Solving the Customer Data Puzzle: How Reltio's Customer 360 Data Product Transforms Customer Data Management

Chris Detzel: Welcome to another data driven [00:01:00] podcast. My name is Chris Detzel and today we have Venki Subramanian on, and he's the senior VP of product at Reltio.

Chris Detzel: Welcome back. Venki. How are you?

Venki: I'm doing well, Chris. Thanks for inviting me to another exciting conversation.

Chris Detzel: Yeah, I'm excited about this. And, it's funny because You, when you and I had our first conversation, we did talk a little bit about data products. And we're going to get a little bit deeper today because Reltio has invested a lot into this, not just concept, today's show is going to be called solving the customer data puzzle and how Reltio's customer 360 data product transforms customer data management.

Chris Detzel: So I'm really excited today.

Venki: Likewise, me too. It's a topic that we have touched upon in a couple of our other conversations, but good to dive into a little bit more details about how relative is thinking about data products and what specifically are we doing around that to enable our customers.

Chris Detzel: [00:02:00] Yeah, and I'm really excited to go deep with you. Finally, we get to talk about this. Before we do dive deep in, tell us a little bit about the concept of data product and how it differs from, traditional approaches to managing customer data. What are the benefits of this key

Venki: things?
Venki: Absolutely. Absolutely. And I'm sure many of our audience would be quite familiar with this. Data product is a concept that came out of data mesh. Data mesh itself is a. An architecture framework you can think of for managing data. And it talks about four foundational principles. It talks about decentralized domain oriented data ownership talks about data as a product or data product itself as a core concept of it.

Venki: It talks about self service capabilities and federated data governance, right? And the reason why it exists is because. There are a lot of problems in the traditional ways of managing data assets for organizations. One of the fundamental problems that we have seen is [00:03:00] companies trying to create use case specific data sets, right?

Venki: So think of a company that is investing in churn reduction, and there might be another initiative in the same company that might talk about. Cross sell and upsell. If you think of what kind of data is needed, probably about 70, 80 percent of the data that is needed is exactly the same, right? The foundational data about customers and the customer behavior is what feeds into either one of those initiatives.

Venki: But the traditional approach might be, Oh, I'm going to create a set of data set, Or some specific assets, reports and other kind of things for my churn prevention, which might be different from what I do for my cross cell up cell optimization. And that approach of siloed thinking about, about data sets in the context of different initiatives has made the data fragmentation worse and frameworks or architectures like data mesh and specifically the concepts of data product emerged out of that as a way of thinking about it differently.

Chris Detzel: Yeah, and you touched upon [00:04:00] this particular question, but not exactly. So tell us a little bit about how Reltio Customer 360 data product. Tell us a little bit about that and what challenges does it seem to solve?

Venki: Absolutely. So let me actually start from maybe A step before talking about LTOs customer 360, right?

Venki: So if you think of data product itself, what is a data product? Essentially it is applying a lot of the principles of product management or product development to data, which means you are looking at starting with consumption scenarios, essentially. Why do I need to create, what problems am I solving with my, with a product I'm creating?

Venki: The same way, what problems am I trying to solve with the data product that I'm creating? So that is one of the foundational sort of principles that gets applied to thinking about data as a product. And then you look at how do I iteratively deliver value? No product is perfect when it comes out in the beginning, right?

Venki: It may meet many requirements, but it's not going to meet all of the requirements. But over time you can think of products [00:05:00] maturing and becoming more and more capable to to address multiple different requirements or use cases. The same concepts can be applied to data product as well, right? So how do data product owners deliver products or data product capabilities that can enable maybe to start with one, two, or three different use cases or consumption scenarios with the data, but over time they can add more capabilities to it, right?

Venki: Those capabilities could be incentive sense of adding more. Attributes, adding more third party data sources to enrich the data, adding more ML models of predictive models as part of it, adding more visualizations or analytics capabilities on top of it. So these are all different ways in which you can enhance a data product over time.

Venki: Those are that is the iterative delivery of incremental value is another key concept that we think about when we talk about data products. So before
Chris Detzel: you go directly into Reltio customer 360, cause I know you want to dive into that, [00:06:00] talk a little bit about product owners. So you're, I hear this a lot, but what I don't hear is who is a product owner, and then how are they using that data? I don't know if that makes sense, but

Venki: yeah. A product owner, essentially for every product to be successful. Let's actually even define what success means for products, right? Having been in product management in the product world for a long time. If you ask me what success means, there's only one true metric, which is adoption.

