 Go. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVersity. We'd like to thank you for joining this DataVersity webinar empowering data consumers to deliver business value sponsored today by Informatica. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them via 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 of questions via 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 right hand corner 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 requested throughout the webinar. Now let me introduce Susan Wilson from Informatica to kick off our speaker introductions and the webinar. Susan, hello and welcome. Thank you so much, Shannon, and thank you to all of you at DataVersity. Welcome, everyone. My name is Susan Wilson. I'm the Vice President of the Data Governance and Privacy segment within Informatica. Prior to joining Informatica about eight years ago, I was responsible for Pfizer Pharmaceuticals Data Management and Data Governance journey for nearly over a decade. And so I work with many of our customers worldwide to help them with their journey to realizing business value with data. Today I'm joined by a couple of experts that I work with very closely in the field. Ryan, we'll start with you with your introduction. Yeah, thank you, Susan. Hi, everyone. My name is Ryan Glossnow. I am a Data Governance domain expert here at Informatica. Similar to Susan, I also came to Informatica as a practitioner. So, you know, been in the shoes of a lot of our customers and helping to stand up and operationalize Data Governance program at several Fortune 500 companies. Great. Thank you so much, Ryan. Looking forward to today's session with you. Now over to Brige from Deloitte. Brige. Hi, thanks, Susan. Hello, everyone. My name is Brige Sharma. I'm part of Deloitte's analytic and cognitive practice and primarily focus on data management services like most data management, data governance and quality and have been helping our clients to innovate and transform into inside driven organizations. Being part of the Informatica family for almost 12 years now working on pretty much all of the different products and solutions from Informatica. So, excited to be here and talk about data governance. Thank you, Brige. And again, looking forward to partnering with you on today's session today. I know you've got some great stories to tell us about practical use cases in the field. Thank you. All right. Well, we've got a fantastic lineup today in terms of our learning objectives. We've developed, the three of us developed this agenda primarily because we work with customers worldwide and what they tell us in terms of what the opportunities they have and some of the pain points that they're trying to overcome. And so this is a collection of what we're hearing in terms of best practices as well as real case studies that we've worked on in the field. So you'll hear us in terms of our panel discussion as well as materials that we're sharing in terms of where the challenges are for today's leaders and how we're seeing that role of the CDO expanding. And they're looking to build data as a second language and truly drive business value within their organizations and how they're overcoming that. Some of the top priorities are our capabilities that they're looking to implement in places that they're getting started. When we've hosted these roundtable sessions worldwide, we've got a series that we run. It's a close intimate engagement with many of our chief data officers and data practitioners. They tell us that, look, delivering trusted data is challenging and it's not getting easier and it won't get any easier because we've got exponential growth facing us with the amount of move to digital objectives that we've got within many organizations. Data is growing quite exponentially and it's challenging many of us are data leaders and data practitioners because we've got a lot of untapped value. In fact, 55 percent of organization's data is dark or unknown and unknown and untapped and we've also got with that some challenges in terms of knowing where our personal and sensitive data is stored and that's about 57 percent of organizations that are citing that. That challenges us for not only our privacy regulations but also data ethics. And then 60 percent of organizations still tell us that data quality is a challenge for them and it's complexity. In fact, I've never met an organization that has said, I've got my quality issues covered. In fact, we're great with quality. In fact, most of them say that they're challenged with it and it actually is impeding their ability to drive business adoption and truly achieve value. And many of you have told us over the last few years that democratizing data is going to be critically important. Not just looking at enabling our IT or our technical specialists and analysts but really looking at self-service to a broader consumer base of business consumers that are non-technical and so self-service objectives are popping up quite extensively. And for many of you that are responsible for your organization's data strategy, the implementation and the adoption, we're hearing that your role is also expanding. In years past, we heard you quite often citing that risk and compliance objectives were your number one priorities. But today, now more than ever, we're hearing self-service analytics insights, the ability to drive business enablement, digital transformation, business transformation objectives are top importance. And we're seeing things such as your programs of work with line of sight to revenue growth, operational efficiency, margin expansion, the ability to build a data driven culture and really getting everybody on board is top of mind for many of you. Because many of you recognize that organizations that have the highest ability to break down those data silos are going to have the higher abilities to innovate with strong data sharing practices. So being able to really build in a strong data practice is critical for many of you. And to double click and put an exclamation on that point, daily democratization is going to be something that you're looking to implement with the broad enterprise. It's not going to be solved by just a centralized team of data experts, you're really looking for data and consumption data consumption by all from your executive teams who are using it for driving the insights that help us to identify new areas or markets of growth or finding more operational efficiencies to your finance teams for financial planning and looking at that cross line of business data to help us get better at looking at how the organization is performing to marketing teams in terms