 And welcome, my name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. We would like to thank you for joining the latest in the Monthly Data Diversity Webinar Series, Real World Data Governance with Bob Sinner. Today, Bob will be discussing the future of data governance, IOT, AI, IG, and Cloud sponsored today by Irwin. 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. First questions, you may ask them in the Q&A in the bottom right-hand corner of your screen, or if you'd like to tweet, we encourage you to share highlights or questions by Twitter using hashtag RWDG. 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 and the recordings of this session, along with any additional information requested throughout the webinar. Now, let me turn it over to Danny for a brief forward from our sponsor Irwin. Danny, hello, and welcome. Thank you, Shannon. Welcome, everybody. Can't wait to hear what Mr. Sinner has to say today as always. Always enlightening, always entertaining. Thanks for having us, and it's a pleasure to be on here. A great topic. What I thought I would do with my small piece of time at the beginning is actually just talk about the challenges that folks are having moving to the cloud and some of the concerns and potentially some of the ways that you can get around that. So, you know, as we look at the future of data governance, cloud is absolutely in there. We've seen lots of numbers that talk about how many people are adopting cloud in some way, shape, or form. And now we have a whole set of, well, they're not all new, but they're new and really coming to the forefront of full cloud-based data platforms. And this is representing a great opportunity for folks, but also a real challenge in terms of a, which one to choose be how to get there fast and realize the value and potential that they can provide, but also ensuring that they're not upsetting the apple cart because the majority of companies have something in place that they're going to move to the cloud. So that talent and not interrupting business operations and making sure that you have transparency and governance through that whole process is important. So if you're not familiar with Irwin, we are no longer just the data modeler. We have put together a full set of capabilities that are designed to really allow you to take a data-driven approach to the business, understand your business at all levels, leverage those artifacts and that knowledge to do things better in a more secure and accelerated way through the whole process, all the time making sure that everybody has visibility into what's going on and a clear understanding of what's out there and available for them to take advantage of for the business. And the reason I wanted to bring this up in terms of our continued leadership in metadata management is as we've worked with lots of customers that are facing this challenge, metadata again becomes the key and the foundation, not just to managing the data on a cloud on-premise or some hybrid of those two, but then governing that environment as well. And so really getting that your arms around the metadata is at the core of what you need to do to really be successful and take advantage of these opportunities to really modernize what it is that you're doing. Now, we put it together in an offering, I guess, that brings together a number of our tools, but really the goal of this is for you to be able to manage and really control that migration to the cloud without interrupting your current business operations, ensuring that you get there again with transparency, visibility, but also the ability to fully take control and take advantage of those new capabilities in that transformation. So just quickly to give you a look at the sort of classic scenario, you know, you'll have an organization that has a number of data sources, you know, some maybe out in the cloud, the majority are still on-premise and then they have an architecture that brings that data into environments where it can be leveraged for strategic advantage, operational efficiency and a number of other things. So generally, you'll have some sort of operational data store. It may be a data lake that's been, you know, attempted on-premise, things like that. Generally, you'll have a data warehouse and some marks that are then taking that data and slicing and dicing and organizing for business intelligence, for analytics and for a number of other reasons. So, you know, the key here is to be able to lift what you have, put it onto the new, you know, onto the new platform with this little manual touch and, you know, without, you know, taking a huge amount of time to do that and then be able to take advantage of that process to actually modernize and improve what it is that you have. So the key things that you need to look at are converting your legacy data structures. So you may have a data warehouse out there. You may be moving more than just the data warehouse out into this new cloud, you know, data platform. You may be bringing in or moving a lake or you may be moving the whole kit and caboodle. So it starts with understanding what you have, being able to auto-document the structures that you have, auto-document all of your data pipelines that exist today to move data around, transform data as it moves into the different stages. And then you have, obviously, you know, it's the tools that have, you know, semantic models that go back and pull that information out and present it to people in meaningful ways. So you really want to be able to take those and automate as much of that as possible. Lots of tools out there that will allow you to, you know, take those structures, repoint them in new technologies and deploy that. You've got to be able to auto-document all of your procedural logic and all of your ETL jobs or any ELT jobs that you may have any other scripting and be able to transform that and point it at new technology. And then, of course, be able to take that data from one and point it to the other, move that data, transform it appropriately, and then from the back forward, convert those semantic models so that they are now pointing at these new technologies. The key being to then govern that entire process. So the real trick here is to use your governance facility and your metadata management to actually do that by activating the metadata, making sure that it's continuously connected to the governance framework and then be able to trace the changes through that. So this is really what you're looking at as the steps required to move forward. These are the things that we can help you with and things that will really help you be much more successful in moving to the cloud. And once it's there, it's just like any other data or any other architecture that's out there. You need to be able to harvest assets wherever they are from a technical perspective, organize them and then curate those things so that they can become meaningful to the business, put all of the policy and control around that in terms of a governance framework and make sure that's well connected. One of the topics today is AI and AI is finding a real role in allowing you to be able to do that faster