Venki: Are people using a product, right? Yeah. Same thing for data. It's no different. So any of the products that are getting created in an organization, which are data centric the measure of success is adoption. So a data product owner as a role exists. So that this person along with it, maybe a team of people can truly understand who are the consumers of data.

Venki: What do they need the data for? How do they want to consume the data? And how can I provide the data along with the consumption capabilities in a manner that drives usage or adoption of the data? So that is the role of a product owner, right? It is really not very [00:07:00] different from a product owner, product manager that exists for a digital product.

Venki: Think of Let's say a CRM product or a code product or a community's product. Any of those products essentially have product owners or product managers who are constantly looking at who are my customers, who are my users, what are their unmet needs? How do I serve them better? And how do I then drive adoption?

Venki: Apply all of those same concepts to data that now defines the role of a data product owner.
Chris Detzel: So tell me a little bit about Reltio customer 360.

Venki: Absolutely. So when we talk to our customers, we realize that. Every customer in one form or the other is trying to create a foundational data product that is centered around customer data.
Venki: In fact, partner in some of the recent conversations even some of their presentations at their data and analytics summit, we're talking about how MDM is your foundational data product, right? So when we started hearing that from our customers, like they are trying to create a product and their product [00:08:00] owners who are responsible for assembling the product and driving adoption of the product.

Venki: It was very clear to us that we have to also orient ourselves and our product capabilities to facilitate our customers, to create data products on top of relative. So customer 360 is the first off such data product we're going to create. And essentially what it does is it enables our customers to create their customer 360, our customer data product.

Venki: on relative by assembling data from multiple disparate data sources, first party and third party, even zero party data that are being managed by customers and then enable consumption of the data through multiple different modes for multiple different scenarios. So essentially relative becomes the platform on which a customer 360 or a customer data product gets built.

Venki: And we are providing a lot of those components. We are essentially providing a prebuilt. Customer 360 data product, which can then be activated on basically hydrated, populated with [00:09:00] the data from our customers. And they have a ready made customer 360 data product available. So can you touch upon a little bit about some of the problems that it solves?

Venki: Absolutely. So going back to the example we were talking about earlier, right? the churn reduction or upsell cross sell. If you think of broadly what customer data gets utilized for inside an organization, it falls into either driving growth and retention with your customer. It falls into improving your operational efficiency and experiences with your customers, right?

Venki: Which could be anything from customer service to other kinds of process optimization, process automation that you do. Thank you. Where you're reducing the cycle time between what customers are requesting from you to how quickly you can deliver those services. And the third category is you're using the same customer data for managing compliance, because again, depending on your industry, there may be less stringent to very stringent regulations around how you collect and manage and use [00:10:00] customer data.

Venki: Yeah. And that other aspect that customer data gets used for, as they, as organizations collect, manage, and use data, they also need to understand and comply to all the different regulations that comes with that territory. So our customer data product, our customer 360 data product essentially enables organizations to utilize, to basically collect, unify, and then use or activate that customer data for any one of those broad areas, whether it's to drive growth Bye.

Venki: Attracting new customers, converting new customers, retaining your existing customers, upselling to those customers, operational efficiency. Or digital processes, automation, and those kinds of things and risk and compliance, this becomes a foundational capability that is needed for all of that.

Venki: So out of curiosity, if it was a product company like us that buys the customer 360 or uses it, is it more of like a customer success sells and marketing kind of play, is that kind of the [00:11:00] group that you would touch or

Venki: Yeah, absolutely.

Venki: It is all of it, right? So think of a company like us and we are a typical high tech B2B SaaS company. We have our marketing campaigns that drive awareness that then take our customers through a process of, evaluating our products, finally converting them to those two customers. And then we retain those customers.

Venki: We enable adoption and, enable our customers to use our services in the best possible manner. So we have. Customer support as part of that customer success as a part of that are our sales account executives. They continue to stay in touch with customers to understand their increasing requirements and how do we deliver those to those expectations.

Venki: And we also have to support our customers. And for that, we need to understand what subscriptions or contracts does a customer have, what have we provisioned or provided to them as products in our case, right? We also keep track of Which individual tenants are provisioned for which customer in which particular region?

