Single Source of Truth Bootcamp

Welcome to the Revenue Room, presented by H2K Labs. Here's your host, Heather Holst-Knudsen. Well, welcome everyone. This is the first of our bootcamps focused on the concept of data driven revenue growth. Today we're going to talk about developing a single source of revenue truth in complex data environments. We'll cover a few things today. We'll introduce the whole concept to you how an SSOT works in a revenue type capacity.

Because it's part of an overarching strategy, the way to create value and track and measure, steps to developing tools and tech, landmines to avoid. We've seen many and this whole concept of write data in, write data out. I'm Heather Roltz Knudsen, the CEO of H2K labs. I have been in media events, digital information for over 30 years. And my bent is data and revenue. So I am very passionate about the subject and our business partner.

Is Chad Rose, the CEO of InsightOut and a software development and data company called Treehouse Technology. Chad, you want to say hi? Hello everyone. Great to be here. I'm looking forward to the discussion and answering any questions you all might have. My background is largely in data analytics engineering, more on the technical side. So. We can get into the details as needed and happy to entertain any questions.

So what we're trying to do today is we're going to review the fundamentals of developing a single source of truth in your revenue organization, the benchmarks for value creation. and understanding and really trying to help you with the opportunity in the landmine identification. This is meant to be interactive by the way, so there'll be points in time where I'll stop, ask questions. If you do have questions you want us to answer and you want to put it in the chat, please feel free to do so.

You'll get a copy of the deck, and we're in the process of building a detailed playbook that'll have more action oriented type of tools that you'll also receive. And if you would like to set up a time with us, both Chad and myself, from both the business and the data tech perspective, To consult where you are with your SSOT strategy. That also is something we were doing for the people who participate. All right.

So I think everyone knows this, but data driven organizations are far more profitable than their counterparts. And this is a study that was just done at the behest of Google through Harvard Business Review. Operational efficiency, revenues, customer retention, employee satisfaction, cost predictability across the board being data driven is a hallmark of leadership and profitability.

91 percent of those respondents, by the way, agree that democratizing access to the data and analytics is important to the success of the organizations. And that democratizing access to AI to drive predictive insights is crucial. And the reason why I bring up this democratization is it is a critical part of becoming data driven.

Many times we'll see companies that invest in the SSOT strategy, but yet they're giving it to leadership or functional leaders, but they're not bringing it down to the whole business. The democratization aspect is critical to become truly, fully data driven. I recently took a course with MIT on data monetization, which is. Really when they say data monetization, they look at it from three areas. It's an improved strategy, a wrap strategy, and a sell strategy.

The improved side is really what you're doing internally to add cash to the bottom line. Improving operations, improving how you're acquiring, retaining and growing revenue, improving processes, standardization, all those things. But it all starts with your data asset. But if you go through this journey, you'll be able to drive excellence across all different areas of the business.

But today we're really going to focus on the one core thing that has to be done right in order for you to go through these capabilities of data asset. That is your master data, how it's integrated, how it's curated, right? When you go up to curation and you have the right platforms. And you have the right data science, you can then actually drive predictive and prescriptive insights.

But one of the key things also to realize is that as you go through this process, these capability building is iterative. It happens over time and it's evolutionary and it's part of your journey. I think sometimes I find when talking to customers and clients that. Bigness of doing something like this is almost like a like a game stopper. And then making that data available to up and down across the organization is really a key criteria for success.

So we're going to do a quick poll here, just so I know where everyone is on this SSOT journey. It's confidential. I'm not going to show the results. I'll speak to them. So feel free to answer. I think this is going to be a very interesting conversation. I think people are in the same boat and starting the journey or on it and a little bit worried about where you are. I'm going to end the poll and let's move on. Okay. So single source of revenue truth in complex data environments.

First of all, let's talk about what an SSOT is. Some people call it a single point of truth. I've actually never heard that, but thought I'd put it up here. Essentially, what it's doing is getting your data into a state where it can be found in a single spot. It's trustworthy, it's clean, and it's meaningful, right? Centralized platform. Yeah. I mean, one way to think about it is if you have a question as to the performance of a certain part of the business.