and more effectively. And then of course, the capability to then socialize that out and visualize that data architecture complete in the context of that data governance framework and then be able to utilize that and provision that out to the people that need it to understand it so that they can be that much more effective in terms of leveraging that data. So at the end of the day, really what you want is a platform that will allow you to take your cloud elements, your on-premise elements, any other type of data elements that you have from conception all the way to consumption. Get your arms around that through modeling, governance, intelligence, be able to transform that, automate that as much as possible, catalogue it so people can understand it and then provide them the capability to understand it in a way that's meaningful to them. So if that's a challenge that you're facing, absolutely come and talk to Irwin, but I'm not going to waste any more time here. I think the real meat is about to come. So I'm going to turn it back to Shannon and to Bob and they can get down to the real reason you came to the webinar. Danny, thank you so much. And thanks to Irwin for sponsoring today's webinar and helping make these happen. If you have any questions for Danny, he will likewise be joining us for the Q&A portion at the end of the webinar today. So feel free to put those questions in the Q&A portion of your screen. And now let me introduce you, our speaker for the series, Bob Siner. Bob is the president and principal of KIK Consulting and Educational Services and the publisher of the Data Administration Newsletter, T-Dan.com. Bob specializes in non-invasive data governance, data stewardship, metadata management solutions. And with that, I will give the floor to Bob to get his presentation started. Hello and welcome. Hi, Shannon. Hi, Danny. Hi, everybody. I'm really happy to have you with us today. Looking forward, I've been looking forward to this topic all year. I think it brings a lot of things that Danny spoke about and a lot of things that we're all thinking about kind of into the fold here. We're going to talk about really the future of what data governance has to hold for our organizations. And, you know, I know Shannon, when she introduced the session, she talks about IOT, IG, AI. Sounds like a whole bunch of letters all sprable together, but everybody knows that this is the future for a lot of organizations. So it's a great topic. We're going to talk about the future of data governance as it pertains to the Internet of Things, to AI, to IG, to the cloud. And the last thing that I want to do is kind of represent myself as an expert in each of those specific technologies. But I do know that they're going to impact data governance programs moving forward for organizations. And that's really what we're here to talk about today. I'll share the bits that I do know and the experience that I have had working in these technologies. But I think most of you are interested in, you know, what does the future of data governance hold as it pertains to these technologies? So before I get started, I just want to run through a real, real quickly a few items to let you know about. As you know with this webinar series, it's on the third Thursday of every month. And in January, in fact, this is the last webinar of the year. I think even for Dataversity, certainly my last webinar of the year, I'm looking forward to 28-21. We've got a lot of great topics in store, some new topics, some new takes on some old topics. But the one in January should be really specifically interesting because you might have heard me say a lot that data won't govern itself and that the metadata won't govern itself. The fact is that, you know, we really need to have people who are accountable for the data. And there's no magic pixie dust that we can sprinkle on our organization to get started with data governance or even to implement data governance as we move forward. So please join us in January and throughout the year next year as well. I do talk a lot about non-invasive data governance. I wrote a book on the topic. It's now about six years old. You know, it's subtitled, well it's called The Non-Invasive Data Governance, The Path of Least Resistance and Greatest Success. So please look for that at your favorite bookseller if you're interested in learning more. I'll be speaking at a couple of data-versity events that are coming up soon. The first one is gonna be in January. So that's just next month where their incredible enterprise data governance online event takes place. And often times thousands of people are attending that or listening to that. So please join us for that. I'll also be speaking at Enterprise Data World in 2021 as well. And so I hope that you'll put that on your calendar and you'll plan to join us as well. A couple other real quick items. Through data-versity, I partner with data-versity in a lot of ways. I have a bunch of online learning plans that are available through them. The first one was on non-invasive data governance. Like I said, I talk about that one a lot. Non-invasive metadata governance is actually a second learning plan. And the one that was released most recently is called business glossaries, data dictionaries and data catalogs. So please, if you're interested in doing some online learning, go to the Data-versity Training Center and take a look at these and all the great courses that they have available. Shannon spoke about the Data Administration Newsletter, TDAN.com. We're getting ready to move unbelievably into our 24th year of publishing on TDAN. And it publishes on the first and third Wednesday of every month. Please go take a look at it. And last but not least, KIK Consulting and Educational Services is my consulting business. And if you go out the kikconsulting.com website has been updated, I refer to that as the home of non-invasive data governance. So let's jump into the topic for today. We've got a lot of things to talk about and only a short period of time to get through them with you. So the first thing I'm gonna talk about today is share a little bit of my knowledge about the advancements in the information technology as it pertains to all of those acronyms and all of those technologies that are listed at the bottom of the screen. I just wanna keep them there on the bottom of the screen because I wanna make certain I keep coming back to applying the things that I'm gonna talk about to each of these technologies. And then we're gonna talk about the impact that these advances in technology are actually having on our data governance programs. And then I wanna talk about what the impact of data governance can have on these advances in technology. We'll spend a little bit of time talking about what the future of data governance looks like and we'll talk about, well, what's it gonna take to sell the need for data governance and what data governance role is in all of these technologies as we move forward. So it'll be really the most futuristic of the topics is, well, how do we sell this to our organizations moving forward? So I guess the technologies that I specifically wanna focus on for this