Hello, everyone, and welcome well, welcome to day three, towards the end of day three of MarDreamin. I’m Lauren. I’m your moderator for today’s session. Um, few keynote or key housekeeping items. I’m sure you’ve heard all these already a couple times today. But, yes, this session is being recorded, um, and you will get it via email after the event. If you have questions, post them in the q and a tab. Um, and, also, don’t forget to join our session chat. Have fun. Throw some GIFs or icons or emojis or whatever you feel like in the in that chat. Um, and then, uh, uh, we’ll answer them as we go through the session. So I will hand it over to Adam Ursell and let him dive into our topic today.

Thanks, Lauren. Thanks, everyone, for sticking around for three long days of MarDreamin. It’s getting to the end of Friday. Hopefully, I cannot put you to sleep. Um, but we’ve got some, hopefully, good tips for getting started with DataCloud. As always with any conference, we really need to thank our sponsors. If you haven’t checked out the marketing resources inside of the event app, please do so. Check it out. Um, they have some really, really cool solutions. Today, we’re gonna get through five tips on getting started. Um, organizational alignment, picking a use case, some success metrics, leveraging out of the box data models, and then being intentional with what you’re trying to build. We’re gonna get right to it. No data cloud fluff. Straight into our first tip.

And with the world that we’re living in now, data management, there’s a lot of data that’s out there. Right? You have data that’s coming from identity platforms, purchase platforms, tickets. Right? Visiting visiting your website and mobile sites or apps and all like, just so much data that’s out there. And recently, there was some study published that’s predicting that there’s gonna be a 100 zettabytes of data in the cloud. Post in chat if you know what a zettabyte is. Hint, it’s a lot of data. Now granted, at least half of those 100 zettabytes are cat videos, but, you know, there’s still an immense amount of business data that’s available to us. And what we need to do with with all that data and bringing in a data cloud or something that is we really need to make sure that the organization’s gonna buy into it. Right? And not just buy into it, but we need to think about who’s gonna own it. And with all that data in a bunch of different places, it can get challenging to see, okay, what’s the right department to own data cloud? Right? Not too different from the discussion and chat that we’ve had with who’s gonna own Salesforce, who’s gonna own some of these wide reaching tools. Now it can be, you know, a center of excellence. It can be your digital or marketing teams. Right? In almost all implementations, all of the groups that you see on screen, they’re gonna be involved in the implementation and in the success of rolling it out, but you still need to have one owner. Right? And it really will depend on the organization, but you have to look at which group is gonna be the most likely to drive this forward and to find success.

So now you let’s say you have your your business alignment, you have an owner. Right? The next thing you need to do with a data platform, and this hopefully doesn’t come as a surprise, is you need to figure out where where is your data coming from. Right? Is the data gonna be good quality? Is there enough data coming in? Right? And for each of those records, how complete is the data? The last thing we wanna do is bring in a whole bunch of garbage into yet another platform and then amplify that garbage and try to do something with it. Right? It’s just not gonna work. So part of the prep work there and with data cloud, this is almost half to, like, three quarters of of an actual implementation is, you know, getting your data in line, figuring out your plan. And that might mean, you know, coming up with spreadsheets for data dictionaries if you don’t already have it, looking at your relationships. Right? How does how does a contact relate to an account? Now that one’s obviously established, but there’s gonna be other examples of that. Right? Then for each data stream or, you know, small group of data, you’re gonna wanna figure out what kind of stream is this. Is it profile information? Is it engagement information? Is it just, like, supporting information? That type of category really matters inside of data cloud. Another thing I wanna call out that, you know, data cloud is a system of reference. Right? It does not become a system of record for any of the data that’s going in. Salesforce CRM still gonna be the system of record for your CRM data. So alright. That’s enough for tip number one.

Tip number two, picking a use case. In other data cloud sessions

Speaker 0: Hey, Adam. Sorry to jump back in. I don’t know if you’re sharing your slides, but I don’t see them. So you wanna retry, Sharon?

Speaker 1: Alright.

Speaker 0: How about that, guys?

Speaker 1: No one posted it in the chat. I was not sure. I’m sorry.

Speaker 0: No one’s sorry about that. It was mine, but we’re good. Thank you.

