In this episode, John Damalas, VP and CTO of Pacific Life, talks about how you can have a successful digital transformation, data-driven decision making, the benefits of third-party data, and much more.
In this episode, John Damalas, VP and CTO of Pacific Life, talks about how you can have a successful digital transformation, data-driven decision making, the benefits of third-party data, and much more.
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Steve Hamm: [00:00:00] John, it's great to have you on the show.
John Damalas: Great. Thanks, Steve.
Steve Hamm: Yeah. Now Pacific life is more than 150 years old yet. I'm guessing that many of the podcast listeners don't know much about the company. Can you please begin by describing the businesses, the companies in, and a little bit about its corporate.
John Damalas: Yeah.
sure. Thanks. So, um, you know, Pacific life has a number of lines of business. Uh, there's life insurance, which includes products that are really focused on financial protection, supplemental retirement income through life insurance. Um, what you typically think of around life insurance term, universal can include benefits like long-term care.
It's also a very robust retirement solutions division, uh, which has products really focused on longterm financial independence. Includes both asset growth and guaranteed lifetime income. Um, you know, we're living longer these days. So products like this really help protect us against outliving our savings.
We have an institutional division. These product products are [00:01:00] really more focused on, you know, large institutional customers, things like retirement plans, corporations, uh, as well as other fundings. and institutional investors. We also have a reinsurance business. PLRB, uh, it's a global re-insurance business specializing in products, um, really that help other insurance or reinsurance companies manage their financial risk.
And then finally, um, Pacific global asset management. That's really, our investment group manages over $18 billion in assets, under management. That's really part of a diverse portfolio, including things like private equity, uh, income, uh, fixed income, other financial vehicles. And the corporate culture is, is incredibly unique, which is one of the reasons why I joined Pacific life.
Even though it is a very large company financially, it is part of the fortune 500, there's only about 4,000. Well he's so it has a really tight knit community family feel to it, which is, um, which is phenomenal, especially for a financial institution of itself.
Steve Hamm: Yeah. [00:02:00] Yeah. That's interesting. I, you mentioned this before the 4,000 people. I mean, it's, it's basically, because of, there's not a lot of processing of human processing of, of, of, uh, you know, paperwork and stuff like that. It just, the digital revolution has basically taken over. Correct.
John Damalas: Yeah, I think, you know, between the digitization and automation of the industry and the fact that our business model, uh, we don't have captive agents. So you won't find a Pacific life financial professional out there. Um, we, the distributor products through, uh, independent financial advisors, financial professionals, which also, uh, is part of that day.
Steve Hamm: Yeah. Yeah, no. I want to get into your role at the company. Now you're fairly new to the job. I believe just nine months. What are some of your prior.
John Damalas: Yeah. Great question. So, um, even though I am the chief technology officer, there's a few different facets to my role. So the first being some of the typical CTO responsibilities you would expect, uh, our enterprise architecture [00:03:00] practice, um, maintaining a pipeline and radar of emerging technology investments.
Direction on enterprise wide technology strategy for things like, uh, integration, um, standards and guidelines on capabilities. Like how do we manage our cloud native data platforms and processes like how we introduce technology into the environment. So that's kind of one set of responsibilities. The second one being our enterprise data program.
Um, so. Kind of the totality of our enterprise data program falls within my organization. It includes both the technical capabilities and platforms, but also things like developing our data governance operating model and coordinating across the teams that are executing these initiatives, uh, within the divisions, but also across corporate functions.
And then the last one really being around digital transformation. So Pacific life is, uh, is underway. Credible digital transformation journey. Um, as you can imagine, this touches all aspects of the company and my office really serves as [00:04:00] the connective tissue, um, to, to coordinate across these work streams, as well as continuing to drive our digital maturity, um, across the various, uh, value chain components of the business.
So it's a really interesting role. Um, you know, a little bit of traditional CTO stuff, uh, obviously. Um, there's some overlap with our data program, but also a bit wider berth of responsibility, specific to data. And then I'm kind of being that, like I say, connective tissue for our digital transformation initiative is a key, keeps things exciting.
Steve Hamm: Yeah. Can we drill down on that a little bit? The digital transformation. I mean, that's a buzzword obviously,
John Damalas: Yeah.
Steve Hamm: it's also a transformation, something real and stressful. That's happening to companies. What exactly are you? Are you, you know, what's the, what's the beginning point and what's the end point and how are you getting there?
John Damalas: Yeah, no, it's it. It's a great question. Um, kind of to, to tease out some of the things that you talked about. I mean, the beginning point really kicked off a few years ago, [00:05:00] uh, when as a, as an enterprise, as a company, Um, in a really started focusing a lot of investments specific to our digital capabilities.