webinar are the Internet of Things, Artificial Intelligence, Information Governance and the Cloud. And so what I'm gonna do is provide for you in a nutshell, what I understand about these technologies and the impact that it might have on our data governance programs moving forward. So I'm just gonna share a couple of more since it is the end of the year and we wanna have a little bit of fun here to share with you. I did some searches on some interesting cartoons or comic strips in regards to these technologies. So if you're familiar with the Internet of Things, a scenario like the one that plays out on the screen in front of you is not out of the question. At some point, the technology can get smart enough to be able to do that. What we really wanna do is we wanna talk about the data associated with that technology or with each of these technologies. So we've heard of B2B, Business to Business and B2C, Business to Consumer and all of those acronyms, but really the Internet of Things is D to D. It's really device to device and it's devices that are exchanging information in real time and using these devices and using the data that's being transferred between these devices in real time to use them for machine learning, putting up sensors around your organization, around your home, for example. I know a recent client, an oil and gas company had sensors on their oil rigs, oil rigs that were out in the middle of the Gulf to let them know what the water temperature was, air pressure and all those things were being said in real time to them to keep an eye on, to monitor, to look at so they make sure that they can protect those rigs as well as they can. So that's just one example of an organization of a business applying the Internet of Things is these devices are constantly collecting information and they're transmitting that data to another place for that data to be interpreted for decisions to be made from that data. So it's really a bunch of wireless sensor networks, control systems and it's automation that automation really becomes important in people's homes, it becomes important in people's businesses and when it comes to their homes and the information that's being transmitted between devices, I think you understand that the data needs to be governed. If people need to, we would need to make certain that that data is being protected, that people don't know where we are or what we're doing and so there's governance that needs to take a place around the data that's associated with Internet of Things and devices exchanging information. Like the one I said, the one that is most commonly understood is the whole idea of smart homes. And I like to think that we have a smart home here, we've got ring devices, we've got Amazon devices all over the place that they're not making decisions for us yet, that would be kind of the artificial intelligence piece of things. But they're helping us with the lighting and thermostats and home security and cameras and those types of things. So you may be using Internet of Things technology and there's data that's being exchanged between these devices, we need to think about that data and we need to think about governing that data. Artificial intelligence, it's getting computers to think for themselves and to make decisions and I wanted to share with you just a quick comic strip here that I found on artificial intelligence really right now that could be the future that the world is being operated more by robots and things that are artificially making decisions and artificial intelligence rather than depending on people within the organization. But just to again, to share with you some of the definitions and things that really define what artificial intelligence is, artificial intelligence is, it really enables the computers and the machines to mimic the human mind, to mimic perception, learning, problem solving, decision making and a lot of those devices and that technology is using data. It's reading data, it's actually making decisions and actually producing data as well. And we need to be concerned about making certain that we are governing the data that's being used, the data that's being defined, the data that's being produced through any level of artificial intelligence that we're enacting within our organizations. So it's basically machines or computers that mimic cognitive functions. So such as learning and problem solving, these are all things that are important as we know that artificial intelligence is being used more and more by organizations, not only around our country but around the world. And we need to be aware of the data aspect and how data is gonna impact this and how this is gonna impact data and how governance is gonna have an impact on all of those things. One of the things I didn't really know to begin with was that this optical character recognition was one of the earliest forms of artificial intelligence. So things that are making decisions based on what they're seeing and the data that's being fed them, that's really what artificial intelligence is all about. Oops, went too fast. All right, so another technology I wanna talk about is really more of a discipline and that's information governance. And so everybody's used to data governance as it pertains to structured data. We also wanna talk about information governance and records management and the governance of unstructured data. So I wanna share a quick funny with you regarding that but give you a definition of what information governance is. It's really the governance of unstructured data and unstructured data could be audio, it could be video, it could be graphics, it could be a lot of different things that aren't showing up in bits and bytes in databases. So organizations are actually focusing a lot more these days on information governance and they're really trying to balance the risk that that information presents to the organization with the value of really governing that data. So legal and compliance and operational transparency, these are all things that pertain to information governance and so that's gonna have an impact. It's already having an impact on organizations that are implementing IG. We wanna make sure that they're focusing on well, how are we governing that? How is that gonna be impacted by the existing governance that we have around unstructured data and what do we need to tweak and to change in regards to the handling the unstructured data as well as the structured data. And the last topic is one I know that Danny talked a little bit about it which is cloud computing and everything or it seems like everything is moving to the cloud. Wanted to share with you a quick funny regarding cloud computing. You may have seen this one before but really cloud computing is making data on demand and making it available for your computer systems and its focus is on storing the data. That's what they call it. They call it in the cloud and that's where the computing power is taking place and a lot of organizations are struggling with well do we wanna store our data on premise or do we wanna store it in the cloud and we'll talk a little bit here about well do we need to tweak our