 And here we go. Hello and welcome, my name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. We'd like to thank you for joining this Data Diversity webinar, Lead Your Data Revolution, How to Build a Foundation of Trust and Data Governance, sponsored today by Experian. Just a couple of points to get us started. Due to a large number of people that attend these sessions, he will be muted during the webinar. For questions, we will be collecting them by the Q&A in the bottom right hand corner of your screen, or if you'd like to tweet, we encourage you to share highlights or questions by Twitter using hashtag dataversity. And if you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the bottom middle of your screen for that feature. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session and additional information requests throughout the webinar. Now let me introduce to you our speakers for today, Erin Hazelkorn and Kevin McCarthy. Erin is responsible for marketing insight for experienced data quality products and developing an understanding of data management and data quality practices through her research and relationships with analyst research firms. She has a unique understanding of digital transformation and data management trends. Erin is a frequent spokesperson and guest blogger leveraging her knowledge to help organizations better understand leading data management strategies and how to build trusted data. Kevin has spent more than 30 years in the data quality and data management space at Experian. Kevin has been leveraging his vast experience with data quality technology and customer implementations to expand experienced data quality portfolio presence in the marketplace. As part of the global product management team, Kevin is working with regional teams in North America, the United Kingdom and Australia to bring practical data quality use cases to the market as Experian continues to grow and invest in its innovative data quality solutions. And with that, I will give the floor to Erin and Kevin to get today's webinar started. Hello and welcome. Thank you so much, Shannon. We really appreciate being here today and thank you everyone so much for joining today's session. Again, my name is Erin Hazelcorn and I am the head of market research here at Experian's Data Quality Business. And really today we've got Kevin on the line. In the past couple of years, Experian has done a wealth of research on data management trends. And we have a new recent study that has just come out. And with this study, we really wanted to take a further look at how successful companies have actually, how they're working to become data driven. And unfortunately we found that many companies are still struggling to make this a reality. And let's face it, data is confusing. It's dirty, it's complicated, it's spread out all over the place. We see a lot of data management initiatives happening and there's investment happening, working to try to fix these issues to wrangle the data. But we still aren't seeing widespread success. But we're starting to see a shift in some of the research in the form of data enablement. And we're gonna talk about more about that as we get into the webinar. So Kevin and I are really gonna try to talk through today how companies can empower a wider data usage and be able to get better at achieving some of the insight that they're looking for. So what we're gonna cover off today, here's the agenda. Much of today's presentation is based off research. So I'm gonna start with a brief rundown of the survey methodology and follow that up with the key findings to kind of frame our discussion for today as a baseline. Then we'll dive a bit deeper, talking about the challenges that we face in becoming data driven, the trends that we're seeing around data and data usage. And then finally we'll get into this rise of data enablement, what that's bringing to the table and finally wrap up with looking at what is the profile of a mature organization and what does that look like so maybe we can emulate some of those key practices. And at the end of today's webinar, we'll have time for some questions. Again, as Shannon has said, feel free to type those into the chat or the question to ask us on Twitter. We're eager and excited to answer those at the end of today's session. So first let's give the background on the data that we're gonna be talking about today. So in July of this year, we worked with a company Insights Avenue to conduct a study on how organizations are enabling the use of data across the business. We spoke to over 500 people in the US who had knowledge and visibility of their company's data management practices. Given that background, we spoke to a number of folks across different departments with varying levels of seniority, right? We all know that data is being used by more people than ever. It's not just folks in IT, it's not just folks with data titles. So we really want to broaden this out in terms of who we talk to. We can see actually that about a quarter of our respondents were actually from the C level, which was exciting. And we saw individuals from a wide range of companies, financial services, public sector, retail, healthcare manufacturing, lots of different types of industries. And as always, again, we kept the population fairly diverse knowing that the individuals who are using and benefiting from data Insights are also very diverse. So here are some of the key findings now that we have the background and some of the themes that stood out to me as we get started at looking at this data. So a lot of companies are working very hard to use data. Very a positive sign. Business leaders understand that without strong and accurate insight, they're not gonna be able to engage with customers in the digital economy. They're not gonna be able to be efficient in decision-making or keep up with the competition. But we don't see necessarily businesses succeeding to the level that they want to. 69% say that despite ongoing initiatives, their organization still struggles to be data-driven. It also takes too long to get actionable insight for over half the business. And there's some clear reasons why we see this happening. Besides the fact that, frankly, data is just challenging to begin with. It's our belief that business leaders are often underestimating maybe the level of data debt that they have within their company. They don't realize that a level of inaccurate data or poor management practices is dragging down an investment in new technologies. They've also traditionally under-invested in this problem. And that shows up in some of these stats that are on the screen. So 64% don't have enough talented data professionals. 