Is there a place that you can go to get that answer with data? Right. If you have to go to a thousand different places or go to Excel or otherwise, you probably don't have that, but it's one way to think about it. Chad, I don't know if you read my blog on LinkedIn the other day, but the forecast was dead on arrival because it took a hundred hours across seven different divisions. And two weeks per division to develop. I mean, it was unbelievable.

Like they definitely do not have a single source of truth. So, and then, and the reason why I like to add the word revenue in when it comes to this particular arena is I find that it's very important to really truly define what the revenue truth is in terms of where the data lies. And it's not just about what's in your CRM. It's way more than that.

It is what anything your customer is touching that you're selling to their behaviors, the way you're engaging with them post sale, what they're doing on your website post sale, all of these things are really critical and important and should be able to be referenced in a central platform in media and events where marketing services are being sold and the delivery is about bringing value to them, i. e. you're bringing an audience to them You're looking at data on the product side because your

product performance is about what's happening on the audience side and how that curation is happening and how that meets the delivery of contracts. That is such a fundamental, profound impact. On your analytics, your forecasting and everything, especially with existing customers, which is your most profitable base, making that connection is critical.

So in the complex data environment, which I just touched on, which is different than what I call a one sided business model or business models that sell hard goods. Or software, for example, where you are selling this SaaS solution, here's what you're getting, and we can track and see usage. Complex data environments do not have it that easy. And it's not just the two sided business model, it's multiple channels and formats.

It is, a lot of these businesses have had acquisition activity, so M& A activity. You're bringing in businesses that are working off different platforms or different markets. It's very complex. And so we put this together to address the complexity part, but Chad and I work together on a lot of client calls and things like that.

And I think one of the things I'd love Chad to talk about is working with one sided, more standard type business models versus This complex data ecosystem, tell me what your insights are and the things that you see. Yeah, absolutely.

You know, I think with regards this segment of the market, you have almost within each business, you have almost multiple different business units that act and behave very differently, almost separate PNLs and probably are in some cases, typically, like you mentioned, Heather, you know, if you have a software company, they have a recurring revenue model and that's how they look at their business, that's the most important metric. And so the data model and the analytics all revolve around that.

There's an element of that here, but then there's also an element of delivering goods and services. There's an element within the event space of having to manage this unusual sort of sales cycle and trying to hit your targets and predict where you're going to land. And so each one of those independently. It is typically within our experience, a single project, so to speak, to develop a single source of truth. It's a, as I mentioned, it's a certain data model.

It's a certain system that they're inputting the data into, but here you have all of them combined, all of them at once, and so there's a real level of additional complexity if you want to get the full picture on the entire business. Not necessarily important to go about it from the start to say, you know, we're going to tackle it all at once. You know, if you do want to get the full picture, there are a lot more variables than you would usually see with a more simplified business model.

Absolutely. And I think, you know, from the finance side, it's very complicated and you touched on it, which I bring up here under the business model, a lot of these business models are now. under pressure or see the opportunity to move into data monetization and move into that recurring predictable revenue stream strategy. So you'll have an old business model going on the advertising, the lead gen, the sponsorship in sync with new. Types of business model, the recurring revenue.

And so you're actually now dealing with all different types of revenue recognition, waterfall forecasting. The levers are different in terms of driving the predictive analytics. So again, it's complex. I think what that means too, is that if you have the ability to put this together. And achieve that single source of truth and you can really differentiate or at least use it as a competitive advantage within your market. I 100 percent agree.

One of the things I always like to say is if you go down this path, your sellers are going to sell a thousand times better. Your customer understanding is going to skyrocket. You're going to outsell the competition over and over again. It absolutely will be a competitive advantage. 100%. This is my view of, I used to run media and digital information businesses.

So I would say when I was putting together those awful spreadsheets every month for my board meetings, I would do my best to pull data together from some of these areas that where it was possible to really try to give some realistic understanding of. What did we believe was going to happen predictively with customer renewals, expansion, upselling, the new business acquisition? How was that being impacted by the marketing funnel? How was that being impacted by the product delivery?

How was that, or are there trends that we were seeing on our website that were happening that would show up or down movement? You know, we talked a lot, we were in the manufacturing and the tech connecting tech with manufacturing. So. This is the whole event side, but there's a lot of data sitting around and the larger you are and the more portfolios and brands you have and the more businesses you've acquired, this is this ecosystem, this.

