Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVercity. We'd like to thank you for joining the latest installment of the DataVercity Webinar Series, Data Insights and Analytics, brought to you in partnership with First-Hand Francisco partners. Today, Kelly O'Neill and John Lowly will discuss advanced analytics governance, effective model management, and data stewardship. 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. For questions, we'll 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 or questions by Twitter using hashtag DI-Analytics. 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 speakers for today. John is a business technology thought leader and recognized enterprise information management authority. His 30 years of experience including planning, project management, implementation information systems, and improving IT functions. John writes and speaks on a variety of topics and enjoys sharing his expertise on strategic planning, data governance, and practical technology applications that solve business problems. Kelly O'Neill is the founder and CEO of First-Hand Francisco partners, an information management consulting firm. She is a veteran industry leader, speaker, and author, and trainer. Kelly is passionate about helping companies leverage the value of data, empowering them to derive insights that inform decision-making and improve results. And with that, I will turn it over to John and Kelly to get today's webinar started. Hello and welcome. Hi. Good morning. Good afternoon. Hello, hello. Well, thanks, everyone, for joining us today. We really appreciate it. John and I are going to share in the delivery of the slide deck today, and we're looking forward to getting all of your questions and being able to have some good conversation at the end. So our goal is always to leave the last 10 minutes for Q&A, and we're looking forward to that this time as well. So today we're here to talk about advanced analytics governance. So, you know, similar to data governance, there's some benefits and some challenges around governing those analytical models, output, resources, et cetera. So thank you, John. Today, what we'll cover, we will talk about some of the synergies and differences between analytics governance and data governance. I'm sure lots of you on the call today already have a data governance program. And so considering some of the things you're currently doing and applying it to analytics governance is a great way to get started. We'll talk about some best practices and ways to leverage those for different analytic use cases. And then we'll focus on ensuring that analytics governance is aligned with your critical business objectives. So this is something that you've probably heard John talk about quite a bit. This idea of a line of sight between your business objectives and your delivery model, whether that be analytics, data, or what have you. And then finally, we'll wrap up by talking about the balance that is necessary between risks and rewards. As more data is available to acquire and consume at very little effort and integrate into your environment, that comes with risk. And the opportunity to share data outside your environment can come with some reward yet also risk. So we've got some good stories to share, and I'm sure that you've seen a lot that's been in the media recently. So this is a great opportunity to talk a little bit about that. Okay, so let's go to the next slide. So hopefully this resonates with some of you on the call.  That can be a challenge, embarrassing and costly. Have you ever experienced where you're looking for some information and the best place to find it is your colleague that you used to work with in another line of business, so you just call them up and they email you an Excel spreadsheet full of a bunch of data. Then what do you do with that? So that usage of the data is sometimes used appropriately, sometimes not because of course when it's shared cubicle to cubicle, it's not necessarily governed in the same way. And then of course you're trusting your colleague to be giving you the right data when in fact that data that you need may actually not be that data that was sent. So that leads to a revalidation process which impacts productivity, et cetera. So I'm sure you've experienced a bunch of these different challenges within your own workforce. And the concept of the Pareto principle of the 80-20 rule, there's a lot of different sources in the media that indicate that there's about 80% of the time spent finding, cleaning, reorganizing data and 20% of data scientists or an analyst time on in fact data analysis. So this is what we're going to talk about today is how do you reduce the Pareto principle to something that's a bit more manageable and more productive within your own environment. Analytics governance just to make sure we're all talking about the same thing. Again, very similar to data governance, it's an organizing framework for establishing and aligning the strategy policy and decision-making process around analytics with the goals and expectations that you want to accomplish from a business perspective. So very specifically, analytics governance is about that process that covers aggregated data, derived data, transformed data, including the algorithms and the models themselves and the, what's the word I'm looking for, and the spread of reports that can occur when there's not an understanding of reports that already exist or how to find and reuse reports. So that's quite a broad spectrum, but the idea is that the difference between data governance and analytics governance is the difference between data and analytics. So why don't we just keep going? And let's drill down into that a little bit more. The big difference between analytics governance and data governance is the content. So the content from a data governance perspective tends to be that raw data. A data element, a critical data element, data that is stored uniquely in different systems, whereas analytics governance is the aggregation or the combination of different data elements into something that is generally considered to be used for analysis. So that combination of different things could be the end result of that combination that is delivered to a user in the form of, say, a report, or it could be that process of derivation in the model. So because the content is different, the usage is also different. The usage is more at the end consumer, where it is the end result of a bunch of analytic processes versus the elements that are put together to create that end result. Many times the timing is also different because analytics is meant to be executed as a way to solve business problems and business questions can come up quite immediately and quite urgently. Whereas data governance, although there is a variability and a changeability around data governance, the data itself might not be changing quite as quickly as the demand arises for changing around analytics and your models and your algorithms might be invented quite quickly in order to address an immediate inquiry or demand from an executive. So the timing tends to be different and the requirement for agility tends to be different. Because the usage is different, the participants are also different. Now, not always, and we're going to talk a little bit about that in the next few slides, but recognize that there are going to be some differences in participants. So then if we're thinking about leveraging the constructs of data governance into analytics governance, there might be some folks that aren't so familiar with it. And then lastly, of course, the differences in technology. There are many capabilities from a technology perspective that are easily accessible and downloadable from an analytics perspective, which means that it would be important to consider the level of governance around that as well. John, is there anything that you wanted to add? Anything that I missed here, possibly? Oh, I think we're good. I'll have a few little things when we recap the section. I think we can move on. Okay, great. So let's just talk specifically about some of the roles that we see. And again, when we're thinking about an alignment process between data governance and analytics, that alignment fundamentally comes down to activities that are performed by people. So we want to actually be looking at the people. So this little kind of triangle here is meant to demonstrate some of the relationships and consistency of the types of roles that occur within data governance as a core capability, as well as other partner roles and then typical roles within analytics programs as well. So I'm actually going to start at the bottom to kind of discuss this slide. So core data governance roles, these are probably very typical and familiar to you, but the idea is that there's a concept of a business lead or sometimes called a data owner that provides guidance and is that key person for a data domain that is ensuring that the data is available and a level of quality to meet the business requirements for usage. They are supported by roles such as data stewards, data quality folks, technical data stewards, not just business data stewards, but technical data stewards. Other people that participate in IT like data architects. And then some organizations are also lucky to have business analysts and data analysts who are part of their core governance office, if you will. That core governance office, of course, works quite closely with other partners in the organization to ensure that they are getting the information they need from those subject matter experts. So they will have partners in the lines of business that are considered to be a data subject matter expert based on their usage of the data. Maybe they are doing analysis of that data so they're considered a data analyst or a data quality analyst. And again, we've got support from technology and IT and then those folks that maintain the data aspects and truly act as a librarian for the data. From an analytics perspective, you also have a business lead or an executive that guides the analytics program to ensure that it is addressing those business requirements and that it is aligned with the business expectations and isn't analytics for analytics sake. You also have someone that explores the data to make sure that you've got a person who is understanding that it's the appropriate data for appropriate use. They could be a data domain expert. They are also, many times, someone that will be creating reports. They will likely leverage data visualization tools. And then, of course, you have the data scientist who is the one developing some of those models and algorithms to do the statistical analysis. So