 Hello everyone and welcome to our next DDW session called Build and Enterprise Data Solution Quickly with Merkel's Data Accelerator, which will be presented today by Shrini Krishnamurthy, Senior Vice President Data Management at Merkle. All audience members are muted during these sessions, so please submit your questions in the Q&A window on the right side of the screen, and Shrini will respond to as many questions as possible at the end of the talk. Please note that there is a linked form at the bottom of the page titled the EDW Conference Sessions Survey. This is where you can submit session feedback and we encourage you to do so. Also, there's a small icon to the lower right of the screen, which will enlarge this window with the speaker and slides. With that housekeeping out of the way, let's begin our presentation now. Thank you and welcome Shrini. Thank you, Ari. Thank you, everyone for joining the session. As I introduced, my name is Shrini Krishnamurthy, Senior Vice President of Marketing Technology at Merkle. My team and I are responsible for building Cloud solutions for clients, and I'm here to share our part of you of using Merkel's Data Accelerator to build enterprise data solutions. What I wanted to do today is to talk a little bit about what's happening in the Cloud space very quickly, and talk about some of the requirements and challenges our clients face as they contemplate their Cloud migration or Cloud adoption. We'll also touch upon what it takes to be a modern data platform, and then we will go into details around how we can leverage Merkel's Accelerators to develop these Cloud solutions to deliver faster business outcomes. Now, going to the Cloud adoption, it really started slowly, but has accelerated tremendously in the recent years. Year-over-year, it is expected to grow at 18 percent in 2021. Specifically, the application services are also called a SAS and infrastructure services are experiencing much higher growth. Earlier, we started to see our clients stakeholders tipping their toes to do POCs and few groups starting to experiment with the Cloud. Now, we are seeing wide adoption where enterprise marketing and IT solutions are all rapidly moving to the Cloud. A lot of this shouldn't be any surprise to you, but if you are not thinking about Cloud, chances are someone in your organization is thinking about it, they are actively working to migrate to the Cloud. Now, going to the Cloud, there are several factors that one should consider as you contemplate that implementation plan. The right approach is one that actually leverages the Demandres Cloud-based capabilities that would set you up for short-term and long-term success. Often, we hear clients say, hey, can I just lift and shift my solution from A to B? That probably would work for some of the clients, but many times it's not the right approach. You really need to understand how do you set your solutions to truly get all the benefits from moving to the Cloud. Talking about creating the modern data solution in the Cloud, what does that mean? We have really looked at that as six key factors that should really be considered as you contemplate any Cloud solutions. Number one, leveraging Cloud-based native technologies. There are a lot of technologies that could work great in your data center. But as you migrate to the Cloud, leveraging the Cloud Platform's native capabilities becomes important because that allows you to tap into the features where you could scale much more efficiently. That's a key factor. The second one is this build for now concept. Earlier, clients would have to invest upfront in a significant way to procure and set up hardware software and build up functionalities not just to support the current use cases, but also anticipate use cases for the future. In a way, they were future-proofing their solution for two years, three years and sometimes five years. That takes a lot of time and effort and cost to get that going. You could spend months, sometimes years to get that initial implementation done. The drawback for that is on day one after you launch a solution like that, it may not be able to produce value until much later on to realize the total investment spent. With Cloud, because of the way you could scale, you really don't have to do that. If you create the right baseline and adopt the right text tag, you can go and just build for what is required for now. Just focus on those critical business use cases. Then as you mature and you have a need to implement more use cases, you can just scale that. That's number three. Getting the right tools and focusing on the right approach with the use cases for now, and then scale for the future is the way to go. Number four is rapid adoption. If you look at how one, two, and three layers on, you're not really creating a huge solution of friends. The amount of technical debt that you're creating gets minimized and you are only focusing on what is required to get your priority use cases and use cases that you want to enable done. That allows for quicker changes and quicker adoption. Number five is importing business users. Earlier, because of the complexity of the solution and it's built to so many use cases, spanning many years, there's probably a layer of abstraction or the solution we may have built and the business users may not have direct access or may have access through the data to another team. With the cloud, there is a lot of capabilities that allow for the business users to get direct access to the data. There are data visualization tools, there are BI tools, there are also analytic tools that enable business users to look at data, consume it in different formats and really empowers their current use cases and whatever planning they may have for future use cases. And number six, which is pretty important is connecting all the data. Having the right approach allows you to seamlessly get all of your data could be at the enterprise level, could be at the customer level or a transactional data set, site behavior data set. You are able to bring all that data together and have it in a way where it is seamlessly accessible with a robust governance layer on top to enable a lot of your use cases. This can be achieved without duplication of data as well. So the cloud really enables a lot of these really