You know, we ha we had been maturing those prior to that. Um, but it was really process focused and now, you know, not just us, but our entire industry, um, including both the life and annuity industry. But also financial services as a broader sector, um, is really investing at a much higher pace, um, because you know, automation, digitization, these are things that are really going to take companies to the next level.
So there's that as a starting point, honestly. I don't really know if there?
is an end point. Um, you know, it's, it's not a, when are you done it's um, you know, how do you, how do you continue on the journey and how do you continue to, to, to maintain visibility on, you know, what capabilities are out there? So I don't really, I don't really think there's necessarily an end point.
Um, you know, we may switch focus to different parts of the organization. Um, but I, you [00:06:00] know, my opinion, uh, digital transformation is, is not a destination. It's more about a, a continual.
Steve Hamm: Yeah, well, I think of financial. Companies and specifically investment companies. I think of massive piles of paper. I think of, you know, huge reports I think of, you know, w publishing and mailing out to two people, these, these massive reports. And I mean, specifically, and that's a lot of trees specifically.
What are you doing about that?
John Damalas: Yeah. I think that certainly is an interesting dynamic because you know, it's easy to say. Digitize, every single process. Let's just have an app for everything. Let's have one app for everything. But one thing that we need to keep in mind is, you know, we want to provide contemporary means and mechanisms to interact with us, but we also need to meet our customers where they are.
So let's say for example, there is particular customers that. That prefer a [00:07:00] paper-based application. It's not really about forcing them to interact with us in a different way, but how can we accommodate those desires and those needs yet still internally taking advantage of the opportunities that, you know, digitization automation, specifically, things like optical character recognition, um, can, you know, can play to help advance us on that.
Steve Hamm: Yeah. Yeah. Now a lot of people are talking these days about making their companies data-driven and I know that that's your mantra as well. What does that mean for Pacific life?
John Damalas: Yeah.
that's a, that's another great question, Steve. I mean, I think a lot of, a lot of companies and enterprises, when they say data driven, you know, they'll immediately gravitate toward technologies and platforms. So, you know, let's implement a data platform or, you know, implement a set of technologies and that's certainly part of the.
But for us, uh, you know, that is only one piece of the puzzle. So, um, certainly includes, uh, building [00:08:00] out our ecosystem of data management technologies, whether it's our hosting platform, data, quality management and monitoring, catalog, uh, access, controls, governance, those types of things. But it also includes things like, um, how can we reimagine and reinvent our business processes if you, you know, if you rewind.
Uh, a couple of decades, you know, there's a big push to digitize processes. So know people have been doing things a certain way for so long and then technology and, uh, you know, platforms and websites and things came out. So now you have to, re-engineer your process taking into account those new dynamics.
And now what we're saying is. Based on the new sources of data, the ways to consume data, we're having another, another kind of, uh, you know, digital revolution as it were where now even digital processes need to reinvent themselves to take advantage of new sources of data and new ways to consume it. So that's kind of the second leg in the [00:09:00] stool.
So the first being the technologies, the second being process re-engineering through this lens of being data centric or data. True. And the third one Really being around talent and data literacy. So, uh, how do we get people comfortable? Um, you know, using and consuming data, how we, uh, implement things like common catalog so that we're all speaking the same vocabulary that we can publish inventories of, of the data that is available to our employees.
Um, so that it's, it's much easier to discover an access to the data that we have. So those are really the three legs of the stool. And it all comes together around decision-making. So that's kind of how we sum it up as data-driven decision-making, uh, where the rubber hits the road. So when you're making a decision, um, you know, questioning what data people are using to make that decision and understanding of the data you have available to make your decisions, um, is really how we see those three, those three dynamics coming in.
Steve Hamm: Really it's, it's really interesting. [00:10:00] There's a bit of democratization of data here. It's almost as if everybody in the company is, has to see themselves as a data consumer and also a data based decision maker. I mean, it seems like this is something that's really spreading throughout the, your, your employees, your, your very few employees.
John Damalas: That's exactly right. That's exactly right. And what you just described, you know, having every employee, every associate understanding, you know, what their role is relative to either the creation, the management or the consumption of data is kind of another way to summarize the journey that we're on. I think that's a great Greta.
Steve Hamm: Yeah. Yeah. Now the company has this. Kind of initiative horizon 2025. How does data-driven decision-making fit into that?
John Damalas: Yeah, I know it is a great question. So horizon 20, 25 is, uh, you know, a broad organizational transformation that, that we are on. [00:11:00] And, you know, we have, uh, this concept of kind of the, the five D's, which is our guiding principles for the entire initiative. And the second one is actually. Data-driven decision-making so it's right there explicitly stated as one of the guiding principles of our organizational transformation.
So you can't can't get any to get any higher than that?
