TNDE - Abbey Keck
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Kate Parker: We are so excited to welcome today's guest to the show, a true pioneer in the world of digital advertising, data, and privacy. Abby is known as a builder of foundational teams and products. She brings deep technical business expertise spanning ad tech, measurement, mobile, social media platforms, privacy, [00:01:00] and she's led strategy at companies ranging from early stage startups to Fortune 100 giants, Walmart, Google, Facebook, Oracle, where she's really been a systems engineer, an architect, and a data usage expert.
Abby focuses specifically on how people's data is used in the product management space, and she has won awards, including David Ogilvy's Silver Winner Honor and the RF Great Minds Award. She holds a cert- certified information privacy technologist certification, and she currently serves as a senior product manager at Google.
Please welcome Abby Keck to the show
Abbey Keck: Thank you. Thank you so much, Kate, for the, uh, extremely warm, warm welcome. Um, I'm very, very glad to be here. Um, I do have a, uh, a quick disclaimer that, uh, while I currently, uh, work at Google, uh, I'm here on my own, uh, behalf. And so my opinions and views, uh, [00:02:00] here are my own and not... don't represent my employer.
Kate Parker: Wonderful. Well, we're so excited you're here. It's great. We're gonna have a great conversation. I think that our listeners are gonna be so interested in your background because you really sit at the intersect of produ- product and data and usage, and it's very clear to all of our listeners that we're going through a huge data and digital transformation right now.
How are you finding that this is impacting the various industries that you've had a chance to work in throughout your career?
Abbey Keck: Yeah. Uh, you know, I think as, as we're sort of thinking about the, you know, new, uh, like, data economy, I think we've actually been kind of, uh, uh, headed this direction for a long time. So, so years ago, you know, uh, people were thinking, uh, you know, sort of data, data was oil, right? Like that, you know, we, we were like, collect as much as you can, and, uh, that is, you know, a, a, a currency, right?
Um, and so there was a lot of drive for companies to collect, [00:03:00] uh, a lot, a lot of data. Um, but, uh, storage kind of has been getting cheap, and now with, with AI, uh, data is a little bit of a commodity, um, because you're sort of trained on, on models. So I think actually where s- some of the industry, I think, is changing is how we can use, uh, data. And so I've been really thinking about how does that impact, right? I think that that is somewhat of a transition in how, uh, it used to be just sort of like collect, you know, everything that you can, but, um, you know, in, in my experience and, and especially now, how you use it and what you have is actually, um, the, maybe, maybe the real oil
Kate Parker: It's so interesting because I can feel that even with our customers and the folks that we serve, we are hearing more and more about new use cases that are really coming online, and that there's this increase in [00:04:00] smart utilization that's in benefit and in service of the people that, that the company is trying to serve.
And so what you're hitting on really speaks to me. When you think about that transformation, tell me a little bit more about what you're seeing. Like, how is that hitting product management? How is that sort of hitting the privacy teams or the data teams when they're trying to get into that utilization mindset, um, which might be different than what they were doing, you know, a decade ago?
Abbey Keck: Yeah, absolutely. So, um, you know, really, really interesting and what I found, uh, you know, uh, joining, joining different companies at various stages is, um, there's actually, uh, now, uh, a lot of kind of old or unusable data
Kate Parker: Mm-hmm.
Abbey Keck: Is potentially muddying the waters, uh, of, of, you know, be- using data for its, its best, um, uh, utilization. So I'm, I'm actually finding at various stages that, um, that [00:05:00] companies, you know, are finding that there's, there's old data or, or conflicting data and, you know, what do you do with this? And, uh, because it's been, like I said, storage has been cheap, and so it's sort of been this like, "Hey, we've got this giant data lake," which sounds good, but, um, it is not ne- necessarily.
Uh, and so you really... W- what it's impacting in product management, um, and, and building, uh, you know, valuable, uh, products for, for people is that you... And, and building the right, uh, making the right decisions for the business is, well, how, how do you use it? Is this the right thing to make, the right data to make the right decision?
Is it up to date? Is it clean, right? Is it the most actually, uh, valuable that we can use? So it's impacting. Now we sort of have to go do a cleanup phase, um, in, in a lot, a lot of these, uh, places, and we sort of have to rethink about how we've been using it rather than, you know, kind of collect everything and how, how are we using it [00:06:00] now, and how do we want to use it.
And it's a good time for us to actually start thinking about that as a shift to the next phase and the future, I think
Kate Parker: I love that. This, that, that notion of the cleanup phase does feel like it really accurately describes what companies are doing right now to meet this moment and to take them forward. You had mentioned a term that's obviously very near and dear to our heart 'cause it's the name of our podcast, but you had used the phrase new data economy.
