Unlocking Data Potential: Streamlining Your Infrastructure for Revenue Growth

Brandi Starr [00:00:36]:
Hello and welcome to another episode of Revenue Rehab. I am your host, Brandy Star and we have another amazing episode for you today. I am joined by Emmanuel Billy Gillis-Harry. Emmanuel is a dynamic leader with over 6 years of experience in analytics, data science and artificial intelligence, specializing in transforming businesses into data driven organizations. As the founder and Chief Data Officer of Tera Bullion, Emanuel leads a consultancy focused on delivering innovative full stack analytics solutions to online retail businesses and beyond. Beyond consulting, Emanuel has a track record of success in marketing and strategy and he is recognized as a thought leader in AI and analytics. Welcome to Revenue Rehab. Your session begins now.

Emmanuel Billy Gillis-Harry [00:01:33]:
Thank you so much Brandy. Appreciate you having me here.

Brandi Starr [00:01:37]:
You are welcome. I'm excited to talk to you. Data and AI are two really hot topics. But before we dive into that, I like to break the ice with a little woosa moments that I call buzzword banishment. So tell me, what industry buzzword would you like to get rid of forever?

Emmanuel Billy Gillis-Harry [00:01:59]:
Well, I mean, I guess I won't really say get rid of, but I would bring it into, into the light of getting misused. And that's pretty much AI slash data driven in terms of organization use. So it's not that it's necessarily bad, I think it's just misuse and sometimes could drive the wrong perspective exception. But you know, upon actually the right usage, I think it definitely makes a difference.

Brandi Starr [00:02:29]:
Yeah, I do agree and I think everyone is calling everything AI now and if there is at least one fact involved, it is data driven. So I definitely see how you have seen people misuse that one. So now that we've gotten that off our chest, tell me what brings you to Revenue Rehab today.

Emmanuel Billy Gillis-Harry [00:02:55]:
You know, I just wanted to kind of bring into the light as the trend is going on of best practices things to consider before I even get into that point of the actual AI use cases, which always leads back into the foundation of the data because AI is only as useful as the data that goes in it.

Brandi Starr [00:03:17]:
I definitely agree and I would see, you know, I would say just talking to people that has been one of the biggest struggles with AI is so much of it is, you know, based on the data and so many people have messy data. And I believe in setting intentions. It gives us focus, it gives US purpose and most important, it gives our audience an understanding of what to expect from, from our discussion today. So what are your best hopes for our talks? What would you like people to take away?

Emmanuel Billy Gillis-Harry [00:03:50]:
You know, I'd like people to understand what is important to get to that goal, which is the topic of today's just modernizing data infrastructure. You know, it the same goes crappy data in, crappy results out. So the better data in, the better results are. So whether it's on a visualization perspective, advanced analytics perspective, or even AI use cases and you know, I'd like people to understand between the framework of, you know, what is modern, what is modern data infrastructure? What does that truly mean? How does it impact their potential business use cases? What are common pain points, pitfalls or challenges that could be faced in trying to get into that modern data infrastructure and potential case studies that could drag attention or just, you know, get people's will spinning on actually taking this information and applying it.

Brandi Starr [00:04:48]:
Okay, and I think the best place to start is exactly what you said with your framework for modern data infrastructure. And I always like to start by grounding us in some definitions. So for those that are not familiar with the term, what does it mean? Modern data infrastructure? It's a whole mouthful.

Emmanuel Billy Gillis-Harry [00:05:11]:
Yeah, that's fair. I like to, instead of going from a definition because, you know, most people may think that just means migrating to the cloud, which essentially that's what you would most likely be doing. But the, the, the, the three, the three words I'll say that really describe it is scalability, efficiency and business alignment. That is what modern data infrastructure should be defined as.

Brandi Starr [00:05:39]:
Okay, we're just, we're, I'm like, we're throwing in all the buzzwords here because I'm pretty sure scalability has been banished a couple times, but it has definitely see that. And so yeah, I do think that just, you know, I've been involved in data projects over the years, whether it be in my own company or working with clients, and I do see that moving different systems into the cloud tends to be the catalyst for, you know, how like when people start thinking about modernize, modernizing this. And I think, you know, putting it in a marketing perspective, when I think about all the different customer data we have, whether it's know what they've bought from us, how they've engaged with us on into all of the touchpoint interactions that are coming out, different systems. So marketing is sending emails, they may be running ads, sales is making phone calls, leaving voicemails. You know, there's all These things that are happening and we have a lot of systems that are collecting data. And so if I think way back, you know, at the beginning of my career when I did have a database management role, when I was thinking about data structure, data cleanliness, use cases, it was really matching, it was like our marketing system, our CRM and our ERP and what's the unique identifier for the customer. And really just matching up data points that are clear one to one, like address in this system syncs to address in this system. When we think about, you know, go to market now and all the technologies that we have, there is so much more data.

