 Here we go. Hello, and welcome my name is Shannon Kemp and I'm the Chief Digital Officer of Dataversity. We would like to thank you for joining the latest installment of the Monthly Dataversity Webinar Series Advanced Analytics with William McKnight, sponsored today by precisely and relative. Today William will be discussing common misconceptions about master data management. I have two 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 by the Q&A section. And if you'd like to chat with us or with each other we certainly encourage you to do so to open the Q&A panel or the chat panel you will find those icons in the bottom middle of your screen for those features. I hope the chat defaults has ended just the panelists but you may absolutely change it to network with everyone. As always we will send a follow-up email within two business days containing links to the slides, the recording of the session and any additional information requested throughout the webinar. Now let me turn it over to Sue from Precisely for a brief word from our first sponsor. Sue, hello and welcome. Hey, thanks Shannon and thanks to everybody for joining us today. As she said my name is Susan Pollock and I'm the senior manager of product marketing for Precisely. Let's jump right into master data management powered by data integrity. To compete and thrive in today's dynamic economy, your core business needs to be right compliant, and just as important it must be available wherever it's needed by the business, faster than ever. We're talking about the core master data that describes your business. One of the misconceptions about master data not to steal Williams thunder is that it's that most people think it's mostly customer or product data. This is not so. All of these are examples of master data that your business relies on to drive data driven decisions, improve operational processes and manage your risk. And as sources get more diverse and complex, and data continues to increase in volume and velocity, modern master data management is becoming a business imperative, yet it has its challenges. Each of these quadrants that you see on the slide represent challenges that every organization faces. One of the biggest obstacles is that we typically see is poor data literacy. This can be something as simple as having exactly the same name for a term but two totally different meanings depending on which line of business within your company you're in. Or you can have things that have slightly different names and you think they're the same but they're not. And you just don't know what the source of truth is you don't know where to go get the right data. And this is all poor data literacy. So if you have poor data literacy that's almost always going to result in the lack of data readiness as modern master data management has emerged, just having data that's complete and conforming, and fits in the field that it goes into in the application is not anywhere close to enough, the bar has moved really way up data now has to be trustworthy, it has to be timely, it has to be the right data the right place the right time, and that process has to be seamless, there can't be any gaps in the data. So if you have poor data literacy, you end up with a lack of data readiness, and that exposes you to a lot of data risk without documented lineage or no traceability of those changes. You tend not to know what to protect and the data that needs to be and not knowing what data needs to be protected and encrypted. And something that a lot of people don't think about these days that is becoming more and more important is that you retain data longer than it's needed for a valid business purpose. A lot of the requirements and rules now say that you're not allowed to hang on to data, if you don't need it. You can't have it if you don't have a demonstrated business use. And the last thing that we see is lack of sustainability, a key indicator here is your data lack, your data updates lag reality. If you don't move at the speed of business because of either manual tasks or complex or incomplete processes, and you have a conflict around custodianship and ownership of the data. Custodianship is who maintains it while ownership is who's empowered to make the decisions about what good data looks like. So successful master data management has to have clear and automated processes. To achieve your goals, you need what we call data integrity. Data integrity is data with maximum accuracy, consistency and context for confident business decisions. But data integrity is a journey and master data management is just one piece of that journey. The precisely data integrity portfolio can help you wherever you are on that journey. Through 12,000 businesses leverage precisely technology across the globe to build value and work seamlessly with both your traditional and modern tech stacks to help you optimize your investments now, and in the future. This precisely data integrity portfolio empowers you with data integration to break down those data silos and derive real time analytics quickly by building modern data pipelines and models, or data governance to manage data policy and processes with greater insight into your data's meaning, lineage and impact. You can observe ability to deliver data that's accurate consistent and fit for purpose across operational and analytical systems and proactively uncovered data anomalies to act before they become costly downstream issues. Precisely data integrity also includes the ability to leverage location intelligence to derive and visualize spatial relationships to reveal critical context for better business decisions or enrich your business data with expertly curated data sets containing thousands of attributes for faster, confident decisions. The precisely data integrity solutions all work together and are powered by machine learning intelligence to optimize your teams, resources and investments. With data integrity powered master data management, you can expect outcomes like enterprise wide understanding, so that you have a recognized and reliable system of record, you've minimized those data silos, your data stories are democratized, and you take action on trusted data. Your data is fit for purpose with enterprise wide data governance, you can look at cross domain intelligence, you've got visibility into lineage, impact and relationships, and you've enriched your data with data sets and location intelligence for more precise data outcomes. And greater data policy management and allows you to believe that you are working with reduced risks, while maximizing your compliance with data that's standardized and normalized, and you're