 Hello and welcome. My name is Shannon Kemp and I'm the executive editor of Data Diversity. We'd like to thank you for attending Enterprise Data Governance Online, the first ever virtual conference produced by Data Diversity. We're very excited to kick off the 2016 year with this new event and have a great lineup of sessions for you today. And of course, a special thanks to all of our sponsors who have helped make it all happen. 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 event. For questions, please use the group chat. If you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag EDGO. Slides and recordings of these presentations will be made available to all registrants following the event. Now let me introduce to you our second speaker for today, Kelly O'Neill, who will be discussing leveraging a data governance framework for operational success. To give you a brief background, Kelly is the founder and CEO at First Time Francisco Partners, having worked with the software and systems provider key to the formulation of master data management. Kelly O'Neill has played important roles in many of the groundbreaking initiatives that confirm the values of MDM to the enterprise. She's also the trainer of Data Diversity's newly released data governance online training program, a seven-part learning program complete with exams. For more information on that, visit the Data Diversity booth in the exhibit hall. You may also learn more about Kelly's company in the exhibit hall as well. I will give the floor to Kelly to get the session started. Hello, and welcome. Hi, Shannon. Thank you so much, and welcome to everyone. I hope you really enjoyed the first session, and I'm really excited to be part of the second session in the EDGO event. So without further ado, let's get started. Okay. So what's the goal of this session? Well, what we're going to do is really just talk about and understand how to best implement and sustain, that's a very important word, data governance using a data governance framework as a tool. So ideally, you will be walking out of this session, or I guess virtually walking out of this session with some really practical knowledge about how you can implement data governance within your organization or improve your current program and implementation. We will review the First San Francisco data governance framework, and then we'll talk about the key behaviors and activities that the framework essentially creates and encourages. So how can you take that framework and then use it to actually implement and drive value from your data? First San Francisco defines data governance as an organizing framework for establishing strategies, objectives, and policies to ensure that your data is available, accurate, sustainable, consistent, has integrity, is auditable, et cetera, et cetera. I think that the most important thing about this definition is the fact that it is an organizing framework, and that's because data governance is never a one-size-fits-all to any organization. There is a lot about the organization's company culture, what a policy means, how decisions are made, who does what, the value of data to the enterprise. There's all of these different things that are unique to that enterprise that drive what the implementation of data governance will be within that organization. And that's why we believe strongly in the fact that governance is a framework, hence the importance of this presentation to you is how to use that framework to implement sustainable governance within your organization. So data governance essentially is the coming together of some of those traditionally technical capabilities around data, like data standards and data modeling, with some of those very business-driven activities and business-oriented activities like communication and metrics and measurement. And because data governance pulls together the enterprise and different sides of the house, if you will, so from the business side and the IT side, as well as sometimes multiple divisions, multiple functional groups within the organization, it is a great platform for decision-making, and so it really ensures that the right people are at the table to make decisions around that important shared asset called data. So that's how we define data governance. First, San Francisco has essentially a seven-part data governance framework, and so we will be visiting each of these components of the framework throughout the presentation. Most organizations, and we encourage organizations to start with a strategy in order to align the organization, create a shared understanding and a common purpose for executing data governance. But other than starting with strategy, it's not necessarily a left-to-right model, because the minute that you start articulating a strategy or you start creating a strategy, you need to consider how it's communicated to the organization. And once it's communicated, then it's important to understand the perception and potential change implications to an organization. So you can see how these different components have a way of playing together, not necessarily in a very linear or left-to-right fashion. The other thing to think about with this framework is each of the bullet points within each of these categories are essentially a bucket list of things to consider and how it would be implemented within your organization and what level of emphasis that it would be important within your organization. So, for example, some organizations actually have very detailed strategy documents, sometimes they're 50 pages in which they articulate very specifically what is going to be done to execute the strategy. Other organizations is a single sheet of paper. Sometimes it looks like a placemat that articulates a strategy very specifically, succinctly, and sometimes graphically. So just because we talk about things like a strategy, there will be nuances that would be considered within your own organization to make sure that it is aligned with the culture of your organization. Some companies are highly regulated, therefore there is a big emphasis on things