In this Community Show Episode, Manish Sood, CTO and Founder of Reltio, answers questions from the Reltio Community about many topics. Some of those topics and questions include: Would like to know more about Manish Sood and his journey on Reltio How does Data Governance and MDM complement each other? Is MDM there to solve the lack of clear data governance or is good data governance negate the need for MDM. How should organizations fundamentally solve the data quality issues that emanate from poor source system data entry problems.If you fast forward five years, how do you see Reltio's vision and market presence? What will you be known for? For Reltio's vision to be ultimately game-changing, APIs need to be very high-performing. How do you plan to make MDM data available to inline decision-making via APIs. And many more. To look at all the questions asked from this episode, please check out the Reltio Community hereFind the transcript here:
Ansh Kanwar (03:19):
Thank you, Chris. Hi everybody. By way of introduction, I'm Ansh Kanwar. I've been with Reltio now for just over seven months and I have about 20 years of experience in different SaaS companies prior at Citrix systems. I was at LogMeIn for a few years. And I've joined Reltio as the head of product management for the platform. And I also have responsibilities on the infrastructure side. I want to begin by thanking Manish really, for being an amazing mentor. Every conversation I've had with him over the last few months, it's been extremely educational. And he's sitting right across the table from me. So when I look up, I'm actually looking at him.
Ansh Kanwar (04:13):
And it just gives me immense pleasure to be able to ask questions that you all have submitted. And I'll interperse with some questions. I may sneak in questions on my own. So I also wanted to thank everybody who submitted questions before this session. And we'll start there, but as you submit more, we'll weave more questions in. All right. So with that, first Manish, I really want to start with you, Manish the person, the entrepreneur. And tell us a little bit about yourself and really tell us about this amazing journey at Reltio that you've been on.
Manish Sood (04:53):
Well, Ansh, first of all, thank you to you, for leading this discussion. And also, thanks to all the customers and partners who submitted the questions. Some really interesting opportunity for a dialogue for all of us. So, to go back to your question about some of the motivation and thinking behind Reltio and how we got started on the journey, I can't really answer that question without going into a little bit of the history. So I started Reltio in 2011. And when I started Reltio, some of the directional things that were becoming very clear and evident, one was the number of applications that continue to grow and proliferate in the enterprise landscape. And it was very clear even back in 2011, that that pace was not going to slow down. There was going to be an increasing number of applications that were going to get created.
Manish Sood (06:08):
The second one was this need for visual transformation. Every company and especially in the last 24 months, we have seen due to the pandemic that every company has to have a digital orientation for their business. Without that, it's a question of survivability. It's not even growth, it's survivability. And the third piece that was in the early stages, the whole notion of cloud adoption. And again, in 2011, there was only one cloud provider, AWS. At that point in time, it was starting to become clear that if you have to manage data, first of all, it will continue to grow. So you need systems that can scale horizontally or can continue to add more data.
Manish Sood (07:02):
And by the way, the best place to build those kinds of systems would be the cloud, and because the compute, and storage, and all those types of lower level infrastructure capabilities were becoming available. So the question in front of us was that having seen some of the past experience and hurdles with adoption of MDM type of concepts, my fundamental belief has always been that MDM is the right concept. That was a little too early for its time and it didn't have the right underlying technology capabilities to support a customer's journey, because some of the problems that we solve in MDM, or think of it as a concept where you have to bring together data from multiple sources, unify it and stitch it together into a holistic answer. Those types of requirements always will have an increasing number of systems that you will need to tackle, will always present you with the challenge that if you don't know what are all the systems that you will have to integrate, how can you possibly know what scheme will those systems have?
Manish Sood (08:18):
And yet, people were trying to define a singular data model that was fixed as a relational construct in the middle of that as the way to integrate. I don't know if I will encounter a record for a customer that has one email address or five email addresses. So why am I defining the data model that has one to one cardinality for a person to have an email address? I'm just using that as an example.
Ansh Kanwar (08:51):
Manish Sood (08:52):
But those types of issues were at the crux of why some of the previous generation type of capabilities failed or ran into problems, because the method of solving those problems was not in line with the nature of the problem itself.
