#188 Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx

Hi, I'm Maggie, VP of Curriculum at datacamp, and I know firsthand how overwhelming it can be to keep up with the rapid changes in data and AI. But staying ahead doesn't have to be hard. That's why our curriculum team has curated the best learning content to help you and your team stay ahead, covering everything from data and AI literacy for busy executives to cutting edge generative AI for developers, and everything in between. Whether you're upskilling yourself or your team, we make learning practical, engaging and flexible. It just works. Power your data and AI transformation journey on datacamp.com data is everywhere and data is only going to Continue to Scale Business leaders are going to continue to invest in the talent to develop their skills around data analytics. That process of data scaling and leaders and people, humans investing in their skill set is going to allow us, the human, the power to solve. Curiously, because we now have the ability and we're not afraid of data. We've got the skills, the talent, the curiosity to be able to drive those insights.

Welcome to Data Framed. This is Ritchie Data analysis is a staple of most organizations and it's been around for decades. However, even with its ubiquity and heritage, many organizations still struggle to get it right. As datasets get bigger and the deadlines for getting insight from data get shorter, it can be hard to keep up, especially since the field is still moving quickly. Today we're focusing on how to scale your organization's analytics capabilities to deal with bigger and badder data sets and get value from your data initiatives. Our guest is a veteran of the field. Libby Duane Adams is a co Founder and Chief Advocacy Officer at Alteryx. She's responsible for strengthening upskilling and reskilling efforts for Alteryx customers to enable a culture of analytics. She also scales the presence of the Alteryx Sparked Education program and she's committed to furthering diversity and inclusion in the workplace. As the former Chief Customer Officer, Libby has helped many Fortune 100 executives drive more revenue from their current businesses using analytics. Let's hear what Libby has to say. Hi Libby, great to have you on the show.

Thank you Richie, very much. I'm looking forward to our conversation.

Excellent. So I know Alteryx describes itself as an analytics company, and quite often people think of analytics as being the same sort of thing as business intelligence. Can you tell me what the difference is?

Yes, I think about today's data analytics. It's the ability to use any and all relevant data sources in an agile environment that's enabling the line of business analysts to be able to react to those data and to updates and provide updates of the analytics at the speed of business. And traditionally, bi, as I think about it from earlier in my career, it definitely was slow by today's standards. It was limited in the data that could be used in those BI reports. It wasn't agile, and it didn't move at the speed of thought, and it certainly didn't move at the speed of business. And so today with data analytics and at scale, it's that ability for business leaders to be asking questions of those data for immediate insights, to be able to drive their decisions.

I think that's a very common problem, is you ask a question to a business analyst and by the time you got the answer, it's like you've moved on, you don't care about it anymore. So certainly speed is very important, getting rid of that latency. Now, you mentioned business analysts. This sounds like a bit of a team sport. So can you talk about what the other roles are that are involved in analytics and maybe which teams are involved?

Yeah, let me start with which teams, because what we're seeing is that it literally is every department of every size organization, whether you're for profit or nonprofit, across every industry, literally across the globe. Data analytics are not reserved for just the Global 2000 or just a finance department. It is literally every department in every size organization in every industry around the world. And business leaders are leading by building this culture of data analytics across their enterprise. And so it's not just for data scientists, which is also, from a historical perspective, traditionally been the person or very small team of people that had access to data sources and they were the ones responsible for the analytics for the company. That model has completely gone out the window. Data analytics is not just for the data scientists anymore, and it's also not just for it. These skills are now required across every team. It's digital marketing, it's supply chain, it's accounting, it's hr, it's it. And it's multiple people on these teams. Richie. It's the analyst that is thinking about how do I understand what data can deliver for me, and that's the beauty of modern technology, is that analytics is, is now for all, as we like to say at Alteryx, is analytics for all because it's a required job skill. So we're excited about where this market's going for sure.

Absolutely. Data's really invaded every part of every business. I think there's a lot of people who've maybe tried to avoid data and suddenly it's invaded their Career. Excellent. So what do you think are the biggest developments in analytics at the moment?

Let me tag on to what I just said about analytics. For all the ability for every leader to be asking, how are we not only going to do analytics, but how are we going to automate those analytics so that they can move into those AI type questions, those artificial intelligence generative AI questions. They're asking the question now. One of the biggest developments they're asking is where does AI fit in our business? So they've got the data, they've invested, or they are investing in the data stack, they're investing in data quality, in data expansion and data capture. Now the question becomes that business leaders are asking is, where does AI fit? And so they have to be thinking as users of AI, where could this potentially impact our business, positively impact our business and allow us to move faster, to be able to consume and use and see the value of the data as the asset that they are. So that's what we're seeing, is that conversation around AI has really ramped up.