leveraging machine learning and automation to be proactive in understanding that risk. And last but certainly not least, there's a more sustainable process by being able to automate and reduce costly manual tasks and rework, you can scale on demand, and you've got the flexibility to adapt to emerging requirements that are undoubtedly going to be coming. The value of data integrity powered master data management is that your business will be able to go faster, be more agile and improve the quality of your most important data. So your business can compete and thrive in today's dynamic economy to quickly find insights and adapt to data changes in your key data. So complete confidence in your critical business data and ensure that your master data management strategy is flexible and sustainable to grow with your business in an ever changing and expanding business environment. As the leader in data integrity precisely as data integrity solutions contain everything you need to deliver accurate consistent contextual data to your business, wherever and whenever it's needed. Thank you so much for spending some time with us today. It's going to be a great conversation. If you want to continue the conversation here. Please visit our website by visiting the scan code, or contacting us if you'd like to have a chat about how we might be able to help you out wherever you are on your journey. Back to you Shannon. Thank you so much for kicking us off and thanks to precisely for sponsoring. If you have questions for a suit feel free to submit them in the Q&A section of your screen as she'll be joining us in the Q&A at the end of the webinar. And now let me turn it over to Anne from RELTO for a brief word from our second sponsor. Anne hello and welcome. Thank you Shannon and thank you everybody for joining. I'm going to share my screen here real quick before diving into my portion of today's session. I'm the Senior Director of Product at RELTO and what I'm going to talk to you about today is how at RELTO we solve master data management challenges that our businesses have. And so if we look at our customer base it's really about getting their core data and creating that single source of truth that they can rely and trust and drive business decisions. What I meant to data is a problem in every organization every enterprise data is typically siloed it's dirty it needs to be cleansed consolidated unified and that's where we come in. But beyond that though we also provide very high performance low latency apis. So our systems are designed to be operational and transactional in nature so we're real time in the sense that anytime data is being changed or updated across the enterprise. So we can ingest those updates and also push out those updates to downstream systems within a very short time frame so that that's really kind of what differentiates us is those real time capabilities. And then the third thing here is we offer very quick time to value so we've proven with our customers that we can get them up and running with their MVP MDM implementation within 90 days. I'll talk a little bit more about this in a second. And then our MDM has been proven to save a lot of costs and drive organizational efficiency because of the way we do things and because of how we set up our systems. So we're driving real business impact for all of our customers day in and day out. Oops. So what the key way we drive business value within 90 days and we can master our customers data within 90 days is because of what you see here. This visual shows you just a high level diagram of what we call a velocity pack, which is a pre configured out of the box data model along with rules and configurations that are all pre packaged and pre created. And in essence, this is offering customers and out of the box MDM where everything is defined by relative because we are the experts we know what our customers are doing with all of their data. And so by industry by verticals, whether it's life sciences, healthcare, insurance, financial services, we have created very specific custom velocity packs. And the name velocity just means that the solutions are very fast and they can our customers can get up and running very, very quickly. And as I mentioned within 90 days we're seeing implementations that have been stood up because of our prescriptive approach. And because we're leaning on years of our experience and all of these verticals and helping guide our customers to set up the exactly the right kind of implementation that they need for their master data management needs. And in addition, we have pre built connectors to many of the popular application integrations and data and merchant providers that our customers typically use. So a few business examples here of how we're driving it savings with our customers so you can see with these logos we work with some of the largest customers in the world. One example that I want to point out is AstraZeneca, a leading life sciences company. They have used relative MDM to consolidate 66 different MDM sources into three. And because of that they're now saving 3.6 million pounds a year because of what relative was able to do and this is just one example. So we have a forester study to show the overall economic impact of MDM. And because of what we're doing, we're saving companies, nine months of time to add data sources, increasing operational efficiency by 78% and some of these other data points that you see here. So the business impact here is far and wide. And then with the forester wave, you can see that relative has been selected as the number one and MDM strategy. And for four years in a row now we've been consistently ranking high in the forester wave. And we've earned the highest score in all of these criteria that you see here, including matching entity resolution, multi domain scalability and so on. And some of these quotes that I'm pulling from the report are the real time AI driven aspects of it that customers really come to appreciate. It's providing that customer 360 view. So not just static entities, but also the transactional and interaction pieces that give you the entire 360 view of the customer. We also have machine learning models that can help customers integrate entities do entity resolution matching merging in an automated fashion. And then behind the scenes, we have graph modeling capabilities that allow us to generate those relationships in an automated fashion. And all of this happens on a secure scalable platform. And then last couple points here is that we are available on all three public clouds Azure Google Cloud Platform and AWS where a cloud native solution. And that's how we were built