of looking at that next best offer and looking at the cross upsell of your customer base as well as your procurement teams looking at areas of opportunity to drive better spend management to manufacturing teams in terms of looking at the effectiveness of delivering to the supply chain and other ways of looking at operationalizing and our data science teams to helping them with data ops as well as our AI machine learning benefits. Because we recognize that data already exists in our organization. If you look at the outer rim of this circle here, we see many lines of business, business functions that are out there looking for data. And what they see on the inside are all of these disconnected systems, business processes, subject matter experts. And what we're looking to do is catalog our understanding, connect it with the business context and enable those connections to happen. But we also need that data to be clean, to be accessible, to be prepared and to be available and trusted for our consumers. And the other thing is that our consumer base is also expanding. On that left hand side is traditionally what we see in terms of our technical expertise or data scientists or data engineers and ETL developers. They're uniquely positioned in terms of seeing the enterprise data and also understanding the challenges with integrating cross line of business data and making it consumable to the business consumers over here on the right hand side. And over here we see some folks that are the citizen consumers and citizen analysts that have those important projects that they're working on to find a new insight to helping us identify new areas of market potential, operational efficiencies. And what they're challenged with is that they don't have the technical capabilities to just have access to these backend systems and to mash up the data themselves. So they're really looking at self-service to giving them the ability to find data efficiently, to understand its trust and transparency and to connect to the subject matter experts. And that population of those individuals is basically the full enterprise. And that's the challenge that many of you have highlighted in terms of the ability for data democratization. It's not only between the capabilities of providing the trusted data, but then also these consumers that are non-technical. So what I'd like to do is to bring in my two experts into a panel discussion and to talk about. We have more on the top business priorities. We've got about three questions. The first question here is, you know, what are some of the top business priorities and business projects we're seeing with data governance and cataloging today? So this first question here, I'm going to ask Ryan to chime in first and then we'll bring in Bridge. So Ryan. Yes, Susan, thanks. I think, you know, when I see and think about the different, you know, kind of business leaning projects and priorities, I kind of break these down into three overall buckets as a part of this. The first one, you know, I think about, you know, the analytic space and the need for rapid acceleration within the space, helping to increase the overall service delivery, making it easier for those end users to be able to find and understand it and really kind of trust that end to end lineage across the overall enterprise as a part of that. A good example is a customer that we work with at George Washington University. One of the things that they wanted to do was be able to turn actionable insights around the student data, primarily focused around retention, enrollment, financial performance. And so, and using that, that really helped to lead to a reduction in overhead as well as accelerating the report compilation and saving as much as 100 person hours per month. Other areas that I'm seeing a lot of focus from right now, especially in times with COVID is around customer retention. And how do they really kind of help not only just initial customer retention, but really kind of creating that customer lifetime connection and that experience there. And a great one that we work with there is a healthcare company. And what they wanted to do was to be able to provide more full spectrum services around the lifelong health journey of their customers. And so this was taking a kind of an older traditional insurance-based approach to that and turning it into the ability to bring in things from further acquisitions to gain better understanding of that and really use the data governance team to be the glue to help align for the understanding of that data, the monitoring of the policies, and the overall trusting of the data and making sure that that was being delivered to the right people at the right time to help gain that view of their customer base. And then the last one, and you hit on this earlier on the slide, Susan, is around data privacy. This is a big thing that organizations are looking at right now in terms of GDPR was really top of mind in 2018. And now we're looking at CCPA. But customers are also taking a much more kind of just focus around that. 69% of global customers are prepared to boycott a company if they don't feel that protection is being taken very seriously around that. So this is a this is a big area of approach that we're seeing our customers come in and helping to prioritize and really using these different types of regulatory data policies to help get their governance program off the ground. Ryan, great insights. In fact, you just described that two sided coin because you started in by the customer focus that then also looking at the the balance of the privacy regulations and even ethics these days, too, in terms of how we're planning to use the data. So thank you for sharing those insights, but I'd like to bring you in on this one. And so please add to what you're seeing with the business priorities in today's in today's world. I think we see very similar priorities what Ryan mentioned, right? Revenue growth and cost reduction and corporate governance are some of the key priorities that we see across industry sectors, right? As companies are looking towards innovating their go to market strategies to to increase their revenue and cost and reduce costs, we see data playing a very key role in helping them become an insight driven organization, right? For example, two of our large tech sector clients, they're looking at increasing and optimizing their channel or partner sales, right? So how they're looking at it is transforming their insight and analytics generation and delivering them in real time to the right stakeholders to make them more actionable, right? Like providing a complete 360 view of their customers to not only