Ansh Kanwar (09:10):
Manish Sood (09:11):
So long story short, the motivation at that point in time was that there is a big problem that is only getting bigger, and there is an opportunity to build a system from the ground up that can scale to meet that need, that customers are going to encounter. And by the way, if you think about the enterprise, everybody has business process automation, everybody has refined how they use applications at scale across the enterprise. But the biggest hurdle for every business today is the number of siloed applications or data silos that they have. And that becomes the friction point for their business.
Manish Sood (09:59):
So the next optimization or the wave of optimization that everybody needs to think about is going to be, how to have data as a continuum while they continue to drive evolution of their business? Because the data that you have today is going to help drive the decisions that you're going to make tomorrow. So creating a capability that would support the businesses for the next 15, 20 plus a year type for time horizon was the opportunity that we saw. And I just couldn't shape that out of my head and I had to go work on that problem. And the answer to that was creating Reltio as the vehicle to go solve that problem.
Manish Sood (10:47):
So long answer to your question, but that was sort of the thought process that I went through.
Ansh Kanwar (10:55):
And coming in at this point, I see the full realization of that thought in the amount of flexibility that we offer in the platform and the sheer number of the variety of problems that our customers solve using our platform. But seeing that 10 years ago, that's amazing. I also wanted to thank [inaudible 00:11:21] for that question and for getting us started. Thank you. Next is from Abby Hussein at Mercury. And this is sort of a system wide question really, which is, how does data governance and MDM, how do they interplay? How do they work together? Does MDM exist to solve the lack of clear data governance? Or once you have good data governance, you no longer need MDM. Can you help us understand the interplay there?
Manish Sood (11:52):
That's a great question. And having lived through various evolutions of MDM and data governance as an overarching concept, my take on this is that it is an ever evolving continuum where these two things have to go hand in hand. Because think about it as an example, data quality. What is good enough, right? Let's say, if you have the ability to claim that you have reached 70% as the milestone in improving data quality, does that mean your work with data quality is over or does that mean that you can go ahead and now tackle the remaining 30%. Or instead of tackling all of the 30%, you will say, I have to improve it from 70 to 75, 75 to 78. And you keep making incremental progress because in these areas, there is a long tail of opportunity and effort that is involved with it.
Manish Sood (13:05):
So what is the best way of solving for these things in concert? I think this is where certain principles have to be thought through where, how does master data management aid data governance, and how does data governance aid master data management? Because both of them have to go hand in hand, and as we bring together aggregate and unified information, we have to think about, is there a different governance model that we can apply to it? Because instead of being fragmented, siloed data spread across all different systems, even if you put a system of governance and a system of curation in place, which is the master data management system, then you have to think about how the data governance policies will drive the continuous improvement on top of it. And instead of having data governance spread out into different parts of the organization, can you do it in a manner where you are empowering more people to participate in that data governance cycle.
Ansh Kanwar (14:22):
Manish Sood (14:22):
So essentially, taking it from a small number of people to democratizing data governance, where if we are working with the business on a specific, let's say, customer, can we contribute and make the quality of that customer record better? Our belief, and this is why we created the UI capabilities, which a lot of customers give us feedback on how differentiated or good the UI capabilities are, but our goal has been that we have to put data into hands of more users, because the more eyeballs that can be on the data and more hands on keyboards that can be aiding and assisting in the work with the data, the better the quality and the governance of that data gets. Otherwise, it's in some dark room or black box, and nobody has a good understanding of what the governance policies and practices are, but by bringing more users with the data together, it drives to a better outcome.
Ansh Kanwar (15:33):
That makes sense. And the full expression really of a solution like Reltio is not just bringing all of that data and mastering it, but really also then, unleashing it and making it available for every department or sort of these distributed data ownership models where all of this curation could happen in that distributed manner also.
Manish Sood (15:55):
Curation at the edge.
Ansh Kanwar (15:56):
Manish Sood (15:57):
Right? Just like you have computing on the edge, you have to go to curation at the edge. And again, more participants and more users of the data drives better quality.
Ansh Kanwar (16:09):
Manish Sood (16:10):
If it's hidden from everybody else, then you're most likely not going to see success with your program.
