From DailyDoseOfWeirdNews.com, I'm Darren Marlar, and this is your Daily Dose of Weird News! Victoria Beckham has trademarked the name of her 5-year-old daughter Harper 7 Beckham. She's not only growing up entitled, she's also now trademarked. The crime problem in Chicago continues where 29 people were shot in less than 18 hours over the weekend. Yay, Chicago! We're number one! The Chick-fil-A in Folsom, California is asking that whoever stole their cow costume last week to please return it, don't be chicken about returning it, they have no beef with you. Sylvester Stallone is suing Warner Bros. for allegedly cheating him out of profits from his 1993 movie Demolition Man. I don't blame him, I mean here we are 24 years later and Sly is just barely making ends meet financially due to not being able to find a job. The Montana State University professor is suing Walmart for libel after he says an employee at the Bozeman store listed his occupation on a fishing license as a toilet cleaner. I got nothing for that, I just think it's hilarious. A three-year-old Missouri girl asked her parents for a poop-themed birthday party.  Uber says its bookings doubled in 2016, but it still lost $2.8 billion. Have you guys considered maybe taking an intro to economics class? Nevada has introduced syringe vending machines to fight the heroin crisis there. So you're fighting heroin by giving people easier access to tools used to do heroin. Oh yeah, I don't see any problem with that plan. It took New Supreme Court Justice Neil Gorsuch less than 15 minutes on his first day to start asking questions. But the other justices didn't mind and quickly told him how to find the restroom. An eight-month-old baby in India has doctors baffled because he's already 84 pounds. I've seen pictures of the kid. Has anybody questioned the Michelin man? The Navy is banning e-cigarettes from ships and aircraft because of reports of exploding batteries. As a side note, all e-cigarettes now come with free Samsung Galaxy Note 7s. An engaged couple was forcibly re-accommodated from a United Airlines flight over the weekend on their way to their own wedding. And you gotta wonder why anybody is flying United at this point. I mean, it's kind of like you deserve what you get now because you should have known better. A Texas woman trying to retrieve something out of her toilet ended up getting her hand stuck. So stuck, in fact, that police had to remove the toilet and carry it and the woman to the backyard. Police then used a hammer to gently break apart the toilet. Pen. Some people just cannot let a dropped toothbrush get away. White House Press Secretary Sean Spicer says President Trump won't be releasing his 2016 taxes because of an audit. When asked if Trump will ever release his taxes, Spicer said that he'd have to get back to the press corps in about eight years. If you liked this video, please give it a thumbs up and be sure to subscribe if you want to see more, and click that little bell next to the subscribe button to be notified when I post new videos. And if you're already an official Weirdo, be sure to leave a comment and say so. Hey, have you signed up for the Marlar Sheet? It is the official newsletter of Marlar House. Subscribers to the Marlar Sheet are automatically entered in monthly prize drawings. Sign up for the Marlar Sheet free today at MarlarHouse.com. For more weird news anytime, visit DailyDoseOfWeirdNews.com. I'm Darren Marlar and I'll see you next time, Weirdos.


Hi everyone, Joe for Jaspy's casebreaks.com with little filler right here. We're doing a prism football Fat pack or value pack break number 26 for those Josh Allen mixer spots being given away But let's do the break first With our first ice roll big thanks to this group for making it happen. Appreciate all of you All 32 are in let's roll it. Let's randomize it four and a four eighth the hard way one two three four five six Seven and eighth and final time after eight. We've got this group in this order right here And then four to four eight the hard way for the teams Joe cook. What's going on? two three four five six seven and eight Well, there's there's a block from Victor women young we got Rams down to Jets May or may not be have some financial interest in how many blocks and steals women young may or may not have Turn to that sort of thing. Here's the first half of the list right there There's the second half of the list Right over here Luca was so so close to what did Luca do something special today. All right So here's the final print out right here hot off the presses So John good to be back and the spurs here actually keeping it close They're only down by eight for the minute left in the game and nice Anthony Richardson rookie card Kurt Warner and Another Anthony Richardson nice pack Joe cook and the Colts You got the red white and blue exclusive To this right here Exclusive to the set and you get the base rookie card Not bad and a chance to get mixer spots All right, so let's hang hang on here. Let's go back to the list. Let's gather everybody's names one through thirty two New dice new list and it's just the top four that will get in top four after Seven we'll get into that mixer one two three four five six and Seventh and final time So from five through thirty two Sorry, Joe, but five through thirty two sad times sad times everybody here But we do have to do multiple fillers of these so if you don't make it this time, maybe try again next time We'll get them next time Top four though happy times for you After seven You're in the mixer Nicole you're in so as Alan Barry and Christopher Congrats to the top four. We'll see you in that mixer video a little bit later. Jasper's case breaks.com


Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer for Data Diversity. We want to thank you for joining the latest in the monthly webinar series, Data Architecture Strategies with Donna Burbank. Today, Donna will discuss building a data strategy, practical steps for aligning with business goals, sponsored today by Digital Realty and Monte Carlo. 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 panel. And if you'd like to chat with us or with each other, we certainly encourage you to do so. And just to note, the chat defaults to send to just the panelists, but you may absolutely change that to network with everyone. To open the chat and the Q&A panels, you will find those icons in the bottom middle of your screen to enable those features. And as always, we will send a follow up email within two business days containing links to the slides and recording of this session and any additional information requested throughout the webinar. Now let me turn it over to Dan for a brief word from our first sponsor, Digital Realty. Dan, hello and welcome. Thank you, Shannon. And thanks everybody for joining. My name is Dan Eline. I'm a senior director of platform planning and solutions, Digital Realty. For those of you who don't know us, we are a company that sits in the infrastructure side primarily in the data center industry. And so you might be wondering, what do we care about data? Well, actually data has a really real impact on our business, right? It's kind of the data in data center means that the more data you all are worried about harnessing and generating, capturing, generating new tends to sit in facilities like ours. So we're very fascinated with the concept of data. And we do a lot of time, you know, talking with our customers, talking with folks like yourselves in the industry and studying what's going on. We're constantly reading about what's going on, right? So the growth of data, looking that, you know, sort of between 2010 and 25, the data has compound annual growth rate of 535%. But, you know, that's something for us to think about where we're going to house all that data. What are people doing with all of that data? To that end, we've actually, we put together in 2022, something that we called the Global Data Insight Survey.  