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In this Office Hours episode, Jacqueline and Juan unpack why marketing technology feels simultaneously stable and deeply uncertain. Q4 earnings show revenue holding steady, yet the market is re-pricing everything around one question: when does AI translate into real revenue? As AI roadmaps stretch into long-term infrastructure bets, pressure to monetize now is exposing the gap between AI adoption and measurable ROI.
They also examine the identity crisis around "platform" positioning and customer data platforms (CDPs), where many vendors claim ecosystem status but operate as point solutions. Increasingly, leverage belongs to companies that own high-quality customer data, not just strong product narratives.
From there, they tackle enterprise marketing's biggest challenge: data fragmentation. With few organizations achieving a true Customer 360 view, they break down why the problem persists and what it takes to build disciplined, commercially grounded data practices instead of buying another dream.
Timestamps
03:49 — The "mirage" quarter: revenue stability vs strategic risk
05:38 — AI doesn't always equal value, and monetization is the bottleneck
06:33 — Category identity crisis: "platform" branding vs point solutions
10:20 — Gartner's CDP Magic Quadrant: hype, churn, and composability confusion
17:56 — The cost of data fragmentation
25:43 — The "Jenga tower" of org complexity and how stacks collapse
45:54 — The playbook: define customer data ideals and don't let vendors drive
Sponsor
Brought to you by Hightouch — Went all-in on a big marketing suite but still struggling to get value? You're not alone. Our sponsor, Hightouch, spoke with 50+ enterprise teams and found 79% are frustrated by high costs, slow innovation, and rising complexity, often needing specialized teams just to keep things running. They'll share the full findings in a live webinar on February 12, plus what they're seeing from organizations updating their Martech stacks. Get the report and register!
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Creators and Guests
What is Making Sense of Martech?
Unfiltered takes on the biggest shifts in marketing technology. We spotlight what matters, who's leading (or lagging), and what's next. In Martech, clarity is power — and we're here to deliver it.
00;00;04;28 - 00;00;07;23
Speaker 1
Welcome to the Making Tons of MarTech podcast. I'm Jacqueline Friedman.
00;00;07;26 - 00;00;09;18
Speaker 2
And I'm one Mendoza.
00;00;09;20 - 00;00;32;28
Speaker 1
And this is Office hours. We cut through the noise and discuss the latest and greatest in the martech landscape. All right. We're going to kick things off with a much debated, much intrigued episode of the hotseat that was released last week with De La Quest and Juan, I know you have a lot of thoughts because you message me halfway through listening to the episode and immediately had a lot of thoughts.
00;00;32;29 - 00;00;36;00
Speaker 1
Did you finish the episode? What were you thinking? What was going through your head?
00;00;36;02 - 00;00;49;07
Speaker 2
Oh my goodness. De la quest. Who are you man? That was an amazing episode. I got to tell you, like as few you know, often martech episodes like things about martech can be quite dry. But he made me think about.
00;00;49;07 - 00;00;51;29
Speaker 1
How dare you? We don't make things dry.
00;00;52;00 - 00;01;22;13
Speaker 2
You know what I data integration. Am I talking about solutions? Talking about data, talking about problems. But Del Equis totally shifted how I thought about email and the value of email. I have never, ever thought about email in any of the categories that he used. One of the main ones he used was that, like every other ad surface, every other marketing surface that's at your disposal, email is also that, you know, his whole thing about, like, you know, email your, unengaged subscribers because that's an impression.
00;01;22;13 - 00;01;35;27
Speaker 2
A subject line is an impression. I thought that was absolutely brilliant. Like where we don't email marketing our business and we don't send unengaged contacts, right. Like, actually LCM tells us not to do that and doesn't let us, you know. So, you know, I was just thinking.
00;01;35;27 - 00;01;38;26
Speaker 1
About it does let you we just have the feature turned on. Yeah.
00;01;38;29 - 00;02;00;12
Speaker 2
Well that's. Yeah. But you know, what's interesting is that like De La La Quest is all about like, it's a the highest data yielding channel. You get all the data in that, you get the contact, you get what they opened or what they clicked on. You get the timestamps on all of those activities and and engagement. And there's no other channel marketing channel in the world that allows you to have that high yield of data.
00;02;00;13 - 00;02;04;29
Speaker 2
You can also get a lot of a lot of volume and a lot of reach as well. You know, and so.
00;02;05;00 - 00;02;08;09
Speaker 1
And it's the most cost effective. Yeah. It's extremely.
00;02;08;09 - 00;02;24;19
Speaker 2
Cost effective. You know, you're like, well no wonder email has persisted throughout the ages. It's an amazing channel. And it was a massive reminder to me that email was just such an incredible base to invest in and why. It's also sort of the beating heart of a lot of martech as well, is that it's a pretty incredible channel.
00;02;24;20 - 00;02;43;11
Speaker 1
Meanwhile, in the back of my head as I was editing the episode, I was like, Juan's going to just say, stop doing another email episode, because email is the backbone of martech. It is, and at least to date, historically. And so I was like, he's not going to like this.
00;02;43;13 - 00;02;43;29
Speaker 2
I love.
00;02;44;02 - 00;02;46;02
Speaker 1
I'm glad I was able to turn that around.
00;02;46;03 - 00;03;05;10
Speaker 2
Yeah, I loved it. And he also drew a lot of historical parallels as well, like when the internet came along and where he was working then versus like all the AI things, like it wasn't just email like he talked about the AI hype cycle in the historical context of the internet hype cycle, and how companies were investing then and how Google won Gmail as well.
00;03;05;13 - 00;03;22;11
Speaker 2
Yeah, it just go check it out. It'll blow your mind. De la quest I hope I get to shake his hand one day, sometime in the future. Maybe he will speak at a MarTech World Forum conference. Let's hope and pray for that. But it's, But it's been a really interesting period because I think he's episode comes at a very funny time in the market.
00;03;22;11 - 00;03;41;01
Speaker 2
You know, a lot of tech companies at the moment are, they're struggling. They are, we're seeing earnings quite a bit amongst the AI native, platforms out in the world. We're seeing meta last quarter was absolutely phenomenal. But in the martech specific world, what are we seeing in terms of earnings? We've just had Q4, looks like revenue held.
00;03;41;01 - 00;03;49;06
Speaker 2
Things are doing okay, but there seems to be a sense of general unease at the moment in the marketplace, particularly on AI plans around a lot of these platforms.
00;03;49;08 - 00;04;21;04
Speaker 1
Yeah. To your point, revenue held margins. Well, even cash flow held, but it felt like all the calls were rather unconvincing. Things were going well and there were a handful of takeaways and the quick headline of it quite literally is a mirage, and that while there's revenue stability, it also can coexist with just general strategic risk. And there's this tension that, yeah, you're having a good quarter, but it's also being reinterpreted through the future spend not your current performance.
00;04;21;04 - 00;04;43;05
Speaker 1
So great examples or investments in future data infrastructure and warehouses and things like that. Like physically however that is a 5 to 10 year roadmap and plan. And typically you're reporting on current present day. And it feels just it's not adding up. It's all I'll say. Do you have any thoughts there?