Speaker 1: Alright. Um, so for our second tip, it’s gonna be picking that use case. Right? And what I mean by that is there’s a lot of use cases that are possible with data cloud. There’s a lot that might overwhelm us. What is the one or two use cases that will really drive further adoption within the organization? And there’s this quote that I read from some famous author that suggests that we start with the end in mind. Right? What’s the goal? What’s that North Star that we’re driving towards? Now when we’re thinking of that from an implementation, right, there’s a series of steps that you’ll go through for, frankly, any implementation in in the world for software. Right? And when we’re thinking of beginning with the end in mind, we wanna start at at the end of this path. Right? What’s the value? What’s the thing that we’re trying to drive? Right? Then from a design perspective, we start working backwards. Okay. In order to see this metric, what are some of the computations that we’re gonna have to do or some of the calculations? How are we gonna you know, what data do we need to drive the calculations to get to measure the value? And you start working backwards from a design perspective that’ll help you build your your blueprint, help you come up with the information that you need. And then you can actually start executing these steps in order to build all the things in order to accomplish that value.

So I mentioned there’s a there’s a bunch of use cases here, and we have some on the screen here, different categories. And what we find is usually for that first use case or two, right, it’s usually gonna be a use case that drives some sort of team optimization. Right? Maybe you’re saving a lot of time creating a report or saving a lot of time and getting a particular segment put together with a few different data sources. Right? So being able to to save that time is often a really good use case saying, hey. You know, we implemented data cloud. We’re saving, I don’t know, ten hours a week on doing this particular use case. It’s shown immense value. Let’s keep going with data cloud and start picking some other ones. Other teams who see that success, hopefully, will get a little bit jealous and they’ll just be a lot more positive buy in across the organization. Now it can’t solely be, you know, saving time. Right? We still wanna drive some sort of positive value to the rest of the organization. Hopefully, it’s some, you know, increased revenue or increased customer satisfaction. Right? So you wanna have something else that, you know, adds to adds to your use case. But picking one or, you know, one or two of these, limiting the scope of your initial implementation, that’s gonna be what helps you find success.

Alright. Tip number three. We’ve picked our use case. Right? How are we actually gonna measure that the that data cloud and executing that worked really well? Right? And you can go ahead and, like, Google, you know, implementation metrics or, you know, what are the things I wanna measure to see if a piece of software is, you know, providing good ROI. And you’ll get a list very similar to this. Right? You can measure your data integration quality. You can measure KPIs. You know, hopefully, nothing on the screen here is novel or surprising. And, you know, they are a little bit generic, and you might think that I might have used a little help to get something like this put together, and you would be exactly right. Um, but if we wanna measure the success of a particular use case, right, that’s gonna be different metrics than measuring the success of an implementation or measuring the success of an actual product. So how can we entice other teams to want to pursue data cloud for their either department or business units. Right? So like I was saying earlier, something that matters to the team. It can be operational efficiency. It can be anything. Right? But that’s very likely to be something that’ll help drive the adoption across the team. It can be business measurements. Some of the popular ones with data cloud are lifetime value. Right? Maybe you’re pulling it across a few different ecommerce systems or order systems. Right? Pulling in that disparate data, harmonizing it to a single value saying, hey. Adam spent $3,000 on cat accessories last year. I didn’t really, but, you know, let’s just roll with it. It can also be marketing metrics. Right? This is a marketing conference. It would be silly to not look at, you know, how can we measure a use case, which is hope hopefully, or or likely backed in a marketing use case. It can be your campaign ROI. You know, did it did it bump up over the period of time that you had this particular data cloud thing put in? You know, maybe it’s lead conversion. It can be any of these. But, you know, figure out what are those key things, simple metrics that you can just highlight across saying, hey. This is what we did. You should do it too.