And we asked them 13 questions. And these folks sat across nine industries in 23 countries, and they were companies sized between $100 million and $1 billion. And we said, you know, what is kind of important to you? What are you thinking about data? What do you see happening in this world of data? And so, you know, the kind of the highlights, the things that I thought really stood out to me that I wanted to share with everybody here. And I suspect you're probably in agreement, or you wouldn't be attending a session like this. But, right? Data first strategies are important to everybody, right? 76% of the respondents plan to use data to improve customer experiences and build new digital products, right? So it's the concept of taking data that sometimes we already have laying around, and creating kind of an industry or a new product around it. The distribution of data is increasing. So whether it's because we're, you know, regionalizing or we're globalizing, you know, growth of business regulatory demands, trying to solve for application performance issues, right? That data is getting distributed. It's all over the place, right? You know, localization is the other side of that point. There are times when we need to have copies of data resident, whether it's because we're, you know, bound by particular regulatory concerns, whether we have an application stack or workflow that requires the data to be in a specific, you know, distance to user distance to application to perform well. And, you know, that's driven by latency, right? So we keep a big eye on latency. And obviously, you know, distance is the largest effector of latency.  Where can all that data set globally? And, you know, so for us, that being the business that we're in, we say, well, all right, like, let's, you know, let's think about what that means. So when we think about that meeting place for data, we say, well, where does it need to be? That leads us to another study we do. As I said, we're big into kind of researching and thinking about what it all means. We put together something that we call the data gravity index. And we've released two additions so far. We have another addition coming out here shortly. But the point of the data gravity index is to take this concept data gravity, which was coined by Dave McCrory, who now works with us at Digital Realty. He says, you know, data has this, there's, there's kind of mass, right? And there's a draw that it creates. So gravity is a great analogy for this. We said, well, that's, you know, awesome, Dave, can you help us predict where that's going to be? And that's exactly what the data gravity index is about. It talks about where we see the largest growth, right? Where are these centers of data? And in the forthcoming release, you know, we're going to talk a lot about the relationships between these locations and, and look at the splits across industries, etc. Because it's not only going to inform us about where we need to build these data centers, but we hope it will help inform you, you know, when you think about how you address your business. And because as much fun as problem admiration is, we also want to be part of the solution. And so we take our platform digital, as we call it, our portfolio of data centers and services. And we say like we use those to create a meeting place. And we encourage folks like yourselves to use that as a meeting place, because part of your data strategy needs to think about how do I get the data where I need it to be? How do I host the applications and services where I need them to be, to support what the workflows are that are important to me. And so, you know, in the short time I have today, I just want to let you know, you know, we have this out there, I encourage you to come and take a look around our website, take grab a copy of the data gravity index and take a look at, you know, what it might mean for you and your business. So thanks, everybody, for enjoying, for joining today and do enjoy the show. I'll be around at the end for questions. And thank you so much for kicking us off and for, and if you have questions for about Dan or about digital realty, you may submit the questions in the Q&A portion of your screen as he'll be joining us in the Q&A at the end of the webinar today. And then let me turn it over to Shane for a brief report from our second sponsor, Monte Carlo. Shane, hello and welcome. Hi, thanks for having me. And it's great to be here. So I'm going to talk about briefly Monte Carlo's data observability platform. I'll start with just super high level. So introduction to me. I'm the field CTO of Monte Carlo. Prior to this, I was the head of data at the New York Times where I spent most of the past decade and oversaw a group of about 150 people in that data organization. Monte Carlo, though, the role I've been in for the last eight or so months, we're essentially building trust and reliability in an organization's data in order to drive the adoption and value from from data products within those organizations. And we are the creators of what's called the data observability category. And you can see there a number of customers we have. I'll explain more of kind of first outline the problem we solve, and then I'll explain what observability is and does. So we start with this concept of data downtime, which maybe you aren't familiar with the name, but I'm sure everyone here is familiar with what it is. Data downtime is periods of time when your data is partial, erroneous, missing or otherwise inaccurate. And, you know, I certainly as a data leader saw this across any number of kind of data products or data assets we were managing for the organization, both kind of machine learning products that had to be available up to the second and we used to drive the revenue of the business, or, you know, more kind of BI and reporting products that were made available to finance or to the newsroom or to product teams that maybe didn't need to be as timely but needed to be accurate and consistent. And really the problem we see with data downtime is that the data producers, the software engineers that are owning the source systems can't see downstream, can't see who's affected by changes that they're making to their systems. The data consumers or their analysts and data scientists can't see upstream. They don't know where the problem is occurring. And very often the data engineers or the data platform teams are stuck in the middle trying to predict all the ways that data will break. Typically, this has been solved with manual testing approaches. And what we've found is that manual testing really only covers, you know, 10% of the types of data reliability problems a team can run into at scale. This is sort of predicting the percentage of nulls you should have that sort of thing. And invariably what happens is your downstream consumers are still finding the issues, telling you on Slack that they've found them and alerting you to the problem. This I'll be super quick. This can range from trivial to existential. We see data teams have about 70 high severity incidents every year across 101,000 tables, I should say, and about 30 to 50% of data engineering time is spent on fire drills. So how does Monte Carlo look at this? The benefit or the good thing we've found is that data downtime looks quite similar across companies. You're asking these questions, is the data up to date? Is it in the right size? Is it complete? You know, all these sort of questions around schema, around shape of data and around the quality of data. And so