Steve Hamm: Yeah, that's really interesting. Um, it seems, you know, it's so funny now in the world we live in now, it seems like obvious. Obviously we should make data driven decisions, just like in medical science. They say evidence-based decision-making medical science where they not using evidence previously. You know?
So I think that the expectations for sophistic, for using data and sophisticated use of data are really, really soaring these days. And like you said, if you're talking about talent, nobody can kind of sit it out. Nobody can sit on the side.
John Damalas: That's [00:12:00] right. It's not just for, uh, not just for technologists. It's literally every employee in the company should know their, their role in the, in the data lives.
Steve Hamm: Yeah. Yeah. Now I noticed that you spent a dozen years at GE in a variety of roles, and then three years at Walmart. Now, both companies are known for their strong focus on leadership and leadership development. What are the most important lessons you learned at GE and Walmart that you're applying to your job at specifically?
John Damalas: Yeah, I know. That's, that's a great question. I would say, you know, my time at general electric, um, one of my things. Biggest takeaways is, uh, adaptability. Um, this interesting personal anecdote in those, in those 12 years, I think I lived in 11 cities and six countries and three continents. Uh, and I worked in, uh, Number of industries, everything from NBC and universal pictures when they were part of the, the GE structure, uh, nuclear energy, [00:13:00] traditional energy, heavy duty, freight locomotives, jet engines, private label, credit cards.
So, um, you know, all, all with a similar theme around technology and data and analytics. Um, but just being able to experience that, those, those wide varieties of contexts, both culturally. As well as from a business and an economic lens, um, you know, you get to learn how to learn, how to ramp up quickly, learn how to prioritize and also kind of draw common themes between those various experiences.
So That's probably one of my biggest lessons learned from GE, um, and, uh, incredibly thankful for my time there. Walmart was another amazing experience. Um, and, and the biggest things I learned about Walmart was one, uh, operating at scale. Fortunately one company, you can't really get, get bigger scale than that, but also an incredibly intense customer centricity.
Um, so, you know, it was very, very apparent. Um, you know, how, [00:14:00] what you did every day, uh, was ultimately in service, uh, to, uh, their retail customers. And I think, uh, especially, you know, here, here in Pacific life, um, it's just as important that focus on. Uh, you know, the end customer, both are financial professionals, as well as our policy and contract holders, particularly because Pacific life is structured as a mutual company.
So, you know, we don't have external stockholders. We're, we're a mutual company, which in essence means means, uh, you know, our, our policy holders, um, you know, our, our not only our customers, but, uh, you know, several stakeholders in many respects.
Steve Hamm: Yeah. Yeah. Very interesting. And so the, the, the interest of the customers and the interest of the shareholders are the same because they are the same. And I think that's a really nice alignment. Uh, in capitalism, you mentioned locomotives. Does that mean you, you w you lived and worked in Erie PA [00:15:00] at one point.
John Damalas: So interestingly enough, um, I was there when the headquarters was based in Chicago, but I had several team members that, that were based in Erie. And I spent, um, many, many fond days and weekends and Erie.
Steve Hamm: All right. Interesting. Yeah. I grew up in Pennsylvania, Western, Pennsylvania. So I'm familiar with all the crazy industrial cities all along the line there. Yeah. Interesting. Yeah. Hey, so, um, let me see, where was I going to go?
John Damalas: Um,
Steve Hamm: Oh, yeah. Let's talk about financial services, the industry. And you know, when we think of the financial services industry, you know, not just banks, but also, you know, the other, the other, uh, elements of the industry. We kind of think that there's the tendency to kind of think of old and stodgy, or there was for a while.
Now Pacific life invested for a long time in the standard technologies, mainframe computers, traditional relational databases, they did the work fine, but then the world changed. Uh, so could you kind of [00:16:00] explain how the companies migrated from the old standards into the new world of technology
John Damalas: Yeah.
Steve Hamm: and also specifically how it got into the.
John Damalas: It's it's actually, you know, a question, we, we talk about a lot in that, not just in Pacific life, but to your point, you know, with financial services, especially longstanding enterprises, you know, our, our policies and contracts are, are tens. If not, you know, 30 40, 50 years long. If you think about life insurance policies.
And so the technology required to service those longterm contracts is, is going to be changing. And really the theme for us is all about intentionality and not just necessarily following a fad because everybody's doing it. You know, everybody's moving from mainframe to data centers. Let's do that.
Everybody's moving from data centers to cloud. Let's do that. It's really taking a very intentional view of the computational workloads that are required [00:17:00] to execute our business and taking advantage of the right technology for the right use case. So for example, those applications where. They can take advantage of the things that cloud brings us like elasticity.