governance program for the data that we're governing in the cloud versus data that we have on premise in different information systems and the like within our organization. So cloud computing is one of those technologies that it's not necessarily even the future. It is now and a lot of organizations are struggling with well how do we implement governance as it pertains to the technologies the data that's being stored in the cloud and IOT and artificial intelligence and all of these things. So again in a nutshell these are the definitions that I use for the four technologies that we're gonna talk about but really now what I'm gonna spend the rest of the time talking to you about today is what impact are these technologies having on the data of our organization and what impacts are they having on the governing of that data. I mean we may be used to governing data in information systems and data resources that we have throughout the organization we need to take a look at what impact these technologies are going to have to the future of data governance. So we can't rest on our laurels and all the great things that we're doing with our data governance programs now at least I hope you're doing great things with them. We need to be one step ahead. We need to be ready to embrace these technologies as we move forward in our organization. So the next thing I wanna talk about is really the impact that these advances have on our data governance program and I'm gonna look at it from four different perspectives I'm gonna look at it from the impact that it's having on roles, the impact it's having on the skills that are required of people within our organization, the impact on the return on investment that a lot of organizations are looking for that ROI from data governance when I always say we should be looking from the ROI that we're getting from other investments that we're making like creating a data lake and making certain that people in the organization understand and know what data is in the data lake and all that metadata and stuff that Danny was talking about. Now we need to make certain that that information is being made available to people so that they can really truly get the return on investment on the heavier investments in the technologies that I'm gonna talk about today. And then last, regarding the impact of these advances on governance, what impact is this gonna have on our data governance program? So let's start with the roles. And if you've seen my webinars before and I hope that you have and I'm very happy to have you here always and have you here now, I always define data governance roles in terms of different levels of the organization but when we talk about the stewards of the data, I oftentimes break them into people that define data and people that produce data and people that use data. Those are the three activities that I talk about the most. And I would say that for most other activities in your organization, they're gonna fall under one of these three things if not more than one of these three things. So we know that the impact that these advances are gonna have on data governance that we're gonna need to have people that are still defining that data and putting good solid business definition to that data. We're gonna need to have people that have the responsibility for producing the data or if the data is coming from sensors, making sure that the proper data is being produced and that it's being calibrated properly and all of those things. And then the third role, the third activity of the data steward is the people that use the data and make sure that the data is being used ethically, that the data is being used appropriately, that it's following all the rules and regulations that we're not only setting up for ourselves, but that are coming from legislation and regulations that are being imposed upon us. So we know we're gonna still need to have definers, producers, and users of the data. And that's one of the impact that these programs are gonna, that these advances are gonna have on governance. We still need to define who those people are, record those people. I say everybody is a data steward in the organization if they define, produce, or use data and they're being held accountable for it. So these people need to be held formally accountable for the data. And it doesn't matter now that that data is being stored in the cloud or that that data is actually unstructured data or that it's coming from artificial intelligence or it's coming from the D2D, the Internet of Things Transfer Data. So we need to still hold these people accountable. We still need to execute and enforce authority over the data. And that's my definition that I use of data governance is that it's the execution and enforcement of authority over the management of data or over the definition, production and usage of data across the organization. And oftentimes as we move to the future these roles are gonna be harder to define in association with the technologies that we're talking about today. And it's more difficult to recognize who the people are that are gonna play the roles. But I will tell you that the roles will continue to be the backbone of your data governance program. And we need to look at those roles as they pertain to all the different levels of the organization. And those levels are the executive, strategic, tactical, operational support, and maybe even administrative roles for the program within your organization. The impact, the data governance actually these advances in technology are gonna have an impact on the skills that are required by our organization. So not only are we going to need people that are skilled in data, but we're gonna need to work alongside the people that are skilled in the technologies that we're talking about here. And the business needs to recognize that this data from these new technologies still needs to be governed. So these roles are still gonna need to be associated with the data in each of these technologies. And the truth is that technology is often moving faster than the governance of data within our organization. So we need to try to keep up. We need to stay a step ahead. Now that's the idea of, now what is the impact that these technologies are gonna have on data governance? Well, we need to stay one step ahead or at least we need to stay in step with what they're doing. And people need to be educated in both data continually educated in data and in the technologies that your organizations are adopting and potentially we're gonna require different people in the organization that have new skills. And so the people that are the quote unquote data governors of the data in our organization are gonna continue to evolve and they're gonna continue to change. I talked about the return on investment and what we know that these technologies that implementing these technologies come with a price not just for technical aspects of it for the people aspects of it and for the acceptability of these technologies by our organization, they come with a price. And so do the adoption and using these things come with a price. And so we need to be able to demonstrate that the value of