what we did is create these five pillars of observability, which are freshness, volume, quality, schema, and lineage. And those are the things that Monte Carlo uses as its foundation to understand through metadata, through logs, through metrics, the nature of the specific data products that are quite variable across a kind of scale data environment. And then so what, in fact, is data observability? It's actually using this capture, the logs, the metadata, and the metrics around your data to detect, resolve, and prevent data incidents at scale. And so the things this comprises is on the detection side, we have, for example, machine learning powered anomaly detection on things like freshness incidents, volume, and schema changes. We also have ML powered anomaly detection on things like quality and distribution of data. And then in the resolution side, we provide the tools to understand the impact radius of a data incident, understand the field level lineage so you can trace back upstream where the incident occurred and also provide all the context necessarily to quickly resolve an issue. And finally, there's tools we have in the prevention bucket, which is really about getting ahead of data incidents in the first place through things like circuit breakers. And I will stop there and hand over. Jane, thank you so much for this great presentation. Again, if you have questions for Shane, he will be joining us in the Q&A portion at the end of the webinar, so feel free to put them in the Q&A portion of your screen there. And now I'm going to do is the Speaker of the Monthly Series, Donna Burbank. Donna is a recognized industry expert in information management with over 20 years of experience helping organizations enrich their business opportunities through data and information. She currently is the managing director of Global Data Strategy Limited, where she assists organizations around the globe in driving value from their data. With that, let me give the floor to Donna to begin her presentation. Hello and welcome. Hello, Shannon, thank you. It's always a pleasure to do these each month and then see some familiar names in the list of attendees and thanks for the sponsors for those helpful overviews. If this is your first time at a data diversity event or this particular series, I just want to call it that this is a series. You know, this is interest to you. There's a lot of other great topics. Or if you missed some in the past that look interesting, like on some of the emerging trends and architecture, all of these are recorded on the data diversity site. So you can always catch these later. So hopefully you can join some of these other titles as well. But why are we here today? Data strategy is definitely a hot topic. Some of the points that the speakers earlier brought up as well. And it's in our name is the company that I work for. So, you know, have a lot of experience with that. But I think one of the biggest things that we hear with a strategy is that it just seems overwhelming. Like I think, you know, everyone, which is a great thing if we're in data that more and more businesses are understanding that data is important. But then, you know, how do I how do I really make that a strategic asset? And where do I start? That's probably the biggest thing. So as with all of these webinars, you know, data can get really complicated really quickly. What I try to provide is real world simplified templates and experience for having done this in the real world that can maybe help you get started. Of course, we can't cover everything. The strategy is big. But hopefully this this webinar will give you just a few points to think of it. There's just one thing you go away with that you hadn't thought of before. I feel like I've done my job. So, you know, as I mentioned, you know, unless you've been living under a rock in which case I'm jealous in this current world, maybe hide, you have certainly heard about this idea of the data driven business.  And even the CIO Journal is talking about data driven business, not just tech, right? So I think, you know, that that's a great place to be if you're in data where the sexiest job of the 21st century if you haven't heard that quote. So that's a great place to be. And if you are a data professional who is looking to more have that business perspective and literally have a seat at the table, I think this is a great place for you. And I think that's where data strategy comes in. You might have heard me say this in a previous webinar. You know, my first, my first degree was in economics and I thought I was going to go and be this great, you know, the business leader and all of that. And then I found data super interesting and felt like I sort of had to choose. And now you don't anymore, right? Because so much of business is data driven, I guess in a lot of ways it always has been, but and more and more new business opportunities are driven from data. And, you know, we at our company work with a lot of C-level folks, a lot of business folks. And I know that they are and we can often fill that gap. But we need more folks at the table to really help them understand the complexity of the data, but in a very simplified business-centric way. You know, because this can get complex. So how do we flip the script a little bit when we're talking about data architecture and data management and really make it more of a data strategy. And that's what we'll talk about in this session. It'll be a little less techie than some of them because it really gets down to this. What are we talking about when we're, I get this question all the time. So I try to get a lot of the questions that I'm asked all the time and put them in some slides for folks. What's the difference between a data strategy and data management? Haven't we been doing data management or data architecture for a long time? I don't get the difference. And so I went back to just the dictionary. It's probably the best way, just the meaning of those words.   Isn't that self-describing or conducting or supervising something, right? So yes, that's the managing day-to-day. But what it makes it strategic is that business vision. We have goals. We have drivers. And a data management is a part of a strategy, just like anything else. Am I just, you know, managing my finances or do I have a strategic goal for my retirement? Right. That's almost the basic definition of the word is really how to think of a data strategy versus data management. And I think for folks on the call who are technical or maybe data architects kind of putting your business hat on when you do a strategy, I think is a key part of that. Of course, there's a technical aspect in the platforms and all that, but the so what is the big part of why we're doing this? The other question I get a lot, and I can kind of relate to this, but what is it? Like my boss just said I had to build a data strategy, like what's the deliverable? Like do I write a document? Is it a, you know, a PowerPoint? Is it a joke about interpretive dance? But I actually had a client that I was getting her master's in data science and one of the deliverables at the end, you could either write your summary of your or you could do it in a poem or a dance. And I kind of laughed, but she was doing a lot of statistics on social science and homelessness and things like that. And I think what they were trying to get them to do is you understand the people and the issues around the data. It isn't just numbers as human beings. And she actually wrote a wonderful poem that brought a tear to my eye about her data. And I think in a way that's kind of maybe a slightly facetious example, but that's what makes your data strategy sing or dance as well. Like, do I have the business view of