I'd love to hear, like what does that mean to you as you're looking across businesses right now when you think about the new data economy that we're in?
Abbey Keck: Yeah. Uh, it, it really, um, uh, you know, and, and I s- I'm, I said this a, a minute ago, but I think it's, I think it's a, it's a shift. And so I think it's a, you know, and a good opportunity for a shift in, in my opinion. Particularly, you know, as someone who, uh, cares about, uh, privacy, I'm, I'm in privacy and data governance and things like that, and I think it's actually kind of [00:07:00] a benefit for users, for companies, for customers, um, and an opportunity to sort of like gain, gain some trust as, as well. So I, uh... When I think about potentially the new, you know, uh, data economy is where, where things have been collected and, you know, creating these data lakes with, uh, uh, massive amounts of data that you may or may not have used, um, we can now start using, uh, even AI to figure out what is the valuable data.
Let's, let's make sure that we're using that in ways that, um, are, are trustworthy and, you know, gain, gain and create that momentum with the customers about how are we using this data to make sure that we're maintaining trust or even gaining trust, Mm-hmm. necessary, to, um, actually build good and valuable products for people, right?
Which is obviously what I'm passionate about. And so where this new data [00:08:00] economy can sort of, I think, get us to is now that, uh, you know, we, used to have a, a bunch of, a, a, a bunch of data. Now if we kind of clean it up and figure out how we wanna use it, be intentional about how we wanna use it, which we can even sort of use AI to model some things like, "Hey, what if I did this?
What if I did that? What if I do with this?" We can be intentional about how we use that, um, and really shift ourselves into something that is value add
Kate Parker: Mm-hmm.
Abbey Keck: Uh, trust, um, you know, trust-based even, um, which, which would be, be amazing. Um, where it's, it's sort of this, um, you know, still using data as, as valuable.
Like d- it's, it is valuable, right? But in and of itself with just the volume and, and masses, maybe not all by itself. We can now more, be more intentional about it and actually create an economy that is value based and trust based and things like [00:09:00] that, which I think would be fantastic
Kate Parker: And I think so many consumers are craving that as well, and even B2B relationships, that trust-based, that values-based description. I know that a lot of companies are really seeking to, to build that for themselves and to show that that's how they show up in the, in the business economy as well. I wanna touch on the privacy piece because you obviously have worked in the intersect of privacy and data for so long.
Um, this is, uh, new news, but we were just named, Transcend was just named as a leader in Gartner's inaugural Magic Quadrant for Privacy UX. And so it's interesting because it's both the first time Gartner has, has done this, which is really exciting and I think speaks to the industry, and obviously we were very pleased to be named as a leader in that category and in that quadrant.
But I'd love to hear from you, Abby, when you think about consent and preferences and data rights and privacy centers working as one connected experience, why does that matter [00:10:00] for the businesses that you've had a chance to, um, you know, consult or work with or just see across the industry? Why is that so foundational to what companies are doing today?
Abbey Keck: Definitely. And, and I know, uh, you know, depending on where you are, this comes up, uh, a lot or, or not so much, right? But I, uh, from my position, I've, I've heard it a, a lot, um, where, you know, even, even consumers, it's, it's not a secret, right? Consumers from several things that have happened in, in years past, uh, with, with other companies, um, uh, or companies that I've worked with, uh, uh, that, you know, consumers know that the data is valuable.
And some, uh, people, you know, recognize that that is fact, but they're not close enough to it to realize like necessarily how it's used in both the good and potentially not, not so good, right? Because there's, there's the intention a lot, es- especially, you know, where in, in [00:11:00] my opinion as product management, if you have it, you wanna use it for, for the good of the consumer, which is, which is great. Um, and that's where you can actually build kind of a, uh, build, build that trust with a consumer. 'Cause if you're providing a clear like value trade-off, I think there's actually an opportunity to sort of use either consent or transparency or some of these things which actually can build trust with the consumer. And that will help them to, you know, maybe even, uh, understand when there's a data breach from, from somewhere else and be slightly more forgiving because they didn't feel like you were just collecting it and hoarding it and doing it for some nefarious secret purpose, right? Oftentimes when you have an opportunity to say, "Hey, I'm going to collect this piece of information, here's the benefit, you know, at a high level that we think it's gonna give to you," um, and there's at least an opportunity to make that, uh, more transparent [00:12:00] or consented even, um, is, is really great because one, then people who are less familiar with like consumers who are less familiar with how, uh, businesses use data, they'll be more comfortable with like, "Oh, I understand what the value trade-off is."
