Hello 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 current installment of the Monthly Data Diversity Webinar Series Real World Data Governance with Bob Siner. Today, Bob will discuss Build and Effective Data Governance, 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. If you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the bottom middle of your screen for that feature. For questions, we will be collecting them via the Q&A section in the bottom right hand corner of your screen. Or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag RWDG. And if you'd like to engage more with Bob and continue the conversations with each other after the webinar, you can go to dataversitycommunity at community.dataversity.net. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of this session, and additional information requested throughout the webinar. Now, let me turn it over to a moment for to Danny Sanwell for a word from our sponsor, Danny. Hello and welcome. Hey, Shannon, thanks so much and thanks, everyone, for joining. It is absolutely our pleasure to sponsor another webinar, the topic, as always, is very pertinent. We're really seeing people making great strides in the data governance space. And I think that the topic is very timely because people are now looking not how to convince their organization to get it done, but now they want to know how do we get it done, what do we need to put in place, and what are all the interactions and interconnectivity between the all-moving parts that data governance represents. So without taking too much time away from that, I'm just going to give you a quick view into Erwin, and in case you're not familiar with us, we're founded in March 2016. We were divested by CA Technologies, the long-standing and industry-leading Erwin data modeler product, and we've turned that into a data governance company with a real focus on helping organizations get their arms around their data in every which way that they can so that they can get the most value out of it, reduce the risk, and make sure that they can leverage that data for all of the things that they would like to do moving forward. So when you look at what organizations are trying to do, we've really set our sites firmly on that, which is to take data-driven insights, mitigate the risks associated with leveraging that data, and then allowing organizations to practice agile innovation based on what those insights are telling them and really transform their business to meet the challenges of today, tomorrow, and the day after that, because really it needs to be a sustainable practice over time that allows you to meet today's challenges, but then there'll be new ones tomorrow and even newer ones after that. And when we look at that, it's really about solving the enterprise data dilemma. That's really where we've put our focus, which is really getting your data into shape to be able to leverage in the way that you want to, starting with harvesting all of the data and understanding that data and collecting that and cataloging it, organizing it in a way that makes sense so that you can understand all of the relationships and associations between the different aspects of your data architecture, your governance framework, and all of those other things, contextualize that data so that people can go at it from a business perspective and to be able to see all of the important information around that data that will help them understand if it's the right data for them, if it's fit for use for the purpose that they're trying to use that, and then how to use it and then how to access it to get that. And that comes with the next step, which is really administering the data. And I think that that's a lot of what we're going to look at today so that you can develop governance frameworks and models that allow you to, in an agile and non-intrusive way, manage that and make sure that everybody's aware of how that data relates to the business, how it should be used and how it shouldn't be used. And then finally, making sure that everybody has access to that in their own perspective in a self-service way so that they can really get to the value of that data without having to wait for a lot of other people to intervene to make that a reality. And to do that, we put together a set of capabilities that we've broken down into some suites, if you will. It all starts with us on the enterprise modeling side. So getting hold of your architecture and being able to understand your business and the technology that supports it, understanding the business processes that you have and how you do business and how you might want to do business in the future and really be able to communicate that and collaborate around that and all come to a common understanding. And of course, when data modeler allowing you to start taking all of your enterprise data and organizing it in schemas and architectures that are going to allow you to satisfy your business use case and meet your business needs. And then from there, building a foundation, starting with the data catalog, where we will capture and manage all of your metadata, allow you to really start to connect that and move it around using the mapping manager, all under lifecycle control with great visibility and capability to understand the quality of that data. And then build on top of that our literacy suite, which allows you to then start putting those business aspects on the stewardship tools, whether it's the glossary and the terminology, your business policies, your business rules and then opening that up through a self-serve portal that allows people to easily search, discover what they have, visualize things like lineage, understand the impact analysis and really understand in a one-stop shopping way, all the aspects of the data and all of the particular characteristics of that data that will allow them to be successful using that. And we make all of that happen using a lot of automation in the platform as well as externally going out and allowing you to automate the harvesting of your metadata, the cataloging and discovery and categorization of your data, as well as leveraging that so that you can activate your metadata through our smart data connectors to start driving out new data management infrastructure based on the metadata you have in that catalog. And all of those things come together to really give you the data intelligence that you need to really start to effectively practice governance to implement the frameworks that you know Bob's going to talk about today. So that's really our goal in life and that's what we have on offer. So if you're looking for technology to start helping you push these things in place, whether it's starting out at the modeling level and really driving and understanding how governance needs to fit into your organization, what framework makes the most sense, and then you know then pushing that all out once you have these things in place and allowing all of your stakeholders to understand what it is that you have in place. And that's what we call the Erwin-Edge or Enterprise Data Governance Experience Platform. Really allowing you to manage your data assets and architecture, the processes, as well as the infrastructure underneath and govern all of those things to maximum business advantage. NetNet at the end of the day, our goal is to get every stakeholder on the same page and enable them to do what they need to do with data for the betterment of the business and to drive the key initiatives in that transformation that everybody is seeking and really needs as we move forward in this in this fast and exciting business environment in which we live. So with that, Shannon, I think I will pass it back to you and we can get to the real topic at hand, looking forward to it. Danny, thank you so much. And if you have questions for Danny, he will be joining us in the Q&A section at the end of the presentation today. So let me introduce our regular speaker for this series, Bob Siner. Bob is the president and principal of KIK Consulting and Educational Services and the publisher of the TDan Administrative Newsletter, TDan.com. Bob has been the recipient of the Dana Professional Award for significant and demonstrable contributions to the data management industry. Bob specializes in non-invasive data governance, data stewardship, and metadata management solutions. And with that, I will turn the webinar over to Bob to get to today's presentation started. Hello and welcome. Hi, Shannon. Hi, everybody. Thanks for attending the session today. I know you've got a lot. Everybody's got a lot to do and I really appreciate you taking the time to listen to the webinar. I also want to thank Danny Sandwell from Irwin. Always great to have them as a sponsor. And there's a lot of things that Danny talked about in the platform, in the Edge platform that you talked about that really relate well to the things that I'm going to talk about today. There's a lot of different components. And as Danny had mentioned, getting people to understand the processes and the roles and the communications and the tools that we can use to be successful in governing our data is so important. And it's really important to collect that information somewhere and to to have a metadata repository or a metadata base for collecting that information. So I'm always glad to have Irwin on board to work with us today. Before we jump into my sharing with you, an updated version of the framework that I presented a couple of years ago, I wanted to walk through a few things that you might want to know about. As you already know about the Real World Data Governance webinar series, next month's topic will be applying governance to business processes. So that's a question that I get all the time is once we've defined the roles and responsibilities and once we've defined the processes, how do we get people actively engaged in these processes? I talk a lot about noninvasive data governance. So there's information about the noninvasive data governance book. I'll be speaking at three data diversity events in the balance of the year. I'll be speaking at DGIQ, Data Governance Information Quality in a couple of weeks, Data Architects for Summit in Chicago and the new event, the Data Governance Vision Event in Washington DC later this year in December. Also, there's two online learning plans available through Data Diversity, one on noninvasive data governance and one on noninvasive metadata governance. So that's the newer one. So please take a look at that if you have interest. Shannon mentioned the data administration newsletter. It's an online publication. It's been around since 1997. Lots of information about the data for data practitioners and people that are working in the data space. And last but not least, KIK Consulting, KIK stands for Knowledge is King. The focus of the Data Governance practice is on noninvasive data governance and also now on noninvasive metadata governance. So that's a little bit about me to get started. I wanted to share with you kind of the list of the things that I'm going to talk about in the time that we have today. I'm going to share with you what I consider to be a customizable data governance framework. And you may have seen other frameworks that are something similar to it. I'm going to provide to you an empty version of it