00;04;43;08 - 00;05;10;17
Speaker 2
Yeah, I think is right now the folks that are able to leverage AI right now in, in the marketplace for actual use cases and to drive products are going to be winning, you know, because everyone is like waiting for really killer use cases around AI, the whole market seems like it's on, on its heels just waiting for some amazing explosive use case or some incredible revenue accelerator from a lot of the martech platforms.
00;05;10;17 - 00;05;35;02
Speaker 2
But I think we're still waiting. You know, a lot of AI solutions, a lot of the sort of the new innovation stuff that's coming out. It's still very early, but the market doesn't want to wait. That's kind of where where I'm coming from is we want to see we want to see returns right now because every company, no matter if it's Braze or Zeta Global or even Oracle, all of these folks are all looking at AI solutions as the next growth lever.
00;05;35;04 - 00;05;38;25
Speaker 2
And I'm not too sure that the market knows yet if that's actually going to come true. Yeah.
00;05;38;28 - 00;06;07;14
Speaker 1
To that point, the next major trend was just AI doesn't always equal value. And the easiest examples are yes, you can be leveraging the platform, but it doesn't actually mean monetization. So it's like this classic infrastructure curve of wide exposure but narrow pay to scale and not to pick on Salesforce. But Agent Force is a perfect example of yeah, it could be cool if it's real and actually are paying users, but that is very unlikely.
00;06;07;14 - 00;06;33;28
Speaker 1
And it just goes to show of some very impatient expectations about when we're actually going to come to fruition with, revenue and results from AI then moving on. Also growth, of course, is getting harder. There's a lot of things at play, and it's not for a lack of weak demand. It's just all of this front loading and we just need to have monetization now as opposed to a future state.
00;06;33;28 - 00;07;00;16
Speaker 1
But I think one of the most interesting components was actually something about a category identity crisis. And I'm seeing this with Live ramp, say, to the Trade Desk and sprinkler to some examples within the martech generally space of like there. But if you read the pitch deck or if you listen to the sales call, but there are actually point solutions in practice and I'm curious what your thoughts are on how like, I.
00;07;00;16 - 00;07;22;01
Speaker 2
Love this idea. I love this idea because every technology company, has a very good incentive to call itself a platform because platforms generate revenue more passively than just specific products. That's a whole idea, is that the customer gets onto the platform. The platform is not only for their customer, but all their solution providers. Everyone else that wants to tap into a platform.
00;07;22;01 - 00;07;27;23
Speaker 2
You know, I think this idea that, yeah, there's a lot of branding of platforms, but in reality there's that point solutions.
00;07;27;23 - 00;07;28;13
Speaker 1
Yes.
00;07;28;15 - 00;07;50;20
Speaker 2
You know, I think that this whole concept of ecosystems and platforms and different companies tapping into different data, the winners at the moment, you know, are the ones that do have the core data. In fact, I did an analyst meeting early today on this idea with the technology CEO, and we were talking about like, who wins in the AI war, who wins in this whole space.
00;07;50;20 - 00;08;15;06
Speaker 2
It's the data. It's the companies that have the actual core data, the data platform, so to speak, that everyone else can tap into. That's who's going to win. You know, it's it's now Salesforce. It's a snowflake. It's the Databricks. It's the even the large CDP vendors as well, you know. So but at the same time, you know, a platform where everyone taps into it like a, well that everyone draws water from, you know, that's very different from, say, a sprinkler, right?
00;08;15;12 - 00;08;34;11
Speaker 2
Or a or a trade desk or even a Zeta global right. They're all selling products. And that was a bit of an interesting one, because they also sell data that a big part of their business is the ad platform and live ramp, as well as in this category, the selling data, you know, so that's kind of how I'm thinking about at the moment, is that this point, solutions in practice is really relevant to some platforms.
00;08;34;11 - 00;08;46;00
Speaker 2
But in another way, I think what's going on is the companies at this can show that they have the best, highest quality data in the market that other brands could tap into and other vendors can tap into. Then they're going to be winning. Agreed.
00;08;46;00 - 00;09;11;16
Speaker 1
And I think there is just one signal to watch and pay attention to. And it's not revenue growth. It's not the announcements or just like vague metrics, but it's really which companies are willing to actually talk about the truth. And that's timelines, trade offs, economics, and not just some big moonshot vision. And so it'll be an intriguing another quarter ahead.
00;09;11;16 - 00;09;21;26
Speaker 1
I'm very curious. Yes, how things are going to shake out as well, especially in the old states of United over here.
00;09;21;29 - 00;09;33;26
Speaker 2
I feel like we're getting more sensibility in the market. I think things are starting to flatten out the hype. Hype always has an expiration date, and it's usually 12 months. You know, it's the AI hype cycle is going on for about three years now.
00;09;33;27 - 00;09;36;05
Speaker 1
Yeah, we're in the trough of disillusionment. Yeah.
00;09;36;05 - 00;09;57;01
Speaker 2
Well, you know if you follow if you follow the classic framework of a hype cycle, yes, a trough of disillusionment, but also a plateau as well. You know, to be completely honest, is not really a lot of things that have come out that has been massively explosive and awesome lately, like the I think we're really at a plateau of what I can do right now, and it's all about commercialization.
00;09;57;03 - 00;09;58;01
Speaker 2
It goes back just it's kind.
00;09;58;01 - 00;10;00;04
Speaker 1
Of like a desert, frankly.
00;10;00;07 - 00;10;07;15
Speaker 2
We're in that. We're in the desert and, I guess martech and leveraging AI in the in the platforms or the point. Yeah, I should say, but,
00;10;07;18 - 00;10;13;18
Speaker 1
I'm just seeing, like, tumbleweeds. Just in terms of actual effective. Yeah. Yeah.
00;10;13;18 - 00;10;20;15
Speaker 2
Tumbleweeds. Yeah. Oh wait. City right now. But like, you gone to the magic quadrants for CDPs. God, that came out.
00;10;20;19 - 00;10;20;23
Speaker 1
Be.
00;10;20;25 - 00;10;40;20
Speaker 2
Magic Quadrant, the MQ for CDP. A couple of weeks. It has been a very busy period for us because we've been reacting to that. Our technology partners been reacting to it. The companies that we work with have been reacting to it. It's a really interesting time. This was a massive shake up. Felt like there was a massive shuffling of the deck on the Magic Quadrant.
00;10;40;20 - 00;10;47;14
Speaker 2
But what was your initial take on it? Because you had quite viral posts, as well on LinkedIn around the Magic Quadrant this time around.
00;10;47;15 - 00;11;16;06
Speaker 1
OOPSes. Well, my initial thoughts are it just continues to expose the cracks and the fractures of the existing status quo. And Gartner is the status quo. I mean, the irony of the day, the CDP, the Magic Quadrant was posted was the day I got Gideon Gardner's biography in the mail to start reading, because I'm intrigued to learn about the man himself.
00;11;16;09 - 00;11;45;10
Speaker 1
And yet it just exposed how volatile our specific niche and industry is, because truly, the changes that were posted as imperfect, as true, as untrue doesn't really matter. It doesn't reflect reality, because what was posted could have been posted three years ago when they first launched the CDP. Quadrant. And so there were a number of shakeups and departures and a brand new inclusion.