from day one, and that's something many of our customers really appreciate and then lastly, we're not just about customer data we also offer MDM capabilities in other domains like products supplier asset reference data and so on. And then we have a lot of options to enrich that data with popular data providers that might exist. I'll end with this slide, which is obviously William is going to go into this in a lot more detail. But we hear a lot of these myths and misconceptions from our customers when they think about master data management. The three that I want to highlight that we come across is that an MDM is a complex project that doesn't really deliver on business value or outcomes. That's definitely not the case. I talked about AstraZeneca and I'm reemphasizing it here where they lowered their overall total cost of ownership by 3.6 million pounds by working with Raltio. The second myth is that MDM takes many, many years to implement, but that's not the case as well with velocity packs with Raltio with our prescribed implementation methodology. We really break it down into milestones that are achievable. So we focus on an MVP implementation. So not the entire sort of boil the ocean type implementation, but getting in a couple of data sources, integrating those creating a customer profile and showcasing the value and doing that in a quick manner within three months. That's been something that's been a game changer for us and customers are really impressed by how quickly they can stand up MDM using Raltio. And then the last one is MDM is not just about data duplication, which many of our customers might think going into a sales cycle. It's actually so much more than that. We offer so many more capabilities than just deduplication. There's data integration, there's cleansing, standardization, there's enriching the data, there's governance and compliance and so much more that's wrapped around it. And so when we work with our customers, we work with them at that holistic level, not just about data duplication. So that's all I wanted to share today. I'll now hand it back over to Shannon. Hi, and thank you so much and thanks to Raltio for sponsoring. If you have questions for Anne, feel free to submit the questions in the Q&A section of your screen as they'll be joining us in the Q&A at the end of the webinar. And thanks to both of our sponsors for helping to make these webinars happen. Now let me introduce our speaker for the series, William McKnight. William has advised many of the world's best known organizations, his strategies for the information management plan for leading companies in numerous industries. He is a prolific author and a popular keynote speaker and trainer. He has performed dozens of benchmarks on leading database, data lake streaming and data integration products. And with that, I'll give the floor to William to get his presentation started. William, hello and welcome. Hello. Thank you, Shannon. And thank you, Sue and Ian. That was a great setup to what I'm here to talk about both Sue and Ian. I am, excuse me, got into misconceptions about master data management. We all see a lot of misconceptions out there. I spent a lot of my time justifying these MDM projects. So if you're out there trying to shape or champion master data management within your organization or for your client as the case may be. And you're finding that it's difficult, it may be because the people that you're talking to and trying to explain this to have misconceptions about MDM and they probably have different misconceptions about MDM to an individual. I started to call this talk top 10 misconceptions about master data management, but I couldn't stop there I think I must have close to 20 for you here today so I'm going to be rattling through them for you. Now, I'm not going to spend a lot of time defining MDM. I did that for you more or less back in May so you can catch that webinar in a rears if you like. But I am going to spend a few minutes level setting just so we're on the same page with MDM and how important it is today. Even today, when things are a little bit more challenged I would say than they were in the past few years. Yes, we are still seeing MDM projects kick off and advance into new subject areas and so on. When all of these misconceptions are cleared off cleared up. So I'm going to start with something that I must say a refrain of a couple times a week. And that is that robust MDM is half of the effort for success of all these projects and many more. It would be impossible to list all the projects that MDM is good for it's whatever data is good for which is just about everything right. Certainly, we see it in fraud detection call center chat box. These are some of the more popular applications that companies are doing today. And if you look on the right side of the slide you see a bunch of subject areas. Now Sue mentioned it's more than customer and product and I'm going to repeat that a little bit later but yes it's about all of these and much more and much more than you and I can even think about today for even your enterprise. So we're going to get into that. But my point is that you can cross reference these applications. And you will find that if those subject areas are really mastered. And in the way that they, they could be at high levels, then if that data just needs to get used for these applications you are well like half the way to success of that application you have to build the application on top of the data at that point. And by the way, this is very true for artificial intelligence based type of applications like we're all getting into right. So those AI based applications need a voracious amount of data. Where's it going to come from. Well the detailed data is not going to come from MDM, but a lot of the supplementary type of data that really puts the spotlight on the data and makes the data alive. That's MDM data. And that's going to come from MDM. Now, what are people using MDM for today? Different things. I would say this probably captures a good 60, 70% of the market here though customer deduplication, name and business matching, the things that you see on there all important, don't get me wrong, but there are some organizations out there really leading the way, and they've got the ball rolling downhill with master data management so they're able to do things like mastering in supply chain management just pick on that delivery shipments carriers transport modes material insights once you do that. How far are you to mastering your supply chain. You are far. And there's so many more uses for MDM we'll get into some of that. Let me start by by let me share with you