66% say that improving data quality, those doing that often don't fully understand the needs of the business. In addition, we see a lot of data management initiatives that are happening within individual departments. Now we'll get into this a little bit more later. And there's certainly nothing wrong with experimentation. And frankly, there are individual department needs that are going to vary across the business. But it's important that we think about data management with some degree of scale so that we have a common framework so that as we continue to improve, we can build on what's working within the business, especially as we try to get data into the hands of more people across the business. So we're seeing the tide starting to turn. I know I'm personally very excited for the new trend of data enablement. Data enablement as defined in our survey was the practice of empowering individuals in the business with the support and tools they need to responsibly leverage trusted data. And they're doing that to achieve real business outcomes. So this is a great initiative. We see 57% of respondents saying this is a priority over the next 12 months. And again, really excited to see that people want to put data into the hands of the masses a little bit more. So with that in mind, here are some of the major takeaways for me from the study. I'll show these again at the presentation. But first, we've got a lot of big data projects going on. We're doing a lot around data, but we're still struggling to be data driven. And a lot of that is because there's a variety of different approaches from departmental to full enterprise. We see project-based kind of components to ongoing disciplines. But we do see that most organizations are missing a key cornerstone of data management success, which is frankly a foundation built on trusted quality data. And then finally, if we're going to improve data usage, there's not just one way to go about doing that. We need to think about enabling the business with not only just the right talent, but we need to think about the technology. And we've really got to think hard about this data culture and how we're approaching data as a business, how it's viewed and understood. So we're going to get into each of these areas in a little bit more detail. But for now, I'm going to turn things over to Kevin to have him walk us through the types of data investment that companies are making. Thanks so much, Erin, and hello, everybody. So as Erin mentioned, we see a lot of investment happening around data, which is great. However, companies aren't necessarily seeing the results that they set out to achieve. So when we talk about a lot of projects, we mean a lot of projects. About 80% of the folks we talk to are actively pursuing multiple big data projects. The top ones include areas like data quality, big data analytics, and data governance. These projects sit in a wide variety of areas across the business. The bulk still sit with IT, which isn't surprising. That's where data's traditionally been managed. But we're also seeing 42% sitting directly with business users. This probably means they aren't getting what they need from the existing data management practices, and they're trying to take matters into their own hands. So they want to try to gather more control over the data from the business perspective. But again, even with all these projects, we aren't seeing the desired results. So let's dive a little bit deeper into the projects that folks have going on. So if you look at the chart, you can see the dark blue sections are projects currently happening. The purple are projects they plan to start in the next 12 months. Light blue are projects that are on the radar, but don't have a fixed timeline. And then finally, the pink color are projects that are not planned yet. So if we take a look, you can see the top three projects are data quality, big data analytics, and data governance. However, we're also seeing areas coming up like data literacy, which is becoming more popular, and of course, things like machine learning and artificial intelligence. So let's first take a look at data quality. So I know, personally, data quality's been around for a long time, but companies have also struggled with building trusted data for even longer. It's clearly a foundational discipline when it comes to an area of investment. People often think of it as sort of a back room activity. And really, these days more than ever, it has an impact across the organization. So part of the problem with data quality is it isn't necessarily the most exciting way to think about your data. It's traditionally something that's been under invested in. And I know there's still a lot of work that needs to be done in this area. It's exciting to see that a lot of people think they've made progress when it comes to data quality. The pie chart on the screen is the response when we asked people if they felt they had made progress in improving data quality over the last 12 months. And you can see 93% said yes, definitely, or yes, probably, which is great. However, we still see a lot of roadblocks in the area, which Erin is gonna be talking about shortly. So it's good that we're investing in data quality and we're also investing in things like big data analytics. So analytics is one of the more exciting aspects of data management. So whereas data quality isn't necessarily as sexy, data analytics is a lot more exciting. So we all want analytics, especially when we can get insight across all the massive silos of data that we've been accumulating. 79% say that they focus on analytics and how to gain more insight with their data. This is very good, but getting insight from this data is hard. 88% report challenges. We see some of the common challenges in this bar chart. A lot of folks may not have access to the information they need. Now that could be because of various regulations, but probably and more likely the data is just set up in a way where it's complicated. Maybe the systems that are in place for analytics aren't built for the data structures that we have today. There's often too much data to analyze. Sometimes these big data initiatives, these repositories become a dumping ground for data, which is a challenge. The issues around the volume to data certainly aren't filling away, but people are also struggling with different types of data that they're going to analyze. So we talked about structured data, baby social media data, that's not really neatly stored. Those things all create issues. It also takes too long to prepare the data. I was able to review a report recently from Stuart Bond at IDC, and he was showing from his new research the large amount of time data professionals spend just preparing the data. It's a huge amount of time and it goes back to needing some of those foundational elements of governance and quality. The last one I'll talk about is not having the right technology. So from my experience, I know there's a lot of different technologies out there. Some are very good and some are designed specifically for different roles. So a lot of the technology that's available today is designed for more technical users, kind of the back office IT users. And so this may require more coding or some level of sophistication to be able to use the tools. The challenge is now everybody wants access to the data and everyone wants to be able to leverage the data from marketing, finance to sales. So tools need to be procured that can allow more people to access the data, not fewer, you know, we want to make it open for as many folks as possible, especially when you think about gaining insights to analytics. The last data management project we'll touch on is data governance. So many companies are investing in data governance and different organizations are at different levels on this journey. This is similar to what we see across other data management disciplines. We see 29% that are relatively mature, kind of taking a holistic approach that involves the governance board technology and data governance related roles. Then we see 27% who are just starting on a program in the next 12 months. 