Brandi Starr [00:07:38]:
And that's where, you know, the big data comes in as a term. And so when I think about modernizing the data structure, it is, I mean it definitely aligns to what you're talking about. Like how do we scale it in a way that we can connect all these things and actually be able to use it for business use cases. And so I'd love you to break down your framework a bit more, especially as it ties to go to market with marketing, sales success, etc.

Emmanuel Billy Gillis-Harry [00:08:10]:
Oh yeah, for sure. And you tapped into one of the biggest, you'd say I won't even put it as a buzzwords, but one of the biggest painfuls which the word is siloed data. So having a bunch of different data sources as technology keeps increasing and getting better and you know, we'll use sales and marketing as a business use. There's a lot in which data can be generated and they all live in their own silo place. And what most businesses seem to have issues with is bringing all that together. The there's no transparency, duplication of the data sources and just a bunch of data assets that kind of end up being siloed. So one of the ways, one of the first steps in data monetization is to be able to actually bring those siloed data together, bring in full visibility. So when you now think of the capabilities of the data, you're being aware of all the data resources that you already have.

Emmanuel Billy Gillis-Harry [00:09:10]:
And we could now talk a bit about the buzzwords that has been banned a bit. But scalability and efficiency in terms of doing that, a lot of these things could definitely cost money. But in terms of actually making it accessible, that's where that scalability matters to where even the smallest companies could actually work with today's technology to get their data infrastructure modernized at a very costly way. And then as they grow, obviously the infrastructure grows with them. So kind of debunking the scalability aspect is that as your data assets grows, these modernized solutions actually will grow with you. So you're not losing out on like, you know, we're not ready or we're not a perfect fit. You know, if you're only generating about 1,000 rows on a monthly basis of data and it's not that much, you can still modernize your infrastructure, you'd say, which now, you know, trinkles down into the efficiency of it. Upon bringing these infrastructures into place.

Emmanuel Billy Gillis-Harry [00:10:09]:
Accessibility, governance. What's the word I'm thinking of? Well those two big ones, Access, accessibility and governance plays a big role in terms of us making it more effective, more efficient, you'd say. And the last but not least was that business alignment. If it's very accessible by, you know, with the right individuals in the businesses, there's a lot of conversations that can happen between the technical individuals and the non technical individuals on how to properly utilize these data assets.

Brandi Starr [00:10:44]:
Yeah. And so in thinking about that siloed data like that is probably one of the biggest challenges that I see, you know, in many companies is there's so much that they want to do. They have the technology in place, but they don't have the data. And this is where I see a lot of projects sort of die because it can be a big undertaking and there's so many different ways to tackle it. I know you have the data warehouses, data lakes, we've got some places where AI has now made it so that you don't have to integrate certain things that can tap into that just on its own. And there's all these different approaches and to your point, depending on the size of the organization can vary. But I often see companies get started, they know they need to tackle this, they get started on trying to figure out how to break down the silos between the data and it just gets so complicated and messy. What is the unique identifier that we can connect records, what can we store, what can we use, what can't we get to? How do you see, see companies, especially organizations without huge budgets, being able to break down these data silos and not have those projects stall.

Brandi Starr [00:12:12]:
And you know, it's like people almost just give up and it's like eh, siloed data is just a thing that has to happen, which I agree with you, it doesn't have to happen. So you know, thinking about the marketers who may have tried and failed or been too afraid to try, how do we avoid those projects falling apart?

Emmanuel Billy Gillis-Harry [00:12:31]:
I would say step one is actually getting with an expert to truly understand capabilities, you know, as everyone in their own rightful subject matter expertise can speak on it, involving the people that actually do know the technology and the use cases aligned with the non technical individuals that have the subject matter expertise of, you know, whether it's the sales unit, VP of marketing or chief marketing officer on the, or all the other, all the other decision makers having those conversations, actually putting a strategy together is usually step one because there you get to actually talk about the fairy tale, the dream of what you envision. And then what happens in those sessions is that you actually now bring it into reality. What's really possible and what's not possible, but you know, maybe could be possible down the lines. And two, in those conversations you're actually being more aware of like what you like what budget is really feasible to even access a modern, a modern infrastructure today. So like I said, in today's world it's pretty much pay as you go. There's a lot of technology that you know, has that, that, that, that cost model. So you're not committing like a, you know, $50,000 upfro to just have this infrastructure. You could, once you set the infrastructure up and as you're growing your data assets and solving the problem of silo data, you'd say it actually grows with you.