And ideally, uh, you're actually making that trade-off, uh, uh, usable where they say like, "Oh, I, I can choose not to give this information and this would be my experience, you know, with this, uh, company or in this app or wh- whatever it is," uh, or, "This would be my experience if I do, uh, pro- provide this." And similarly, when we're doing B2B, there's like obviously a whole network of companies that are, um- Uh, dare I say, you know, s- said candidly kind of, uh, transacting a little bit on data, right?
There's, there's sharing and there's usage, and businesses specialize in different aspects of data usage and measurement and things like that. But even with B2B, um, [00:13:00] there is a, "Hey, we wanna make sure that who we do business with is also going to be considering that, uh, because our-- we care about our end users."
You know, as, as a company, like I, I hope m- most companies do, like, care, right? They, they do wanna be like, "Oh, that's the best thing." But they oftentimes don't know, um, like necessarily how to, how to navigate that when they're doing, you know, B2B, uh, business. And they can ask some questions about like, you know, "How, how is this data collected?
Let's make sure that we're doing the right thing and make sure it's deleted when, uh, it's no longer necessary," things like that. Um, and there's some things you can do to make sure the entire kind of chain, uh, in the background, the supply chain, so to speak, of, uh, of data, is actually also doing it in as responsible of a way as possible
Kate Parker: it's so important that last piece as well around the supply chain that you mentioned, because making sure the customer choices or that relationship as you- as you've mentioned and you've defined it [00:14:00] carries through across all of these systems. I know that we're seeing that consumers are becoming more aware of that.
You know, their data might go in one door, but then it's going down into these different companies that really specialize, to your point, in helping to make the entire ecosystem better. But they want to know that it's actually carrying across that system. Um, so th- I think that's such a good jumping off point for us.
When you find that companies are able to show customers that their choices are enforced across every system, that that relationship is really clear, what do you think that unlocks for companies? What have you seen from a product management and more of a revenue side of the house that you're finding it really drives?
Abbey Keck: Yeah. Really, uh, and, and it, and it does. So what the outcome is, is you're, you're building, you know, the, a, a relationship, um, th- with, with your customers so that you're clear, uh, you know, there's, there's a clear value trade-off, right? And so that means that your customers are building [00:15:00] a, a good relationship.
They trust you more. They're actually willing to provide more data, and, uh, that ends up being like a r- a really nice flywheel, right? Because if you k- if they know what value you're pr- you're providing in your products or your services, um, or they have a, a good sense of it, uh, then they're willing to actually give you more information about themselves, and that i- that will be a company's biggest value driver.
Kate Parker: Yeah
Abbey Keck: you know, I said before that sort of the data collection and collect as much as you, as you can earlier, uh, was, was really im- was really key. Uh, but I think now it's what are the specific things, you know? How, how ... It's, it's proprietary cus- business data that you alone have, rather than this sort of commodity of, like, what e- what everyone has. It's, it's, um, ab- the ability to actually have a re- a relationship with your customer and allow you to collect, you know, more, but of the right thing. And so now it's kind of business [00:16:00] proprietary information that might, that's, that I think is maybe the most valuable now 'cause this'll be unique to you.
It's unique to you and the customer relationship that you've built, which, uh, should lead to better products being built, better value proposition for your company, and, you know, ultimately more revenue, um, because n- a- a- and, uh, not only in the immediate future, but kind of in the long term, uh, you're going to have that relationship that you can maintain and build on that, uh, relationship and data that you have about people and know what their value trade-off is, uh, right?
They did agree to do this, and you w- you got a lot of, uh, a lot of usage out of these products, and so you can build on that and build further into that, on your proprietary information with that relationship that you've built with that customer. Um, and I think that has, you know, not only immediate revenue unlock but, uh, certainly long term, uh, to maintain people coming [00:17:00] back and maintain that relationship over a longer period of time
Kate Parker: There's something really valuable there, and I'm curious from your perspective, does it compound with AI and personalization and everything we're seeing today? Does that value proposition, as you've defined it, become even more valuable as businesses are developing that closed ecosystem of, of utility of data?
Abbey Keck: Yeah, I think it does. And, um, but in maybe a, a little bit of a different, uh, way. So, you know, I, I actually think that AI, um, you know, AI's gonna be really good at telling you what all of the other companies are doing, right? Like, here's, here's a general thing, though. And but what you could potentially use AI for is, uh, you know, testing value propositions, uh, doing, doing small tests like, you know, um, and using AI to sort of like bounce ideas against or, or say, you know, what, what could I do with this, or, and, and [00:18:00] drive new and, and different ideas to, uh, to get, uh, to get products, uh, to build products that, that, um, are gonna be really valuable to, to people. Um, and so I th- I think AI is actually really interesting in this space. And, and AI lives and dies on how, o-on, on what you're, uh, building with it really. It's, it's actually the quality of the data that matters when, when AI is, is involved because it's actually really good at taking all of the data that you have.