they are the ones that are exploring the data that's managed by the analyst and what they're exploring is kind of the trends in the anomalies that you see within the data. And again, we've got support from the technology organization to make sure that this is automated as much as possible. If we think about this kind of triangle, it's highly possible that that business owner and that business manager, business SME or data owner could potentially be the same person within a line of business because they're most familiar with the data and they might be most familiar with what that line of business demands from the data. The analyst role could be that line of business data analyst as well, for they are both working within that data domain. They're working with the raw data to ensure that it is fit for purpose. That data scientist, even though they're doing certain modeling and they're working on the algorithms and that sort of thing, a data quality analyst might be doing a similar sort of modeling but much more basic from a quality perspective. So they're looking at how the models and thresholds for things like accuracy and consistency are created so that it can actually serve the data scientist. And then of course, IT has an opportunity to share some of this, I guess, economies of scale where there are understanding requirements from both the data perspective and IT perspective. So you can see how there's quite a bit of overlap or at least partnership in some of these roles where depending upon the size of the organization, they could in fact be the same individual or they could work very closely with each other so that there's an organizational synergy, not just a functional synergy. We also want to talk a little bit about the concept of these governance models and how you can leverage the models to align governance across either data or analytics. And fundamentally, there's centralized, decentralized, and then of course everything in between. And as you start to think about what sort of governance model works for you as an organization and how centralized versus how decentralized you want to be, these are just the two extremes and the conversation would be where do you want to land somewhere in between? So if you have a highly centralized model where you've got an analytics organization and a subsequent analytics governance organization that is very similar to an organizational chart, it's great for providing consistency of output because there's so much consistency in the actual individuals that are participating. You of course can leverage the same technology and you end up with a skill set economy of scale based on the representation of those individuals in more of a centralized way. Now because everybody is centralized into kind of a single sort of organizational unit, if you will, one of the things that happens over time is their experience and their subject matter expertise from a line of business perspective starts to wane. As a result, then their applicability to the enterprise starts to be reduced unless of course you very proactively recognize this and create alternatives for that. The other thing is sometimes with a centralized model depending upon the demand, that demand could exceed the resources available and so there's less agility available with a centralized model. Now of course a decentralized model is going to give you the exact opposite, right? So with a decentralized model where everybody stays within their line of business versus coming together in a kind of a centralized analytics team or analytics governance team and because they stay within their line of business, their business relevance is maintained and that means that there is a constant link between business demand and what that organization is doing. It may have a significant amount of agility and responsiveness as a result, but the challenge is if you're trying to get a highly networked organization to create some consistency of output, there's a lot of communication and negotiation that happens to make sure that everybody agrees on the same technology, the same look and feel in the output, the same reusability of resources is virtually non-existent. So again, most organizations don't end up at one extreme or the other, but where you're thinking about economies of scale versus agility, that's how you end up somewhere in between. And we've got a couple of examples of this coming up. So here's an example of a model in which the centralization between data and analytics occurred via a forum that was made up of different executives that came together on a regular basis and through their conversations, their planning and their execution, they were able to create that consistency while being a decentralized unit. Supporting that data and analytics leadership forum, there was a separate forum that was really focused on data stewardship and data governance, and then another separate forum that was focused on the actual analytics process and output. The two forums, the data stewardship forum and the analytics forum, they primarily focused on their own spheres or their own community, if you will, although there was some activity that occurred to unify those forums as well. Primarily that unification happened at the leadership forum. So this is a highly decentralized model where there's a recognition of the requirement to create consistency, and this organization was able to do that via a very involved leadership forum to create the consistency of planning and the consistency of execution. Okay. This is a build slide, so I'm going to talk through it just a little bit. And so this is another example, but this has a slightly more centralized model where there was a head of business analytics that ran the analytics organization for this company. And that head of business analytics had a group of folks that actually ran analysis on behalf of certain functional units within an organization. So in this instance, you could see that they've got market analytics, credit analytics, client analytics, et cetera. And they had those data scientists and those data analysts who were exploring the data and creating the models and algorithms to support those groups that were doing the analytics in a specific category. Now, they did consider that they needed to reach out to those line of business units that were doing some more of the operational reporting to ensure that there was a level of consistency, and that's what they called kind of the analytics working group. This head of analytics reported up through the CFO, and there was the recognition that they need to have a little bit more cross-functional involvement from an executive perspective. So they leveraged the presence of the Enterprise Infrastructure Committee to get feedback to ensure that the analytics direction was serving the full enterprise versus just the finance organization. So this is what the analytics group looked like. Again, a little bit more centralized from an organizational perspective. So if we... Next slide, please. If we compare that to what the data governance organization looked like, it was a little bit similar where there was a centralized data governance office that had capabilities within that office to manage the data governance program itself. And then the data governance office had a bit of a decentralized model where it leveraged business steward leads that maintained their role within the line of business, had data stewards within the line of business as well, and an IT support group that worked all as a working group and a kind of decentralized body that was unified by the data governance office. In this way, data governance was a bit more decentralized than the analytics organization. The data governance ultimately reported up through the chief operating officer, but in another way, in kind of a similar way as what we saw on the analytics side, that leadership forum that provided the relevance and the guidance to the data governance office was the same leadership forum, the Enterprise Infrastructure Committee, that was being leveraged by the analytics group. And if we consider what was occurring at that kind of more execution-oriented level of this operating model, the reality was that some of those business steward leads were actually those same people that were on the analytics side because of their usage of the data in the analytics practice. It made them good representatives to be a business steward for that line of business. Some of those data stewards were also people that were acting as data analysts on the line of business side. And there was even some crossover in terms of the people that ran the operational reports because, of course, we want to have some data stewards and business stewards that have an operational perspective, not just an analytics perspective. So you can see that in an organization, this organization was able to leverage some of the capabilities across these different models with a varying level of centralization and decentralization because when it came down to it, the people and the individuals themselves were actually the same. John, did you want to add anything to these discussion points or this section? Just to recap a couple of things. One thing that leaps out to me is, and I think some other people will be hearing this, is that a lot of this sounds familiar to perhaps something that is just said about BI reporting versus analytics. And again, the core difference is content, but don't think that the content difference is going to take away some basic blocks being and tackling that you have in these operating frameworks. You can't base your operating framework or the way you operate this on a particular type of architecture. Classic corporate information factory type thing or modern dupe-centric type thing. You have to base it on what works for your organization. It's not an either or one of the different or that's old or that's new. I think that's the takeaway from all of this. Should you be decentralized? Should you be decentralized? I don't know. It depends on what you require. Remember, the expectations are very, very high for these types of analytic environments. And your operating framework has to work and communicate very, very well. So spend the time to do it. Just don't throw some boxes up on the board and say, yeah, that'll kind of work. Put some conscious effort on this type of, this aspect of your analytics environment. That's all I had. You ready to move on to the next section then, Kelly? Sure, absolutely. We'll talk about best practices. And I believe I'm talking about that for a little while. Sure, go ahead. Well, I think that was the plan. Feel free. Okay. No, that wasn't the plan. Actually, no, I had something