Data ecosystem on the revenue side gets quite significant. But the good news is you don't have to include everything. It's really important to look at the data that's meaningful, and we'll talk about that later, and do not include data you don't need. Someone may say, well, you're putting HR on there, but we need to know headcount and payroll and things like that. Finance has that on their side. Right. Chad, you, and you have a strong opinion about this.

Yeah, I do think it's just as important to consider what not to put in, what not to include in the single source of truth. I think there's a lot that we have here. And the important point to note is if you had all of this combined and you had all these sources, you would be in really great shape to get there. You don't want to tackle them all at once.

And you really shouldn't, but we'll get into that a little bit more, but we also try to call out things that just really are necessary when it comes to. A revenue, single source of truth and wasted effort that you would put it into trying to bring that data together. Right. How do you know you need a single source of revenue?

Truth Heather, real quick, given where everyone is, if you don't mind me jumping in, I'd be curious to understand or hear a little bit, if anyone has an opinion, like if they're on the path, why did they start Why are you trying to develop one? What pain points or where did the priority come from? If anyone has an opinion on that, it'd be great to hear. It's all of those things. Yeah. And we've been kind of, it's not a secret. It's not like an aha. Oh my God. All these things exist now.

We've known a long time. It comes down to then the investment, meaning people spending the time to do the assessment and then migrate to whatever the SSOT is going to be. Was there any finally made the push over the edge there to get started? In my opinion, I'm Intel.

I feel like the shift in economy over the last couple of years and our lack of ability to predict our revenue and count on our historical retention rate, and then to figure out why and what to do about it kind of came to a head in the last, I would say 24 months. And if I can add to this too, it's not only these bullet points, but even in our business, we had a problem. Different teams would give different answers. Like they didn't know who to ask.

They may go to one part of our finance or accounting function. They may go to rev ops. They may go to some Salesforce admin and like everyone then had a different approach as to trying to answer the question and the requester has to be different answers. So it wasn't even just the data sources who in the organization is responsible for reporting on some of these key business questions, which were maybe is the easier one to sort out versus. Some of the data and tech issues.

Good afternoon, everyone in our case, I had a chat with Heather about this a few months ago, where we knew what was coming. The pandemic certainly through a little bit of a wrench in that from a, from an end point, but we ended up. Buying 3 companies during over zoom during the pandemic, so 2022 was like. We didn't even know what we had. Now the dump truck has backed up to my house and dumped all the information on the driveway. And I'm like, great. I need to get my teams to sort all that out.

And so it's all those things, but a different arrival path. That's pretty common too. Just the M and a route ends up being unmanageable unless you can get it all together. We're able to introduce certain shared services, but I know this exists within these bullet points. Right. But it's like. Just the tech stacks and the various legacy tech alone can certainly make this. Like from the top, something that we could do better, but then we will still need to change some of the other logistics.

Anyway, I'm not going to take up other people's time. No, that's a good, it's a fair point. It's a good point. And that's another way of looking at it. Generally, it's an added benefit of doing this, but yeah, absolutely. Yeah. And you'll see, I use the same time last year on this third to last bullet point. Life is not the same anymore due to a lot of different reasons. Buying patterns have changed and your demographic is changing and expectations have changed.

So. The one, the historical back view does need to be complimented with real time trending data, where the machine learning and the AI comes in, where that actually allows you to pressure test. What people are telling you, right? And that's why, and I'll just back up here really quickly. So the unstructured sources at the bottom, the, there's what's being put in manually into your CRM, for example, versus what's happening actually.

So if you have customer insights, I talk about this all the time. If you sold something and there is a contract with product performance metrics that need to be met, and those aren't being met or they're delayed. That does have an impact. That's something that is data. You need to know that most times does not get surfaced when you talk to your sales team. They think the deal's in there. It's going to close on this date and for this amount.

But yet, because they're not really connecting like, Oh, we haven't delivered yet. It's going to be delayed. So that's going to push my deal out to here. Or we're noticing that while we may have the same amount coming in on a renewal, We're getting less traction, you know, at events, but there are other things that are happening outside the CRM that help with not getting caught, not knowing what's going to happen.

So again, I'm just underscoring that this is part of the SSOT strategy that's really critical to keep in mind when you are going through it. So let's go forward. I think we already asked that this question was going to come after this list of how you know you need a single source of truth. But I'll say that if you're spending a lot of money on manual labor, additional headcount, and spreadsheets to generate forecast reports, that forecast dead on arrival example I used before.

