Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVersity. We'd like to thank you for joining the latest DataVersity webinar, Conformed Dimensions of Data Quality and Organized Approach to Data Quality Measurement. 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 upper right-hand corner for that feature. For questions, we will be collecting them by the Q&A in the bottom right-hand corner of your screen. Or, if you'd like to tweet, we encourage you to share highlights of questions via Twitter using hashtag DataVersity. 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 introduce to you our speaker for today, Dan Myers. Dan is a principal educator of Data Quality Managers, excuse me, Data Quality Managers, an e-learning company focused on providing information and data quality learning material. As an information quality practitioner, educator, and thought leader, Dan conducted a robust comparison of key IQ authors' lists of dimensions of data quality and proposed a way to align the data management community by using common definitions. In 2016, he proposed a standard first set of dimensions based on his research and called it the conformed dimensions of data quality. Previously, Dan worked as an applications developer, data modeler, and manager of data governance and data quality. And with that, I will give the floor to Dan to get today's webinar started. Hello and welcome. Thank you very much. Really excited to be here first time doing this, having done the conferences many times, but never the webinar, so look forward to this. Here's the agenda here, and I wanted to just cover some of the basics of the dimensions of data quality. I'm sure we have a broad range of different individuals attending today. So I'm trying to cover all of those different areas starting out with some of the basics. And then, of course, if you're joining for the statistics and a little bit of explanation around the white paper that's come out in the 27 annual report on the dimensions of data quality, then, of course, we'll get to some of that at the end. And then if you have any specific questions, we'll cover that in the Q&A session. So what are the dimensions of data quality? Probably my guess is most of the people on the call have heard of them before, so let's just revisit that in order to make sure that we're on the same page. The definition that I typically use to communicate is that the conformed dimensions of data quality, so already jumping kind of to the conformed dimensions, but the dimension of the data quality in general also are categories used to characterize data and its fitness for use. Now I understand there's a little bit of nuance to that with some of the authors also applying categories as groups of dimensions, but for the layman, the terminology category is often best used to describe the dimension of data quality. And the application, they can be applied in any industry to assess, major, track and communicate information and data quality. And the real goal of today is to show you some information that should help your information quality initiatives further communicate the quality of the data that individual consumers of the data are expecting, as well as the producers of the data and the way that they present that. So here's a slide with just some of the definitions kind of thrown out there to give you a feel for what we're talking about today. So why do I need the dimensions of data quality?  And that kind of depends on what role you're in, both in the business side and the IT side, having the dimensions of data quality offer a lot of different things. So let's go through a few of those. So the act is a quick reference, a checklist, and a guide to quality standards. So when you're implementing projects, you can reference this checklist. Oh, did I cover XYZ? Is the data that I'm asking for out of this new system that I'm developing? Is the way that I'm modeling it going to represent the data in all of these dimensions of data quality? It kind of gives you a checklist too often as humans where we go through things fast and we just forget items. So I frequently travel at least once a month, and I'm always referring to different checklists that I've developed in order to ensure that I have everything. And that's just a good practice relative to data management. So then also they can be used as a framework to segment your data quality efforts. So it doesn't mean that you necessarily focus on one without review and use of all of the others. But honestly, everyone can have their fair opinion on this, I believe. But I espouse using completeness as one of those fundamental dimensions that you start with. Because if you don't have the data there, then there's a lot of other things about the data that are going to be difficult to diagnose and work with. Now, I understand there's different situations for that. I'm sure that we'll have a lot of discussion around that later if we get into it. But that's something to think about. How can you carve out your efforts and prioritize things using the dimensions of data quality as a framework for your efforts? And enable people to communicate current and desired state of the data. So I mean, you can basically look at this in context of things like Lee, Papino, Funk and Wang. So these are initial authors that espoused some of the early dimensions of data quality. And in their book, Journey to Data Quality, they have the comparative approach, which basically allows you to take subjective information, your survey of your information consumers, and join that with the same kind of data analysis that you would do in an objective way. So you may have some computer programs or profiling results that you profile and analyze your data in an objective way, but then also using those same dimensions to reflect on the survey results and joining those together so that you get both sides of the coin. And reuse of existing categories and definitions enables faster implementation times. So we'll talk about it a little bit more, but during the interview of two of the respondents that had responded to the survey and their interviews are available in the white paper, one of the gentlemen said, you know, hey, listen, the conform dimensions lays it all out for you, and it really prevents fistfights. Because at the end of the day, people can get pretty emotional about the way that you define different things. And if that's going to prevent you from moving forward in a timely manner, then why not use something out of the box that has a certain level of rigor to it? And we'll talk about that rigor a little bit later. And then matching the dimensions against a business need and prioritizing which assessments to complete first. So, you know, I really appreciate this from Danette McGilvery. Her book in 2008, you know, kind of lays out the dimensions of data quality according to her methodology. And she puts forward in there the fact that, you know, you can look at how you want to approach your project and or your program, and looking at which dimensions of data quality, where your focus will be, can determine different outcomes. And it really helps you prioritize things with the business in terms of business goals and objectives that you want to achieve. And understanding what you will and will not get from assessing each dimension. If you're new to the dimensions or come from a background that isn't as heavy in data, then communicating your needs and what you expect to get out of each enhancement on an existing legacy system or development sprint in an agile methodology, communicating with the dimensions of data quality facilitates an environment of communication that is what we really need in order to ensure success. So, where are they used? And we'll get into a lot more of the detail of that as we go through this in the next slide. But we use them to define, you know, measures and scorecards, dashboards. And IQ International actually has a seminar in about a week or two on developing dashboards. And you'll find a lot of the dimensions of data quality discussed within that. If you're interested, I would check that out. And then also, you know, almost most importantly, at the end of the day, dimensions of data quality are just about communication. They're about naming scenarios, naming the way that we look at our data, the way that we want our data. And so the way that we have conversations can either be challenging if we're talking over each other, not understanding the meaning or getting confused because of the terminology. So if we have, as I propose, having a conformed set of dimensions, then conversation becomes easier. At least you don't need to necessarily agree on the way you define things, but at least if you understand how each other communicate those and then reference those accordingly, then at least you're one step ahead. And you can also embed these in the instructions or forms and other parts of your application. So if you're explaining to non-technical, non-data-orientated people how to do data entry, clarifying what you mean by completeness and or integrity, validity, these things like that can help facilitate your data quality improvement efforts. And also in terms of validity on forms, obviously the underlying concepts or the sub-concepts that are espoused in the conformed dimensions enable you to develop your requirements and design documents for IT in a more cohesive manner. And then also if you're in a data provision in some sort of organization that sells data, provides data and or within a business intelligence context, obviously we all have a boss. The boss sometimes is our data consumer. And in that context you have service level agreements oftentimes. So consider using the conformed dimensions in order to better communicate and keep your definitions consistent throughout time. And then as we'll talk about in the next slide, you can really integrate the dimensions of data quality into the software development lifecycle. And so I'm going to switch over to that now. So if you think about it from an ideation perspective, ideation being the first step that you do within the project lifecycle. These names may be slightly different than yours depending on how your project management team at your organization has defined the steps. But generally you have some step of ideation where the innovation starts with the customer and the data. And most importantly it's the knowledge of your data and using the quality lens to ensure that success. And new ideas really need data to be executed and to major success. So I live here in Silicon Valley and a lot of startups are dependent on how well they execute. And a lot of times the focus can be on a functional perspective. But if you don't have the quality data then no matter how good your UI is, your data that's driving that can impact the functionality and confuse customers and or drive them away. So there's a high need for data quality and the measure of that. Also profiling data aligns well using dimensions of data quality and allows you to understand the usefulness even at the ideation phase. So I don't know that most people kind of have gone through every phase and analyzed, okay, am I using data at this phase and then am I using information quality principles and methods of measurement at each of these phases. And one of the biggest things that the effective implementation of new projects really requires a leap in balance of connecting data, different dispersed data together in order to develop some sort of knowledge or some sort of product that customers haven't had in the past. And you can only do that when you have high information quality, when you have the quality that's required in order to make that join or that connection. The next phase is conceptualization and initiation. And in this phase, you know, it's about improving the customer service. So it depends on if you're in a slightly slower industry, say for instance, insurance. And there's not a whole, whole lot of innovation. There are some innovative things that are coming online. But in terms of the actual product that you're providing, there isn't as much innovation. Rather, the customer service aspect is of critical importance. And how can you better serve that customer primarily comes down to the way that you treat them and your customer service representatives, but the data that you have about that customer and the transactions of that customer so that you treat them well, you