like policies, processes, standards, and that sort of thing. Other organizations are more entrepreneurial and as a company culture they really are more averse to things like policies and standards because they feel that that limits their entrepreneurial spirit. So again, how this is implemented in your organization does need to be considered. What we're going to do is walk through each of these categories, drill down into what they mean, and we will also go through how some of them are implemented and in some instances provide you samples of the artifacts or deliverables that come out of each of these categories. Now suffice to say there are seven categories. This presentation is meant to be 40 minutes. Some of them we will go through quite quickly. You will have access to both the recording and the slides going forward. And also if there are any requests for additional information about any of these categories or the framework in general, of course please reach out and we can communicate via the booth on the session. So for our first category within the framework, so strategy. Strategy ensures that there's business justification and alignment across things like projects, programs, goals, and objectives. It's really a key foundational element to ensure that there's funding and resourcing for the program. So key components of the strategy are the vision and mission. That articulates your core beliefs and the idea is that you want to establish those beliefs around your data which will then drive behavior, which then drives results. The vision and the mission are articulation of those beliefs and starts to create a picture of that future state. We'll revisit this concept of vision and picture as we go through the presentation. So you'll see how these start to create some interdependencies with each other and how each of the components of the framework relate to each other. Objectives and goals starts to get more specific. So it starts to define activity and how you're going to measure against that activity. Of course, this needs to be aligned to corporate objectives. So linking to the strategic statements that the company provides, business goals, stakeholder goals, this can be very personal in the sense that it can inform the stakeholder communication on a one-on-one basis or it can be very general, aligning with corporate objectives. It also, of course, needs to align with more specific business operations. So how does data governance work with projects and programs? How is data governance embedded into other business operations to ensure that data governance becomes a discipline within the organization? And then finally, the guiding principles are a technique to translate beliefs into behavior. And ultimately, the behavior that you want to encourage with data governance is done through policies, processes, and standards. It's done through the organization, et cetera. So these guiding principles help to drive some of the other components of the framework. Now, some of the statements around alignment, there's multiple ways in which you can align your strategy in the sense that you can align your vision, mission, and guiding principles to those that are explicitly stated within your own organization. So think about this as kind of a tiering effect or a cascading effect where you have your corporate or enterprise vision and mission. Maybe you even have divisional vision, missions, and guiding principles. And those for data governance should align to your divisions and to the enterprise as appropriate so that there can be this line of sight, if you will, between what the data governance program does and how it impacts the organization. So that alignment can occur in multiple ways. And also, just remember that if your starting point is smaller in scope in the sense that maybe you're starting with a specific division or region, not the enterprise, that's also okay. So then when you are going through and establishing your vision, your mission, your alignment with objectives and business operations, it's done on that divisional level. Always remember, however, that you have an enterprise to consider. So anyway, this strategy needs to work within the scope of your organization. And we'll talk a bit more about guiding principles as we get into the discussion around policies, processes, and standards. So we're going to take an example now of how you would align data governance with corporate objectives. We're not going to go into each of these categories. So just suffice to say that this would be a standard statement of corporate objectives where there's a focus on the client, a focus on core capabilities, articulation around the importance of tactical execution, maintenance of the brand, enforcement of the people, sorry, endorsement of the people, and support of the people. So just keep these categories in mind as we go through this next example. What we want to do is we want to take your vision, take your mission, and discuss how it drives those corporate objectives. So here we just have an example. Again, we're trying to make this really practical and usable for you. So in this instance, the vision talks about best in class client and account data capability that facilitates strategic objectives, positive impact, and management of risk. So it's linking those corporate objectives from the very beginning and from the articulation of that vision statement. From a mission perspective, then you go to that next level of detail in the sense that in order to execute the vision, we are going to create a culture that recognizes data as a corporate asset. So it gets a little bit more practical and tactical, whereas the vision statement is very lofty. And then we want to get specific into how it actually drives and facilitates and supports those corporate objectives. So if we just take the first one as an example, data governance improves and aligns with client satisfaction because through improved speed to delivery on products, address change processing, online account management, et cetera, it is easier for our company to do business with clients and