where you can look for the MDM opportunities in your organization. And a lot of you are trying to shape MDM for your organization. We have to find a business target for it. We can't just say it's a technical exercise so and so said that we should have it, and so on we have to find some business targets business targets that need data shouldn't be that hard to find really. The products that you make the supply chain for those products, your business operations, the intelligence that you use in designing your product and services and the intelligence used in the marketing and approval funnel. What haven't I covered here. It's a lot but MDM really can affect all of these things I think that's, that's really a big part of the problem with getting MDM projects kicked off. What you can see it is, it can be once you get past the misconceptions, it can be a lot. And there may be, I don't know let's say it can do 20 things which probably about true. And you don't need 15 of them that doesn't mean that you don't need MDM or that doesn't mean that MDM won't pay for itself or be beneficial to you just means that you can ignore those 15 for now. Because I think a lot of the functions of MDM are interesting as you go forward so it sets you up very well for getting into all of those additional functions. Alright, so this is a reference architecture for MDM. This is an Azure example we have sources of data we have our data lakes we have our data warehouses and so on. This is right in the heart of this, of all of this, and if I showed all the arrows in a, in a, in a very elegant architecture or just make a mess of the slide because there'd be so many arrows from MDM, because it really does interface with everything that's fully manifest within an organization. So, yes, it sits somewhere in the, in the, in the organization either operationally. Well, I would say operationally, but it affects all the operational environments and the analytical environments, or it can, it can grow into that. So I would say by way of definition, I want to share with you that it's not just about the core attributes, which is how you get started, I would say most people get started that way with all these things that are, for example, one to one to a customer date of birth customer status marital status accepted things you see there but also these was what a lot of people call analytical attributes I call them empowering attributes because that takes your value of MDM up to another level. So MDM can be operational and analytical, and we're going to get into that that's actually misconception so let's get into our misconceptions about MDM here we go. These by the way, are good misconceptions, meaning, meaning there are things that you need to add on to your MDM program for it to succeed. Some of them are really misconceptions that people have about MDM the discipline that hold you back from getting more into MDM like for example this first MDM isn't necessary. We have ERP or CRM but I think a lot of times, people who say this that they know that MDM fits somewhere within a Venn diagram in their organization. But it's a lot of work to kind of ferret out. Okay, what's MDM going to do and what's ERP going to do and what's CRM going to do. And when you, when you don't want to go over that hill, you can easily leave MDM aside, and that's that's that would be wrong to do, because MDM fills the gap, even if you have ERP or CRM or both. Either one of those are really great distribution systems. ERP is not great for for example data quality CRM is not really great for instantiating the data on and on really MDM does a lot of things which leads me to my next misconception. It's kind of a big blanket, big tent misconception MDM is just blank. Yes, depends on your perspective, but people come at MDM, and I can tell I'm always trying to figure out okay what's this person think MDM is data integration. Or maybe it's about architecture maybe it's about data modeling we're not doing modeling very well so let's do MDM. How about data merge and match. That's huge data quality. Yeah, that's a big part of it. How about workflow for for for building out the master data. Department A passes to department B etc etc till we have a master record how about that yeah that's a part of it too and also what about hierarchy management. Not as widely used as the others but some shops will use that the most. So you see it all kind of depends on your perspective and your business needs. That's why I want you to effectively fit MDM into your environment and use the functions that you need out of MDM, knowing that the others are there, and that you'll probably grow into it but don't try to borrow the ocean here with MDM find a business need make it happen. You can load it scaleably so you can grow out within your enterprise and which leads me to something that I also say a lot of and that is that MDM should be enterprise MDM. It shouldn't be just about a singular application. Yes, it can start there, but once you build out customer and product for example, who doesn't need customer and product which application out there doesn't need customer or customer. Okay, there's a few, not too many. Most of them need it, and most of them will home grow their own. If you don't have your data as a service to provide them from your MDM pub, which is how I express MDM within the organization is data as a service. So it's all the things I mentioned again, data integration architecture modeling data merge and match data quality workflow and hierarchy management. Now another misconception is that it's only about technology and it. So business users don't have to be involved. That's an it thing that's a technology thing don't give me these buzzwords because I'm you know I'm not going to hang with that just just show me the results. It's not going to work like that so this is a bit of a warning that you need to get the business involved in MDM so this may be a warning for you MDM champions out there. Who are going to do MDM to not do it this way and to not even really get into it with this thinking. This is this is a hill I will absolutely die on for a project, meaning that if if I know the client thinks of this, and I can't disabuse them of it, and they're not going to give me any business help in the project to define the data define the data quality rules, help me place it in the organization, so that it's doing maximum benefit for them. Yeah, it's probably not a non starter for MDM I hate to say it that way, but it is, you've got to get more than technology and it involved in these projects and also. It's not a one time project this is again another kind of warning to MDM champions