>>Hey, everyone. Welcome back to accelerating analytics, maturity. I'm your host. Lisa Martin, Alan Jacobson joins me next. The chief data and analytics officer at Altrix Ellen. It's great to have you on the program. >>Thanks Lisa. >>So Ellen, as we know, everyone knows that being data driven is very important. It's a household term these days, but 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. What's your advice, your recommendations for organizations who are just starting out with analytics >>And you're spot on many organizations really aren't leveraging the, the full capability of their knowledge workers. And really the first step is probably assessing where you are on the journey, whether that's you personally, or your organization as a whole, we just launched an assessment tool on our website that we built with the international Institute of analytics, that in a very short period of time, in about 15 minutes, you can go on and answer some questions and understand where you sit versus your peer set versus competitors and kind of where you are on the journey. >>So when people talk about data analytics, they often think, ah, this is for data science experts like people like you. So why should people in the lines of business like the finance folks, the marketing folks, why should they learn analytics? >>So domain experts are really in the best position. They, they know where the gold is buried in their companies. They know where the inefficiencies are, and it is so much easier and faster to teach a domain expert a bit about how to automate a process or how to use analytics than it is to take a data scientist and try to teach them to have the knowledge of a 20 year accounting professional or a, or a logistics expert of your company. It much harder to do that. And really, if you think about it, the world has changed dramatically in a very short period of time. If, if you were a marketing professional 30 years ago, you likely didn't need to know anything about the internet, but today, do you know what you would call that marketing professional? If they didn't know anything about the internet, probably unemployed or retired. And so knowledge workers are having to learn more and more skills to really keep up with their professions. And analytics is really no exception. Pretty much in every profession, people are needing to learn analytics, to stay current and, and be capable for their companies. And companies need people who can do that. >>Absolutely. It seems like it's table stakes. These days, let's look at different industries. Now, are there differences in how you see analytics in automation being employed in different industries? I know Altrix is being used across a lot of different types of organizations from government to retail. I also see you're now with some of the leading sports teams, any differences in industries. >>Yeah. There's an incredible actually commonality between domains industry to industry. So if you look at what an HR professional is doing, maybe attrition analysis, it's probably quite similar, whether they're in oil and gas or in a high tech software company. And so really the similarities are, are much larger than you might think. And even on the, on, on the, on the sports front, we see many of the analytics that sports teams perform are very similar. So McLaren is one of the great partners that we work with and they use TRICS across many areas of their business from finance to production, extreme sports, logistics, wind tunnel engineering, the marketing team analyzes social media data, all using Altrics. And if I take as an example, the finance team, the finance team is trying to optimize the budget to make sure that they can hit the very stringent targets that F1 sports has. And I don't see a ton of difference between the optimization that they're doing to hit their budget numbers and what I see fortune 500 finance departments doing to optimize their budget. And so really the, the commonality is very high. Even across industries. >>I bet every F fortune 500 or even every company would love to be compared to the same department within McLaren F1, just to know that wow, what they're doing is so in incre incredibly important as is what we are doing. Absolutely. So talk about lessons learned, what lessons can business leaders take from those organizations like McLaren, who are the most analytically mature >>Probably first and foremost, is that the ROI with analytics and automation is incredibly high. Companies are having a ton of success. It's becoming an existential threat to some degree, if, if your company isn't going on this journey and your competition is it, it can be a, a huge problem. IDC just did a recent study about how companies are unlocking the ROI using analytics. And the data was really clear organizations that have a higher percentage of their workforce using analytics are enjoying a much higher return from their analytic investment. And so it's not about hiring two double PhD statisticians from Oxford. It really is how widely you can bring your workforce on this journey. Can they all get 10% more capable? And that's having incredible results at businesses all over the world. An another key finding that they had is that the majority of them said that when they had many folks using analytics, they were going on the journey faster than companies they didn't. And so picking technologies, that'll help everyone do this and, and do this fast and do it easily. Having an approachable piece of software that everyone can use is really a key, >>So faster able to move faster, higher ROI. I also imagine analytics across the organization is a big competitive advantage for organizations in any industry. >>Absolutely the IDC or not. The IDC, the international Institute of analytics showed huge correlation between companies that were more analytically mature versus ones that were not. They showed correlation to growth of the company. They showed correlation to revenue and they showed correlation to shareholder