Ansh Kanwar (16:17):
Yep. No, that makes a lot of sense. There's an add-on question here from Jed Prakash, and that has to do with best practices. Any data governance frameworks that we've seen working across various clients and their feedback?
Manish Sood (16:33):
So there have been different models of data governance that different customers have adopted. And a lot of the customers started with the highly centralized data governance type of a model. It worked in very controlled environments, but what we are seeing is that even those customers are now trending towards enrolling a more distributed type of governance model, the challenge previously was that their systems were fragmented. And hence, they could not go to a more distributed system of governance, but now, by centralizing the data, they're able to distribute the governance of that information into a single data repository. So they may seem like opposing constructs, but unifying and centralizing the data, but distributing the governance is the model we see evolving that more and more customers are adopting and finding successful.
Ansh Kanwar (17:40):
Yeah. That's a really interesting way to think about it. Since you mentioned data quality, I want to pick up the question, another one from Abby, but I know that there's a whole theme here around data quality and the questions we've received. And that goes, how should organizations fundamentally solve the data quality issues that emanate from poor source system data entry problems? That's a pretty specific question, but in general, all hosts of issues in our native quality as they're screening into our systems of record, to help us sort of [inaudible 00:18:15] through that.
Manish Sood (18:15):
I like to call it, the method to the madness. There's a sequence to approaching it. For example, let's think about the state where you don't have a master data management or a central 360 degree view of the information that you're working with. In that case, all of the data is getting created in the applications. And a lot of the consumption of that data is taking place in those applications. And most of those applications don't have good data quality type of capabilities or ability to prevent duplicates from getting created even within those applications. So instead of trying to rewire each system on day one, I think the sequence that works or has proven to work is, start centralizing the information because instead of going across 50 different applications and trying to manage quality in each one of them separately, you have to get to a shared understanding of the data quality.
Manish Sood (19:24):
And you put a system for this in place, which is unifying, aggregating, or aggregating and unifying the information like an MPM system. You get more eyeballs on that data because then, you can understand not only the source level contribution, but you can also understand what is the aggregate impact on that information. And then you have to go back to the source systems to inform them where their processes are lacking. For example, now that you have a central repository in place, should you empower these source systems or applications with a search before creating the type of capability, so that they can look up records in the central repository, and then onboard from there versus having to fat finger in or key in something erroneous every time.
Ansh Kanwar (20:22):
So more of the same information.
Manish Sood (20:24):
More of the same information. So you try to reduce the quality issues at the edge, but it's a round trip that you have to go through.
Ansh Kanwar (20:35):
Manish Sood (20:36):
So the sequence says, don't try to solve it in all the peripheral applications. Centralize that information, get better insights of where you stand on the data quality paradigm, and then, with that centralized information, how are the experiences, so that you can control the creation points in a manner where it is reducing the noise at those edges?
Ansh Kanwar (21:00):
Right. Right. So the observability gets centralized, and then very actionable metrics can get pushed out to the access-
Manish Sood (21:07):
Actionable metrics, as well as actionable tangible capabilities that can be tied into those applications.
Ansh Kanwar (21:16):
That makes sense. Let's move a little bit beyond, but not too far beyond data quality. And really, this is about the efforts required to delve into data quality. But really, this is from Loic Tordo at Schneider. And he's asking a general question about match and merge decisions. All of these activities still take a lot of heavy lifting, a lot of manual operating to get right. And this question is, what is our strategy? What is the relative strategy to help move towards more automation or greater efficiency?
Manish Sood (21:55):
That's a great question. If you think about any of the deployments and it doesn't really matter if it's Reltio deployment or any other flavor of master data management capabilities that customers have been using. Loic is absolutely right, but there is a lot of manual effort even after the data has been ingested, after it has been matched and merged because you still have to resolve some of the manual matches, or you have to steward some of the changes to the data. And there is a process or a set of processes that customers have to define as to even how to identify some of the gaps or anomalies in the data, and then fix them. Can we automate those areas? Can we bring ML or AI type of algorithms that can assist in that process?