adopting these technologies really will come from the way that we're able to govern the data that's associated with these things. We need to be able to tie the governance of the data associated with the internet of things and artificial intelligence. We need to get people in the organization to buy in that this data is the same as any other data. It's just coming from a different source but it too needs to be governed. So in the future, as we start investing in these technologies, we're gonna need to be able to demonstrate the return on investment that data governance is gonna provide to each of these technologies. And that from my experience and from what I've seen it's gonna be a challenge to us and it's gonna be somewhat more difficult. And we need to understand that the best practices that I talk about a lot associated with data governance are you gonna continue to be the same they're not gonna be impacted by the technologies. In fact, when I say that the first best practice is the senior leadership must support, sponsor and understand the activities of data governance that needs to continue. In fact, that might need to be strengthened a little bit. So they understand that the data governance they need to support, sponsor and understand the governance of the data that's associated with these things we're talking about today and somebody in the organization has to have the responsibility for doing that as we move forward. Let's talk about the impact that these advances are gonna have on our data governance program. And so the model that I have shown on the lower right hand side of the screen is one that I talk about a lot. I'm not here really to talk about this today, but the idea is that we need to cover all of those core components of a data governance program from all the different levels that I talked about earlier when it comes to roles and responsibilities and people's perception of those core components. So I break down the core components into the data, the roles, the processes, communications, metrics and tools and down the right hand side I list the executive, strategic, tactical all the different levels of roles. We still need to make certain that we are addressing these things for the data that's associated with these technologies. So we need to formalize accountability for the governance of data. That's gonna continue as we move our programs forward. We need to execute and enforce authority and the truth is when it comes to implementing these new technologies that's not necessarily gonna be easy. Not that just regular data governance is easy but when we start to implement the future of technologies in our organization we need to be able to adapt our program and adopt these technologies and make certain that we're governing the data associated with these technologies. So now let's talk about the impact that data governance has on these advances. And I mentioned before that there's really three primary activities or actions that people can take with data. They can define it, they can produce it, they can use it. I also wanna talk about how this is an opportunity for us to improve data stewardship. Because data stewardship is one of the key items as we start to approach data and how we define who our stewards are and how we get them engaged is going to be a big indication of the success that we can have with our governance programs. So it's not just the ability to improve definition production and usage and the ability to improve stewardship but we need to continue to realize that the data and the metadata from these devices are not gonna govern themselves. It's gonna require that we get people engaged and that is the topic of the webinar that we're gonna be giving in January is about data and metadata not governing themselves. We need to formalize accountability for data across the organization. And I'd say probably now more than ever, when I say a lot too, is everybody in the organization is a data steward. I'm actually gonna add to that here in a couple of minutes. I'm gonna add something that might be memorable to you because it may actually go beyond just everybody that's a data steward as we move to the future of how data is gonna be used by our organization. So the impact of governance on these advances and we wanna be able to improve the ability to define the data and that requires a resolute effort from our organization to make certain that we're governing the data that's associated with the internet of things, artificial intelligence, IG and the cloud. We need to make certain they're not gonna govern themselves. We need to have a specific effort in place to do those things. And the formal definition of this data needs to be provided to the organization. So how can we use this data? Where do the data come from? How is it being calibrated if we're bringing it in from an instrument? People want to know if they're gonna trust the data to make decisions and there are still gonna be people making decisions we're not all only gonna lean on artificial intelligence we gotta provide good business definition for that data. And the truth is that good business definition of the data oftentimes leads to improve production and usage of the data. So we're gonna continue with the definition of standards and the definition of the regulatory and the risk rules. In fact, these things are gonna be emphasized as we get people to understand that they need to govern the data associated with these technologies we're talking about. And in fact, I'm seeing it already and maybe you're seeing it as well that additional scrutiny and auditability is gonna be required when it comes to the data and how we're collecting the data and how we're defining the data and even how we're producing the data. I mean, people are going to want to see improvements in data production that are returns on the investments that we're making in the four technologies that I'm talking about here. So they're gonna wanna know where the data was produced and that's gonna increase the importance across the organization. We need to know if the data was not being manually entered by anybody, where did it come from? How is it defined? How can we use it across the organization? There's also gonna be improvement in the account there needs to be improvement in the accountability for the data production and that's gonna be addressed by further scrutiny by your legal team, by audit team, by internal and external auditors to your organization. They're gonna want to know who has responsibility for the data production. And so auditability is gonna be a key factor for us with this data, especially data that's coming from the internet of things, from devices that we have across the organization. The auditability of this data is going to increase in the future and data governance needs to be ready to address that. So the production of the data from these different sources will continue to remain constant, but the way that we are addressing it and governing that data needs to improve if we're expecting to get the most value out of these technologies. And certainly the legal consequences of the new technologies are gonna continue to grow too. If