partners which therefore drives client satisfaction. So again, what we want to do is take these concepts that are specific to data governance and link them directly through these stated ties to the different business objectives. You can go through each of these on your own if you'd like to. Now we want to look at what is a technique called business information requirements. This is another technique to start aligning your output of data governance to the goals and requirements of the enterprise. So this is generally we do this in a spreadsheet, so it's not super complicated, but the idea is if we start at the left, what we're doing is we're identifying what the business driver's vision and strategy is, in this case operational excellence. There's an enterprise strategy that's stated around commitment to operational excellence, effectiveness of the organization, execution in terms of day to day efficiency, day to day responsibilities, and then you go through priorities, goals, objectives, outcomes, et cetera. And the idea here is that you want to identify how data governance and maybe some of the other aspects of data governance like metadata facilitate those vision strategy priorities, goals, et cetera. We call those levers. And those are the levers in which the organization and the data governance program specifically supports the delivery of the other corporate goals, objectives, and priorities. So this is just another example of how you can articulate the way that your data program, your metadata program or your data governance program will facilitate your business objectives and drivers. So the important point here is that these levers or these aspects of managing your data need to be very clearly articulated and linked to that goal or objective. So we want to make that statement very clear. So when you're writing these, think of filling in the blank for this statement. We will use data to blank thereby enabling some specific business action. So it's a very clear statement around data, enabling a specific business action. So that's a technique that we've used to facilitate alignment as part of a strategy. Now we're going to talk a little bit about the organization. People tend to think about organization almost immediately after they think about strategy because really it is all about the people. So people jump to organization quite quickly. In our view, the organization has a lot of different components in order to make it sustainable and in order to make it a discipline within your organization. So we start with an operating model. An operating model is exactly what it says. It outlines and articulates how data governance will operate. It is the foundation for an organization but it isn't the organization and it is many times a single piece of paper graphically represented that talks about the decision-making process, roles and responsibilities, ownership accountability, et cetera. So many of these subcategories that you see on the slide here actually roll up into an operating model. But each of the categories are also important in and of themselves. Excuse me. So starting with the operating model and then it's important to determine the escalation process, the arbitration process, et cetera. And of course that is articulated in the operating model. But by defining what the escalation process is, it ensures that there is the appropriate level of decision making so the decisions actually stick. And again, this is going back to some of the cultural issues within each organization. Decisions are made enforced and mandated at different levels in the organization. Some organizations like to push decisions down to make them very, to make sure that each level within the hierarchy is very bought in and empowered in the organization. In other organizations, decisions are only made at the very tippy top. And it's a very kind of mandate-oriented organization that comes down from the top. This needs to be considered when you're identifying decision-making processes and ensuring that the participation level is one in which those decisions will actually be adopted and implemented. Roles and responsibilities start to create some standard titles and expectations across an organization so that when we say that someone is a data steward, for example, everyone knows what a data steward is. The marketing division is the same thing as a data steward that's in the product division. Now, the type of data that they are stewarding might be different, but everyone knows that there's a consistency around roles and responsibilities. Ownership and accountability are really important to make sure that there is an identification of ultimate culpability around the data governance and data management and that the organization agrees upon where that ultimate culpability occurs. This links back to the decision-making process and making decisions that stick. And then lastly, we look at the data governance organization members. And we really use the staffing and the identification of individuals to fill the role as the last component of an organization. But this is what starts to identify the individuals and make it really tangible within the company. So what is an operating model? Well, here we've got some sample operating models. And I've chosen two that are most commonly implemented in organization. So the hybrid model is a structure that tends to have some centralized capabilities like a data governance office and some decentralized components like a cross-functional data governance working group. The federated operating model is really a more complex representation of a hybrid model that tends to work in larger organizations where there might be multiple geographies, multiple lines of business, et cetera, where it's difficult to coordinate all of those different organizations and they might have actually different requirements, different needs and different priorities, which may make it more appropriate to govern data in a federated way rather than trying to have a mandated one way across the organization. Now, in a federated model, you also see a couple of additional