26% say the process depends on individual departments and then we have 16% who have a governance board but have not decided how they want to move forward with the technology or enforcing policies. So again, not surprising, it's similar to some of the other data management disciplines but when it comes to governance, we see some clear directives as to why people want to invest heavily in these programs. Companies want to understand how their data is used. So they also want to comply with regulations. Now, this is what governance was traditionally thought of for compliance. However, in recent years, data governance has started to encompass a much broader definition that takes into account areas like data privacy, data usage, data context, others. However, compliance is still the central component. So we also see companies want to improve the quality of data for decision-making. They want to be more data-driven, which are all great reasons to invest in data governance. Well, there is a reason to invest. There's also a lot of challenges to be aware of. It's hard to get people to follow the data rules. We're all aware of that. They don't have the right technology or enough knowledgeable resources. There may also be a lack of executive buy-in. Well, these are all challenges that can cause problems when implementing a governance program. They can be overcome with the right approach. Governance program is certainly worth doing to get the benefits of accurate and trusted data. So now that we've talked a little bit about some of the investment we're seeing, I'm gonna turn things back over to Erin to talk about some of the roadblocks that we run into as well. Thanks, Kevin. And as Kevin said, we're making a lot of good investment in data management programs, but we aren't achieving the results that we really wanna see. So part of this is because of the way, I think that we're approaching certain aspects of data management. And some of it comes down to the fact that we're stuck in a bit of a data rut. And again, a lot of us are investing in it, but we're struggling to be data-driven. There's also still a lot of inaccurate data that's undermining initiatives. So it's not just data management-specific initiatives, but when you think about the number of initiatives that are dependent on data, we're talking about things like customer insight, customer experience, things like operational efficiency, even down to compliance, all of those different areas are dependent at this point in the digital economy on some sort of data insight. So think about all the ways that you use data now. And when it's inaccurate, it can cause a lot of problems for the business. Right now, when we see, as Kevin mentioned, only 29% of companies taking a holistic approach to data governance. And while that's a stat specifically for data governance, I think it's indicative, frankly, of a lot of the different data management initiatives that we see people undertaking. And so we're all kind of approaching data management initiatives in a different way. And sometimes we get carried away with the latest thing or the most exciting thing. And we forget about some of the foundational elements that frankly take a lot of time and effort to get right. And right now we see a lot of different approaches to data management. Now let me first say that there is no one-size-fits-all approach. There's no silver bullet to managing data assets. Each initiative isn't going to be and shouldn't be set up the same way for every company. But the research shows certain organizations are having more success than others when it comes to being data-driven and getting value from their data. So there's four common areas that we looked at in terms of how people are approaching these initiatives. So first we're gonna talk about the scope of the initiatives, where they departmental, where they enterprise. We'll look at the level of maturity of certain foundational elements, specifically data quality. Then we're gonna look at the management approach, was it a one-off project or is it kind of the look at is more of an ongoing discipline? And then we'll look at data debt. So we're gonna tackle each one of these variations and the data behind it a little bit more detail. So first we're seeing a lot of data management initiatives happen, but again they're happening in individual departments or in individual pockets within the business. 69% of reporting data management initiatives occur in the individual department, only a few are happening on the enterprise level. Again, that's not necessarily a terrible thing, but when we think about some of the challenges that folks are running up against, it is causing a few problems, right? So we can see on the screen some of the departments that are most advanced when it comes to data management. They're not gonna be surprising. So folks like logistics and operations, finance, marketing, segmentation, senior management, decision-making, all of those tend to be more advanced departments and they do have some varying levels of need in terms of what they're doing with their data. Now the siloed approach for individual departments happens across all data management disciplines. And there's a risk that companies may not be leveraging best practices from around the various departments or across the business in general, if they're taking this kind of very siloed approach. And that can lead to a lack of insight and certainly so much variation that it can cause a lack of trust in the data. So we're seeing some challenges there. Now let's move on to data quality and talking about that a little bit more. And again, as Kevin mentioned, a lot of times it's thought of as a general data management initiative. A lot of companies will brush it off and say, well, we already covered that a long time ago. We're good when it comes to data quality. ITs handle that it's not something we need to think about. But the problem is without quality or trusted data, you're not gonna really do a great job of driving some of the initiatives that you want to. How are you supposed to drive analytics if you don't know the accuracy level of your information if you can't trust the