Speaker 0: Hi, all. Welcome to MarDreamin. Uh, welcome, everyone. We’re really excited to have you here today. My name is Sarah Gantz. I will be moderating this session for you. I’m from Circante. Hello. Hello. Uh, before we get started, just a few housekeeping rules, which if you’ve joined us today yesterday, you probably heard already. Yes. This session is recorded. Yes. We will have it available on demand after the event, um, and we will be following up via email as well. Um, if you have a question, please post it in the q and a, and we’ll be monitoring moderating this and answering these questions, uh, throughout and at the end. Um, lastly, please use chat. Um, if something resonates with you, give us a thumbs up, send a gift. We love interaction. Um, so let’s get started. I’d like to introduce you to Jacob. Um, Hayes, he’s has an awesome session for, uh, for us today called Data Cloud: Bridging the Gap Between Data Consumers and Data Producers. Jacob?

Speaker 1: Yeah. I always love a long intro for any session that I do, so it’s always fantastic. Thank you for everyone who’s joined, and thank you, Sarah, for the intro. Uh, look forwarding look for looking forward to hopefully having a good session today, and you guys like the topic at mind. I am going a little bit in a different direction than typical data cloud conversations. So we’ll see if it either hits the marker or if people are like, Uh, but feel free to ask any questions. Uh, don’t want this to just be a TED talk, uh, on what’s going on in the ecosystem.

So, yes, who am I? Jacob Hayes located just outside Washington, DC, um, with Accenture. I’m one of the senior managers in financial services, but I’ve been in the Salesforce ecosystem for over twelve years, helping with not only financial services clients, healthcare clients, media, uh, you name it. I’ve probably done it for a fortune 500. So excited to talk with you all about this. But before we go into any content in-depth, I wanna be able to say thank you to the Marjorie Eman team. Thank you, Sarah. Thank you, Sir Conte. Thank you all the sponsors because this is my second time coming to Marjorieman. It’s a fantastic conference. I love virtual nature. I have a three and a half year old, so I like to not have to travel all the time. Uh, but I think all the content you’re probably seeing and will continue to see throughout, you know, our dreaming conference is very interesting. It is very unique. Make sure to come back again next year and continue to see con pieces this year.

Okay. So with that being said, I guess I should have asked this in the beginning. Sarah, you can see content. Everyone’s good. Hopefully, nothing’s freezing. Yep. We’re good. Cool. Got it.

Okay. So what are we gonna try to accomplish today? I wanna hit on four main pieces. If I can talk fast enough, it’s a thirty minute session. If we can leave a little bit of time for the end, I’m more than happy to have a conversation. Feel free to ping them in the chat. Sarah’s gonna help me. I’ll also keep a chat open in one of my three windows, you know, first world problems, uh, with my home office. I’ll keep track of it. What are we trying to hit on? I wanna hit on challenges that clients and customers are facing, especially within the CDP and the data cloud ecosystem. I then wanna talk about a tactical framework in two different ways. How are we distributing workloads between data producers and data consumers? And how is the data and experience ecosystem aligned to those persona based themes? And then finally, what are some key operating principles you guys can take away with, uh, not only your clients if you’re an implementation partner or if you’re working directly with an organization as a Salesforce, you know, admin or architect, what you can use to help hopefully get these two persona bases that sometimes are in conflict to talk to one another.