So if we have a lot of activity, you can ramp up the resources you need, but when you don't have a lot of activity, you can ramp them back down. There's certain types of workloads and applications that can take advantage of that. And there's some that. And if they can take advantage of it, those are things that we prioritize moving to cloud.
So, um, from a, a general cloud strategy standpoint, that's been our approach and then specific to, uh, to data. Um, I think one of the, one of the catalysts that really led us to evaluating and eventually migrating to cloud-based data platforms is. We're moving beyond just storage. So databases are no longer a technical capability where you just park data and query it every once in [00:18:00] a while.
Uh, you know, we're moving into much more, um, analytical consumption use cases that require not just storage, but more sophisticated compute capability that can handle not only the increased volume of data. But the complexity of these types of use cases and that ability to right size, not only the storage, but this compute that's on top of it, that is, could be, uh, you know, can be a bottleneck or can't be a bottleneck.
If you use the.
right technologies. I think those are some of the advantages that we see against specific to cloud-based data platforms. Um, uh, you know, one of the reasons why, um, we're, we're excited to take advantage of platforms like snowflake to help advance those analytical use cases.
Steve Hamm: Yeah. Yeah. Hey, let's talk about snowflake. Now. When did the company start having a relationship with snowflake and what were kind of the initial reasons for, for anything?
John Damalas: Yeah. Now, interestingly enough. So our, our snowflake [00:19:00] journey actually started, um, uh, a couple of years ago in our. Retirement solutions business with their data science and advanced analytics teams. And they were grappling with a particular set of challenges, including, you know, they they'd spend a ton of time wrangling data that was siloed across a few parts of the organization.
Um, they were, they were wrestling with some performance issues, uh, relative to some of the queries and models they were working with. Um, I know, uh, for example, one, one extremely complex query that. They were wrestling with, went from, I think it was, it was like an hour, hour and a half an execution time to a matter of seconds once they re-engineered it on snowflake.
So that was really, you know, us dipping our toe in the water, um, specific to the platform and the partnership. From there, our life insurance business caught wind of the impact that the capability was making started deploying some of their use cases on it as well. And then, you know, [00:20:00] really it caught on like wildfire and we said, Hey, this isn't just specific to one of our divisions.
There really is an enterprise opportunity for, uh, kind of a more robust implementation of the plan.
Steve Hamm: Yeah. So are all of your divisions using it now or is it just still kind of migrating from one to the next?
John Damalas: Uh, so I would say the primary use is in, uh, is right.
now in our retail division. So life insurance and retirement solutions, um, as I alluded to we're, we're on a journey. Uh, actually, as we speak to, to implement more of an enterprise shared service related to management of the platform and, uh, on the heels of that, we certainly have a number of use cases in the backlog that, um, we're teeing up to migrate, uh, you know, once that shared service gets up and running.
Steve Hamm: Okay. Hey, I, I think I want to drill down now a little bit. What are a couple of the most important applications for snowflakes that you know, that your divisions are using and what kind of [00:21:00] benefits as the company got?
John Damalas: Yeah, I would say even two things. I I'd certainly call out one B. Uh, our ability to collaborate on enterprise data sets. So, you know, removing the technical friction between sharing and accessing with appropriate controls, which are also built into the platform natively, which is great. So that, that ability to break down silos, Um,
you know, particularly for our data science and analytics teams, but also for, um, say our.
Are, uh, supporting functions, being able to build reports and visualizations without having to know who to go to, to get certain data sets at being, um, kind of curated and managed in a, in a single repository has been a great benefit. And then. Um, introspectively, you know, being able to orchestrate that collaboration, um, but only, you know, only writing the data at once.
So, you know, the snowflake platform is as you well know, you land the data once and then you [00:22:00] can create a myriad of logical views. So I don't have to worry about all these dependent chains of jobs that transformed data and you know, which one might've run last night or failed, or, you know, who's looking at.
What time period of dataset, you know, all of that is, is, uh, kind of taking care of by the platform, which has been great. So we give our users the capability they need, um, without a lot of that administrative overhead that you would have in a, in a traditional data warehouse platform. The second thing I would call out is really the central management of third-party data.
So. As you can imagine, and this isn't specific to Pacific life, but we source a number of third party data sets, whether it's things like Experian data or discovered our work that we use, um, through a number of use cases. So having the ability to source and manage that centrally and publish it across the enterprise is another, uh, you know, a huge benefit that we're seeing from the implementation.
Steve Hamm: [00:23:00] Yeah. Interesting. Yeah. So basically, I mean, the third-party data, is that something that's relatively new or is it something you've always had, but just, it was kind of clunky to gather it and make it available in the past.