insights coming from your analytic program? How are you gonna understand, if you don't understand the quality of your information, how are you gonna build a governance program or know where to start? If you don't have good training data, how are you gonna train your new machine learning or AI algorithms that you've invested probably a ton of money in? So we found that actually the more mature an organization is in terms of data quality, the more mature they typically are in other areas of data management. But we actually, again, from the research, see most people admit they've underinvested in this area. We do see the tide turning a little bit. Most people get the sense and have said in the study this year that they've seen an improvement in their data quality in the last year. And that's fantastic. But we still see, we asked about different levels of maturity and we only see 11% that are at that top segment of that chart that say, we're mature in terms of data quality. When we looked at maturity, we thought and asked about things like, what's the data talent that you're working with? What types of technology? What's the automation that you have in place around your data quality? What kind of processes are in place? That's what we were asking about to determine whether folks fell into that limited emerging, developing or mature. And you can see actually from this chart over half of companies are actually still in the bottom two levels of limited data quality understanding or they're just really starting to sink their teeth into it. So we really do have a long way to go when it comes to data quality and establishing the level of trust that we need to within data. Next, we have the approach to data management. So we typically see two different types of approaches when it comes to how we look at projects. So some people look at it in terms of a project. It's technical in nature, right? They start with a scope of the initiative. They may go out and buy a piece of technology. They're gonna solve the issue in a set amount of time. And these organizations typically do an initial cleanup and then the information should be all set, right? There's a technical scope. There's budget allocated. It's all around a specific period of time. Now other companies take a different approach when looking at data management. And they see it as more of an ongoing business initiative and frankly are trying to tie it to specific outcomes. So for example, a company may look at data quality as an ongoing initiative, right? You're getting new data in all the time. So you need to look at how to prevent that bad data from entering your system. How do you keep it clean and up to date? And then you're hopefully gonna tie it to something like customer experience or revenue generation but that discipline actually never stops. We're always working on it. So we see half of companies taking one approach and the other half taking another. So very evenly split in terms of the full survey group that we talked to. And then finally, we've got the concept of data debt. And if you look in the report that we pulled together, we actually included in that Gartner's full definition of data debt. But to explain it in as non-technical way as possible as I can, it's basically the cost that comes from suboptimal data assets. So if you think about it, we're not ever gonna be 100% effective in terms of our data governance or our full data management practices, right? There's so much time and effort and cost. There's so much data coming in. We're trying to get as close to perfect as we can but we're probably never gonna meet it. So we're really looking at the deficit between what's the ideal condition that we're talking about that's required for the needs and then what's actually available. So it's a lot like technical debt. If you're operating with a large degree of poor and accurate data, it frankly sometimes doesn't matter how much you're investing in projects like machine learning or analytics or AI. If you don't have some of that foundation, you're not gonna necessarily get the same degree of benefit from that new investment as you were potentially hoping for. So you may not achieve the same positive outcome. So organizations really need to understand the quality of their data and maybe take some measurements on that so they can tackle other types of initiatives with confidence or understand if they need to take that step back and relook at that foundational element. So now I'm actually gonna turn things back over to Kevin to get us started talking about that exciting new trend of data enablement. Thanks Aaron, thanks. Yeah, and I love that concept of data debt. That's a great term to use for that. So, and another good term is data enablement. So with the backdrop of all the types of investments that people are making in the way that they're approaching projects, let's talk a little more about data enablement which I think is a great new topic sort of in the world of data management. We haven't been seeing this wide reaching success in leveraging data that organizations wanna see. So we're starting to adopt new approaches. And as we were talking about previously, data is not just for IT anymore. Ideally, it's going to move throughout the business. Organizations are turning to data enablement to empower more people to be able to leverage data insights. So if we wanna start with the definition and I think Aaron mentioned this, if we talk about data enablement, we're really speaking of the practice of empowering individuals in a business with the support and tools they need to responsibly leverage trusted data and achieve real business outcomes. So well, it's a relatively new concept in terms of terminology. We see a lot of businesses starting to gravitate towards this. This is something that really has been going on for a few years now, kind of migrating data out from IT and into the business. But just recently, we've been able to develop this term of data enablement around it. So data enablement is a key focus over the next 12 months for 57% of businesses and a further 41% report that it's a focus to come. So it's getting to be top of mind for folks. However, we also still see challenges related to these projects. When we looked at the ways that people can enable the better use of data, we're looking around data talent and the right technology and also creating that cultural shift, turning it not from a project but to really a culture change within the organization. So why are companies thinking about data enablement? Well, here are the outcomes that we saw from our survey from improved data usage. Not surprisingly, we see at the top of the list, complying with regulations. And again, we talked about governance and managing around compliance with new regulations around data privacy and industry specific regulations. Compliance is top of mind for many companies and better data usage can certainly help with identifying issues and risk as well as with compliance reporting. But compliance