layers of that unification or centralization where you've got the enterprise understanding of what data governance means via an enterprise data governance office as well as potentially divisional data governance offices. But anyway, these are just sample operating models, so just what to consider an operating model could look like within your organization and then how this is used as a communication platform, et cetera. Now, another thing I wanted to talk about as part of the organization is how you establish ownership and accountability, and many times racies are great tools to do this. Here's an example of an actual deliverable or artifact called a racie. Many of you might use racies within your organization to define other sorts of roles and responsibilities or accountability. But really, the purpose of a racie is to make sure that there's clarity around roles and responsibilities at a skilled level and ensure that the proper people are involved in activities or decision making. Again, it's a way of documenting the way the decisions are made so that everybody agrees upon who makes those decisions and therefore the decisions actually stick. One of the really important things about a racie is that we want to minimize finger pointing. So this is the reason that we put together a racie is we don't want to be the poor guy in the middle where everyone points at data governance and says, look, it's governance responsibility or it's governance fault. So we want to make sure that different participants in our governance ecosystem participate in decision making processes so that it is truly cross functional and people really do agree with decisions that are made and reduces finger pointing. So next we're going to talk about policies, processes and standards. So policies, processes and standards are kind of the stuff that comes out of data governance. Many times people consider data governance to be policies, processes and standards. As you can see, we think that governance is much beyond policies, processes and standards and really it's a way, the whole framework is a way to make sure that when you create policies and processes that they are actually implemented because we have linked them back to a strategy because we have organizational support for them because we have an approach to communicate them, et cetera. But this is what people think about many times as the stuff. And so let me just go through what the purpose is of these different categories. I think most people understand what they are. So I'm going to talk mainly about the purpose and why they're valuable. Well, policies and rules really establish enforceable directives. So we can't actually expect people to behave a certain way unless we tell them what that behavior needs to be. Policies document and make it very clear what the expected behavior is and what the enforceable directive is. Policies can also create a basis for auditing in which you want to say is this person complying or not complying. This is important especially for regulated industries. Now processes start to articulate how to behave in a consistent way across the organization and importantly data governance processes should be integrated into existing behaviors and processes. And it's important that the existing behaviors and processes are considered. Sometimes they are adapted. We'll talk about that a little bit when we consider the change management component of the framework. But the idea is that there's an understanding of how a data governance process such as a data profiling process or a data quality check or what have you or the utilization of a definition is incorporated into other processes and behaviors. Again, many people think about data governance just as control. Control is important and controls establish how data assets are protected and they are a way of ensuring appropriate data access, data sharing and data use. Standards and definitions are again very important in data governance to ensure that there's commonality and consistency of the data and that it creates data understanding. So being able to define the data and create standards of how data is defined really improves the way that people understand the content of the data. Now metadata taxonomy classification cataloging, et cetera, these are really the more detailed documentation of standards and definitions. So these bottom two are quite related. But in this context we are separating the data definition from other metadata that further describes the data asset. So whether it is the relationship to other data elements or additional information about that element such as context, usage, ownership or technical attributes, we are separating these just slightly. Some organizations consider the definition to be part of metadata which is perfectly okay. We're just trying to make this bit more granular. But the definition of the metadata and the classification starts to support the way that you want to measure and audit your data because you've got the additional context, you've got the definition of ownership, you've got a bit more understanding of how that data is created and should be used across an organization. And importantly it starts to link that business definition to technical implementation. So metadata is many times considered to be a technical capability because systems and applications have metadata. But of course it's not the only use of metadata. So let's look at how some of this is done and a couple of examples. So remember I said that we were going to talk about guiding principles again when we talk about policies, processes, and standards. So here this is why we bring in the discussion around principles. Because when we develop policies, processes, and standards, we start with principles. And the reason is that principles are that statement of shared organizational beliefs. And policies codify principles into very specific statements, guidelines, and rules. So you need to have agreement upon the principles before you start to jump into policies. Standards are the next level of detail that creates specificity and controls