00;11;45;10 - 00;11;48;21
Speaker 1
And so there's a lot going on there. But I want to hear your thoughts now.
00;11;48;23 - 00;12;05;26
Speaker 2
I think the KDP Magic Quadrant in the martech space gets a lot of hype and a lot of attention. And I think the big reason why is because of how duplicitous, how duplicitous it is that CDPs are categorized in a certain way. They are, you know, what is a CDP these days? Who knows?
00;12;06;02 - 00;12;12;09
Speaker 1
A great question, because everyone has a different answer. And that's it's a perception problem, too.
00;12;12;09 - 00;12;36;27
Speaker 2
And Gartner has this has always had this role in the market saying with Forrester, IDC, the other big and other small analyst firms, they're trying to categorize and put a frameworks around technology so executives can make decisions around them. You know, particularly for Gartner, a lot of it's for investors to make decisions, right. You need to categorize things, put a taxonomy around certain technologies in order to be able to invest in them and to figure out which ones are performing well.
00;12;36;27 - 00;13;05;25
Speaker 2
But, you know, for the third year running, yeah, I don't I think we're actually getting further away from clarity then they're getting better and better at it. It seems like we're just moving further and further away. It's becoming more chaotic. The Gartner Quadrant, for example, you know, high touch goes from invisible for four years, to a leader overnight, you know, and you said this really well, Jacqueline, that this is one of the like it's very, very rare that a tech company will go from not being in the Magic Quadrant at all to or being a leader in the in its first appearance.
00;13;05;25 - 00;13;06;10
Speaker 2
Yeah.
00;13;06;13 - 00;13;09;00
Speaker 1
The debut is kind of wild. Yeah.
00;13;09;03 - 00;13;09;12
Speaker 2
Yeah.
00;13;09;18 - 00;13;17;15
Speaker 1
And we're quite literally not paid to say that for what it's worth. Like, this is truly our unbiased opinion of that is crazy.
00;13;17;17 - 00;13;43;04
Speaker 2
Yeah, it's crazy, but it's also a lack of recognition of composability. You know, there's other folks like, for example, Unifor, they acquired action IQ, last year, and now they've been subsumed into the Unifor platform. Now Unifor, let's face it, there are contacts in the technology business. They're not a CDP. This made it very weird, right? Because you got this contact center platform now being traded as a cool CDP leader that confuse things as well.
00;13;43;04 - 00;14;05;23
Speaker 2
However, action IQ was one of those leaders in composability as well. So you can like there's been this weird categorization of composability. I don't think Gartner is really grappled with a properly and what it actually means. But there's an interesting yeah, this is interesting chaos that's going on as the CDP segment continues to change. But, you know, there's other ones like Salesforce right up to the top right hand side.
00;14;05;23 - 00;14;24;17
Speaker 2
You know, again, like hard to separate the product merit from the spend here. It's what we hear from our customers constantly that, you know, that's not the story. But again we're talking about the commercial strength of a particular technology. Not necessarily the customer satisfaction. You know, people often confuse the two, right? The Magic Quadrant is about customer satisfaction.
00;14;24;19 - 00;14;43;13
Speaker 2
Not necessarily. It's about it's commercial performance. That actually can be a separated thing. And, you know, there's a few other interesting ones here, like Zeta was on the CDP Magic Quadrant. Now it's not. And particle they got acquired by rocket recently as well. They fell out. Red point didn't really want to invest this year, so they didn't rank as well.
00;14;43;13 - 00;15;03;19
Speaker 2
And, you know, I just think this whole idea of these multi category approaches, is making things more complex for buyers out of a one, you know, okay, this group seems very unusual and a little bit weird in some cases. And then you've got brand new entrants actually right to the top. And then you've got established players as well.
00;15;03;26 - 00;15;14;17
Speaker 2
It just seems as always, it's getting harder and harder to see how this connects to the day to day reality of folks using these platforms, selecting them, buying them, using them in their everyday work. It's becoming increasingly challenging.
00;15;14;17 - 00;15;29;25
Speaker 1
I agree, it reminds me a lot of a certain CEO of a CDP a couple years back saying composable is just product marketing being outperforming what it is. And I wonder if that CEO's eating their own words at this point.
00;15;29;25 - 00;15;36;17
Speaker 2
Yeah, it's that's totally true. You know, but let's move into our next section, which I've been dying to talk about.
00;15;36;17 - 00;15;39;12
Speaker 1
Yes, you have ever since it came about.
00;15;39;15 - 00;16;00;07
Speaker 2
I know a little recap for everyone. We did a really cool research project at the start of the year, looking at all of the, the data that we collected from the enterprise brands last year, looking at things like which platforms are happy with what they're not happy with. But the one thing we focused on was, what are the core enterprise challenges in marketing technology?
00;16;00;10 - 00;16;32;15
Speaker 2
Now, among that 400 enterprise brands that we, that we surveyed, we've had conversations with all that data we collected. There was six major categories. Now you can go back a couple episodes on the Making Sense MarTech podcast to check that out. We did a quick overview of the six. But the first pain point we're going to drill into now, which is all about data fragmentation and lack of a single source of truth, siloed systems, inconsistent identities, poor data hygiene, and unfinished custom made through 60 efforts.
00;16;32;17 - 00;16;50;25
Speaker 2
So I've been chomping at the bit to talk about this, because I think at the heart of most martech programs, there is this problem. Every large organization have data problems. It's never going away. And I've never spoken a company to that said, yeah, we got this on. We got it, got it. Yeah. It's fun.
00;16;50;27 - 00;16;51;29
Speaker 1
I've spoken to one.
00;16;51;29 - 00;16;52;15
Speaker 2
You have.
00;16;52;16 - 00;17;03;24
Speaker 1
Pre acquisition. Wow. And then post acquisition not so. And it wasn't a small company two but one. Yeah. Total.
00;17;04;01 - 00;17;21;03
Speaker 2
And and I'll say that on the six we had an amazing response from from our subscribers shout out to Anna Morale. She heads up martech at the, company Stanley Black and Decker. She did a, a visualization. You can check it out in a LinkedIn profile. Of all the six challenges, we didn't ask you to do that.
00;17;21;03 - 00;17;39;01
Speaker 2
We certainly didn't pay you to do it. But, you know, we've had an amazing responses like that where folks are, like, taking these challenges, putting them into their business, creating content off the back of it as well. It's really exciting me, because getting underneath some of these problems, I think can really help that enterprise laid out, navigate some of the biggest problems in martech.
00;17;39;03 - 00;17;56;03
Speaker 2
And often it does take some insight. It takes conversation and great relationships to actually figure out a path through. So do we want to dive into this space, data fragmentation and a lack of a single source of truth. Do we want to get into the swamp now? Is it time to, like, get in there and figure out what's broken?