their sales reps, but also their partner account executives to help improve with their win rate or retention and renewal for their customers, right? Now for achieving this, the organizations are playing very important role in ensuring trusted high quality data is available throughout the value chain, right? Not only in their analytics platform, but across their value chain in their source applications all the way to your analytics platform, right? And then the second thing, as Ryan mentioned, corporate governance and privacy, that's taking center stage with new stricter regulations and data privacy requirements, corporate governance is becoming more and more important. Again, similar example for tech sector with COVID, most of our online or as a service provider organizations, they've seen exponential growth in a very short span. That's leading to higher risk of exposure and reaches and threats, right? So what they're looking for is tighter governance controls for securing their data assets. And like that starts with defining their sensitive data, knowing where it exists, and then identifying gaps where they might have exposure. So very similar themes that we see across the industry sectors. Very good. Thanks so much for sharing that, corporate governance course being top of mind for many organizations, especially as we start to bring and break down those data silos and taking a look at the data that we have. Thank you for that. Bridget, in fact, I'd like to keep you on the second question, which is around what are some of the priorities and capabilities, the capabilities that organizations are looking to implement in terms of addressing those business objectives and outcomes, both you and Ryan just cited. So I'm going to use this slide as our backdrop for the conversation. So, Brish. Sure. I'll continue on the same theme, right? Based on some of these business priorities and outcomes that we just talked about, there are a variety of capabilities that organizations are looking at. Managing master data across various domains, be customer partner, product, practitioners or agents, that continues to be a table safe capability, right? However, what we see as a shift is that organizations are now looking at next-gen capabilities, like integrating social media feeds into their customer masters or master data management systems, right? Or AI and ML capabilities for use cases like sentiment analysis, right? Yeah. Second, what we see for organizations to provide more actionable and trusted insights, metadata management, both business and technicals, including cataloging and creating a clear end to end lineage of the data flow that has seen tremendous traction across industries, right? Data management communities and users, be it your business analysts, retail writers or on the extreme and your data scientists, they want to spend the least amount of time finding the data or worrying about if they can trust the data they have and instead, spend time on generating insights or creating new predictive models for business to act upon, right? So, those two areas, we definitely see a lot of traction. Then the third area we see definitely is data privacy management, right? Organizations want to look at or identify what their vulnerable spots are and then how can they put more controls and governance in securing data not at just, and I'll use more technicals down here, right? Both from data at rest as well as data in motion, right? How can they secure and avoid a situation where they have exposure? So, those are some of the key capabilities based on business priorities that we see. Very good, Brish. And yes, I'm hearing those same things as well. Of course, we're working very closely with our customers jointly and having the ability to have these capabilities integrated is critically important for that feed and reduction of risk and lower total cost of ownership, right? I'd like to also bring you in on this because you, of course, work with many of our customers worldwide in terms of core capabilities. What are you seeing? Yeah, thanks, Susan. I think one of the things that I really love about data governance is there's so many different entry points that you can come into as a part of the governance program and kind of represented on the slide here. And what we're hearing with an organization is really they're looking to be able to apply the automation to help enable their business. So, when I'm talking about business, I'm talking about the people and the process piece of this and marrying that up to the data so that way they have a better opportunity to be able to scale to the success of their new modern data environments that they're looking at. And so, similar to what Brish was talking about, we think about things with GDPR and CCPA, right? How do you begin to automate the discovery and classification of that PII data, tying that into a policy and then also then assigning ownership and accountability as a part of this. And so, then when you kind of continue down that path, you're thinking around, you know, all right, well, now how do I begin to apply data quality? I'm looking at these policies. Susan, you mentioned the beginning of the presentation around the fact that, you know, 60% of organizations are still continuing to be challenged with data quality. Well, now how do we bring data quality into our regulatory piece of that? You know, making sure that you have the appropriate rules that are lined up to the policy to be able to cleanse and enrich that data. And then, you know, and then carrying on to what Brish was talking about around, you know, master data, right, making sure that you have the processes there to help embed the enforcement of those as they are going into the overall master data component of that. So, you know, I think that, you know, this slide is just a really great representation of what we're really kind of hearing of where different organizations kind of based on the maturity and their use cases are choosing to enter into an overall governance program. Yeah, Brian, right, it's fine on. You know, the other thing that I also love about this, to your point, I'm just going to put an exclamation point on it, is the different personas that we're engaging here, that this isn't going to be solved by just a centralized team of data experts. It's really engaging the full enterprise when it comes to democratizing data. And they all play a role. And it's critically important that we give them the proper capabilities and the experiences to truly engage in, you know, a centralized platform of shared data and metadata. And that's, you know, basically the source of truth there. So, good