With that being said, let’s go a little bit further. Let’s talk about the challenges first. I think I love to lead off with this slide. I have talked about challenges and themes for enterprise IT and business IT, as well as business teams to CMOs, CIOs, CDOs, and every time we talk about the customer data divide. So enterprise IT and business teams within an organization often share the same end goal, but approach it from different perspectives, which can lead to misalignment. And enterprise IT teams typically focus on the technical architecture as you can see, while business teams prioritize actionable insights. How do I personalize experience, help with journey management, etcetera? Enterprise IT might think they’re producing valuable data assets, but typically, business teams may view these assets as lacking the agility they need and some sometimes even seeing them as obstacles rather than tools to help them on their day to day. This disconnect not only impacts efficiency, but also hinders the company’s ability to deliver coordinated cross channel customer experiences, which not only in the consulting space is what all of our clients expect to do, but also as consumers ourselves with with multinational brands, you expect to have information at your hands readily, quickly, and personalized to you. So by bridging this divide, organizations can create a more harmonious relationship between these groups, ultimately, and hopefully ultimately, fostering better customer outcomes. So like I said, I’ve shared this with CMOs, CIOs, and CDOs, and every time I get a thumbs up, it says, yep. These are my themes. This is what I’m working on. I’m sure this will change as I go into, you know, fiscal year twenty twenty five in a couple months, but the main themes are what people are trying to do in these organizations that are often silent.

So if that was kind of the 50,000 foot view of a theme by, you know, a team, what are some of the themes as you go down to a 10,000 foot view? And I’m gonna cover three main areas, incomplete picture, data bottlenecks, and inaccurate data. A lot of this you probably have seen or hopefully people have seen or are dealing with on a day to day basis. So one of the first pieces on the incomplete picture on the left hand side, I won’t read everyone off to you, all the little bullets and pieces that I put on, but when we deal with data cloud and CDPs within enterprise space or also within mid market client space, we’re often seeing different CRM instances by line of business that are done for a reason. Siloed data and siloed teams sometimes has a purpose within an organization, especially in regulated industries that I work in. But multiple reps and individuals in a sales service or financial services cloud instance are probably working on the same lead account or contact as a customer, a patient, etcetera, works through, you know, the customer journey with client. But the biggest thing that we see and the theme that we have problems or the customers have problems with is, do I have the right data at the right moment so that an agent can fulfill and close a case quicker? Or so a sales rep has additional information so they don’t go into a conversation and say an example that I literally heard from a CIO was our one wealth business line went into a conversation with a multinational company and they didn’t realize another business banking line had talked to them a week ago. Just understanding what the left and the right hand well, backwards on my screen. What the left and the right hand are doing or talking to one another in organization is crew. And I think the final piece is proactive versus reactive data. Very generalized statement. Good job, Jacob. Putting a generalized statement there. But how are we housing data in Salesforce, and how are we housing data in the enterprise technology stack? Are we literally just throwing an extra field on a record page because we gotta store it, or what is the actual value? And what you’ll hear consistently throughout this presentation today is, does it align to a use case? And is that use case aligned to an internal experience for an individual to see a Lightning Web Component on a contact record or account page, Or is it external facing? Meaning, it’s actually an email or some sort of communication out to a customer.

Data bottlenecks. I mean, I think we all know or hopefully we’ve I think unfortunately dealt with inefficiencies in data flow that can lead to a frustrating customer experiences. I’ve dealt with it as a consumer myself. So, you know, imagine these different scenarios and how they occur. But, I mean, I think we can look at this and say, of the four bullets on the screen, number three and number two are the most Sorry. I don’t know why I did number three too. Number two and number three are the most interesting in a lot of the conversations we have, which is CIOs, CDOs, and others are being inundated with different software as a service technologies at the moment. So it’s a lot of the conversation. Should I be throwing data into a data warehouse? Should I be throwing it to a data lake? Should I be throwing this data into data cloud? What is the right design framework? And what we typically have a conversation with is, and I’m a this is why sometimes I feel like a decent consultant, but I also get mad at myself, is it depends. Does a use case need for you to be does a use case require you to be quick and nimble, or can it be batched and processed? That’s where these different systems come in. That’s obviously why we think about how do we provide a quick and nimble enterprise framework that also doesn’t introduce unmanageable tech debt. I mean, Salesforce continuously upgrades its systems three times a year. Data cloud is now monthly, providing 10 to 15 different feature enhancements every month. That’s a lot to keep up with. And so we need to make sure that our technology solutions continue to scale and also just don’t provide unmanageable pieces or unmanageable tech that that could break in the future.