John Damalas: Yeah. So it's actually, it's, it's a bit of a ladder, so we've always, um, in a subscribed and sourced certain third-party datasets, whether it's, uh, you know, relating to economic data or, or things of that nature. Um, and now having the ability, uh, and having a mechanism to do that central. Um, and do it again, cutting down on the administrative overhead.
Uh, So you don't have to worry about, well, what, what team actually is sourcing that data? Who do I know that I can get a copy of, you know, how do I get a copy? How do I make sure I have the latest data set all of those things now through, you know, through the platform, uh, and, and particularly the data marketplace that we've started leveraging for things like experience, um, you know, see that just again, we're able to deliver the capability to our business [00:24:00] users.
The administration and, you know, the sausage making, uh, required to make that happen is dramatically lower.
Steve Hamm: So you don't have to set up kind of separate distribution channels for each supplier. You just go straight to the marketplace and kind of like you publish it and your people know what they can get. That kind of thing.
John Damalas: That's exactly Right.
Yep. So, you know, subscribe to it through the marketplace or where, you know, if they aren't marketplace participants, um, you know, using more traditional mechanisms to source the data, but still having a single platform to publish it and make it discoverable by the enterprise has been, uh, has been phenomenal.
Steve Hamm: Yeah. Yeah. Now data science is becoming kind of the lifeblood for many companies. A lot of companies are struggling between kind of the need for governance, but also the need to allow their data scientist community to really have almost free range, different kinds of data. Cause they're, they're, they're mashing up different kinds of data sources there.
They're really doing very creative [00:25:00] things. So how do you manage this balance? This challenge at a Pacific.
John Damalas: Yeah. no, that's, um, it's certainly. A dynamic that has been picking up, because if you think about a data science team, I mean, they're, they're only as good as the data that you feed them. So you can have the most incredible, you know, smartest data science, uh, science, scientists, and modelers in your, in your group or team.
But if you don't give them the data, it's like having the. The, you know, the best, uh, most exotic, you know, see that you're trying to plant, but if you never water it or you don't give the soil proper nutrition, then you know, you're not going to ever have a, have a plant or harvest. So, um, certainly that, that need to democratize data and reduce the friction of discovering and accessing data as parents. But when we think about governance, we kind of think about it in two, in two veins. One is governance of the data itself. So, [00:26:00] uh, giving transparency to our data, scientists on our, what is that dataset? Where did it come from? When was it last refreshed? Do we have any information relative to the quality of that data so that they can make an informed decision whether or not that dataset is a fit for purpose for whatever model they're building.
So that's kind of governance on the data side. And then governance on the analytic side. Um, not, not just proliferating, a whole constellation of science experiments, but really having controlled experimentation on, right. How do we. You know, go broad, but then at a certain point of the experiment, be very intentional about whether we pivot or persevere.
Is this an experiment that we want to continue investing in or, you know, maybe has that, has that well-run dry eye. I use the analogy of, you know, you drill a bunch of test Wells before, uh, you know, you maybe decide [00:27:00] where you're gonna, where you're going to troll for oil, but, um, you know, at some point you have to, you have to cut it off and say, well, you know, is this.
Is this test well gonna going to bear fruit or should we put it on the shelf? So really governance for us comes down to governing the data and transparency into what data sets are available and the quality and pedigree of that data. And then governance just around, like I say, this concept of controlled experimentation.
Steve Hamm: Yeah. Now data scientists are kind of a special breed. I mean, they're, they're kind of the, the wizards of, of, of the data business. And they like to use a lot of different languages, kind of purpose-built for different kinds of queries or different kinds of, of analytics they're doing. You know, SQL very popular, very well understood, very powerful, but there are a bunch of other languages that, that people want to use.
And I wanted to, without getting too far down into the technical weeds here, how, you know, how are you dealing with that, with [00:28:00] that kind of, you know, the desire to, to be able to use all those different tools, uh, on the same data or the same? Well, the same data warehouse, I guess you'd say.
John Damalas: Yeah, no, it's a great question. I'd say.
there's a, there's a couple things that we're doing to try and manage that, you know, one is, um, you know, looking to partner, uh, with platforms that allow a certain amount of flexibility in the language that is used for modeling. So, um, you know, I know, uh, with snowflake, we're excited about some of the snow park, uh, functionality that will allow for Python execution within the environment.
Um, so, you know, things like that. Um, again, to allow flexibility, at least in the early development and experimentation stages of analytics, um, analytical modeling, but then also, you know, again, being very intentional. So yes, a data scientist might have their, have their language of choice, which is perfectly apt and fine for prototyping for, like I say, the test.
[00:29:00] Well, But then when it comes time to, um, you know, maybe formalize or harden, a particular model, uh, for a production use case, you know, then we will take a little bit more, um, I would say intentional look at the languages and the, and the design and architecture behind that model. And potentially align it to closer to one of our enterprise standards, just so it's more consistent relative to what's running in production, but allow for a bit more flexibility again, during the earlier experimentation and test 12 phase.