stuff. Right, Brian? Absolutely. Go ahead. If I can just kind of make one more point on that. You know, I think, you know, part of it too, is it's not a one-time project, as people are really kind of looking at this or as an initiative as a part of that. And, you know, expanding on, you know, there's, you know, all of these different personas that are involved, but it's changing in a way the role. So, there's that change management piece that comes in to the roles that are a part of that overall end-to-end process of that data pipeline. You know, being able to treat that data as, you know, trusted govern data supporting all of this, right? It's not just a one-time activity and then people go back to the way things were prior to. Exactly. Which is why we focus a lot on operationalizing it so we could reduce, you know, the simple documentation aspects and the aspects that oftentimes crush these teams and focus on the real insights and value of data. Very good. Thanks so much, Ryan. In fact, let's stay with you for the second question, because this is also going to be very important for us to talk about, which is where are programs getting started today with data governance? And what are you finding are some of those high-value targets from a project perspective? Because this is about change management. This is not just about a tool implementation. It's also about getting those, I call it the aha moment, in building the muscle memory into the teams in terms of what data governance can truly do for an enterprise. So, this next slide is basically, you know, the backdrop of, you know, think big, start small, scale fast. So, Ryan, I'm going to stick with you on this particular question. Where are you seeing customers getting started today? Yeah. You know, again, kind of similar to the last slide, you know, we're seeing them coming from a variety of different angles as a part of this. But I think, you know, the key thing to the success of these governance programs is making sure that it is anchored into a key strategic business driver. And we talked about some of those different business drivers that we're seeing, you know, across the industry globally as a part of this. But it's really making sure that you have that anchored into that piece of it. And then, you know, kind of tying into this slide, it really is as simple as it seems as, you know, thinking big, starting small and scaling fast, but creating that repeatable process, right? Kind of thinking about, you know, what are the things that are going to have the, you know, highest value, the lowest level of effort in order to achieve that. And making sure that you're beginning to get those wins quickly and early on because of the focus in organizations, especially of how quickly they're moving in today's current environment, you know, people aren't going to have the patience to wait six, 12, 18 months to see results yielding from an overall governance program. So, by kind of using this blueprint, if you will, you know, I think that that's really where we're seeing, you know, having some of those smaller use cases coming in, building that and then having that repeatable process that you can use in order to help scale that across the enterprise by bringing, you know, people in, you know, one at a time. And my prior role before coming to Informatica, you know, I was leading a governance program, reviving actually a failed governance program as a part of that. And my goal coming in there was to go ahead and find where are we seeing the overlap across the different use cases within the, within the different organizations across the enterprise that we can then hone in on. So, we're making sure that we're creating kind of that enterprise value. And we're not making, we're not creating solutions that are just going to be niche for an individual line of business, but something that can be represented across all of those lines and then bringing those people in and then scaling it kind of as a part of that. That's great, Ryan. Very, very good. Bridge, I'd like to bring you on this question to your focus quite heavily on implementation. And we always put in some of these great plans in terms of, you know, what the roadmap looks like. And we're looking at incrementally releasing value to the organization because this is going to be done in a big bang approach. So, Harry, what are you seeing and how are you helping customers to help them think through their roadmap for getting started? Absolutely. So, how we look at, you know, from a starting data governance organizations or data governance programs, right? It always needs to start with defining your data strategy that aligns with your organization's strategic priorities or initiatives, right? What we tell our clients, your goal should not be solved, should be solving a business problem using data and not solving a data problem, right? We see a variety of priorities across different industries like customer centricity for insurance, banking and capital market, or improving your 360 view for both providers and members for life sciences and healthcare, or for that matter in the tech sector as well, right? The customer 360 view or a partner 360 view, those are some of the key areas where our clients are investing to improve either their revenue, reduce costs, or improve operational efficiency, right? So, when you define your data governance initiative, your data strategy needs to enable these business outcomes, right? So, that's your first step. What we tell our clients. Next step is to look at what are the data management or CDO services you would need to focus that will help solve for or accelerate some of these business outcomes, right? And solve some of the key business pain points, right? So, these could be capabilities, should you focus first on your master data management, or is data quality your key issue, which is, you know, impeding business to achieve operational efficiencies, right? Now, the other aspect what Susan, you and Ryan mentioned about is definitely you need to keep in mind that you have to start small, right? Align, define your business strategy based on your organization's priorities, but start small. Look at where those quick wins are that can quickly, you know, provide value to the business, prove success of governance programs, and then continue to scale, right? Continue to scale within the business functions across the enterprise, and that then it becomes a repeatable process. And, you know, it also enforces that data driven culture within the organization, right? Now, to take an example, similar to what Ryan mentioned, right, we were working with our real estate or recline the real estate investment