values. So across really all of the, the, the key measures of business, the more analytically mature companies simply outperformed their competition. >>And that's key these days is to be able to outperform your competition. You know, one of the things that we hear so often, Alan, is people talking about democratizing data and analytics. You talked about the line of business workers, but I gotta ask you, is it really that easy for the line of business workers who aren't trained in data science, to be able to jump in, look at data, uncover and extract business insights to make decisions. >>So in, in many ways, it really is that easy. I have a 14 and 16 year old kid. Both of them have learned Altrics they're, Altrics certified. And, and it was quite easy. It took 'em about 20 hours and they were, they, they were off to the races, but there can be some hard parts. The hard parts have more to do with change management. I mean, if you're an accountant, that's been doing the best accounting work in your company for the last 20 years. And all you happen to know is a spreadsheet for those 20 years. Are you ready to learn some new skills? And, and I would suggest you probably need to, if you want, keep up with your profession. The, the big four accounting firms have trained over a hundred thousand people in Altrix just one firm has trained over a hundred thousand. >>You, you can't be an accountant or an auditor at some of these places with, without knowing Altrix. And so the hard part, really in the end, isn't the technology and learning analytics and data science. The harder part is this change management change is hard. I should probably eat better and exercise more, but it's, it's hard to always do that. And so companies are finding that that's the hard part. They need to help people go on the journey, help people with the change management to, to help them become the digitally enabled accountant of the future. The, the logistics professional that is E enabled that that's the challenge. >>That's a huge challenge. Cultural, cultural shift is a challenge. As you said, change management. How, how do you advise customers? If you might be talking with someone who might be early in their analytics journey, but really need to get up to speed and mature to be competitive, how do you guide them or give them recommendations on being able to facilitate that change management? >>Yeah, that's a great question. So, so people entering into the workforce today, many of them are starting to have these skills Altrics is used in over 800 universities around the globe to teach finance and to teach marketing and to teach logistics. And so some of this is happening naturally as new workers are entering the workforce, but for all of those who are already in the workforce have already started their careers, learning in place becomes really important. And so we work with companies to put on programmatic approaches to help their workers do this. And so it's, again, not simply putting a box of tools in the corner and saying free, take one. We put on hackathons and analytic days, and it can, it can be great fun. We, we have a great time with, with many of the customers that we work with helping them, you know, do this, helping them go on the journey and the ROI, as I said, you know, is fantastic. And not only does it sometimes affect the bottom line, it can really make societal changes. We've seen companies have breakthroughs that really make great impact to society as a whole. >>Isn't that so fantastic to see the, the difference that that can make. It sounds like you're, you guys are doing a great job of democratizing access to alter X to everybody. We talked about the line of business folks and the incredible importance of enabling them and the, the ROI, the speed, the competitive advantage. Can you share some specific examples that you think of Alter's customers that really show data breakthroughs by the lines of business using the technology? >>Yeah, absolutely. So, so many to choose from I'll I'll, I'll give you two examples. Quickly. One is armor express. They manufacture life saving equipment, defensive equipments, like armor plated vests, and they were needing to optimize their supply chain, like many companies through the pandemic. We, we see how important the supply chain is. And so adjusting supply to, to match demand is, is really vital. And so they've used all tricks to model some of their supply and demand signals and built a predictive model to optimize the supply chain. And it certainly helped out from a, a dollar standpoint, they cut over a half a million dollars of inventory in the first year, but more importantly, by matching that demand and supply signal, you're able to better meet customer customer demand. And so when people have orders and are, are looking to pick up a vest, they don't wanna wait. >>And, and it becomes really important to, to get that right. Another great example is British telecom. They're, they're a company that services the public sector. They have very strict reporting regulations that they have to meet and they had, and, and this is crazy to think about over 140 legacy spreadsheet models that they had to run to comply with these regulatory processes and, and report, and obviously running 140 legacy models that had to be done in a certain order and linked incredibly challenging. It took them over four weeks, each time that they had to go through that process. And so to, to save time and have more efficiency in doing that, they trained 50 employees over just a two week period to start using Altrix and, and, and learn Altrix. And they implemented an all new reporting process that saw a 75% reduction in the number of man hours. >>It took to run in a 60% runtime performance. And so, again, a huge improvement. I can imagine it probably had better quality as well, because now that it was automated, you don't have people copying and past data into a spreadsheet. And that was just one project that this group of, of folks were able to accomplish