is more of a defensive play for data management. It's sort of a cost and somewhat of a burden for folks where there's a lot of offensive outcomes where we can look to drive business growth that companies wanna see from better data usage. If you look at the next few, we've got enabling better decision-making, improving the customer experience and better understanding of the customer. So these are much more offensive, meaning looking at your data as a revenue generator. How can we bring in more business and enhance the bottom line? Here are some of the common ways companies are looking to improve that data usage. So first, they're looking to provide standardized data across the business. Then we see things like better utilizing data governance to ensure the right data usage, putting data professionals in specific departments and consolidating certain sources of information. So for those that have undertaken these initiatives, more than 95% report that these actions have resulted in better business outcomes. But there are also a lot of challenges that companies face when looking at these practices. So 89% of the individuals we spoke with reported having challenges when enabling the use of data. They often lack skilled human resources or data professionals. And those are the folks that are typically the bridge between IT and the business. So it's interesting that we see that more and more that these data professionals are an important component to kind of the data management triad, I'll call it. So we've also found that communication can be challenging between silo departments. They may not have the funding that they need or they may lack the data literacy within their staff. People have to know how to deal and speak data in some ways. So all of these are serious challenges that need to be addressed. Companies can't frame their data-enabled program purely around the data. They need to think about the data. They need to think about the talent. They need to think about the technology that they're using. And again, they have to think about is data part of the culture of the organization? So with that, I'm gonna turn things back over to Erin to go into each of these areas in a bit more detail. Thanks, Kevin. So again, let's dive a little bit more into the people, the talent, as we should say, and some of the challenges there. Let's talk about how we address some of the challenges around technology, and then finally dig a lot more into the culture. I know I've seen a few questions come through on that one, but let's start with data talent, right? So with any new initiative, there's often a rush to hire. And so data professionals right now are in very high demand. So a lot of people are competing for this talent. It might be part of the reason why we're seeing that 64% don't have enough data professionals in their organization. It also relates to the top challenge of enabling data. Kevin just mentioned there's that big lack of skilled human resources. So without the right talent, it's gonna be difficult to move forward with the right initiatives and move forward in the right way. But I think we need to think about this a little bit more and reframe it, right? So when we're talking about data enablement, we're talking about how do we empower the masses? How do we create that cultural change so that more people know and understand and how to work with data? That means that you don't necessarily need an army of data professionals. You need that core group. You do need some of those professionals, but then you need to think about how do you empower more folks, some of those citizen type roles with technology or processes or other resources so that they can leverage data more effectively. So for that core group of data professionals, the bar charts, so there's some of the common roles that are being hired, right? You've got data analysts, you've got data engineers, chief data officers, data governance managers. Which roles that you're gonna hire depend on, frankly, your focus areas, where you need more strength within your business, right? So you don't necessarily need X number of each type of role for your core data, professional group within the business. It's really more around what's gonna fit for your business and where your priorities are. And some of that depends on the state of where your data is today and how you're driving data within the business. I will say, kind of pointing out on this chief data officer piece a little bit more, there's some interesting stats recently, Forrester announced that they're actually seeing 58% of companies that now have a chief data officer. That's certainly a larger number than they've reported on in the past. And I wanna point out too, there's a common misconception that a chief data officer is just for a large enterprise and that is simply not the case. We're seeing CDOs pop up across all different kinds of industries, all different size companies, right? It really depends on that business, but I think the CDO, while they're third on the list here, they're often the cornerstone of that data professional group within a business. So certainly one to watch out for here when we talk about data talent. Next, we're talking about technology. So part of the data enablement is you wanna expand the use of technology. You wanna ensure it's business friendly, you want more people to leverage tools and insight. And as Kevin mentioned earlier, a lot of the traditional data management technology that's out there was frankly built for IT professionals. It was a little bit more technical, maybe it required coding, but that was when data was just relegated to being more of an IT function or something that was more technical. We're now seeing more people wanna use data. And so luckily more vendors are starting to produce software specifically designed for more technical, more non-technical users, I should say. So on the screen, we see some of the tech that's being used for data enablement. So you'll see tools like data preparation, you'll see Excel and you'll see data quality. And while some of you may be rolling your eyes online going Excel in a second, like what do we mean by that? But many business users haven't been given an easier to use technology. What does everybody have on their desktop? They've got Excel. And so with Excel, they're generally, people know how to use it at a very basic level. So they're doing what they can to be able to leverage information. Is it probably the most secure way? Does it fragment data? Absolutely, but it's what people have been given and so they're using it. There's certainly better options out there, but again, it's very, very widely used. But we are seeing some concerns with some of the tools and tech as we saw in our survey. So 87% of companies are reporting concerns with the tools around the technology specifically for data enablement. There are concerns that they don't have enough training on the tech. There's concerns that there are too many different technologies. They don't