rich features to be brought into effect as you contemplate building your own solution in the cloud. Now, as we speak about the modern data solution, what Markle has done is, we have been a pioneer in the space and we have worked with many clients to take their solutions to the cloud. We have also moved our own solutions to the cloud and we have captured a lot of the common use cases and we have created accelerators to help our clients to adopt the cloud and really get the value from those moves in a quick way while mitigating some of the pitfalls and the risks associated with those moves. We are custom build accelerators that I'll cover in a short while that really focuses on data ingestion, data management transformation and also there are accelerators that focuses on integration to marketing technology platforms. That these accelerators really enables us to deliver these solutions really quickly in a matter of weeks and we are able to enable that business value realization cycle in a much faster timeline. Okay, now this slide provides a little bit more detail view into how the accelerators can help with your own cloud implementations. As I mentioned to you before, our accelerators are purpose-built and they are focused on specific capabilities. We have accelerators that are like primed for data ingestion, I'll go into a little bit more detail on how that ingestion would work, but those accelerators getting data into one place becomes a breeze. We have accelerators that can take the ingested data and transform that into a common data layer that multiple use cases can be enabled. We also have accelerators that focus on integrating to leading technology platforms like Adobe and Salesforce. We also have accelerators that could take the data and basically drive identity resolution on that data. You know, a lot of people, you know, say we got to get all the data into one place, but it becomes very difficult to get that data together and identity resolution plays a big role, especially when you're driving use cases to understand your customers better. You want to enable that superior customer experience, it becomes much more important to get your customer data, your transaction data, your site behavior data, or other prospect data that you may have and rationalize them to a common identity and we have accelerators that drive that as well. You know, basically taking all these accelerators together, you could build your enterprise data platform in an applet fashion. You know, these accelerators are developed in a wave and you could plug and play based on what your demand or use cases may dictate. You know, we have the core architecture principles that drive these accelerators in a way that you could scale on demand and you are starting with a lower cost of entry and you are able to enable that access to the data to your analytics and business users that would help with your adoption of the solution you're building. You know, want to spend some time on talking about what are we, what do we typically hear from the market? You know, a lot of this should resonate with you. You know, we see clients say our data is not clean. You know, we need to help cleansing the data. We have clients say we have data about our customers and their transactions, but it's not in one place where we can go get access to the data. Or the data is in a much more granular form, we need that aggregated, our vice versa. We also see customers say, you know, it's the data has to be brought together but we need to make sure that is done in a secure, you know, privacy compliant way. You know, with CCPA and GDPR type compliance regulations rising, it's important that any solution you create can scale, but it's also secure and in full compliance with all the laws. And then this is common rate where many of our clients say, listen, I hear that, but I just want to start small. I want to test and I want to learn and then I want to expand. And many times they are looking to prove value and that should help them try a business case to expand further. You know, all of this are typical, you know, feedbacks or interactions that we have with our clients and, you know, Markle's data accelerator is created to specifically address a lot of these requirements or challenges from our clients. In this slide, I want to cover like what actually happens when you implement the data accelerator. We have four simple steps specifically to the data accelerator. We have ingest, we have identify, we have load and we have enable. In the ingest step, we just take all the data from our clients to for storage where we are able to prioritize, validate and essentially stage the data for the cloud. In the identify spaces where we are taking the data, we are cleansing it, we are standardizing it. We are matching the data with one another and we are now stitching identities to create that identity graph to bring your customers prospects and also us together. That's key to, you know, to get the customer 360 view or other, you know, use cases that clients would look to so far. You know, during that phase, we are, you know, basically applying a lot of the customer data identification processes. We could apply if the clients wanted the NCOA process and essentially it creates a persistent ID for our clients to look at any data that they've just ingested. And then in the load phase, we are now extracting the stage data, the clients data, and then we are transforming them for, you know, consumption for putting them in like destination tables through automated pipelines. We are reporting the progress through dashboards and we are also able to provide that secure compliant environment as well. Lastly, in the enable stages where the client is now able to get access to the data that we just ingested, identified and loaded. You know, that enable phase is where you are also able to plug in your visualization tools, drive data science, drive AI, ML type use cases. So you are able to see that value realized. Now, in this slide, I want to talk about, you know, in a visual way how the data accelerator is being brought into life. The underlying layer for the accelerator is the data. That is the core asset. It could be digital data, it could be the CRM data, it could be third-party data or many times the clients procuring data from