that had huge ROI, but now those people are moving on and automating other processes and performing analytics in, in other areas, you can imagine the impact by the end of the year that they will have on their business, you know, potentially millions upon millions of dollars. This is what we see again. And again, company after company government agency, after government agency is how analytics are really transforming the way work is being done. >>That was the word that came to mind when you were describing the all three customer examples, the transformation, this is transformative. The ability to leverage alters to, to truly democratize data and analytics, give access to the lines of business is transformative for every organization. And, and also the business outcomes. You mentioned, those are substantial metrics based business outcomes. So the ROI and leveraging a technology like alri seems to be right there, sitting in front of you. >>That's right. And, and to be honest, it's not only important for these businesses. It's important for, for the knowledge workers themselves. I mean, we, we hear it from people that they discover Alrich, they automate a process. They finally get to get home for dinner with their families, which is fantastic, but, but it leads to new career paths. And so, you know, knowledge workers that have these added skills have so much larger opportunity. And I think it's great when the needs of businesses to become more analytics and analytic and automate processes actually matches the needs of the employees. And, you know, they too wanna learn these skills and become more advanced in their capabilities, >>Huge value there for the business, for the employees themselves to expand their skillset, to, to really open up so many opportunities for not only the business to meet the demands of the demanding customer, but the employees to be able to really have that breadth and depth in their field of service. Great opportunities there. Alan, is there anywhere that you wanna point the audience to go, to learn more about how they can get started? >>Yeah. So one of the things that we're really excited about is how fast and easy it is to learn these tools. So any of the listeners who wanna experience Altrix, they can go to the website, there's a free download on the website. You can take our analytic maturity assessment, as we talked about at the beginning and, and see where you are on the journey and just reach out. You know, we'd love to work with you and your organization to see how we can help you accelerate your journey on, on analytics and automation, >>Alan, it was a pleasure talking to you about democratizing data and analytics, the power in it for organizations across every industry. We appreciate your insights and your time. >>Thank you so much >>In a moment, Paula Hanson, who is the president and chief revenue officer of ultras and Jackie Vander lay graying. Who's the global head of tax technology at eBay will join me. You're watching the cube, the leader in high tech enterprise coverage.

>>Hey, everyone, welcome back to the programme. Lisa Martin here. I've got two guests joining me. Please welcome back to the Q. Paula Hanson, the chief Revenue officer and president at all tricks. And Jackie Vanderlei Grayling joins us as well. The global head of tax technology at eBay. They're gonna share with you how an all tricks is helping eBay innovate with analytics. Ladies, welcome. It's great to have you both on the programme. >>Thank you, Lisa. Not great to be >>here. >>Yeah, Paula, we're gonna start with you in this programme. We've heard from Jason Klein. We've heard from Allan Jacobsen. They talked about the need to democratise analytics across any organisation to really drive innovation with analytics as they talked about at the forefront of software investments. House all tricks, helping its customers to develop roadmaps for success with analytics. >>Well, thank you, Lisa. Absolutely is about our customers success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts, of course, with our innovative technology and platform. But ultimately we help our customers to create a culture of data literacy and analytics from the top of the organisation starting with the C suite and we partner with our customers to build their road maps for scaling that culture of analytics through things like enablement programmes, skills assessments, hackathons, uh, setting up centres of excellence to help their organisation scale and drive governance of this, uh, analytics capability across the Enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practises so they can make better business decisions and compete in their respective industries. >>Excellent. Sounds like a very strategic programme. We're gonna unpack that, Jackie, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the I. D. C report that showed that 93% of organisations are not utilising the analytic skills of their employees. But then there's eBay. How Jackie did eBay become one of the 7% of organisations who's really maturing and how are you using analytics across the organisation at bay? >>So I think the main thing for us is when we started out was is that you know our especially in finance. They became spreadsheet professionals instead of the things that we really want our influence to add value to. And we realised we have to address that. And we also knew we couldn't wait for all our data to be centralised until we actually start using the data or start automating and be more effective. Um, so ultimately, we really started very, very actively embedding analytics in our people and our data and our processes. >>Starting with people is really critical jacket continuing with you. What was in the roadblocks to analytics adoption that you faced and how did you overcome them? >>So I think you know, Eva is a very data driven company. We have a lot of data. I think we are 27 years around this year. So we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them, um, to move forward. The