Emmanuel Billy Gillis-Harry [00:14:11]:
So as you, you know, utilize your data assets, actually make money, you can reinvest that into scaling your, your data assets on the next level I would say. But it, it really always starts with the conversation, you know, finding yourself an expert finding or you know, employing an expert within your company to really strategize that and really bring, like I said, bring the dream into reality is, is where you could get rid of that bottleneck.

Brandi Starr [00:14:37]:
Yeah, and I think you hit on a really good point here in that there has to be someone who owns the data structure and whether that, and going back to the governance and accessibility and all of those things and whether that's an internal resource or an external resource, you know, can, can vary based on a number of factors. But that is another place where I've seen people go wrong in that it is no one's job to actually understand how the data flows, what data is available, how it's being used. And you know, sometimes there's the pushback of oh, there's not enough to do on a day to day to have a full time person. And so for those that don't currently have a resource, what should they think about in figuring out whether they hire for that, whether they outsource That I know the debate of internal versus external resources comes up in a lot of places and this is one that I see that happening a lot, especially when you know, marketing is feeling the pain. But if they get an external resource that may not, you know, sit in their department because so it becomes a whole different, you know, corporate discussion. So I'd love to hear your perspective on, you know, if I'm sitting in a company where I don't have that person that really owns this, you know, is focused on modernizing it, like what should I be thinking about in considering to figure out how to get that resource?

Emmanuel Billy Gillis-Harry [00:16:20]:
I mean personally I would say this may sound like I'm pitching my services already, but I would say getting that external handprint first to be able to kind of understand and actually build out some proof of concepts, you'd say. And then upon that you actually make the strategy to now bring in your own in house team which you know, most third parties would be able and willing to do that. So not only do you actually get to get it right in the first place, you could actually strategize to start bringing in house at the right time. You'd say, because you know, one talent sourcing could be expensive and if you actually bring in talent that you are not even ready for, you know, that could, you know, mess up the company culture, wasted budgets and a bunch of other negative impact. So actually getting it right in the first place with the proof of concept and then scaling that out would be usually where I point people to. And that could look like, you know, bring in a one time consultant and have them on a contract basis or actually you know, partnering up with an analytics boutique like my, like Tera Bullion and actually have that come in place and you know, set up that foundation and over time you could actually hire because at the end of the day, you know, they say in today's world if you're not being data driven, AI driven, you're slacking behind. And there's some truth to that because at the end of day what this does is bring more efficiency to decision making, to insights generations and then the use cases of actual of bringing in AI could definitely fall into place there.

Brandi Starr [00:18:01]:
I, I definitely agree because quite often the challenge with hiring is the person that you need to help untangle and unsilo all of your data is a much more senior experience skill set where you may not need that in an ongoing capacity. Once you've got it kind of, you know, running well, you could have a different level resource that is able to govern, maintain Etc, I want to shift a little bit to the business alignment piece because this is a place where I've got a strong opinion. Not that I don't have a strong opinion on most things, but from my perspective and in some of the projects that I've been involved in with clients, to me, the business alignment and business use cases has to be where you start in figuring this out. And what I see is most people start with what data do we have, what technology do we need to put it all together, and where I have seen challenges with that, and I'll give you a specific example and let you react to that. We worked with a credit union and they asked us to integrate two systems. They didn't want any, you know, consulting efforts to help them figure out, like, how that should be. They basically were like, we want this to go with that this way. So, fine, we did that project.

Brandi Starr [00:19:39]:
They ended up, you know, we ended up having a conversation afterwards about some of the campaigns that they wanted to run. They were doing email marketing and, and long story short, through the conversations, they had like these 18 use cases or 18 campaigns they wanted to run. And of the 18, they could only actually execute on three because they didn't have the data for the other 15. And so that was, you know, and that's one of many examples that I could give you where there were business use cases that they had thought about, but that wasn't where they started in figuring out how the data needed to flow. And so although there was a lot of work to match systems and bring these things together, in the end the business alignment to what the actual need was wasn't there. And so, you know, thinking about it in like a chicken and egg sort of situation, I feel like what has to come first has to be the clear use cases of what are we going to do with the data and then back into the rest. I've heard people that will argue that that's not the place to start because so many people in the different functions don't really know what they want to do and this, that and the other. So I'd love to hear, you know, since business alignment is a part of your framework, I'd love to hear where that fits into the process.