this? That said, if you do have this deliverable, I am a bit biased and I always say, put it in a PowerPoint. That may not be your only deliverable, but the nice thing about a PowerPoint is consumable and do it like you're going to present to an exec. If you can't say it in 20 slides, you know, it's not simplified. It really helps you crystallize your vision. I just, we've come in too many times with strategies that are well thought out, but it's like a 50 page document and it's on the shelf and no one looks at it. You know, that may be a deliverable you need. I know a lot of government agencies, a lot of universities do publish kind of their five year strategy and it's in a document that's been thought through. You just don't start there, right? You need to kind of sell this and part of your job is always marketing, right? You need to really make it, you know, understandable. And that's hopefully what this webinar will help you do. You know, you're back to data really driving the economy. You know, this one was passed to me actually from a client a little while ago. And I really like it because this is the world economic forum, right? This isn't dataversity. This isn't Dama. This isn't data people saying the data is important. This is the world economic forum saying it. What they're saying, you know, seems like a long time ago, but really wasn't just in 2013 when you look at the companies that with the highest market cap, they showed things or their focus was things, you know, Walmart, Exxon. And in 2028, really the focus is data. And yes, Amazon's still there. But when you think of it, you know, they sell things but they're very data driven, their recommendation engines are focused on data, Alphabet, Google, Microsoft, you know, they are data companies and they're saying that actually today's economy data is the valuable asset, not so much the tangible physical objects. So I found that really interesting and something to think about when we're thinking of data strategy. Another thing I like to kind of the balance when we're talking about a data strategy and neither one of these is right nor wrong, but just think of your organization. There's a difference between business optimization and being a data driven company and business transformation or being a data company. Now, what's the difference? I would say an optimization is kind of doing what you do but do it better. And you could argue, hey, we've been doing this for centuries with data, that, you know, how to be more efficient by either, you know, removing manual efforts to either manage data or business, you know, processes that are inefficient because again, I don't have the right data or can we understand our customer enough to have better marketing campaigns or, you know, understand our product usage, et cetera, et cetera, et cetera. But, you know, many companies are now where data is the product or monetizing your product or even within your company is an entirely new business model. We're going digital and data is the business. You think of an Uber or he has is it a taxi company or is it a data company? Right. And I am working with several companies that, you know, it may be even to mitigate some risk of a recession or if your industry is up and down, is there data that you can monetize within your organization or new business models driven through digital or data they think of. And again, maybe it's both, maybe you're trying to optimize your business or transform your business, but, you know, kind of different ways of thinking about it, which makes, again, me excited about being in data because you understand that, you know, if you're doing a data strategy, it should be driven by your business strategy. It's not done in a vacuum. That's what makes it a strategy and not data management. But more and more, what I find exciting and the data can drive your business strategy, right? What data do we have? What data can we optimize? How could we, you know, do more things with data to drive our business? So how do we turn that into a strategy? This is the framework we use and you may have seen in other presentations. What I like about this, and we've gotten some good feedback, it's just sort of almost a checklist of things we need to do. So definitely from the top down, what's our business? How does that align with data and vice versa? How do they feed into each other? But also from the bottom up, what are we talking about? Is it everything in a relational database? Is it big data streaming, documents, content? You know, there's a lot of things and then moving up the stack from how do we secure it, integrate it, and metadata around it to, you know, how are the maybe more leveraging that for strategic advantage? Do we have BI and analytics or machine learning? Is the quality of good, good, you know, source? Do we have a single view of our customer or patients or students through master data management? All of those are important as well. And then the key glue, I think, that level two that kind of links the business with the tech is that data governance. And I'm a big fan of calling it governance and collaboration, right? Because that's really the way to collaborate between IT or tech and the business to really make this a business focused event. Do you need to do all of these things in the end level of detail all at once? Absolutely not. And that's another part of a strategy that I, and we'll talk about it towards the end, which is a roadmap for execution, right? A strategy might seem theoretical, but it's absolutely not. What makes a successful strategy is that roadmap for execution and then re-strategize, you know, another question, the, you know, how long should this take? They should be fairly short and sharp so you can get, if you're doing strategy for too long, you know, it shouldn't be here because you're together, right? You know, make it a month, two or three months, and then move on and start, you know, tweak it over time, but really get to the meat of things. The other thing to think of, and again, I'm throwing a lot out here in a fairly quick period of time, but again, if you just have a few things that help you tweak what you're doing it and maybe frame what you're doing than I've succeeded in this webinar but, you know, are you offensive focused, you know, on, is it creativity and profitability or is it more on, you know, defense and think about and there's probably a combination of both kind of that purple middle, but when you think about because at the end of the day you're going to be selling your strategy, right? You know, are you a very compliance-driven organization, maybe insurance or you've just had a fine or, you know, it's healthcare and it really is more we have to reduce our risk, you know, aspect and we, we don't want to be there or are you a brand who start up and it's all about, you know, growing the profitability and customer satisfaction, you know, is that the messaging or is it somewhere in the middle but, you know, I've not that I've ever made a mistake but if I had, you know, it's, it's kind of thinking of your audience or even within your company, maybe how you sell to legal or finance might be different than how you sell this or explain this strategy is sales who might be more on the offense side, right? So something to think about is kind of that theme or the message of your strategy overall and then what whichever ones are probably a combination I would argue that almost every company has some aspect of that purple, right? Your bit offense and bit defense is just more kind of the level. What are those quick wins or the levers that you can where data can be that fulcrum, right? Because there's a lot you can do and you want to do a lot of things quickly over time that build rather than wait for that one year, you know, we're not going to give you anything into a year, right? So there's a lot of data you could manage is there a new marketing campaign that's coming up and we can get better customer data or location data or whatever.  