Venki: How are they, how are we serving their traffic and things like that? [00:12:00] And we also need to understand the active deployments or completed deployments for those customers. So we, you think of the, if the full life cycle and every single engagement that we have with the customer also needs to be understood so that we can provide a seamless end to an experience.

Venki: That's what a customer 360 is all about. So we are essentially using Reltio for a lot of that to support our sales marketing service success kind of efforts using Reltio.

Chris Detzel: Yeah, I've seen it and love the, whenever you go to the dashboard, you can see just the 360 view of the customer and accounts and what they're doing, the usage, all that kind of stuff.

Chris Detzel: It's really cool. So there's this thinking around real time, right? And the customer 360 data product delivers, the kind of our spill is, that view in real time across the full customer journey. How do we achieve that? How does Reltio do that? And why is it so important?
Chris Detzel: I

Venki: think it's really easy to answer the

Venki: second part. Why is it important? I think I'll [00:13:00] quote our CEO, founder CEO Manish Su, right? Where he was in a, one of the recent conversations he was saying he has never heard a customer ask for things to be done slower. No, you will not hear someone say that, no, this API is too fast for me.

Venki: Can I interview some latency, right? So every business essentially is trying. Nobody has

Venki: patience anymore, Venky.

Venki: Okay. But I think that starts with all of us as customers, right? We don't have patience. We want things to be done snap of a finger. So I think that is a reality that we live in, right?

Venki: We are living in a highly connected digital age where everybody needs the ability to find information, self serve or to be serviced. Instantaneously, and that requires real time capabilities. So your data or data products that we are talking about, right? The data products are only relevant and useful if they are able to meet that real time nature of the world that we are living in.

Venki: And that has been a foundational principle for Relgeo from the very beginning. This was part of Manish's vision, right? He wanted to [00:14:00] create a highly scalable, real time oriented data platform that can serve master data management or customer, data products capabilities. And that's exactly what we have done.

Venki: Give you an exam. Just

Chris Detzel: began. We've just begun. So this is the first one, like you said. So thinking about CDPs for a minute, they've been around for a while the performance has been mixed. What are some strengths around CDPs and why does their use persist? Why is it still there?

Chris Detzel: And then, tell us a little bit about the shortcomings.

Venki: Absolutely. So CDPs, by the way, they solve a critical problem in companies where primarily they are focused on marketing and that is how the category emerged, right? So my personal opinion, the term customer data platform probably is not very appropriate because it's more A marketing data platform than really being a customer data platform.

Venki: But to their credit, what CDPs most CDPs have done is they have really simplified the process of [00:15:00] collecting signals or events, interactions, behavioral data about customers from different touch points and connecting that information to identify the right segments, and then connecting that to the different marketing automation processes, whether it's email campaigns or.

Venki: Text messages, phone calls, all of those kinds of things that you can think of, which are marketing oriented. Yeah. You have done a good job, but you also see the problem that it creates, right? Chris, because if best case CDPs are able to do exactly what they are meant to do, which then creates just another silo database or a data store within an organization where customer data now exists.

Venki: It doesn't mean that, CDPs are now, or have been serving every different interaction, whether it's a, Think of a mobile application, a web experience, a contact center engagement through, chat or phone call or email or other kind of methods for customer service kind of scenarios, post sale [00:16:00] scenarios.

Venki: TDPs have not traditionally been really good at that, right? And their focus have been, and will continue to be, on marketing. Whereas what I believe is enterprises require an enterprise wide customer 360 or an enterprise wide customer data product that can support multiple different use cases. And marketing is absolutely one of them.

Venki: So when we look at Reltio, for example we have many of our customers using the foundational data, customer data, they're managing in Reltio to power their contact center experiences for call routing or agent, to reduce the first call or improve the first call resolution time where agents require a 360 degree view of the customer, they are hooking Reltio into those experiences.

Venki: We have seen customers use us for privacy and compliance kind of capabilities, managing that information in a centralized manner across mobile web, multiple other channels. So for us, A CDP or a marketing automation is one of the many use cases that we can enable with customer [00:17:00] 360.

Chris Detzel: You already answered the next question, Vicky, and gave some nice use cases and it's really cool first time I've heard that.