and also a filled in version of it. The filled in version is going to be most specifically towards noninvasive data governance, but the framework itself can be used for really any approach that you're looking at. And I'll get into that real quickly here in a minute. I'll be talking about six core components of a successful data governance program. And these are the six core components of the framework. And if you see I have the letters, the word six highlighted in red, that's because before it was five, and I've added a core component, and you can guess which one that is, and we'll be showing that to you in a minute. We're going to look at those five core components from five different perspectives or different levels of the organization. I'm going to provide to you a detailed description of everything that exists in the framework I'm going to share. And then we'll talk about using the framework to help you to identify what's the most appropriate approach for you to use for data governance within your organization. I'd like to get started by sharing my definitions of data governance. I don't want to spend a whole lot of time on this, but at least we need to start off on the same page. And so I word my definition of data governance quite strongly. I say that data governance is the execution and enforcement of authority over the management of data and data related resources. The fact is that however you define it, if you define it as being the cooperation or the bringing together of IT and business and process and things like that, at the end of the day, we need to make certain that we can make good decisions with our data, and that we're doing the right thing. So I'd like to word my definition of data governance strongly. It's the execution and enforcement of authority. And when I talk about stewards, typically I talk about the fact that potentially everybody in the organization can be a data steward, if they have a relationship to the data, and if they're being held formally accountable for that relationship, then potentially they might be a data steward, and we might want to help them to understand how to make their life more efficient, more effective, and formalizing some of their accountability around the data that they define, produce, and use. And since I talk about non-invasive data governance, really non-invasive really describes the way that governance is applied to the organization. So we're not going to rewrite all of our policies. We're not going to rewrite all of our processes. What we're going to do is we're going to apply governance to these things and hopefully be somewhat transparent, supportive, and collaborative in our approach. The idea is that we don't want to have an iron fist approach, meaning a command and control approach. I'll talk about that a little bit later in the webinar today when we get into some of the different approaches and how the framework might help you to identify what the best approach is for you. So let's talk about first the customizable data governance framework. And before I show it to you, I want you to know a few things about it. So this is really a generic approach, a generic framework that you can use to insert any approach to data governance. It includes what I consider to be the core components, the main elements of a data governance program that you need to concern yourself with. You know, looking at the organizational levels from the executive level all the way down to the operational and support levels. I'm going to share with you, like I said, a blank framework, but then I'm also going to share with you one that's filled in with information that would be more likely to be used for an organization that's taking a noninvasive approach. And then last, I'm going to show you a comparison version where we can take the different components of the framework and look at them from several different approaches. And last to talk about the, with the framework is the inspiration for the framework. And some of you may be familiar with the Zachwin framework for enterprise architecture. It's a very popular framework that's been around for a long time. It really, what it does is it describes the who, what, why, when, where and how of the organization from a bunch of different perspectives. And what I've done is I've taken what John Zachwin has done and I've molded it into a framework for data governance. And that is what you see on the screen now. And so with this framework, basically it's the matrix kind of similar to what John had put in the Zachwin framework. But I've defined the core components of the program across the top. And that's the data, the roles, the processes, communications, metrics and tools. If you've seen this framework before, I didn't have a data column. So we really need to talk about what specific data is it that we're going to govern. And that's why I've added that as a core component of the program. Again, looking at it from the executive all the way down to the support perspectives of the organization. This is a blank matrix. And if you can't read what's in the upper left, I wanted to make that a little bit more readable to you. So the date by the data we're talking about the assets that are being governed, the roles, you know, basically who's going to be held formally accountable for what in the processes of data governance. We'll talk about the processes, the communications, metrics and tools. And then I identify the five different, at least what I consider to be the five different levels of a typical organization, which is the executive piece of the organization, the strategic piece, tactical operational, and then all of those different functions within the organization that support the program. So we've got the support function as well. So this is the empty framework. Let me show you here real quickly at least what the completed framework looks like in terms of noninvasive data governance. So if you look at the top where it says noninvasive data governance framework, it said just data governance framework on the prior slide, because like I said before, you can customize the framework to what makes sense for your organization. And so I'm going to walk through each of the pieces across the top, each of the pieces down the side. And then we'll get into the details of looking at data from the executive perspective, from the strategic tactical operational and support perspective. So it's a very simple five by six matrix, again with the core components. And you may have different components, but these are the ones that I've experienced in my practice. So these are the core components that we're going to discuss. And then, like I said, you may have the different organizational structure where you might not have as many levels where you may have more levels, but you want to make certain that you're addressing each and every one of the different perspectives of people within the organization. So let's start out by talking about those six core components. And what I do is I have a smaller version of the framework in front of you. And I'm going to highlight in yellow. I'm going to do this kind of throughout the presentation. That's to the part of the framework that I'm speaking about. So when I said roles processes, data roles, processes, communications, metrics and tools, that's the top part of the matrix. And if you can look underneath them and just kind of sneak a peek, if you look under roles from an executive strategic tactical, it has a name of a typical type of position that might be assumed by somebody at that level of the organization. That's just as an example. And then from processes, if you're looking at it from each of the different perspectives. So let's first walk through the different components of a data governance framework. And the first one and the one that's been added since the older version from several years ago of the non-invasive data governance framework is the data piece. And basically, the scope of the resources that are being governed within your program is really what the data column is all about. Are we looking at just data? Are we looking at information, records, knowledge? What are the specific types of data that we're looking at within the organization? Some organizations call the program information governance and some call it data governance. Others have called have called it records management or records and information management. So one of the things that you want to identify first is what is the scope of the data resources that we are looking at and what do we call these things? Is there an understanding as to what the difference is between data and information and information and content and things like that? So we need to be very clear in that definition as we're getting started. In the non-invasive approach to governance, you know, it's very similar. You have a very similar approach. In fact, most of the components of this program will work for any type of data that you're going to be looking at. So whether it's structured data, unstructured data, content, records management, you're going to need some of those same core components when you're addressing the governance of those resources. So the program's going to look somewhat similar depending on the type of resource that it is that you are attempting to govern with your program. The second core component, it's one that I talk about all the time. In fact, the last month's webinar with DataVersity shared a complete set of data governance roles and responsibilities. And my suggestion is that, you know, the way that you go identifying the roles for your program can be a real predictor of the effort that's going to be required to govern the data within your organization. As I said before, I'm often known to say that everybody in the organization potentially is a data steward and that's going to need to, that's going to really dictate how the program is going to move forward as compared to if you're going to only have a handful of people that you identify as data stewards. You know, the idea that everybody is a data steward, if you use data, you got to use it effectively, you got to use it the right way. If you define data, you've got to catalog all the information about the data in your repository and you've got to have clear definition to the data. So really the roles are based on people's relationship to the data and it'll also help to dictate how many people in the organization are going to need to be involved. I mean, you may need to communicate to a wide stretch of people across their organization if you're considering that everybody in the organization is potentially a data steward. And I spent in the webinar last month, I talked about an operating model of roles and responsibilities. And the idea is to put together a detailed document that says what people are going to do as part of the program. All the way from the executive standing committee level, all the way down to the operational data stewards that are defining, producing and using data as part of their job, you also need to define what role IT plays. So all of these different components fit into the roles and responsibilities. That's going to be one of the first questions that you get is, you know, who does what? When do they get involved? How do they get involved? You know, I suggested right here that there's several