00;17;56;03 - 00;18;32;01
Speaker 1
You have to wade into? It is like your other alligators or kind of feels like it. Yeah, well, so nearly 50% of respondents said that they didn't ever achieve alignment on the 360 view. And that completely matches with what we all know and hear about a very complex data unification process, if that even exists. And there's an interesting component to this, where I think, especially in the enterprise, a significant number of these companies are legacy, as in they've been around the block.
00;18;32;03 - 00;19;01;06
Speaker 1
They're not digital native companies. And as a result, they have tech debt that far exceeds many lifetimes, often. And so as a result, it's a lot harder to make changes. And it's clearly showing up in the numbers where nearly only 14% of organizations say they've achieved a 360 view. They know what their customer is up to. Meanwhile, over 80% aspire to it.
00;19;01;06 - 00;19;27;15
Speaker 1
So if an over indexing at 14% of companies means 1 in 10, if you're lucky, has a decent understanding. And if that is all enterprise companies in the fortune, we'll say 500. It's not a good look, but well beyond just that I think is is also an underestimation. But Gartner estimates that poor data quality cost organizations nearly $13 million on average.
00;19;27;18 - 00;19;51;20
Speaker 1
I think that is such an undervalued number, I would dare say the same amount of revenue that a company is up against and what they're hoping for. That's how much it costs them, because they don't realize the unrealized cost of employees, estimated hours spent on trying to unify things, traversing different data components. But I'm curious where your head's up.
00;19;51;26 - 00;19;59;02
Speaker 2
Yeah, I those stats, just just really show you the problem of data fragmentation in the enterprise.
00;19;59;02 - 00;20;01;08
Speaker 1
It's depressing. Honestly.
00;20;01;10 - 00;20;18;06
Speaker 2
And there's a lot of folks listening along going, yeah, I've been there. Yep. Nothing's working. Yep. Can integrate this. Can't do that. Use case comp. This is broken up upstream. I've just launched something. And then someone in the engineering team just broke this, change something and now everything is broken. Have to start again. Identities are mismatch.
00;20;18;06 - 00;20;31;11
Speaker 2
We've got seven. I talked to a person. At an event two days ago that said. Wow. Yep. So we have six different identities across our, logged in experience and also in our CRM. And then also in our CTP.
00;20;31;14 - 00;20;33;02
Speaker 1
Having heart palpitations.
00;20;33;08 - 00;20;55;16
Speaker 2
And and the person's like, I can't do anything more than very basic personalization. I can see the opportunity, but it is so frustrating getting anything changed like, unpacking all of that complexity, and different identities, reconciling them. It's so hard. Oh my goodness. It's one of those things. It's like, man, try to like, you know, upper earth, a big shade of concrete with your bare hands.
00;20;55;16 - 00;21;16;09
Speaker 2
That's what it feels like. You know, a lot of the it's set in stone. There are all these problems are so cemented into the companies, it makes it so hard to actually get out and and fix those things. But, you know, I think one thing we want to start with here, understanding the problem of data fragmentation is, I guess what you said was that customer 360 is still a lie.
00;21;16;15 - 00;21;33;19
Speaker 1
You know it is. Unfortunately, I want it to be true. I want there to be a magical fix. And I know CDPs have promised that for years and years and years, and very rarely has that become a true statement. Yeah. I'm still waiting for the true statement to be told.
00;21;33;21 - 00;21;53;09
Speaker 2
Yeah, well, I don't know if it's a lie. I think it's a promise. I think it's maybe an ambiguous goal, like going to Mars or, you know, having, self-driving cars. It's kind of feels a bit more like that where it's a unrealized promise that it's ambiguous as to the value of it. And if you can even do it, you know, like, could we can go to Mars.
00;21;53;14 - 00;21;55;22
Speaker 2
What's the value of going.
00;21;55;25 - 00;22;00;06
Speaker 1
Sounds like an exact recap of our Q4 earnings report.
00;22;00;08 - 00;22;09;22
Speaker 2
Exactly. Yes, yes, that is very true with space, etc.. Cool. We can go to the moon. We can go to Mars. Cool. We can go to Mars. But should we?
00;22;09;27 - 00;22;10;29
Speaker 1
Are you ready to populate it?
00;22;10;29 - 00;22;11;15
Speaker 2
Yeah.
00;22;11;17 - 00;22;11;29
Speaker 1
I don't know.
00;22;12;00 - 00;22;31;04
Speaker 2
Cool. We can create a golden record and a customer. 360 and every single thing that our customer does will know it. And it's all against one single beautiful profile. Sounds amazing. What's the value? That's great for engineering. Great for data. Data nerds. But if you're an executive trying to drive growth, if you've got if you're a public company with shareholders, where's the value?
00;22;31;05 - 00;22;48;05
Speaker 2
You know, and I think maybe, you know, a lie, maybe is to me a bit too much of a strong word. I think it's a promise that is so ambiguous about everyone's been trying to do it and no one really knows where they're going. You know, it's like this journey without an end. This whole customer 360 thing.
00;22;48;07 - 00;22;48;28
Speaker 1
Agreed.
00;22;49;00 - 00;23;19;15
Speaker 2
And let's talk about AI. AI is only exacerbating this problem. Yes, I'm thinking recently, just this week, can't name the brands but talk to brands and the AI pressure they're getting. And this will be another pain point we'll talk about. But the AI pressure they're getting is even exacerbating the problem around data fragmentation, because I know that connecting data, connecting the customer profile is only going to enable real revenue generating, use cases, whether that be using LMS or AI, decisioning or even in content.
00;23;19;17 - 00;23;42;08
Speaker 2
You know, all the data disconnection is making. It's making the pressure from above to implement AI experiences even harder to deliver on. So, you know, that's adding more, more pressure, more difficulty here. And I'm just getting, as you said, palpitations, thinking about this because it's so stressful right now. If you're in a big, complex organization trying to manage customer data, my heart goes out to you.
00;23;42;11 - 00;23;43;11
Speaker 2
It's very difficult.
00;23;43;14 - 00;24;03;06
Speaker 1
It's very hard and sometimes disheartening, especially if the status quo is what's expected and you're trying to be that trailblazer or that maverick changing that up. And it's sometimes you're up against a wall every single time. So it's worth the fight, even if it's really hard.
00;24;03;08 - 00;24;13;29
Speaker 2
It's it is worth it because there is so much value to fixing this or even just fixing pockets of it. You don't have to fix the whole thing. Getting unlocks of data connectivity, especially around the customer profile, can unlock a lot of value.
00;24;14;02 - 00;24;36;28
Speaker 1
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00;24;36;28 - 00;24;55;06
Speaker 1
They're sharing those findings in a live webinar on February 12th, where they'll walk us through the patterns are seeing and how some organizations are slowly updating their martech settings. If this is something you've been thinking about, join the live session to get the full report. Register today and high touch.com/suite fatigue. And now back to the episode.
00;24;55;08 - 00;25;27;13
Speaker 2
So this is what I want to do. I want to break this down into these three big problems that are causing data fragmentation okay. So there's three pain points. Organizational complexity I'm going to call this the vendor problem. And and the investment unclear investment operational complexity i.e. the vendor problem and not clear investment. Unclear investment. So let me break this down because what I'm going to what I want to do is think through, okay, here the three main drivers that we see.