trust client, right? They were looking at basically starting a data governance program across the enterprise, across all their business functions and business units, but they took a very academic approach to it, right? They started defining what are our governance charters, what should be the operating model look like, and, you know, from the get go, they did not have any business line or business loss interest, right? When we started engaging, we flipped the conversation around. Instead of looking how you need to organize yourself, first look at what is the business problem you're trying to solve, right? For them, data quality was the one of the big issue, right? So we started with a very small initiative around solving their data quality issue first, that then led to, you know, quick wins, quick impacts on their business process, and then that became a repeatable process to expand it to other business functions, other geographies, became a very successful data governance program, right? So as you mentioned, think big, start small, and then scale it up, make it repeatable. That great example is just so important because oftentimes organizations are at their second, their third, or fourth attempts at data governance, because I think that what they focus is almost too much on just the charter and the organization structure, the roles and responsibilities, and not the with them. What's in it for me, meaning the people that are going to have to engage and get part of this program, they're not thinking about it in terms of what's the value to my organization, how do I accumulate that value in clear terms? And so that great example that you just provided with spot on, and it's critical because what you want to do, you want the momentum to continue to build on each and every one of these examples, because it's going to then expand to more teams, you know, knocking on your door saying, I want this too, I want to engage, I want to be a part of that, because governance isn't about slowing people down. It's really about engaging your enterprise, being able to really start to connect the dots across all of these rich subject matter experts and this rich data that you have within the organization to truly democratize it. And so critically important that your road map builds in the with them, those business objectives and how we're going to achieve those. So thanks so much for sharing that bridge. So that's powerful. And one of the things that Susan, I would say it almost becomes a bit of a self-funding organization, you know, after a while. You talked about the with them and how organizations will then begin to actually knock on the door of your actual program, what you're trying to do versus going out there and trying to actually sell that value, because people have seen the, you know, the cost savings, the time savings, you know, being able to solve those different business initiatives that the bridge was talking about versus data challenges. And so it makes it much easier going forward to be able to request, you know, that funding for maybe additional headcount or additional technology as you begin to mature as part of your program. Exactly, exactly. In fact, that is a fantastic segue to bringing us to, I want to share a bit more about a live use case that we worked with many customers. In fact, you're going to see this through a bit of overview and demonstration of these capabilities in action, because what you're hearing from both bridge, as well as Ryan, is that, look, we've got a broad set of business objectives, and we also have to serve a very broad base within our enterprise. And with that, you don't have a whole lot of time, you don't have a lot of opportunity to take some risks with these use cases and these capabilities. And so what Informatica offers is something truly that helps organizations to get their faster in terms of delivering on those business drivers and outcomes. So let's take a look at this short video, and then we'll also bring it to summary and then stay tuned. We're about to also enter our Q&A as well. So hang on and let's take a look at this quick video here. So the purpose of this demo, I will take on the role of Rosie to work for Analytics. Rosie comes in to act on to receive tasks within her Analytics project team, as well as finding a good place to understand and contextualize data. This is why she has these dashboards in view. One is related to retail in general, an overview from Court Crypt that has been shared with her. The next is configured to focus on Analytics, a key safe where Rosie and her colleagues keep an eye on the best available data. Finally, there is her own personal dashboard, which is stoked to show her actions and interests. Her open task widget is showing that she has an overdue task, so she better investigate. By clicking into this task, Rosie can see that her project manager has nominated her with taking on this GT to find the best available customer data. Rosie knows she has a widget saved of the published customer data set, so she returns to her dashboard to view it. By clicking into this widget, Rosie is isolating the relevant data sets and will only see the ones that have been published and contained customer data. She can then choose to see a scoped map of bees to make her decision by clicking into maps. She can even expand this map to make sure she will be able to see all of the relevant details on one screen. Rosie should consider how this map has been scoped. The orange icons represent the systems with the published data sets that she was meeting, so she will ignore the black ones for her purposes. By adding the attributes overlay and the physical field additions, Rosie can begin to get an idea of what data these systems contain and begin to check about what will be most useful. She will also immediately rule out the Tableau report, as aside from having limited data, it also does not link to underlying physical fields via EDC, so it may be more difficult to gain access to. The data quality check shows that there are no rules on the tool system at the start, so she would like to bypass this as well. Also, it looks like it has incomplete data as opposed to MDM. Finally, Rosie adds the privacy overlay to ensure she understands the protections around this data. MDM does have protections in place, but she should still be able to use the data for analytics in an anonymized manner. Rosie has made her decision. Based on this context, she will request access to the MDM customer data. To do just that, Rosie will move into the marketplace area, so she can locate this data and request access to it through the official governed route. She knows the data set