And on the inaccurate data perspective, I definitely won’t read this one to you. Um, I’ve been dealing with inaccurate data for at least the twelve plus years that I’ve been working in the Salesforce ecosystem. It’s not new. But I think the biggest piece and one of the items that I call out in the second bullet on the screen is I’ve dealt with a lot of organizations where because of the initial design of framework of how Salesforce has been set up, sales service reps become to not trust the data. Meaning, they’re tech typically using it as a Rolodex or as an Excel sheet, but it’s so much more powerful than that. So how can we start to design and take bite sized, you know, approaches to different technology pieces, whether that’s Einstein focus items, now Agent Force, to say, how can we regain your trust and not just think of the CRM as an administrative tool, but actually providing you value? And then obviously to that point, if I can provide you value, what data do you need in the process of your day to day or your flow? That allows you to say, I trust the system and the data is accurate, which means now I can resolve this case quicker. I can do cool features that we always talk about in the Salesforce ecosystem.

Great. Did a long spiel. Let’s talk about this tactical framework that we’re gonna introduce today. And so one of the biggest things that I wanna talk about before the tactical framework is uh, a lot of this information comes from a very interesting individual who I’ve followed a lot, David Chan. He works at Deloitte. He’s in customer data platforms and other pieces. He introduced this framework that was called the dual zone approach. So this is what I’m building off of, just so everyone understands. The dual zone approach worked for CDPs. I’m focusing into a tactical framework and how it could work for data cloud because a lot of the concepts and pieces that David has introduced into the ecosystem, which shame will shameless plug for David, go read his Medium articles, uh, are quite intuitive and will allow us to actually scale Salesforce data cloud and the Salesforce technology stack. So I think one of the biggest pieces is bridging the gap between those data producers we’ve talked about and producers uh, sorry, data consumers and data producers isn’t just about storage or data composition. It’s about optimizing who does the work and where the work gets done. The tactical framework that we’re talking about today provides what I believe is a structure that allows us to reframe Data Cloud and CDPs not as a standalone technology, but as an organizational capability. Meaning, it this approach hopefully allows you to avoid the trap of inverse Conway’s Law, which is organizations unintentionally build structures around software limitations instead of aligning the technology to their or operational needs. This framework is about distributing responsibilities in a way that aligns with business goals, creating efficiency and effectiveness without unnecessary complexities.

And so the the design on the screen is again, we’re gonna go as you notice a little bit, I go 50,000, sometimes 30,000 to 10,000 and down to a 5,000 foot view. This is what I would refer to as that 50 or 30,000 foot view. Typically, from a very high level, this is what we talk about with enterprises. They’re going to have multiple different data sources that either need to be provided in real time or via batch to different modern data stacks. That modern data stack could be, think of it, your cloud computing softwares or your hyperscalers of the world, could be Azure, could be Snowflake, could be Databricks, could be other systems. Uh, but how do those get aggregated and organized and then provide them to Data Cloud, which how I refer to data cloud is the orchestration and activation engine of Salesforce. Meaning, it is the centralized hub or brain that allows you to push segments, audiences, specific calculated insights about, I don’t know, transaction levels down to your MarTech platforms, meaning marketing cloud engagement, marketing cloud personalization, marketing cloud account engagement, or sometimes outside of the Salesforce ecosystem because, again, there are tools outside their Salesforce ecosystem. We aren’t just living our little Salesforce bubble. But the main point there as you followed me from left to right is to say, that’s typically where it stops, where it stops in most occasions and we may need to make sure it doesn’t. Meaning, the MarTech platforms need to provide some sort of back feed. How are we providing what someone is doing on our engagement platform, uh, of choice, whether that’s an omnichannel campaign that’s hitting SMS or an email to a sub segment audience in a region of The United States, But how is that feeding back into the modern data stack so that can continuously be updated? Then how is it feeding back into data cloud so data cloud audiences can become smarter? This is the high level. We’re about to get into a much more granular focus into each one of these in a second.