Alright. Tip four, leveraging the out of the box data model. In data cloud presentations and maybe if you’re lucky enough to be in some demos, they’ll show you how easy it is to map data map fields from wherever wherever it’s coming from into what data cloud can work with. And when we’re looking at doing that, right, the information on the left might be from your your raw data source, so to speak. You know, maybe it’s come from Salesforce. Maybe it’s coming from Marketing Cloud. Maybe it’s coming from an Azure database. Right? Doesn’t really matter, but it helps you take the information that came from your business and mapping it into what data cloud can work with. And, you know, c r CRM or Salesforce core, it gives you custom objects. Data cloud’s the same way. And in both of those solutions, as much as you can and it makes sense to stick with the out of the box objects, you really should try to do that. Not only does it help from a scalability and extensibility perspective, but third party system or third party applications that might come out after you know, maybe there’s gonna be some new data cloud app exchange apps. Right? They’re gonna be working off of the same core objects. And if you’re thinking today about using those, it will accelerate your your value in the future once those AppExchange apps are available. Now with that said, like, you don’t want to, you know, pound that square peg into a round hole. There’s gonna be some situations where it really makes sense to have custom objects. And, again, in data cloud, that’s 100% possible.

Now when we’re thinking about data, right, we need to align it like I was mentioning earlier. But data cloud works in a little bit more of a normalized approach than we’re used to in sales cloud or service cloud where it’s a little bit more flat. So it makes sense that we have to do just a little bit extra mapping. And not only do we need to to normalize it, right, but we have to in some cases, we might need to transform it. And what I mean by that is, you know, you might have a a single contact record with multiple email addresses. Right? And that’s a that’s a denormalized record. It’s pretty flat. You have lots of attributes on a single record. In data cloud, you’re gonna wanna split that so that you have a record for each email address. Right? So there’s tools inside of data cloud to help you with that transformation and getting it into the right fields and the right schema. Excuse me.

Now when we’re when we’re thinking about data coming into data cloud, we really need to understand that there’s a a split between ingestion and mapping. Right? So they’re they’re completely different steps. You start off with the ingestion. Right? Let’s bring the data into data cloud. Once it lands, it’s gonna be turned into something called a data lake object. And from there, you can map that to the fields that are available in your data model object. And it’s the data model objects that their cloud has a standard model. It’s got a bunch of diagrams on the website, um, far more elaborate than we wanted to show here in this presentation. Once you’ve highly technical term. Once you’ve smashed all that together, you gotta, you know, lay it out nice inside your data model objects, that’s when you can start doing some of the features that we hear about with identity resolution, segments, activation, etcetera. Sorry. Chad got me distracted.

Alright. So we got our we got our organizational alignment. We have our data coming in. Everything’s looking pretty good. What are we what are we doing with it again? Right? And this is where we’re gonna wanna make sure that we’re being intentional with the data that’s coming in. Right? There’s a there’s a few different ways that we can do that inside of data cloud, and we want to make sure that we’re picking the right tool in order to accomplish that, hopefully, one or two use cases that we’re working with. Right? So if it’s from a analytics perspective, we have a few few platforms that we can do this with. We have some calculated insights, which will let us sum up or, you know, figure out what that one metric is that we’re gonna wanna associate with someone. This is where lifetime value might work or age, age that the contact has been with our organization. There’s a few that you can, uh, come up with. You can use Tableau to just browse and do all of the fancy things for, you know, exploring your data, and you can also leverage that data inside of marketing cloud intelligence.

Now if you’re looking for something beyond just analyzing and visualizing your data, right, you actually wanna act on it, right, that’s where you would have, um, data cloud segments or segmentation. Right? That lets you look across all of your different data sources and say, I wanna find customers that have made a purchase in the last thirty days without any support cases and that have hit the website. So that’s cool for grouping and, you know, getting a list of your population. But maybe you wanna activate it. Maybe I want to send that to a marketing cloud data extension. Maybe I wanna send that to one of my ad platforms so I can start targeting them with ads. Right? Thinking through what that use case is, what the end goal of it, actioning your data is gonna be, how you how you move that data around.

Now with all that said, there’s also some really cool things with data cloud around AI. Right? You can use either Einstein Studio for building your own model, or you can bring your own model using SageMaker and, uh, couple other tools too.