And if you have a lot of low-quality data, it's going to munge that together and not give you good bus- uh, business outcomes. So it's actually really great to try to qualify that data, figure out how this can actually be, uh, used. How do we, how do we tease out what is, um, you know, proprietary information that our business has, and what, you know, do we want to, uh, do with that? [00:19:00] Um, and I think that's, uh, a, a really great opportunity, um, with, with AI to actually try to use it to what other, you know, companies could do with it. But if every company knows, you know, name, uh, address, you know, even historically U- USPS knows that too, right? So, uh, like, you know, those, those are things that are very commoditized and, um, but what you can actually drive value in using AI is, um, I think something really cool, uh, to bounce ideas against.
See, see like, hey, if I have this, what, um, what other, uh, not, not ask it questions, but bounce your own ideas off. Like human creativity along with AI is actually, I think, a really powerful, uh, proposition
Kate Parker: it reminds me of what you said in the beginning of our conversation, which is this cleanup phase, to your point of around the quality of the data. Like, if you're in that e- [00:20:00] experiential innovation mindset, trying to drive some really interesting new value propositions for your business, the quality of the data that you're working with matters so deeply, which puts us, like, back into this clean...
Like, companies are in cleanup phase so that they can create and break through what just every company would come up with, right? Like, how do you drive something really clean and clever? And it comes back to the quality of the data. So I love, I love that. When you think about the use of data that creates value for the company, how are you finding boards or, um, you know, senior leadership, like, how are they thinking about it?
And when you're presenting to either a board or executives, what are some of the words that you're using to connect data strategy and data protection to revenue and activation unlocks? Your view as a product manager is, is so perfect for that because that's part of your job, right? To combine those two things together [00:21:00] for, for folks.
So walk us through what you think about as you present.
Abbey Keck: Well, and it depends on, uh, what stage the, the company is at, of course. Um, so when, um, you know, in, uh, when you're, when you're in a r- you know, smaller company where you can iterate really quickly, uh, which, uh, where ... In a place where anyone can sort of iterate quickly, uh, you can, uh, you know, do, do more tests.
And so when I think about presenting to a board, usually I'll prepare and sort of have, um, you know, these are, these are where we're seeing, um, uh, better benefit of customers. We have more interactions. We have, right, we're, we're seeing that this is something that, uh, the people are coming back for, right?
And so that, that's, uh, that's useful. Um, and you know, of course the, uh, protection and trust is, is important, but that's usually not the place to, you know, start, start in the, in the, in the boardroom, depending on where, where companies are. Sometimes it is the best [00:22:00] place to start. Uh, but, um, the Of, of course, you know, it's, it's connecting it to, um, to the value, but how do we get the, uh, the revenue?
And so you can have the sort of long- longer term, uh, trajectory to say like, "Hey, we actually need to, uh, you know, build, build trust," and, um, that leads to longer term relationships. But if I'm thinking about how do we get, uh, short, short-term revenue, there's more, uh, you know, tests that we can do, uh, these days, and you can do A/B testing and things like that.
And I, I oftentimes rely on that because you can show, uh, early or, uh, early stage tests. But how, uh, how is the company thinking about like their, their overall goals? Obviously revenue is, is, you know, top. Um, but is it, hey, we need to, um, also gain, uh, the number of, of people that we're [00:23:00] touching? Or maybe there's an opportunity to go deeper with the existing, uh, customer base that, that you have. And um, again, it may not be the, the collection of that data. So oftentimes I'll also look at, hey, this is existing data that we have, and we do have to do a cleanup. I've had a lot of conversations with, uh, co- with companies where, uh, we tried to get something just right out a door and we got extremely messy results and, and potentially bad business decisions. So I have had to convey that, uh, before. That, you know, hey, we're getting something that doesn't make any sense, uh, based on, on this data. It is showing that there are, uh, you know, 12 bill- billion people in the US. Uh, that's not true. So, you know, how do we like actually ... So I, we, we actually need to put some more value, and I've had to have these conversations about put more value on the actual cleanup of the data.