else. Go ahead, Kelly. I got something else to go ahead and do that. Not a problem. And of course, to those of you that haven't, that this is the first time dialing in, John and I do go back and forth quite a bit to make sure that we have this shared experience that we can discuss with you guys because we each have different backgrounds and we bring different things to the table. So generally it covers all bases. So since we just talked about some of these models to kind of align data governance and analytics governance, there's also other aspects, not just organizational aspects, that you can leverage across both analytics governance and data governance. And so data governance can help facilitate analytics governance, but analytics governance can also help facilitate and provide direction for data governance as well to make sure that the way that data governance is operating is providing value to the analytics organization as well. And so whereas one provides a focus on data as a foundational asset so that it can be used across both operational and analytics aspects of the business, then the analytics governance helps to ensure that that voice is heard within the data governance forums as well. Now data governance traditionally defines data standards to ensure data consistency, whereas analytics governance would be defining analytics standards, model standards, algorithm standards, and in order to ensure consistency and appropriate use, they will think about the model management process, for example, and the model development lifecycle in a very similar way that a governance organization is going to think about the data lifecycle and how data may be created or acquired, brought into the organization, protected, managed, maintained, archived, et cetera. The same sort of process would come up around analytics as a process and the actual algorithms and models themselves to ensure that there is reusability of those models as well as maintenance of those models to ensure that as things change both in the business and from a regulatory perspective, that those models adapt and are changed appropriately as well. So there's a lot of ways in which governance can facilitate analytics and then analytics can help guide and ensure that governance maintains relevance within the organization. Next slide, please. Some of you have seen this next slide previously. So this is the First San Francisco data governance framework, but the idea isn't necessarily to talk about it from that perspective. It's more that when you're considering data governance, that you think about these primary categories and how they're relevant to your organization. So thinking about the strategy and how the strategy aligns to your organization, what that actual operating model and organization looks like. And the idea is if you break each of these components down, it's a similar sort of question or consideration when you're setting up your analytics practice. For example, how do you make sure that your analytics and analytics governance practice maintains relevance to the organization and adapts and changes in the same way that your regulatory environment may adapt and change or your requirements may adapt and change? The concept of directive, so policies and rules around how algorithms are created, shared, and maintained and how you create traceability and how those algorithms are created, maintained, what data it uses, who uses those, why do they use them, are they authorized to use them, etc. So it's very similar to data lineage as well. You want to make sure that you are measuring the output of an analytics program in the same way that you would have proactive measurement around governance. And then a couple of other things to point out. I mean, obviously there's a difference in the way the technology supports it, but activities associated with communication and change management are highly leverageable and reusable across an analytics governance practice. And the way that you go about doing a change readiness assessment, for example, would be virtually the same regardless of whether your content is data or your content is analytics or models or algorithms or what have you. So these sorts of best practices can be repurposed and reused and the success that you've had on a data side will help to accelerate your analytics program as well. So the next slide talks about a few design principles and in the interest of time we're not going to drain this slide. As usual, you will all receive a copy of this. But a few things that I just want to highlight here because they are really important and they do cover analytics and data governance from a best practices perspective. Ensuring that you have a clear goal and that clear goal is aligned with business objectives. If you do, chances are you will also be considering the enterprise even while you're thinking about lines of business requirements because, of course, there's going to be specific requirements around analytics that will be different for the finance group, for example, from the HR organization. You're going to be working with different models. You're going to be... There will be different data scientists, what have you. But they might also be using some of the same data and so there might be things that can be leveraged across the different lines of business. So you want to be thinking about the enterprise even if the implementation or the exercise is within a line of business. So there's some other kind of design principles here in terms of thinking about flexibility, simplicity, usability. And the idea is that the demands on the organization is going to change and so you need to be available... You need to be ready to change with the demands of the organization. A few slides ago we talked about one of the big differences between data governance and analytics governance is the timing and the requirement for agility because the questions that the executives will ask may change quite regularly. So that will be important to ensure that you're able to respond quickly from an analytics governance perspective as those requests and demands may change. And then finally, the concept of communication as being a huge validator and as an important component to ensure that the rest of the organization understands the good work that you're doing is incredibly important and should be considered as a proactive, funded, and resourced exercise. Communication is time-consuming, especially when it's done well and that does definitely need to be considered. I think now we're moving on to... Oh, well, did you have anything you wanted to add to this section, John? Again, by way of summary, there's kind of a theme with this section. And some folks might be saying, well, again, there's a lot here that isn't explicitly exclusive to analytics. And I think that's a good thing because it's not that there should be... It's not that we're applying even lessons learned from one or two generations before now this current generation of using data. It's more that there are essential principles and capabilities in running any effective organization. And don't overlook those. Don't overlook some solid principles to do that. Don't necessarily... Certainly, there will be specialization, but the whole theme here today is kind of a Pareto principle. There's a 1% of activities that are going to get you 80% of the way there. And we just highlighted a few of those there. And I do believe the next section... Now, I've finally got my notes in order. Kelly, I think I've got the next section here as lead. Sure, keep going. On that. And that's the analytics and BI strategy kind of blending them together and having an alignment. Again, this is... And the reason I take this section is because this is a message that we continue to hammer on everywhere we go or work or speak. And that is that if you're not doing something explicitly for the business, you can't measure... You don't know for first and foremost whether you're making any progress or not. And even if you are making progress, you can't prove it to anybody. And since a lot of organizations have a bit of change and cultural stress when they move into highly governed or highly exposed data-driven type capabilities, there's always pressure to go back to where you were. And if you want to be sustainable, you have to show some alignment. So we talk about a super high value exercise called line of sight. And that makes sure that what we're doing is... And what we're focusing on is what we're doing for the business. A great many folks will say, well, we want to build models that answer questions that we've never asked and all those wonderful things. And that becomes more or less the objective, but that's not the objective. The objective is, of course, to do something for the business. So the kind of the process we go here through is, first of all, capture what the business is about. What does business value mean? So all businesses have strategies. They have major initiatives. All organizations. We will talk about government or nonprofit. You've got major directions you're going. Capture that. Where are you going to improve value and fulfill the mission of your organization? Those have to be measurable, right? Once they're measurable, and I have an objective that has a measurement on it, I now have a data requirement. I am now in the realm of, now, how do I meet that data requirement? And if I meet it with analytics or big data or whatever, fine, or I'll put this simple BI reporting, fine. I need to take the entire population of those needs. And it could be an analytical model. It could be operational BI. It could be a traditional managerial monthly report. But you do need to build kind of a meta layer of this or a way to understand what your broad needs are. And then each one of those requirements, as it were, for using your data. Let's just say we have an analytical model that is going to predict medical outcomes for a certain population. And I'm just saying that because I've done a lot of healthcare lately. It's kind of in front of fine. So I'm going to build some sophisticated models. Well, those models have to be managed. They have to be tracked. I have to assure the consumer of the results that the results are accurate. Well, that is data governance and data management. So now I need to say, what am I going to do to assure quality, assure timeliness, assure trust in that information? And of course, there's also some ancillary things with that, which we learn about in analytics or anything else. You know what, data quality is important. There's some dimensions that I want to use, or I want to group the data by certain populations, and that reference data is deficient. So the reference