Brandi Starr [00:21:11]:
If, you know, it's like, do you feel like that's where people start? That's like, where does it come in in the conversation of modernizing data?

Emmanuel Billy Gillis-Harry [00:21:22]:
It definitely comes first because at the end of the day it's going to cost you money. So before you even spend, you want to know what the True return on your investment could be whether it could actually be, you know, increasing revenue or it could be optimized. Optimized times in terms of the insights that that generates from it. But having that business alignments comes first. But the next important thing that actually comes post that is the audit side. So this now goes into where you're bringing in the idea into reality. So, you know, you could have all these business use cases that, that, you know, definitely make sense on paper. But then if you go through the audit, you know, looking at your data sources, do you actually have data? Are you generating data that could actually serve the business use case that you have? If not, then you got to strategize to properly collect the data and have the infrastructure to, you know, maintain the data and actually utilize it.

Emmanuel Billy Gillis-Harry [00:22:20]:
The other part is, is what's the, what, what's the, what's the validation of the data? Is it actually clean data? Is it structured? Is it unstructured? That now goes into the next step of like, hey, this is something we actually have to do. But the business case comes first. The strategy of solving the business case with the foundation of the data side, which is on the data collection and data storage, now comes next. And then from there, you could actually go through the process of massaging and applying methodologies to the data to then start utilizing it for dashboard generation, machine learning development, and the list kind of goes on. But without the business use case, you could be generating data and you don't know if it's actually useful or, you know, not useful. Does that make sense?

Brandi Starr [00:23:10]:
It does, yeah. And that's been my perspective as well. So you talked at the beginning about, you know, different pains and pitfalls of this process. And I know that we hit on at least a few of them, but I'd like to come back to that to understand are there any pitfalls that we should be thinking about that I haven't thought to ask about?

Emmanuel Billy Gillis-Harry [00:23:33]:
No, I mean, you, you talked on the first one, which was the lack of business alignment. So a lot of people, you know, have these ideas that, you know, trends kind of lead people's dreams of capabilities. So actually having that business alignment comes first. The second part is validating, and I'll put that under the category of, like, where people end up over engineering a problem. So, meaning you don't need to go bring an AI use case into solving something as, oh, we just want to look at this historical data for, you know, this time period. You know, don't try to solve problems with complex solutions when it doesn't really needed. So again, going back into that strategy, although yes, it does sound cool, it's trending and X, Y and Z, what is the best way to actually execute that would bring more value back to the business? And the last one is just change management. You'd say in this case what we'll specify the data culture.

Emmanuel Billy Gillis-Harry [00:24:38]:
So it's one thing to be excited about it, it's another thing to understand what comes about it. And you know, speaking from a non technical standpoint, a lot of these things are thought to like, you know, happen within a blink of an eye. Like, oh yeah, there's technology out there and it could make it work for us with this specific time. But coming back into reality is that some of these methodologies and use cases and you say application may actually take more time than anticipated. So does that truly align with the decision makers? You know, this solution that they want to have, they, they may want it within the next week, but in reality, let's say you just, your infrastructure is just at the bare minimum, that may take, you know, up to like 5 to 10 weeks to get that up and running. And then you can start actually putting some POCs into place. So having proper understanding of what it takes and being patient to, you know, actually learn if you don't know about it. But those are usually the three, the no business alignment over engineering and poor data culture change management within there.

Brandi Starr [00:25:47]:
Okay. And the term data culture, I really like that because one thing that just made me think about in using that phrase is stepping away from the technology and the actual data project. Thinking about data culture, there comes a point of where humans have to impact the data integrity. And I think that as a part of that change management that is something that gets overlooked because you have people that enter data into different systems and the sort of mentality and approach around that has a direct impact. And you know, I think about a lot of times the CRM system is generally the biggest culprit. You got sales folks who are, you know, they're trying to make the money, they're trying to move fast. Getting salespeople to put, you know, complete, accurate information into the CRM has been a pain that a lot of organizations have struggled with. And so I do feel like in, you know, that that change management process of once you get this done, changing that data culture, that is a place where you really do have to do that, not just within the team that is actually fixing the data, but you've got to do that in all of the teams that have any sort of Hand in the data, otherwise you're fixing a problem while also recreating it at the same time.