That's really fascinating, the idea that actually what you want to do is answer business questions and you'd be able to move them from just sort of answering simpler questions with data to using AI to answer more complicated questions. Okay, so can you talk about some of the implications for the existence of this powerful generative AI and other types of powerful AI for analytics?

Absolutely. And it's ironic because the potential analytics value and the value creation for the business from generative AI is potentially huge, not only for individual departments, but for businesses and industries. But we also know that those business leaders have been blocked or stopped, I'll say, by ethical questions, by governance questions, certainly security and privacy concerns around those data that they have access to or want to have access to. And also what are the requirements on data privacy and data security that they have to work within? And so what Alteryx has done is we've developed our AI platform and I'm going to refer to it as Aidan, because every C level that wants to harness that trusted generative AI in an end to end unified platform, they have to ensure that those platforms and technologies are meeting their ethical, governance, security and privacy concerns that's going to help them accelerate their analytics delivery and be able to make better, faster decisions at scale and in partnership with data security, data privacy, governance across the organization. So they're really teaming up with their CIOs and IT leaders to meet these criteria. And Alteryx Aden platform is just one example of a very powerful implication of enabling AI for analytics, that's absolutely fascinating.

And I'd like to dive into that a little bit deeper. So it seems like you want to harness the power of generative AI throughout your business, but then there are also these risks that are associated with that. So if you have any privacy problems, if you have security problems, then it's a disaster and it's worse than not investigating. So do you have any tips for how you can ensure that you meet all those business requirements? Like how you meet the privacy requirements, the governance requirements, the security requirements when you're adopting AI?

Yeah, and I'll say, Richie, it's really all about this ability to harvest the data. The quality of data does not go away. The data is getting bigger, but that quality is still the first thing that every business user, every data analyst and every business leader wants to ensure. And so what we're talking about with our customers and what's changing, what's really the game changer of analytics is you have to own the data stack. You have to govern those right data assets. To your question, that's the first part of this. You've got to be able to own the data stack, own the quality of those data, own the security and the governance of those data. And that's what we're seeing a lot of, is the investment in those data resources across every size company. You can't, and this is a conversation I get asked a lot is, well, with AI, doesn't that replace what people are doing around data? Isn't that going to automate that and those people won't have to worry about that anymore? The answer to that is absolutely, positively not. That's going to be the number one or very early on. A very big part of this is you've got to worry about those data quality, you've got to worry about how you're harvesting those data. And to ensure to your question that that data is within the boundaries of security and governance so that the private information remains private.

This sounds a lot like when parents told children, you gotta eat your vegetables before you can have your dessert. You gotta do your data quality before you can play with the AI.

Absolutely.

So are you seeing any differences in adoption of generative AI either from different industries or from different roles?

I love the question. And if you were to ask me this question, probably, let's say five years ago, four or five years ago, when Gen really started to become a thing that developers were thinking about and focusing on, I would have said yes. There probably were a couple of industries that were leaders, those industries that have Gotten a jump start, I will say is the health and life sciences industries as well as financial services. And when you think about the quantity of data, the data assets and resources that a healthcare or health and life sciences company has access to, that's why they really took on this forward looking initiative around Gen AI. But I will also say that that game has now opened up to every industry. It is no longer just for health care, it's no longer for just financial services. It is every industry that we're talking to, they're asking the questions, what do we do? How do we do it? We certainly saw it in the news media, in Hollywood's perspective on AI, I believe it was last summer where they had the writers strike and the actors strike. Those are examples of yes, AI is going to impact the entertainment industry as we're already seeing from samples from Sora that's already coming out. But that doesn't mean that all these people are going to be unemployed, their jobs are going to change, but they're still going to be required as part of the entertainment industry process in getting product, quote unquote to market. And so it is, literally every industry is thinking about or already taking significant steps on the adoption of Genai for sure.

Okay, I suppose that makes sense that it'd be industries where you've got a lot of data and a lot of resources that would be going first in this. But I suppose this comes back to your previous point that data and analytics is everywhere and now so is generative AI. And actually I guess just going back to that idea, just the fact that you've got these sort of powerful AI tools, does that make analytics more or less accessible to people who don't have a data background?