Chris Detzel: Yes, a CDP wants to solve the problem of marketing, but then creates potentially another silo, and it feels like you, I think you were just saying, Reltios customer 360 kind of takes those silos away. And in a lot of ways so I like that. So let's have a conversation about AI, I think that's always gonna come up nowadays.

Chris Detzel: And I think for good reason. So the conversational AI powered data exploration capabilities seem quite innovative. How do you see this empowering business users and changing their interactions with customer data?

Venki: Absolutely. It's a great question and I wanted to drill into a little bit of that conversational experience.

Venki: But before that, just to take a step back, Chris, and we have talked about it probably in other conversations, the purpose of AI is essentially to simplify our day to day [00:18:00] tasks, automate away repeated tasks and get more richer insights out of, multitude of data sources or amount Yeah. So we use AI as an enabler for a lot of the capabilities that we create.

Venki: AI is not just, AI is not an end goal for us. AI is how we do things, how we enable better experiences, faster outcomes for our customers. So that is why when we look at customer 360, we are applying AI in a few key areas, right? One is for unifying the profiles of customer, understanding who the true customer is so that we can associate all of the interactions and behavioral data to that customer.

Venki: We are looking at AI based predictive models that can predict certain outcomes. Like he talked about, we talked about churn prevention. It might require us to predict a churn score or a risk score associated with customer. How do we do those kinds of things with applications of AI and what you just asked me about the conversational experience.

Venki: It is all about [00:19:00] reducing. The level of effort it requires for a person or a data analyst or for a data steward to interact with the system. Traditionally, relative is known for its great user experience overall, but even that requires someone to actually understand where to go, which button to click, what kind of action to take to be able to get to the data.

Venki: But what if you don't need to know any of that stuff? And just like you can ask me a question or I can ask you a question, what you could ask, what if you could ask a question to relative and say, find me all customers in New York who likes red cars. And the system is able to interpret what red car means and what entity type to search on, what kind of attributes to put a filter on.

Venki: And then it brings back the results. And here are your, let's say 20, 000 people who are interested in red cars in the state of New York. Isn't that much simpler?

Chris Detzel: Yes, it is. I've seen it in action and it is really cool how we're not just talking about AI, but we're actually using it. Like you said, it's not the end goal.

Chris Detzel: It's just to make it simpler [00:20:00] to use. I love it. I love it. You actually answered one of them, one of my questions, but. I'm going to go, I want you to get a little bit deeper into this particular question. What are some other AI field advancements in Realtio customer 360? You mentioned one. Is there any others that you want to mention?

Venki: Yeah. So I touched upon a few of them, right? Let's just go back and quickly recap that. So just talked about the conversational experience that we are creating where using natural language prompts someone will be able to identify a segment of data, a segment of customers. Or even create a chart out of that so that it can be visualized and added to a dashboard inside rel to you.

Venki: So there are capabilities around that, which we are investing in expanding or deepening, right? So we will have more such conversational capabilities available, which can even do things like summarize a profile for me, tell me more about this customer. And then it will provide you a summary. Think of a sales executive who's preparing for a meeting with a customer five minutes before the meeting.

Venki: She wants to go [00:21:00] into the into relative and say, Hey, I'm going to go into meeting with XYZ customer. Tell me what I need to know. And the system is able to summarize all of that information and present it much better than what they have to do today. Go through and click through screens after screens and trying to find the information they need.

Chris Detzel: Even patient stuff tell me about this patient, if a doctor, maybe it's not a doctor, somebody wanted to know that information. I assume it's some of that same stuff, right?

Venki: Exactly. Whether it's a patient, whether it's a supplier, whether it's a provider, a member, whatever it is, would be right?

Venki: So that is one where, which we are really excited. We are still in the early stages, but we are getting a lot of great feedback from customers and we continue to invest in that. The other area, which is very core to relTO is the problem of entity resolution or identity resolution. How do I know?

Venki: That ABC carp is the same as ABC corporation, which is entered probably in 10 different ways in my system. And that happens all the time. So how can we collapse that information into a single true trusted representation of the company that you're trying to do business [00:22:00] with, or a person that you're trying to do business with?

Venki: How do I know Chris Detzel is the same person as Christopher Detzel? Who lives in, Phoenix, Arizona, whatever. There's that kind of information. So again, application of machine learning AI specifically with large language models for comparing different versions of, a person or an organization with its context awareness has proved to be a, produce much better results and.