data and data quality, all these things come to bear, but those are all data management components. So you want to decompose from what your business needs to what your data governance and data management needs. Here's a simple example. And again, since healthcare is front of mind to me here recently, we threw one in there. And that is the initiative of the strategy, the organization is to improve outcomes and wellness through a predictive model of some sort. But that's tied to a particular financial goal, which is reducing readmissions by a certain percentage. There's a bunch of metrics. We're not going to empty this slide out. I like the way Kelly refers to that in great detail. But you can read this if there's a bunch of stuff that we want to have our model support and measure. So if I have a model and it tells me to do something and readmission should go down, I've got to also measure the readmission. That's something else a lot of folks overlook is that that model is going to drive business activity and we need to measure that business activity. So then I have some use cases or some way, I'm going to leverage my data and we have a lovely big long list of things that are going to happen there. And I can look at all of those, I can say data management wise, I need to have some reporting, I need a registry, I have some domains that I need to pull data from. And from a data governance standpoint, I need to assure data quality is correct. I need to have my semantics of the data correct. I need to manage my models. And I'm going to have to require a certain amount of data lineage and or a certain amount of data provenance or provenance or however you say that. I keep referring to an area of France and it's not an area of France. Anyway, this is a simple decomposition. Now, here's where it's helped you. You get challenged by someone to say, oh, we don't need to get out of this. We just need results. By gosh, we need results. We tried this data management stuff 10 years ago and it was no good. But you can say, okay, call it what you want. Well, we won't call it data governance anymore. But we need to do these kinds of things. Do you think that's smart? Like, oh, yeah, well, of course, that makes sense. Just don't do data governance. Fine. Proceed as requested. All right. But you've done the alignment exercise regardless. This also, everyone wants to advance their data maturity to be competitive or efficient or stand out in your particular industry or segments of government or market. So, and we all know that some of us started some spots and we want to go to somewhere else. Any formal alignment exercise will help advance your maturity. So you don't need to be at a certain maturity to do this exercise if you have nothing. All right. And you do the alignment. You're going to find yourself leapfrogging to a level where you can see commonalities of bubbling out and economies of scale and things like that. If you're starting perhaps a little more advanced, we already have a level two on the second part where you've got some policies and some governance and along comes analytics. And now you're going to do these models. You'll discover that you're going to have a really good driver for enterprise level policy enterprise level of data. You'll have further support of probably something you've been striving for a while anyway. So there's a wonderful opportunity to advance your maturity. Kelly, anything on that section here? And then we'll let you continue on here. I think you covered it all. All righty. Onto risk and rewards and the next slide. It's you. It's me? Oh, my goodness. I thought I went back to you. Anyway. I've talked enough. All right. All right. I'm, but everyone here, the paper, John's going over to his next page. You know, my gosh, it is me. How about that? There's a lot of risks associated with this. Another reason to manage your models really, really tight. And it's really, we have a really cool example here. It's come out here in the last few weeks. And I think if unless you've been in a cave, you've heard about the Facebook problems. And this is a classic example of, of data modeling and data analytics run amok. And it's, I don't know the market cap of Facebook was down some 40 billion dollars or something like that. Some amazing amount of money. And if you don't think there's a cost associated with no governance, now you know differently. There are obviously ethical aspects to using your data. And the more powerful the model, the more you need to consider the ethics of data usage. You need to consider that there, that your constituents in this modeling process are have to be considered even if they are the subject of some type of data driven action. And of course, consider the tone of privacy. American privacy laws are looser than Europe. But after this, you might see some tightening. So consider the entire context of the privacy world. As you're, as you're going through your modeling exercise, a real significant capability within the management of analytical models is an ethical capability and a risk management capability. And if there's anything to add to that Kelly or I'll just going to move on here. I think in the interest of time, let's just keep going. All right. Okay. The other kind of a balancing force here is the fact that when you start to work with analytical models and with data so much, when projects become data centric versus becoming process centric, your methodologies are going to change. Even if you have an SDLC or your agile, whatever, you're going to find that a lot of those processes don't fit. You feel like I've had someone describe this to me as a round peg in the score hole trying to use corporate PMO and a corporate SDLC and corporate agile work and then doing these heavy data centric model type analytical projects. It just doesn't feel right. And that's because they don't fit. So about again dumping this whole slide out, this is something we've shared before and we're happy to share with folks again and again. There is a different mindset or a different process which we call data centric. But the takeaway here is think about that your go-to market process is going to be a little bit different with anything that's model driven. And then lastly, we couldn't do this any better than Gartner did it. And so we brought this in here. And that is depending on the different type of platform, you might have some different drivers, some different characteristics. You might have some different governance objectives with them. But at the end of the day, those capabilities we've talked about are going to be very, very consistent. All right. So if I take, for example, a standard information portal versus a data science lab, we'll take one side or the other. I'm looking at efficiency and integrity and quality while looking at that portal. The predictive model is looking at some type of viability of that model, the transparency of the data. And I have to worry about the privacy, the legitimacy of what I'm doing. But the capabilities underneath those visible objectives are very, very similar. So if we go back to our prior sections, you can see that we've got a bunch of core things that have to be done. A basic alignment has to be done. A basic balancing of risk and rewards with your using your data on that. So let's move on into our summary then. And we'll have time for questions. We do have a few questions here, which, and we'll take a minute here, that if you do have a question, we do allow time now to answer them as much as we can. And if we can't get to them all, we'll get to them some ways somehow, but we'll get your answer to you. So please don't be shy and enter a question here. It's not like YouTube, you don't have to click like below and or anything invasive like that because we've considered our data risks and rewards with this exercise. We just want to help you get your questions answered. So feel free to put one in. And while you're doing that and now we get prepared to answer your questions, let's just do some takeaways and Kelly, let's just kind of toss these back and forth here. Sure. All right. What business attempts to do an analysis directly influences what you do with your data? Form follows function, right? Absolutely. Yeah. The analytics requirement is a key driver for what you are doing from a governance and process around your data, most definitely. And then we talked about existing constructs, existing ways we've done things are perfectly appropriate for figuring out what you're going to do with environments too. Absolutely. There's this, you know, the don't reinvent the wheel idea. And yes, the content's different. Yes, your participants might be different. They might be the same, et cetera. But recognize that the ability to implement an analytics governance program will be significantly faster if it doesn't feel foreign to the organization. And so leveraging what you've done from a data governance perspective and what's worked will give you a sense of what you're doing and what's done in the way that you're going to do. And so that's why Gephardt will allow you to accelerate your implementation. We talked about risk and rewards and balancing by other factors other than regulatory compliance. And of course we use that example of Facebook but there's many others out there that, you know, of balancing. The more sophisticated we get with our data, the more we're going to have to do things about how we use our data that we've never thought of before. And these analytic models, you are now on the forefront in your organization of that type of thought process. That's absolutely right. And although a lot of the regulations focus on the data aspect, there are already regulations that are ensuring that the models that are created are not discriminatory. And so there will be a requirement to create, to have transparency, validity and be able to expose what those models are easily in order to demonstrate compliance. And that's just going to get more and more pervasive because the more that companies are basing decisions on analytical models and that sort of thing. And the risk of those models being discriminatory or something like that becomes higher and higher. So it isn't just data regulation. It is algorithm and model regulation that is coming. Absolutely. Absolutely. And we hammered again on our alignment and form following function and alignment makes things a whole lot easier in the long term. So let's just take time for the questions. I'll read through our questions here and we'll go from there.  