell me what your thoughts are on AI, and maybe kind of the ethics of it, the use cases of it, the things we need to be aware of, as we move forth in this new frontier?
Yeah, yeah. You know, my, my previous company, you know, we were, we were playing with AI for, you know, best part of a year fairly Elementary, this was actually you know, fairly quite a bit before kind of Gd g t, p three, and Bard and other things like that came out so slightly behind or prior to that real revolution, but certainly exploring, you know, large datasets, you know, the methodologies of, of how we get there, you know, the inputs versus the outputs, etc, etc. And, you know, the fundamental piece behind it is, is, you know, AI is is a fantastic tool. It's not truly intelligent, you know, this is not article is artificial intelligence, as in, you know, can reason, certainly, in terms of what is being offered right now, yes, there are systems out there that are doing more in that space. But, you know, these, these much more commercial applications are massive, massive, you know, search and filters and things like they're super quick, and there's tools to help cut down on, you know, the effort that, you know, we have to take fantastic I can't argue with them as a as a technical tool. But I have, you know, I'm very much like you I'm not a hugely technical person, so I can't speak to how, you know, the technology and things like that operates but what I do have a huge interest in is As you mentioned, the ethics behind it, we everybody is rushing towards, you know, AI, everyone wants an AI for everyone wants their platform to be aI powered. Lovely. And again, many isolated systems perfectly fine. But at no point is anybody of, you know, more broadly looking at the ethics of what of this, I spent a long time looking at this, you know, there's there's real pitfalls, you know, in terms of bias ism, there is a huge lack of transparency at the moment from all of these companies about how they are using it, what datasets that they are using, they are training these things upon, you know, we're all assuming that every single AI tool out there is completely infallible, which using the latest and greatest technologies from open AI or wherever. And there isn't any of that, you know, there's nothing at all, it's just we've got an AI tool, and everyone's falling over themselves. This is fantastic, and brilliant. There are limitations, you know, to the technology. And in particular, where, you know, many companies are suddenly stopping, you know, implementations for their own internal needs, because they're now starting to look at where are you training all of this data upon? You know, they're massively billions rows, datasets, things like that. And we want to, we want to, you know, compare that to our internal customer, but don't databases or our internal security database and things like that. And everybody's, then, you know, being very, very cautious about what happens to that data? No, no, we don't want to share that data with the outside world. So you can go and check on it. Can't we have our own internal peace as if they want to buy the underlying technology, but they don't want all that all the data behind it. And of course, that's that's a massive, massive part of the massive investment that these companies took part in. So there's, there is a massive lack of understanding amongst technology companies that are going after this kind of gold rush of AI. Not really understand. They just, they just see AI, oh, you know, Joe Bloggs down the road, they've got AI, we must have an AI too. And in fact, you know, my last employee, you know, that was essentially the term they used, we, you know, come up with an AI tool, you know, what, but what do you want it to? What's the what's the purpose? What's the outcome? And is AI the best thing for if AI is the best method to get to that outcome? Right. But don't just say you want something I don't care what it is, we just got to have something AI. And again, this is part of the problem that I see is that there's just not enough understanding. And there's not enough understanding black people that are then professing to say, we have got AI and it is fantastic. Look how great we are with it. It's a it's a giant sticker. bumper sticker right now. And that's my real worry over to what the next couple of years holds for for AI in general.
Yeah, I agree with you. I think I you know, I'm kind of toggling between excitement over what it can be and trepidation of or what it can be. And, you know, I think I think a lot of us are feeling that way that it can, it can truly, potentially be, you know, groundbreaking in so many ways, but there's so many possibilities for negative outcomes or impacts. And I think that's the folks that are building it and, you know, building it in the right way, or thinking about those things. And, you know, funny, funny, you know, anecdote you told there, but it's so spot on, like, go but go build an AI tool. Hello, what is? What does that mean? What do you want? You know, what problem are we solving? And I think it's happening quite a bit around around the product world around the tech world right now. So I knew you would have a perspective there. I love your insights on that. And I think they would just have to keep an eye an eye on it. Right. And see, I think there'll probably be some regulations and you know, some some things that can put some parameters around it, and not saying that's good or bad, but I think we all need to expect some of that to come along. If this continues on, which looks like it will.
Yeah, yeah. No, I fully agree. Yep.
So Graham, last question for you just you know, any final advice for someone who's in a product seat and maybe they don't feel like they're there. They're utilizing data in the right way and coupling with you know, the different inputs and really, you know, being informed by it in the right way. Any any advice you would give to them on you know how to It started how to learn a little bit more how to just jump in and do it any resources, any advice that you would share?
Yeah, I think, you know, my in terms of advice, I would say, from forget about data, what is it? You want to know? Okay. There's, there's a really old term I remember doing when I was a university that I read, or what someone told me, which was, the difference between data and information, data becomes information, when you have a reason you have a meaning behind it. Otherwise, data is just hit that wall piece. So forget about data, what's the information that you want? Okay. Go to that end space, go to the NPS. You know, what is the outcome that you want? And how are you going to get that? How are you going to analyze x y&z. Now, conceivably, you could do that without kind of jumping into database and things like that, you know, in the old olden days, yeah, we would have done that. Lovely, almost only you're not going to, so then, you know, plan back from that end piece and say, you know, how do I get out this, what's the easiest way for me to get out this, and then then the issue investigation piece, look at the data that you want, tell it to yourself as a story, first, map it out in your brain, on a on a memo board or whatever. And, and then, and then work iteratively from there, find the piece of information that you want. You know, I again, ideally, you know, if you if you've got some database skills, SQL skills, things like that, you know, that that's, that's going to be great. But you know, what you don't need those sorts of things as well, if you if you can hook databases and things like that up to, you know, visualization tools, like Power BI Google Data Studio to have both things like that, look, you know, what you can do, you can do so much just with drag and drop tools there as well. Ultimately, your again, it doesn't really matter how you're doing it, you're trying to find the insights. I mean, probably you could go and do on Excel, and everybody can do Excel these days. So, you know, forget about the technical side of it. Look at the look at the much more business side of it. Exactly. As you said earlier, Jody, I think that, you know, look at that business side of things, what is it you want, and then only bring in the tech as and when you need now? Yes, almost certainly, you're gonna bring the tech in, you know, and the analytics and things like that. But then you can build in, in a very controlled in a very controlled way.
I love that insight. I love that advice. I think it's, I think sometimes data and the use of data, especially those of us that aren't as technical can be a little intimidating. And I think that, you know, your your advice allows people to just get in, right and think about what they're trying to do in the first place. And then go from there. And so I think that's, that's wonderful advice. This has been a great conversation. I knew it would be love talking with you always Graham. So Graham Reed, thank you so much for sharing your insights, for sharing your wisdom and your experience. I really enjoyed it. And thank you so much for being here. Oh, you're
very welcome. I loved it. So and thank
you all for joining us on product voices. Hope to see you on the next episode.
Outro (the incomparable Sandra Segrest):38:12
Thank you for listening to product voices hosted by JJ Rorie. To find more information on our guests resources discussed during the episode or to submit a question for our q&a episodes, visit the show's website product voices.com And be sure to subscribe to the podcast on your favorite platform.