00;25;27;13 - 00;25;42;02
Speaker 2
And then I'm going to at the end of this episode, we're going to talk about what you can do about those. So it's not all doom and gloom. We're going to talk about some practical things that you can actually take away to really think through solving some of the data fragmentation problems in your organization. Okay. Are you ready for a Jacqueline.
00;25;42;05 - 00;25;43;05
Speaker 1
Let's do it.
00;25;43;07 - 00;26;03;26
Speaker 2
All right. Let's dive in. Now. The organizational problem I call this the Jenga tower problem. Now a Jenga tower is multiple blocks stacked up. And then when you take one out, the whole thing falls over. That's the whole game. You know, people go around in circles and there's usually four players, and they're taking out pieces of the Jenga tower at a time, but you have to still build a Jenga tower.
00;26;03;26 - 00;26;30;21
Speaker 2
Initially. And what happens is, those four people, let's say we call them different names head of product, head of marketing, maybe CTO, and then maybe AI person or your data analytics person. As you build a Jenga tower, people are adding blocks as they go. There's a use case over here that's really valuable or, you know, the execs want to see this experience delivered or, you know, there's an opportunity here to drive more revenue from our data sets.
00;26;30;21 - 00;26;49;00
Speaker 2
So we'll do this thing over here. And what ends up happening is you own the Jenga tower, you just adding blocks as you go. And as the organization gets bigger and bigger and more people come and go. So in this circle of Jenga, people leave and then another person starts adding blocks in different areas, and maybe the blocks are standing upright instead of horizontal and they're ones are happening.
00;26;49;00 - 00;27;08;24
Speaker 2
This big Jenga tower happens and it's a monstrosity. And then one person takes one little block out and then the whole thing collapses. Now that I think is what goes on. So organizational complexity has all these different layers to it. You have people coming and going from companies. So building up complexity, AI, building a capability and then leaving.
00;27;09;00 - 00;27;25;15
Speaker 2
And then you also have folks say, for example, coming in and wanting to build a specific use case and build a little pocket of capability around data in a certain area. Then you have it that have very different incentives, wanting to use customer data for certain things, and then you have the strict commercial legal aspect of data.
00;27;25;22 - 00;27;49;03
Speaker 2
And all these people are starting to build this up. And what that overlapping strategies, that thread of strategies and different people wanting different things, that is what's causing the organizational problems. It's multiple different investment incentives. Often I could I could count probably 20 to 25 different major okay offers that align to data that I map across the organization and for our puny human minds.
00;27;49;03 - 00;28;01;07
Speaker 2
And, you know, maybe I maybe could solve this one day. But, right now, the overwhelm of all of that happening makes it extremely difficult to even understand what's going on with with the data. It makes the systems.
00;28;01;07 - 00;28;02;08
Speaker 1
Makes my head hurt.
00;28;02;10 - 00;28;12;29
Speaker 2
Yep. It makes it extremely opaque. So no one really knows what's going on inside the machine. And yet we all need to use it. So that's the organizational problem. But what do you say to that? How do you say it?
00;28;13;01 - 00;28;32;11
Speaker 1
Well, the Jenga tower is a perfect example. Or just a house of cards. Like if you keep holding it by duct tape and Band-Aids, it's going to come down. That's why you have to focus on true architecture that stands up to the test of time, as well as very painful storms. Mother nature comes in and there's a shake up.
00;28;32;11 - 00;28;39;03
Speaker 1
There's an acquisition you things you can't anticipate, and you won't be able to scale accordingly.
00;28;39;03 - 00;28;55;10
Speaker 2
Otherwise, you know when you see a like a chief data officer, that what they really want is simplicity and clarity often, but they can't get it. The larger the organization, the more and, you know, you've got to think about like the bigger companies that have multiple divisions, and different companies and different things. We did a case study.
00;28;55;10 - 00;29;05;12
Speaker 2
It was a year and a half ago now with, Hewlett Packard, HP, and they consolidated, I think it was like more than 100 data systems into 20 using Databricks at the time.
00;29;05;12 - 00;29;09;13
Speaker 1
And now that.
00;29;09;15 - 00;29;12;06
Speaker 2
That was a two year labor of love, to get there.
00;29;12;08 - 00;29;15;05
Speaker 1
But it's that's pretty quick. I have to say.
00;29;15;11 - 00;29;16;10
Speaker 2
That's just quick. It's a.
00;29;16;10 - 00;29;17;09
Speaker 1
Quick turnaround.
00;29;17;11 - 00;29;34;14
Speaker 2
But but HP, because I have so many different lines of business of the B2B that B2C, the consumer side, and then they have the technology division and then they have software division. You know, they have all these different areas that they had to consider for as well. But you know that the organizational problem, it's the overwhelm of complexity, I think that's driving it.
00;29;34;14 - 00;29;55;07
Speaker 2
And the overlapping incentives you know, everyone wants to use data for certain things. And the way that happens in organization often can be quite chaotic. When you get beneath beneath the surface of what happens day to day. I've been in so many situations where we've tried to analyze, for example, customer onboarding for AI, for a particular kind of like a consumer software product and, customer onboarding.
00;29;55;07 - 00;30;03;12
Speaker 2
We could not access the data beyond Salesforce Marketing Cloud. It was a total black box. We could not access it. We tried and the data was so African.
00;30;03;14 - 00;30;07;28
Speaker 1
Cloud kind of a black box in and of itself. So you're kind of screwed.
00;30;08;01 - 00;30;28;04
Speaker 2
That's right. Yeah. But also many other engagement platforms have a very full subset of data. They don't have the full, the full picture. And so, you know, even in that project, when we were trying to get that data out, couldn't trust it, we didn't know what all the compliance around that was and what that fields or meant and what they you know, all kinds of things were causing issues there.
00;30;28;04 - 00;30;52;27
Speaker 2
And so we ended up throwing up and say, hey, we just can't get this data. It's just not possible, you know? And that's a problem for everyone that works in martech, you know? And so, yeah, I think that the organizational problem, it's a complex threat, a lattice of just so many different things, teams, products, data requirements, you know, commercial use cases, business cases, all that stuff's happening at the at the same time.
00;30;52;29 - 00;31;03;14
Speaker 2
And then, as I said, with the Jenga tower, you pull one thing out, little falls apart because no one has a real sense of the picture. So that's the organizational problem. Let's move on to the vendor problem.
00;31;03;17 - 00;31;34;26
Speaker 1
Why must you sell a dream that doesn't exist? StarHub. You can't productize data solutions. You cannot fix what's broken. There's a reason why we spoke about organizational and operational problems for so long. Because they're complex. You can't productize it. It's not something that can be fixed in one fell swoop with one feature, one capability. They can be supported, it can be helped, but also it can often create more silos too.
00;31;34;28 - 00;31;48;08
Speaker 1
It can just add to the problem. And so I want to be excited for vendors. But at the same time, I know how often the dream is old and folks end up being both disappointed, dissatisfied, and discouraged.