things that you want to include within that operating model, which is a description of their responsibilities, typically bulleted out. You know, who's going to participate in each role and how much of their time is going to be necessary. You know, all of these things that people are going to ask, when you're going to ask them to get involved or you're going to go to them and say, hey, you're a steward of the data, we're expecting you to do steward stuff, but they're going to want to know what that means and how you went about recognizing them as being data stewards in the organization. The third core component or the third foundational component is the processes. So it's real good if you can define what data you're going to govern and it's really good if you can go and define the roles and responsibilities, but if we can't apply them to something and we can't engage people in process, then we're not going to have a successful program. And at the beginning of the webinar, I stated that the non-invasive approach really talks about how we're applying governance to process, rather than redefining all of your processes or having a single data governance process. My suggestion is that you take a look at the steps of your process and you get the right people involved at the right time for the right reason. I typically call that a data governance bill of rights, you know, doing the right thing at the right time. And in non-invasive data governance, we're looking to take processes and make them repeatable, have a good cross-reference of who does what during the steps of the process and what the outcome is going to be of those steps of the process. So processes are certainly an extremely important part. You know, a lot of people talk about people and process or they talk about data and process and technology, but processes seem to be at the heart of what almost everybody is thinking about when it comes to implementing governance programs. Communications, we've done webinars in the past on putting together a communication plan. And my suggestion is that communications is a huge part of a successful data governance program and therefore needs to be a huge part of the data governance framework. How we're going to communicate with the different people in the organization at the different levels needs to be spelled out. We're certainly not going to communicate with the operational data stewards the same way that we're going to communicate with the executive branch of the organization. So we need to make certain that we spell out a communication plan understanding what we're going to share with people and how it's going to be shared, understanding that it may really depend on the audience that you're communicating with. So really every aspect of the program has to be communicated and typically I break that down into orientation communication on boarding communication and ongoing communication. So certainly we need to make certain that we keep people involved. We help to orient them to the idea of data governance. We onboard them when it gets to the point where they need to be actively involved and then we have ongoing communications which might be closely related to the metrics and the measurements that you use to demonstrate the value of your program. And that just so happens to be the next component of the of the framework is the metrics. We know that we need to be able to demonstrate value. Sometimes it can be tangible financial value. Quantified financial value. Sometimes it just increases inefficiency and effectiveness. And sometimes we can build on those things by by building governance into the processes and taking benchmarks early on to understand how long does it take us to do something now and how long does it take us some point in the future. We can actually measure what data governance is doing for the organization and what value it is adding to the organization. So metrics are another component of a successful data governance framework. And the last component is tools. We all know that tools are important. I'm sure Danny will admit that tools are very important to successful data governance programs. There's a lot of different tools and templates that I share over time but they really become the enabler to your program and to help you to deliver value to the organization. So tools are used to help to manage the data, manage the metadata or the information about the data, improve knowledge about the data, improve knowledge about the data and the rules and the processes so that Joe user understands what the rule is about using certain data certain ways and that he can refer to that rule very quickly. The tools are very important for the success of the program and we're going to look at those from each of the different perspectives as well. Now let's talk about the five different perspectives that we need to look at this from and I call them the five different levels of the organization and typically if you think about your own organization, you've got an executive level, potentially a strategic level, tactical, operational and then there's a lot of supporting functions in your organization. Things that might already be considered data governance. You know IT security, audit, human resources, even maybe some level of support for your program. So we need to understand where you know who participates in each of these different levels, how they get involved and I'll share with you a quick diagram of what the operating model rules and responsibilities looks like the one that I spoke about in last month's webinar but we're not going to go into a lot of detail about that but what we want to do is recognize where roles play and fit into the framework that I'm sharing with you. So let's run down the levels real quickly so then we'll start getting into the meat of the framework which is where the rows meet the columns and there's a lot of information to provide there. So there's the enterprise perspective which often is referred to as the C level and I think we have almost a C level position for every letter in the alphabet, CAO, CBO, CIO. There's a lot of different executives but we know that if our data governance program is going to be successful that we're going to need to have support and sponsorship from people at this level of the organization. So we need to define you know from an enterprise perspective what what do we know about what data do they care about what role are they going to play what processes do they get involved in if they're going to get involved in communications all of those things we've got to look at all of those different components of the program by each of the levels so we're the first level is that executive is that top tier of people within your organization the strategic level a lot of your organizations might have roles you call a data governance council or a data governance committee or something named like that typically that's the top strategic part of the organization and we need to again look at the the components of our data governance program from the perspective of the the people that are playing a strategic role in the organization so we've got executive we've got strategic perhaps one of the most important pieces of a data governance program is the tactical level i often refer to those people as data domain stewards or i refer to them as subject matter experts for the data now these are people that have a tactical responsibility for governance and they're looking at the data they're looking at the roles they're looking at the communications and processes tools and metrics they're looking at them from the cross business unit i you know typically i talk about the the tactical level being where we break down the silos where we have people that have responsibility for data across business areas rather than specifically within their individual business area so we'll talk a little bit about how the tactical um the the tactical level of the organization pertains to each of the different components that i've walked through so far and then we've got the operational level and those are the people in the organization typically that daily are defining producing and using data as part of their job they typically consist of almost everybody in the organization and a lot of those people have relationships to the data but we need to look at the data and the roles and all of those components from the perspective of these people at the operational level because they may not look at it exactly the same as the tactical strategic or executive levels of the organization and the last level i want to share with you is the support level and that's the one that's most vague out of all the different levels because those are parts of the organization or the people in the organization that are there to help you to execute and enforce authority and that may be as i mentioned before the it part of the organization might be a support part of the organization well we certainly need to look at the framework from their perspective and it's it security we need to look at it from their perspective so levels is the final perspective or the final level of the framework that we're going to look at each of those components by each of those levels and so we'll talk about the support level as well okay the next thing i want to talk about is the detailed description of where each row meets every column so we want to look at we want to look at the the data from the executive perspective and the data from the strategic and the same with the roles and the processes so we're going to look at the meat of this diagram and we're going to go through each of those relatively quickly because we only have a limited amount of time today if you're interested in more information about the framework i'm going to share with shannon a white paper that describes the whole white the whole framework in more detail from the empty empty framework to the one that's filled in but i'm going to go through these relatively quickly because i think it's important that we at least discuss them and touch on them when you're thinking about building a framework for your organization you don't necessarily need to use the things that i'm implying that you would use it within the framework but you really want to define it at from the perspectives of these people within your organization so when we talk about data by the different levels well oftentimes the people with the executive level are most interested in the data that's going to help them to effectively lead the organization and a lot of that information comes to them in their KPIs and their dashboards but they certainly have a perspective they're not necessarily looking at the data from the operational perspective or even really understanding that we have multiple definitions of the data and the data is used different ways in parts of the organization of course they care about that but the things that they care most about are the data that they need to help them to effectively lead the organization from the strategic level you know they're looking at it as an enterprise asset and they're looking at it as an asset that's going to help us to improve performance within our organization at the tactical level as i mentioned before those are the people that are now looking at the data from across business unit from a