All of these are signals you need it. So we already kind of opened up the discussion, but Mintel is there. You do research, right? And, or you're giving insights and intelligence for improving marketing outcomes for customers. If I am understanding what I saw on your website, correct? Yeah, that's the gist of it. All right. Are these customized type programs or is it like a, is it productized and standardized?

About 80 percent of the money we make is in the form of a subscription, like a subscription license to our platform. And then we've got a professional services arm that does kind of customize one off deliveries. Okay. And the subscription, I'm assuming at the large ticket item subscription that renews annually. Okay. When you look at customer adoption and usage with your platform, how do you define that? We have usage stats to be able to say like number of active users on our platform.

We can look at their downloads. We can look at what they're looking at. So it's one of the client health score metrics that we then provide to the client success manager or the account manager to be able to assess like the health of the account and as we get closer to it, the risk or lack thereof and renewing that account into the next year. Okay. And is that data applied to forecasting and pipeline and things like that to adjust based on the customer health score?

Nope, all of the forecasting right now is subjectively categorized into different sales stages by the account manager. So we give some training and guidance to say like, okay, if this has happened, this stage, if this has happened, you're in this stage, but they still have kind of the ultimate call day and their manager, there's kind of a review of it, but they, it doesn't factor in usage data or things like that, at least systematically, like the account manager may know it in their heads.

Like, Ooh, this is a risk. I blow usage here at this account. So I'm going to categorize it at this stage, but it's not systematically built into the forecast. No. Okay, got it. So, anyone else want to talk about the complexity at your business that we may have missed or may add color to our conversation? All right. Well, I will then move on. So let's talk about value.

One of the biggest complaints that I hear is we've invested all of this money and time and effort into building, you know, this data driven culture and strategy, and we're not seeing value. Well, that's a result of actually not defining it and making a part of the strategy, but let's go into what value can look like.

So there's tons of value with an SSOT, everything from, you know, making it easier to discover information, reducing the cost to create those reports, including headcount that you won't need anymore, or the headcount can be reallocated to value creation and added value activities. It helps with data governance. It helps across the board. But the real big thing here, the message I always like to say is that if you are going to move forward with an SSOT strategy.

You need to look at it as a continuous creation of value across multiple areas of the business. And you want to track and look at those and have that top of mind as you put your plan together, track the progress of the plan and evaluate outcomes of your plan as it goes through milestones. Yeah. And actually just real quick Heather on that prior slide. So a lot of our customers, the best when they're using the analytics and the single source of truth.

I mean, the means that it was really meant to be, they are able to deploy new marketing efforts or change certain tactics in their sales process. And then simultaneously in real time, kind of see how that is impacting their business. Right. So again, as it relates, like, do I have a proper single source of truth, am I doing this correctly? You should really be able to try new strategies within the business, adjacent to that, see the performance with little effort to develop like that.

To develop that insight or to develop that visibility into how that is performing within the market. And that... It enables people to try things and then turn them off or turn them back on depending on how they work. It really goes to Heather's point of enabling that value creation going forward continuously. So the way that I look at it is this. The first part is standardization. Regardless of whether, where you start on the SSOT strategy, you need to look at things in your business.

And how you can use this process to standardize them. I look at it that the SSOT strategy comes first, by the way, because you want to know what to standardize, what makes sense, and test it out versus doing that first, then driving SSOT side. But it's everything from processes, how you measure things. What does conversion mean to sales versus a conversion act mean to sales versus marketing, for example, it's the platform standardized as much as you can on them and it's also products.

This is more towards a media company, for example, or events organization, but you should not have 100 different newsletters that you're selling with all different ad formats. You'll never be able to track true performance of your newsletter channel. And add types and things like that.

If you are deploying all different kinds of normalization, standardization needs to take place, you should have a customer engagement process that's standardized, but this data strategy could help you do that and do it with low risk and low destruction or disruption to the business. If I talked about the values through data and systems, revenue excellence is an area that this strategy can truly drive everything from. I use the word the revenue room, I trademarked it.

It really is a mindset, it's a culture shift, but it is an actual organizational construct where you're using data to connect all of what the revenue critical functions in the business and have them in some way or another jointly accountable for outcomes, right? Revenue customer outcomes. I'll use an event example, but I've seen this a thousand times where sales sells a sponsor, you know, very large sponsor promises all of these outcomes.