out there. It can be it can start as a one time project doing something for one application, but I say that on the side, you need to be developing a strategy, you need to be developing a strategy of where it's going to go and the reason for this is that MDM it will it'll pay for itself with with one project but it will weigh more than pay for itself the more it goes the more use you get out of it. The more you build that thing once and you use it many many times over that's that's the my goal for you is to build true enterprise MDM. Some of my misconceptions again have been trying to help you get to MDM. This one is a bit of a warning of something that you need to do. I'm not trying to burden you with strategy and doesn't have to be a huge deal, but to get the maximum benefit and really sometimes just to justify the project. It needs to, you need to have a strategy around it. Okay. Next, MDM is expensive. You need to get into dollar numbers here in terms of how expensive is MDM because it really does depend on several scope features but these are true things about MDM here as an enabler. It means improve project results that's what it's all about MDM by itself and if you think this way inside of an enterprise that Oh if I build that data warehouse over there if I build that data lake over there. That's not good. Or if I build MDM that's no good. What was the ROI on just that we got to think a little bit beyond. Okay, into the uses of these things like MDM. And for MDM it's going to reach out and touch a project or projects within your organization. What is it going to help make them better. The easiest thing I found to do is to justify the are the, the TCO, excuse me the TCO, meaning you're going to save money by actually doing MDM because you're going to do it right you're going to do the data part right. I'm sure then saying well we're going to sell, you know, 50,000 more donuts if we do MDM on this program to sell donuts. That's a little bit harder. So MDM should be made in conjunction with most projects yes most projects that are on the docket MDM should be a part of and project benefits are indirect. And that turns that turns people into naysayers about MDM but these are things you have to go through. MDM also enables lower operating costs. And here's another one, MDM is only for large organizations. I think small organizations midsize organizations I've seen them do very well with MDM their scope is smaller and that's okay. They're just lower, and they're actually able to get through a lot of the value proposition for MDM a lot quicker and that's all good. And that lowers costs and that means it's not just for large organizations, all organizations out there that want to compete really need to compete on their data. This is this is MDM is the elegant way to compete on data today to be that what's the word for it. Data champion, if you will, data driven data driven organization yeah that's it. Okay, next is another to do slide for you staff MDM projects entirely with technical people I kind of touched on this a little bit ago. Yeah, kind of bears repeating and a lot of times what I've seen is that this is acknowledged upfront that oh yeah we have to have business involvement and, and yeah we'll offer up this and this person and that person at 10% and so on and then we get into the project. And they're not there and then we go back to okay it's just for technical people to do. It's not so you want that commitment. That's a misconception. Okay. Okay, another bit of warning here. It will fix all our data issues instantly like data quality. I actually have had conversations with people who want to install an MDM tool because it will fix our data quality problems. And it will not I could probably do a misconceptions about data quality presentation I would have enough material, trust me on that. But one of them has got to be that MDM or anything really a data quality tool, a data integration tool will fix all your issues instantly. It won't it's a misconception. Misconceptions. This is a similar misconception to what we buy a tool and implemented and we're on our way to the promised land without having to do all the messy work around it. Sorry, this is another to do a warning out there for you MDM champions data quality needs business input back to my prior misconception. If we get these cleared up though we're going to we're going to do quite well with MDM. MDM is another data warehouse. Well, if you squint your eyes at it. It kind of looks like a data warehouse there's data coming in, and this data going out. Yes, but there's a lot of differences. MDM is operational. It's not analytical. It's, it's very transactional in terms of moving data around. MDM is for master data only you wouldn't put your transactions in MDM. Now you might bring transactions into the hub to glean some analytical value out of it. You can put updated analytical attributes in MDM. That's all great. That's not what I'm talking about here I'm talking about as a storage mechanism. Right now we are certainly living in a world where there's no one size fits all you need your MDM you need your data warehouse. You don't want to take and on and on and on. Maybe time over time that will abate a bit, but right now we need them all MDM is real time by the way most data warehouses are not. There are similarities though in terms of data quality and data integration. Now data quality. What I'd like to do with my architectures is for MDM to feed the data warehouse, it's data. So all those, all that dimensional data for those of you that know what I'm talking about all that dimensional data in the data warehouse, that should come from MDM, because in MDM that's where we're really cleaning the data up and that's the dimensional data that the data warehouse needs so MDM should feed the data warehouse it is not another data warehouse. Right, similar to that one. The MDM hub is downstream analytical and post operation. No, it's, it's very much operational if you're not familiar with post operational. That's a term I use a lot to mean the analytical environment except when I say analytical environment people tend to think it's just the warehouse and maybe this or that analytical hub but I mean everything post operational. Everything that collects data from an operational environment and an operational environment, meaning one that is transacting your business. Okay, I hope that helped and didn't hurt but MDM hub is not a downstream analytical after the fact kind of database and by the way, most of, most MDM implementations out there and certainly most of mine do involve an MDM hub, sort of the physical MDM, not the logical school of