treat them the way that they expect to be treated. And then innovative products, as I mentioned before, in conceptualization, need to connect the dots. So understanding your quality up front, the beginning of the project is so, so important. And then Dimensions of Data Quality offers that to you. So requirements phase, right? So in the new application development, it's really eyes wide open about what data is available. If you don't know what data is available in your system, if you don't have that metadata. So having the metadata is actually part of representation and documentation of your data and it fits into the Dimensions of Data Quality. Maybe you haven't used it in that context before or your organization hasn't adopted that to that context. But as you'll see, it's definitely within the conform dimensions of data quality. And a discussion about why you need that data and why the design of the system needs to be done in a certain way in order to facilitate the collection of that data in that manner, in that high level of quality. So the design level, of course, data models and the level of abstraction, it really helps you accomplish things faster. But to the extent that you abstract things and build your models in a more abstract way, data quality and specifically metadata around those data quality requirements and integrity that either you build into the application or into the database itself are so important. And that can't be really done well without the Dimensions of Data Quality because it's really a communication framework for explaining your requirements. And also the strong focus on error handling inevitably has benefits as well. So then the build phase, if sample data is available, the unit testing outcomes are improved. So one thing to point out here is that, you know, a lot of times developers having developed data that is representative of the real world. So we have data security requirements that allow that the data cannot necessarily be the production data in the development environment or even test environment. But to the extent that you use the Dimensions of Data Quality to characterize the data in your production environment and then replicate something that's representative of that in your testing environments, even including your build phases for your test cases, can be extremely important. And to the extent that your developers, programmers, understand and are able to communicate using Dimensions of Data Quality, your organization can be massively more efficient than other organizations that may ignore that completely. And in the test phase, similar to the build phase, for instance, test cases, I know a lot of different developers or testers who, in developing their test cases, rely heavily on metadata and existing data models and understanding of how the program should work, how the system should work, and other discussions with the customers. To the extent that you understand the customer's needs better with the Dimensions of Data Quality, you can understand how to better write better test cases. So it's really important that you understand these and then also one way of engaging business users is to make sure and use their data with their own Dimensions of Data Quality to overlaid on them within the UAT process, a user acceptance testing process. And then, of course, go live and support. So go live is focused on the customer and improvement in the prior steps relative to the data quality. But this means the faster response to the customers if you're able to understand customer issues in terms of Dimensions of Data Quality. So imagine if all of your issues were understood in terms of data language in your customer service division and that tickets were filed relative to the way that they heard the customer say those things. In other words, they've already kind of translated the issue using the Dimensions of Data Quality. So the question then kind of becomes, well, okay, you've got me sold on the Dimensions of Data Quality, but which one do I use? Because I know there's a lot of them out there and obviously the DMBock is out and has a lot more information on the data quality. And it's a good place to start. But let's look at some of the other things that are available to us in the field. So we can look at Wang and Strong, their 1996 paper, and then obviously the others they contributed in 2002, and then Journal of Data Quality as I cited for 2006. They're all very good beginnings. They're solid, but there is some confusion between timeliness and currency that I think is better articulated in the conformed dimensions, which I espouse. We'll get to that. And then McElvry's 2008 book I think has a very practical, business-focused approach to naming the Dimensions of Data Quality. They aren't probably the most typical dimensions. She extends those for her methodology, and if you're using her methodology, then the use of those makes a lot of sense. And then you look at Larry English 2009 or 1999 and Redmond 1996. They have a strong technical and logical basis. Redmond has really his discussion around views. It's really helpful to conceptualizing the Dimensions of Data Quality if you're getting into it to a deeper level and you want to understand all the different ways that individuals have thought about it. I really like that one. I recommend a read of that if you haven't done so already. And then, of course, if you have manufacturing or other ISO connected applications or constraints, perhaps you do business in Europe requiring some level of ISO, the way that you communicate things in terms of ISO standards. So ISO has the 25012, 2008 Dimensions of Data Quality. So I have a paper coming out soon in the Information Quality Journal discussing the conformed dimensions versus the ISO standard. I think that there are some strong documentation aspects to the ISO standard that are very helpful. It does lack a level of hierarchy that is available in some of the other frameworks and overly is context sensitive, meaning that it relies on the individual customer to apply that dimension in a different way each time, which can kind of make it difficult to make it actually harder to standardize or harder to repeat the same thing in different situations. And so that doesn't quite work for me either when I started looking at the landscape. On the left-hand side here is the title of the academic paper that Brian Blake from Axiom and PhD candidate at UALR. And I have just finished and will be presenting that at the International Conference on Information Quality in October in Little Rock, Arkansas. So if you're headed out there, consider attending the presentation that we have there. The title is on the Evaluation of the Conformed Dimensions of Data Quality in Application to an Existing Information Quality Privacy Trust Research Framework, which is the work that he had previously done and kind of overlaying the work that I've done with the conformed dimensions and his work in the Privacy Trust Research Area. So about 2013, I sat down and started, as I was working on my IQCP, the Information Quality Certified Professional Credential, and reading all the different authors on this and trying to map it out in my notebook. And so I started kind of looking at this and started to diagram it out. And the way that I came up with doing that is to basically create a column here for every dimension, just like the single word dimension. And then identify what is it that all the authors refer to within that dimension. So making little bubbles down on the green section for each of the concepts. And then to the extent where it was mismatched, like for instance, let's take a look there at definition of metadata. So definition and metadata oftentimes is cited as a dimension of data quality. Well, things that they included within that, that's the second to right most column, is clear, easy to understand definition. Does it include the measurement units? Does it include the values consistent with the definition? And then it has complete and available metadata. But one author might include the concept of complete and available metadata within the dimension called metadata. Or another one might say that complete and available metadata is an underlying concept within presentation and identifiability, a different dimension. So at the end of the day, there's a little bit less argument around what are the dimensions of data quality, meaning what are the names of the dimensions and the top row there in the gray. Everyone can kind of get to maybe 6, 12, 15, some sort of number of dimensions. But then the question goes, it comes, well, what do you include within that? And that's really where my work has begun, is identifying all the underlying concepts that different authors have espoused and then trying to reconcile that into some single version of the truth. And to the extent that we're able to, we try to say, well, how many authors think this billing belongs to validity? How many authors believe that this concept belongs in accuracy? And to the extent possible, it's kind of a democratic approach. It's saying, well, there's four authors that believe it's an insight of accuracy. Let's keep it there. And there's only two that do otherwise. So my paper, the series of articles that I wrote in InformationManagement.com titled The Value of Using the Dimensions of Data Quality, really stepped through that in terms of six authors comparing all their different definitions of the underlying concepts and saying, hey, here's a way that we could get to some reasonable conformed. And I say conformed in the business intelligence way where we have a star schema and then we have dimension tables on the outside. And we try to reuse those if they're conformed and reuse those dimensional tables. Then they have some standard and they're reusable because they can be used in different contexts or to answer different business questions. So in the same kind of context, I said, well, hey, the dimensions of data quality need to be conformed. There's just too many things floating around out there. So that's why I proposed this name called the conformed dimensions of data quality. So then in 2015, I said, well, wait a second. You know, this makes sense to me.  And so I said, well, maybe there's a survey that I can put together to kind of understand how many people would be really interested in a standard and how would that work? And the reasons to agree upon the standard, what would be the reasons? What would be my elevator pitch for someone if I saw them just for a short period of time? And really, to me, the primary thing comes right down to communication. It provides language to communicate your data quality requirements. At the end of the day, it's all about how I'm talking to my coworkers about what I'm trying to achieve. And recently I saw this article around how naming is the hardest thing that you do. And you probably don't think naming is really, really important. But I mean, I ever tried to do it. So I had my son, my wife, and I had our son five years ago. He just had his fifth birthday. It was really hard naming my son. I mean, there were so many really, really good names. And then when you get to computer programming and reusable code, you want names that a spouse, engender, the kind of idea that you're able to conceptualize what that program does, what that module of code does, what does that entity, that table, what does that column include, and how should I use that? It all comes down to naming and the definitions. So to me, communication is the number one thing. We have many, many different definitions for the dimensions of data quality. Then inevitably we're going to not be communicating in an efficient manner. So efficiency also is one of the keys here relative to implementing things faster. And as we now have the agile kind of methodology rather than waterfall, how fast can we do things? Now we could say we're going to put the data out there in the Sprint 1. And then Sprint 2, we're going to start looking at the quality of it. You know, that's fine. You're going to decide when you bring the quality lens to it. But once you do, wouldn't you want to be on the same page? Wouldn't you want to have the same definitions of that quality across your team? And with using the conform dimensions, that's what you achieve. It discourages repetitive philosophical arguments. As I mentioned before, one of the interviews respondents of the survey that is interviewed