[00:24:00] You can build things at the same time, right? You can do the A/B test and sort of like not just pause and do a whole data cleanup. That's- unrealistic most of the time. Uh, but, you know, having a parallel path is, I, I think, a really good strategy, especially, uh, if you're going through some sort of, uh, growth, growth phase.
Um, if you have a data strategy about like, "Hey, let's tag data. What is the metadata that we have attached to it?" AI is going to actually really, uh, uh, be, um, uh, hook into metadata and, uh, tagging of data and things like that. If you don't have a good strategy about how you're gonna start to get that done, um, then it's gonna be harder and harder.
So usually a parallel path, uh, system is what I think is, um, uh, usually best at most phases. Um, but depending on how much you need to actually invest in the data cleanup, [00:25:00] depending on what you've, uh, where, where, what's happened historically and, uh, where you wanna go. So usually some amount of investment in data cleanup is valuable while you do your other, uh, "Hey, let's do
This was already on the roadmap, and what can we do with the existing data that, um, that, that we already have plans to do our A/B test?" Um, those two things, in fact, together of A/B testing plus cleaning up data, you get to that intersection of, "Oh, we have some information that shows us that this would be actually be valuable and a differentiator for our company.
Oh, and now we have clean enough data to actually execute on it,"
Kate Parker: Mm-hmm.
Abbey Keck: That can really be like the, uh, unlock the best business decisions. And that's in a really exciting place that you could be, um, where you're creating value and differentiators, um, based on that, that cleanup. And you say, "This is what we wanna do with it, and, and what we, um, not just what [00:26:00] we could. It's what we want to, based on what our customers are telling us from even A/B testing
Kate Parker: You're saying something so important for the Fortune 500s, for the enterprise companies, for even the, you know, the high-growth smaller companies, that doing both at the same time is a very effective strategy. Identifying the cleanup and putting more value into the underlying data, while also creating that space for the innovation and the A/B tests, because there are so many companies that, that, you know, they don't want to slow down.
The boards are saying, "Faster, faster. Let's not miss this wave." But we also know that ensuring the underlying data is in a healthy place, that it's gonna deliver strong answers, that it's gonna protect the brand, that it's gonna have everything encoded and laminated into it that it needs, is just as important as moving fast in this world that we [00:27:00] now live in.
And I think as executives, we see that on a day-to-day basis, just with our own interactions. You know, the more quality you put in, the higher quality you get out. But sometimes it's harder for us to apply that on a big scale when the pressure's coming to, you know, get something big out the door. So I, I think you're saying something so important, that being clear on that data foundation work stream alongside of the innovation and how they're going to m- mix and mingle together is a really effective strategy.
Abby, you've worked across so many companies, and without naming names, I, I know that there are success stories that you have seen from a product and privacy perspective where there might be an initiative or a product launch or just even a really strong lesson that was learned when you combine data together and you think about data protection alongside of data utilization.
Are there any lessons or recent success stories that come to mind for you that you'd like to share with our, our [00:28:00] audience?
Abbey Keck: Success stories, uh, you know, and this, this actually, uh, probably was top of mind because, you know, sim- similar things have happened when you do, you know, uh, do, do that cleanup alongside with the AB testing. 'Cause of course you, uh, uh, it's, it's mostly unrealistic to sort of pause and do a, a full, full cleanup.
But, you know, some, some really good, uh, places that I've, that I've seen success is, uh, finding, um, uh, your analytics team. You know, as businesses grow, they have more and more analytics, uh, and, and, you know, you want to, you know, have good quality data while you're doing those, uh, analytics. And so as, uh, you're, you're making the business decisions and you have your analytics teams, you know, help you uncover what is actually, um, uh, usable versus, versus not, what is more useful versus not, and this is where, you know, high quality [00:29:00] data scientists and analytics, uh, people are, are highly, um, uh, valuable, uh, in, in helping you discover this. So, you know, that kind of, uh, marrying of, hey, you know, what are, what, what do, what do we have? Um, but it can lead to really good business decisions of, uh, you know, "Hey, we actually didn't realize that, um, that this data that we had was lower quality and we were getting messy results." Like, I've been at a company, like I said, the 12, 12 billion people in the US and like, okay, well, that didn't...
That's not right. And so it was actually really important to figure out because then it was how do we say like, oh, most, most people actually think this. Mm, I'm not sure if that's true and could lead to potentially a bad business decision. Um, because I've seen, uh, that a lot as, as well. And so where you can do, um, high quality sort of research and [00:30:00] analytics, um, and then, and, and have those, those data scientists, those analytics, uh, teams, not just like pump out numbers of how, how good things are, right?