subject area perspective rather than a siloed business unit by business unit perspective at the operational level they're looking at the data that they need to use to do their job within their business unit to serve their function of the organization so they're certainly going to look at the data a little bit differently than the folks at a tactical level or strategic or executive level and then the data from the support level you know they're most concerned with accountability and making certain that we've inventoried all the data and metadata one of the main assets that help us to add context to the data so we can turn it into information that's some of the data that the people at the support level are looking at so let's jump into the roles and this one's probably the easiest one to describe and i'm sharing with you a very small version of the operating model but like i said the webinar last month goes into every level in every detail of all the different roles and responsibilities so you may want to take a look at that but at the executive level when it comes to the roles of the program that's going to be your leadership that's going to be your steering committee that's going to be the people in the organization that say that data governance is important enough that we're going to apply resources to it and that we're going to focus on improving the value of data to the organization at the strategic level they're the ones that typically kind of preside over the program and they're the ones that have responsibility for making certain that the different parts of the program are in place this is the group that most often the people that are administering the data governance program go to they go to the council to share with them the status of the program things that are being worked on to get some level of priority as to what needs to be worked on first second and third the tactical level of the organization that's those data domain stewards the subject matter experts a lot of people refer to them as data owners take a look in the most recent issue of tdan.com there's an article that I wrote about the difference between owners and stewards and when we should use each of the different words to describe the people in the organization I try to shy away from the use of the term data owner because it implies some things that I don't think we really want to imply that these people own the data when the organization is really when the data is really owned by the organization at the operational level we've got the operational folks like the data stewards the people that we refer to as users of data from the support level we've got people that have responsibility for managing the program whether it's your data governance office team administrator you've got work groups that you put together to solve problems and then you've got partners of the data governance team or the data governance office and that can be as I said before it security it audit legal hr all of those are potential partners of the data governance program so when we look at the roles we want to make certain that we're addressing each and every one of the levels that we've defined in the framework when it comes to the processes there are certain processes that different people in the organization at different levels have have as their focus you know at the executive level they want we want them to endorse and enforce the program we want them to authorize the program we want them to act on things that we have uncovered as part of the program at the strategic level they're really directing and approving and prioritizing and resolving issues that get escalated up to the strategic level at the tactical level what's their responsibility to processes they're there to facilitate and mediate and promote effective practices within the organization at the operational level these are the people again that day to day are managing the data are handling the data that really need to understand the rules associated with the data in order for us to successfully govern data across the board and then from the support perspective these are the folks that help us to formalize it here and enforce different roles rules are you le s that they that they are implementing within the organization to make certain that we as an organization are are complying that we're securing and making private the data the way that we need to so when it comes to the the processes we can look at them from all five levels as well and I've given you some of the idea some verbs to help you to understand what are some of the processes that people at these levels are most concerned with when it comes to communication this is really important as well I mean we want the executive level they need to support sponsor and understand what we're doing with our data governance program if they don't and I've mentioned this when I talk about best practices before it's immediate risk for the organization it's the people at the highest level of the organization don't support sponsor and most importantly understand what you're doing with your data governance program at the strategic level we need to provide status to them they're evaluating the program they have the responsibility of also commending those people within the organization that are doing what they're what's being asked and and help people to understand that there is somebody that's noticing that they're becoming effective data managers within their parts of the organization at the tactical level most of the communication resolves or revolves around standards and subject areas and different projects that are going on for example if you're focusing on customer data that might be the subject area that that you have projects going on you may be creating a master data solution for your customer for your product or for your vendor or material whatever those subject matters are that's where we need to help we need to work with the people at the tactical level and communicate effectively to the organization information that's going to help people to understand data within their subject area make the best use of the data within that subject area and also protect and and and make certain that we're keeping private the data the way that it needs to again within that subject area at the operational level well as I mentioned before we need to make certain that we're orienting people across the organization to data governance and helping them to understand what it is when they need to get involved we're going to onboard them so we might have additional communications for these folks who let them know what their role is in the program and what types of activities they're going to get involved in when they should look to get involved in the program so we need to orient them to data governance in general bring them on board but then we also need to be able to provide ongoing communication to them how effective have we been are we measuring the appropriate things how do we know when something should instigate data governance to get active and get go and get involved and those things are ongoing types of communication that we're going to provide to basically everybody within the organization and from the support level we want to make sure that we're working with them to communicate how this is planned how it's being developed and how it's being delivered to the organization so again take a look at the communications which is one of the core components and think about how we need to communicate to the different people across the organization and what they're interested in so we can make certain that we hit the mark on providing them the information that they need the metrics by level so typically the executives need to approve the metrics that we're going to share with the organization but even more importantly than that they need to act on the results in the earlier version of the framework I didn't have the word act in there because and that's been pointed out to me by several people that you know why are we doing these things at some point somebody needs to approve that change is going to take place they need to act on the results that are being brought to the council and then being brought by the council to the executive level of the organization at the strategic level they're looking for acceptance participation performance metrics at the tactical level it's mostly focused on those specific subject areas that the people at the tactical level are most interested in at the operational level we can measure accountability and efficiency and effectiveness as I mentioned before and at the support level we need somebody to help us to make certain that we're collecting and that we're reporting the appropriate metrics across the organization again the metrics are another one of those core pieces that components of a successful program and the last one is the tools and there's different tools that are being used by people at different levels of the organization so at the executive level we don't expect them to jump into our data modeling tools and to jump into our repository tools but the tools that they're most concerned with are the policies and the directives and the audits that are taking place across the organization at the strategic level again these people might not necessarily be as hands on and some of the tools we might want to consider for them are the charter for the program where the best practices the guidelines and the roadmap of the next steps that we're taking like I mentioned before at the strategic level that role has the responsibility for approving things and prioritizing things moving forward at the tactical level they're worried about the tools that will help them to document standards document requirements and workflows at the operational level we've got a lot of the tools some of the things that Danny spoke about which were the glossary and the dictionary and the catalog and the repository typically that information is going to primarily come from the operational level of the organization from the support perspective excuse me you know we're talking about the data governance tools tools like Irwin that would help you to catalog and to record all of that information that's going to be of value to people in the organization so those metadata tools all of those artifacts that I tend to share with you throughout the different webinars that I give I consider those to be tools as well because there are ways to help to get you jump started in collecting the appropriate information within your organization that will support your data governance program so basically we talked about the levels we talked about the core components we talked about the core components by each of the different levels the last thing that I want to share with you today before we turn it back over to Shannon is a way to use a framework like this to compare the different approaches to data governance and to help you to identify which one's most appropriate for your organization so the names that I give to the different approaches that I often talk about are the command and control approach it's top down it's you will do this the traditional approach which I like into the movie the field of dreams which is if you build it they will come so it's going to be certainly a responsive action of people to get them