and has a section in the white paper on it. He says it just eliminates the fist fights. So everyone's going to have their take. Maybe you slightly disagree. Even you'll disagree with the standard. But at least we can join at some focal point, some hub of understanding. And there's been a lot of work by myself and now Brian and others to vet out the conform dimensions of data quality. And one thing also I might say is that the living standard, it's an open standard available for free for other organizations to use. But myself and continuously I steward it and others contribute over time is still alive. We're updating it and fixing it. We have release versions available on the dimensions of dataquality.com. And there isn't any other set of dimensions of data quality that I know of that is comprehensive and alive, literally improving as we move along like that. I mean, all of these other published works are static. They're finite. They do not move. And that just means that they're very, very good maybe, but maybe not to the extent that we have within the conform dimensions and they won't ever improve because they're not moving. They're not alive. And having said that, the measurement, I mean, if it isn't measured, it can't really be managed. We always say that in data management. But having consistency between organizations really enables comparison. So you can benchmark within your organization. Even when you have turnover within an organization, if you stick to the standard, stick to the naming and make sure that it's communicated within your culture, that the conform dimensions are the way that you use it, then, of course, you're going to be ensuring a level of consistency that is desirable. And then providing a framework to define more detailed measurements and associated sub-concepts. So a lot of people, as far as dimensions of data quality, and they have some free-form paragraph about that dimension, but they don't really define it. That's one of the biggest challenges I found with trying to normalize all of the author's content was they often described them in a textbook, but they didn't really provide definitions that were concise. And they didn't articulate each of the sub-concepts, which really kind of lead you to metrics or individual measures that you may define within your organization to track that. And that's really where the rubber meets the road. And also, lastly, the teaching. It provides a solid framework for teaching. I can't tell you within the e-learning efforts that I've done within DQ Matters, having a real structure around what are we teaching and having that basis of the dimensions of data quality offers you a framework to begin and explain different concepts, especially individuals that are new to data or laymen who know good data quality when they see it, but they don't exactly know how to explain it. And so offering them the dimensions of data quality with examples, which is why this year in 2017, I started the blog, the Conforming Dimensions Data Quality blog on the website. In order to just do that, we've published about seven different articles over time, which talk about each of the different aspects of the conformed dimensions. So in our last survey here in 2017, if the question was asked if an industry standard set of dimensions of data quality was available, how interested would you be in using that at your organization? And it was pretty revealing that, again, most people do indeed want to have some standardization. So over 50% are very interested and these levels have stayed relatively consistent over time, as well as then somewhat interested another 30%. So right there you have around 80% of individuals saying, yes, we really want some sort of standard. So really quick dive into the website dimensions of dataquality.com. Where do you find things? I just wanted to throw this out there, the conform standard menu on the top left. If you look underneath there, you see about the standard. It tells you more about the effort that's gone that we've done so far. List of the conformed dimensions, and that really is at the summary level, meaning what you would see in a textbook and definitions for each of those dimensions. And then the detail level is really at the underlying concept level, which is that each of those smaller components that goes into that dimension. And the blog, of course, is under the news and blog menu item. And then you can see actually the screenshot itself is from the blog page, and the blog archive lists those by each of the months. Usually I do one, maybe two a month, but that's the nature of it so far. And so then Shannon has the white paper and she'll be distributing after the webinar for those attendees. It's been developed and completed for 2015, 2016, and 2017, so the prior years you can get those from the website. So let's take a look then at the actual survey results. So how was the survey conducted? It was a web-based survey over a one month period of time, so each of the years it's been conducted typically in the month of April to coincide with Enterprise Data World. And oftentimes I present, actually only one time of the three so far, I've presented on this topic at Dataversity Enterprise Data World, and it turned out really well, got a lot of individuals participating in the survey that way. It was also advertised on LinkedIn, Twitter, the conformed dimensions website, and through referral and prior year sign up. So last year in 2016, we started collecting individuals' names and email addresses in order to offer them the ability to answer the survey year over year so that we can start tracking some of the changes within their organization if there are any, and also ensure a stronger repeatability of the survey year after year in order to save time and effort and marketing to people to try to get them to take the survey. So I think when you read through the white paper, you'll see a lot of valuable information, and it's just amazing how many different things you can pull out of the survey with the data and the questions that we ask. And so I really highly encourage you to take the survey next year. There's links inside of the survey, inside of the white paper itself to sign up to be reminded about the survey next year. So please join that opt-in to get that survey next year. Now, I do have to say that as we go through the survey results, there's some response bias, we say, given that the response bias would be that the people that are responding to the survey behave in some way different or more or less than general individuals, general organizations in the broader context. So my assumption is that there is some level of this. We haven't quantified this specifically, but to the extent that you're aware of the dimension of the data quality concept and maybe even believe that there's some value in it, you're more likely to take the survey. So when the survey says that X number of organizations use the dimension of the data quality, it's more than likely that the people that are taking the survey have used them in the past and know their value, and therefore they're going to be answering that they use them. So the numbers you might expect to be biased a little bit high in terms of their association and comfortability using the dimensions, and therefore the answers bias a little bit in that way. Now, how could we resolve that in the future? Obviously collecting more data would be the number one way to do that, collecting more data across a broader landscape of individuals in business and IT that use data. So data scientists maybe not necessarily the ones that are collecting the data through IT systems, but rather data scientists on the consumer and do they understand the dimensions of the data quality and use those to communicate to IT departments and or business customers about the levels of quality that they need and then could even be answering the survey. So the goal is really to gain more respondents in order to eliminate some of the response bias that exists. So diving into some of the results and how often, so one of the questions that we asked was how often does your organization classify data-related defects using dimensions of data quality? And so the percentage of respondents for the 2017 was on an ongoing basis, 40% of them. So I mean, again, given that bias, say that the number is a little bit inflated because the people that are answering already do this. That's right, they were attracted to the title of the survey. But wow, 40%, that's pretty good. But then again, on the other hand, why is it that 8% only used it once, 29% or considered it but haven't used it and 15% of endeavor to their knowledge used it and that 8% really didn't even know about it or they're totally unsure whether or not it was used in their organization. So if our goal is to maximize information quality within the community, then we should look at this and say, hey, wait, if dimensions of data quality is valuable, yes, we think they're valuable. Whether or not we use the conformed dimensions, let's just say dimensions in general. But the dimensions of data quality are valuable. Why wouldn't the pie chart say 100%? So everyone's not using them. Something's going wrong here. So my guess is that everyone's describing and communicating data quality issues. They just aren't using what they consider to be the dimensions of data quality. They haven't uncovered the resources that are available to them. You could do even better to use a standard like the conformed dimensions of data quality. But this is just kind of the survey's take on the survey communicating what level of improvement we still have to go. So we have a long ways to go. 60% of this, I mean, eventually we would hope that 100% of organizations are using the dimensions of data quality in some way. So does your organization have a method of categorizing data quality issues using the characteristics of the data and its fitness for use like the dimensions of data quality? So how do they frame this within their organizations and how well-governed is it? The question then becomes, yes, there's one, and it's quite actually amazing that in many organizations, nearly 30% of the respondents, so again, the question is, can you extrapolate on this and say that within the industry as a whole, 30% of companies are using dimensions of data quality, and there's only one, like standard within their company, right? So it can be done in a smaller organization, really hard to do in a larger organization, but is it really 30%? So probably not, I mean, given the survey bias a little bit with the response bias, but wow, there are companies that are doing this, and they have better communication among themselves because they're doing this.  