The contract comes in and both marketing and the enablement team are looking at this and say, there's no way we can do this. And it was over, it was oversold. They didn't deliver, everyone's pointing fingers. But if you use data now to actually co sell, co deliver, co create with the customer. And data's at the ready to say what you can do and what you can't do. You can really optimize revenue performance in that way.

Product excellence, if, again, if you're standardizing around product sets, and you can actually see what's working, what's not, you can then leverage that across all of those solutions regionally, for example, or across portfolios. to really drive better outcomes. And then organizationally, everyone talks about data driven skills. You are not going to be able to acquire data science and data skills across your organizations. It's impossible. What you have to do is you have to build them.

So if you're utilizing an SSOT strategy, you're making that data available, you've got the right governance, so the right people are seeing the right data, you're actually going to embed. data capabilities in the daily flow of work, because if they've got the right dashboard that's going to help them do their job, it's going to see how they're performing. It's going to help them make better decisions. They're going to learn a lot about data and then business value.

I mean, up and down across the board, it helps with risk identification in time to actually activate it or mitigate it. Identify new customer opportunities in terms of revenue, and it allows you to lead the business versus the situation leading you. And if you are looking to sell your business, then we'll build value evaluation multiples the more data driven you are. So before I move into steps, any thoughts or comments on the value equation?

Did we miss something you want us to answer, or did you want to share how you look at value at your business? Not on that one, but just a point of clarification is a single source of truth always a place we're talking about, i. e. This is going to be some software or ERP system somewhere. And it's a matter of choosing the right ones and properly, or is the solution now in 2025, you're always, it is a system that will be that and it's just setting up the system correctly.

And I'll let you answer that one first. Generally it is like, if it's an application you're logging into, right. And that can take a couple of different forms of how you want to do it, but. Ultimately, yeah, it's one place versus going to your CRM to get the revenue or to get the sales numbers and your finance system to get the profitability and marketing to get the leads and so on.

So it's cutting that all out and how you get to that, how you implement that technically, you can take a variety of different approaches, but the goal is to get to one place where you see it all together. Okay. I have a question. Should I post it in the chat or can I say it now? Is there a sweet spot of size of organization that you have found this really can be the most effective, you know, plus or minus one iteration on the size? In my experience, and then I'll let Chad weigh in.

I actually am pretty astounded at this. Large companies, enterprise level companies that the forecast is dead on arrival, that came from one of the largest event organizers in the world. Okay. Billion dollar company. So I think that the data issue moving in this direction is it's for large enterprise, but also for a midsize company, like if you want to compete and you are competing for dollars, you may be competing against part of a brand portfolio within that large company. It's for everybody.

We worked with companies that are a couple of million in revenue, all the way up to fortune 500 to Heather's point. You want to be mindful of ROI here and like how much you're investing in this technology and what you're expecting to get out. So for the smaller companies, there might be significant pain points within certain parts of the business that can be solved through the single source of truth or through a certain slice of it.

So as long as you're mindful of that, depending on your size, and you're not going as a small organization, going and grabbing all the data that really won't drive much of a difference in terms of the business. If you're considering that in the process, then you're probably doing it the right way. As the business gets bigger than small tweaks in the marketing or operations can make. Big differences in the dollars. And so the ROI is there to bring those systems and those datasets. Absolutely.

And two other points. And the reason why I brought up this slide is, which Chad said is totally spot on. It's like, where in the business can data, a single source of truth really help you accelerate, right? And especially if you're smaller and you have limited resources, but you have to make big games, this could be a very significant, profitable investment for you to make. But it's very important to focus in on that area that you want.

You may not need the predictive waterfall forecasting for your CFO to deliver to your board. So you won't need to connect into all of those other systems, but focus in, it can deliver value. Absolutely. I also think it's important to understand how you approach it. And here is a standard layup that we see successful. Which is one you do need to create a cross functional team.

Whether it's three people or ten people, it's the functional roles who are going to benefit from the data outcomes to activate them to drive the ROI who need to be on this team. It's important to, once you have your team to discuss, document, connect, and prioritize the business outcomes that you feel are being thwarted due to these data issues. And when I say connect and prioritize, certain issues are connected to the other and have to be solved first, right?