Manish Sood (22:48):
And again, one of the reasons why we created the Reltio foundation in the manner that we did, that we have been long-term believers and the impact of AI and ML in this area. But in order to create a system where we could provide recommendations that would assist our users, we had to build not just the framework, which would allow us to create those capabilities, but we also had to ingest in our system a reasonable amount of data from customers, so that we could start training some of the models that would aid and assist those customers or users.
Manish Sood (23:33):
And the great news is that we are now at the point where we are starting to double down on the AI, ML investment. And a large part of the benefits that you will see out of it are things like detecting anomalies out of the data, so that you don't have to go fish for those anomalies. The system can surface those anomalies, bring to you, and then, even suggesting what type of fixes can be applied. Because in some cases, you will get a recommendation about, you should use an enrichment provider that will fill a gap and give you completeness of information. Or in some cases, it's really something that was fat fingered and a minor direction will fix that data.
Manish Sood (24:25):
So creating those capabilities that would aid and assist the users, those are the types of directions that we are starting to invest in. And the first step in that direction was creating the capability of the data quality dashboards that we have been in the process of rolling out to the early adopters of the capability. And the next step from there is to start identifying some of the anomalies that the customers would be able to see from the application itself, so that it reduces the amount of effort that they have to go through. And you'll see continuous improvements in those areas.
Manish Sood (25:06):
In fact, even in the area of matching and merging, the match IQ type of capabilities are not just for matching the data, but also for... You have lots of manual matches that are sitting there in the queue to be processed by your data stewardship team. And there are not just thousands there. In some cases, there are hundreds of thousands of manual matches that have to be looked at. In those scenarios, the assistance from the match IQ algorithms or models would help to double verify the results of the matching algorithm, so that then, the users can accept those results. So again, these are just some examples, but the team at Reltio is very excited about the next set of capabilities that we'll be able to unleash with the application of AI, ML for the specific purpose.
Ansh Kanwar (26:03):
I will double down on that. I could not be more excited about the roadmap that we have for the rest of the year and cannot wait to talk to the customer community about those things. I'll switch gears just a little bit and focus on the ecosystem for a second. A couple of questions. We'll go on sequence. First one is, what is Reltio's strategy to increase, attract applications and services to make their data, those provider's data and applications available through integrations with Reltio?
Manish Sood (26:39):
Great point, integrations. Going back to something that I just mentioned, which was, mastering is only 20% or less of the equation. Making data more usable across the varied application landscape, across every business process, across every analytics and data science type of an outcome is the big part of the equation and the big win. So one way to drive that is by making sure that the integration options are easier and faster for our customers. And there are different ways in which we are going about that process. One is, if you look at the recent rollout of Reltio Integration Hub, the sole purpose of that was, that before the release of Reltio Integration Hub, we had some API level capabilities, which are great for developers who understand APIs, or we had some connectors, or MuleSoft, SnapLogic type of integration tool sets that are being used by customers. And you can simply use a Reltio connector for it. You can drag and drop different types of things.
Manish Sood (28:03):
So again, making it easier. But now with Reltio Integration Hub, we want to bring the low code, no code type of flexibility into the mix, so that we can have citizen developers developing integrations and taking data from Reltio, to various applications or from various applications into Reltio, so that you can have the closed loop of integration available to you. And if one developer, citizen developer creates that capability, they can share with the rest of the community, so that every customer is helping solve the other customer's problems as well. So we've created that platform that we rolled out, but now, we ourselves want to make use of that. And in the past, we have had the third party enrichment data providers that we have integrated with. Dun & Bradstreet was one.
Manish Sood (29:02):
And in life sciences, we have had some sources that we put into the data tenant type of orientation. We just added Bureau Van Dijk for organization data to this mix. But we are creating a roadmap of different types of data domains and the different types of third party data providers that we can have as pre-integrated capabilities. So different flavors of integration, some for enrichment, some for managing the entire life cycle of data into Reltio, and out of Reltio, into applications, and back and forth. But our goal is to create a holistic set of these integration capabilities that can be made available, so that more and more of you can benefit from what already exists, instead of having to go create something [inaudible 00:29:53].
Ansh Kanwar (29:54):
Absolutely. And we're seeing some really powerful use cases come out, that integration hub capability, where our vision of really having non-developers be able to extend the platform. Every week, it seems like we have a new use case and a new story that we're able to share with our customers.