Great question. And as a user of AI, and I'm talking now about the human, not the developer, not the developer of AI, but the user I'm the consumer of. I always like to pick up my phone and say apps on our phone are examples of AI and you don't have to be a developer to use a lot of the apps that we have access to. And so yes, AI is going to make analytics more accessible to people, but those are probably a lot of those people won't be touching the data, they won't be building the AI app use cases, they'll be consuming them. And so yes, I do believe that AI is going to make the prevalence of data analytics even more pervasive than it is now. And so there will be people that are not going to have a data background, but they're going to be able to ask questions of data and trust the answers that they're getting. And that will happen over time.

Yeah, I like the idea that apps on your phone, they use a lot of AI, but you don't really need to understand how they work. I suppose if I want to put a filter on the photos to make myself look better, I don't necessarily need to understand the deep learning that's going on underneath the scenes.

Exactly.

With that, do you think there are going to be any particular data skills or AI skills that everybody needs to know, like what should be widespread?

I love the question, Richie, because in my work here at Alteryx, I lead our Sparked Education program. And the Sparked Education program is all about putting modern analytic technology in the hands of students and educators so that they can learn what modern analytics is about. And it is about working with with data and being able to be able to solve questions. Have that curiosity factor of I've got this question that I've been asked. I think I can use these seven data sources to answer it. Let's go see what we can build. Building that curiosity is what the Sparked Education program is all about. Because to your question, today, data skills in the workplace are a huge asset for people at all ages, all levels, all parts of an organization. And so I believe that the data skills that are being taught today in the workplace are only going to continue to scale the use of data faster and wider across different applications, different industries, different companies. And data's not going away. Data is with us for a very long time and it only keeps getting bigger. And so having those data skills and the confidence to work with data as an individual are really going to help organizations get better and also help the individuals on their own career journeys. Because I view data skills as now a required skill for a job. Whether you want to be a communications major or a philosophy major or an accounting major or an engineering major. Comfort with data is now that skill that's required, one of many skills that is required.

So I think some of the things you said there that are going to be very exciting to people, a little bit terrifying. So the idea that data skills are now just table stakes to get a job in almost any organization, that seems to be true, but I think it might be frightening to some people. And maybe the scarier thing you said was that data sets are only going to get bigger and you need to be able to scale your data function. So, yeah, I think a lot of organizations are saying we're struggling with that data already. Surely we have enough. If there's going to be more data. Can you maybe talk me through what are the main blockers to scaling data capabilities in your organization?

I'll break this down into four different areas. It's data governance. Every organization, no matter what industry you're in, whether you're for profit or nonprofit, you have to ensure to our earlier conversation that data is used within the right parameters. So data security, data privacy, all of those are part of the data governance conversation. You need to know where those data are, where they're being used, who's using them, who should have access, or who should not have access. Analytic governance quickly follows on that same track because analytics is obviously run by all of those data resources and data assets. And so you've got to govern the analytics because those data are going through those analytics processes, those analytic workflows. And so that is the second part of what leaders need to be thinking about in order to scale or what may be blockers to them today. The third are people we have to invest in the learning, the training, the education. That level of comfort I spoke about just a few minutes ago, you have to ensure that your people, so what, while they're in university or while they're in K through 12, or while they're in that stage of learning what their next career is going to be, we have to give them these skills. We have to give them the opportunity to develop these skills so that they can compete for those jobs. And lastly, and I'm intentional about putting this last, it's the technology. And you're saying, well, Libby, you work for a tech company. Why wasn't that the first thing? Because until you can understand and answer all of your questions around data governance, analytic governance, and the skill levels of the people, how analytically, early in their career or early in their analytics journey are they or how advanced are they? You don't know what technologies you're going to need. And so that's where I put those blockers, in that order. And those are the investments that businesses have to make to continue to scale this analytics function and build this culture of analytics as we refer to it at Alteryx. You've got to be thinking about those four things.

I appreciate the honesty there that you can't just buy a technological solution to all your problems. You have to think about who your employees are and what their skills are and what your processes are as well.

And it's interesting, Richie, I'll stop you just for a second because I've had prospective customers of Alteryx say to me, well, Libby, why aren't you? Why are you asking me about my data and where it's housed and who has access? Or why are you asking me about who the users are going to be? You should be here pitching me on Alteryx. And I said, I can't justify the use of Alteryx until I know those things. And those are the blockers. Those are the things that hold organizations back. That. That's the power of what businesses need to be thinking about in order to scale this culture of analytics.