and then retail customer MDM, so now it's down her search to focus on this system. Once she has located the data collection in question, she can click into it to understand more about what she will be receiving and do some additional checks before requesting the data. One of the things she wants to check is the data quality. It looks good in general, but there is an issue with the transaction ID. As this is not direct customer data that she needs, Rosie doesn't worry about this for her purposes. Next, Rosie will check the delivery options to make sure that she can have a copy made of the data and provision to her via EDC, so that she can manipulate it as she wishes. Her final check is on the policies. They seem to be as she expected as she's dealing with personal data, so she has no concerns in agreeing to them. Rosie is now happy to check this data out, keeping off the process of it being provisioned to her. Rosie is happy to justify her request as she knows this project has a company's attention right now. She also expresses her preference for EDC provisioning and requests this in the comments box as well to make sure. Rosie agrees to the terms of use related to handling the personal data and anonymizing where necessary. This is standard request since she's EDC's ADA, and she's used to entering into this kind of data sharing zealot. Rosie then submits her order, happy that she has kicked off this relevant task, and it will soon be picked up by the appropriate owners to action. She can keep track of its status in the My Orders section in Marketplace, and will do so until she is updated and can get to work on her Customer Analytics project. All right, very good. So let me just move on from that. Okay, we're going to go ahead and then now start to bring us home in terms of the summary as well as closing comments, and then we'll get into the Q&A in just a few minutes. But in summary, the data landscape is certainly changing, and it's also becoming challenging. It's not going to get easier for us. And so the exponential growth of data as well as about a third of the organizations still don't know where their sensitive data is, and that's challenging them in terms of not only the privacy, but also the ethical use of data. The other thing that we heard on today is called by our panel, as well as what we're hearing worldwide is that data democratization truly drives business value, and that the CDO role is no longer just tied to just risk and compliance. It's looking at some of those broader business objectives and truly building in a data-driven culture, really bringing everybody on board. And that 80% of organizations are also shifting to a self-service model by 2024. And so many of you are being getting prepared for that, as well as starting to incrementally build out your road maps to address this opportunity. And some of these tough opportunities for capabilities is that data is really embedded as a part of the business strategy, and that we're also investing significantly in AI and in machine learning around data consumption. So critical capabilities that we heard on today's call, things such as being able to bring in a marketplace for data, being able to also really bring on board our full enterprise to help us, not only for visibility with cataloging, building transparency and trust with quality, and also helping us to secure our most important data with privacy. So those are tough minds. So with that being said, we'll go ahead and get with the questions that we have at hand. And actually before we do that, I just wanted to say that let us also help you with advisory and consulting services. We've got a broad set of opportunities for us to engage you across both Deloitte, the partnership with Deloitte and Informatica. We can help you with that business case development, road mapping, helping you with business adoption as well as technical adoption as well as platform implementation, a whole host of different sorts of services that will help your organization to succeed. And then if you're like many of us that are also binge watching quite a bit of things that are on the digital platform, we also have our binge worthy channel of our customer stories that we call it our data empowerment series. In fact, you'll hear from customers like New York Life, UNC, Genworth, and Vesco that have told their stories from our data champions, where they started, where they're going with their program, how they've been able to overcome challenges and leverage Informatica and our partner in terms of helping them to achieve business outcomes. In fact, you'll see also UNC Health is a great one too because we just talked quite a bit about anchoring your programs to top line business objectives and having that executive sponsorship. UNC Health, Ratchany, Sallie talked about how she built her program from the ground up and looked at incremental projects. So we've got basically a story that has quite a few dynamics that will help you with helping you with your program. And then please feel free to connect with us over LinkedIn as well as over email in case you would like to get further information from us, from those that presented today on the call and as well as our Informatica experts. Okay, Shannon, I'm going to go ahead and turn it now over to you for Open Q&A. Thank you everybody for this great presentation, been a great discussion. Thanks for facilitating Susan and thanks everybody for joining us so far just to answer the most commonly asked questions or if you have questions for everybody please do submit them in the Q&A section in the bottom right hand corner of your screen. And to answer the most commonly asked questions just a reminder, I will send a follow-up email to all registrants by end of day Thursday for this webinar with links to the slides and links to the recording. So diving in here, data sharing is showing up a lot in conversation with CDOs. What are some examples? Great, thanks so much. I think Ryan we're going to go ahead and turn that question over to you. Yeah, thanks so much Shannon for that question. I think examples that we're seeing and it was just working with a customer on this specifically around self-service analytics and Susan mentioned you know 80 percent of organizations are shifting to the self-service model by 2024 kind of moving away from that previous IT centric model as a part of that. But we're really seeing a focus in here and kind of tying it back to this use case was just the plethora of requests that thinking about a BI or analytics team is receiving across the organization for very similar reports and people not knowing where to go really kind of thinking about that end