Manish Sood (30:12):
Not only do we have a new use case or a new story that we will be able to share with different customers or all of the customers, but the pace at which this is increasing. That is tremendous because we are now, instead of spending three months, we are spending a couple of days and seeing the new example come to fruition.
Ansh Kanwar (30:35):
Very good. There is a specific question about DMV, but I think it also speaks to the ecosystem. Does Reltio have any plans to provide shareable data beyond DMV? I know you partly answered that, but maybe we could spend another-
Manish Sood (30:54):
Yeah. So there are several third party data providers that are relevant for the person domain, or the organization domain, or the product domain. And our product management team is now looking at prioritizing some of those sources. In fact, we would really love to get feedback from our customers, what third party data sources are relevant to them. For example, I think this question came from Loic, where he had asked about the third party data providers. Bureau Van Dijk, as I had mentioned, was one. But Loic had brought up our denied party screening process because of the various sanctions the organizations that you do business with. And in fact, Loic's team is helping us identify what are all the sources that they would like to see integrated. What are the integration methods and mechanisms that we could put into place? And a large part of that is going to be informed by our customers. So we would love to get your suggestions or your requirements of which data sources are you prioritizing, so that we can learn from it and help you solve some of those problems.
Ansh Kanwar (32:20):
Absolutely, absolutely. Perhaps we'd love to do a community webinar, in which we talk about these data sources. And you can share the candidates that we're thinking about and get feedback.
Manish Sood (32:31):
That would be great, because I think, once again, customers and partners are the best source of helping us understand that landscape and really tell us about which sources are more relevant than ours.
Ansh Kanwar (32:48):
Absolutely. Absolutely. Let's switch gears a little bit and take a question from Ashish [inaudible 00:32:55]. And he asks, how does Reltio, a SaaS product company, ensures customer with data security concerns or assures customers with data security concerns?
Manish Sood (33:11):
We are in the data management business. We are running a SaaS platform where all of our customers bring in their core critical business data into Reltio. And one of the parts of the responsibility that we have as a service provider to our customers is to ensure the security of that data. There is a large investment at Reltio that goes into not only building up the compliance type of policies, but also investing in security technologies that surround the perimeter of our product capabilities. In fact, not just the perimeter, but they're an integral part of how we create the product, how we have engineering working on the code that gets rolled out into different environments, and all the checks that it goes through, in addition to, how we secure the data itself?
Manish Sood (34:17):
So again, that's a core part of our offering. In fact, if you have any questions, we are happy to walk you through and your security audit teams through the details of what is being put into place already in place, and what is the evolving roadmap of those capabilities? But it is one of our biggest investment areas after the product capabilities to invest in. And this is not only an investment team, but also a significant differentiator for us because we become a part of your security infrastructure. We become an extension of that, and we provide those capabilities on your behalf, so that you don't have to go create that additional security capabilities around the data that you are managing inside Reltio.
Ansh Kanwar (35:16):
Absolutely. There's another one that I'd like to pick up from Loic, such a great set of questions here. And he is looking at CDP, customer data platforms. And his question is, as technology stack allows capturing of real time PII and non-PII, like cookies, IP addresses, et cetera, how does Reltio position itself in this space? Are there plans to evolve the platform accordingly, or are there capabilities today that customers can start using to solve some of these problems?
Manish Sood (35:56):
It seems like we have to sign up for another webinar on this topic, because this is a very relevant and timely topic for a couple of different reasons. One, the entire life cycle from anonymous to known PII type of information, and how should you manage the life cycle on of that data? We have a concept of progressive stitching inside Reltio. And if we take a step back and think about why we've created the Reltio capabilities to be available through an API, is primarily for the purpose of being able to stitch that digital journey together. Because in every part of the digital journey, when you go to a website, you are only a cookie because you haven't yet created your own profile by registering it to the site. But that cookie, with some of the minor details of the IP address, other types of details, can be instantiated inside Reltio as an unknown profile.