But this is a good time to have them help tell a story, right? Have them try to uncover, uh, this for, for your business and, uh, you know, here's, here's where it looks messy. Tell-- You know, have them tell you, uh, where it looks messy, where it doesn't completely make sense. And so then you can sort of develop the strategy for, okay, uh, well maybe if we go this direction and do, you know, these, these AB testing or things like that, um, you can actually find your sweet spot,
Kate Parker: Mm-hmm.
Abbey Keck: Which is your differentiator, your proprietary information.
You can actually like tease out that, that sweet spot for you, um, and, uh, and your company. And I think that that's, um- Exciting, uh, right? That, that, uh, for, for, for anyone. [00:31:00] Um, but particularly I've, I've seen good success in there. Lean into people telling you the bad news, uh, people telling you where it doesn't look good.
Um, and it's really easy to sort of bypass that these days because you can also have AI just say, "Tell, tell me what looks good. Is it green? Is it, you know, yellow? You know, is it... Are we, are we good?" Um, and you wanna share that, of course, share the good, but don't be afraid of the not so good, 'cause there's opportunity there, right?
There's, there's this is not so good. And so then okay, put a little bit of investment here to, to get you over to the next, um, quarter or year or, uh, what have you
Kate Parker: it's really interesting seeing data analytics, product management, data protection-minded folks come closer together than ever before. And it feels like they're start- starting to have conversation that perhaps 10 years ago, you know, they'd all, all be off doing very separate [00:32:00] s- you know, siloed items.
And so, uh, particularly with our customers, we see that more than ever. You know, it's, it's the VP of data analytics, it's the head of digital transformation, it's the data protection officer, and they're all working towards the same end outcome, but just from very different vantage points. But it's, it's more collaborative than ever before.
Are you seeing that as well, those sort of different personas come more closely together than perhaps you saw at the beginning of your career?
Abbey Keck: Absolutely. Uh, and I, I hope and I hope to see more of it, to be perfectly honest, and I think that we, uh... I do, I do hope to see more of it. Because y- yes, you're exactly right that, you know, the, the VP of analytics and the VP of operations and the, uh, and product and, and privacy, uh, because these things must work together, uh, right?
Because even the VP of privacy, you don't want them to just [00:33:00] be like, "Hey, you're not allowed to, uh, do this." Um, right? You wanna actually find, you know, this is, uh, this is the right thing for our company to do, and what is the analytics team finding? What are we finding in our operations that, uh, is, you know, supply chain, right?
This is not just supply chain of, of physical goods, but supply chain of data and supply chain of, uh, information that we have. So I, I've definitely seen this also, and I highly encourage this on, uh, you know, at all levels, um, not just at the VP levels. But I would love-- I have seen it and would love to see it even more at, at VP levels, um, where you can have these small sessions of, "Hey, what, what are you seeing, both good and bad?
how can we leverage this, this information to help us to grow and, and be stronger?" But as much as, uh, you know, I've-- I'll, I'll touch on AI for, for a second, and I'll say something slightly controversial. Uh, AI doesn't necessarily make someone an expert. It do- [00:34:00] doesn't at all make someone an expert who is not. What AI can help you do is actually be the expert you are and extend slightly in both directions, and now we can create more overlap
Kate Parker: Mm-hmm.
Abbey Keck: Between expertise. And it can really help you do that and have those conversations where historically, if you've been siloed and are not used to having these, uh, kinds of conversations, then AI can help stretch to, uh, help to kind of bind these things together and how do you actually have these conversations.
Kate Parker: So and that, that, that's such an interesting... My- I just wanna visualize that for a second. So the privacy person, while still being an expert in privacy, can get a little halo of product management, and the product management person, while still solely an expert in product management, can get a little halo of privacy and, and data anal- Is that what you're saying?
And it kind of makes the conversation and the work product more effective in service of the company and the outcomes? It... 'Cause that's such a beautiful visual. Is that what [00:35:00] you're, for us? Yeah.
Abbey Keck: It really helps people to stretch and, and find the, uh, find how to talk to each other and find the absolute business value to, um, because you do need to be the expert, and you need that expert in the room. Um, but you don't necessarily want to come at it from conflicting silos. You want to be able to say, "Let's find the bridge," because you can build a stronger product, you can build a stronger business outcome, uh, for the future and actually, like, collaborate, um, better than, than I've ever probably seen, uh, across those, um, those disciplines. And I, I-- you can see-- you can do that at all, all levels. Like, this is, this is-- I'm, I'm doing that in my day-to-day as well. I'm finding that, uh, you know, talking to engineering directors, like, all, all of that, uh, is, is happening. Um, and it's... That is where I think it can be really, really powerful
Kate Parker: So awesome. And what a end benefit as well when all of those conversations can be more [00:36:00] fruitful, it does help. 'Cause there is this notion of speed right now that I think companies are very, uh, focused on for all the right reasons. You know, they wanna gain market share, they wanna protect, uh, what they have built.