Emmanuel Billy Gillis-Harry [00:27:17]:
No, I definitely agree with that. And that kind of circles back up into the topic of our here today, data modernization with infrastructure. So within the steps, best practice usually has a validation aspect in there. And you know, sometimes people tend to skip over that, but it can be very tedious. But it's a step that definitely has to be done because again, going back to the face, trash data and trash insights out or trash results come out. And fortunately, and I'm sure the people with the AI buzzwords would be happy to hear this, there are AI technologies that aid those situations, you know, kind of really solving those bottlenecks of data entry situations. But beyond that, it is a process that should definitely not be overlooked because if you're going to rely on the results of your data, you need to make sure that the collection aspect is properly done in the first place.

Brandi Starr [00:28:18]:
Yeah, and you hinted at the last thing that I want to ask about, which we can't walk away from this conversation without talking about AI. That is obviously, you know, the hot topic right now. And I, I, you know, although I use AI pretty extensively, I definitely still feel like a newbie. And so some things that I have learned recently is how, number one, how AI can take unstructured data and, or data that's either unstructured or structured differently and be able to match and recognize patterns. I've also been learning about use cases where AI is able to, and it may be AI, it may be machine learning. I can say I am one of those people that do sort of mix up the two, but where it can help to better identify those patterns in the data. And so there's a lot of places that when it comes to modernizing the data, making it more scalable, more usable and applicable to the various business use cases, there are so many ways that AI can be used, and I know so many people out there are just really scratching the surface. And in some cases, the hardest part is understanding what's possible.

Brandi Starr [00:29:47]:
So I would love to hear your perspective on what are some of the common use cases where AI is being deployed to help with this data infrastructure and to help people be able to better leverage the data that they have.

Emmanuel Billy Gillis-Harry [00:30:04]:
Yeah, I would say on the infrastructure side there's use cases, but I would always leave the human touch in there because, you know, we have to define, for example, what data we're collecting, like, you know, what is the definitions of it, what does it mean to the business, what could come out of it, which you know, we'll refer to as a data dictionary. Want to make sure the data lineage makes sense, but an AI could assist in validating that process. But beyond that there is a lot of use cases at this early stage could be somewhat costly. But I'm sure you probably heard about the terminology of like agentic AI and that's pretty much thinking of like automation. You'd say, you know, you set up, you set up a step by step or logic you'd say, and an agent can go and execute it repetitively without your involvement, without being supervised on that. But as long as you properly do the validation stage, you should kind of have a good setup and let it run by itself. But then even beyond that you could see where you talked about the pattern recognition with clean data already on your, on your infrastructure layer, you could expect to be seen use cases of AI agents actually giving you these insights. So for example, asking like you know, out of this, in this campaign abc, you know, what were the success patterns that happened? You know, where the click rates, open rates, engagement within and all that.

Emmanuel Billy Gillis-Harry [00:31:46]:
But as long as the data is clean and ready for those kind of analysis, you could definitely see the utilization of AI agents actually doing that for you, which makes a data analyst role even a bit easier and accessible to those that are not too technical. And you could even foresee, even on the advanced side in terms of like predictive analytics upon building a predictive model you could, you could kind of see the use case of embedding that with the, with the agent to actually run a specific data set through the model without you having to do that by yourself. So a lot of automation and then kind of filtering back into the insights and advisory. So now I think it bridges the gap with those decision making aspect of you know, if you saw, if you show a stakeholder or the end user dashboard with specific insights and all that, they still have to kind of like click through and filter through. You can see these agents actually answering those questions without doing that. So kind of like taking some of the footprint off of that. But again it goes back to the foundation. If your infrastructure is not ready for that, don't expect the most accurate results to come out.

Emmanuel Billy Gillis-Harry [00:33:04]:
Because like you said, AI could take structure and unstructured data in, but it's not always correct. Right. You know, there's still some validation piece that goes in there and how you close the gap on that is your foundational aspect. If it's properly set, if it's clean, if it's you know, validated, you could definitely rely more on the output of these AI agents.