And then, yes, there's one, but it isn't well-defined, 30%, again, right? And then 20%, there's various methods across the landscape. And if that's where you have to start, that's fine. It is hard, as you know, in data governance, to change anything once you've started it. So if you're thinking of picking it up from scratch, maybe consider the conform dimensions. Or if you're going to use something else, at least use it consistently. So drumroll, which one is the most used? Well, probably kind of figure that out, and it's pretty logical that most people, at the end of the day, probably due to some of the confusion around what aspects fit within accuracy, they listed accuracy as the most important or the most used. And this is bounced around a little bit over the years of doing the survey, but we see accuracy right up at the top, and typically completeness in the top two or three in terms of usage. It's a lot of fun to kind of look at this for the first time, but if you're like me and you've seen it a lot of times, it kind of gets old after a while. The question kind of becomes, well, how have these jumped or changed over time? And is it really representative? Is my sample size with the survey at 48 respondents, is that really large enough to say, yeah, there's something going on when something jumps? So I like to do this. I mean, you need to be careful with inferring too much around this. And some of the summary here on the left-hand side posits some of my theories on that. I'd be really interested to hear from you guys as well. So shoot me an email and give me your theory on this later on if you have time. But accuracy was reported to be the most used dimension in 2015, and back now in 2017. It wasn't in 2016 as you see there. It really confirms our earlier observation that these two dimensions are really at the heart of most organizations' data quality efforts between completeness and accuracy. And accessibility jumped all the way from tenth to seventh. And we don't obviously have a total picture, we don't have the full picture of this, but I kind of loosely associate this with a larger focus on data lakes and having more data available in one place. It seems that the big focus right now is around big data, data mining, sorry, algorithm design, AI, deep learning, and so forth that often requires access to a lot of data in not only in volume, but in veracity and variety. So these different sort of dimensions of big data as we call them. And where accessibility is really the key to understanding that. So that's my take on why 2017 has such a jump in accessibility, but I'm interested to hear what you guys have to say about that. And consistency clients back to the third position from the fifth position. In a lot of what I do for clients and day-to-day basis is setting up controls that balance between different systems, whether it be from a more accounting perspective and a top-to-bottom or a cross-system balancing where you're ensuring that the data from two different systems reconciles and so forth. That's really fundamental to the consistency dimension. So yeah, that's interesting. There's a comment here on security and the dimensions of data quality. A lot of times, so the way where I put security is not in it as its own dimension based on my research, rather as part of accessibility, the security components come into that. And we discussed that a little bit in this paper, but the actual, though, the white paper with Brian Blake that's going to be coming out in ICIQ has a lot more of that discussion. So shoot me an email if you want to discuss that more offline. And then, Tennessee, please choose in which industry organization is categorized. One of the kind of the problems or concerns that I had and is brought out in the white paper is such a lack of use of the dimensions of data quality in certain industries. So not only do we need to get 60% more organizations using the dimensions, but specifically certain tiers are just not represented at all in the survey. And again, that could be due to the response size. But what I did is I said, well, rather than tell you all the different industries that were in the survey, which you see there on the right-hand side, it's just too many pie pieces. So I said, well, let's do this. Tier one includes the finance banking and accounting, and I split those out in case you're in one of those categories and you want to understand how your peers are doing relative to yourself. 21%, 10%, 11%, all make up tier one. So these are the guys that use them, and they're pretty using them pretty decently. It's a highly competitive market environment and very much a for-profit environment. And then the tier two is industries that only make up 6% of the responses in the survey. And that tier two is the government, state government, retail, manufacturing, software development. You can see those actual industries listed out by tier below. So if you're in one of those industries and you're willing to talk to me about it, I'd really like to understand what are the things that are inhibiting your organization from implementing the dimensions of data quality at your organization. And it might be part of a broader data quality or data management as a domain in total is hard to get by in from our leaders in the education industry. You know, whatever that is, you know, that would be interesting for me to understand and be able to include that in the report next year and then handle that bias in the survey by soliciting more responses from organizations in those industries so that we have a better representative sample. So with that, I'm done and want to leave time for questions. It's my professional profile. Follow me on Twitter and LinkedIn. You know, connect with me so that you can get the latest and greatest. I usually present two or three times a year at various conferences and have other professional events and what forth that I participate in. So I'd love to get connected. And Shannon, I'll turn it back over to you to facilitate some of the answers and some of these questions. Dan, thank you so much for this great presentation. And just to answer some of the most popular questions that come in, I will be sending a follow-up email by end of day Thursday to all registrants with links to the slides, links to the recording, and anything else requested. And one of the first things requested is where can we find your blog? So if you send me that link as well, Dan, I'll make sure and get that in the follow-up email for this for our attendees here. So how many, regarding the survey, how many incomplete survey was there? How many incomplete? Incomplete. So the problem with the web surveys is that there will be like thousands of people that want to know what the survey is, and so they'll call me, maybe not thousands, but there'll be a lot of people that click through to just get the introduction to the survey and then drop it having not taken anything. Most people are that way. There are very few that actually started it and didn't finish it. I don't remember off the top of my head how many of those were, but there were some that had to be disregarded. For the general rule, I think there were like maybe two responses that were not completely finished. It was like the last two questions. So I was able to salvage those two, but it was a very minor number. Sure. That makes sense. I've certainly experienced that with ours. So how is accuracy measured? How is accuracy measured? So for that, why don't I go to the end of the presentation and I threw in the conformal dimensions. And at the dimension level, accuracy measures the degree to which data factually represents its associated real-world object, event, concept, or alternatively matches the agreed-upon sources. So this is the definition at the dimension level. And then you have things called underlying concepts called agree with real-world and match to agreed source. There's only two underlying things within that. And then if you go to the next slide, you get a little bit better picture when you look at the underlying concepts, the definitions. So all of this is included in the presentation for reference, but please don't necessarily use this. Use the website because that's where we keep everything up to date. And accuracy, so you see the two sub-concepts, agree with a real-world, which says degree that data factually represents its associated real-world object, event, or concept. That's the most used definition. But of course, a lot of times when there are things where, say, I buy something on Amazon and there's an event tracked, that event is the system of record for that. I can't go back in time to review that event. The only mechanism that exists to record that event is within Amazon's operational systems. And so that's matched to agreed source, which is a major of agreement between the data and the source of that data. This is used when the data represent intangible objects or transactions that can't be observed visually. So, you know, while you're there too, the question came in, you know, what is the difference between timeliness and currency? So you've definitely displayed it in the previous slide in this slide. Anything you want to add to that? So I think that's where weighing is strong and the original work in the dimensions of data quality had a little bit of confusion. And I think the conform dimensions clears up. To me, the way that it works and then based on, you know, most of the authors that I read, a clean way to separate that is that timeliness is the expectation of when it gets to me and how well do I have access. It's more of an availability issue. Is it available in the time manner that I need to get it? Versus currency is, you know, how well that reflects the real world. So even though I get a report daily, that information, even though I get that information daily, it might have been recorded weeks before. So maybe I have a biologist collecting data in Russia and they have to, you know, ship it on some, literally on a ship or some boat or there needs to be some analysis done to it. So the data is no longer current and the currency really measures how it is to the real world. Timeliness is how quickly I get that information. It's confusing. Well, you know, another good topic that we've got going on here, too, with that is, you know, what are your thoughts on security as a dimension of data quality? I've seen it here and there on lists, but not often. However, security is often core to determining if data is fit for purpose. Yeah. So that's really where I... So here's the problem within information quality. We have this domain called information quality and we try not to look too far abreast and other things like Infosec communities and how they define things. And that's why working with Brian on that paper that's coming out in October has been really fun because he's taking kind of a different approach to it and he's able to find the controls and descriptions of the things that he... the way he looks at it from that Infosec perspective under access control because security is really measuring what level of access someone has to that information. And, you know, obviously this probably can be expanded a little bit more in terms of refine. The question is how many tiers do you want in the conformed dimensions? Do you want to have, like, a dimension level and underlying concept level and a sub-concept level of detail? And so the real goal of the conformed dimensions is that we keep it as simple as absolutely possible but not ruin the veracity and the real conceptual strength of it at the same time. But I'm completely open to exploring ways to enhance this, especially if there's enough academic rigor to support, you know, adding another underlying concept and articulating the existing concept in a better way. I also have a paper coming out in IQ International that discusses the ISO dimensions of data quality compared to the conformed dimensions of data quality. And there's some security-slash-system-specific ramifications there. So if you're interested, subscribe to the blog. My best solution and my best recommendation is everyone who's subscribed to the blog, so that when I publish these papers, you'll get them and or you'll get the summary in the blog so that you can stay up to date. Awesome. Love it. So are there differences in dimensions for unstructured versus structured and any suggestions on standards for metadata for unstructured data? Yeah. So actually, Batini and Scana Capito, I'm not saying that second author's name very well, but Batini, B-A-T-I-N-I, is... So look at their book. So it's Carlo Batini, 2016. So their 2016 book has a decent coverage of the dimensions of data quality with respect to unstructured data like maps and web documents. My goal is to stabilize the conformed dimensions on mostly relational constructs first and then move into those areas. My wife works in geographic information systems and I come from a little bit of that background as well. So I'm really... I'm already salivating over spatial data quality concepts that I want to get included eventually, but there's only so many hours in a day, especially when you have kids. True. So could you comment on the dimensions in relation to the information steward application? Information steward application. I'm sorry, I'm not sure I understand the context of that, the meaning. So maybe we can get a clarification from the questioner. So while we wait for that, let me move on to the next one because I'm not sure. Do you apply data quality dimensions by report or at the source? Both. So I think the question will be on the report and really Olga Medanczyk, who's presenting, who's her... As far as I understand, her husband's also wrote the book Medanczyk in 2007. I think it was on the data quality assessment. Both of them are really great at data quality and so I encourage you to attend her IQ international webinar in about a week and a half. But where do you apply that? She's going to talk about dashboards and reporting through dimensions of data quality. The problem with dimensions of data quality only in a report is the business sometimes doesn't understand them to