we have something making decisions for us or if we're storing data off-premise and we need to protect the data, the legal consequences of the definition and the production and usage of these data are gonna continue to grow. And certainly around the production of the data, but as I mentioned, the definition as well. I just wanna touch on the usage of the data here real quickly too, because we need to improve the governance around data usage, from the data that's coming from these devices and from these instruments and the legalities of the data to data, data usage, data to data, data usage is still being defined and it's being debated by organizations. And when we have artificial intelligence taking place within our organization, we're gonna need to, somebody's gonna need to be accountable, we can't hold these machines accountable. They're making decisions based on the use of artificial intelligence, but ultimately at some point, it's not gonna be, you're not gonna be able to point at the machines and say, well, it was their fault. There's gonna be people that are gonna be behind those machines, kind of like the wizard and the wizard of Oz. There's gonna be somebody there that's gonna be accountable for that data. Certainly when it comes to information governance and unstructured data, the same disciplines apply, as applied to regular data governance or should I say structured data governance. And then the cloud usage of this data stored in the cloud will continue to be, will stay consistent. We just need to make certain that we're defining, producing and using that data appropriately. We're protecting that data appropriately for our organization. So the regulatory and the privacy rules will increasingly, if they're not already at the forefront of your governance program's focus, they're gonna increasingly become a part of the focus of your data governance program. And what is the, what's the impact that the data governance can have on data stewardship or the impact that these devices will have while stewardship will continue to be the driver of data governance. The governance is gonna focus on the formalization of the accountability for these people. And as I've said before, everybody is a data steward. You know, that's gonna become in jeopardy because we're gonna be able to point to devices that are creating data. So maybe, just maybe we need to change that statement to say that everything is a data steward. All right, maybe not. But certainly everybody is and the people that stand behind the curtain with these devices are gonna play a bigger role in governing the data from our organization because we're gonna become more and more dependent on these devices within our organization. So everything will require formalized accountability. I don't know if I'm just ready yet to say that everything is a data steward. No, my office chair is not a data steward, but these devices that are associated with this technology, you know, they're stewards. The people that work with these things are stewards of the data. So let's spend a few minutes talking about what the future of data governance looks like. Well, the first thing I can tell you, there's gonna be more and more use of the internet of things. There's gonna be more and more use of AI and of information governance. That's becoming much more prevalent in organizations. Although organizations have been doing records management for years and years before we ever even started to talk about data governance. And information governance is really the next coming of records management. So we can learn from people in our organization what they've been doing to manage records across the organization from the earliest days of records being managed within our organization. So there's gonna be more and more of that. So we need to make certain that the governance programs that we're defining are not just applicable to structured data, but they're being applicable or applied to the unstructured data. And that's really where a lot of organizations are focusing on information governance rather than data governance. And more and more organizations are gonna start to include data in the cloud. And I wanna share with you what some of the newer technologies are. And I would say that these are even beyond the future that I'm talking about today. But the fact is that a lot of these additional technologies I'll share in a second, they're already being utilized. And there's already data being transferred between them. And we need to make certain that we are governing that data. At least personally, I know that that's important to me, but for your organization, as you move to these technologies, we are going to need to continue to govern that data that's being shared certainly between devices. I'll talk a more about that in seconds. But okay, there's gonna be more internet of things. There will be more occurrences and there seem to be more occurrences every day of devices talking to other devices. So your refrigerator may notice that you're out of milk and it might let your store know and it'll put it in your cart. So when you go into a curbside pickup these days of the goods in your supermarket, it'll be ready for you. You don't even need to add it to a list. You don't need to go looking for it, it's gonna know. Yeah, that's just one example. I've seen things like smart mailboxes that will let you know when something's been delivered to your mailbox. So you don't have to go out and look five times a day like we do a lot. There's gonna be more device to device data management that's gonna be required. It's gonna require more security and more scrutiny and organizations will continue to be held more accountable for it. So the internet of things is not going away. The way that the internet of things is going to prosper in our environment and the world is going to only improve and it's only going to increase over the next weeks and months and years. There's gonna be more artificial intelligence. Organizations are starting already to use artificial intelligence more and there's gonna be higher scrutiny paid for the accountability of the decisions that are being made by artificial intelligence or the decisions that are being suggested by artificial intelligence. We're gonna see more scrutiny but certainly artificial intelligence is not a passing phase. A phase is something that's here now and it's going to be here into the future. So there's gonna be more and more artificial intelligence which means there's gonna be a need for more and more governance of that data that's associated with artificial intelligence. And over time, the rules are only gonna become stricter as the government and as organizations recognize the importance of that data it's gonna require governance of that data. And I don't wanna talk too much about information governance because like I said before it's just the governance of unstructured data and if we can leverage the roles and the processes and the technologies and the metrics, we wanna make certain that we know and understand that information governance is only gonna continue to grow and grow in our organization. So unstructured data is growing at