other thing is that you know, people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals, right? And there was no we're not independent. You couldn't move forward. You're dependent on somebody else's roadmap to get to data to get the information you want it. So really finding something that everybody could access analytics or access data. And finally we have to realise, is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy and that is not so daunting on somebody who's brand new to the field? And I would I would call those out as your as your major roadblocks, because you always have always. But most of the times you have support from the top. In our case we have. But in the end of the day, it's it's our people that need to actually really embrace it and making that accessible for them. I would say it's not to say a road block a block you want to be able to do. >>It's really all about putting people first question for both of you and Paula will start with you and then Jackie will go to you. I think the message in this programme that the audience is watching with us is very clear. Analytics is for everyone should be for everyone. Let's talk now about how both of your organisations are empowering people, those in the organisation that may not have technical expertise to be able to leverage data so that they can actually be data driven colour. >>Yes, well, we leverage our platform across all of our business functions here at all tricks. And just like Jackie explained that eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jackie mentioned, we have this huge amount of data, uh, flowing through our enterprise, and the opportunity to leverage that into insights and analytics is really endless. So our CFO, Kevin Ruben has been a key sponsor for using our own technology. We use all tricks for forecasting all of our key performance metrics for business planning across our audit function, uh, to help with compliance and regulatory requirements, tax and even to close our books at the end of each quarter. So it's really remain across our business. And at the end of the day, it comes to How do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other to problem solve and, along the way, maybe earn badges, depending on the capabilities and trainings that they take and just have a little healthy competition, Uh, as an employee based around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jackie mentioned, it's really about ensuring that people feel comfortable that they feel supportive, that they have access to the training that they need, and ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >>That confidence is key. Jackie talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >>I think it means to what Paula has said in terms of, you know, getting people excited about it. But it's also understanding that this is a journey and everybody is the different place in their journey. You have folks that's already really advanced. Who's done this every day. And then you have really some folks that this is brand new and, um, or maybe somewhere in between. And it's about how you could get everybody in their different phases to get to the the initial destination. And I say initial because I believe the journey is never really complete. Um, what we have done is that we decided to invest in a group of concept when we got our CFO to sponsor a hackathon. Um, we open it up to everybody in finance, um, in the middle of the pandemic. So everybody was on Zoom, um, and we had and we told people, Listen, we're gonna teach you this tool. It's super easy, and let's just see what you can do. We ended up having 70 injuries. We had only three weeks. So these are people that that do not have a background. They are not engineers and not data scientists and we ended up with 25,000 our savings at the end of the hackathon. Um, from the 70 countries with people that I've never, ever done anything like this before. And there you have the results. And they just went from there because people had a proof of concept. They knew that it worked and they overcame the initial barrier of change. Um, and that's what we are seeing things really, really picking up now >>that's fantastic. And the business outcome that you mentioned that the business impact is massive, helping folks get that confidence to be able to overcome. Sometimes the cultural barriers is key there. I think another thing that this programme has really highlighted is there is a clear demand for data literacy in the job market, regardless of organisation. Can each of you share more about how your empowering the next generation of data workers Paula will start with you? >>Absolutely. And Jackie says it so well, which is that it really is a journey that organisations are on and we, as people in society, are on in terms of up skilling our capabilities. Uh, so one of the things that we're doing here at all tricks to help address the skill set gap on a global level is through a programme that we call Sparked, which is essentially a no cost analyst education programme that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this programme is really developed just to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with sparked we started last May, but we currently have over 850 educational institutions globally engaged across 47 countries, and we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises when we close gap and empower more people with the necessary analytic skills to solve all the problems that data can help solve. >>So >>I just made a really big impact in such a short time period is gonna be fun to watch the progress of that. Jackie, let's go over to you now Talk about some of the things that eBay is doing to empower the next generation of data workers. >>So we definitely wanted to make sure that we kept implemented from the hackathon that we don't lose that excitement life. So we just launched a programme for evil masterminds and what it basically is. It's an inclusive innovation initiative where