have the right people in place. Again, it shows the multiple angles that we've got to think about when we're thinking about data enablement. And then finally, we've got this cultural shift that needs to take place. And when an organization is more properly aligned to achieve data insights, they're able to get that from a broader group of people, right? So when we look at some of the biggest challenges that Kevin just went over, there are some that specifically point to culture, like communication between departments and this concept of data literacy. So again, I mentioned before, a lot of data management initiatives are happening in silos. That means when we move forward or maybe when we have a success, there's not a lot of coordination. And while experimentation is certainly a good thing, there's a lack of efficiency and scale if there isn't communication. So a lot of times something like a data governance board or some sort of central group that sits and talks about the use of data that has folks from across different departments represented can help bridge the gap in terms of some of that communication. Then we've got data literacy. Again, this is something relatively new. So data literacy is defined as the ability to read, work with, analyze, and argue with data. The goal is the more people within the business that are data literate, the more insights that you can empower. And we're seeing 45% of organizations working on data literacy. Now, again, there's a lot of different approaches and this is there relatively new, right? Sometimes we'll see a chief data officer that takes the lead on data literacy. But really it can be anybody. I've heard actually of human resources teams taking charge on it, right? We're talking about training of staff and empowering staff. Sometimes that's something that human resources takes on. So data literacy is something at a basic level that everybody needs to know. Now you're gonna have different levels of maturity and data insights. Obviously somebody who's a data professional can understand and argue with the data a lot more, but you wanna make sure that everybody across the business can get a piece of information or insights, read that data and either ask more questions of that information or at the very base level be able to understand it, right? So you can't advance the amount of data that people can leverage if they don't know what they're looking at. So data literacy becomes that really important piece that training of how to be data driven. And it's another one of those kind of core foundational elements to me. So with all those kind of different areas, before we jump into questions, and I know quite a few that have come in, I wanna quickly walk you through what we see is the profile of mature business. Somebody who's probably doing data enablement well. And it's our belief that actually somebody who's more mature in data quality, it's a good indication that they're doing something right around data management. And you can see from our research, we see that companies of all industries and all sizes are in this level of maturity. It doesn't have to be a big business. It doesn't have to be somebody with a really robust and huge data professional organization. We're seeing folks of all different levels, right? That are reaching this level of maturity. So there's a few common characteristics that we see that these companies share. First, they're more likely to undertake a broader set of data management projects. They're also more likely to focus on data enablement. So of these mature 11%, 83% of them have a focus on data enablement in the next 12 months versus 57% of the norm. They're also more likely to have data roles in place, especially a CDO. We see 64% of data quality mature organizations with a CDO, it's 38% for the others that we surveyed. So data management is really important when we think about that kind of talent area. Then we're also thinking about how are they approaching these different practices, right? And we're more likely to see than think about data management initiatives as continuous processes. They're more likely to see these as ongoing initiatives than kind of that finite project basis. We're also just as likely to see these projects sit with IT as we are within business and individual lines or departments. And then finally, we're actually less likely in these to see data quality undermining key initiatives and we're less likely to see them actually experience data management project delays when they tend to be more mature. So those were some of the common kind of characteristics that we saw when we looked at the research. So right before we get into questions again, I just wanna put these key takeaways up again one more time. You know, we're seeing a lot of investment happening, that's great, but we're not always seeing the desired outcomes. And so we really feel like companies are missing some of these key elements for data management success, specifically that foundation of trusted data. And then we also see that to move forward, we've gotta focus on data talent, we've gotta focus on technology and we focus on culture. Just focusing on one of those areas, you're gonna move the needle a little bit, but it's not gonna reach to the level that we wanna see in terms of data insight. We've gotta look at this more holistically than maybe we have in the past. And with that, I'm gonna turn things back over to Shannon for questions. Erin and Kevin, thank you so much for this fantastic presentation. Just to answer the most commonly asked questions, I will be sending a follow-up email to all registrants by end of day Thursday with links to the slides, links to the recording and anything else requested throughout. Now, you know, Erin and Kevin, one thing I love about doing webinars with Xtreme, you guys have such great content. So educational. But we're having a great opportunity here. We've got a lot of people asking about tools. What tools do you have to help solve these issues, especially around data quality? What does Xtreme have available? Yeah, Erin, I'm happy to jump in on that. That'd be great, Kevin. Yeah, that's great. So we have a variety of tools. I'm gonna talk a little bit about our data management platform. We have a platform called Aperture Data Studio, and I'm gonna break it into kind of two parts because really when you're trying to solve these, so the data quality problems, the very first thing you wanna do is learn what you're up against, right? So we've got a pretty powerful data profiling portion of the platform that goes in and is able to give you a bunch of information on the data you have today. So depending on how many hundreds of thousands or millions or hundreds of millions of rows of data that you have and how many hundreds of columns of fields that you may have, it can go through and analyze the various literal values that are in there, the various sort of formats. So use phone number as an example. Phone numbers can come in in hundreds of formats. Some