unprecedented rates and it's gonna continue to grow at unprecedented rates and people as we move forward they're gonna need to be held accountable for all of this different unstructured data and the examiners are getting smarter, they're getting stricter as to how this unstructured data must be governed within our organization. And we know more and more data is moving to the cloud which means that more and more metadata, information about our data is gonna be stored in the cloud. And so organizations will need to make certain that they are governing the cloud storage, that they're securing the cloud storage. I've even had clients that are securing the metadata that's being stored in the cloud because a lot of these catalog solutions store their metadata in the cloud. Well, if somebody can get access to the metadata they can know where our data is and it becomes a lot easier for them to be able to find exactly what they're looking for. So that governance of the data and the metadata in the cloud is just gonna continue to hold all the cards as we move forward with data governance in our organization. And I just wanna share with you a few of the futures even beyond and maybe incorporating some of the things I've already talked about. We're gonna be seeing more and more wearable technology. We're gonna be seeing much more virtual technologies and then we're gonna have nano technology and hologram technology and robotic technology as really being those technologies that are taking advantage of not only the internet of things but also the artificial intelligence that we're moving to as a world. So we know that we've got more and more technologies that are coming the better we can get our hands around the technology that we already have and the technology that we're starting to work on the more adept we are to being able to handle those things the more adept we're gonna be able to handle the data that's being transferred and the data that's really a part of these additional technologies which you know what, they're not coming. They're here already. I already wear a smart watch. I already have devices around my home. The future is now. So we really as organizations we need to be thinking about the future of data governance and what it looks like and how these technologies are going to apply. Okay, so last thing I wanna talk to you about today is how to sell data governance as role moving forward. And I wanna tell you again as I tell you a lot that at least consider that there's an alternative approach to data governance and that may be there's a command and control approach. There's a traditional approach which I always compare to the field of dreams. If you build your program, they will come to it. That's kind of the traditional approach organizations have taken. And then there's the non-invasive approach which starts with the premise of you're already governing data. We can help you to do it better, more formally, more efficiently and more effectively in the organization. You wanna sell to the organization that you're gonna gain more return on investment from the technologies, from these integration efforts, from these digital transformations that are taking place in the organization and organizations are looking to improve efficiency and effectiveness through formality of responsibility, formality of accountability for the data across the organization. So by staying non-invasive, start with the premise of the idea that governance is already taking place in your organization and let people know if it makes sense that the non-invasive approach looks to leverage those things that you already have. Recognize people into their roles at first rather than assigning people into roles. I always talk about if you assign people into roles, it immediately feels as though it's over and above what they were doing before. But the bottom line is we need to be able to execute it in a forced authority and we can do that through formalizing accountability. And as I suggest, take the path that's gonna provide least resistance and greatest success to your organization. Organizations are gonna look to gain more ROI from these technologies. So return on investment, like I said, doesn't necessarily come from governance itself. Oftentimes it comes from the investments, the return that we're getting from these technology advancements. And the ROI from the technologies will certainly come from the value of the data that's being associated with each of these investments. In immediate value, you can look to improve efficiency and effectiveness. In the long-term, it's gonna come from sustained use of these technologies and becoming efficient and effective in how we use them. And really, the ultra long-term value may eventually come from replacing humans with machines where they're making decisions based on data. Well, there's a lot of governance that's gonna go into the data associated with that. Now, these integration efforts, if you're moving to a singular CRM platform or ERP platform, we're already reducing the systems and the data that needs to be maintained. We're reducing the people to maintain these technologies. We look to reduce the number of errors and people are looking to automate these processes as we move into the future. So organizations will look to gain more ROI from integration efforts. They're gonna look to gain more ROI from these quote-unquote digital transformations that are taking place. They're adopting new technologies. They're replacing technologies and processes with new ones. They're enabling innovation and creativity rather than just continuing to do things in a manual way. So governance will be required to become digital and to become data-centric as we move forward with our organization. So the last thing I wanna talk about is kind of going back to that data governance framework that I spoke about. And we can make this available to folks who are attending the webinar. There's a white paper on the framework and how we need to address each of these components from each of these different levels. And that's gonna continue to be a focus of governance programs, not only around here but around everywhere. And this is really what the future of data governance holds for us is we cannot sit and rest on our laurels of what we're doing and what we're doing well. We need to understand that the world is not gonna stop and wait for us to catch up. It's gonna keep advancing and we need to advance with it. So today I spoke about the advancements and information technology and shared with you what I know about these advances. Oops, we talked about the impact that these advances will have on governance, the impact that governance is gonna have on these advances what the future of governance looks like and how to sell data governance to our organization moving forward and tie it into the advances that are taking place in information technology within our organization. And so with this, I would like to turn it back over to Shannon and see if we have any questions today. Thank you so much for another great presentation. And if you have questions for Bob or for Danny, you can submit them in the bottom right hand corner in