to ensure that those policies are applied to different data categories and data elements. Some organizations have a single high level policy and all of the other rules and lines are considered to be standards. Some organizations have multiple policies that follow the life cycle of the data. So again, back to cultural norms within your organization, organizations do this differently, and that's perfectly okay. And then of course the processes tend to be activity driven, talk about how you implement the policies and standards, and then the next phase after processes are things like procedures and necessary tools specific. But anyway, this is how we would go through the creation of policies, processes, and standards. So in the spirit of giving you a couple of examples, I'd like to give you an example of a policy. This is a high level data governance policy that was created. That is the single policy that drives the implementation of governance across the enterprise. This might be very simplistic to some organizations. I've seen other organizations where a single policy is, you know, a dozen pages or more. But this is what worked for this organization that followed their policy template. It was very clear in terms of what the policy is, the scope, the participation, and then also what are the rules and responsibilities that implement the policy, how do we report it, how do we measure it. And then also how do we govern the policy? So the category at the second to the bottom row talks about the review and renewal period. Just to be clear, policies need to be governed in the same way that anything else needs to be governed. So anyway, here's an example for you of a high level policy. So hopefully that's helpful for you. All right. Next category, measurement and monitoring. So this is one of my very favorite categories. It tends to be one that is difficult in the sense that it's not just the measurement of progress. It's also the measurement of impact to your organization. So we'll talk about both of those. But essentially there's several categories of measurement and monitoring. So we want to first look at kind of statistics that we can then analyze. So what are those quantifiable measurements that start to demonstrate proof of value to the organization? And when I say proof of value, I talk about how the data governance provides value back into the organization, how it makes business operations more efficient, how it makes compliance to regulatory events more efficient and secure all of those things. Measurement and monitoring also includes tracking of progress. So this is taking some of those statistics and applying it to the measurement of how you are delivering according to your roadmap. Now we all know that road maps are meant to be changed, but the idea is that you are tracking along a plan that is articulated and people agreed upon the progress. So you need to be able to track that progress, articulate that progress, and also identify what resources do you need in order to continue to make progress. Monitoring of issues is an important aspect of measurement because those issues that are identified can start to indicate whether there is adoption by the organization. So this is something where if you create an issue management process or a case management process, as some organizations call it, and the data governance organization starts to receive data issues or requests for data changes, et cetera, many times the monitoring of issues, it identifies a huge influx of issues in the early days of the governance program. And the more issues you are getting is actually quite a positive thing. So the quantity of issues isn't necessarily the indication of importance, but it shows adoption by the organization. And depending upon the type of issues that are identified, it actually can inform whether your standards are really effective. Are people understanding the standards? Are people utilizing the standards? Are the standards improving the data such that issue identification is dropping? Of course, there should be an aspect of continuous improvement so that this is a constantly improving process. And then score carding and dashboarding is a way to maintain measurement of the program itself and to start to identify what is the value we are providing back to the organization as we have articulated it based on our business case or our justification. So this is where we're starting to share and show the impact not just the progress back to the organization. So here I'm going to talk about a process to establish metrics. And the metrics that we're focusing on here are the impact metrics. So progress metrics are pretty easy in the sense that progress metrics are just measuring back to the execution of your roadmap. But the idea around impact metrics is that you're focusing on addressing business issues and challenges and that you are focusing what you're measuring based on the problems that you're trying to solve. So the way that we do this here is we actually start with that business challenge and then create the measurement and metrics that address the business need. And sometimes this process will help to inform the progress metrics that you are measuring. One of the challenges of progress metrics and governance is that there's so many things that you can measure. This process helps you to identify those metrics and KPIs that are important to solving and addressing that business need. So the way that we go through this process is to start with the issue or the business need. And the point is to clarify the issue, what is meant by the issue, why is that issue specific to see. So then we start to move into the goals. And just by going through a detailed line of questioning of what the issue is and what the goal is, then you can start to come up with those ways of tracking what you need to do from a data perspective to impact the goal to address the issue. So for example, if you identify an issue and a goal, the next step you want to do is you want to identify what are the processes and the data that are involved with that issue and that goal. By