involved in the program if you take kind of that traditional approach in the non-invasive approach we're really looking at it as a fact that a lot of these people are already doing these things and that we're going to help them to do them more efficiently and more effectively by putting more formal accountability around the things that they do so these are the three primary approaches and this is the comparison matrix that I have put together so if you look at this matrix what I've done is I've taken that what was across the top of the previous framework and I've put them down the left hand side of the matrix where you have the data down through the tools and across the top now what I've listed is command and control traditional and the non-invasive approaches so we want to look at each of the different core components according to each of the different approaches that organizations are using for data governance and so this I just wanted to blow up it was in the upper left hand corner so you can see it better you know typically the command and control approach really has limited coverage within the organization because you're identifying that handful of people with accountability for the data the traditional approach as I said before it really depends on how people respond and how they want to be engaged in the non-invasive approach if you're looking at everybody being potentially a data steward you have complete coverage of the organization that might require additional communication because you're talking about communicating with a lot more people but we know that we need to make certain that anybody who has responsibility around data understands what their responsibility is what the program is and how the program can help them to improve the quality of the value the protection of data within the organization so let's go through each of the different each of the different components that I mentioned from each of the different perspectives and so from a data perspective and the command and control approach organizations oftentimes just talk about governing the data and it doesn't necessarily go to a much deeper definition than that typically when I see in a traditional approach is that they go a little bit further than just say oh we're going to govern the data of the organization and they say it's going to be the data it's going to be the metadata it's going to add context to the data to turn it into information in the non-invasive approach typically you can be looking at any type of data or information resource within the organization from data to information to records management to knowledge management and such so there is a difference between what the focus is on data according to the different approach that you're going to take to implement governance in your organization when we look at the different approaches by roles there's a big difference in command and control approach people are assigned into roles rather than being identified that you know this is a role that we would like you to play or being recognized and saying that somebody does something with data so therefore we're going to help them to understand how to do that thing better so it's a much more aggressive it's much more invasive to assign people into roles it's less invasive but still somewhat invasive to identify people into roles but if you're going to recognize people into roles based on their existing relationship to the data that's really what non-invasive data governance is all about we're not we're going to try not to give people more than what they already have but understand that we may help them to do some of the things they've been doing in the past a bit better or should I say at least a bit more formally from the processes perspective the command and control approach typically people look at things as being new processes or in a traditional approach oftentimes I hear people refer to something as the data governance process as if data governance can be a single process typically in non-invasive governance in non-invasive data governance we're looking at the processes that we have and we're applying the roles and responsibilities to that process like I said before the Bill of Rights getting the right people involved at the right time for the right reason to get you to the right solution at least most of the time communications is very different as well and the command and control approach we're going to we're going to tell people that they are going to do this you will do this versus you should do this in the non-invasive approach saying you know what you're already doing this let us help you to figure out ways to do this more effectively for the organization from the metrics they measure different things as well typically in the command and control approach they're looking for measurement of return on investment versus data quality which is really the traditional approach to metrics within a data governance program in a non-invasive approach we're going to focus on the advancement of the program meaning how well has this been accepted into the organization how many issues has it solved we can get to the point by saying you know how of these issues related to or translated into dollars and cents so understand that the metrics component can be looked at in different ways from the different levels of the organization that we spoke about before but also in the different levels of approaches that organizations are taking metrics may be viewed a little bit differently depending on the approach in the command and control approach with tools a lot of organizations will buy the tool first and try to fit their program into the tool versus the traditional approaches let's look at what we have in the organization and let's leverage it wherever we can and then in non-invasive approaches we're going to leverage what we have we're going to build tools we're going to apply purchase tools to our solution but we're not going to limit it to one type of tool we're going to look at what we can build versus what we can buy versus what we already have in the organization to identify those specific tools that are going to add value to our data governance initiative so just kind of going back to the beginning I want to go back and just kind of share the blank matrix with you because all the things that I've shared with you all the nouns and all the verbs that fill in each of these different blocks are they came from me they came from the idea of being non-invasive in your approach but you may not be calling your approach non-invasive data governance so my suggestion is you might want to start with an empty template like this one and work in it start filling in well how are the strategic people viewing the roles or what is the strategic role for data governance in the organization or at the operational level how are we going to communicate with those folks and if you can answer what you're doing in each of these empty blocks in this framework you'll be well on your way to putting together all of the necessary components for implementing governance within your organization so today in the webinar I know I ran through it kind of quickly for you I shared with you that customizable data governance framework we talked about the six core components of the framework being the data the roles the processes communication tools and metrics and then the five perspectives being executive strategic tactical operational and support and we use the framework to kind of look at it from the perspective of the different approaches that organizations are using for data governance so that's what I walked through today as Shannon mentioned I've blocked off some time after the webinar to go to the I'm sorry it's the the diversity community we go to community.dativersity.net and I'll be there to answer questions or to chat with you if you have questions or comments about anything that I've shared with you today and with that I'm going to turn it back over to Shannon to see if we have any questions today Hi Bob, thank you so much as always for this great presentation and just to answer the most commonly asked questions the reminder I will send a follow-up email by end of day Monday for this webinar with links to the slides and links to the recording and anything else requested we do have we do often send the matrices from Bob on those follow-up emails so diving in here so to both you Bob and Danny this is a large task that is spread over various IT organizations so who's the right owner to establish and run this? Danny do you want to take that first or should I take a stab at it? Yeah that's a tough question I think I'm going to listen to what you have to say Well typically there's not one place they should reside in every organization every organization is different some people tell you that if your data governance program resides in IT it's going to fail I'm not one of those that will tell you that it really depends on the purpose and the scope and the expectations that you're setting for data governance so if you're setting it up as a program to affect primarily IT related things that's where people are going to think it should reside most people will answer the question of where it should reside in IT or a business they'll answer the question very quickly business but the question is what part of the business should it reside in and so there's often it can be in an institutional research part of the organization it can be in a current risk part of the organization or a shared services part of the organization so there's not really one single answer as to where governance programs should reside I don't know Danny have any different take on that than I did No I think you nailed it what I've seen in dealing with some of our customers it's where is the pain and it generally starts there but that's by no means the place that it ends up at the end of the journey so you know if as you say it might be in a credit risk you know there's a lot of depending on the line of business or the type of business that you're in you know my answer to the is it IT or business I always say both which is not really an answer because it doesn't give you a single throat to choke but I don't think that's really what we're looking for so but but yeah I generally I've seen it where where there's you know a good sponsorship and a council at the at the higher level and then they they they sort of cross those business lines and everybody kind of gets on board because they've seen that or it's it's the line of business that's leveraging data taking the biggest risks with data or feeling the greatest pain with data where it starts expand that out across as other people see all the goodness that comes with it yeah and I'm very similar to what you answered when people ask me if it should reside in IT or in business my answer is yes it needs to reside somewhere and somebody has to have responsibility but if it's going to be in IT make certain that there's heavy business involvement because it's not typically in IT for IT's sake we're really trying to solve the business's problem with the data and IT is really the the conduit to get there to apply this framework in a less mature organization um you know what what I would do is I would look first to see what already exists so a lot of organizations will tell you that they're