the Q&A section of your screen. And just to answer the most commonly asked questions just a reminder, I will send a follow-up email to all registrants within the next two business days. So by end of Monday for this webinar with links to the slides, the recording and anything else requested. So diving in here, we had a couple of questions Bob coming in even before we started. To help implement a data governance strategy and data warehouse, would you recommend engaging a team of independent consultants or partner consultants team would be more beneficial? Well, I'm a consultant so you can guess where my answer is gonna come from. It's always valuable to engage consultants and things but you know what, it really depends on the knowledge level that you have within your organization and the skill level that you have within your organization. Yeah, I would say it's important that if you don't have the skills and the knowledge from people that have done these things before that it makes sense to go out that it's gonna be money that's well spent. I mean, if it's gonna get you there quicker if it's gonna get you to prevent or prevent you from hitting some of the hurdles or from getting over some of the hurdles for implementing not only a data warehouse which was the example that you provided but you know the data lakes and the implementation of the internet of things and all these things that I talked about today. Yeah, I think it makes sense. You've got to evaluate your organization and determine whether or not it really makes sense for you to bring in external consultants who may have been down this path before in organizations your side, your size and may help you to get to your end game a lot quicker than if you try to do it on your own. Do you need anything you want to add to that? No, I think Bob hit it on the head there when we're dealing with customers and again, we're generally coming in from a technology enabler perspective. So they have a lot of that sort of what are we going to do and what does it look like in our organization? If you don't have the opportunity to hire in somebody that's going to lead that effort for you then I absolutely agree that having those consultants and folks that have been through, it's like anything else. If they're generally capturing best practices on the way if they can help you avoid a couple of rabbit holes or hurdles that they've seen before and you're set up to walk into that's probably going to pay it back right there. So generally what we're seeing is there's somebody with a vision that comes in whether they're using other people's vision and reusing that they come in as an employee maybe it's the CDO role or maybe there's a chief governance officer there's a lot of different ways that they skin that but at the end of the day coming down and looking at consultants I think to get those best practices it's invaluable because I see one of the questions there in terms of ROI and always challenge selling your governance not just in terms of this is what we should do but this is what we've been doing and this is what you're getting for it. It's important and you don't want to not be able to show that so those consultants can often get you over the hump. Another question that came in early on maybe not necessarily relevant to the topic at hand but is there a basic difference between data governance information governance or are the terms used interchangeably? Well, it depends on the organization. I have a funny real quick anecdote is that I worked with a company that was calling in information governance before I got there and I asked them why it was called information governance and they told me that they had tried data governance three times before and it had failed they called it something different this time so they called it information governance that's not necessarily the best reason for calling it information governance but oftentimes what I'm seeing in the industry is that data governance oftentimes applies to structured data that resides within as I said before databases and systems and data resources and maybe stored in things like Snowflake and other applications that you have in the cloud. So that's what a lot of people refer to as data governance when it comes to information governance my experience has been that organizations are focusing more on unstructured data so document management, records management, audio, video, those types of things that you're not necessarily gonna find in rows and columns in databases but there's still data that is in a different format and the organizations that are implementing information governance right now at least the ones that I see that are calling it that for the right reason they're learning from the past experience of people in the records management field and how they've always governed documents and governed records within the organization. So I draw the clean distinction between the two is that data governance and this doesn't always hold up but it's a good way to look at it that data governance is for structured data and information governance is for all the rest of the data which may actually be more plentiful than the structured data that you have in your organization. Anything you wanna add to that? You know, I have, I think what I've heard is primarily in line with what Bob said. Sometimes when we look at organizations that are incorporating data and then the insights that they gain from that data they have called it information governance sort of to encapsulate it all because they were going in terms of, you know, what data do we have? Is it appropriate? Who owns it? Who's responsible? How should it be used? But now you've got the work product from, you know, analytics, business intelligence which is technically information as opposed to data because it's got a purpose that generally has, you know, something that it's trying to show you or an end result if you will, you know, that it's taken from that. But, you know, content management, there's a lot of things where folks, you know, try to mix all that in with data governance. And what I think it does is it degrades both sides. So, you know, there's things that we can learn from both sides of that equation. But I think that information governance is something that's bigger, different. It may incorporate and can learn from data governance and vice versa. But I think that they are truly different things and it really depends on the organization and how they're using the term. Yep. So that is all the time we have for this webinar. Thank you both so much for these great presentations and Q&A and thanks to our attendees for being so engaged in everything we do. It has been fantastic year in terms of webinars. I will say that. And this is our last webinar and event for the 2020 year. I think we're all looking forward to 2021 and hopefully we'll bring a lot more tomorrow to the world. I know we definitely have a great lineup of events for you here. So, well, thanks everybody. Stay safe, have a safe holiday season and see you all in the new year. Thanks everybody. Thanks Bob and Danny. Thank you for being here everybody. Happy holidays. All the best. Bye.