But having that as an element of speed, it's so much easier to talk to someone when you feel like you already have common ground and already understand. I'd love to hear, Abbie, you have been working across this industry for a, a long time, and you've been doing it from this place of shared knowledge across data, across utilization, across product management, revenue, privacy, like these things that don't actually naturally go together.
What excites you right now about the future of this new data economy that we're headed into?
Abbey Keck: I, I, I might be slightly, uh, primed from, from the last thing, but I actually ... That is, that is the most exciting for me. And I, uh, w- and especially the, the, the part about how can I [00:37:00] stretch now, now given, uh, I'm, I'm already, like, quite interdisciplinary.
Kate Parker: Yep
Abbey Keck: but there are places where I'm certainly, um, uh, not an expert.
And so it helps to, yes, like you said, find common ground, but be more effective, be more efficient. Um, and, uh, you know, back to, back to old days of fail quickly, that is still a good principle, right?
Kate Parker: Mm-hmm.
Abbey Keck: That's still a good foundational, um, uh, like, first principle of fail, fail quickly. It's not just build, build garbage that, uh
Right? It actually means try something and see what failed, and then say, "Uh, this is the part that worked and this is b- the part that didn't." And it can, uh, help us to move faster and be more efficient, but, uh, you know, also, you know, for, for good of consumers and good of products and things like that, actually be useful and beneficial for, for everyone, which, uh, I feel good about, right?
Like, that's something that, that drives [00:38:00] me is, uh, you know, building the right thing that's good and, and valuable and useful, um, in, in, uh, day to day or things that are hard. Um, you know, uh, I think that that's s- that's very exciting, where I c- you know, we can stretch to understand other people a little bit better, a little bit more of a lens. Um, you know, these tools that help us ma- make us more efficient. AI is a good tool to make us more efficient. That's, that's what I think its most powerful, um, value proposition is. Um, and let that happen to all of the experts and ... Or all the people who are even learning, right? This helps you to grow as an individual, no matter what level you are, and help you to kind of understand and test those, um, test those assumptions and help you understand a little bit better.
And then you can be very efficient, very effective, uh, at talking to other people, finding common ground quickly, [00:39:00] getting fast answers. I'm literally having meetings about extremely complicated topics in 30 minutes where people are like, "Oh, okay." Uh, because I'm able to leverage these tools, and that is making a, You know, can make people move faster.
It's making me move faster
Kate Parker: so awesome. How did you get into this interdisciplinary space that you now occupy from project management to systems engineering, architecture, privacy? Like, take us back in time. How did you, how did you get there?
Abbey Keck: Yeah. Uh, I've, I alway- uh, I'm, I'm naturally actually interested in everything, so that certainly, uh, you know, bene- benefits me. Um, and so I am naturally, uh, interdisciplinary. Um, I find most things interesting, and so I also hear, uh, people out, so, you know, that helps, that helps in a lot of, a lot of areas. Um, but then I also, uh, [00:40:00] to, to the chagrin of my team sometimes, am interested in the hardest problems. Uh, right? And so, you know, like, can... I'm able to kind of connect the dots and find the commonalities or find the similarities between something that is not typically thought of in a particular area. And so I love that about, you know, product management. So I kind of graduated from, you know, where, um, just, just learning phase, obviously, at the beginning of my career and trying to help, you know, kind of operationally to, uh, oh, well, if we actually built this, that would be easier for users.
I have the, I have the gift of being, um, uh, lazy. One of the one-- and I say that in the... Some of the b- some of the best product managers are actually lazy because you recognize, like, sure, something could be done if you take these 12 steps. And I'm like, "Okay, well, I'm not gonna do that." Right? No one's gonna do [00:41:00] that. So that's very unlikely. And so, like, as a product manager, to gain adoption in something, you have to be able to have, you know, easy. What, what is no-brainer? What is automated? What i- you know? And, um So both completely interdisciplinary and interested in everything, and also recognize laziness of, okay, I don't have to...
But also I don't have time, you know, all of those realistic things. And so I love that about y- you know, getting into product management. Then management, depending on what industry or what you're trying to do, can use a lot of data. And, uh, so I started to also be really interested and passionate about let's make sure that we're not just, um, mining.