Steve Hamm: Yeah, that makes a lot of sense. No. I understand you're implementing a shared services framework at Pacific life, and this allows the central it team to own snowflake, but still allow some autonomy and flexibility for your, for your different divisions, retirement, life insurance, that kind of thing. Um, why did you decide to implement that framework versus [00:30:00] allowing the businesses to basically run their own shows from their own environments?
John Damalas: Yeah, no, I, one of the things that we found, especially. You know, once the, the, you sent adoption of snowflake graduated from a single division two, you know, across multiple divisions is one, there were just a lot of administrative tasks, things like object provisioning, user provisioning, monitoring the environment, monitoring jobs, things like that, that, you know, in the absence of a, of a centralized shared service, um, you know, would be done, uh, similar in several different places.
It wasn't really a differentiated capability or a differentiated tasks specific to the business unit. So the opportunity to take a lot of that administrative work off the shoulders of the division or resources was appealing, uh, you know, in, in a, in a number of different ways. One. The divisional technology teams, weren't burdened with that administrative work, but then also, uh, you know, we [00:31:00] could put some consistency around how those tasks were being done.
So that was kind of one compelling reason why we went down this path. The other one being just the increased need for collaboration on data sets between divisions and between. Uh, say corporate supporting functions, individual, uh, functions, um, third-party data being, you know, being a great example. So a lot of the third-party data that we source, um, is consumed by multiple divisions.
So again, having a centralized shared service and a centralized infrastructure, uh, allows for a lot more seamless sharing of things like that, but also even. You know, other datasets that are, that are generated and managed by the divisions themselves. So those were probably two of the most compelling reasons.
Again, one, um, just optimizing our execution of administrative work, but then also facilitating and, and reducing friction, uh, to collaborate on data sets between divisions is, is kind of the other big, compelling reason.
Steve Hamm: Yeah. Yeah. I [00:32:00] would imagine that kind of in the, in the pre data cloud era, it would be very difficult for divisions to share. to have it generated in one place and used an access than another place is not something that really is absolutely enabled by, by the data cloud. Or is there something that was done could be done kind of manually and ponderously before that?
John Damalas: Yeah, I think it would, it was possible before, but to your point, you know, it just, it required a lot more effort and there was a lot more friction in doing so, and, you know, just like just like water and, uh, people find the path of least resistance. So when, when there was a lot of resistance and a lot of friction, or, you know, or, or it was easy, They siloed versus coming together.
I mean, that's just human nature would, would execute in that manner. But now that things like, uh, you know, cloud data platforms, um, reduce a lot of that friction. We're starting to see that [00:33:00] tide turn a little bit where people are realizing both technical and functional, but it's actually, it's actually easier to collaborate. it's actually easier, uh, you know, to, to leverage some of these centralized platforms versus staying siloed.
Steve Hamm: Yeah, it's really interesting to think about, because you talked about how stable, so much of, of the company's businesses and how these long contracts, but so you'd think the technology, you know, is kind of the same kind of thing. It would be. It would, it would be something in parallel. It seems like you've got a very stable long-term business, but, but your technology and the technology needs are changing rapidly and you are changing them rapidly.
I mean, it's like, you're, you're, you're running like in a horse race or something like that. So it's interesting. How do you, how do you kind manage? Is, are there frictions between the stability of the, of the core business and the need to, to rapidly innovate on the technology?
John Damalas: Yeah. I mean, [00:34:00] that, that, that is certainly something that has, that is always top of mind, you know, not only, um, our, our understanding and, and managing our risk profile relative to technical change, but also, you know, to your point, as we, as we change technologies, as we change business processes, how do we maintain, uh, the continuity.
Of our existing book of business while particularly because, you know, even though we have a very stable, long running, uh, segments of the business, we are, you know, we're writing new business every day. Um, and in, uh, bringing the, the great financial products of Pacific life to new customers and policyholders every day.
So how can we leverage technology? Particularly for, um, accelerating our, our, uh, our writing of new business, but also, uh, realizing, uh, operational efficiency in our existing book of business is, you know, certainly a focus of ours, but, but to your point, Um, you know, you have to balance [00:35:00] that with, uh, what's the risk profile.
Uh, what are the requirements of servicing our existing in-force, uh, book of business while also, uh, being able to take advantage of these new digital capabilities, particularly as it relates to new business generation.
Steve Hamm: Yeah, that's interesting. I hadn't thought about that. I mean, new business, new business for life insurance is always going to be with the younger people with new people somehow. And I mean, I live in a town full of. Uh, of young people, Yale is here, other universities, stuff like that. You know, these young kids today, they're different, you know, they don't drive cars, they don't have TVs, all this kind of stuff.