MDM. Okay, here's a big MDM is for customer and product only and we're kind of doing a half job at that we're, we're getting by so I don't want to make that investment I hear that a lot. Well, I doubt that half job, or whatever you're doing outside of MDM is really good enough for giving you the most value for your book. In terms of what you're doing for customer and product let's start with that but the misconception here says MDM is for customer and product only. There's other subject areas that it's for Sue listed a few I listed a few earlier real quick, but there's no limit. So, I'm not trying to limit you, we're not trying to limit you here. It goes beyond I have one client that uses MDM for DNA. I couldn't have thought of that when I walked in the door and we started talking about customer or product I forget what it was, but eventually they got the hang of it. They figured out, oh, this is what MDM is. This is what it can do for us we want to do more with it. We get beyond our first target we get beyond that customer product which that's where most companies get into MDM. And usually they go to the next one customer or product after that one, but beyond that we're probably talking a good six months to a year into the program here. You're actually at the point then when I hope that you're able to spin up multiple scrum teams not just one doing one subject area at a time. You're able to do multiple and really pick up the pace there. Now, that being said, you might, you might think from what I'm saying that some customers are done with MDM they've gone through all their subject areas, some are close. But I don't know anybody that's done done with their business by the way of MDM. There's been, there's probably some out there. If so get in touch with me I'd love to do a case study on you but most likely everybody's still on the journey it is a journey which might be another misconception that it's not a journey. It's definitely a journey, although any journey in business today must have points at which we can say okay now we're delivering ROI now we're delivering the value that exceeds the cost for what it is that we're doing here. And MDM certainly has to be that as well so don't get off on some theoretical exercise with MDM make sure you stay in touch with those applications that need the data. And you provide that data as a service to those applications and do be open minded that MDM can go well beyond customer and product into many subject areas and by the way we could probably spend some time talking about how you define subject areas I won't do that right now. But I'll move on to the next misconception which is about MDM and workflow. These are the misconceptions here. And the M has workflow that's not a misconception by the way, it certainly does. And we must use it that's the misconception that you must use the workflow if you think you already have master data in the environment somewhere. Okay, great. You can just move that data into MDM for all the other functions of MDM, especially the data integration part where that data is going to get integrated into all your operational systems or analytical systems and so on. So, MDM is still great in those environments that think they have MDM because it's probably sitting in a CRM system or an ERP system or somewhere, where it's not really a great place to distribute that data, or do hierarchy management or the other things that MDM does. Okay, great. But you know, oftentimes, I find that even though some people think that they may have great master data that really don't at the end of the day. So, by scrutinizing that data, you might find that you actually do want to use the workflow inherent within these MDM products. That's really where I land for most subject areas. I have one client where we did some subject areas with the workflow and we did some without that's okay too. That could be another misconception you got to do them all one way you don't. And the next misconception is MDM doesn't have workflow. So we must bring the master data to us so we still got to do that function. Well, if you have to do that function outside of MDM, then MDM's value proposition is drastically lowered I would say so all the things that MDM does to create master data. It's fairly unique to MDM in terms of technologies out there so MDM does have workflow you don't have to use it, but it's very valuable. Okay. This is one that most people don't resonate with me on this. MDM is not for syndicated data. That's a misconception I think it is. Usually, the reason that it's, this data is not brought into the MDM hub and now here I'm talking about customer attributes, product attributes, things that you get from a third party marketplace, or you're buying data which third party has taken off recently so there's a lot of data out there. Think about that data though in terms of is it isn't really master data. Is it something that many applications will be interested in in my enterprise and you'll probably find it yes, many of that much of that data is, especially around customer if you're enhancing the customer dimension. That's just customer data. That's just customer data from a different source. So, MDM is for the syndicated data most syndicated data, I would say, although it doesn't seem to work out that way and many enterprises because the people who manage the syndicated data are fairly removed from enterprise data architecture. Well, maybe that's what has to change to make it happen. Next misconception, and this is a two-parter. MDM is for operational data or MDM is for analytical data. It's for both. Okay. We can definitely use MDM to bring in operational data, change it, generate the analytical data from there. Like I showed you before, the empowering attributes, add them in there, get that exponential value uptake from MDM with that analytical data. If you have to start with operational data, that's fine. But keep in mind that what you're doing there is you're solving the problem probably of real-time data integration, which is a big problem. Okay, great, solve that problem. But do know that you can go beyond that into these analytical attributes. All MDM data will go as slow as the first one. The first one took an egregious amount of time or at least in somebody's view. Who's to say really how long it should take. I guess an experienced consultant could help you out with that one, but ultimately there are different challenges along the way for sure. And in my experience, having gone through multiple subject areas with some clients, we do pick up the steam that I was talking about before. We do get to the point where you see the value, you want the value, you