not very mature with data governance to start but if you say to them well do you have an executive level of the organization okay yes they do okay well at the start do you have a strategic level of the organization well maybe you have something that you can leverage to be a strategic part of the governance roles and responsibilities you know my suggestion is take a look at what you already have what are some of the existing practices within some of these support areas of the organization that have a governance component to it so look for things that are governance like if you're starting out with a very immature organization and you might find that you're more mature than you actually think you are any insights that you want to add there no I you know I I've seen a lot but uh you know it's it's again where's the pain point and where where do you have some maturity that you can leverage and and you know if you're starting at the at the bottom level at the support and the below the top but I can't remember the frame right now you know that's great you can start to solve some pains and put some pieces in place and then you know do the evangelizing up at the top and then start to to leverage that to get the shoulder behind it that you're going to need eventually when either money runs out or or you know somebody decides it's not a good idea so if you take an empty framework and you just put a check in the block in the box where the row meets the column and recognize that there's a need there I think that's one way again to you might find that you have a lot of empty blocks if you're really immature but it will at least give you a starting point and give you some things that you can work on to improve your levels of maturity and we don't have a ton of time left but I think we have time for one more question but do keep the questions coming because in the follow-up email I would will include the answers and bottle right up the answers to any questions that are outstanding so any tips on which of the six core components to start with first or prioritize higher well my suggestion is to start with the data and the roles calls first because you want to know what data resource it is that you're going to govern whether it's going to be structure of data or unstructured data or records management or content or whatever knowledge management any things that you're looking to govern so we've got to start with the data column but I say look at the roles column first because that's really the backbone if you noticed all the I know that the templates that I shared in this webinar were very small but there was a role component to each of those in the racy matrix the roles are being cross-referenced with the steps of a process in a communication plan the roles are being are being cross-referenced with the types of communication that was providing so my suggestion and Danny I don't know if you have another suggestion maybe you're suggesting another component but I would say start with the data and the roles and that will set you in a good course of motion for your program Bob as usual we're in violent agreement you know the data if you don't have your if you don't start getting your arms around the data as to what is critical and what's important what's first you know you're going to have some challenges but many hands make light work and if you can start to put people and get them to accept that responsibility and give them the accountability and the power then it can start to do some of the other columns across in terms of doing the investigation and bringing in the right people that can help with that but if you try to do it all and then have a finished thing to give to somebody you know that that's going to be a challenge so I would agree with you it's it's data than roles okay thanks all right well thank you both again for this great presentations and for the time I thank all of our attendees for being so engaged in everything that we do we love all the chat and the Q&A coming in again I will send a follow-up email by underday monday with links to the slides links to the courting the matrices etc from this presentation as well as answers to questions that we haven't had time to get to and again if you'd like to continue the conversation and continue to chat with Bob you can do so at uh in the data diversity community at community.dataversy.net and thanks to Irwin for the sponsorship today appreciate it as always thanks everybody hope you all have a great day appreciate thanks to you all thanks Bob


Good afternoon, brilliant humans, and welcome back to stunning Stanford University here in Palo Alto, California. My name's Savannah Peterson, delighted to be spending International Women's Day with you this year, broadcasting live all day long here for theCUBE at Women in Data Sciences Worldwide Conference, annual conference. We've had three guests already on the show today that have absolutely blown my mind and inspired me and we are starting off the afternoon just as hot. Sam, welcome to the show. Sam is with Starfish Space. I'm really excited. You and I both talking about the fact that we are admittedly nerds. I hope I'm not throwing one of the best super nerds and big space nerds. So I'm super pumped. I've been looking forward to this segment all day. I know we're gonna get into it, but give us a little bit about your background. Tell us about Starfish. Yeah, so I've been interested in space ever since I was in the fifth grade, so I always knew I wanted to go into space of some form. I didn't exactly know engineering quite yet, but then when I got to college, my dad was like, yes, you need to do a good career, the good career path, so just choose wisely. I really appreciate that he left it open for me and he was always very supportive of whatever I wanted to do. So I decided to combine my love of space and engineering and to aerospace engineering, and so I attended the University of Michigan for undergrad. I got an aerospace degree master's as well following that undergrad. So I did the four plus one program, but I did it in three and a half years and then plus one. So early grad and then I went off to Starfish. That's my first job as a new grad. So fifth grade you mentioned, what happened in fifth grade that got you interested in space? I guess I'll have to shout out to my teacher here. They made us do this project about the solar system as I'm sure a lot of people do, and we had to choose a planet. And I still remember I chose Neptune and I was just, it was gorgeous. It looks like a blue gem. And I had all of these pretty pictures and I learned about the planet and the gas giants are just so different from us. That just blew my mind. I had no idea before that point. What was your teacher's name? I don't remember, but I remember this project. That's all right, that's all right. No, I was going to say that that's so cool. Actually just a personal shout out to all the teachers out there, especially in those early stage years, don't always get all the love, recognition, and certainly don't get the compensation they deserve.  So you've got your aerospace engineering. This all makes a lot of sense. How did you know you wanted to go work for Starfish? What attracted you there? So of the companies I interviewed, I think Starfish, the founders just had incredible energy. And I really have to thank them because they were very supportive and not just of me during the interview process, but even afterwards, post-interview, they told me that if you don't want to join our company, that's okay, but here are some life tips for you. And I just thought that was very unique. That's very unique. Oh, I just got goosebumps. What a good culture of empowerment there. It was an incredible culture. And so I talked to a couple more people and the people at Starfish, I truly think I'm blessed because they are some of the most supportive and incredibly talented people that I know. And they're also humble. They're like PhD people. I think Trevor has a PhD and then Austin went to Stanford himself. So, but they never show off how incredibly brilliant they are. You know, work speaks for itself to a degree. And I think if they're attracting people like you, they're showing off how brilliant they are in the most subtle and humble way possible, which is, which is really exciting. Just in case some of our audience isn't familiar, what does Starfish Space do exactly? Yes, so we're a, we're a startup and we aim to provide satellite services. So we just launched our Otterpub spacecraft this past June, 2023. Exciting, congratulations. Thank you so much. Thank you. And we have a couple more missions planned. We ultimately want to get to an Otter servicing vehicle. That'll be our final end goal. And then we want to expand that to be an entire constellation and fleet of Otters. So we want to provide services like life extension, debris removal, maybe even like in the future, in space manufacturing and assembly. So really. Wow, that's really cool. So you're, Starfish is all about supporting existing space systems. And cleaning up space, because we do know that is a huge problem. It's a ginormous problem. And I think if we don't act now, especially with the ease of launching satellites these days, it's so easy that even, I don't want to downplay these university students, but it's university students have the opportunity to launch their own CubeSats into orbit. So that just shows how available it is. Anyone can launch a spacecraft if they really want to. But that means that there's so many spacecraft in orbit now. And we really have to do something or there's higher risk of like them colliding, creating more space debris. And in the future, if we don't act, I think we could be trapped on earth because then if we try to get to the moon or to get to Mars, it'll be hard to navigate around all of this debris. Oh my gosh, what a future forward thinking. I've never thought about the space debris actually imprisoning us here on this planet. But you're absolutely right. And there's a lot of expensive technology up there floating around too. So it's kind of an egregious, it seems like the definition of that, that especially this is why we can't have nice things, over these nice things. And now they're all just flying around everywhere, kind of willy nilly. And we definitely need companies like Starfish to address that problem. What's your favorite part about working at Starfish? Yeah, I think I really love my role. So I'm a satellite systems engineer, which is an engineer that kind of monitors all of the aspects of the satellite. But right now I'm actually working on operations. And I never thought I would be working on operations. So I'm running tests that I then get to send to the spacecraft, run on the spacecraft to see whether our hardware is functional, if our software looks good. And I'm doing this for the Otterpup1 mission that launched. So it's just super cool to code something and then have data collected from the spacecraft and know that that's my code that did that. What did it feel like the first time you sent that test up to space? Yeah, I think Austin, Trevor, and then my boss, Jesse, they all, they all were super supportive. I was the one who was nervous. They were like, I know your code is excellent, we've reviewed it, you've got this, Sam. But I was very nervous. And it was incredible and surprising that it worked. But also it makes sense that it worked because we go through a very iterative process and we make sure that we have enough data to validate that the test is successful, but also that everything's functioning on ground before actually sending anything to our very expensive spacecraft. Yeah, very expensive edge device in this scenario, which is pretty, which is crazy to think about. I love that they knew it was going to be great, but I know what you mean.  