You know, this is me personally, again, we're not just mining data for, for the sake of it. We're actually, like, having a good value proposition because I'm also a human that lives in the world, uh, and I care about these things. Um, and so, you know, that is a really then interesting [00:42:00] balance where I can intersect, uh, you know, doing, doing good things hopefully for the world that's useful for people and valuable in their day-to-day lives with, um, making sure that it's not, uh, you know, d- that it's valuable in a way that is not creepy or weird or, and, you know, gonna get people in trouble for, for no reason,
Kate Parker: Mm-hmm.
Abbey Keck: Know, in, in ways that, that we don't want.
And so I, uh, I, I, I love this. Product management is just the best place for me to be able to tackle these hard problems and, uh, think about them in interdisciplinary ways. How does operations team or the analytics team or the engineering team, uh, think about them, um, and, and solve, solve them together? Uh, product management is a good intersection across multi teams as well, and I love that.
I love hearing everyone's perspective
Kate Parker: You in your free time, I understand, do puzzle escape rooms. You love puzzles, you love [00:43:00] scavenger hunts. I f- I mean, I can already see how that intersects with where you have found yourself in your career. But what have you noticed when you think about puzzle escape rooms or when you're in that environment that translates to what you're seeing with data and data usage?
Abbey Keck: It's such a good question, um, because there, there is, um, yeah, scavenger hunts and escape rooms I, I love. I, uh, love running around. Um, while I said I'm lazy, I'm high energy, so, uh, so it- it's a good outlet for me. But where this kind of intersects with, uh, e- even data, some things when you, when you're ... I'll- I'll take an escape room to, uh, um, as, as you mentioned. Sometimes you are looking for something, and it helps you to find something when you're not looking for it sometimes, right? You're like, "Okay, let me take a step back and actually look at the big picture." That's something that actually really helps you in escape rooms sometimes. Sometimes it's poking around and being like, "Okay, let me test [00:44:00] this," right?
That works with data. I'm talking about these things, and it, it helps you to, like, either go, go deep and see, well, maybe let me poke around an analytics t- team. Let me do some analytics. Let me go deep dive into this. Let me see if there's something here or if there's a thread I can pull, right? Uh, or let me take a ba- step back and look at this big picture.
Oh, there's this giant painting on the wall that's staring me at the fa- in the face, and I didn't notice it because I was looking for the details, right? So it's a good practice to actually be able to do both. You wanna look at the big picture. What am I trying to do? What is the world telling me? What is, what is, uh, the zeitgeist, you know, telling me about where people are, wanna be headed and where the industry and, um, product should be headed?
But then also let me go deep dive and pull, pull at a thread. Um, that's really interesting. And then also in escape rooms, there's red herrings.
Kate Parker: Yeah
Abbey Keck: when you pull the thread, and you're like, "Oh, no, this is not worth my time," right? This is not, this is not it. And that is some, some intuition. You wanna [00:45:00] trust your teams to, you know, have that intuition of like, okay, yes, go, go do it, but then don't, uh, you know, know, know when to stop when it's a red herring and it's maybe not gonna help you. Um, and, uh, tr- translates actually a lot, um, uh, to that, I think
Kate Parker: I lo- I mean, there's so many pieces there, right? There, it's the, the taking a step back and seeing the big picture, the not fa- you know, not falling for it, you know, when there's the red herring in the room, and being able to balance all of that together is really cool. And I think it leaves us with something very visual of being in that room, and sometimes it can all feel like we're in that room, right?
And particularly in this moment of time of whether it's the, our company or, you know, a project that we have at work or something that we're trying to launch, and how do we get out? Like, how do we get the benefit of getting out of that room? And there's so many lessons for us to take from that. I wanna thank you, Abby, for joining us on today's podcast.
It has been such a, a full [00:46:00] conversation. I have learned so much. You sit really uniquely at the center of a lot of important threads that are happening right now, and I feel that we've learned so much from you in our, our time together. So I wanna thank you for joining us on our podcast. I wanna thank the audience for listening as well.
Hopefully, you've taken some really good notes from this conversation. I keep coming back personally to it being cleanup time, and importantly, being able to do both at the same time because no company wants to slow down right now. Every company wants to take maximum u- use of this moment that we have to grow their business, to grow their market share, and being able to really cleanly articulate, as we heard Abby today on our podcast, that doing both in parallel is in great service of the business.
Um, so I hope you all enjoyed our conversation. Uh, tell someone about this podcast. Share it out. [00:47:00] Thank you again, Abby, for being here and for our conversation. Um, it's been wonderful. This has been another ex-
yeah, this has been another exciting episode of "The New Data Economy." We'll see everyone next time, and thank you again, Abby, for joining us.