have all these subject areas now that you want to get that value from, and you can spin up multiple Scrum teams and pick up the pace that way. Here, I'm really just talking about first time through, you don't have your processes in place, you're putting them in place, you're building the airplane in the air, if you will. But the next time through, as long as it's not five years later with a brand new team, you are adding, you are going much faster. So there's no way for anybody to say, okay, a subject area takes three months, this subject area is going to take six months, well, it depends on a lot of things. How are you staffing it, what kind of experiences on the staff, what kind of numbers is on the staff, what kind of skills and training, and which tool did you buy, is it a good fit for you? What are you trying to do with MVM by the way? What are you trying to do with MVM? Are you trying to do all the things that it does? That might take longer, but if you're just trying to do basic data integration with the MVM, it'll take a lot shorter. So there's no one-size-fits-all with this. You know, I like round numbers that say you have to break things up into quarters. So in a quarter what are you going to get done? You may not be the whole subject area, by the way. In fact, your first subject area, people pick customer or product, like I said before, those are a couple of the most difficult subject areas in the enterprise, but I get it. That's where a lot of value can be derived from, but don't think that they're all going to go that way, especially if they're a smaller subject area. So, subject areas, yeah, you'll pick up the pace. What everybody's talking about, and I had to get it into my presentation, generative AI. MVM will be unaffected by generative AI. Generative AI will automate many of the tasks that are currently performed by human analysts, such as data cleaning, feature engineering, and model training. As this generative AI begins to take on the analytics function in organizations, it will utilize the data in master data management. This has yet to be done, or at least in my walk, but I'm sure that it's coming. Customer segmentation, for example, generative AI can use MVM data to segment customers into groups based on their demographics, purchase history, and other factors like that. And this can help organizations to target their marketing campaigns more effectively. For example, this is also true around product recommendations, fraud detection, risk assessment, demand forecasting, etc. So, MVM is for fully centralized organizations. Now back in April I gave a presentation and I got into data fabric and mesh, so if you're not familiar with that. You can check out that presentation. A lot of people are talking about data fabric and mesh and these decentralized types of architectures. I am too, by the way. I am somewhat becoming an advocate of doing things of this nature. I think I already have been doing it for quite a while and now we're adding a little science to our data architecture environments, which is great. But the choice of whether updates should be performed centrally or decently within the organization. It's a key consideration to master data management as well. A decentralized approach may be simpler to manage within a large organization because it leaves data closer to where it's created and used. So instead of having one big hub for the whole organization, you might have a few. You might have them out in different departments and then they might come together in some sort of centralized hub or they may not. But either way, MDM fits into these decentralized architectures. And by the way, I love to put MDM on the data mesh, because that just makes that data much more accessible by all the applications within the environment, which I hope is our goal. And finally, my last misconception for you today, you won't need organizational change management with MDM and nobody's job will change as a result of MDM jobs will change if you do it if you do it in a big way. Jobs will change if you do it right jobs will change if you want to fully utilize MDM. And one of the big ways that it will change is in the use of the workflow remember the workflow where data gets passed around from department to department before a master record is formed at the end of the day and pushed into the hub for distribution. So whatever process you're using today to do that and I've been in environments where literally involved file cabinets and faxes. And this is, yeah, 20 that was 2022 and various other environments that do, maybe not faxes and file cabinets, but they do other forms of master data management, if you will, and those jobs absolutely change with MDM. And that might be a big example but I'm sure within every MDM project that jobs will change and let's not be afraid to, to say this upfront early and often and sometimes even to make it happen with the job description of the people involved. Yes, I go to that, I go to that extreme, because I want people to understand how important MDM is we're not just doing something casual. So, remember, when you say MDM upfront, half of the people are going through the, the seven, whatever it is the seven stages of distress where they go through the denial the anger and all that all that stuff right. And, but what coming out the other end of your MDM projects is going to be success so they're going to have to get around to acceptance. They're going to need some organizational change management. And by the way, OCM is not just about job changes it's about communication it's about people understanding what the new world is going to look like, once you have MDM in place. And this gives me an opportunity to say, come back next month, because next month, organizational change management with these data projects is going to be the topic of our advanced analytics webinar so I'll see you then and we're going to talk in more detail about organizational change management for now. I'm going to summarize the presentation. If you have any questions, hopefully you've been putting them in the q amp a go ahead and do that now. If you have any questions for me, pursue for iron. We're going to answer your questions in just a minute here. Once I finish up the summary, the MDM world is full of misconceptions. Have I made that clear. I made it clear that not only is it full of misconceptions there. Everybody's coming at it with a different set of misconceptions. So you as the champion of MDM have your work cut out for you. MDM is much more versatile than most people realize. Yes, and don't boil the ocean with MDM find your little wins. You check in quarterly with what the ROI