Let's just start near the top. What is your view on a bottom-up approach versus a top-down approach if you're decentralized? Sure. I'll take a first swag at that one. Yeah, so decentralized is fundamentally a bottom-up model. So it's not decentralized and being top-down. In fact, I don't think I've ever seen that exist. And many times, all sorts of programs are started bottom-up because it's the people that are on the front line, the people that are delivering the value either to the client or internally to an internal client. They're the ones that see the pain in the first place. And so I would say that most programs start as grassroots programs. And the trick is, is going from grassroots to more repeatable and sustainable models and kind of crossing that chasm, if you will, to ensure that you get some top-down mandate to authorize and empower you to spend the time actually doing that work. The challenge in maintaining a decentralized model is that most times in a decentralized model, you don't have that function in your job description. And guess what? As resources get tight, your management team is going to reference back to your job description. And so might your compensation. So that's where the top-down needs to come in place, is to give that mandate and that authorize it. Hello? Sorry. I don't know what happened there anyway. Okay. Hopefully you were hearing me. I don't know if you wanted to add anything to that answer. We have a bunch here. So we're just going to charge ahead with them. I'll take this one, because I have a kind of a quick answer for it. Can you please address how the activities of data scientists who after all are on a voyage of discovery with no a priori knowledge about what they're looking for should be governed? Well, again, let's kind of go back to the theme here. Maybe we could have called this thing, there's nothing new under the sun thing today too. The good answer is why wouldn't they be governed? Your organization, they're going to do something that could put your organization at risk or could do really, really wonderful things. There needs to be an expectation at the beginning of what the boundaries are.   This question was answered, it's time stamp, it was answered before that Gartner group thing. We will still reset. Gartner group kind of addresses that as well. Next question. Let's see here. When data stewardship is assigned at the source or a transactional data level, is it common for a data steward within the operational data warehouse and data march to mirror and always trace back to the source? I would think that that's basically lineage is kind of a required capability these days. And if it's not done manually by the steward, we want to automate that. Let's see here. Kelly, one for you. Does the Gartner slide recommend to apply different levels of governance based on how the data is used? For example, exploration versus a decision model or corporate support. And we'll go back to that. There's one other question to go back and put slide 24 up. So we'll kill two questions right there. So why don't you take that one, Kelly? I think the answer is kind of sort of yes, but why don't you explore that a little bit more? Yeah, I would say it's an absolute yes. And again, form follows function. So it depends on what the demands are. So if you are looking at and we'll take the two extremes again, if you're looking for as an information portal, then there's a lot more demand for quality of information that is pulled out of that portal because there is the requirement for to adhere to the corporate standards. So for example, and so there is a need to ensure that the data that is pulled out of that in order to deliver on analytics does comply with any sort of corporate standards. So it is much more governed, if you will, versus the data science lab. The focus here is I would actually like focus on that bottom bullet point, calling it legitimacy. And from a data science perspective, the idea is not to limit what happens from a data science perspective, but to ensure that the activity is legitimate. Is it legitimate based on the requirements of the company and the culture of the company? Is it legitimate based on regulatory requirements? Or are these people exploring in such a way that it's not relevant to the goals of the organization and therefore the value isn't relevant, right? So there needs to be some level of guardrails around what those people are doing from a data science perspective. And so although they are being, they are experimental, it does need to be within the bounds of what's considered acceptable use within the organization. So yes. And so there might not be a care around levels of quality from that perspective, right? So again, the levels of governance and rigor would differ depending upon that use case. Yep. You know, the key there though, it's different flavors or different levels or intensities of governance. But there's still the essential components and capabilities within data governance that are brought to bear with maybe some different visible results. But what we see often, right, Kelly, is that people say, well, we're different. We don't need governance. And that, that is not correct. It's just it might look differently to an outsider. But yeah, the question is, what is the governance that you need, right? Because there is risk associated with zero. Lots, lots of risk. I think we have time for another one here. Do you have any recommendations in terms of assigning accountability and in prior and aesthetically ownership, assuming you recommend assigning accountability for analytical data and reports? And that's one that's a topic Kelly and I both like. I'll give Kelly the first shot at that for a minute and then I'll take Yeah. And what I would want to highlight is that kind of clause that comes after the question is that it's about accountability. And the challenge with ownership is that people tend to think about ownership as being, you know, by one individual. So accountability, especially as you get to a more distributed, either distributed model, or you get to greater amounts of experimentation and the type of data that you're using is more variable and things like that. There needs to be inherent accountability by that consumer of data. And so what they are doing in their innovative experimental lab, they need to be accountable for the data, the algorithms and the models that they are creating. So therefore they need to understand those guardrails that are available. So I think that's what what I would focus on is that concept of accountability as opposed to the concept of ownership. Ownership may work from a data perspective in the sense that someone really needs to be drilling down into quality thresholds and that sort of thing from a from a ownership perspective that might not necessarily apply quite as stringently depending upon your analytics use case. Yeah. Yeah. My short version of that is there's two concepts that in my last 20 years now of working on governance have caused immense headaches. And that is the term ownership and the term data storage. There's a tendency to just grab those right away. And you immediately get if there's a hundred people in a room, there is a hundred different pictures in the front of their minds as to what that means. But the term accountability or responsibility or control or informing like a bold fashion racy chart, those are pretty discreet. And those are much more important words to use than a steward or an owner or something like that. Now, let's see here. We've got one minute left. I mean, just I think we've got the big ones here. There is a question here. What are the major activities to initiate data governance of an organization? Pretty broad questionnaire. But our little proun slide earlier in the presentation is a really good start to take a look and kind of take a look at that and go, wow, well, this is kind of all the little various shades and shapes and forms data governance can appear in and what needs to be done to get that up and running. And then, you know, send Shannon and voter something. We've got other papers and resources and presentations we've explained that in. At that point, I think I just saw the clock tick to the top of the hour. Anything to do for a wrap up here? No, I think that I think that you're absolutely right. If I were to coach someone in terms of starting data governance, I would ask yourself, why? Why are you doing this? And then I would ask yourself, how are we going to make decisions? And those two big questions are the best place to start. Yep, all righty. Back to you, Shannon. Thank you both for another fantastic presentation. And thanks to our attendees for being so engaged in everything we do. I just love all the questions. Just a reminder, I will send a follow-up email to everybody by end of day Monday with links to the slides and links to the recording of the session, so you'll get all those wonderful charts and such and that. And I hope everyone has a great day. Thanks so much. Thanks, everyone. Thanks, all. Bye.


How's it going everybody? Tata Doi here bringing a highly requested team actually, Vegito Blue and then of course the components of Vegito Blue, Goku and Vegeta. I thought it was a pretty good team, sounded like a lot of fun. Obviously Vegito Blue is just a lot of fun in and of itself and the same can be said for Goku actually. So really I guess if you got blue hair in this game you uh you live in life good. Oh yeah I forgot to mention we're going against MLG Weed Scope 420 so my expectations are high and uh so are the frames. We're getting bounced around here pretty bad. It has been a few days since I've played but I'm not too worried. Watch this. I'm just gonna auto combo it out there because I don't think anything would. Oh I messed up. That was not a part of the plan that was just an accident. All right well looks like we're gonna have to be switching out into Vegeta here. Oh this is actually going to be kind of difficult. Okay it's two reflects. Going over my stubby stubby arms. All right that's fine let's get out of here reflect going up again going ah. Missed that one a big. There we go just delayed a little bit. Oh no how did I get on this side. Oh the key bl- ah. All right we need to uh we need to work on getting Goku Black. Yeah nice. Oh wow I missed time my um maybe. Oh that's absolutely fine. You know what I never really cared for the islands. I'm more of a desert kind of guy. Oh that was bad. That was a bad switch. Oh is that gonna tag? I don't think so I don't think so. And then again I don't think it mattered because I dropped that combo anyway. That's fine going down.  Are you kidding me man how bad do you gotta be. Oh get that out of here man it's budgie no time. Combo. Wow that was just an awful strung together combo. Honestly I think I forgot the real one. Oh no you know what what is a team if not for its weakest link and uh now introduce us to our weakest links. Not bad. Oh neutral go ahead Super Dash or that. Oh let's go I'm going to go for it all here. Boom excuse me while I wipe him off the face of the earth and we still got sparking so I'm about to bust it all out here.  Alright let's uh let's see if we can't put in a little more work with Fujito. Neutral dodging it. Oh nice wow he attempted to go over and then he did so if you can dream it you can succeed excuse me can I just if I could just recover here real quick oh we oh this is a feels good scenario this is um not the combo but I'm just happy at work honestly.  Yeah, crush them. Shields up again. Okay, my shields were up, but my chest was open, so I got a sweet special beam to the face. That's fine, because with reads like these, who needs them? Let's go. Let's go. EX. Oh no, what are you doing? Okay, I'll take it. You know what? That looked pretty sick, so I'll roll with it. I wanted the EX legs though. Reads again. Okay. Whoa, whoa, whoa. Take it easy on me. If I can land one hit, it's all over. Okay, Vegeta, I'm gonna need you to say a little... Let's go. Let's just hope this kills. Oh, and we're gonna take it to the bank, baby. Too nice with it. Nice.  And they did a great job. What can I say? This was a pretty good team, pretty fun. Thank you so much for suggesting it down in the comments below. Of course, if you have any other ideas for a few Zamasu and Vegeta blue teams, feel free to leave those down there as well. Hopefully you enjoyed this video. I'm Dr. Doya, and I'll see you in the next one.