looking at the processes and the data that contribute or are used in that to create that issue or that business need, that's where you start to identify your data measurements. And ultimately by going through a process like this, then you can easily demonstrate the impact of the data improvement to the organization. So let's just take an example of this so that you can kind of see what this means. So in this instance what we've said is we've got some goals there on the left-hand side and then we've gone through to determine what sort of measurements that we want to make based on those goals. So the top three goals lead to the bottom line or the bottom line in the table, which was really the business issue. So the business issue was increasing report quality and accuracy. The goals or the change that they would like to see was improved data understanding, improved data transparency and reducing manual remediation. And essentially what you want to do is be able to identify those measurements, those targets and frequencies, which changes that you need in order to really demonstrate an indication of performance improvement and performance impact. So the KPI and the ability to improve report quality and accuracy by providing better transparency, understanding and a more efficient data remediation process is really the message that you want to provide back to your organization. So there's an example from a metrics and measurement perspective. Okay. So just a couple more categories left. Actually three more categories left. We're going to talk a bit about how technology is used within a data governance program. And the way that we consider technology is that technology really provides the automation, efficiency and measurement capabilities to support policies, processes and standards. So the idea is that you can govern your data and the role of governance is to create the policies, processes and standards and technology are great tools for facilitating easier implementation of those capabilities. And in this context we'll be calling out the technical implementation of concepts that we introduced elsewhere such as metadata. So you'll see metadata represented here and represented in the policies, processes and standards because here we're talking about metadata as a tool to create a repository for that metadata. And you know there's a belief in the governance community that these tools will progress in such a way that once they're set up using those standards and processes that have been determined then governance will be automated and non-compliance to governance rules will be virtually impossible and will be so well audited that the enforcement of governance is very easy. Okay. So quickly what are the categories of technology that are primarily supporting governance? Well you have collaboration and knowledge management tools and these really assist in involvement, participation and consistency across an organization. So having a place where all of the data governance artifacts and definitions and policies and participation and all of that are in a single place that really facilitates the adoption of those stated policies and processes and roles and responsibilities. Now data mastering and sharing so this is the concept of master data management as well as some data integration and the idea is that you want to leverage technology to ensure data consistency across the enterprise and it's a way of implementing those business rules that establish the requirements for data accuracy and data movement. Data architecture, security and life cycle management are really core capabilities that create a secure, robust and consistent foundation for the data and of course support the complete data life cycle including data retention in a disaster recovery sort of infrastructure. Now data quality and work and stewardship workflow are those user interfaces that are leveraged to facilitate the stewardship activity. Many times these are data quality capabilities so the steward has an actual tool to measure the quantifiable dimensions that are identified to ensure trusted data. So it really helps with efficiency and productivity to have workflow to have tools that can measure data quality efficiently. And lastly in metadata management by having a repository really reinforces those definitions and standards and can really improve that enterprise cohesion that you are trying to create through the program in and of itself. So let's just take a quick example of what a technology structure would look like with governance. So this is a master data management services framework that we use to define what are all of the capabilities within master data management and the idea is that governance provides the shared cross functional participation to make the decisions about how these services are implemented in order to address the business requirements. And you can see that in this definition it calls out other sorts of capabilities like data quality, security, metadata management, et cetera. But the idea is that governance provides the input, the business requirements and the business rules to make that technology provide the efficiency and the automation to support the data governance program. So two more categories that are both tightly related. So the first one we're going to talk about is communication. And communication is something where it really does impact this next category which is our final category called change management. And communication to us is not just the one on one communication or meetings. It also incorporates the training strategy. So a communication plan is really an effective way to document what you're trying to do from a communication perspective. And then that training plan helps to make that a bit more specific to each of the organization. So sometimes communication is thought of as awareness and orientation. But really that is also part of the training strategy. So the idea is that you want to be able to articulate to the organization, to the enterprise. Vehicles and mechanisms are really just the way that you want to delineate the techniques