Manish Sood (37:06):
And as somebody goes through a conversion process and engages in that life cycle, at that point, that profile goes from an unknown profile, to a known PII profile. But even at that stage, the amount of information available is no more than a cookie ID and an email address. But stitching together that information, and then progressively building on top of it because there'll be multiple channels of interaction, you go from registering as a user, to actually buying a product. Once you buy a product, then you have to provide additional information. You will have to enter your address information for delivery. You will have to enter your name information or some of the additional details for contactability beyond email. And that adds more attribution to the profile. So there is this concept of progressive stitching, where it's not necessarily data coming from 50 different sources on day one. You start with a single source, you start with a few elements of the profile attribution, and then you keep adding more details to it.
Manish Sood (38:21):
And yes, along the way, you also add more sources because as you get an email address, you have the ability to match it to other data sources inside your organization. As you get address and phone number information, you can then do more from a matching and unification point. So that's how we have been able to help some of our customers. And in fact, more and more customers, especially with the evolution of the CDP platforms that are coming to us to better manage this type of profile information in a central place before sending it down into the various marketing systems. And something that I think we should definitely take Chris Detzel up on organizing a webinar for.
Ansh Kanwar (39:15):
Yeah, and the workflow that you described, almost the retail workflow that you described is almost a perfect example of this, because even in that sequence, you may get login information as a prerequisite to purchasing something, certain workflows, but in others, you may end up through the whole process, do the checkout, and at the payment point, you may enter your log-in or choose to enter a log-in. And so the sequencing of all this information, the flexibility that our progressive stitching provides, that allows for different workflows to be created and not be dictated by-
Manish Sood (39:55):
Absolutely. The workflows can be different, but the progressive stitching concept is primarily that you will receive piecemeal information that you'll keep adding more attribution details.
Ansh Kanwar (40:06):
Exactly. Yep. Some of these things are very exciting for me to learn about, the capabilities that we can unlock. Now, this one is about performance, and this is from Mercury Insurance. And I really like the way it's phrased. For Reltio's vision to be ultimately game changing, APIs need to be very high performing. How do you plan to make MDM data available to inline decision making via APIs?
Manish Sood (40:44):
So Ansh, one of the things that you and I discussed this very often as we talk about the roadmap and the future capabilities that we want to unlock, day one, when we started Reltio, we said that this has to be an API enabled, API led paradigm because more and more organizations at their own pace are moving towards the time to action being reduced down to milliseconds. There was a time when every company has had data for the longest period of time, but their ability to act on that data was seven days later or 30 days later, because they would wait for all the data to flow into a data warehouse, and then they would look at it. But most of it would be rear view mirror type of information, because the engagement already happened. The transaction already happened, but as we talk to most of our customers, they're all moving towards reducing that time to action and bringing it to the point of engagement.
Manish Sood (42:02):
I'll use the retail example because we are all familiar with it. We all go shop in store online, all those types of things. But at the point of checkout or at the point of purchase, that is where the retailers are trying to enable action with the information. And this is where the real time nature of the APIs is extremely important. And not just real time, that ability to do it as a... I'll use a technical term, a synchronous response where a request can get a response in less than 300 milliseconds. We have now customers who are saying, "300 milliseconds is not good enough. We want a hundred milliseconds."
Manish Sood (42:52):
So our directional goal is to improve our core set of APIs. I don't mean APIs that export data out of Reltio. Everything in Reltio is an API, but there are core set of APIs, like the get a profile, or search for a profile, or update or insert a profile. These are real time APIs that need a real time response back and that has to be down to milliseconds. And that's where we are making additional investments, so that we can drive our orientation to be even faster than where we are today, and really help every organization be in that digital forefront where they're able to impact the experience that they're delivering. It doesn't matter if it's supplier that you're working with, consumer that you're engaging with, or a patient that you're trying to service. It has to be at the point of engagement.
Ansh Kanwar (43:54):
Right. Right. And there's another aspect that I've seen bubble up over the last few months, which is constantly optimizing the implementation. Because over the years, we've introduced new APIs, things that we, in the past, could do through searches. Now we're able to do through straight look ups. And so, looking at the current usage pattern of the APIs, we've often been able to help customers speed up certain workflows, right?
Manish Sood (44:24):
Absolutely. Again, going into the technology details, a get API call is always faster than a search API call. So, if we see a certain pattern that comes up consistently through the use of search, we are saying, should it really be a part of the get type of an API call? And can we optimize the system to deliver better performance on it?