So identify what those connections are. But second is always focus on the quick win, right? When I say quick win is, it's going to be the one that can be implemented With the least disruption to your business first. As well as we'll have the best outcome and you'll see in our landmines, a lot of people miss this step. They go full boat and that actually, especially for a small business is not recommended.

It's very important to communicate up and down the organization, what you're doing and what to expect, you know, as you're going through this process. Not just that you're doing it, but how things are going to start changing. The adoption cycle, all of those things and start preparing for that. And then everyone needs to agree on success. And the agreement part is actually interesting. It's also, you'll see in our landmines is getting agreement on what things mean is actually really challenging.

And it's, I think Chad's seen a few situations where that has really impacted the outcomes. And then you need the commitment and then obviously finding partners to help you. Because it's important that your business keeps moving forward while you're doing this versus stopping it, right? So you need to fill any capabilities gaps that you may not have in order to get it done. So quick win approach, and we thoroughly believe in this.

Is once you've identified your quick win and you have your partners, you then go back into the current future state, you define your goals, you do the architecture. Chad, I know you have, you're on the tech side. I don't know if you want to talk a little bit about this wave here of architect, validate and develop. Yeah, absolutely.

Going back to Mike's question, I think the technology you need also depends a little bit based on the size of the business number of systems you have, the types of where your data is residing.

If you're in more modern solutions and CRM and finance systems, it's a little easier to deal with, but sometimes organizations have outdated systems that are much more difficult to pull data out of, but regardless in the quick winter approach, you try to implement as little as you can just to get to the outcome. And then continue to add on as you take down priorities one at a time. So it's in this cycle here is really just an iterative cycle, right?

You're starting, you're identifying the priority, defining it and going about getting it done and then moving on to the next one. Once people see how it can work and see, it can believe in the outcomes. Yeah. And I'll, during that MIT course, they use Microsoft as an example and how Nadella basically went from very old school sales practices, selling solutions on the Microsoft Dynamics side, and then they moved to the 365, which was the subscription based.

And basically, they did their single source of truths, they launched dashboards and they started out with three areas they were going to measure first, then added the next one on and so on. And it was like a flywheel effect, but iteration scale and leading from the top and ensuring adoption because leaders are using this is very critical to also this quick win approach. All right. Landmines. So we talked a little bit about this, but starting too big, biting off more than you can chew.

This is something I, Chad, I think you've seen this just many times, and maybe you could talk a little bit about your experience with the starting too big. Yeah, we've mentioned it. I don't know if.

Folks have questions on what we're referring to here, but I think generally what we mean is that most of you have decided that you're going to go down this path, there was something that initiated that some sort of visibility or issues that you had trying to tackle all tackle them all at once and getting a handle on how marketing is performing, how sales and then finance all together performing at the same time is challenging approach. And that's what we're trying to advise against is.

You know, really take a single system or a single metric, get that going and build on top of that over time. I'll talk to a few more here. I think the IT owning the strategy is another one we see. The strategy really needs to be owned by the CFO or the COO or the finance team. That's what we see due to what the outcomes are, but that's another landmine to avoid. Yeah. What you'll potentially end up with is a more overly technical solution. That may not meet the needs of the business.

And so it's really important to have the business side represented and delivering and determining what's the priority, what the investment is managing to that investment and so on. Exactly. We talked about the disagreement on definitions and there's a way to solve that. I also see the once and done mentality. It's, this is an ongoing commitment to data integrity and data driven culture and mindset. So it's, you don't do it once and then you're done.

You need to have the data governments and rules set to ensure that you're keeping consistent and that you're taking it to the next level when the business is ready. Heather, one thing I wanted to add here too, just given that you guys, a lot of you are already in the process. You typically do need a technical representation to actually make it happen, to get the right pieces in place, to create the solution or the dashboard. If those people that you have doing that are.

Coming from a background that does not involve data analytics, that's a big red flag. So if they're more of a website developer or an application developer or some other internal IT resource, transitioning over to be a data analyst or data scientist, whatever you want to call it is a pretty big change in mentality and approach. And so if you're leveraging internal team that you're moving over from one technology to another, this technology being analytics, that can be a very. Risky move.