we firmly believe that innovation is all upscaling for all analytics role. So it doesn't matter. Your background doesn't matter which function you are in. Come and participate in this where we really focus on innovation, introducing these technologies and upscaling of people. Um, we are apart from that. We also said, Well, we should just keep it to inside the way we have to share this innovation with the community. So we are actually working on developing an analytics high school programme which we hope to pilot by the end of this year. We will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, But also, um, how to use all tricks and we're working with Actually, we're working with spark and they're helping us develop that programme. And we really hope that it is said by the end of the year, have a pilot and then also makes you must have been rolled out in multiple locations in multiple countries and really, really, uh, focused on that whole concept of analytic school >>analytics. Girl sounds like ultra and everybody have a great synergistic relationship there that is jointly aimed at especially kind of going down the stock and getting people when they're younger, interested and understanding how they can be empowered with data across any industry. Paula, let's go back to you. You were recently on the cubes Super Cloud event just a couple of weeks ago and you talked about the challenges the companies are facing as they are navigating what is by default, a multi cloud world. How does the all tricks analytics cloud platform enable CEO s to democratise analytics across their organisation? >>Yes, business leaders and CEO s across all industries are realising that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organisations. Last I checked, there was two million data scientists in the world. So that's, uh, woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CEO s with business leaders is that they are integrating data analysis and the skill set of data analysis into virtually every job function. Uh, and that is what we think of when we think of analytics for all. And so our mission with all tricks analytics cloud is to empower all of those people in every job function, regardless of their skill set, as Jackie pointed out, from people that would are just getting started all the way to the most sophisticated of technical users. Um, every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organisations. So that's our goal with all tricks, analytics cloud and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyse and report out so that we can break down data silos across the Enterprise and Dr Real Business Outcomes. As a result, of unlocking the potential of data >>as well as really listening that skills gap. As you were saying, There's only two million data scientists. You don't need to be a data scientist. That's the beauty of what all tricks is enabling. And eBay is a great example of that. Jackie, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where all tricks fits in as that analytics maturity journey continues. And what are some of the things that you're most excited about as analytics truly gets democratised across eBay >>when we start about getting excited about things when it comes to analytics, I can go on all day, but I'll keep it short and sweet for you. Um, I do think we're on the topic full of data scientists, and I really feel that that is your next step for us, anyway. Is that how do we get folks to not see data scientist as this big thing like a rocket scientist it's something completely different and it's something that is in everybody in a certain extent. So, um, game partnering with all tricks to just release uh, ai ml um, solution allowing. You know, folks do not have a data scientist programme but actually build models and be able to solve problems that way. So we have engaged with all turrets and we purchase the licence is quite a few. And right now, through our masterminds programme, we're actually running a four months programme. Um, for all skill levels, um, teaching them ai ml and machine learning and how they can build their own models. Um, we are really excited about that. We have over 50 participants without the background from all over the organisation. We have members from our customer services. We have even some of our engineers are actually participating in the programme will just kick it off. And I really believe that that is our next step. Um, I want to give you a quick example of the beauty of this is where we actually, um, just allow people to go out and think about ideas and come up with things and one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution. Where, um, you know there is a checkout feedback checkout functionality on the eBay side, There's sellers or buyers can pervade them at information. And she built a model to be able to determine what relates to tax specific what is the type of problem and even predict how that problem can be solved before we as human, even stepped in. And now, instead of us or somebody going to debate and try to figure out what's going on there, we can focus on fixing their versus, um, actually just reading through things and not adding any value and its a beautiful tool. And I'm very impressed when we saw the demo and they've been developing that further. >>That sounds fantastic. And I think just the one word that keeps coming to mind. And we've said this a number of times in the programme. Today's empowerment, what you're actually really doing to truly empower people across the organisation with with varying degrees of skill level, going down to the high school level really exciting. We'll have so stay tuned to see what some of the great things are that come from this continued partnership? Ladies, I wanna thank you so much for joining me on the programme today and talking about how all tricks and eBay are really partnering together to democratise analytics and to facilitate its maturity. It's been great talking to you. >>Thank you. >>Thank you so much.