Ansh Kanwar (44:51):
Manish Sood (44:51):
And that's where some of it is being informed by the customer's usage patterns and how they're engaging with us, asking for better capabilities, but at the same time, our own internal desire to have our APIs perform better, because we know that every customer has to get to that real time orientation for the digital business that they run.
Ansh Kanwar (45:22):
Right. Makes sense. There is a direct question here from Deepak around support. His perception is that we can improve our support services. And he's asking, what are the plans for that?
Manish Sood (45:36):
Great question. Some of the feedback that we have received from our customers was that 12 months ago or before that, our support needed a lot of improvement. And over the last couple of years, we have been investing heavily in not only adding more team members, but how we train them? How we make our support processes more robust? What is the path to resolving some of the issues and how quickly we respond to those customers? A large part of it is us training and enabling our own resources to be the trusted extension of a customer's team, understanding the contextual relevance of the questions that are being raised by the customers. And that awareness allows us to lead to the responses in a faster, better manner.
Manish Sood (46:40):
And also, constantly getting feedback from our customers because a large part of this is informed by, we have made improvements in a certain area, but Mr. Customer, what are the things that you are not yet seeing? And is it because we haven't tackled those other parts of the support equation, or is there something that we need to evolve in our support engagement structure that we have. So once again, I would love to get Deepak's feedback on where he sees some of the areas or opportunities for improvement that we can go after, so that we can further improve it and enhance our support capabilities.
Ansh Kanwar (47:25):
Absolutely. And this may be yet another opportunity for perhaps a community webinar or an engagement where we can bring Dan [inaudible 00:47:34] or-
Manish Sood (47:35):
Absolutely. Just like for example, with the documentation. We have a lot of support from our customers and partners where they are providing advice to us as to how we should improve those capabilities. And if our documentation improves, our ability for our customers and partners to go do more with our system improves.
Ansh Kanwar (48:02):
Manish Sood (48:02):
In a similar manner, we would love to seek their guidance and advice on what other things we should be thinking about. And maybe even having an advisory council around it that we can go after and get some valuable data from.
Ansh Kanwar (48:18):
That's almost a perfect segue into the next question I was going to ask. And this one is also from Deepak. He says, what is the plan to engage customer side decision makers with Reltio? Specifically, he's asking about roadmaps, QBRs for roadmaps, consultation on new partnerships. What are the mechanisms that we may be thinking about?
Manish Sood (48:40):
So some of the mechanisms that we are thinking about, first of all... And again, these are different tools, but I would encourage all of our customers to use all of these tools. Community, that is a great way to get input back to Reltio. For example, you have your standard engagement model where you are engaging. As issues come up, you go to support, you open up tickets. Our engineering and product management teams review those types of details. But community is the vehicle where if you have something that is outside the bounds of a support ticket, then please provide that suggestion or discuss that in the community, so that we can take that input and further inform our roadmap. On the customer success side, we are in the process of revamping our model, where we can have a systematic process for engagement with the customers and their teams on roadmap discussions.
Manish Sood (49:55):
Reltio is a pretty wide platform in terms of capabilities. And a lot of times, we find that some customers are only using a portion of those capabilities. Even though they have access to other capabilities, they haven't gone down the path of adoption of those additional capabilities. So through that engagement where the CSM team can help the customers understand some of the opportunities for further adoption with what you already have access to, and also in return, getting feedback on some of the roadmaps that you are thinking about. Because it's not just about the Reltio roadmap, it's also about your roadmap for the next 12 to 18, or 24 months, where if we get an understanding of that, then we can look at how to incorporate some of those things into our product roadmap, or provide guidance on how we should be jointly solving for some of the things that will not fit into a product orientation.
Manish Sood (51:07):
So those are some of the vehicles for engagement, but Ansh, you've been leading the charge on this. So from a product management standpoint, any areas that stand out to you, that would be additional vehicles for customers to engage.