have dashes, some have spaces, some have parens, all that. There's just a whole slew of statistics that are able to be pretty quickly and easily provided around the state of your data today. And so that's always a recommended first step in any data quality initiative because then you can see, okay, some of this data looks good. I've got a yes-no field here, but I'm seeing threes and Xs, all right? Maybe I wanna recode some of those. Maybe I wanna find out how those entered the system in the first place. But again, you wanna sort of have an idea of where your data sits as it is. Then the next part would be to build up a workflow to actually go through and manage that data. So there is a methodology to your data quality process. So we'd start with profiling it, then add in any specific transformations or scans, especially if you're dealing with some contact information around names and addresses and phones and email. You wanna make sure that's as cleansed and viable as possible. So we go through the process of potentially scanning out data that may have been put in an errant fashion. Sometimes people will throw an ID number in an address line, you wanna pull that out. Before you start to do things like address verification, and this could be really on a global basis, but you wanna verify, make sure that not only is that information standardized, but that it's accurate. Is there a one, two, three main street in this particular city? And is it spelled right? And do I have all the correct components and directionals and things like that? Add the zip plus 400. So address validation, I'd say, again, if you're dealing with contact information, you can do email verification, you can do phone verification, but once you've gone through and done all of that kind of standardization of your data, it really sets you up to get the best results for looking for duplication too. So a lot of the initiatives that we're involved with revolve around people trying to identify duplication within their particular data warehouse or maybe they're combining with M&As going on, people might acquire a company and wanna see what kind of a cross-pollination is from one source to another source. So matching ends up being a bit of a hub. So we standardize the information, get it as close to apples to apples as we can, and then use matching logic to be able to determine those relationships. And it's all configurable. So you can identify, do I wanna look for an individual? Do I wanna look for a household relationship, a customer relationship? And maybe that customer is specific to the marketing department, but maybe I've got another definition of how I want a customer to look to finance or to customer support. So we've got flexible technology to be able to define the way that we build those relationships. And then you can also decide if you wanna keep records for historical information or maybe you wanna build this sort of single customer view. And you've probably heard that term a number of different ways between a 360 degree view or a golden record, but it's all just building relationships and potentially consolidating and merging some data in the end. And kind of the last piece of the puzzle is being able to take that data and put it back into whatever particular warehouse or storage area you want. We don't really talk about building another silo and having another repository. We wanna be part of the pipes to be able to take your data, pump it through, go through those processes to cleanse it and load it back into your systems. And really the final point I'll make is once you've cleaned up in batch, then we wanna be able to talk about implementing these processes in real time and adding API driven processes around those standardization, verification and matching components, so that now that you've cleansed your data, you're not introducing that data into the mix after that. So there is a method to the madness. There's a methodology that I would say around implementing data quality and kind of the best practices around that. And those are all things that experience able to provide. I love it, that is fantastic. So to both of you, we get this question a lot in a lot of our different webinars. I love your take on it. Do you have any recommendations on how we can improve for people to understand the needs of data quality? Yeah, I'll take that one first and then Kevin feel free to jump in. But I think in terms of understanding the needs for data quality, Kevin touched on it briefly before, but I think that the first kind of step is really getting that audit, right? So everybody's data is in a different state depending on where it is and what practices that you have in place right now. So we always recommend that kind of robust profiling to start. Let's take a look at the information and run some basic out-of-the-box checks, right? So how complete are your fields? How accurate are those fields? And also thinking about what's the information that you use most within your business, right? Not all data is necessarily created equal. So you don't necessarily need to spend the same amount of time making sure every single thing is perfect, but you certainly want to make sure information say about your customer is right. That's typically very broadly used information when we're thinking about, you know, the value of a customer or their contact information so we can just get in touch with them or send them packages or deliverables, right? So we certainly want to take a look and get an audit and an assessment of the data to get started. And then depending on the importance level that you're placing on those areas and the current state that it's in, that's going to help dictate, you know, where you want to get started or maybe where you want to invest first, because frankly, you can go in and try to do everything but you're not going to boil the ocean at once. It's best to take kind of a phased approach to this. And so that data audit is really going to be helpful for that, but Kevin, I don't know if you have anything to add there. Yeah, no, I think you're 100% right. You know, I just was going to joke a little bit that, you know, odds are there's something wrong with your data, you know, and I've been doing this for a while and I've yet to find the perfect database or everything was 100% correct. So, you know, but there's levels, there's levels of issues. So doing the audit, exactly as you said, Aaron, that's a great way to see how big of a hill you have to climb. And, you know, we've talked about the kind of data maturity level of companies and, you know, and some people have a smaller hill to climb and some people have a giant mountain to climb, but you don't know the difference without doing that audit. You know, Aaron, you mentioned, and you dig over that there was questions coming in about the cultural aspects of the poll and there's a lot of questions as well. So I don't know if you want to expand on that further. And there's also questions on which companies did you