Oracle. Or, you know, it could be our data, you know, someone's, you know, maintaining some error data and a spreadsheet and they want to bring that in and they want to integrate it. We are able to take all types of data and we are then able to basically plug in the accelerator on top and go through those four phases. I just talked to you guys about the ingest, identify, load and, you know, enable. Ultimately, all the data is running in a data lake. We are able to then provide an analytic environment. It could be any of the leading cloud providers, right? We have listed some key names here. It could be on AWS, Azure, our GCP, it could be Snowflake, BigQuery, Redshift, what have you. You know, our accelerator can work with any of these environments. So your data assets that are integrated is now available for analytics. And that is enabled by, go ahead. Sorry, Srinu, just a quick note. We are about four minutes away from our Q&A. So audience, if you have questions for Srinu, please do drop those in the Q&A now. Great, thank you. So we are, just to close this out, we are able to then, you know, integrate that analytic environment with data visualization tools like Tableau, CDPs like Lytex, TDM, Action IQ. You could enable R and Python for advanced analytics. You could integrate with Merkel's publisher platform, or you could go and integrate with marketing platforms like Adobe or Salesforce. You know, here I want to share some examples where, you know, some of our clients have used the accelerator to drive their projects. We have examples where a large auto manufacturer was able to use the accelerator to build their foundational data management environment. And they, you know, their use case was to drive analytics and they were able to do it pretty quickly. We have another example where a large beverage company wanted to create an analytics environment to ingest more than 50 data sources. And their use case was to understand their customer's journey and event stream to drive, you know, modeling and segmentation. And they were able to do it. And then there are, you know, other examples here where a large electronics company was able to bring their CRM digital site data. Again, for advanced analytics to drive modeling and segmentation. And we have an example with nonprofit as well. This is just to illustrate that, you know, companies big and small across multiple verticals are able to essentially leverage the Merkel's data accelerator to enable their cloud migration or adoption to drive value pretty quickly. You know, this is the last slide I have to show, you know, how quickly you could get started in the cloud. There are three simple steps, you know, starts off with the proposal and kickoff there, you know, we would scope and, you know, engage in a contract and then, you know, the process starts. We have preconfigured series of processes to create the cloud environment. You know, could be an Azure or GCP or AWS, whatever the cloud, you know, platform that the client may need or want to be in. And we would then start ingesting the data and, you know, get the configuration and development then we'll validate the processes and the data and then we deploy it inside the analytic environment in a matter of weeks. And then we also offer ongoing support to continue to monitor the solution. You know, this is where the initial onboarding should be thought of as built for now. You're just getting it set up, you're enabling those one or two use cases, you're seeing the value realized and then we jump into the ongoing support mode where you are now adding more use cases, adding more data sources, adding more integrations as you mature and seeing more use cases come to life. So, and I show, you know, using the data accelerator, it allows you to really leverage clouds capabilities to get started quickly and scale as you go while mitigating a lot of the risk associated with, you know, privacy compliance, security and integrations. All that is addressed with using the escalator design pattern. With that, I will hand it over to Eric for any questions and thank you for taking the time to listen to my session. Thank you. Hey, Sharini. So we don't have any questions from the audience yet, but I had one. So when you're just starting a deployment with the client, what is the most common concern or question you hear from them? So the most common concern we would hear is, you know, we wanted to really have a flexible environment that can ingest any type of data into this because this is really a test and learn environment for their analytics. So they want to have that flexibility and that security. So, you know, we are able to explain how, you know, we have multiple layers of security that we have created around a client install inside the cloud. And then we are able to demonstrate the flexible nature in which they could ingest data in any format, API, batch, streaming, what have you. And, you know, that allows them to then take those capabilities and then start enabling their use cases. But it's really around flexibility and security as they contemplate their first move. Yeah. Is there anything else that they would need to have in place and be sure they're ready for on their end before they were to engage with you? Yes. So typically, you know, the clients would have, you know, use cases that they are looking to prove out or enable. And, you know, really the data, that's required to support that, right? Those are the two things that a client would need. The rest of the, you know, set up and get the project configuration set up and all that, you know, can be done by Marko. It's really the use cases and the data to support it is all they need. Sure. Okay. Well, we are just about out of time. So we'll go ahead and wrap up. Thank you, Srinni, for this great presentation. Thanks to our attendees for tuning in. Please do complete your conference session survey on the page for this session. Between sessions, you're welcome to continue networking with other attendees within the Spot.v app. Don't forget to check out the sponsors section for information about the tools available to support your data management programs. The next sessions will start in about 10 minutes. And so with that, I will thank you all. Thank you so much, Srinni. Thank you, Eric. Thank you, everyone. Bye. Bye.