the extent that you use them in your report. Who's your report audience? They need to understand what they're reading. You need to define those in the context of your customer then that's more appropriate. Having said that, there will always be somebody in their organization that needs to be looking at it from a conceptual dimensions of data quality level, which a set of standard reports along those is really important. So another kind of exciting thing that I can't promise anything on, but I'm starting to work with some vendors to see how they can include the conformed dimensions and their tools so that as we're comparing data quality tools such as a data profiler, we have more apples to apples comparison of each of the vendors and each of their vendors functionality. Can they perform XYZ underlying concept of the dimensions of data quality and so forth and so on. And I've had actually a generally positive response. So if you're a vendor on the phone, let me know if that's interest to you. I'd love to explore some of those options. Love it. Lots of questions coming in. So where can I find definitions for the data quality dimensions listed on slide 15? And if you maybe again send that to me, I can get that out in the follow-up. Yeah, 15. Well, so yeah. So the survey is all based on the conformed dimension of data quality. So in the white paper, in the appendix, it lists out all of those dimensions of data quality and the definitions that were used in the survey. But that's stagnant. It's fixed. It's finite. And it isn't up to date. So you want to use the dimensions of data quality website. So right here, that QR code, if you just scan the screen, scan that QR code or go to the site, dimensionsofdataquality.com, then that's where the definitions are. That's what this page is explaining where to go to get those definitions. I love it. I think that's the first time we've had a QR code presented. That's awesome. Well, usually the paper base is better, but I figure if somebody's going to scan this screen, so it might as well. Just one question. In your inquiry asked about a unique data quality index. How can we build one with all dimensions normalized? I think I would need to unpack that. There's a lot of things in that that I need to understand what the person asking is, what's the context to asking it. I mean, you could do, so I have actually some clients that are implementing the conformed dimensions in their organization and they are developing this survey at their organization just for them to kind of do that baseline. And yeah, I think that's what they mean when they say index is basically reusing this survey. And I don't mind at all giving out the language to this survey for other people to implement within their organization. There's nothing proprietary about asking, using the language of the conformed dimensions. So just ping me and I'll help you get what you need. Well, Dan, that does bring us right to the top of the hour. Thank you so much for this great presentation and education and thanks to our attendees and community for being so engaged in everything we do and all these great questions that I've come in throughout. Again, just a reminder, I also have a follow-up email by end of day Thursday to all registrants with links to the slides, the recording and all the additional things that have been requested throughout the Q&A here. So I hope everyone has a good day and Dan, again, thank you so much for this presentation. We just really love it. Thank you, Shannon. Have a good day. You too.


Yeah, so as Mike was talking, it's kind of hard to manage access to Cloud and see who has access and everything. So in this talk, we're going to talk about kind of like some of the security best practices that I've learned working at Microsoft, and then like the tool that my company created. So we're going to start with a little bit about myself. So I'm an identity security engineer with years of experience creating identity and PKI services for Microsoft, and then after a few years decided to leave and co-found Keto's where we create PKI services, easy to use PKI services to kind of make the world a password less place. And then there's my Twitter handle. If you have any questions, feel free to send them there and also send them into the Cloud Village and we'll answer them at the end. And here's a picture of me outside. Since I didn't know we were going to talk about rowing, I wanted to show that sometimes I go outside too. So let's start with something that we can all agree on.  Like they're slow to respond when there's an outage. They get hacked because they don't follow your recommendations and they can't scale. What do you... They don't follow your recommendations.  They'll still click that phishing email. And why? Because we're humans. We make mistakes. They can't scale. We can't go all the way to Cloud Scale and having hundreds and thousands of machines. As Mike was saying, like automation is taking over and we should take over. It doesn't make sense to people still going in and clicking through and creating VMs. So we have to try to get them out of the equation as much as possible, but also understand when we're designing our security policy, they're not going to be perfect humans having, doing everything perfectly, like count that they might choose a bad password and help them do the right thing the easiest way. So in this talk, I'm going to start by first talking about like kind of like the cloud best security best practices. Going to go through each one of them. So I'm not going to read them out right now. So first one is keep management identity ideally to zero. So Mike in the previous talk was also talking about PIM and just in time access. So it's something we're going to talk about. But the first thing we have to do is infrastructure as code. If you don't have the humans go in and click through and everything, and they don't really have to go to the portal as much, it'll make it easier for them to not have access to the infrastructure and harder to hackers to get in when humans don't have access. And the way to make this is making it easier for them. So one of the things we have learned at Keto's is that one of our most visited sites is our docs page where we talk about how to deploy cloud VMs with already SSH certificates in them. The reason for that, it's easier to just go to one site that has all the information than to have to go and figure it out. And if that information has the best practices, then your users are just gonna follow the best practices. So incurred arm templates, terraform templates, Pulumi templates, whatever your organization uses to make it easy for the user to do the right thing, maybe create some with the ports closed and all that stuff. And then as I was saying about just in time access. So one of the things that just in time access gives you is first of all, you don't have access all the time. So it makes it a little bit harder to scan. And it also gives you the ability to have dual key approval. Dual key approval helps because then you need another account compromised and you need to convene someone at Microsoft we had to do with that. Like I would go in person or like call my manager and be like, Hey, I need access to this. Please approve this, which is very hard for a hacker to be able to imitate that. So having dual key approval, having some policy that the user has to actually talk to the other person will really help mitigate some of the attacks. And the other one, it also makes it hard. It makes it hard to get into the infrastructure, forcing people to do the first thing that we were talking about infrastructure as code and automate some of the stuff. If it's gonna take you a while to get in every time you have to get in, then you're gonna find ways to automate yourself out of it. And then we have talked about how we're trying to get out of it but you also have to have a strong identity. So one of the things that we do at KITOS and many companies do as well is having a completely isolated identity. That your email identity is gonna be compromised because you're checking email, you will get phished and bad things are gonna happen to that account.  So the user is kind of like train the password and move on with their life. So if you create an isolated identity that you only use for production. So you're only using it in AWS or you're only using it in Azure. That kind of helps your users only think that that identity is for that and it's harder to get phished. So something to keep in mind that you can have two different identities. And those identities should have MFA. Nowadays with cloud, there's no reason you shouldn't have MFA. I'm a big fan of FIDO and PIV and Ho for Business because I worked on some of those technologies or because it's the best way to work with, for humans, like it's way harder for a human to remember a 20 character password and have 400 different passwords for all their accounts. But it's very easy for a human to grab their UB key and plug it in and get access to enter a pin and get access. So it's way easier to remember and it's a better user experience. I actually prefer having this and typing six things instead of having to have a big password and cell phone. And the other one is you can do conditional access that I'll cover a little bit more in another slide, but basically it allows you to have, if the user hasn't logged in in a while, like ask for MFA, although I recommend asking MFA for each time. And if the computer is not compliant, you can block the access. And then as I said, humans are always gonna try to find a way to get in the lazy way. And one of the ways that they do that is creating a service principal, a service account with a password. And then they can set the password whatever they want. So if you set up all the MFA and everything, they can still set up a password and bypass that. So have some policy that you cannot create with passwords. And also you shouldn't use passwords for service to service. Certificates are out there, use PKI. And the last one I wanna talk to is remove VM passwords. So most VMs in cloud like AWS and Azure and GCP will come with a default password that you set. And it's like a local account. Make that, automate that in your scripts when you're creating, that it's just a random password that no one has access to. And don't use that password. The reason for that is you don't wanna have a shared account. And also the engineer that is creating the VM will have that password. And if they leave or in the insider thread, like they can still, when you're saying your Azure Active Directory passwords or basically they need to provide their passwords to access your VMs. And there's a lot of tools that do it, like you can domain join your machines and for Linux, there's many tools we actually create in Keto is one of those tools. So there's a lot of tools out there that you can use. And then the other one is keep your network closed. I don't know why everybody decided that it was okay to open all the ports. Maybe it's about earlier that it's just people don't, like before it was like the network engineer making sure that the firewall and everything was closed and now everybody has access. But from going from like completely firewall to being completely open, we should still keep just necessary ports open, be very strict about your IP ranges. So no, your whole corporate doesn't need access to manage your VMs. Like your management port should probably be closed all the time and maybe have some IP addresses. And same with service to service authentication, not all services talk to each other, just have the specific IP addresses of the services that talk to this specific service have access to the service. There's no reason to have a completely open network. And then networking JIT, so that goes back to the management ports. So Azure offers this networking just in time access, which means that when you need access to the port it's always closed, no IP has access. And, okay, sorry. No port has access to the machine. And then when you need access it will just request access for you. I got a hurry up here. So I'm gonna keep going. The strong device, so a strong device.  