Venki: It's an exciting development as a patent pending area, application of LLMs for us. And that's a second area, which again, we will continue to invest and enhance those capabilities. We are also looking at other areas like anomaly detection, identifying anomalies and data so that we can detect those before they start polluting your downstream consuming systems.

Venki: So there are applications of AI in that space. And there are possibilities. There are multiple other possibilities, which we are exploring. But like I said earlier, a lot of it our thought process really is about how and where [00:23:00] do I apply AI so that it is able to produce better results, faster results, or results with less cost, less effort.

Chris Detzel: Yeah, that's look I, we had to touch upon the AI stuff and I think it's really awesome what realtio is doing in that space and truly applying, real life scenarios to some of this stuff. So it's really exciting times. And we've started talking about a lot on our community and then your team has been.

Chris Detzel: Getting out there and about and so really appreciate that. And it's really fun to watch. I can't wait to see even more We always want more. So let's shift

Venki: It's probably you know, what just a shameless plug, right? Like you said We have detailed sessions on relative intelligent assistant, which is the conversational experience we have created.

Venki: We have had a couple of sessions on FERN, the Flexible Entity Resolution Network, which is an AI powered entity resolution capability that we have created. All of these are available on the community and you've done a great [00:24:00] job of curating some amazing set of content. Which is available to everybody. So just a quick plug there.

Chris Detzel: Yeah, I appreciate that. So we'll get off a little bit about the AI stuff and talk a little bit about data governance and security. They're hot topics these days. How does customer 360 data products simplify governance and protect sensitive customer information?

Venki: I think we briefly touched upon this at the beginning of the conversation.

Venki: So let's just click down into that, right? Customer data has a lot of different regulatory requirements or, requirements around it. Depending on your state or country, you have all heard GDPR, CCPA, CPRA, multiple such such regulations that are applicable. Now, if you were to take this into.

Venki: other more regulated industries. Think of healthcare with HIPAA at the requirement. So while companies have managed their customer data and there is a it's a wealth of information that they have. If used appropriately can [00:25:00] produce amazing experiences for customers and provide growth opportunities for companies.

Venki: It comes with a huge responsibility of how to manage this data in a compliant manner, in a manner that is. legal, ethical, all of those aspects, right? And there was a lot of conversation off late about ethical AI. How do you use data in a responsible manner for AI and all that, but even without AI, before AI, even just talking about basic management of data and putting it to the right use in the right manner requires a lot of capabilities.

Venki: And it starts with, first of all, the basic set of security capabilities on how you're collecting and managing this data. That is something that is built into relative, right? As we bring data together, We support all of the security requirements very stringent security requirements for large enterprises and even regulated industries.

Venki: We are HIPAA compliant, for example. So healthcare companies, life sciences companies can trust Reltio as a platform that they can use. In addition to that, though, there is another important aspect, right? When you have a centralized repository of information like [00:26:00] Reltio managing your customer data, which also understands the end to end lineage, as in it knows where, what slice of information came from.

Venki: So just imagine the power that it can provide when you have to comply to requirements, like your data service requests. When a customer comes and says, tell me what information you have about me or forget all the information about me, right? Companies have to clearly implement a clear process, which identifies what are the different sources of information that we have used to collect the data.

Venki: And which part of the data has come together from which place, and what are the consuming systems that are using this data? So using the relative ID, they're able to now trace that entire lineage from start to finish. And now they can go and take actions to. Provide that information or to obfuscate hide, tokenize that information, all of those kinds of possibilities now become easier with the capability we provide.

Venki: So while we talked a lot about the, the growth initiatives or [00:27:00] the, process optimization kind of initiatives with customer data. It is equally important to understand that this is essential for every business to have these kinds of capabilities so that they can comply with the different regulations that that appear and continue to change in our landscape.

Chris Detzel: Wow. I feel like we're hitting all the buzzwords today, AI, data lineage, governance, all those things, but it's really good.

Venki: And I hope it's not just the buzzwords, right? Important conversations that absolutely. Every digital experience leader is having.

Chris Detzel: No you're exactly right. And you mentioned earlier that the customer 360 data product is described as like the first in the series of planned data products, maybe without giving away too much, can you talk a little bit about some of the other data products in the pipeline and Reltio's overall vision?

Chris Detzel: In this area.