And I'm sure that there's something parallel going on in kind of their, how they view their financial future or their security or, you know, their risk profiles and things like that. So there's a lot of learning for you guys to do kind of on the fly.
John Damalas: Yeah, I would say, you know, especially given [00:36:00] our intense, intense customer centricity and our focus on my customer experience, you know, staying, staying in line with and understanding the changing dynamics. Um, and not, you know, it goes back to one of the themes I talked about earlier of meeting our customers where they are.
So how can we. Provide experiences and capabilities that do cater to, you know, some of these newer demographics or, or evolving or changing demographics while still, you know, enabling our existing customer base to maintain a very close relationship with us and preserve their experience as well, because, you know, Um, you know, let's say, say for example, uh, newer, newer demographics, you know, younger folks, they prefer texting versus calling.
Um, I actually got this question earlier this morning, you know, I actually prefer calling versus texting just because, and maybe it's because I, uh, you know, I've been stuck in my house for the last year and change, but, um, even, even [00:37:00] newer generations, they are homogenous demographics there's variation. So how do we.
Leverage digital capabilities to meet our customers where they are either across demographics or within demographics is a big opportunity and a big focus for us.
Steve Hamm: Yeah, no, that's interesting. Hey, I want to talk about the future a little bit now. Uh, first the near future, what do you expect will be the major trends in data management and data analytics over the next year or.
John Damalas: Yeah, it's a great question. I would say, um, you know, starting on the analytics side, uh, really embedding analytics directly within business processes so that you can keep users in their workflow. I think, um, you know, historically, or over the past few years, a lot of the focus on analytics is are, and I've got a big data set.
How many, I'm going to use that dataset to generate some sort of statistical model or maybe a forecasting or predictive model. Um, you know, we'll put that output somewhere on a report [00:38:00] that someone can go access and say, you know, what's our forecasted or predicted outcome. How can I use that data to make decisions, but that report or.
that output was maybe separated from their day-to-day workflow. And if you, if you think about things like, um, on the consumer side product recommendation engine. So if you go to amazon.com, walmart.com and you're about to buy something and you say, well, these other products are recommended for you. It's Right.
there in the same workflow. You're not going to a separate site and saying, well, what other product recommendations would, you know, would someone have for me that I can now go and add to my cart?
It's Right. there in the workflow. So that trend of embedding. The impact or the results of analytics directly in a, in an enterprise users workflow? Um, I, I see that in a being incredibly critical, uh, particularly over the next year and several years on the data side. Um, I think it really, it is about that last mile of [00:39:00] delivery of data sets to the consumers.
So there's been a lot of advances. And the capabilities of managing data, particularly, you know, on the backend around where, how do we host it? How do we transform it? How do we transfer, transmit it between sources and where these repositories are? How do we govern it? But really now it's about, well, how do we, how do we reduce that friction?
If I'm a consumer, whether I'm a data scientist or another enterprise user, how does. How do I discover what data is even available to me or is relevant to my role and, and how, and should I have access to those data sets? So it really catalog, um, you know, catalog and access. I see on the data side being a near-term prior.
Steve Hamm: Right. All right. All right. Hey, now I'm going to ask you to kind of put on your visionary cap for a minute, looking out five years or more, what are the major changes you see coming in, in the data sphere and how will they change the [00:40:00] game for businesses and even for society?
John Damalas: Yeah. it's a great question. So if I put my, my futurist hat on a couple topics come to mind, so the first being. Uh, ethics and, you know, the appropriate use of data. It's certainly a hot topic right now. And, and rightfully so, uh, where I see this evolving is really to the point where you'll start to see actual technical solutions, uh, playing a larger role into managing, not just, you know, can I have access to a certain data set?
Uh, you know, there's certainly, um, you know, access controls built into. All data platforms at this point. Um, you know, if I'm a certain user, what objects can I query and all that good stuff, but now, you know, the appropriate and ethical use of data speaks to the use case itself. So, um, you know, our, we source this, this dataset either maybe contractually from a third party or from an individual that data set was given to [00:41:00] us, you know, with some.
Uh, expectations around how that data is is used. Um, so how can we, you know, tag datasets and how can we tag analytic use cases to help, uh, take the, take the decision-making, um, you know, Yeah, out of the hands of the data scientists, but be a lot more consistent in how, how those uses are applied, I think is one, one trend where again, the, just the natural evolution, the conversations are happening now.