But then if you have a device with malware they can still store your token and do stuff like that. So you have to have your device manage, make sure that they get scanned and they have proper policies. So you can see for the me, my prefer Intune is way easier than AD GPOs and it's cloud-based and easier to manage. And you can use conditional access based on this device's health. So if the device has been scanned and everything they give you access. If it doesn't, it'll block access even if you have the correct credentials. And then currently we use isolated device for production. So this is something that some companies do. It's kind of going in the extra mile but we're an identity security company. So we go the extra mile. That you have a completely different device that you only access production with. So you have your dev machine that is where I'm presenting this. You have your email and everything actually touch the production. You have to open another device and log in with production identity. So your production identity is always in that device. So a compromise to this device wouldn't give access to the device identity. And the last one is use cloud tools. So you have the Azure Security Center that will help you with compliance and help you with some stuff when you're moving to the cloud that you might oversee. Like, hey, you left all the ports open. You should probably close some of the ports. The other one is manage service identities. It allows you to manage your identities for you. And this is all Azure. I would mostly use Azure. So all my terminology is in Azure but most of these cloud providers have the same. So you just have to translate it to your cloud provider. So manage service identities. The cloud provider will manage the password for you. This makes it hard for a user to get at the password and use it. And it also avoids outages such as bad password rotations or anything. So using manage identities is great for availability and it's great for security. And for the passwords that you can use that you should use Azure Key Vault, KMS, any of the password vaults that will allow the services to access the vaults and you can rotate them and have all your policies to do all that stuff. And once again, if you make it easy, put some code samples out there and be like, hey, this is how you get your passwords from a Key Vault or this is how you authenticate with the manage service identities. It makes it easier for your developers to use the right tools and instead of them having to figure it out on their own and also makes a better experience for the security team. I always thought that security teams should, people should come out with security meetings instead of with more tasks to do, with ways to improve. So they look forward to those security meetings instead of dreading like, oh, I have to go to another security meeting. So let's see how we get delayed this time. And then I already talked about PIM, NetworkingJet and Intune. So those are some that I already covered in the talk. And then monitor everything. So you should have your SIM up and running with alerts Azure Security Center. It has some alerts, and then turn logs on every service. Even if you're not reading all the services, you have to turn on the logs just to have forensics. If something bad happens, you wanna be able to go back and look. So don't save those extra few dollars, turn on logs on every single you run. And also run your own custom alerts in code because some of the things they get through infrastructure, but one of the things that we did great at Microsoft was we had some trainings that said everybody had to do them and we would talk about security. The Red Team would go up there and say, like, hey, this is how we get in and this is how you guys can stop us. So then like the engineers are aware of it. You write different alerts in here. I have an example of one that we actually write. We have, I'm not gonna show exactly what we log in where this is, but somewhere in our code, we log this and the engineer on call will get a call saying like, hey, someone is trying to mess with our APIs. Let's track that. So basically, if you follow this, we solve security. You'll never get hacked. No, I'm kidding. This is just, this is not a silver bullet. These are just best practices. And that's the thing. Even if you follow all this, you can make a mistake. Something's gonna happen and that hack can still get in. So I'm gonna talk about some of the backdoors that I've seen people do. And then I'm gonna talk about CloudWatch for a tool that we have created. So the first one is adding, so if you have your groups that are echoed, like you might have like your engineering group and that group gets hackled into your subscriptions. If you add an extra member in there, it's a group that you don't check the membership that often. So having that extra member will give them access to all the stuff your engineers have access, but you won't notice. Having an administrator. So this, once again, Azure specific. Classic administrators was a way of doing things like four years ago, we have been trying to facing them out since then. And now they have moved them to another tab, which is great, less chances of people creating them. But it's also harder to detect when someone adds one. So if like a hacker adds one, it's harder to detect that there's one and that they have a stack. Another one that I used to love when I was doing pen testing is adding a service principle as a contributor to the subscription. And if you put like the employer somewhere in the name, no one wants to break the deployments. So no one is going to touch it. So something to make sure that you're keeping track of and usually deployment has contributor access because you have to create and delete resources. This one is more developers being lazy that it's removing the Azure Active Directory authentication only. So they might want to create a password for SQL and just use that instead of their Active Directory account. So having that checked forces them to use Azure Active Directory. Add more permissions to IKV. So this one is if you have all these different permissions like let's say a developer only has list permissions but it doesn't have get. If they get the get permission, now they can steal the code, the secreting use it. So keeping track of that is kind of hard because when you're looking at the keyboard, you're like, yeah, they should have some permission. So that's about right. Then adding IP addresses to a firewall. This one is one match when you have like a long list of IP addresses and you just add a one. They might be a developer being lazy, maybe a hacker from else trying to get in. So catching one is kind of hard. If you have, if they try to remove the rule or something is easy, but catching one is kind of hard for a human. So we created a solution that kind of monitors all those back doors. And it's basically a PowerShell module that runs in Azure Automation. You can get it from the Azure PowerShell Gallery. It's all free. We basically used it internally and decided to open source it. Like we, this is not one of our products or anything. We don't make any money out of it. We just wanted to share with the security community. Scans your subscription and detect any of the configurations changes that I mentioned and alert to if a change has been detected. So in here you can see no changes have been detected. This is kind of like a run that would be fine. And in here we show one that it hasn't been detected and we have it that it fails the run book whenever something is detected.  So in here you can see we added some stuff and it failed. So what it covers, our break changes. So if something gets added to the RBAC or it'll detect it and let you know because production access should be very monitored. Classic administrator changes. So we'll alert if a net classic administrator has changed. Azure resource provider changes. So this one is Azure resources get access through Azure resource providers. And they get kind of like automatically added to your subscription, but you can create custom ones. So for example, like one of the ways that I like explaining it is like the Azure resource provider for Key Vault has access to the VMs so they can encrypt the disks. So it can give you some access to the resources. So we just like to keep it clean. So we added it for monitoring, research creation of deletion. These might seem a little bit harmless, but if someone adds a VM that then they're using for tests, but it's not production network and then that doesn't get patched, it can be a way in later. So it's important to keep everything clean. And also if someone deletes something in a production subscription, you should be alerted because you don't know if it's something that happened by mistake. And all of a sudden you're one of your ability sounds is down and you'll eventually have a disaster or something. So it's important to keep track of that. Change of group members, we only do nested groups. Sorry, we don't do nested groups. So we only do first degree. So any group that is echoed anywhere in your subscription will monitor the membership to make sure that nothing has changed. SQL Firewall changes. So if a firewall rule gets added, a single IP or something will do. SQL Server, AAD admin changes. So as I mentioned, if they like uncheck the AAD only or they change the admin group or anything, will alert Azure Key Vault access policy. So once again, those are the ones that I talked about that like changing the, how many permissions you have into the, Azure Key Vault firewall changes. So I'm gonna address the elephant in the room. Maybe a few of you might be thinking like, he already talked about cloud tools. Why isn't he just using Azure Security Center and like Sentinel? And the reason is like those don't cover all the stuff. So we use all of those plus this. So we have all our detections and everything but this is more kind of like making sure that nothing has changed because Azure Security Center is not gonna alert you if someone adds an extra person to a group that seems pretty normal or if someone has an extra IP address that's something that might service got a new IP address or something. So this one we keep it just because we wanna make sure that everything we get alerted and we know if something changes. So now I'm gonna talk about how we set it up. This is a great picture of how we actually set it up. And then here I kind of make it easier. We just have one cloud watcher in this one what every subscription would have a cloud watcher watching in the other subscriptions. And you have your production subscription in this case that is the one we're watching. You have a cloud watcher in this subscription that ideally has different identity management than the original. So like you don't have the same administrator so a compromise in one identity doesn't compromise the other one. And then you have the baseline in the different subscriptions. So a compromise in this one doesn't compromise the baseline which this one only has read app. And we actually keep this one in a different tenant, a whole different setup. But so yeah, so this one will read the baseline which is basically a JSON file then it'll compare it to your resources. So what's next? The next thing we wanna attack is create a UI to visualize the resources and kind of like alert on certain things that might be a vulnerability. So like we would show kind of like all the resources and say like, hey, like it seems like this is very open. You should check that. And that will help us because we think security reviews don't like all diagrams. So like having something up to date that you can actually use for a security review and actually being based on the scan of your subscription instead of what you think the service looks like will help a lot with the security. Then add more resources, as you saw right now it's kind of limited but we're a small company that doesn't use many types of resources. So that's one of the reasons we're open sourcing it. If you wanna use it and you wanna add your own resources please feel free to make a pull request and add them. And then the last one is we wanna integrate with Pulumi Terraform and all the other providers to be able to match the baseline also to your infrastructure as code to make sure that nothing is changing from your infrastructure as code to the actual console. And once again, thank you so much. Here's the link to the GitHub project. Here are the slides and I actually also created a set of videos so make sure to check that out. I'll just go through the whole things of how to set it up. And that's it. I don't know if there's any questions, Jeff.