presentation of TFNN. The Tom O'Brien Show is produced every business day. Tom takes your phone calls toll-free at 1-877-927-6648 internationally at<PHONE_NUMBER>. Let's go to Alan Homassassa. Hey Al, what's going on? Hi, it's been wonderful. This gentleman here with the gold report right before the market fell apart ended up with TAAF, we have a 98% gain in the year. And I mean, we want 99% proof like Irish whiskey, but we had a good game there. He always told us to do what we feel comfortable with. And if I lose a little bit of money on the table, I will, but I know that I just pocketed $8,000 or $9,000 in two weeks. That's a beautiful thing, man. Now, Tom O'Brien. This is Tom O'Brien of TFNN. We have five days a week. We go seven hours a day. We go 24 hours a day on the internet at TFNN.com. Always remember folks, whatever you think about, you bring about whatever you focus on, Garose, hope everyone's having a great day, safe day. It's making a great night, folks. You have the power to create. Your power is so strong that whatever you believe comes true.  I like that one. Mockin' the wise. Let's take a look at it out here. We have the Dow Industries up 134, NASDAQ up 33, S&P's up 850. Gold, gold contract trading down $3.30 at $19.47 an ounce. We have Silver up 7 cents, $22.80 an ounce, LightSweet Crew down a buck 37. $83.04 a barrel, notes and bonds. A 10-year note, down 17 ticks, trading out at 1.10.27, the 30-year down a full point plus four ticks at 1.21.20 and KingDollar. KingDollar's the mover out here, folks. That's the bottom line. KingDollar right now, rejected lower price. KingDollar is trading at a price point up 35 ticks at 102.526. Our phone number's<PHONE_NUMBER>. Give us a call, folks. Want to know what's going on in your world and the world of the S&Ps. Let's take a look at them. What do you have? Well, you talk about some price spread out here today, man, it's pretty intense. You know, the spy, we got up to $451. And $451, the last, that high volume when we came down was $452.89. And it couldn't even make it, which is pretty weird. Bottom line is that you still got a small ABC structure on the way down. That's how this is set up. You know, bottom line, it gave it up, it gave it up in spades. We'll see how this shakes out coming into the clothes. But as I said in the update, I suspect coming into the clothes we're going to go right after this low. But it's dicey. It's a dicey one. It's not, we don't have a real high volume low here. Let me show you this. You know, normally if you, it's not a real conviction play here, but you still do that the volume is still there. You know, nothing big. But the bottom line is that, you know, we stair stepped all the way down all day long. We go into the end cues. Well, first we go to the cues. We take a look at the cues we have with the cues. Got to a price point out here of $370, no, $374, $374.36. You're at $369. Now what is intriguing here, there's no doubt. Now you're going into the volume of $61 million. Well, we've already done $61 million. So that was actually pushing up with volume and it gave it up on price. So that's kind of dicey because when you do that, you can go right back up, you know, right to that level, that's how this shakes out. And what has happened with the cues, the cues are coming into that $100 million, I mean, $100 million share price, but that is at $372. So now what you can see is that you're not holding price inside the NDX100, which is a problem. We go to the end cues. We take a look at the end cues out here. We have with the end cues right now, there we go. So we take a look at the end cues, put these, pull these back. You talk about the price spread in the end cues. We went for $15,442 to $15,136. And yeah, the same setup, man. You're coming into volume here, your probabilities are gonna, they'll run it down because you can see what's happening now. The last two bars, we had 10,000 contracts and 8,000. And that's going into 15,000 and 20,000. Yeah, check this out. I'm gonna go back to the S&Ps for a second. Yeah, I see what's happening here. Let me go back. See these two bars, that's what you're going into. And yeah, you got price going, you know, the last 30 minutes, but those two 10-minute bars, that's 10,008, it's the second one. So you got the contraction there and you're going into 15 and 20. That's saying we're going down. Go to the ES, let me do this. It's gonna be the same setup, but I just wanna see specifically this number. Okay, so pull this out. Okay, so there it is right there. Okay, so let's put this right here. That's what you're going into. Okay, so we did, last bar was 26,000. Bar before that was 34. That's going into 43 and 53. Yeah, this thing doesn't have it. It doesn't have, well, it doesn't have it to make it through it, that's what's going on. So can't bust them up, guess what? You'll bust them down, coming into the close. We go over and take a look, let's go take a look at the note and bond market because the 10-year, what's gonna be interesting about the 10-year right now is that, we had a couple days up, I wanna tell you how this is back and down. Whoa, we're back down all right. Oh, holy cow. It back down all right, look at this thing. Okay, so the 10-year gave it up in spades. You get 1.6 million contracts. Yeah, that's saying that we're gonna go back and test the lows again. And that lows we're talking about is 109.24. And then if we go to the gold contract, we take a look at gold. And we have out here with the gold contract, back right down to 1945. You swing as 1939. This is getting dicey, man, particularly going into a Friday. This is dangerous, man. We're gonna have our anti-mort on today too, but this is dicey. Because that's telling me that this actually wants to get down probably to this, could even be 1904. See, if you break that 54, no, not 54, 39, and on a Friday, we can do that in a second, and particularly how the dollar's set up, the dollar's set up, that it wants to go higher. So this goes down here on a Friday, you're gonna go against the strength of how we came off the bottom in February. That's how that baby's set up right now. There is certainly a huge amount of action. If we go over to the VIX, I'm sure that we get an elevated VIX now. There's no two ways about that. Look, and it's even down, that's even crazier. That's even, you know what? That's even more bizarre. The reason being, folks, is that that's saying there's no fear, and if there's no fear when the market has just gone down 55 points, 55 S&P points, that is saying quite a bit, meaning that's when markets can go south. Down, down industrials right now, down 142, we get the NAS, that got 44 S&Ps off 11 1 1⁄2, they're right there folks that come right back. Well, that man Mr. Tim Ward. 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If you're not satisfied, let us know and you'll get a full refund within 30 days of signing up. Subscribe to the Fibonacci 24.7 newsletter today. TFNN.com, educating investors. Now toll-free at 1-877-927-6648 internationally at<PHONE_NUMBER>. Welcome back folks to Dow. Dow investors right now trading up $145, we get the Nasdaq up $53, S&Ps are up $12. Let's get over to our mam, Mr. Tim Ord as we do every Tuesday and Thursday, and remember folks, you can reach Tim every trading day at www.Od-Oracle.com, that's www.Od-Oracle.com. Tim Ord, what's going on? Well, I sent you over actually four charts and let's take a look at chart one. Okay. I have it. All right, which is basically the SPYs and I have two shaded areas there that are pink. Anyhow, the first one I have open gap that day was September or July 12, and that day had 91 million shares. So when that was a gap up, so when I sent you over this chart, we didn't touch that gap yet. Well, in our day today, we did touch that gap, so the day is not over yet. We got about 70 million traded right now, but if we hit less than, well, 10% less than 91 million shares, which is what 81 or 82, somewhere 82, 83 million shares, then that would be a test of a gap on lighter volume and that would be what might be gonna be support. We also had yesterday, we had a trend close to 1.17, I like to see 1.2, it's not written in stone, but around 1.2 seems to be enough panic in the market to really suggest the low, but last Friday we had a trend 1.79, pretty close to the same area. So if you notice, when these trends get in panic areas, they all kind of do it in the same area. Yes. So that's what's happening here. So we went down and broke a little bit below Friday's low, but we got panic again. And I like to see the trend or the tick rather close around at least minus 200 preferably lower than that. And last Friday we had 414 and yesterday we had 208. So not quite as much panic, but still panic, so I'm thinking we're making a low, I think today. Again, and when you're saying the ticks, you're looking at the closing tick, right Tim? Yeah. So it goes, when everything jailed, interday, I've tried studies in the past. Yeah. We used to have two of them. You get the first one, you know, within three days, 70% of the big one, right? Yeah. Right. Okay. All right. So yeah, these are all in a close. And so yesterday's volume picked up a little bit when I put my bicycle out yesterday. I didn't know what the volume's going to be. I thought it was going to be a liar turns out to be a little bit heavier and that implies yesterday's low is going to be tested while it is testing. We also came, didn't touch the gap yesterday, but today we did touch the gap, even though this chart doesn't show it because it's interday, but we touched the gaps on concluding probably today's volume is going to be liar, 10% liar than the gap volume.  