Brandi Starr [00:33:28]:
Yeah. And I think, you know, thinking about being a leader in go to market, a lot of what I have to do and my peers is a lot of pattern recognition and then being able to make decisions and pivot based on those patterns. And it's one of those things that as you progress in your career, you know, you've looked at so many spreadsheets, you've seen all the things, like, you get really good at that. And I do think that that is one place where AI really has an opportunity to help executives is exactly what you're saying is like taking some of that heavy lifting off of your plate because it can, you know, recognize patterns that we never see with, you know, just the human eye and being able to surface those things and, you know, answer the questions that you're referring to so that then we can take our own human expertise and experience and determine the way forward. Because I think all roles are stretched thin, but executives, especially executives leading a go to market function, are extremely stretched thin and the expectations continue to grow. And so I do think, and, you know, maybe that's the justification for tackling these projects is the ability to make better decisions or to, you know, have those patterns surfaced without someone having to go in and do that analysis for themselves. Because I do think that that can be a hard part for some people. Like, I've talked to some leaders who, who are like, you know, I'm not, like, I'm not good with the data.

Brandi Starr [00:35:17]:
Like, I know what to do with it. I know, you know, I have the expertise to lead the organization, but the analysis for them is a weak spot. And so that becomes an opportunity for AI to be able to lean in and help to close that gap, which I think can be huge. Like, for me, in thinking about AI, like, I'm always thinking about how can it help to account for my weaknesses or accelerate and streamline my strengths. And so I appreciate that input. It gives, gives me even some things to go think about it and figure out. Like, agentic AI is the next thing that I'm trying to get my arms around and fully understand. Because it's like, just as soon as you get to know, you know, generative AI, now we got to throw agents in the mix.

Brandi Starr [00:36:12]:
So it's, it's a constant learning.

Emmanuel Billy Gillis-Harry [00:36:16]:
No, most definitely. Most definitely. But, you know, like you said, the, the excitement and the use case is there. I just always like to bring into that importance again why your foundational data makes sense to really invest in in the first place because that's where all your next assets in terms of this, in terms of this technology will definitely be kind of like, you know, follow through.

Brandi Starr [00:36:42]:
You'd say, well Emmanuel, talking about our challenges is just the first step. And nothing changes if nothing changes. And so in traditional therapy the therapist gives the client some homework, but here at Revenue Rehab we like to flip that on its head and ask you to give us some homework. And so I'd love to hear what is your one thing for those listening who have recognized that they have not modernized their data infrastructure and that they've got some opportunities, where should they start? What's that first action?

Emmanuel Billy Gillis-Harry [00:37:15]:
You know, if there was one thing to definitely take away from this in the topic of modernizing data, just understand it's not just only about the technology. And I say that because there's going to be a bunch of shiny tools which has proper use cases and all of them could definitely be relevant. But really looking back on what your business or what you're engaged in, you know, what, what, what, where does your business stand on in terms of the agility to even embrace these things? You know, audit your current data stack. What does it look like where you lack and speak to a expert to help you even understand that beyond just what you could see, identify a lot of inefficiencies in that. And then from then on, once you start understanding that, you put out your goal and start aligning where your gap is, do the gap analysis on that and that should give you a good idea of where you need to start from. You know, some people may have to start from ground zero. Some people may be already like 20 or 30% there, some people may be 90% there. But just self awareness in that aspect should be the homework.

Emmanuel Billy Gillis-Harry [00:38:28]:
You know, go audit yourself, go talk to an expert to help you with that process, get proper deliverables that would help you then align you on what next to do. You know, I like to say that you'd say data should not just, you know, it, it shouldn't, it shouldn't just be an asset and something to say that you have and you're saying you're data driven, you know, you should start seeing some impact in terms of revenue. You know, it's not just okay to say yeah, we have dashboards and we're data driven but you know, you want to truly see these things be decision drivers. You say, but again it starts back with your self audit. That's where I'll say everyone should start from.

Brandi Starr [00:39:17]:
Awesome. Well, I have enjoyed our discussion, but that's our time for today. But before we go, tell our audience how they can connect with you and definitely do the shameless plug for Tera Bullion.

Emmanuel Billy Gillis-Harry [00:39:32]:
No, definitely. Y'all could definitely find me on LinkedIn. I am very active on there and responsive and definitely do check out our website. We specialize in aiding from the strategy all the way to implementation and even post implementation and also staffing too to help actually build that internal team in the long term.

Brandi Starr [00:39:56]:
Awesome. Well, we will make sure to link to that. This has been such a good discussion. Always love to learn more, especially when it comes to the data and the analytics and the technical side of getting all these things done. So thanks so, so much for joining me.

Emmanuel Billy Gillis-Harry [00:40:17]:
Definitely. Thank you so much for having me, Brandy.

Brandi Starr [00:40:20]:
And thanks everyone for joining us. I hope you have enjoyed my discussion with Emanuel. I can't believe we're at the end. Until next time, bye. Bye.

Emmanuel Billy Gillis-Harry [00:40:29]:
Bye.