Nervous just mean we care. It doesn't mean we don't think we did a good job. But now I do it on the daily. You just say that casually.  That is awesome. Is it still magical to you? It's still magical, every time. Yeah, I would imagine it kind of has to be. It's so, it's so cool. Space was one of the catalysts for high-performance computing in the rise of processing power and a lot of the things that we do. Yes. Why is space and data science so important right now? Yes, I think, especially with space, it really transforms the way that we view these technologies because I think there's a lot of discussion on whether AI is good or bad. I think space provides a platform for us to see what AI can really do. So our spacecraft, we're actually wanting to run it all autonomously. So it would go to our client satellite and dock with it or connect with it, kind of like interstellar here. Yeah. And all autonomously in an affordable manner. So I think data is really key to that. I think data is really key. So this is a question I wasn't expecting asking, but you just spawned it. Do you think we'll have autonomous docking before we have autonomous driving? Oh. I hope so because that would be our company successful. So I hope so, but I know Tesla is working fast. Yeah, I mean, I think that what a great race to have happening both on space and on the ground here is the ability to do things more safely and more efficiently and more cost effectively. Yes, absolutely. Which is exactly what you're doing with those. So awesome. We're both huge space fans. What expites you most about where we're at in our exploration today versus where we might be going in the future? Yeah. I think it's really exciting to be in this point in our journey of space, because I think before we had like Challenger, we had the space missions, but right now we haven't decided on what our collective next goal is. And this is something I learned about in my master's program actually. We don't have end goal yet. And we did, for example, for the moon, we had a goal of getting to the moon and then we had a goal of getting to Mars. But I think right now, people and different, depending on which company you're at, they have a different goal. So as a collective society, we haven't decided what our next goal is. So it's really exciting to be at these crossroads. And I think I would really like to see us explore exoplanets more. I would really, I know it's really difficult to do, but I think we can learn a lot. And it's always been something at the back of physicist's mind, but I think if we decide to do it, then we can really do anything. If we can get to the moon, that we thought that was impossible, we thought it was impossible. Totally agree with you. Yeah. Yeah. And so what do you think we could learn from the exoplanets? Yes, I think, well, we have the question of how did life start. There's so many questions in space that are not exactly facts, like the Big Bang. That's actually still a theory. It's not facts. And so I think with pushing for exoplanet exploration, we can gain a lot of data on, and hopefully information about how different planets formed, how we have life here on Earth, and maybe even how the universe started, why we're here. There's just so many questions. Casual questions that no one's ever asked before. I mean, I love that. I think it's certainly something that probably inspires you every day is to answer some of these questions. What motivates you? I mean, I think we're both excited about space and we're nerds, but it's not always easy being a woman in data science. It's not always easy building products that are implemented not on this Earth, I can imagine, and you're also working for a startup. So there's a lot of challenges. What drives you when you get up in the morning? I think I always have this desire to see our industry, aerospace, advance and do really cool things. The industry itself is super cool. Just that in itself is, it's enough to get me up in the morning, but then what really keeps me going when things get hard, for example, is the end goal of I'm actually doing something that will answer questions for people and it could even improve humanity. For example, the microwave was invented. I know we were talking about that earlier. So it's really unexpected discoveries in space that improve humanity down here on Earth. 100%. Microwave GPS? Yes, GPS. I mean, there's so many things when people, space programs cost billions of dollars or whatever. They also are the only programs that enable solutions for our planet and humanity at good. I mean, climate change, there's gonna be so many different things that we learn through this. So I'm sure that there's gonna be a lot of opportunity for Starfish to support a lot of these different endeavors and help with that cost efficiency. I'm curious, since you are a rising star in this space very clearly, but have finished your education at least at this stage and are actively in the workforce, what's your advice for a young woman considering a career in data science or space? Yes, I think my advice is never say no to opportunities. Always seek out as many opportunities as you can because you never know where it'll get you. Like I never thought that my CubeSat mission that I was part of in undergrad would probably get me this job at Starfish. I think that it really made me learn the key skills that aren't taught in classrooms that allowed me to get a full-time offer, for example. So never say no to any opportunities. And then I actually also did, I did an internship that it's related to space, but it was more in like trace gas, atmospheric, sustainability research, and that was that Goddard. Oh, cool. Yeah, thank you. And so that led me down this interest of data because my project was with machine learning and AI algorithms, and we took the data from trace gases of our atmosphere. NO2 specifically for me is what I focused on, and we took that, we ran it through machine learning algorithms, and they produced really beautiful pictures and graphs that would take humans like weeks and weeks to make. So, that's so cool. And all of those things combined helped me get opportunities like today, I'm here speaking at a data science and space conference merging two fields that are so incredibly important. And a lot of people don't realize they're related, but this intersection is something I didn't think I would be at. Is this a milestone moment for you in your career? Absolutely. I feel that in my chest actually I'm just sitting here looking at you, I'm really proud of you. Congratulations. That's so exciting. You mentioned never say no to an opportunity, and so how would you, what would your advice be to those folks who are just getting started who maybe don't even know how to find those opportunities? Yes, I think everyone's really willing to help. So, talking to professors is really great. Talking to colleagues, networking with professionals, and even if they say no to an offer now, keeping in touch later because you never know, maybe you could help them in the future and then maybe they could help you. Yeah. These things that I think I didn't realize as a freshman, I would go to these networking events and get really exhausted. Everyone would say no to me at the time, and it would just be really demotivating, but then as I grew older I realized, no, I should keep doing these things because eventually they will lead to opportunities, so. I think that's actually a really great point of advice you said was just get used to rejection. Yes. But also don't be afraid to keep asking because you can't get what you don't ask for and you're going to learn, hopefully you learn from every one of those rejections and even like when you were applying at Starfish, they said, hey, here's some thoughts just in general, no matter what your call is. That would have been a slam dunk for me to say yes to the job in that scenario, so I'm not surprised you did that. That's beautiful. We are here celebrating International Women's Day. You are obviously a very empowered woman and it's great to see that you have both the support of your team and clearly your family as well. What would be your advice to some allies who are watching today on how they can support women like us in our industry? Yes, I think in aerospace and also in data, the ratio is still not equal. I'm a private pilot in training and so the ratio there is incredibly different so it's mostly male dominated but I think it's really important for allies, for example, my flight instructor and my best friends at university and my family. All of these people always were supportive of me. When things got rough, they would encourage me through rough patches, they would sit me down, talk to me, so I think just talking to people who are going through hard times and telling them, no, you can do this, it's difficult but you can and you will, you just have to get up and do your best. So I think just really being there for the people and women in your life who might be considering these opportunities but who might be too scared to try or just might be going through a rough patch, yeah. I love that and congrats on your flight lessons, that's rad. Thank you. How far down the process are you there? So I was really close to getting my VFR which is like the first certification but I actually, I moved to Starfish so then I stopped for a while and I'm hoping to pick it up in Seattle. That is so exciting while there's just a little bit of aviation history in Seattle so I'm sure you can find some fields to practice in. Final question for you here. Are there any women who have helped you on your journey that you'd like to thank today? Oh, that's an incredible question. Thank you so much for asking me that. My mom is probably one of the strongest people I know and whenever times get rough I think of her and I'm like, if my mom can struggle, she came from India, my family moved here from India. If they can come here and my mom can get a job in America with having minimal English experience then I should be able to do anything and then my best friends, I want to call up for Nika, Katina and Shui because you guys are incredible and truly I don't think I would have survived undergrad because aerospace is super hard without you guys so big shout out to those people. I love it, Sam. Thank you so much for being here with us today.  I look forward to interviewing you many times and hearing about the many accomplishments across what will be certainly a decorated career and thank all of you for tuning in to our fabulous coverage here alive all day at Women in Data Science Worldwide Annual Event at Stanford University here in Palo Alto, California. My name's Savannah Peterson. If you're not inspired yet, I don't think anything's gonna inspire you. Thanks for tuning into theCUBE. Your leading source for Enterprise Tech.