Absolutely. And I thought it was interesting that you differentiated between data governance and analytics governance. I think those two things often get lumped in together. And so maybe if you could just tell me a bit more. Is it going to be the same people involved? Like quite often you'd have, say, a chief data officer or someone in charge of data governance. Is it going to be the same person in charge of analytics governance or would that be someone else?

It actually, on your question of same person in charge of the analytics governance, it will in a lot of cases be that chief data officer or chief data and analytics officer. But we also have the business leaders. So it's the chief marketing officer if it's in their silo, it's the chief financial officer if it's in their team. Because they also need to have a voice, voice from an analytics use perspective around what's being built, who should have access to it, and then how it's being used. And so in the business side, in the use case side, we will often see a second team working with the cdo, the cdao, CIO on these initiatives.

Okay, that makes sense because you want the chief financial officer to decide how the financial data is being analyzed and the chief marketing officer to decide how the marketing data is being analyzed. All right, so the other thing you mentioned out of your four steps on the data governance, analytics governance, it was upskilling and technology. So there was a process aspect to that, particularly around upskilling. Can you talk me through what sort of process changes an organization might typically have to make in order to get better at analytics?

Yeah, sorts of process changes. We are looking at an initiative now with a lot of our customers, with most of our customers, where it's really about how do I scale what is the foundation of what we have to be able to use? And so it's in partnership with IT leaders, it's that cio, it's that SVP of it. And business leaders really want to continue to advance analytics within their own teams and then across the enterprise. And what we're seeing And I haven't talked a lot about LLMs yet, large language models, which is the foundation of generative AI. But when you look at the foundation of LLMs, it's all about the right data assets cleansed, ready for modeling. And then you get into the whole data governance conversation. And so one of the processes that we've had inherent in our technology since its launch is the ability to document those processes, document those analytic workflows so that I could build one and hand it to you. I don't even have to be in a meeting with you or on a teams call to, to work with you. You can read the documentation and say, oh, this is what Libby built. Analytics governance is required. And so one of the steps that we've taken around AI is what we're referring to as workflow summaries. It's AI generated documentation of the workflows. Because guess what? The one thing that a user doesn't want to have to do, nothing annoys me more than spending a couple of hours building a workflow that I'm going to pass off to you. And then I've got to spend time documenting it. I'm like, no, I don't want to do that. I just want to build the workflow and then run it. And so we took on the initiative using generative AI and built AI generated documentation of workflows directly into our platform because that checks the boxes of governance, data governance, because you know what data is coming through that workflow, analytic governance, because you know what the workflow is actually doing. And then it also checks the box of auditability. So for large public companies, they now have an audit trail of what exactly is this analytics workflow doing? And so those are the kinds of processes that are going to need to change, that are going to have to improve and continue to scale.

That actually sounds brilliant because I don't know anyone who really enjoys writing documentation about what they've done. And yet as a user, when there's no documentation, like, well, what is this thing? How does it work? There's no documentation. So that's very common. Sort of cross team conflict I think is like about the documentation. So documenting workflows to sound incredibly important.

And I'll also say, Richie, the worst thing is six months from now when the auditor steps in and says, what is this? You or I are going to have to remember what we were working on six months ago. And highly likely, if you're like me, you're not going to remember exactly what you were working on and you're then going to spend time, but it builds in that security, it builds in that auditability that every organization is looking for and every business leader wants to ensure so that they can say, hey, six months ago this is what we did and this is why we still stand behind that work today. So it's as much as nobody wants to do it, it is required.

Absolutely. I'm right there with you. I can't even remember what I was working on this morning. Do you have any other examples of how you might be able to automate things in order to scale?

Yeah, one of the other things that we've done is we've built in using generative AI. Inside another part of our analytics platform is a capability called Auto Insights. And so we've automated the ability to be able to recommend high value analytic use cases that are tailored based on the user's business and their role. So if I'm the senior analyst in digital marketing, for example, a playbook will get generated based on the data that I have, those data that I have access to and be able to recommend those use cases for me as a digital marketer. And so these playbooks now generate realistic synthetic data for each use case to enable really rapid prototyping for faster time to insight. Because you and I could be working on the same project and if we don't have that at least ideas of where we should focus. That's exactly what these playbooks are designed to do. And it's taking advantage of generative AI to give users like you and I that creative process of hey, have you ever thought about this kind of a use case? Because the technology, the gen AI is looking at those data that we have access to and so it'll give you a rapid prototype and then you say, yeah, that makes sense, let me take that a little bit further. Or we've already done that. That's not relevant. It's run over here by this other team. We don't have to worry about that. And so that constant creativity is going to get and help each team be able to drive more value.