used to communicate. So these are things like newsletters. You would call out things like meetings or what have you. But we want to be able to incorporate creative techniques such as using video and other sorts of interactive capabilities to make it much more real for the recipient so that you're not just emailing things out. And of course we need to create reusable content and identify accountability for the different aspects of communication. Communications are really big category so if you don't reuse that content it can be very daunting in terms of the amount of work that is done. So this is a communication framework that we have used to help support change management. So it's really an approach that brings together many aspects of the data governance framework. So you can see it starts with a statement of a vision and a purpose which would be covered within the strategy category of the data governance framework and it goes all the way through to participation which is also something that is called out in the organization category of the framework. But the idea is that you want to create and articulate what is the desired future state. So what is the value of that future state to the company? This is the vision. This is a statement of a strategic goal and the why. So what is it and what's the why? That's the purpose. And the picture is really just that. It's a picture of what the future will be like once data governance and data management is implemented. The plan is the how and then of course the participation is the who and what is each person's role within that plan. The idea is this starts to make it very tangible to the organization so they don't just know the vision but they know how they participate in the plan to create the future state. This is something that was adapted from a very valuable change management approach by William Bridges. So that takes us into change management. And this is going to be our last category. I feel bad at being the last because it's also one of the most important. So a few components of change management, most fundamentally it's identifying what is the impact of data governance to the organization and how ready are they for that. So successful data governance inevitably is change and change to the way that information is created, managed, shared and consumed across the organization. And fundamentally you are asking people to do something differently. And so it's important to understand what that ask is and how they'll potentially respond to that change in behavior. So whether this is an incremental change or a big change, some of this still needs to be considered. Leadership alignment is formally identifying what the sponsorship role is and how they need to be supporting the organization. Stakeholder management is a similar sort of process but with a broader group of stakeholders in the organization. And really the stakeholder management plan assists in promoting the internalization of the value of the data governance program. A couple of things to make it very specific. There should be a change plan with very clear and practical tools and checklists to make sure that this plan is actually delivered upon. And then fundamentally over time all of this should be transitioned to just a sustainable discipline. So the idea is that change management is helping people through a shift but it is ultimately to transition to the business as business as usual and an ongoing discipline. So there's two sides to change management. The one that we're all very comfortable with is the situational. So what are we doing? Why are we doing? When are we doing it? What's the process? What's the rule? Give me the policy, et cetera. But then there's the psychological side which is the response that people have as they're being told to do something new. And this is the most difficult part of change management. So in order to make it successful both of these need to be addressed. And as data professionals we tend to be very linear which is something that falls nicely into the situational change but the psychological change sometimes we feel as much more airy-fairy but the reality is people will respond how they will respond. And if we deny that there may be some resistance or psychological impact then we're not going to be successful. So considering we're all very linear thinkers, not all of us so sorry that's a gross generalization but we tend to be more linear thinkers. Well here is a very practical way to put together your change management plan as it aligns to data governance phases. So the idea here is that you're doing this proactively, you're doing it purposefully and you're recognizing that this is an important aspect of sustainability. So for some final considerations here we want to make sure that we are being very clear on what we're trying to accomplish so that it is aligned specifically to the goals and objectives of the organization. So we want to be consistent, we want to enable people to think about the enterprise, so to think globally but also to act locally. We want to make sure that it's simple enough that people are actually accepting and using the guidelines that we're providing and the tools that we're providing through data governance. Many times using that alignment process can help to encourage people to use the data governance programs, tools, artifacts, et cetera. We also want to make sure we're deliberate on who needs to do what. So again, thinking about that process of vision to purpose to picture to plan and participation, we need to be very clear on that. And lastly, it's important to communicate, communicate, because that's really what ties this all together and creates an effective change. So with that, I'm going to wrap it up and turn it back to you, Shannon. Thank you. Kelly, thank you so much for this great presentation. Kelly, we'll be joining you in the group chat over the next 10 minutes to go over questions and comments of the presentation. Following that, there will be a 10 minute break where we will hear from a panel discussing how to govern data definition through data modeling.