Now with data actions, it’s a it’s a specific feature in data cloud that lets you actually action your data in real time. Right? So information that’s flowing into data cloud in a real time way, that can be a data cloud objects. It could be coming from the mobile and web SDKs. Right? It might be actual streaming data, maybe from from the website. Right? That streaming data, live record changes, those are the things that you can use to drive data actions. Now maybe it’s, like, through a flow and you do some interesting things. You might be able to enrich the data before you send it out to a Chatter post or a Slack message. Right? Some really interesting things that you can do with these data actions. And a lot of times when Salesforce is talking about the real time nature of data cloud, this is specifically what what they mean.

Now when we’re thinking of activation, right, this is where we might take our segments in a batch approach and send it to marketing cloud or an FTP location. Right? And it’s basically building your lists and plopping them somewhere. It’s the the marketers that are usually gonna use this and, you know, take that information and either use it in an ad platform or, you know, send an email out to them. But that’s that’s usually what is meant when we’re talking about activation within data cloud. Data actions, like I was just mentioning a little bit earlier, this is more of the real time, you know, if you think of a river. Right? As as data changes in the stream, you’re able to act on it in a in a real time way. Typical users here are you know, sometimes it’s engineers or your data team. Right? Because they really understand the the nature of the data, how it ties together through the various systems and timings and and all that fun stuff. When you’re working with a data action, you don’t really have access to all of the rest of the information that’s in data cloud. So the the scope of the information or, you know, the water that’s flowing through the river, it’s a lot more focused and specific.

So for a tips recap, right, we wanna be very clear on what we want to do in data cloud. Right? Pick that one or two use case that’s going to really show off the success and the power of the platform and hopefully make other team members jealous and help, like, snowball the desire for it inside your organization. Being intentional. Right? Going in with a plan. Now I know, you know, there’s a free data cloud right now, kind of like a trial. And I don’t know how many other people here in this session have signed up for a free trial with, uh, you know, full intent of proving something out, and then something shiny rolls by, and then the free trial kinda gets wasted. I know I’m way too guilty of that. Same thing with data cloud. Right? You have a free trial. If you’re gonna go with it, make sure you have a plan. You know what you’re gonna do, and then sign up and execute on it. Make sure you have that organization alignment. Right? Which team’s gonna drive it? Which teams are supporting it? Is everyone bought in? Is everyone kind of excited? Hoping to see that success from the first couple use cases. Are we sure that we can measure it? And then lastly, that data model. Make sure as much as you can, stick with the out of the box data model. It’s quite robust. Um, I would argue it’s even more robust than the Salesforce core data model, so definitely take a look at it. And bonus tip, right, leverage the experts. There’s other people in the community in the ecosystem who have gone down this path with data cloud. It is an extremely rapidly evolving product. Um, the last, uh, technical deep dive for the release, it was three I think it was three two hour deep dives. Right? A lot a lot of changes in the platform. They’re making it more robust, more feature rich. So, you know, find someone else who’s in the data cloud ecosystem who can help either guide you or, you know, do some of this work for you so that you can find the most success with it.

If you’re interested in learning more about data cloud, there is a lot of resources that are available. There’s some that are, you know, self learning through Trailhead, and there’s videos. If you’re a Salesforce partner, there’s lots of really good content in the partner portal. They just recently moved some of that partner content to Trailhead as part of making the certification open, but there’s a lot of really good in information. Like, not just Trailheads and things like that, but workbooks and guides and a lot of really good resources to get you going if you decide to go with it, uh, on the solo side. For those trailblazers among us, right, there’s Trailhead, there’s the community, there’s a couple of, uh, community groups that are data cloud specific. There are expert coaching sessions by Salesforce. They have support office hours, and then, uh, we have some other resources here too. They’ll all be shared in the slides that I think come out later. I don’t know those details, but, uh, that’ll be available to everyone after. And that’s it. Thank you for sticking around. Hopefully, either you were able to stay awake or you have enough time to wake up until your next session. Um, looks like we have a few minutes for q and a. I don’t know. Looking at the app here, I’m not seeing any questions. But if anyone has some, I’m happy to answer. Otherwise, I think we have one or two sessions left in the conference before it’s the weekend, and, uh, time to relax. Alright.

Speaker 0: So yeah. Thanks, Adam. Um, I was like, yeah. There’s no questions in there, so, uh, take a few minutes before your next session, everyone. And, um, enjoy that break, and have a good weekend.

Speaker 1: Alright. See you, everybody.