user within an organization of being able to find relevant data that is important to their roles. And so you know when we're really kind of talking about that ability to self-service our data that's kind of an example of what we're seeing is creating that platform of a trusted repository of reports where that non-technical user has the ability to go in there have that seamless user experience finding data understanding the data seeing what it means across the organization understanding the data quality of that data as well and then being able to go through a simple checkout process and then having that that data then provisioned for them to be able to to access. And so that's something that really kind of on this one around you know the data sharing and and the the value of the trying to get out of that is really trying to save a significant amount of time and then also allowing that that BI and the analytics team to really focus on additional value added activities within their organization. Right and just to add to that Ryan you know how we explained it all of these capabilities are now helping business functions across the value chain like unblock new insights that can be more actionable right for example back in the day auto management data or support tickets and support issues data wasn't readily available for your sales reps to look at right there was different ownership how where the data is what report should they be using at right now making the data shareable readily available sales organizations are running reports and generating their own insight using data across the value chain right what it results as an outcome the sales rep is already able to see what the customer owns what are the some of the key issues that they have with the products or what trainings have the customer taken where is my cross-sell upsell opportunity right and that's helping sales reps have more personal conversation or more you know conversation that is more impactful for either renewing or upselling crowd selling to their customer so when a sharing data across your different business functions organizations that that's unblocking newer and newer optimizations across your value chain. That's a great example Bridget and that actually reminds me of a use case that I was working on back in my prior role as part of the governance organization similar you're talking about with the sales organization and allowing that you know account manager to have that understanding what's going into their accounts but you know another great example of that was you know from the opportunity to cross-sell and upsell but making sure as you're going in and having a conversation that it's not getting derailed for example of hey well we've had 15 support issues that have been outstanding for the last x period of time and the conversation completely pivots from the ability to cross-sell and upsell to more triage with with the customer so being able to have all additional insight around the customer is is extremely helpful to be proactive and nurturing that relationship. That's great Ryan. Susan anything you want to add to that? Oh no I just thought a great question coming in from Doug Graham regarding access requests maybe Shannon you could do the honors with asking the question. Any change from a system-based access request to a data set-based access request what are the major changes that need to be considered and what capabilities need to be in place to support the conversation? Yeah I like this question maybe I'll start and then I'd like my fellow panelists to weigh in. When we think about getting access requests at the data level it's going to be critically important that we think about that business context to data so not only do I need to understand it from a physical perspective but you know what is where does this data come from like from which business processes you know what are the who are the subject matter experts that are going to be important to us to to basically connecting to understanding this data. The other thing is to also be able to understand the importance around the quality and the transparency of the data collection is it appropriate for us to use at this point in time and so important for us is to have that context around the on the business angle the technical angle the quality angles and to be able to provide an efficient process for facilitating that data that data request because this is oftentimes those individuals that are in that line of business that are non-technical and we need to make it easy for them to understand that they've they're requesting access to the right data and that they can seek to understand more about it if they choose to do so maybe Ryan or Briggs anything else to add on to that? Yes Susan I think you know and even more particular at the at the data set level because multiple organizations you know thinking about an ERP tool or something along those lines are all using that tool but yet the ability and kind of access control around the particular data attributes or elements within there might not necessarily be appropriate for those users so being able to see you know how that data is actually being used across different organizations so is it something from a financial perspective a great example of this is a process that I put into place when I was at Microsoft standing up there governance organization and corporate finance was when the Microsoft surface initially came out Microsoft immediately became a competitor with their OEMs in this case and so now all of a sudden you could not have even though it was in the same system you could not have certain people having access to surface data invites versa into data against the the OEMs on the overall financial performance of that so I think that's kind of an example of same system and it needs to be broken out by that data set access for an individual and their roles and responsibilities within the organization. Great point. Yep I think we're seeing a shift from you know user saying I have access to this application this table this column rather than you know moving to than moving towards saying I have access to our sales data or I have access to our opportunity data right that is more curated it has more business context added to it I'm looking at our opportunity win rate rather than saying I'm looking at this table this column in my Salesforce instance right I think so that that's a shift that we're seeing what this and to get there is again what you mentioned Susan you need to catalog your data you need to add business context to it you need to add the trust factor so the quality to it so that your end user which is your either