He has a lot of things we could do. And I think, and again, this may be obvious, but when you're thinking of a strategy, what are those quick wins that are going to drive ROI and really drive business value towards the end? And when you think of what ROI or return on investment might be, there's a lot of several ways to look at that. There's a lot of things you could do. I've found that over time, generally, they fall into these four buckets and probably having something from each of these buckets as you're trying to make your case is probably valuable. So, I would argue, sometimes the easiest thing to quantify at the end of the day, because, you know, you're going to have to prove that your strategy has some value or why else are we doing this, right? Decreasing cost. There's always so much wasted effort and, you know, Shane talked about that in the beginning of, you know, for not checking our data ahead of time, you're going to be cleaning it up later and how much effort does that take to, you know, fix things or all your business processes and efficient because you don't have the right data at the right time and data quality and issues like that. That's a sort of an easier one to quantify because time or money or whether, you know, wasted mailing is sent to people with the wrong address. You know, some of those are really easier to quantify but also think of the revenue, the, you know, the carrot and we're not just always the stick, right? Do we have better pricing through analytics or marketing, better marketing campaigns, you know, et cetera or, you know, this isn't always about a for profit. We've got some customers, you know, how can we write better grants by understanding who our constituents are for a nonprofit, right? But sometimes a little harder to quantify, you know, well, so much we can talk about but you know, having champions for your effort, one of our strategies, we actually had marketing come with us to the presentation to the CEO and say, you know, we will commit to driving revenue by 10% if you can help us get a customer master data system because we can do a better understanding of customer. A little bit rare that someone's going to stand up to that level of accountability but they needed it. They knew they needed it and you'll probably find those champions in your business or, you know, maybe again, do a nonprofit. We can't write these grants if we don't know who we're serving or whatever it is, try to think of that increasing revenue. Also the risk don't always love to drive with risk. You know, but we have GDPR, HIPAA, you know, FERPA, all the other regulations but not just regulations, right? Is it, you know, I'm a product company and I don't have the right product information, you know food, I'm a food product and I don't have the right, you know, allergens on the website. I could get sued or do we just, you know, kind of to the one on the right, do we look bad?  So, so much more and then social media, people do have a voice to say things.  And so much is up, hosting on reputation and brand, brand trust, right? I'm a bank and, you know, they don't have my, my name right or my account number right. I start to lose much, should I be putting my money in this bank? So, you know, just think of those things, those different categories. Also, and this one is often forgotten, is there risk of doing nothing? So I don't, you know, almost every company has that inertia. We've been doing this for 20 years. You know, what, why do something different? Often, we come in with a company right where that, kind of that scaling point, you know, it might be a million, the multi-billion dollar company and they've been doing well. But in order to get to that next level, you really have to be investing in data management to get that next level. And that's often kind of hard to explain to an exec, well, tell me how to run my business. I've been successful. Yes, you have, but think of the risk of doing nothing and include that in your ROI analysis. You know, doing nothing has its own risk as well. This next slide and I'll be jumping around across a lot of things because the data strategy does, it's a big part of it. And I will be pulling off some of the research that I'm also kind of like Dan, a fan of statistics and numbers and proven the case, data person, right? But we do surveys with Data Diversity, a little bit of strategy partners with Data Diversity each year. And I love to reach back to these because this was a nice one I saw in 2020. When you're looking at the big things, folks are looking to implement in the next few years, which is now, it's data strategy, data architecture, and data governance. And I am not surprised at that at all because to me, those are almost the three pillars of success. That data strategy can't be successful without the data architecture, which is the key to make things drive. You know, if data strategy sort of sets the business vision and the roadmap for execution, think of your architecture as that technical foundation and your governance is the people process and culture part of it. Yes, there's also a technical aspect of governance. That's not lost to me either, but I think those are kind of the three pillars and I'll kind of touch on that in the rest of the session of how all of those really fit together. And if you're missing one, it's going to make your strategy a little bit weaker. This is a framework we use for governance. And again, it really kind of naturally ties into the idea of a strategy. If you don't have a good vision or strategy for why you're doing governance, it's going to be really hard to move ahead or that culture and communication. We'll talk a lot about that in this session around it. You're not going to have success. Everyone has the what's in it for me. Why am I doing this and why does it matter? That's a valid question. We can't answer why we're doing this. We shouldn't be. And that's really a part of a strategy is tying it intrinsically with the dripper drivers of the business. A lot of that's people, a lot of that's process. Data management is close to all of us and tools, of course. It's hard to do data lineages. As Shane mentioned, you don't want to be doing that one by hand, right? But that shouldn't be where your strategy starts. What are you doing for a data strategy? Well, we're going to buy a data lineage tool. Yes, you might. But then you got to frame it in the right way. What are we doing? Why are we doing? Who's doing it? How's it going to affect things? And then one of the means to an end will be a tool to do that. Another thing to think about when you're building a data governance, I'm kind of in this first pillar here with an organizational framework is how you organize your data governance structure and it really should match that culture because they're driving that business. And think and the one on the right is one method of kind of developing a data governance framework where you may have a exec leadership. They're really going to be driving your strategy and the steering committee are really going to be developing that plan. Often your strategy is kind of driven by the steering committee as well as the execs but also kind of that in the weeds, governance committee doing the definitions, work groups, et cetera. It's a very common way to kind of put things together but not the only way. Are you a more federated company? Are you more agile? Are you more driven in different ways? And maybe you don't need all these committees. Maybe you need one or maybe you need just some working groups to begin. That's another way we've seen that can either make a strategy sing because governance is so intrinsic to the way of working and the way your company runs that you almost don't, you know, it's just part of the DNA of the company. Or it feels forced. You know, maybe this is too hierarchical and it's just not going to work with this company. We need something else.  