is, etc, etc. And one thing I love that MDM is not an option you got it, you got to have master data in some way shape or form. Again, which application doesn't need it. I can't think of any MDM is not an option. What is an option is how you do it. If you do it the right way with a great tool, and you know everything that's involved, and you have a plan, and you execute on that plan, but, you know, the halfway that many folks are doing today and which I did for years and years and years to write is to do things like, you know, let's let's use the data warehouse for MDM or let's use this operational database and we'll squeeze it out of CRM and stuff like that. Okay, though that's your option to really but to do master data itself, not an option. So let's dispel the misconceptions out there about master data management. Shannon back to you and let's see if we have any questions. William, thank you so much and just to answer the most commonly asked questions just a reminder I will send a follow up email by end of day Monday for this webinar with links to the slides and links to the recording to diving in here lots of great questions coming in. This question came in early, you know, do you need RDM before MDM. And Sue I know that this was something you wanted to look at as well. So go ahead. Actually, I RDM before MDM I'm not sure that's something that we did talk about I'm assuming we're talking about requirements doc right do you need to have the end reference data management. Do you need reference data management that I was looking at it a couple of different ways. So just on what your, what your use cases, right. I think you really need to look at what it is that you're trying to accomplish. If reference data is part of the use case that you're looking at as far as whatever the domain is that you have. I can jump in there real quick so I would say RDM is not something you need before MDM and the reason I'm saying that is because as part of what we offer at relative we do have reference data that we provide along with the MDM solution. So we can get that all set up for you. And this is something that rubs people the wrong way sometimes when I say and I'm not going to have time to really develop the statement but I'll just make it and I think that reference data management is really just MDM light. It's MDM for subject areas that really don't have a lot of complexity to it you can still handle them through MDM as Ian just said, but you need a plan for which which subject areas are important to to the applications under development. And you need a plan for bringing them together in time for those applications needs. And if that means that some of these are going to get classified as reference data management then so be it. Perfect. So I, so I'm curious on the AstraZeneca case study, I mean, or example, to consolidate how long was the timeline to consolidate their 66 MDM systems to three. Yeah, that was as you can imagine a pretty complex undertaking and task. And that took about a year to rationalize all of that data from all those systems and it's a global company data spread out everywhere. And so we had an implementation team global GSI partner, and all of us worked on it together and then got them to that outcome and saving them that amount of money. So, how is generative AI being used to automate the manual process of MDM tasks for example matches that are below a minimum threshold and require human review, have many vendors incorporated generative AI to improve MDM results. So, I will let the Sue and Ian answer respectively for their products but I would say that I do see, I do see this getting into products these days, and I think that there's a lot of opportunity for automation within MDM using generative AI, especially around that workflow which the questioner kind of touched on there right. These decisions that are made in the workflow can be automated definitely the ones that say have so and so review it and answer yes or no, or whatever to the to the workflow that can definitely be automated. I just understand what so and so is is is doing when they actually look at the record and let's bake it in. And if it's artificial if it's to the complexity of an artificial intelligence need and then so be it with generative AI so that's a couple areas that generative AI will affect MDM. Absolutely, we're definitely seeing AI bringing being brought in for doing things, as William just mentioned, but also like looking at recommendations for quality improvements recommend recommendations for enrichment. And actually this is a big one recommendations around policy enforcement so looking for PII and information that may be coming in that needs to be scrutinized further along that line so it's definitely going to impact things. Yeah, and I'll jump in there. You know, obviously, many of our customers are expecting some sort of advanced automated solution in the future, using generative AI like large language language models and LP. So the first thing that we have done we're continuing to do is just using machine learning models to automate the process of matching. So instead of putting in rules and manually creating the rules for matching and getting to that single consolidated view of a record, have the model automatically detect and create that matching mechanism for you. So that automates the process of matching records that are similar generative AI obviously adds even more capabilities on top of that the area that's interesting for us at least is using natural language processing so one example is, as you try to search for records within the system, you could have a conversational interface where a user types in give me all of the customers that are located in this region that meet criteria X, Y and Z. And simply just type in the results are populated so looking at MDM holistically there's a lot of ways to incorporate that technology and we are certainly going to incorporate and integrate that work makes sense and automated and automate bits and pieces of MDM moving forward. Perfect. I love it's such a hot topic right now right. How is generative AI going to be incorporated into anything and everything. I love it will. So many great questions but I'm afraid that is all the time we have today, William and I and so thank you so much for speaking today, and thanks to precisely and thanks to relative for sponsoring today's webinar and helping make these webinars happen. And just a reminder to everybody I will be sending a follow up email by end of day Monday with links to the slides and links to the recording of the slides recording of the webinar and so hope you all have a great day. Thanks everyone.