Ansh Kanwar (51:25):
Yeah, definitely. I think three avenues I can think of. One is providing feedback just as far as ideas go, improvements to the product go. We have ideas portal that we share all of our customers ideas with other customers. So please go in, take a look. Work with either your CSM or we're happy to put product management time into talking to you and really walking through some of those top ideas. And please add your books. That's how we get the signal that there is demand from multiple customers and we use that every planning cycle to prioritize some of these requests.
Ansh Kanwar (52:09):
The second area is around QBRs. And we are making it a consistent practice to talk about the roadmap in those QBRs. The way we think about the roadmap going forward is the current release for us. So we have major releases three times a year. The current release is locked, but the next release, we really lock it with about 60% confidence. That means, we leave room for these conversations and to be able to move things around, to fit in what we learn from you as we're talking to you in those QBRs. And of course, further out, the third release is about 20% confidence. So there's definitely a lot of fluidity there for our customers to be able to influence that far out. And that's what really your comment about aligning roadmaps really comes into its own. If we know where your large projects are going, then we can definitely try our best to align with those.
Ansh Kanwar (53:08):
The third area I wanted to talk about are advisory councils. And so I would say, we're growing up, we're maturing and creating subject specific advisory councils where we'd love your input on, for example, the way we have our UI set up and the usability. We're in the process of improving the UI and releasing a brand new version, which we feel is significantly more usable and cuts down on interaction time or time to task. And that's a very specific example, but just an example of how targeted we want these advisory accounts close to be. And by doing that, we're hoping that we ask you for very targeted input. It's efficient in terms of your time and it's efficient in terms of outcomes that we can drive to improve things.
Ansh Kanwar (54:01):
So those are three avenues that I can think of, but as always, we're open to ideas. Whenever you see we're not following best practices as a company, please tell us, and we're happy to open additional avenues. Good job, Manish, in turning around the conversation. I want to take the last question here, which is also from Abby. It's about vision and it's about where we're going as a company. And he asks, if you fast forward five years, how's do you see that year's vision and market presence? What will you be known for?
Manish Sood (54:46):
So Reltio's mission is to be the real time operating system for data. And when we say data, it is the core data that your business runs on. So think about customer information, product information, supplier information, asset information, employee information. These are all the different types of data domains that are relevant to running your business. And given the fragmented state of the enterprise or the siloed state of the enterprise, there is a dire need to have central system that will govern, manage, aggregate, unify, and provide the single source of truth for these types of data domains. And that's where we see Reltio plane, because my long term hypothesis towards the evolution of the space is that applications will create data, consume data, but they will not be governors or owners of data.
Manish Sood (55:58):
Applications will need data to be available in an equal manner across all the different parts of the business process at any given point in time, as a real time asset that they can tap into or contribute it. And that is going to come from the Reltio type of systems that will act as the data plane, a real time data plane, or a real time operating system for data, but the business processes will contribute or consume information at any given point in time. Analytics will depend on data coming out of this. Data science will depend on it because this is how the data will become the continuum of everything that we do in the enterprise.
Ansh Kanwar (56:56):
Wow. That's a very expansive vision. Thank you, Manish. And I also thank everybody for being part of this webinar and really engaging questions. And as Chris said, we'll follow up with any unanswered questions and make sure that they're published on community. With that, Chris, I'm going to turn it back over to you.
Chris Detzel (57:19):
Ansh, Manish, wow, this is really great. And somebody asked me, "Do you think you'll get an hour in?" Yes, we did. No problem. We didn't even get to all the questions. So thank you for your time. Thank you everyone, for your time. We do have two great community shows coming up and I continue to get more coming in. So Ansh and Manish already promised you four more from what I heard. So we'll definitely start thinking about how to book those and push those in.
Chris Detzel (57:53):
By the way, here's the swag. You see my shirt and you see my hat. Right now, we are out of medium shirts, but we're getting some more in. We do have some others coming up. So what I'm going to do is it's not too late to get your swag. Ask a question on the community with the link that I just got you. So hopefully, you like that... or yeah, that I just sent over. Joseph, I haven't forgotten about your questions. I'll get those posted on your behalf on the community. And I got the others there too. So thank you everyone for coming. This was really cool, really awesome. We'll do this again. And you guys have a great day and keep the questions coming on the community. Thank you.
Manish Sood (58:36):
Thank you everyone.
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