Do you understand the business capabilities and how we apply that to data stewardship? It's not just something to think of. And again, this can and will be a whole webinar but just again, one of those touch points to think of is you're building out the strategy. And then data architectures. I mean, if you think of those pillars across, you know, governance and architecture and strategy, a big part of that is the data sources and what platforms to use. I guess I wouldn't lead with that, right? What is your data strategy? We're moving to the cloud. That's not a strategy. That's a tactic. It's one of the things to do, right? I do like to look at this though because that is a big part of the decision. How and what tools do we use? Again, this comes from the Data Diversity Survey we mentioned. Still, although everyone loves to talk about it's demise and I will argue with that person, the relational database still is sort of king where most companies either have a relational or on-prem. What keeps me up at night is that Spreadsheets is up there. Spreadsheets are fine. They're not an enterprise data management tool but you'll see that there are a lot of other choices in the market as well which leads us to the next slide. What are people looking to do in the future in terms of their data and their data strategy? Again, relational does not go away but you will see that it's much more cloud focused and I think this idea, I'm including a data lake as well as this, whether it's a data lake house or another part of the survey was one of the questions was do you use a data warehouse? Do you use a data lake data warehouse only? Data lake only or a combination lake and warehouse? And the order was absolutely combination of both then data warehouse and data lake by itself which is very not common because a lot of folks know that they have their value but to really get a lot of those business value out of it you have to kind of transform that lake into something else. So that said what I also like about this slide is that yes, you'll see the relational databases are great for what they do. I don't think they'll go away because they've referenced on integrity they're good for data quality. But where you saw that that was kind of a peak before and conditions that people can use relational databases and other things like graph databases, real-time streaming and key value but non-relational and they're excellent but you don't want a graph of garbage graph databases are great but you have to ensure that some of these core data quality and things are done as well. So I'm heartened by this that there's a lot of choices and it is something you definitely should think of in your strategy of maybe we're not going to throw away relational databases they definitely have their place but should we be adding to that? Are we not looking at some of these new technologies that could offer us value? Should we be looking more real-time? If we're using data to drive the business should it be more real-time? The business runs in real-time, you know? Do we need a graph to really understand kind of that knowledge graph of the or... There's a lot of things you could be using and something to think about as you do develop your forward roadmap are we missing something? Are we not looking at some of these newer technologies or machine learning or AI and things like that? But really when you're looking at kind of this idea of a data strategy and all of the things we're building that we could talk a whole webinar on each metadata quality, governance, privacy... The idea is to get these trusted data sets and all of the things around your strategy are yes the sexy stuff and yes the analytics but that can't be done unless you have this idea of those trusted data and it takes a village really to start making that which is the again not only the strategy of the why and what do we prioritize the data architecture around it but not the data governance since kind of the what do you call it the synergy of all of those is what makes trusted data sets and then what do we prioritize along the way? One way I'm a big fan of doing things again I mean I understand it and I'm old enough to have been through some of these phases of your architecture sometimes seems onerous like are you gonna make me do you know an entire enterprise logical data model before I can move ahead and is it gonna take a year? No, I think the idea of picking these targeted business values is part of your roadmap what are those quick wins but that doesn't mean a quick fix right what was the minimum viable product for architecture we can do to solve a business problem and then you're gonna get the buy-in you're gonna start building things in a much more excuse me agile way but you're not skipping the harsh stuff right so pick a problem you know this was a like kind of a fictitious this insurance company it might be any of these you know how do we best price our policies how do we support our brokers how do we support our customers you know pick a problem and then build the models around it again that's what makes it more strategic and less just architecture what's the business data model around our pricing and what what can we include in pricing or not what's the business process for understanding the you know is it online pricing do we have to go through a you know any actuaries and things like that you know what's the data architecture for this problem where the business rules and glossary around it what's the data quality for this focused area you know rather than and you know maybe this is obvious we don't boil the ocean and look at every piece of data in the organization to the end level for a data quality dashboard that's part of the prioritization of strategy what's going to you know and you may have several dashboards or views right which are customer data quality for this area right and you're really getting those targeted wins along the way that'll build to something more enterprise but that's you know really target it until the story around it it's always kind of my my best advice here and then how do you turn all that into a roadmap and again that can be really overwhelming because there is a lot of stuff so one one thing is to what are those chunks what are those quick wins we can turn into a warehouse and then a warehouse into a roadmap and then the classic who, what, where, why, when how and what so you know the why is the if you skip anything don't skip the why because you really get me a what what why are we doing this is a valid question and it's an offense it's a defense how do you message it we talked about that who not only who are the stakeholders who will benefit eventually you're going to have to keep selling this and then who are the data stewards do often they're kind of discovered in the org because you probably have champions over this effort that you can get back to the how how will you organize your governance your architecture all of that and then the what the what is huge what are those quick wins how do we build out the full road map but in small wins you know is it customer rather than product data or you know different analytics use cases etc and then the when how do you roll this out and there's a bit of a magic sauce there of how do you roll it out what's the timing and then what other initiatives either