So I'm thinking during expiration week, I don't know if the rally really starts tomorrow or Monday, but probably expiration week, we're going to go up and test the upper gap, which is the August second gap. And the same thing will apply if you test that those gaps on lighter volume, I didn't write down the volumes, but you look like close to 100 million shares in that range. So we go back to 454 and volume drops say to it, 90 million shares, that gap's going to be resistant. So I'm thinking, this is what's going down right now is in A, we go up to a B, then we go down to a C is what I'm thinking was happening here. It's quite a sell-off today, man. I mean, we had a spread in the S&P of 60 points from highs to lows. Yeah. Yeah. I know I was close enough. I thought we'd go test the gap and here we are. I might got the closing low. The day before, it wasn't really that much room because if we do go to 454 and find resistance, it's only about a percent up. And to me, that was not worth the risk if I got to more percent than I would take the trade. Yeah. And if you take yesterday's close and we do get to 454, that's at least 2%. So that's worthwhile. All right. So anyhow, that's what it looks like on a short-term basis. So I think we're making a low, probably some sort of rally during expiration week. We get up to that gap up above and we can't get through that, then we'll turn back down again. So in the more times you test the gap, the less resistance or support that gap has. Yes. Today, if we do rally up and we only touch that top gap one time and find resistance, we go down again. That gap won't be, that gap at July 12th won't be support anymore because it'll be the second time you test that gap and normally the second time you pass through. So we'll see how it works.  So it's not like a trending market, but so anyhow, it is what it is. Right. No, no, I get it. I get it. Trust me. I get it. Yeah. Which is what I think I'm in. Right. No. The, go ahead. I'm sorry. I'll go to the second chart. Yep. Or are we talking more about this? No, no, second chart. Let's do the second chart. Okay. So I have this. Right. This is a kind of, this is a bigger view of what's going on. And to me, it's bearish. The reason why the second window up from the bottom is the 21 day average of the trend. The next window higher is a six three day of the trend. And both those trends got into bearish territories here on this rally and those red line show the times when those trend readings got into bearish territories. Most of the time it came at close to highs, or right at highs, whatever. So in other words, when the trend stays low for a long period of time, it kind of implies exuberance, I guess. Okay. And the market needs to knock off that exuberance. So Zoe's mark goes from exuberance to panic, back to exuberance, back to panning. This goes back and forth over time. So if the market's going up and there's no exuberance, it's going to keep going. And once you get kind of exuberance, it's, this is time to be cautious.  So I'm thinking this is worthwhile high that's forming here, even though we may rally next week. We'll probably, we're going to rally next week, hit some sort of a high up around that 454 SPY range and probably go back down. And I'm hoping on the next decline, you know, we get panic. And like we said in the past, you know, for a bottom to form, you got to have panic. You don't have panic. It's going to go down until you do get panic. There's only place of bottom forms is on panic. My bottoms don't form just out of nowhere. You know, bottom forms and everybody's kicking and screaming. Yes. Tim, when, you know, it seems that the folks have been panicking very quickly these days, right? Yeah. Yeah. So I don't think this is going to be a big decline at all.  Yeah. But yeah, you're right. We're starting to see panic pretty close off the highest.  Just stay there for a second. We're going to get a quick break. Tim, I would myself, we're going to be right back folks. Our phone number is 8779-276648. The hours up 79 now is except 29 S&P is up five. We'll come right back. The gold report. As a precious metal, gold is still king. It continues to hold the most effective safe haven and hedging properties across the global major trading hubs of the London OTC market, the US futures market and the Shanghai Gold Exchange. The gold report. Tom O'Brien publishes his weekly gold report every Monday morning for subscribers, consisting of coverage of the XAU, HUI, GDX, the Dollar, Bonds, the South African Rand, as well as 25 different mining equities with specific buy-sell recommendations. The gold report. New subscribers get a 30-day money back guarantee so you have nothing to risk. Subscribe to Tom O'Brien's Gold Report newsletter now at TFNN.com. 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We have the Dow Industries up 58, Masik up 16, S&Ps up flat. We are talking with Tim and we have a long-term chat here of the S&Ps that we're talking about in the trend. Okay, go ahead. I'm ready. We're done with that chart. We can go to chart four, actually. Okay. We've changed. I haven't. That's pretty much all I think I need to say about the long-term chart. It got kind of exuberant up there because the trend got so low. Yes. Over an extended period of time. It needs to just adjust. So, nothing other than that, and I think the adjustment, you're right.  We got trend rings kind of high already. That's a really good sign.  So, that's a good point you made. But anyhow, moving on to chart four. This is the chart. And actually, it's the bottom window with the 50-day average of the up-down volume. And I circled in red at the times this indicator got below minus 20. The time it did, it was a bottom. It was a bottom in that the market quit going down, but it didn't go up right away. It went sideways. And sometimes it went sideways. Back in 2016, it went sideways for like six months. And we've been going sideways now because this happened about June 15 when we hit below minus 20. And pretty much we've been going sideways since about mid-May. So it's been, well, now we're mid-August almost. So it's almost been three months. The market really hadn't gone anywhere. And so, but what's important here is the market's gone sideways. This indicator has been going up, which is a positive divergence. But the rally really, from my experience, really doesn't begin until both those indicators, which next one above that, is a 50-day average up-down volume bass climb indicator. But the bottom one, once you get above zero, that's usually when the impulse rate starts. And when I sent you this chart, it was 2.84. And as I'm looking right now, we're on 1., this is minus 1.88. When I sent you that chart, it has minus 2.84. So we're off a close to zero. And so once this gets above zero and holds above zero, that is the time the impulse rate starts. That's a pretty cool indicator, Tim, as the aspect. Because what happens, folks, when we go sideways, sideways many times is building cause for the next move. So it frustrates everyone. We know that, Tim, right? Yeah, you're right. It would be going sideways, but these internals are actually getting stronger. Because if they were weak, they'd be holding down close to minus 15 or something like that. They're not. So it's kind of a workshop move to the downside. And the mark's gone sideways. And you're building a base here. And this chart kind of shows that. So the blue shaded areas, I didn't shade them all, but just the major ones. The blue shaded areas are the times when this indicator is above zero. Okay. You can see all those shaded areas are when the GDX was in a rally mode. And sometimes these rally modes last just a couple of months. And sometimes they lasted quite a long time, especially the one back in 2019. That rally lasted over a year. I don't know how long this one's going to last. Right. But I'm thinking we're going to rally above the previous highs on GDX, which is 36. So I'm thinking there's a trend line up there, too. So let's wait and see. But this is by mean, it's not bearish. It's just bullish. So and everybody's kind of, especially we get authority people, you know, the gold market is dead and all this other stuff. Oh yeah. That's kind of the feeling of what the market is doing. Right.  It's going to be interesting. What's going to, how is the ratio on the VIX coming out? Because this is so weird today.  So it's like, you talk about, that's almost saying there's no fear. It's so weird. Like the VIX is trading 1557s. That's what you want to see. You know, when the S&P said, well, we're actually just touched, we're closing low right now. You went, you went actually, when the market's going down and the VIX is not going down, it's bullish. When the market's going up and the VIX is going up, that's bearish. So the VIX right now is kind of saying the same thing. The VIX is, yeah, we're up. We're pretty much unchanged from yesterday. So far as the market's not saying a lot, but the VIX is actually lower now than it was several days ago. If you notice that. Yeah. No, I know. That's, I mean, and you know, I'm looking at a market that just sold off that many points. That's my point of it. I guess it's like, okay, that's pretty wild. Yeah. Yeah. The VIX gives quite a bit of information. So I watch it. I do stuff with, you know, the trend. I'm so familiar with it because I've been looking at it for quite a long time. Yes. And the VIX is not new to me. I'm just trying to figure out some new indicators with it. Right. You hear me? Interim indicators that work really well. And, and I noticed that if the VIX is going with the market, you know, if the VIX is going down along with the SP, that's bullish because the VIX should be going up when the market's going down. Right? Yes. So, and if the market's going up and the VIX is going up with it, that's a divergence too. That means the market could turn back down. So if you notice again, you know, last, you know, we're about, I don't know, this last four days, you know, five days ago, one, two, three, four, you know, five days ago, the VIX was over 17, looks like on a close, now we're 16. And the S&Ps is pretty much setting out as lows. Right. So that to me is bullish because the VIX should be higher. I get it. Okay. Cool. Okay. So, so it's kind of confirming that probably, you know, and then we're hitting this gap today. And that gap again is 91 million shares. So I like to see volume 10% lighter because when you test the gaps like testing previous high on 10% lighter volume, gas works the same way. If you test the gap on 10% lighter volume, that gap has support. So we're going to come in, we started, let's see, we're at, well, we're at 75 million now on the spy. So we're going to end up with. Okay. Yeah. The July 12th is where that gap formed. And that had 91 million shares. I'm looking at the SPY. Yeah. No, that's what I have up. Cool. Okay. Okay. I get it. Right. Right. Yeah. So, so you want to come in, you know, nine million shares less. So it'd be what 82 million give or take. Yeah. Even 7% liars is pretty good. So we're probably going to get over that though. If it's 75 or ready, we get, you know, 20 minutes left. But the last 20 minutes, they'll throw 20 million in there. We're probably going to come in about 90 million. So that's going to. All right. Then that would probably mean today's low could be tested again. Again. Okay. Okay. So that would, it was a left way to see. So. Well, listen, it's always a pleasure, Tim. And folks, it's real easy to get hold of Tim more. 