Venki: Absolutely. So as relative has had a multi domain master data management capability for the longest time. That is what we are well known for. That's what any of our customers are using us for. [00:28:00] And if you think of what foundational data. Or data products companies are trying to create, they fall into a few different areas like, found fundamentally it is data about organizations, people, products and locations, but when you start thinking about it more in the business terms, it's data about customers, suppliers, products locations or stores.

Venki: Or different industry variants of that. There might be a specific interpretation of what a customer 360 is in banking, which may not be the same as what you, what it might mean in insurance. When you extend that to other industries like healthcare, for example, they might talk about a provider 360 identifying, understanding all of the different providers, healthcare providers they work with, or the member 360, which are all the insurance members, like the clients, customers they have.

Venki: It could be patients. You mentioned that multiple times during this conversation. It could be patients for life sciences. It could be also providers and payers and other kinds of information for life sciences companies for pharma and others. So [00:29:00] each one of those areas or those domains.
Can have a domain centered data product.

Venki: And our vision is to enable our customers to put all of those data product, the foundational data product capabilities on Reltio and trust us as the platform that gets them to building out those data products and driving consumption of those data products across the organization.

Chris Detzel: I like that. So thinking just broader, right?

Chris Detzel: Just in, in each kind of industry, like focused, um, and speaking or building it the way that companies think, I love that.

Venki: You're very well aware of this, right? One of the things that we have always focused on is that shorter time to value, which comes with a semantic understanding of this data in specific market segments, and we want to continue doing that same thing in customer 360 as well, right?

Venki: So customer 360 is not just that a single horizontal capability. We will also look at how do we create a customer [00:30:00] 360 that is more relevant for our optimized for fine, tailored for. Insurance banking or other sub segments within financial services, high tech and others.

Chris Detzel: Yeah. And funny that you mentioned community earlier is.

Chris Detzel: We go deep into some of that. So if anybody wants to, get on community. relta. com, you'll find some deep dives into a lot of these things that we're talking about today, and if we don't have them, we will at some point so what are the most transformative aspects of delivering customer data as a product?

Chris Detzel: And how do you think this will change the way companies operate and innovate?
Venki: There is a question. Yeah, it is. And I think it is. It's also the probably the most important question to ask, right? So what is different about it? And how can companies get more and more return on their investment? I think absolutely.

Venki: It's a very relevant question. And I will go back to what I said in the beginning about why data product, why does that data product orientation help? It is all about [00:31:00] supporting multiple use cases or usage of data. With a foundational data product that can then iteratively get better over time, right?

Venki: Deliver more value, support more use cases. And what there is a nice blog and article that McKinsey had written about this, where they talk about data product and how you can think of the incremental cost of enabling a use case. A new use case becomes less. As you add more and more use cases to the same data product.

Venki: So that is foundational. The reason why we want to adopt that thing that thought process and enable our customers to build data products is essentially to enable them to get it done faster, get it done cheaper, because as you do more and more with the same foundational investments, It just makes more economic sense for our customers.

Venki: So that's what I, I would want everyone to take away from it.

Chris Detzel: No, that's great. One last question. So for companies interested in learning more about customer 360 data product, what are the next steps? And How should they take? What should [00:32:00] they take and what resources are available to help them and guide them?

Venki: So a ton of resources, as always, I would say, community is a great place to start. There are sessions that we have done. We have our documentation and other materials. We will always continue to have case studies published out on our website. One of the exciting things that I want everyone to remember is, we have our industry conference coming up data driven, there's going to be a ton of coverage about customer 360, not only from the relatives perspective, but more from our customer and partners perspective, how they are leveraging relative as a platform for their customer 360 and and adopting that data product orientation with it.

Venki: So if you have an opportunity to attend data driven, That'll be another great place to network with your peers and learn more about it.

Chris Detzel: Yeah. And I'll put all the links in the show notes so that you can have access to the resources that are available about customer 360 and data products, and how you can get in touch with us also, we'll put the information about data driven 2024.

Chris Detzel: [00:33:00] That's in October in Orlando. So that you, if you haven't signed up that you can sign up For Data Driven 2024. Venky, thank you so much for your time. Really do appreciate it as usual. Really great stuff that you guys, that Reltio is doing. I say you guys, but we so thank you everyone for tuning in to another Data Driven Podcast.

Chris Detzel: I'm Chris Detzel