pull? Yeah, so in terms of the cultural type aspects, right? I think it really, the biggest thing that we see as challenges is that communication, right? So if we're not openly talking about data, if we're trying to relegate it to one kind of, you know, part of the business, say just it's IT and the data should just work for me, right? It is something that we hear. Then you may have some problems, right? People need to be an active participant in terms of the information that they're leveraging. And so that comes down to communication. Now, one, do you have that kind of understanding of your information and where it is today and the structure of what you want to be doing, but understanding the needs across the business and how the data is actually being used is hugely important to determining what steps that you want to take on the management side. So if you know that, you know, marketing has, you know, they're using data in X-Way to do kind of this new customer engagement campaign or these loyalty efforts, or we're looking at, you know, we've got some big challenges and operations in terms of shipping and return packages, right? When a business talks about what challenges that they're having with the data, it gives an opportunity for that data to actually be fixed for folks to understand, wait, this matters in more just like an address being accurate. It matters in X number of ways to our end customer. And when you can get around with data storytelling to articulate that and understand that across the business, it's huge in just creating that baseline understanding and communication and just opening up the dialogue about data I think is huge and the first thing in terms of the cultural perspective. You've obviously got data literacy challenges in terms of people need to understand, you know, what they're hearing you ideally want that data talent where you have some of those data professionals that are leading the way that can, you know, take in some of those business challenges and interpret what that means and how it can evolve from a data management perspective. But I think first and foremost, you know, the core aspects there is communication and how do we talk about data more across the business and across departments. But again, Kevin, I don't know if you have anything to add there. Yeah, no, the only thing I'll add is, you know, and we mentioned in the presentation, it's the difference between people thinking about data quality as a project or an ongoing initiative. You know, it's really, it's ongoing. You know, it's not a one and done activity. It's, you know, as soon as you've cleaned everything up, tomorrow you're going to get more data and you've got to make sure that it's conforming to the standards and that you'll be able to match it to your existing data and build those relationships. But it's an ongoing process. And I think, you know, what's helped to build a culture around data is like the role of a chief data officer. That, you know, more and more companies are starting to pull in that CDO role. And in some ways, that's an indication that, you know, they're getting a little more serious and a little more broad in leveraging data within the organization. And, you know, a lot of times that CDO role is the advocate for data as part of the culture within an organization. So, you know, I've seen a lot of companies over the last few years really start to adopt that role and really start to make better use of their data through that sort of stronger cultural approach. And then one thing I'll add just to answer your, the last part of your question, Shannon, that came through was, which companies did we pull? We had it, we anonymized. We don't know exactly the list of the companies that we pulled for the survey. But again, they were US companies. They were businesses with over 250 employees in terms of size. But we surveyed across all departments and roles. We surveyed, you know, from a C level down to a managerial level. So really, again, kept it fairly broad in terms of what we were asking about in terms of these data management practices because, again, so many people leverage and manipulate data today. Sure, absolutely. And, you know, there's a question about receiving a copy of the paper. I'll coordinate with Emily from Experian to make sure y'all get a link to download that paper. So just, we've got, you know, less than a couple of minutes here. So let me throw this last question in. If an address is input that is correct but incomplete, for example, the country, state, city, zip code and street name are entered, but can Experian software offer suggestions to complete the address? Yeah, there's different facilities. You know, depending, obviously sort of depending on the components that are available, but we do have some facilities to be able to help sort of fill in information as well. Some sort of reverse appends on some of that data. As well as, you know, as being part of Experian, you know, we also have access to additional data sometimes that people are looking for like around demographics, for example, you know, knowing people's, you know, genders and marital statuses and number of kids and things. And, you know, we do have folks that's kind of the icing on the data quality cake, you know, being to add some of that additional demographic information and really kind of enhance your ability to build those relationships with your customers. And I'll add one last kind of component on that, on the address front, right? So there's the addresses that you already have in your system, which we can certainly take a look at. But, you know, best practice is, you know, one thing that Experian does really well is we actually have verification software that we can put in as addresses or other pieces of contact information are being entered to make sure that they're complete upon entry so that you don't end up with kind of those gaps in some of your data aspects, particularly around customer contact information. It's such a crucial component of the data. So we certainly have best practices around how to streamline that data entry and making it easier for the customer and then also making sure it's complete on your side. But again, as Shannon mentioned, we'll certainly make sure and coordinate to figure out how to send around the report. But if you want a copy today, it is available on our website, eq.com. And you can go there to download the report. Well, thank you both again for such a great presentation. We really appreciate it. And thanks to our attendees for being so engaged in everything we do, love the conversation going on and the great questions. But that is all the time we have for today. Just a reminder, I will send a follow-up email to all registrants by end of day Thursday with links to the slides, links to the recording. And as mentioned, a link to the report from today. And I hope you all have a great day. Thank you so much. Again, Erin and Kevin, thank you. And thanks to Experian for sponsoring. Thanks everybody.