data scientist business analyst ETL developer they're not struggling at looking at tables columns trying to make sense of the data but they're looking at you know a final product that they can start using into their reports of insight generation. Great point you know we had a couple questions come in earlier on you guys cover this a little bit but just want to just dive in a little bit more here at what level is data catalog tag to data glossary whether at logical or physical model layer and then just add on to that you know how is data dictionary being addressed in order to address the data lineage part not sure who wants to jump in there yep I could take that linen bridge maybe you can add to it so it's critically important that the business language gets appropriately associated to the technical understanding and so that automation is important in fact you know through the video one of the things that was highlighted is the automatic business glossary associations because you know that activity might require for example a DBA an architect a data modeler to be able to understand for example customer ID maps to miscellaneous text one you know in the table in some foreign database so the business glossary associations are critically important for that transparency and understanding and that so that does need to happen at that level from business to technical and then that helps us with the lineage and understanding because at the same time I'm also harvesting the technical metadata that gives me a sense of the source to target the transformation logic and that also gives richer understanding for both the business and the technical in terms of is this the proper data that I'm looking for is this the data that I expected is there are there other data sources this also helps with impact analysis when I am looking at changing my data landscape and need to appropriately notify those individuals that that are impacted perhaps maybe I'm moving you know to a staff solution maybe I am decommissioning things that of importance of us in terms of being able to manage our data states are critically important for that business to technical mapping and that keeps anything to add on to that absolutely I think we we definitely need to start with your business glossary what are the critical data elements what are their business context or business definitions and then go into looking at your technical metadata where do they where do these critical data element exit like across your different applications in the case right what are the different how is that data in those applications are looking at your data quality then next right so starting with business definition then looking at your technical metadata and then creating the lineage how is this data flowing from across your value chain from one system to another for all the different use cases you mentioned impact assessment technical migrations business transformation programs right with cataloging lineage both business and technical metadata that makes the impact assessment so much easier rather than doing the research absolutely brazier yeah I think you know and and you guys have done just a tremendous job explaining you know how things are related and impact analysis and assessment of looking at that but I think there's a there's a big underlying piece that organizations are looking for in order to get started with this and that's really around the ability to automate those pieces that you both have spoken about and leveraging the different you know artificial intelligence and the machine learning that is out there now that really helps to accelerate and operationalize those different capabilities that that the organizations are looking to drive exactly yeah we're going to find more information on any prerequisites for data provisioning via EDC reference here okay so we can certainly yes we can certainly send that out afterwards as far as links to additional documentation around you know the enterprise data catalog and those different prerequisites um support of that I love it yeah anything I can do that in the follow-up email that goes out and so diving in here I think we've got time for one more question so where did the phrase data democratization come from democracy makes people in organizations think of voting of course most organizations do not vote on who data owners are or vote on what system is in the system or of record or how to calculate sales um the term causes many people angst in organizations you know organizations living to popular voting of definitions quality measures and people are who are responsible today are for data or is there any more insight on the term which is becoming more popular so that's about one and then I'd like to turn it over to my panelists but uh the I think of it not so much as voting I think about it to that point about visibility and transparency making it understandable to everyone in the enterprise I mean we're not leaving anybody behind when it comes to building data as a second language within the enterprise and so um and and while and that means I make it transparent the transparent the transparent so they can find the data they can also request access to they can connect to the subject matter experts but they can also comment on the data I wouldn't say whether might be some voting features in terms of like a yell burning for data the ability for us to comment on it and to build a community around um collaboration is is what we're seeing um the definition of around data democratization um Ryan would you like to add anything a bridge yeah I think um that you know that's a great point and you know I really kind of think of it as as being able to provide broader access to uh data and really trying to help eliminate those different barriers um to access that exist um today right helping to streamline that overall process as a as a part of that and then allowing as Susan said you know that that access to to that data and the understanding of what that data means within the within the organization but certainly not to meant to create a you know a bureaucracy around data all right well that does bring us to the top of the hour thank you again so much for this great presentation and thanks for our attendees for being so engaged in everything we do we just love it again just a reminder I will send a follow-up email to all registrants by end of day Thursday with links to the slides and links to the recording thanks everybody and hope everybody has a great day and stay safe out there and thanks to informatica for sponsoring today's webinar thanks all thank you all thank you thanks bridge thanks Ryan BQ thank you thank you