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This program is brought to you by Vista Gold. Traded on the NYSE American and TSX under the symbol VGZ. Brian! Welcome back folks to our Dow. Dow investors right now have 25 and ASX up, four SAPs are down, five and a half. And folks, as you come over to our website at TFNN, right under featured content, you're going to see our man Mr. Teddy Kegstad. He's going to be doing a candlestick pattern stock and option strategies coming this Monday. So check it out on the front page. It's going to be 4 to 5 p.m. What this is going to be, he's going to be talking about, he has a book on candlestick charting, folks. Specifically, it's going to be candlestick charting. Candlestick charting, where you should be getting in and where you should be getting out. So check it out on the front page of TFNN right under featured content and the bottom line is that it costs only $97, folks, okay? Right on the front page of TFNN. We get over to the markets, we take a look at the markets and the bottom line is that they are going after the close, after the lows rather. We take a look at them, they just went all negative. So they're going after it. Bottom line is that this is quite a move, man. We haven't seen something like this in a long time, man. What we also had in here, folks, is that we also had jumps of here. I'll show you these jumps because this is how the market was reacting three or four months ago.  Then we had the big bounce off the bottom that it went from 44.73 getting up to the 45.01. That's trading from like three or four months ago, man. Volatility, folks, is your best friend if you're trading.  Well, if you're investing, just don't look at the market. That's the easiest thing to do. That's the bottom line. But this is, when I take a look at this, it's like, okay, let me put the spy up for a second. Let me put the dollar up because it's the dollar that's throwing a wrench into this thing and the dollar looks to me like tomorrow is going to be a heavy day, man. It looks like to me they're going to blow gold out of the water. See this move on the dollar. Well, this is how this works, actually. Oh, look at this. This is going to be intriguing. So see this move here on the dollar. They rejected 101784 and were 102.629. Now, this is intriguing because Teddy's doing the workshop on candle six. This actually is a hanging man. Now, it did reject lower price, but it's not a hammer because hammers come at the end of downdrafts. This is certainly not a downdraft. That's been going up there. So now the question's going to be, okay, was it just a rejection or are you going to have an ABC up? My take is that we're going to actually have an ABC up. And if that's what we get, we're going to get some real action here, man. And that ABC up is going to bring you not to the first swing point, which is 108.156. No, I'm sorry, not 108.102.224. Not there. It's going to bring you up to the next one, which is that 104.699. That's how that seems to be checking out. And then it's like, okay, you're not too far away from the 105.883. But let's just see how many, let's see. That's 102.800 to 99. So it's approximately just right of three, which would get you to about 104. What's that up there? Yeah, we're going to that 104 up there, man. That's where we're going. And that is going to raise, I think that's going to keep selling pressure on the market. And as I said, as I started the program today, what we have seen, we saw it happen today. We saw it happen yesterday. And I forget whether it's Thursday or Wednesday. Let me look at the S&P's for a second. This is the third day that surprises came out. Yeah, here it is right here. So yesterday, oh, it's more than that. One second. Let's see. Let me put this back. Ten days. Okay. So, yeah, here it is right here. So let's see. This is Thursday. The surprise that down side happened today. Surprise that down side happened yesterday. Yesterday is Wednesday.  Same deal. So it was yesterday, today, and last Friday. You know, that's when, to me, that's when trends have changed. That, you know, when you have surprises to the upside, you're in a bull. When you have surprises to the downside, you're in a bear. That's kind of how it works. And what I mean by surprises is that they come out of nowhere. I mean, that won yesterday. You know, I want to thank Basil for doing the show, but that came out of nowhere, man. That was like, boom. And the one today was the same way.  And then someone came in the market and just croaked them. I mean, you can see this thing. There was no volume up there. That's for sure. That top tick only had 71,000. We came off it with, well, the first bar wasn't bad. The first bar was 49,000. The second bar was 58,000. And then see you later. Then we went up to 84,000. And, yeah. Let's go take a look at the silver market. So the silver contract's laying right at the lows, too. Yeah. This is telling me on a Friday we're going to break this thing, man.  Because where the contract itself says 2277, 2254, 2234 is the low. The strength is the top of the strength is 2239. The bottom of the strength is 2110. So if I go to the SLV and take a look at that, you're going to see the swing low is 2066. And the problem is that where the strength actually comes in is way down here. There's a lot of strength, but that's the bottom of that is 1911. And we put this one here. My point is coming in on a Friday, that's where this gets dangerous, meaning even in the broad market, because if that dollar continues to go high, you can see the dollars tick for tick today. That's the bottom line. Inside the NDX100, the strength versus the weakness out here, you had, what is this? Let's see. Oh, trade desk is down 4.5%. Lucid automotive is down 4.3%. You got broker, oh no, Baker Hughes. Baker Hughes is down 2.9%. McConnel Libre is up 5%. Datadog is up 4.5%. And Zscaler is up 2.4%. Dow. Dow Industries right now trading up 41. Nasdaq is up 11. S&Ps are down 2. We'll come right back. Tom publishes his Daily Market Insights newsletter every market day before the market open, along with updates when warranted. Stay ahead of the game with Tom's real-time analysis and trade recommendations delivered straight to your inbox.  Ready to join the ranks of successful traders? Head over to TFNN.com and subscribe to Market Insights today. Don't miss out on this opportunity to supercharge your trading results. Market Insights comes with a 30-day money-back guarantee for all new subscribers, so you have nothing to risk. Don't miss out on this opportunity to revolutionize your trading game. 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If you're not satisfied, let us know, and you'll get a full refund within 30 days of signing up. Subscribe to the Fibonacci 24-7 newsletter today. TFNN.com, educating investors. Don't forget, you can listen to TFNN live on your mobile device 24 hours per day. Go to TFNN.com, then hit Watch Tiger TV. That's TFNN.com, then hit Watch Tiger TV. Welcome back folks to Dow. Dow industry is up 43 and has like 15 S&Ps down one. And folks, if you live in Florida, right, you know, we've talked about this before, citizens insurance, right? You're going to hear a lot about this in the next couple of days, because now citizens insurance, and that's the state nonprofit, is the biggest insurance company in the state of Florida. And I suggest that you, if you're getting insurance bills, right, from a regular private insurer, they're insane. They're like, yeah, I'll just give you with the difference. The difference is, let's say in a $500,000 house, you'd be paying like $10,000 to $11,000, right? Versus a citizen's policy of $3,700, OK? Now, this is what I want you to go through, because you're going to hear this, that policy holders themselves, OK, could be on the hook for more money if, in fact, we get a catastrophic hurricane because they're the biggest. Well, you're only on the hook. This is how this works. You're only on the hook for 40% of the premium that you paid, and then you could do an additional 50%, 10%. So it'd be like, just put 50% in there. Well, I can tell you, I insure plenty of places, right? You can put 50% into the premium you're already paying, OK? And that's still going to be half of what the insurance costs of all these other insurers. And, you know, the bottom line, because it's the state behind it, you're going to get paid. So, you know, you're going to see this. It's just coming across the Bloomberg right now. Florida's last resort. Property insurer is now the state's biggest, and inside of that there, I'm sure folks are going to get, when I was just reading, it says, oh, I'm on the hook for what? But when I just did the math behind this, it doesn't matter. It really doesn't matter. That's the bottom line. And it has to do, you know, unfortunately, in the state of Florida, there's so much fraud. It's disgusting. And that's what hurts all of us, and it's discontinued. Go from car fraud to freaking house fraud, and, you know, and, yeah, it's not good. It's sad, actually. Always remember, folks, the bank and claw your hideout, the bull can run you over, and thank God, there's always another trade. Health, happiness, and prosperity. Have a great night, folks. Have a safe night. Come back and visit Tommy tomorrow morning. There's going to be action just like today, folks. 9 a.m. Real! Well, get him, folks.