That does seem very cool. And it seems like there's two separate ideas there. So one is about having AI help come up with ideas for you and improve your creativity. And the other idea is around synthetic data. I know this is particularly a problem when you're dealing with customer data or health data or financial data where it has to be sensitive and maybe not everyone in your organization is allowed access to it. And so have you seen any success stories where organizations have really gone to town? They've invested in their analytics capabilities and then they've seen some results.

Absolutely. I could go on for a while, Richie, way longer than the time we've allocated. But one of the things that we see, and we've actually made it part of our learning, training and onboarding of customers is to help them think about the impact and the value of the analytics work that they're doing to get to those easy wins. We're working with customers on developing their own value and impact muscle because every customer has different steps, different processes to understand the value and the impact. And so we work with our customers to understand before they even start step one of an analytic process. Think about what do you think the impact's going to be? What's the value to the business? What's the value to you? And I'll start with an easy one. Customers with our technology very quickly will say, well, I just went from four days to 12 minutes. And I always say that is phenomenal for you. What are you now doing now that you've been given four days back or three days and whatever number of minutes 12 minutes is. But it's that idea that it's not just about time savings, it's the fact that I can now deliver a report to you in 12 minutes instead of you having to wait four days because it then impacts your decision making process or your own reporting capabilities because you can get that report whenever you want it. And so the value conversation, the impact conversation we have with customers is a continuous discussion because people don't do analytics just for the sake of doing analytics. There has to be some end result, some end impact to the business. And so when we have customers, I'll actually share a quick story without naming the company, but we had a very large tool manufacturer using our technology and they've been an Alteryx customer for probably about seven, eight years. And their CEO was very clear to say that over this time, as they've been building this analytics culture across their enterprise, they've saved or impacted the business by a half a billion dollars. That's where analytics really starts to improve the business because they see what levers they have to pull to continue to get those. Improved performance, better efficiency, reduction in inventory costs, and the list goes on. Those are the kind of wins that we want our customers talking about and those are the abilities to calculate that value and impact. That's where you get those half a billion dollar conversations at the C level.

Yeah, certainly. I think a lot of businesses would like that extra half a billion dollars. So it's worth paying attention to. And your point about how it's not just about you saving time as an analyst, it's the knock on effects for the business as well. So when you give someone a report four days faster, that's then going to improve the productivity in another team and you get this sort of cycle of benefits. I like that. So on the flip side, so we talked about success stories. What are the common mistakes that organizations make as they're trying to scale their analytics?

And I mentioned this before, Richie, one of the mistakes to take advantage of those data assets that the company is now harvesting, I could say it's one of the single biggest. One of the others is that they're not amplifying the message around the impact of data analytics. So I spoke about value of I do a project, it took me four days, I'm now down to 12 minutes. We have to amplify those results for the business. The C level should be hearing about this. That story I just shared with you, that half a billion dollar savings quote came from their CEO. That's the kind of level that these impact stories, these value statements need to reach in every company. And how do you do that? You've got to amplify the skills of the people. You've got to develop their talent, you've got to invest in them and show them that you're investing in them and then reward them when they come back. And they say now that I went from four days to 12 minutes. Richie can now answer a vendor's question and move on with contract negotiations. I'm making up this story, but that's the kind of impact that business leaders need to know about. And so that's the other thing that I'll say is I want business leaders investing in their people, investing in the skills development, make time during the work week week for people to invest in their learning. And then the second is amplify the value of the work that they're doing and the impact that they're making through these analytic approaches. Because that goes to building that analytics culture across the enterprise that we've been talking about.

I love those suggestions and certainly the point about making time for learning throughout the week, it just seems incredibly important. I know that's often a difficult task. It's like people are working full time, they've got deadlines, and yet they do need to make time to improve their skills in order to make a long term sort of improvement in their job. And so the other thing about adding value, or at least talking about the value you're adding in the data world, that seems like a slightly trickier proposal sometimes because if you are like a salesperson, then it's very clear you're hitting your targets and how you add values for the organization where sometimes for data people it can be slightly more nebulous. So you gave an example of like, okay, we know we've saved a half a billion dollars. How do you quantify this sort of stuff in general? Do you have any advice on that?