So if we look at that 50,000 foot view, let’s now talk about where does it actually get done from a data or an experience ecosystem. So as you see, we’ve expanded the diagram a little bit. So a cohesive data and experience ecosystem is not simply a collection of disparate technologies cobbled together over time. Instead, it has to be a deliberate strategy designed to provide the right data to the right team at the right time. That statement has been used in marketing for years. We use it as the North Star approach of, uh, right audience, right message on the right channel at the right time. Now, we’re changing it to be more of a data focused or an IT focused statement to say, the aim is to streamline that operational efficiency we talked about, but while also maximizing customer engagement. Because at the end of the day, the customer is the key piece here. So this framework enforces a disciplined approach to clearly delineating responsibilities. So the data ecosystem on the left hand side is typically the backbone. I’m gonna focus on identity for a second. It’s typically where core identity resolution and data management occurs. Think of that core identity resolution as that baseline identity from a name, address, that zero party information that an organization has, while the experience ecosystem augments that identity with device IDs, cookies, IP addresses for more actionable insights. So if we just take that micro moment of just identity across this ecosystem, we can see that this this distinction ensures that each part of the ecosystem has a specific function, preventing overlaps and unnecessary redundancies, which at the end of the day, ultimately, it helps us by not overbuying or overextending on capabilities across the SaaS technology space.

I think one of the other pieces that’s really critical here is the thinking faster thinking stuff. We’re gonna get into actually one of those in a few minutes or in the next couple slides in the key operating principles, but I wanna talk about it again. Because in the first diagram we talked about a second ago, we’re thinking about back feeds in real time. Now, if you look at this diagram, you would probably look at it and say, Jacob, this is a very linear approach. That’s not the case. I’ll explain why in a minute. But when we say think fast, we need the actual edge decisioning of what should be done based off of real time personalization on a web or real time personalization in a cached email depending on the feature set within Salesforce. We need that to be happening in that experience ecosystem within data cloud, journey builder or journey orchestration tools, and then personalization. Again, you can pick your personalization tool choice. And then the same way, as we gather that data, we wanna make sure that we’re thinking slow. How are we building additional components into the data ecosystem so that not only is our data warehouse, which is probably aggregating all of the information, how is that continuously getting updated from the experience side, but also how is it continuously updating each other in a relationship. And again, I’ll go into this in a second with more details.

So we’re gonna hit on two slides that talked about key operating principles for the rest of the time, and I think we’re doing well so far. Uh, but this is typically where I pause for a question. It doesn’t look like there’s any. So let’s dive into the principles.

So we’re gonna go into the first four. The first one being experience first operating model or what I’ve call it on the slide is think experience op model, not c e p op model. Whether you refer to it as the CDP or data cloud or package or composable CDPs, Don’t care. We’re trying to put this all together in a very high level. Rather than a traditional CDP focused model, think of this tactical framework that we’ve talked about, those diagrams, as an experience operating model that fosters collaboration between those IT and business teams. The reason why I say that is, I think most recently with the emergence of a lot of data and AI, it has actually forced IT and businesses to work together to achieve success, propelling the convergence of these teams. In the past, these teams have multiple degrees of separation, but they now actually need to come together and be in lockstep. So there are quality data products, quality data science models, and quality customer experiences being delivered.

The second bullet, avoid data duplication. I think, well, to our point earlier about data, um, one of the biggest pieces when you looked at that, you know, last model is you wanna make sure you’re not duplicating data between the data ecosystem and the experience ecosystem. Try to minimize it. There’s always going to be an occasion where there is data, but if you can minimize it, you minimize cost. So how do we look at it? Typically, raw data should primarily sit in the data ecosystem with only the necessary process data flowing into the experience ecosystem, system, making sure that the approach avoids excessive storage costs and reduces synchronization issues. Now, we’ve also talked about it a little bit. It depends on the use case. I’ll talk about that more in a second.