00;31;48;08 - 00;32;09;04
Speaker 2
Ultimately, yeah, the vendor problem is a real one. It's what ends up happening often is the vendor sets the definitions of things, particularly in the customer data platform category. They'll do the pitch, they'll do the vision. The biggest issue is that when the dream is sold and then actually bought by the, the by the brand or the practitioner, it's very quick.
00;32;09;04 - 00;32;15;02
Speaker 2
It only takes weeks to realize often that was not it was a dream. It was always a dream was never there's no reality there.
00;32;15;09 - 00;32;18;06
Speaker 1
Smokescreen.
00;32;18;08 - 00;32;40;15
Speaker 2
And I think there's a reinforced SaaS siloing that happens that exacerbates a data fragmentation problem. SAS platforms want to collect data. There's a adversarial thing going on with brands and this and the software solutions, because the software solutions know that the more data they collect into the company, they're going to be create more vendor lock in. And so easier retention, right?
00;32;40;15 - 00;33;03;26
Speaker 2
That's good for, operating metrics for software business. And it means that also and this is more of a positive thing, is that the vendor wants to deliver really great use cases. They want to deliver that great revenue. And often the way they do that is, well, you need to have the data and our systems and our platforms to actually make that work, though there is an adversarial problem there where the brand is wanting to have the freedom to control and move data constantly.
00;33;03;26 - 00;33;21;15
Speaker 2
But then you also have the SaaS companies having this, really strong incentive to motivate the brand to bring as much data or customer data into that platform as possible. I'll give you an example from one customer data platform. This was from 5 or 6 years ago. Now large entertainment company had a really great well known CDP. I won't name them names.
00;33;21;18 - 00;33;44;28
Speaker 2
They, we're collecting all their customer profiles that were collecting all of the variables that they needed and the most valuable insights for their customer base, content consumption, log ins, you know, engagement. All of that was feeding into a single profile that was created by the CDP vendor. The company started getting more sophisticated about like, data science and wanting to run everything off their own data cloud.
00;33;45;01 - 00;34;01;24
Speaker 2
And when I asked them, hey, can we pipe this data into AWS so that our data science team can do analysis and we can do some probabilistic work, like a really good use case for it? The vendor turned around and said, that's going to be an extra $20,000 a year. And in the grand scheme of enterprise pricing, that's not huge.
00;34;02;00 - 00;34;05;12
Speaker 2
That's a drop in the bucket. But the very fact that you have to pay, that.
00;34;05;12 - 00;34;06;16
Speaker 1
Makes me mad.
00;34;06;18 - 00;34;21;13
Speaker 2
Yet again, the very fact that we paid to get data out of the vendor you have to pay extra for that means that it's tells you it's a tell that for the lot of the vendors, it's a they do not want data leaving their ecosystem. They do not want that happening because as soon as it.
00;34;21;13 - 00;34;22;11
Speaker 1
Goes into promote.
00;34;22;17 - 00;34;42;26
Speaker 2
It becomes a threat. And it becomes a threat to their retention. You know? And we're starting to see this with the engagement platform category as well. It's not just CDP. CDP is wanting to do more things like AI, decisioning and more. They are stepping more into customer engagement. For example, Treasure Data recently announced their marketing super agent, which is a marketing automation customer experience platform.
00;34;42;29 - 00;35;06;12
Speaker 2
You know, their CDP, they're moving into this space because there's, this customer engagement space is struggling because, the CDP one to collect and own more of that experience and be responsible for activation. That traditionally was the engagement platform space. So be seeing like where does this data live? And often when I go into a company they'll be yep, we've got three engagement platforms.
00;35;06;12 - 00;35;30;07
Speaker 2
There's data in all of them. Then we've got our sales, we'll CRM, maybe we have a helium CDP, and then we have something else in the background going on as well. And then, oh, we have a massive data cloud that no one can access either. You know, big part of this problem is orchestrating. So what we get often is requests for things like consolidation help us consolidate the data, help us to consolidate the platforms because so that the platforms can consolidate the data.
00;35;30;12 - 00;35;37;00
Speaker 2
And that can be probably one of the easiest wins here, which we'll talk about in a minute. But, you know, the vendor problem is a big one. It is a massive problem. Yeah.
00;35;37;03 - 00;36;08;27
Speaker 1
But I would say just as important as that is actually the business case. So it's really figuring out how and when to invest and proving it that I think often it leads into kind of the organizational problems. But if you're having to convince your leaders that this is a problem, you've got a problem. If they're understanding of the complexities, the downstream effects, you're in a great place.
00;36;08;27 - 00;36;33;22
Speaker 1
Yes, you have to still build that business case, but it is a much easier sell versus you have to gain trust. You have to prove with small initiatives and wins and hopefully free wins. Ultimately. But I'm curious what your thoughts are, because investments are the hardest thing to make possible, because the returns take a while, especially when it comes to data.
00;36;33;26 - 00;36;51;11
Speaker 2
Yeah. How much is that going to cost? Again, you know, it's it's a classic question. Right? Okay. Most people that work in data, they couldn't build a business case to say they love. You know, I'll just say that straight out. They're not business. They're not business practitioners, that technology practitioners in a very different part of the business, they can manage costs.
00;36;51;11 - 00;36;53;17
Speaker 1
Storytelling is a is a lost art.
00;36;53;19 - 00;37;03;27
Speaker 2
Telling a story? Yeah. Tell me, tell me someone that works in data and analytics every day, but then can get up on a stage in a, well, a crowd for 30 minutes, right? Very few. Very far.
00;37;03;27 - 00;37;05;10
Speaker 1
Few between unicorns.
00;37;05;13 - 00;37;25;09
Speaker 2
And. And why is this a big part of the problem? In data fragmentation, it's a problem because a lot of businesses, they need guidance. They need a strategy for what their data does. And the only people with power is the executives. And then by extension, at the board, they have the power to enact change more than anyone else in the organization.
00;37;25;12 - 00;37;46;05
Speaker 2
They're the ones with the mandates, their quarterly and annual plans and their five year visions. You know, they're the people setting out, okay, if we're going to go and invest in these things, this is the expected return. They're accountable for it. Those are the people that in reality, they're the only people that can really make a change. As big as fixing data fragmentation in an organization.
00;37;46;07 - 00;38;08;29
Speaker 2
But often when it comes to it. Okay. Well, we've got say, all right, we've got 250 million customer profiles and maybe 30% of duplicated. Okay. Just take an example. Now what's the cost of that to the business. What's the opportunity cost of that. What's the cost of, of, of of managing that the data storage cost, maybe integration compute.
00;38;09;01 - 00;38;13;18
Speaker 2
Okay, okay, I get the cost, but what's the opportunity?
00;38;13;20 - 00;38;15;28
Speaker 1
What opportunities are harder to sell?
00;38;16;00 - 00;38;41;20
Speaker 2
Yeah. Are you mean what do you mean by opportunity? You know, there's this whole gap of figuring out how to quantify data in a way that it makes commercial sense. There's a whole education piece there that needs to happen with many organizations, because if you invest in a badly, you know, if you say something like, hey, if by doing this, we can, for example, if we unify all of those profiles, we're able to do personalization and we expect an X amount of uplifting conversions.