The need is, is white hot right now. And so I see technical capability coming, uh, you know, very fast following, uh, and continuing into the future. The other trend that I really see is, uh, individuals becoming much more explicitly aware of the value of their own personal data. Um, you know, just as enterprises have, have started doing over the past few years, even, um, you know, in, in the companies I've been a part of, and a [00:42:00] lot of the forums that I participate in this, this notion of data as an asset, Has really been circulating, particularly in, uh, in the enterprise world for awhile.
And, you know, just like you think about your house being a personal asset or your car, or, you know, the, your, the money you have in your bank account or your wallet or your investment portfolio being an app. Personally this notion of, uh, an individual's data being, um, you know, just as much of an asset that they'll make explicit cost benefit decisions.
Uh, now when they, when they go to exchange that data with someone and are getting something of value back, um, so I don't know quite yet, technically, you know how that manifest itself.
Steve Hamm: been people, people been talking about that for about 10 years, but how does an individual monetize their data?
John Damalas: Exactly. Yeah.
Steve Hamm: you'd have to have a company that kind of sets up a system for doing that, recognizing it because an individual can't just [00:43:00] demand it, you know, that kind of thing.
John Damalas: That's exactly Right.
Yeah. Monetize it. Or at the very least put, put it, put more of an explicit or quantifiable value around it so that, you know, you understand right. If I'm going to sign up for a service that is, you know, quote unquote free, but the, the, the compensation or the exchange is part of my personal data.
How can I have more of a quantifiable value, uh, in order to. It will make that cost benefit analysis of well is, is the data I'm giving up, worth the service I'm getting, getting back versus it being a, uh, you know, a one for one exchange.
Steve Hamm: that's interesting. So maybe, maybe an exchange. You know, a degree of openness, you get premium services or offers or various or discounts. I mean, there, there are different things you could do. That's very interesting.
John Damalas: That's exactly right. Yeah.
Steve Hamm: Yeah. John. We'd like to end the podcast on a personal note.
Now, like [00:44:00] many other people you've been working from home for about 19 months, this whole COVID thing. Do you have any fun stories that have come out of that evolving pets or kids or both pets and kids?
John Damalas: So I, uh, I had to Chuck away or said that I do have one. And, um, so, so for, for folks that know me, I have a, I have a five-year-old, uh, Belgian shepherd mix and we named her whiskey. Uh, when we got her, which is an amazing name, I still say, it's an amazing name. She's this great, like kind of caramel, brown color.
Um, and it's just, you know, when people like, oh, what's your dog's name? that's, like, oh, her name's whisky, which is, which is great. Everybody, everybody thinks it's amazing. Um, and, and. Now I have a two and a half year old. And one of the, uh, uh, humorous byproducts of having a dog named whiskey is, you know, my daughter's first words were mommy, daddy, more?
Thank you. Milk. And. She [00:45:00] can very clearly articulate the word whiskey, which has led to some, uh, fun conversations. Um, plus, I mean, it's just, it's just funny having her, you know, stand on the balcony, screaming whiskey and seeing the looks from people walking on the street. But, um, but yeah, that's, that's kind of a funny story.
And then, you know, if I'm on a video call and people don't know, I have a dog named whiskey and. She went on something off screen and I snap my fingers and yell whiskey. I have to make sure that they know I'm not asking someone to bring me a drink at 10 o'clock in the morning.
Steve Hamm: that's, that's really nice. I mean, you know, it's interesting. We have a learned a lot about each other on these zoom calls. I mean, when you, you know, when you see somebody's true environment, you see the pictures on the wall and you see whether they have. You know, any kind of interior decorating taste or not all this kind of stuff.
It, it really does kind of deepen the relationships. And I think that's something that, that we knew people didn't anticipate, never had this kind of experience and suddenly, suddenly you're having it. And [00:46:00] it, it is kind of strengthening in a sense. We, you know, goes back to almost back to the culture that you talked about at the top.
I mean, If you see your, your colleagues more as, as people, as individuals, not just in their roles in the company, uh, that I actually think that that yields better relationships and better working relationships and better results. So
John Damalas: Oh, yeah, I couldn't agree more.
Steve Hamm: Yeah, yeah. Hey, this has been a great conversation. You know, just looking over the things we've talked about, we talked about so much, but I was like the highlights, something, this whole thing with horizon 20, 25, that, you know, the big initiative, the big, you know, corporate wide initiative for the company.
You know, the fact that data-driven decision-making is one of the five key elements. Is really, I think, reflects the important the data has these days in business. I mean, it's, it's really been elevated. It's, it's [00:47:00] recognized and it's absolutely essential to every company. And I just, it's really, uh, kind of really striking to see how important you, what kind of important to your company puts on it.
So thanks for explaining that.
John Damalas: Oh, definitely.
Steve Hamm: All right. Thanks very much.
John Damalas: All right. Great talking to you. See, and, uh, appreciate the time.