So I just wanted to throw that out there and make sure that folks had that in mind as well. And another one that's not on here is that, but I think it has a lot to do with the wrong data in wrong data out is to get right data in right data out. It's not just what you want to report on. It's what you want the data to tell you for the future. Right. And that may require. additional data sets that you're not considering.

So it's really critical to pressure test that or else you'll just get very nice looking reports. Right. And you're looking for, you want your single source of truth to drive prescriptive and predictive insights. The next slide is really a question related to landmines. Are there any landmines you've already encountered or you want to share that either you would like to brainstorm about or you found a solution?

So one of the things that I encountered throughout my career was the disagreement on definitions. I once worked on a project where we were trying to revamp an internal system and I remember working on this for about two years based on definitions. So how does that process work where you sort of get everybody together to hop on board and agree on a definition? What is one strategy?

I'll tackle that quickly and then Chad, I'm sure you've got perspective, but there actually is a strategy which probably was not available to you during the time you were doing this. That is, if you, people get threatened when you're going to start messing around with their actual system. Let's say you're using a CRM.

Maybe you're all on Salesforce, but it's partitioned off by this division, that division, or there's a way to agree to make it a lighter lift, whereby you're actually agreeing at what these things mean, but you're doing it at a level at the data platform that you're using for the SSOT and the analytics and the visualization. And you're mapping so that you're actually not disrupting what's happening in the actual source of the data. So that's one, one solution.

Should I do anything to add to that? Yeah, we did add a couple notes on the prior slides. But one thing that's really important in that process for us. Single owner, preferably an executive that is sponsoring initiative and has the ultimate discretionary power to say yes or no to the definition they need to be a very, they need to be very committed to the process and to the, and to what you're trying to achieve and they need to help generate the buy in.

I think if you don't have a single person who is owning this, that's much more difficult to get consensus. And then secondly, I think it goes back to our start small process approach.

If you want to define every metric and every definition across the company, all at once, it might take two years, but if you start with a couple and then you go through and you actually deliver, you know, a unified version of revenue for everyone across the company, that they can slice and dice how they want, they might be able to filter it by certain areas or otherwise, or adjust it based on their preference, but if you give them that end result, then they'll see the benefit of agreeing to a

consensus definition. Thank you. And what that would lead to, and then you can add on to that going forward. And I'll add a third thing in, tie back to what Chad said, but taking a step further. If you're, and I'll use revenue as a revenue org. If you're actually aligning outcomes across the revenue critical functions now, and people are jointly accountable in, in unique ways, but have accountability for revenue outcomes.

And you're providing them the data set to allow them to see how they're doing, but it has to be unified. You're actually creating a benefit. There's a need for it and a benefit above and beyond just saying we're doing a single source of truth. There's value for the end user as well as the functional leaders, so there's, there would be a way to get better buy in. But eventually the business will say, this is how we're measuring, right? And this is how you're going to be tied to success.

Utilizing the way we're doing this. We're just, I want to do a time check cause there's actually a little bit more to go through, about eight minutes. Chad, why don't you go quickly through the tools and tech and considerations, and then we'll move into the write data in write data out. So just a few components that you probably need to consider or at least have for developing this and putting this together. First one's data warehouse, which is really somewhere that stores.

All of the data that you've aggregated or collected from all the different systems. Typically in the cloud nowadays, there are a variety of providers out there who offer very good solutions. Second is more of a data extraction and data cleansing capability or tool. Oftentimes this is also sold independently within the market.

So there it's basically what it means is it's a tool to bring the data out of your CRM or out of your source systems and then to transform it or to clean it up and get it ready for report. The third is a data access and data management layer or tool, which is a means to make sure that only the right people are seeing the data that they need to see and also a means to adjust the data as needed.

You want as a business user, as a business in general, you want to enable the end users to manage that information that's in the single source of truth to the best of their ability without technical intervention. And that's what we were calling out there with that capability. Force something to display it. So a way to look at the results, look at the data period or dive into it, so to speak.

And then lastly, a predictive ML AI capability, which can sit on top of all this information and hopefully give you more advanced insight into the business. And generally these can. Either sold and purchased independently in the market or unified within a single platform. And what is ETL, RETL? I don't know what that stands for. So that's extract, transform, load, and then R is reverse. And that's a little bit newer technology.