00;38;41;23 - 00;39;08;21
Speaker 2
Now that is a really great story to tell. However, the person that tells it is usually the marketer or the head of personalization that is saying we're going to do these specific use cases to drive uplifting conversion rate. Oh, we're going to run these experiments to drive uplift and conversion. Right now, if you're thinking like that, you've already lost because that person is thinking about the tactical execution and not including the data capability that enables it.
00;39;08;23 - 00;39;26;13
Speaker 2
And so even that aspect where you separate the tactical execution from the data capability enablement, what ends up happening is the the business, the executives go, oh, we just, you know, invest in the tactical execution. We don't need to invest in those capability stuff. Why would you need to do that? Just go run the experiments, go build a personalization.
00;39;26;15 - 00;39;51;28
Speaker 2
And then and then those personalization managers or those people go, oh, okay, how are we going to do that now? Oh wow. There's a mountain of costs. Oh well we can't do that now. You know, and then all of a sudden everyone is going, oh, we can't do that. We can't do what we promised. And so I think often that's the problem is that these organizations stay the way they ask for money, doesn't include the real costs and the real opportunity calculation of investing in data.
00;39;51;29 - 00;40;05;06
Speaker 2
And so that's my kind of view on it. Is that the business case area, the top down in investment, that executive sponsorship of fixing data fragmentation is is a very, very far from being mature in most organizations.
00;40;05;12 - 00;40;13;05
Speaker 1
Yeah. You're you're preaching to the choir but also I don't like problems. I only like a solution. So what are you going to do about it? Give us some.
00;40;13;05 - 00;40;32;17
Speaker 2
Ideas. Well I want to ask, what are you going to do about it? You've been in this space for a while. So let's talk about the three organizational complexity, the vendor problem, and then back to the, the unclear investment. Okay. So organizational problems, how would you tackle some of the data fragmentation issues we've talked about?
00;40;32;19 - 00;40;57;27
Speaker 1
Who to your point, actually, I would turn all of these problems into part of the solution. So I would build my business case with the help of a vendor, and then also, hopefully this is all dependent on a couple of different variables. One, your level, how long you've been at the company, ten year, what level of trust in you know you have within your leadership.
00;40;57;27 - 00;41;24;03
Speaker 1
But I would use the bottom two problems of investment and vendors to your advantage. If the most bombastic concept how much money is wasted, start there. It's easier if you're talking about something smaller and kind of building your way up, but a good place to look would be. How much are we spending on our snowflake data bricks data warehouse each year?
00;41;24;05 - 00;41;51;02
Speaker 1
How much are we wasting? And start quantifying those duplicate records, not to mention estimated time and hours of employees on a adjusted salary basis. Thinking through how to really prove the point because it's going to cost a whole lot to fix it. But you want everyone so bought in before you get to that. And so you have to sticker shock correctly.
00;41;51;02 - 00;42;15;18
Speaker 1
You can't just make it up out of thin air, but with that, you have to story tell. You have to be all inclusive and represent every team, not just marketing. And this is where I think martech in particular is so unique, because we really are at the center of customer excellence. And that's not even self-aggrandizing. It's truly we can't do our job without it.
00;42;15;24 - 00;42;38;28
Speaker 1
We can't do it without product. We quite literally can't do it without marketing and analytics and data science and engineering, you name it. Being that nexus point means we have to be that connector and that glue to get everyone on the same page and aligned. It is not easy by any stretch, but in my eyes that is how the most successful solutions come about.
00;42;39;01 - 00;42;39;21
Speaker 1
What are your thoughts?
00;42;39;26 - 00;43;01;16
Speaker 2
No, I, I completely agree. I think there's like what you often see with big cross-functional projects like this or, you know, well, I shouldn't say it's a project. It's an ongoing effort, you know, is, a center of excellence, a center of excellence, seem to often work, you see, center of excellence and I personalization. And even just like pure operations as well, you see CEOs happening.
00;43;01;16 - 00;43;20;13
Speaker 2
Data orchestration CEOs can be in a very, very great way to try and solve some of the organizational challenges. You know, having representation, talking through the key areas in the business where there's major data problems, having a forum for where you can filter up a lot of those complaints and a lot of those challenges from the organization.
00;43;20;15 - 00;43;38;10
Speaker 2
I think starting there and building a shared awareness with the right leadership around what are the data challenges in your organization right now? And my filter on this is not all data, it's just customer data. So having that code for data orchestration for customer customer data would be like, I would say really great place to start if you don't have that already.
00;43;38;10 - 00;43;49;07
Speaker 2
Again, as you say, you need a bind for that to work. You need to go tap on the exact shoulder. Chief Data Officer CTO sometimes CMO, depending on the business and how they organized, you need to go there firmly.
00;43;49;07 - 00;43;50;24
Speaker 1
All of the above.
00;43;50;26 - 00;44;15;22
Speaker 2
Executive needs to set it up. It can't come from a marketing technology team. I just can't actually, I would say my advice to folks that are martech trying to make your data holy goals just stop. Like, you know, you're not going to get anywhere. And, and it's really sad because that's so self-defeating. Well, you're working against the tide of, say, hundreds, potentially thousands of people working in a large organization that have all different incentives.
00;44;15;24 - 00;44;29;11
Speaker 2
You know, you have you have better luck trying to, try to push back a tidal wave, you know, with your bare hands. It's all going to happen. Your executives can your executives have the mandate? They have the leadership. They have the, the capital to be able to do that. So I would say move first with the CEOs.
00;44;29;16 - 00;44;50;10
Speaker 2
Go talk to your executives about it. Have that conversation about we need representation, we need forms, and we need discipline and a cadence to actually start solving this problem. And it's like everything most things in life is about going to the gym and building up muscles and building up skills. This is a skill area where your organization needs to get better in.
00;44;50;15 - 00;45;07;14
Speaker 2
So having that forum and having those conversations, you can start to get onto the treadmill and start losing some of that weight that, you know, start building some muscle and then potentially now start making progress. But the whole idea of a of a 12 month or a 24 month transformation, you.
00;45;07;14 - 00;45;14;02
Speaker 1
Totally missed out on saying all of that technical weight. All right. A technical debt.
00;45;14;04 - 00;45;34;04
Speaker 2
Or a technical weight. Yeah. Well, that technical debt. But, the organizational problem really can be it can be solved with discipline and, expectations around time and then making small bites at it, you can start to unpick it. But you need discipline. You need a time horizon to do that. A 12 or 24 month, say, transformation project.
00;45;34;04 - 00;45;54;00
Speaker 2
We have an external consultancy coming in often, not just saying that making the problem worse because no one's bought in that consultancy come and comes, fixes a certain up to a point and then they leave. You know, you need to build discipline within your own organization. The only way to make it work, discipline is the key here. But I want to move on to the vendor problem and how we solve that.
00;45;54;01 - 00;46;21;13
Speaker 2
You know, how we solve this vendor isolation of different platforms and technologies and things like that. You know, this thing is always one of the most difficult ones, especially in martech. But the first thing I would say is create your customer data, practice ideals, your vision, and your values. Don't let a vendor define it for you. Set down the rules of engagement for customer data, and then every vendor has to sit underneath that.