So we're continuing on with our example problem looking at an air conditioning application that has humidification as well as a heating section. Yes, we have heating and humidification and we have what's seen coming in. And what we did at the end of the last segment is we were able to get to the point where we were able to compute the mass flow rate of dry air. So what we're now going to go ahead and do is look at an air balance. So we have mass flow rate of air at one equals mass flow rate of air at two equals mass flow rate of air at three. So we're not adding or removing air at any points. That one's pretty simple. And then the one for the water balance, the place where we have water addition is between two and three. So that's where the interesting things happen. What we can say, first of all, is mass water at one is equal to mass flow rate of water at two. We're not adding or removing. And so with that, we can reexpress this in terms of mass flow rate of dry air. And with that, we can say the specific humidity at one is equal to the specific humidity at two. I'll put that in a red box because we may use that in a moment. Now for between two and three, what's happening there, we're doing the wet steam injection. Actually, it's a saturated vapor that we said. Now I can express the first term in terms of the specific humidity. So omega two and then the mass flow rate of the dry air, mass flow rate of water coming in. And that has to be equal to what is leaving the right hand side of our duct. With that, I can isolate the equation for mass flow rate of water in. And we get that expression. And I will box that as well, because we may come back to it. Okay, so looking at this equation, what we know thus far, we know the mass flow rate of dry air, and that's it. We have specific humidities here. So if we can start to determine what the specific humidities are, we can start solving some of these things. So let's take a look at specific humidity. And this goes back to the equations that we looked at in an earlier lecture. Now we determined the vapor pressure that's right here, that one, and that was the, for the moisture, the humidity in the air, the water vapor. And consequently, what we can do is we can plug it into this equation here. And with that, we determine first thing, specific humidity at one is equal to that. And then at two, it's going to be the same value because we're not changing the amount of moisture or water vapor in the air between one and two. So that's good. Now let's take a look at the humidity at point three or the water vapor specific humidity. Now we know there, the dry bulb temperature we were told was 20 degrees C. So what we can do, we can go to the steam tables in the back of our block. And at 20 degrees C, the saturation pressure is 2.339. So that's from the steam tables. And with that, we can then go and calculate the partial pressure due to the water vapor. Once we have that, we can put it into the equation that enables us to calculate the specific humidity. And P here would be the 95 kPa, our atmospheric pressure. And then we get 00933 for the specific humidity at state three. So there we have specific humidity at one, two, and three. What can we do with that? Well, let's go back and look. If we look at this equation here, we can now calculate the mass flow rate of water coming in. So let's do that. And there. So what we've determined, that was what they asked us to determine for part C of the problem. So that is the mass flow rate of water coming in. Now the next thing that we will do in solving this problem, we've done our mass balance for dry air and water. The next thing we'll do in the next segment is apply the first law. And with that, we'll be able to put together the other pieces of the puzzle.


Hello, Joel Reid is my name from the CFA Fleet and Protective Equipment Department. Today I'm going to share three driving tips on our new 4-4C heavy tankers. The focus is going to be about the automated manual transmission. So driving tip one, park brake start. I'm now on a bit of an incline and as his vehicles configure with an automated manual transmission or in other words a smart manual transmission when I bring the vehicle to rest the clutch will be disengaged. If I was to take off normally the vehicle could roll back hence I need to use my park brake. To start off I build up the revs to at least 700 rpm and I can feel the clutch start to re-engage and the vehicle to take off at the same time I disengage my park brake. Driving tip two, the exhaust brake. When driving off road the exhaust brake is either on or off. However you can activate it in two ways. In setting one the exhaust brake will come on when you apply the service brakes or setting two is when you take your foot off the throttle. So driving off road like the situation we're in now I'm going to have it in setting two. Also when driving off road I select semi-mode on the transmission. Semi-mode means the driver intervenes on all gear shifts. I'll also engage low range through the transfer box and I'll also put the center diff lock in. All of these modes are done when stationary. Driving tip three, when going down a tricky descent I've now brought the vehicle to rest to get over a small obstacle. At the same time the clutch is now disengaged. To continue my movement I need to ensure that I've got full control of the vehicle. So I have my exhaust brake on the second setting as always when I'm driving off road. So now like we learned on the Park Brake Start I build up my RPM to about 700. I feel the clutch start to bite and re-engage and I take off again. Okay so just a recap. Driving tip one, when taking off from an incline remember to use your park brake. Driving tip two, when driving off road make sure the exhaust brake is in the second setting. And driving tip three, when tackling an obstacle going downhill in an off road environment also use a park brake start to ensure the vehicle is in full control. I would just like to thank Peter, James and Phil from the Axtar Brigade for their time and effort to help put this video together. Thank you guys. Thank you.


So yesterday in one of my live stream videos one of the Viewers wrote in a comment and she said Jonathan while I love your content and it and it totally resonates with me Quite frankly, I don't feel very I don't feel much hope Given the current state of affairs as I call it the dating marketplace and as I was reading it I really empathized with how she felt in in the sense that it is quite frustrating to Find ourselves in a society where we're quite disposable quite frankly And it's certainly shifted in the last 20 30 50 years if you will I think the mating process in general To actually find a partner who really is truly aligned to who you are and what you want in life And I can understand that and so to experience that kind of relationship where you feel adored And or How does that happen? How does that happen given the state of affairs? And I think one of the challenges we have to recognize that the reality of life is and I think this is what she was Reflecting upon the reality of life is to find a truly healthy partner is a needle in the haystack I'm just going to be candid with you. It is truly a needle in the haystack the question is Do you want to become the pin cushion and what I mean to say and not in the derogatory way? Do you want to find yourself in a position where men are attracted to you? Men want to adore you or are you going to come from the pessimistic view that it's come? It's almost impossible Listen part of the problem is right now We are we are a society of there's a lot of mental health issues going on and I mean, let me reframe that emotional mental health issues going on Unlike ever before because in the past ladies, I want you to think about this really think about this for a second throughout history Women have been subjugated to the whims of men. They have absolutely had to settle Based on survival, that's right women had to settle on whomever who would accept them Based on survival needs and that's certainly changed in the last 50 years. Women are no longer dependent upon men for their survival. I Think what's happened from a DNA perspective, and this is just merely my opinion. I think to some degree Women throughout history have been so dependent upon men From a survival perspective that it's bled into now Dependent on men to love them for them to feel good about themselves. That's right I truly believe women more so than men are dependent on men to love them so they can feel good about themselves Now I don't mean all women. I just mean that this seems to be the challenge today Maybe not with the younger generation Maybe not the Gen Z's and the Millennials, but certainly for those of us who are in the baby boom generation and the Gen X Generation fact sadly, I think we're suckling on the nipple of I need you to love me so I can feel good about myself I need you to validate me so I can feel good about myself and this is true of men and women alike This is one of the reasons why I wrote my book what the heck is self-love anyway a journey of personal development Self-help and spiritual work by the way, there's a link below get a copy of all the books. I recommend Why I'm sharing this with you is If you want to experience a relationship where you're adored And first you have to find that space within yourself to adore yourself You have to find that space within you To genuinely adore yourself and sadly I believe here at least here in the United States And I think this is a global issue. We're suffering from a place of I don't feel good enough I don't feel lovable and I don't feel likeable. That's right. I truly believe that's what we're experiencing In many degrees this this fear of not being loved not being enough And sadly dating triggers this like nobody's business because when you think about the repetitive nature that shift What I mean about I'll explain repetitive nature in a second In the past when we mated with people we mated within our tribe we mated within our village we mated within Our city or town most likely there was only one degree of separation And now because of our devices because of our devices Online dating or swipe apps have Literally leaped to the number one place to meet people and sadly we're meeting total strangers And when you meet a stranger, there's not one degree of separation There might be three or four degrees of separation what I mean to say is How can we trust somebody who's a total stranger? And yet sadly we have been so indoctrinated that chemistry equals relationship success So if there's even remote physical attraction for someone That means that we're completely Perfectly aligned for one another and that's now if you're listening to this and you're hearing this right now I want you to know that that's delusional thinking if you're not familiar with my relationship iceberg chart I share this with you attraction