So that's mean that all the tools During but from the beginning of the shaping until the polishing at the end when you have the final product will be Abrasive the abrasive powder. So you will have different type of hardness and coarseness. Okay of these things for instance So like carborundum and silica. Okay, not at this time. It was Let's say emery or corandum. Okay, the diamond powder as well. Okay. Let's mean that for instance here. This is pieces of emery. All right. Okay So these is a harder stone than Rock crystal and so it was crushed into powder. So I've got here some example of Corandum powder So this is a very fine natural powder. Okay, obtained from crushing bead stone Or here, but this is an astreal corandum. Okay. Okay. So this is the type of things So this is for the beginning of the process, but when you want to obtain something finer, right? You are going to use finer powder. So for instance for the polishing process It is aluminum oxide, which is used still nowadays. Okay. Yeah to polish Rock crystal, so this is aluminum powder. So yeah, this is oh, yeah, this is very fine the one. Yeah, it looks like yeah baking powder So all these powders was you were used of course with water Okay, right and also for the coffee process and maybe for some of the polishing process with oil. Okay Right


I'm looking forward to the challenge to helping Chief White create the new structure of the police department and to move it forward. I was promoted to commander in September of 2003 where I took command of district one. It's northwest Denver. My bilingual skills served me greatly in that community. I served there until January of 2007 when I was made division chief and I took command of the special operations division. I served in that capacity until May of 2010 when I made a lateral move over to the patrol division which was my last assignment. I think the desire to want to help others is embedded in all of us. I just think it's important for the community to look at us not just as the people in the blue suits in the cars but as human beings and personalize that we do have feelings, we do make mistakes and we're there to serve. I think depending on the cultures that you deal with they have different views of law enforcement and we have the greatest law enforcement system in the world and I just think it's important for us to be able to reach out to all segments of the community. Again being bilingual helps me to do that on a daily basis. I have high expectations of myself and I carry that onto the people that I supervise and really I just would like people to continue doing their job like I know they're capable of doing. In the eighth grade I actually took an aptitude test and I scored high for serving in law enforcement and I kind of took an interest to it so I started to do some research and talk to people, officers in the district that I lived in, that I grew up in which is District 4. As a fact I remember as a young high schooler talking to an officer who ended up, I ended up working with him in District 4. As a recruit officer once graduating from the academy going to District 4 you are just nervous as heck. I remember walking in with probably more stuff than I needed carrying a briefcase and everybody's looking at you because you and your fellow classmates are the only ones who are pressed and ready to go especially on that morning shift. Everybody's drinking coffee and we're standing at attention ready to go and I just remember my TO at the time just kind of looking at me and telling me not to touch anything until he was ready to tell me what to touch. It was winter and this training officer is just, I'm sure just beside himself looking at this kid who's 22 years old and not knowing anything according to him and I remember getting, we didn't have computers in the cars obviously so I remember having a log sheet that I just kept making mistakes on and I think he set me up because he took the whiteout and he put it right by the heater and towards the end of the day he asked me to fix this so as I opened up the whiteout it exploded and I had all this whiteout on my pressed and ready to go uniform so I was quite embarrassed walking back into the station like that and I think everybody knew what he had done because obviously I wasn't the first victim of that.


At the end of 2020, over 82 million people worldwide were forcibly displaced from their homes, nearly half of them children. Many people and families who were forced to flee must often undertake perilous journeys to reach safety and asylum. The devastating effects of displacement on human life and economy and challenges for governments around the world are clear signals that we need a better understanding of migration and migrant journeys. The authors of a new book from Springer's Methodos series hope to provide the tools for that undertaking. In Towards Bayesian Model-Based Demography, Agency, Complexity, and Uncertainty in Migration Studies, researchers across several disciplines offer a much-needed blueprint for modeling modern migration processes. The authors pull from recent advances in demography, computational modeling, statistics, cognitive psychology, and computer science to tackle the uncertainty and complexity that shroud international migration. Their approach relies on agent-based modeling, a choice that reflects a focus on the migrant journey, the information migrants gather on their journey, and how they make decisions based on that information. Embedded in the real spaces across which people migrate, this method provides a way of mapping the dynamics of migrations and how they evolve over time. The authors discuss how to source and integrate various types of data into their modeling framework through an iterative process, taking the Syrian refugee crisis as a timely and compelling case study. As the title of their book suggests, the authors use Bayesian techniques to help readers design ways of quantifying uncertainty and complexity and calibrating agent-based migration models. Statistical models, of course, are only as good as the data on which they're based. To ensure migration models reflect the reality of migrant journeys, the authors detail examples of data sources that can inform models of asylum migration. They also discuss new types of psychological experiments that could help researchers address current limitations in gathering empirical evidence. To help execute this new vision for migration modeling, the authors describe their formulation of a dedicated programming language that is flexible, efficient, and easy to use. Naturally, many facets of migration studies can't be solved by modeling alone. The human mind and spirit can't be reduced to a set of equations, however sophisticated they might be. But by making informed and transparent choices along the way, modeling provides a useful place to start exploring important questions and learn new ones to ask.