going to are going to get in a way of this if people are busy with other things or can you champion with you know there's a new digital transformation for our supply chain can we support them with our strategy and get the supply chain data excellent move on right so that's a win-win you're helping the org you get some visibility or is it a marketing campaign or a new product launch or whatever and sometimes that's just a hard switch and having lived this you're in the weeds of tech and you just need to get the stuff done and just put on your business hat and be like okay if I were the CEO and what are their strategies I mean what's their goal and how is data becoming an integral part of that it's just flipping the script again a little bit and making sure this becomes strategic and not just data management if that makes sense so how do we build the roadmap that's almost the trickiest part sometimes if this again aligned with the organizational vision you want to be you know I sort of joke we're all human and at the end of the day you're you know venting about work how can this other project get all the funding everyone cares what they do we're doing way better work than they are well answer that question why are they probably I don't think their project is inherently better or worse it's probably more aligned with the organization's vision you know maybe they're driving a new sales campaign or the new you know student you know enrollment campaign or whatever it is and so do make it especially if you're on the tech side you know have you read your company's annual report if your public company is generally out there and often those are very clearly defined what the vision is what the goals are and then think okay so how can whatever project I'm doing whether it's analytics or MI or ML or glossary anything how does that support that and you know there's a lot of ways or do we listen when we hear the company updates is it just you know Snoopy's or you know Charlie Brown's mom got a mom or are you really listening and adjusting and thinking okay how does this drive what I need to do and then also think there's so much to get this right also what what is a quick win for your org and you know is it cautious is a quick win maybe within six months we can start something or is a quick win you know your startup and it's you know a sprint cycle you really need to kind of understand that and what you need to plan because again people have a day job is the beginning of semester start you're trying to get all the faculty together to do a data governance launch you know maybe that's not the best times and maybe that's obvious but sometimes again you're in your weeds just remember you know people are in their other weeds and how can you help them and then yeah you're supposed to sorry so the other part is explaining the vision so what I was sort of headed on that last slide is that rocket ship right how do you be part of the rocket ship moving yourself ahead in a way you know a big part of the story is building that story because it's going to take many steps and you know the analogy is maybe you know we're trying to climb Everest and yes that's an exciting thing and it's exciting and and we'll have a great success at the end and we all want to do it but you know people die along the way it's a hard thing so not only that is it takes a long time so unless you continually build that excitement you know people they're in base camp and they've been stuck in the tent for three days and they haven't eaten and they're going to start to wonder why the heck am I doing that and that'll happen when you whatever it is you might not be starving in a tent but it might be that you know the the data quality is as great as we want we can't get the analytics until that's done or you know whatever this but you have to continually have that excitement so people not only are bought into the vision because the so what is a big deal what's in it for me it should be the question but then how does everybody fit in does the finance analysts or you know counts receivable clerk really understand why they need to be a steward and why this business rule is going to help what we're trying to do right so so a bit of it is explaining what their role is right I might not understand why I'm carrying this heavy pack but it's because you've got the food for the trip to go to Everest right you everyone only has a piece of the role so it's hard to see the full journey and that is you as a champion for your strategy really need to kind of keep that moving it is partly a marketing effort the other thing is the idea of your organization maturity so back to that analogy I might be climbing Everest great am I already a mountaineer I've got my core team and we're just ready to run or if I've been sitting on the couch for six months and I don't even know how to climb and I need to really start and neither both folks can get to the goal you need to understand that and then really understand it by discipline because not everybody's pointed in the right direction and it doesn't it's not your answer right so the one in the middle is where you are today the one on the right is where you aspire to be and maybe not everything is as important as everything else maybe for this company you know data warehousing is more important than you know the metadata management or the integration or you know maybe we don't need analytics we just need bi right now right and then where you are today it doesn't mean you always feel the gap it might be that you play to your strengths you know that's the that's the bit of the subjectiveness you know do we start with the thing we're already great at and get people really bought in or if we can't we're great at analytics but we're terrible at data quality we really let's get the data quality fix when we publish the analytics because they're going to be wrong right that's kind of the cost benefit but you need to understand where you are before you can see where you want to go and there's plenty of maturity assessments out there you can use or you can kind of do your own this is ours that we use you know with kind of those areas of the framework that we talked about but in any case you know it could be a finger in the wind or you could have an external person come and do this for you it's kind of benefits to both to get a objective view as well and then from that again that that isn't the answer that's one of the other inputs there's a lot of inputs and then what are these quick wins you know how do we knowing that the full strategy might take years you know think of a strategy you might look at your company strategy or government strategy generally it's the strategy 2030 or the strategy 2050 right strategies by definition on the term but if you don't see those efforts along the way you know your team on Everest is going to give up and go home because they don't see why I have to do all this hard work to get to the peak if you don't always see that full vision so do these small wins you know take pictures of each base camp or whatever killing my analogy and then deliver a quick value and over time what is a quick win right it is worth kind of thinking that through so what it's going to solve something high value it's going to be a proof of concept but also have a foundation for future efforts it's not a I'll talk about that in a future slide still do it right back to that architecture slides still do data models still do a glossary you still you know do the right things but do it in a quick way the idea is to get that light bulb moment for folks that maybe didn't get data or didn't understand how they fit into a data strategy so that's a big big piece of it and this is my my slight rant I'm holding back on rinse