Number the third bullet about, you know, expensive workloads or what I call optimizing workloads, make sure that computationally heavy processes are ideally running in the data ecosystem. Why? Because costs are lower and scalability is higher. Makes sense. Why do I say that? Data cloud is not meant to be a data lake and a data warehouse tool like others out in the market. That may sound weird based off the naming convention structure of the of the tool and the platform, but it’s really supposed to be used based off of specific use cases to optimize down into MarTech channels or into the CRM and associated technologies to, again, provide an experience to a customer or to the end state user in Salesforce technology. Okay. But I probably pulled up a soapbox, so I’ll take it away. Uh, but by keeping the experience ecosystem on real time actionability like we talked about before, organizations can balance the need for cost efficiency. And so I I I’ll bring up an example. So let’s take a FinOps mindset, uh, for a second. A lot of the times, they’re trying to analyze total cost of ownership or TCO, and they start to find, uh, much more value offloading a lot of these activities that we’ve talked about in the data ecosystem into that ecosystem itself. But then we look at zone two to say, maybe we can save some money in roles and responsibilities and operational teams by understanding where things happen and for the inverse of Conway’s Law that we talked about before, what tech should do it in these two sections.

Number four is identity resolution. Like we talked about a little bit, it’s a two step process and look at it as a flywheel, meaning the data ecosystem again establishes that core identity and the experience ecosystem refines it into what we call an actionable customer profile. The reason why we say it, and I won’t belabor, the point is it clarifies each team’s role in identity management, and it helps limit any confusion between overlapping capabilities. Probably asking Jake, why are you saying this? I’m sure if you’ve talked about Data Cloud in some capacity or if you’ve looked at it from, you know, a client or customer perspective, you’re like, I have MDM tools out in the market. What’s the difference between a golden record and this actionable customer profile? Why are they needed? And, you know, that could be its own session and its own right, but the reason why we talk about it in this stage and this setup is you’re going to need both tools at times depending on their use case. MGM tools are great. They’re still out there and they still create pieces. And the actual profile is also very good for specific use cases in the Salesforce ecosystem, but I’ve found many clients who have both working in tandem together to create a GUID or UUID for a customer base so that they better understand Jacob or Jacob in a household in a financial services example.

I need to speed up a little bit so I look at time. Alright. So we have another five of these left, so, hopefully, I won’t bore the t bore everybody with them, but let’s talk about Matt those macro and micro edge decisionings that we talked about before. I think, like we talked about, both ecosystems play a distinct role in the decision making. The data ecosystem handles the macro level insights, analyzing customer customer clusters and behavioral propensities, while the experience ecosystem focuses on real time edge decision based on that contextual data. The combination of both delivers a better customer experience. And so I think from an experience ecosystem perspective, because I feel like I’ve belabored the point a little bit on the data ecosystem. So scores that are typically generated in the data ecosystem, they have propensity to buy, propensity to churn, any predictive models. Those need to be appended to an actual customer profile. You don’t need all the raw data associated with it in data cloud to come to that pro that actionable, you know, score itself. But that’s typically where, like, if I need to do a propensity to churn or propensity to buy use case, we’re passing those scores that are done in those BI processing tools and data ecosystem into data cloud to then be provided in the end state, meaning the end state data extension in Marketing Cloud has that score so I can do a personalized email or some sort of segmentation to last mile in Marketing Cloud engagement.