Yeah. And this is actually where we work directly with customers, Richie, on a case by case basis, because you're spot on. It does get unique by customer and even by team within a particular organization. And so we'll work with customers to help them. I use the phrase earlier of building the muscle. It's just like personal training or working out or running or swimming. You've got to develop the muscle to be able to do this. And so it's this muscle that we're developing to calculate that value. And so what organizations will do is. To your point, it's not Libby does work and Libby drives a half a billion dollars in value. No, that is a very long process across multiple teams being able to stitch that story together. And so we'll work directly with customers to show them in their environment what they have to do. And when business leaders know that they're going to be able to walk away with, how am I calculating value? This is how it's being calculated. This is the analytics behind the value of analytics. That's really what the driver is. And so we work closely with customers to build these skills, to build the thought process. And as I said a moment ago, it'll vary within one company. That value process could be different across teams, but at the end of the day, it has to stitch back together to get you to that value for the company, impact to the company.

It sounds like there's a bit of an irony there. Is that like that the data analysts in general not very good at calculating the value of their own data analysis.

Yeah. What we've seen is that people will take on analytic processes just because their boss says, I need you to do this. And that's why we say, but I want you to stop. Not that you're going to say no to your boss, but you're going to stop before you start it and you're going to say, what do I think the impact is going to be when my boss says, if you can get me this report, it's going to allow me to do XYZ faster. That's part of that value or that impact statement that you as the developer, the analytics analyst, that's what you have to be thinking about is where are they taking these data, where are they taking these insights? How is it affecting them in some way? And so we always want to go fast, but I always will say to people, stop and think about it before you start the process because otherwise, to your point, they just dive right in and 20 minutes later they've got a workflow that they can pass off. And if, see, say, here are the results you were looking for, boss.

I do kind of like the idea of just questioning your boss. Are you giving me a stupid task to do or not? It's a dangerous approach, but it might work.

Yes. And there's a way to question and then there's the wrong way to question. But if the boss says, I want you to do this, I would respect anyone who says, well, tell me, what are you going to be able to do when I get this done for you? What do you do with these data? And hopefully it's a learning journey for me as the analyst and a learning journey for the boss because they realize there's now, I say a paper trail, but there's now a trail of where these insights are going to go that they've asked for.

Ah, yeah. So by just asking those questions up front, then you're sort of more likely to get some kind of impact at the end of the project. Excellent. And so just on that discussion of impact, I think there's maybe a trade off between when you're trying to start scaling your analytics process, do you start with the things that you think are going to be high impact or do you start with the things that are going to be quick and easy?

I am all about quick and easy. Start and automate the mundane. That's a key lesson, Richie. Start with what you know. If there's a report that you do in a spreadsheet every day, every week, every quarter, start there. Because you know what those data are, you know what the calculations are, you know what the results need to be. Start there, automate the mundane, get those off your plate. Then you get to focus on the, as I like to say, the big bang, without it being the big bang. But if you can automate the mundane, it's going to clear up, free up your brain to be thinking about bigger projects, higher value projects, higher impact projects, and it gives you more time for discussion with your boss as opposed to just being reactive.

I like that. And I guess mundane tasks, not the exciting bits of your job anyway, in General. So that sounds good. Automate the boring bits, the stuff you do every day, and then you've got time for the cool new stuff.

Yes.

All right, Excellent. So just to wrap up, what are you most excited about in the world of analytics?

Oh, this will be a great, great answer. And I said it a few minutes ago, Richie. Data is everywhere. And data is only going to continue to scale. Business leaders are going to continue to invest in the talent to develop their skills and around data analytics. That process of data scaling and leaders and people, humans investing in their skill set is going to allow us, the human, the power to solve curiously, because we now have the ability and we're not afraid of data. We've got the skills, the talent, the curiosity to be able to drive those insights. And it is truly limitless of what we are able to do now and will continue to be able to do. As skills continue to develop, scale technologies, continue to scale, and partnerships with CIOs, senior vice presidents of IT around data security, data governance and analytics governance in partnership with CIOs. This is going to allow the world of analytics to continue to go like this, which will drive impact, value and profitability for any organization.

That's absolutely fantastic. Data is kind of almost everywhere now. It's really, really going to be everywhere soon. Yeah. So time to start learning some data skills. Excellent. All right, thank you so much for your time, Libby. That was great.

Absolutely. Thank you very much.