What that is being able to take the data that you've aggregated and push it back into your source systems, grab data from finance and Salesforce and the CRM, potentially being able to push that. I mean, it's back into the Salesforce or CRM, which is funny. Is this a picture of like comprehensively what you would need? Eat all of those elements in your, you generally need at least data warehouse, data extraction, or ETL, and then a display. If you don't have those three, it's tough to get by.

You really want a data access and data management to do it properly. And then the predictive is more something you can add at the end. If you want to, once you've gotten the data combined, we're trying to keep it as simple as possible in terms of achieving them. Thank you. So just quick key factors, we'll run through these, I want to make sure we get to the last part is obviously the consideration, do you have the staff to do it or do you need to outsource it?

When, you know, your time to value is really important. You don't want to be spending two years defining things, right. And agreeing you like you want to go, your investment goes out and you want to start activating it, enabling it and iterating it. There's the complexity calculation, like how much and again, that's why the quick win part is really important is what we're being pitched or what are we looking at?

Is it adding complexity that we don't need and it's going to stop us from getting to the time to market. And. How disruptive will this be on the business to go forward or non disruptive? It's important that whoever you're working with understand your business and organizational model and how are you going to calculate your investment and then the payback period, right? How will this investment pay itself back plus? And the real biggie also is make sure it's built for business users.

And then, so, write data in, write data out is a very important concept. Not everything needs to be measured. It's not all meaningful. Tracking everything will jeopardize the long term value that an SSOT strategy delivers. The next slide. And it really needs to be mapped to stakeholder goals. So the next slide we'll go through the stakeholders and how you need to understand who you're addressing.

And Steven, this may go to your question also is, you know, which part of the organization are you trying to drive value through data? But the first stakeholder are gonna be your investors. And no particular order, by the way, the second stakeholder is clearly the C E O. The C F O is right there. C r O. The C m O. The c o o. The chief product officer, and then your functional teams, right? All underneath those layers. And again, you'll add yours, but build your map of stakeholders.

And then the next slide we'll, let's focus on investors. So investors, if you are either PE owned, or you are looking to be PE owned, or maybe you are a private equity, oh, and you own. The map, the goals, right? They're looking for due diligence for new investments, operational oversight of current portfolio companies, proof of investment thesis, identification of hidden opportunities, and examples of types of data they need go cut across. Pretty much.

I would say all of the executive dashboard, if we wanted to look at the CFO, which is the next stakeholder. Your CFO is a critical person in the organization, and there's a huge plate to, of things that they need to do. Everything of reporting financials to board and shareholders, to maximizing assets and resources, managing EBITDA and EBITDA growth.

Their data requirements are, I would say probably the most extensive and probably tap into the majority of the data out there because they're looking at costs. They're looking at margin, they're looking at growth, revenue, they're looking at customer lifetime value, and they're looking at revenue per employee profitability.

And then the next one example that we'll share is the CRO, who is driving the revenue and mapping their goals like we'll include in our deck, a little blank slates of this, but you would map your, what are the goals, the data required to hit those goals and some examples of how you measure for those stakeholders. And then that goes into how you're going to build your strategy and how you'll prioritize. Right. Maybe the investor.

Data isn't as important as the CRO data because revenue right now is your most critical, urgent issue. You would start with revenue. Or it could be that you're spending hundreds of hours building forecasts that are dead on arrival, and your board's really sick of it, so the CFO is going to come first. And it'll dictate the data that you're going to attack first. Because we're over by a minute. We had opened up for more questions.

If you'd like to set up a call with us to dive into this deeper, we're offering an hour time, like office hours. So just email myself and I will get that set up. You'll get this deck. We have another bootcamp for September 21st. This is all about the actual predictive analytics side. And once you get a single source of truth going, how can you use predictive analytics to drive? Top and bottom line growth and manage risk and capture opportunity.

So the registration link for the 21st is in the chat, whether you want to just copy it for later or we can also email it to you. I hope everyone found this helpful. Thank you, Heather. Yes, thank you. Thanks, Chad. I think we have our work cut out for us because we are really just in the early stage. And this was very useful, especially at like a summary level of things that we can talk a little bit more about internally and give us some sort of path to go by. Terrific.

We really appreciate you joining us and hope we'll see you on the 21st. I'll also reach out to see if there you need any help in between now and then. For sure. Thanks everyone. Thank you everyone.