Segmentation is a team sport. Love it. I actually stole that from David Chan. Sorry, David. I typically talk about it as a collaborative segmentation. Uh, it is truly a team sport, meaning both groups need to understand core profile data and how they’re gonna use it to target segments in the experience ecosystem. Uh, one of the things that I like to talk about, especially if you look at that FinOps example we talked earlier, data ecosystem should establish some sort of intake process to govern the request implementation of key profile attributes that the business teams require need or required in order to build those audience segments. I am working with clients all the time who don’t realize the people in process component of people, process, data, and technology, the four main pillars for success or implementations. They forget about the people in the process. And so let’s go a little bit further into the example in FinOps. Uh, let’s actually go in this example, let’s say, well, segments can be created in a marketing automation platform, marketing cloud engagement as an example. They really need to start moving these audiences into that centralized brain, which is data cloud. Again, aligned to a use case and aligned to consumption based structures. But things like exploratory data analysis should be performed in the data ecosystem since, again, we’ll have a richer dataset and we’ll be able to handle, you know, more flexibility in the tooling because that’s what those tools are built for. While Data Cloud can do it, doesn’t mean it should.

Alright. Data flywheel, I’ll go a little quicker since we’re almost at time. So data flows are not strictly linear between, again, the ecosystem, experience ecosystem like I talked about before. Real time data required for rapid rapid personalization may flow directly into the experience ecosystem, while batch data can be processed in the data ecosystem. That is okay. But, again, the decision of where the data source should land first should totally be dictated by the use case. Keep use case in mind and keep the end in mind and how you will report on it.

Okay. Why did I say reporting? Because reporting needs to happen where it needs to happen, meaning localized. Reporting should stay in the ecosystem where the data lives. Sometimes that’s clear as day. For instance, journey analytics and experience ecosystem is suitable for real time insights. While more extensive BI reporting should occur in the data ecosystem, whether attached to a warehouse into, you know, the specific other platforms, so you can draw on the comprehensive datasets.

Clean rooms, I will admittedly say clean rooms, I am still learning, uh, because they seem to be changing as well as much as everything else in the ecosystem. They serve different functions depending on the ecosystem. Sometimes they’re best suited in the data. Sometimes they’re best suited in the experience ecosystem. Data is probably related to in the data ecosystem, it’s probably more focused on data collaboration and sharing. Or in contrast, advertising ClearRooms fit very well in the experience ecosystem, helping to provide the right teams with the right data for experience pieces. But again, the main key item there is clean rooms should reside where they think they should where they align to a business use case at the moment.

Alright. We’re almost there. So operating principles. Let’s wrap it all up, and I do see I think there’s some questions coming through, and I’ll hopefully, I’ll leave a minute or two to answer them. Uh, effective data strategies are grounded in the collaboration between producers and consumers. Hopefully, you’ve gathered that from my session today. Uh, tactical framework distributes that workload. So who does it? It needs to align you need to align responsibilities between IT and business teams. And then where does the work get done? They serve their purpose. So each one of these ecosystems serves the unique purpose of balancing that real time need with the efficiency. And then finally, the operating principles can help companies make the most of their data investments. That’s why we’re here as consultants. We’re trying to deliver seamless customer experiences while also keeping costs down. So again, when aligned altogether, people, process, data, and technology come together to bridge the divide between these to help enable a more connected and responsive organization.

So with that, I wanna thank you all for sitting in my on my TED Talk. If you wanna find out a little bit more about me, connect, feel free. Um, I’m on LinkedIn. Here’s my email. I’ll stay for another minute or two, Sarah, depending on how much longer we have. I’m also a marketing cloud champion, so feel free to ask any questions. I’m more than happy to answer them. I will look at the chat now.

Speaker 0: And I know we’re about at time. So thank you all, um, again for attending today. Jacob, thank you so much for a great session. Um, you know, just keep in mind, this is not possible without our sponsors. So, again, thank you so much to them. Um, if you don’t know where you’re headed next, check out the agenda. Some great sessions now and into the afternoon. Um, and then, of course, any questions or if you’d love to connect with Jacob, um, information is here.

Speaker 1: Thank you again, everybody. Thank you. Have a good rest of your day. Enjoy March Eamon. Yeah.