Is above the waterline? That's the first thing we see is chemistry But compatibility is how you experience a relationship where you feel Genuinely adored by your partner and that's when you share the same values your lifestyles are blendable and most importantly They have the emotional maturity to lean into a healthy happy relationship So what's it going to take for a man to fully feel a be fully Be fully be capable to adore their partner. I've been noodling on this because I'll be candid with you. I'm in what I call a juicy delicious healthy happy relationship It's taken over 50 years for me to experience this I wonder when to think about this I certainly got married, but I don't really believe that was real love When I first got married I was a baby. I didn't know any better I was experiencing love attachment style And then my most second my most significant Relationship after my divorce was another woman I was I was I thought I was in love with her but and I am clear that I was it while there is There is care for this other person deep care I was experiencing my anxious attachment style reliving my experience from my childhood of having a mother who was emotionally disconnected If you're not familiar with the book Attached by Amira Levine and Rachel Heller as well as the book getting the love you want by Harbell Henryx and Helen Hunt I highly recommend check clicking the link below in the description to my books. I recommend I lay out all the books that I recommend I didn't pull that out today because I want to lean into a deeper conversation It's taken me over 50 years. I had to be in my late 50s To meet this special person. Why did it take so long? Why did it take so long to meet someone? That could actually be capable of adoring Because ultimately if a man isn't in this space that i'm about to share with you in a second If he's not in this space, then it's going to be very difficult for him to lean into Leaning in not leaning back leaning in to the space of adoring you Every man has to experience this to be capable of doing this For the long term look at I know many of you can experience short-lived relationships You can play a lot of the dating coaches games out there Manipulative games text this when he says this do this when he does that Folks all of those are for short-term gains You can play the leaning back in your feminine energy which creates anxiety in a person Which can create anxiety in a person not always That he might temporarily want to pursue you but at the end of the day if he doesn't have this foundation in his life To actually lean into a healthier happy relationship. It's going to be very problematic. This is why folks I I know this is the depressing news i'm about to share But here's the reality. This is about emotional maturity and relationship skills and again This is not a fact. This is merely an opinion I believe 20 of the population has clinical issues And while I state 20 of the population is emotionally healthy As good skills, I believe most people are dysfunctional So you can play the games Or you can do a better job of weeding men out And what I mean to say is to weed out the men who are most likely Incapable of leaning into a healthy happy relationship Now I want to recommend a book right now that I think would Totally change your life and help you. Okay, look I know you all love the idea that men are the leaders of the relationship And they just will come in and sweep you off your feet. I know you love that idea It sounds fantastic as movies have been made about it The reality is is Men throughout history might have been the provider protectors from the physical realm of the relationship The end of the day women Predominantly have been the emotional containers of the relationship They are the ones that can lead by example to actually open a man's heart up to deeper love Unless he's in this space This is why I want to recommend this book by Barbara DeAngelis How to make love all the time Why I love this book is these are great examples of creating deeper connection with someone And by doing this you actually can weed out the men Who are capable of leaning into a healthy happy relationship? And let me explain. Okay 80 percent of men aren't there. That's the reality of it My job as a coach This is what I do as a coach is help you help to put the odds in your favor to get to the other 20 percent Who are actually capable Of doing what i'm about to share with you I want to put the odds in your favor by the way There's a link below to schedule a free discovery call with me to see if working with a coach is right for you And if you can't afford coaching check out my link to my group Midlife love mastery. This is a great group for less than 20 a month You can have direct access to me and if you join today I'll shoot a personalized video just for you. So check out the link right there A man has to be in a state Of commitment readiness The only way he can genuinely adore you beyond the surface adoration I'm talking about looking in your partner's eyes and really appreciating your partner and say I want you not just from the physical Sensual sexual perspective. I want to embody I'm not embody. I want to embrace all of you When you can experience a man who wants to embrace all of you that to me is the definition of adoration And he doesn't look at your flaws I don't even like the word flaws the flaws you may think you have within yourself. I think of my girlfriend She points out her flaws all the time her physical flaws What I mean she pointed out is Like I appreciate her capacity To be vulnerable authentic and transparent because she knows I'm not going to run away So she shares some insecurities with me I'm so tired of the rhetoric out there right now that we can't be vulnerable authentic and transparent It is through that that we can build the deeper roots of emotional intimacy with our partner But the same time as I said a moment ago For a man to be capable to fully adore you He has to be in a state of readiness for commitment So ladies look it I know you love the idea that first date second date third dates He's romantic and he's chivalrous and he does all these fantastic things to woo you Romance should be reserved For those in a seasoned relationship not as a precursor to get into a relationship You've been sold this idea. You love this idea. You buy into this idea and what happens over and over and over again Remember the tinder swindler. He romance the heck out of her private jets everywhere until he used And abused this person Romance is not the entry point to a relationship character values Lifestyle emotional maturity those are the ingredients along with chemistry and attraction But ultimately We are sitting in a we are in a sea of opportunities. What I mean is there's so many options There's so many options right now that it's difficult It's difficult to sift through these options This is why you have to cut to the quick lay your cards on the table be radically honest right from the get-go And what I mean to say is push his boundaries by testing him To see his capacity his readiness for commitment. This is why I definitely today I want to remind you of this book eight dates by doctors john and julie gotman Folks, I want you I really only want you to read chapter one Here let me open it up chapter one page 38 page 38 spare with me What's the it's called lean on me trust and commitment trust and commitment Without establishing this very early on Very early on you might be setting yourself up For an experience that will let you down As I said earlier in this broadcast Dating triggers are number one emotional health issue of I'm not good enough. I'm not lovable. I'm not likeable Without establishing the ground rules very early on Right from the get-go why listen, what do you have to lose? Do you remember the Seinfeld episode where? Where george decides to do the opposite of what his natural instincts are What do you have to lose by trying to do something different by by literally laying the ground rules right up front right on that first phone call By weeding out men that are not capable of commitment You've got basically listen 80 percent of men aren't capable of this by the way This is true of women as well.  What are the ground rules to what this commitment look like for you? And how can I trust you how are you going to how are you going to Demonstrate trust and trust isn't just merely about fidelity trust is do you have my best interests at heart? You know, thankfully there are a lot of good men out there that genuinely look out for a woman's best interest right from the very get-go I've kind of been 50 50 on this But ultimately Determining his state of readiness for commitment Is your vehicle to actually being adored by someone because without it You could find yourself being used by someone And ladies, you know before I you know my recommendation is before you become physically intimate with someone Do a better job of vetting them? I know listen. I recognize Jonathan. This seems like a lot of work. Shouldn't this be easier? No It's not It's going to be hard if you want something of value. It's going to take monumental effort to make this happen No olympic athlete who wins the gold walks up and says i'm not going to train. I'm not going to do any work I'm just going to run and do the you know the one mile run and i'm just going to win it They train for years. Well, guess what if you want a juicy delicious relationship? And I got to tell you I've been training for a decade and a half after my divorce I've been training for this relationship So I am a big proponent of individuals having some self-awareness that they've done some Introspective work some personal development work some self-help work some spiritual work to prepare them to be in the state of readiness So they can actually lean into the experience of adoring you And that is my hope for everyone. So do you want to feel adored? Choose a man who's capable of commitment and readiness And if you too hit it off you have a great chance to experience adoration in this relationship All right, I think that's enough for I want to share today I'd like to hear your thoughts on this Please post a comment below if you like this video, please hit the like button. Please hit the share Please tell your friends about my channel. Please subscribe to my channel Check out the links in the description below for the books. I recommend joining my group midlife love mastery or Scheduling a chat with me to see if working with a coach is right for you And I'm going to wrap up this video as I always do first off. Give myself a big gigantic joth america self love I'm going to reach into the camera and give you a hug of love if that's okay I'm going to ask you to turn to someone pet teddy bear pillow Give enter them a hug of love because hugs are a great source of love and let's face it. We could all use more love in our lives Thanks a bunch. Bye. Bye. Bye