00;46;21;15 - 00;46;43;28
Speaker 2
And they have to deliver solutions and products and customer support to enable that. Don't do anything. Don't buy another vendor. There is no vendor that can fix this for you or solve for you. What will fix it is having a set of guardrails and a practice that the vendors have to align to, and being very judicial. And, I have to say pretty ruthless with how you enforce that as well.
00;46;44;01 - 00;47;10;11
Speaker 1
Oh for sure. And if you're looking for a starting point, if you're like, what the heck is a customer data practice? What are these ideals? What are these values? I love looking to any and every company's core values. Everyone touts them. Not all. Subscribe. However, I love looking to those as a starting place because if those are truly fundamental and core to your business, it should apply elsewhere.
00;47;10;14 - 00;47;31;07
Speaker 1
And of course, you can iterate, and not everything is going to completely be a 1 to 1 transfer and make sense in this context. But it's a good way to start. And also it's a good way to show upward. It's a good way to show leadership that you're thinking with the business mind, in addition to the specific problem you're trying to tackle.
00;47;31;07 - 00;47;39;14
Speaker 1
So it sounds weird, but it's an important one. And I know you've got some thoughts on vendors doing vision workshops.
00;47;39;20 - 00;47;57;18
Speaker 2
Yeah. Yes, I know, just to say, I don't feel like there's a place for learning about what a vendor does and how they see the world of data. I'm I'm totally on the board. The idea that the vendor probably has more insight than most folks about what different brands are doing with their data. You can learn from them, but don't give him a steering wheel.
00;47;57;21 - 00;48;13;28
Speaker 2
Don't give him the wheel. Because what ends up happening is that if they set a vision and then your executive starts nodding along, then you're in the grasp of a technology vendor that has very different centers from yourself, you know? And often those incentives can be aligned. You know, they can be they want to see you drive revenue because they want to be successful.
00;48;13;28 - 00;48;31;06
Speaker 2
And so to you, that's great. But it goes back to the problem that we talked about, earlier. The vendors want to have a lot more data than they maybe should, often about your customers in order to keep you as a customer. You know, they want to keep your customers so they can keep you as a customer.
00;48;31;08 - 00;48;46;00
Speaker 2
And so, you know, be very careful about bringing a vendor into an executive session or pitching to your CMO or chief data officer. You know, I think there's a obviously often a very good place to learn to learn from them. They've got a wealth of experience. I am not discounting that at all, but it is about influence.
00;48;46;02 - 00;48;59;11
Speaker 2
And you want your executive to be listening to you and your standards of practice and how your vision and not, a variety of vendors. It may actually just confuse a message and highly encourage you to try to stay away from the vision type conversations. Yeah.
00;48;59;11 - 00;49;24;15
Speaker 1
And if you do want an additional solution within this solution and an idea, I love using case studies as leverage. So if this vendor is going to pitch a vision that you're going to be part of, great. Put a timeline to it. We'll give you a logo. Right. And that shows initiative to your executive as well that you're thinking far more than just what this vendor's promising.
00;49;24;18 - 00;49;41;21
Speaker 1
And it puts pressure on everyone to do the right thing. And so, yes, the incentives are still slightly aligned and misaligned in a couple of ways, but it brings a little bit more neutrally so that everyone has to play nice. And by the book.
00;49;41;23 - 00;49;57;21
Speaker 2
Yeah. So let's say, you know, I'm going to give an example here around when a vendor is really helpful, when you give them a real problem that's based on your data fragmentation issues, you should see how they try and solve that or how they communicate that solution. That's a really great test. If you're going to bring a vendor on to support some of these areas.
00;49;57;21 - 00;50;16;17
Speaker 2
For example, let's say you're running unica, a lot of enterprise platforms still on Unico, very old school system. Let's say that the API, runs every day, and at midnight, several customer lists a refreshed for a loyalty platform as an example. And then often that once a day batch breaks and then the data doesn't get transferred over and then you've got stale data.
00;50;16;19 - 00;50;36;04
Speaker 2
Now that's just a very simple unica to a loyalty platform type set up. Now, in that example, if you give that to a vendor, how would they solve that? How they overcome that, give them that opportunity to tell you how would they fix that daily batch? What what's the issues with it? Give them the schema. Give them the API in details, give them the info, see how they solve it.
00;50;36;07 - 00;50;52;05
Speaker 2
Because if they are it become a solve to some of your data fragmentation issues. Then you'll be in a really good spot. So there's some really great things the vendors can bring to the table. At the same time, just be very clear and create that customer data practice ideal. The visions and the values. Don't let the vendor trying to find it for you.
00;50;52;05 - 00;51;12;06
Speaker 2
And moving on to the last one, which the investment problem is a really interesting one, where, you know, I often say that there's a massive challenge around how you communicate the data and the value of data, and it actually can be really simple. I have to advise folks there's data and there's money. You know, cow, you convert data into money.
00;51;12;08 - 00;51;33;14
Speaker 2
That's the job of martech, effectively. You know, the data of your customers. How do you turn that commercial commercial outcomes? That's a whole story. And I think that simplifies is a massive, massively important part of solving the investment problem. You have to get someone. And this is so hard to find, but someone that can understand the technical problems, quantify them, but then also talk to the commercial reality.
00;51;33;21 - 00;51;52;17
Speaker 2
I think often martech people can have that influence. They can talk to both the commercial reality and then also the data problem. They can understand those things in both worlds, but you need the right person to have that conversation. But do you have any quick tips or advice for folks trying to overcome an investment? Lack of clarity around investment in data.
00;51;52;20 - 00;52;26;20
Speaker 1
Yeah, this is one of the hardest translations and parts of martech. If you don't have a marketing background, it makes it that much harder. I don't have a sales background similar, and this is where I think the alchemy of martech comes into place of okay, how can I not just Dorito, but how can I explain what the problems are here and not let it take over or not let any and every other team take over and really come to the table with solutions.
00;52;26;23 - 00;52;46;14
Speaker 2
So it is interesting, you know, this whole idea of this data investment, the golden nugget of the data sitting in a system like focus on that, focus on the commercial value, do the work to figure out the commercial value of your customers, what the retention is, what the revenue drivers are, what the engagement and the retention that you can drive off the back of that as well.
00;52;46;14 - 00;53;10;25
Speaker 2
Understand and quantify those things. Work with your data science teams. Work with you people to figure out what is a commercial value. Create the story. And simplified as much as possible for an executive. But that's folks, is all we have time for today. And it's been a massive episode all about data fragmentation, lack of a single source of truth, siloed system, inconsistent identities, poor data hygiene, and unfinished customer 360 efforts.
00;53;10;25 - 00;53;28;02
Speaker 2
Thank you to Jacqueline for an incredible episode. What an amazing way to think about how to solve some. I would say the biggest problem in enterprise martech is a data fragmentation problem, but if you'd like to come check us out, you can follow us on LinkedIn on making said Symantec. We also have a YouTube channel, like and subscribe their sales on Reddit.
00;53;28;02 - 00;53;32;08
Speaker 2
We have a great community as well, hanging out there and as always, stay curious.