today but a quick win is not a quick fix and I you know often you know folks don't like that idea okay it's going to be quick it's another POC that's going to be crap or I'll end up putting it in production and that's not a right thing a quick win for a strategy is again it's a building block towards that full foundation stop the duct tape on the wall is again back to that slide I had with building building it the right way understanding the vision understanding the business drivers picking the right subset of data understanding quality and the architecture all of that get success there and then move on so you're building you know the house with a good solid foundation and not a bunch of just random stuff that that's some of the why it's a strategy you're sticking through this strategically another way to look at this we're a big fan of doing a lot of this stuff whether it's an architecture or you know brainstorming you know through through workshops you can get a lot through some of these collaborative workshops and this is one a we use all the time a priority grid I found out after I did this that Boston Consulting Group Christ to call it the Boston grid but I don't know this is sort of obvious we all come up with whatever give it to them but again like of all the things you need to do even done that I've had a lot of good success with it just sticky note it right we need to do a customer MDM that's going to be high benefit but it's also really hard is that where we start or is there maybe something that's high benefit maybe not as hard doing some address validation for customers maybe those are steps throwing the way maybe there's some business process change that has to happen maybe that's somewhere in the middle and I folks kind of self select and people start to realize you know because you might have someone saying we need customer MDM or we can't do everything well maybe that's too hard maybe we do start there because that's the hardest but people are kind of collaboratively seeing it or you know do we need to migrate all the legacy stuff now does that have the same a lot of benefit or or whatever right so it's a good way to kind of again that finger in the wind of where do we start what's going to take longer how do we get there along the way and kind of a collaborative workshop be type environment I do want to kind of stress that I see a lot of these when I see you know we often come in to kind of clean up a roadmap of a roadmap that mean I'm sorry a strategy that maybe didn't feel strategic and I just want a strategy and a roadmap shouldn't just be a laundry list of things right that you should have some sort of thing again so I'm going to go climb Everest it's going to be hard we I don't just get this thing of you know pack your bag and then train and do all these things get your oxygen there has to be some theme of the wine and really it's east level kind of explain why folks are doing things so definitely do have a roadmap understand the staffing and training around that as well across all of the areas maybe governance is going to go one pace maybe it's a master going to go through another and do we have the right people to really support this effort that's going to be big part of it oops sorry but that's a part of it of kind of how we roll it out but a big part and I do want to touch on this but then also give some time for questions if and I guess I didn't always include this in my strategy presentations and now I always do the more I understand about this idea of culture and organizational change management that's probably what's going to make or break your strategy yes, you have to have the vision yes, you have to have a great plan you know I have to tie it into business benefit but if it's just me and not everyone else is into the ride it's not going to be successful no matter how good it is and if you go look at Peter Drucker you know culture each strategy for breakfast you know don't love that because I like to say I'm a strategist but he's fine yeah you really have everyone else around it so you know I was you know it's been just past several years for me that have really understood organizational change management and once I'm bought in I'm fully bought in we add into all of our projects right this is not change management like tech change management like your you know database change management this is how you change the culture of an org and we've found where we might come in or even one of our own projects where it seems to be lagging after a few years like what's wrong we've got the warehouse we've got analytics and we've got governance but something missing is generally this organizational change how do we change the culture so that everyone's going to be data driven they're actually going to use those reports and make data driven decisions think of it just like anything else has its own roadmap and journey again we tend to as the tech folks go right into kind of knowledge right we've got this new thing I'm going to train you on data governance or train you on master data management or and that's folks aren't necessarily there yet right even the awareness what is it what is he even this new thing be what is data driven what is it and what's in it for me and then sometimes this starts to get the momentum oh everyone's talking about it and Joe brought analytics to his meeting and made his case through analytics maybe I should do that too right and then how do I get trained and then and then reinforcement those success stories Joe Joe made a decision based on data we didn't have and it really helped the ROI of the company wow we need more of those dashboards right and then it becomes this wave but it's a planned wave right doesn't magically happen you have to plan those quick wins in the ROI you do management but we often jump right into training this is how you use the dashboard this is how you do data governance but do we go backwards then back to that motivation as well a lot of slides here you can kind of go through and kind of look through but what's interesting is change happens at many levels it might start with the organization I mean I could talk today the individual right the person who's sitting at their desk going what's in it for me valid question and change rolls up into a project that might be your first quick win while that project was successful or how can how we get the right people involved and then it goes and branches out to the organization and you really need in your plans to look at all all levels and I would add into this you know yourself is one of those individuals where are you are the change of journey across all of this right and think of your own motivations too good way to think of that and then marketing party of jobs marketing it shouldn't be technical jargon it should be business driven we're going to help drive the organization we're going to get better students and healthier patients you know better community whatever it is through data and then you know don't be shy do the swag do the posters do the you know you know but make it very business-centric because again that's the way it's a strategy and not data management another thing you will get resistance again the most motivated person trying to climb Everest is going to be tired half the way up right and there's going to be times even yourself right why is there resistance to change and then just think through and like anything planted out what are some of the root causes you know is it that been through this before and they're chained in is that they're busy is that they don't understand it right and do it at each level from the folks you know hands to keyboard to the execs to middle management and out and then proactively go against that resistance plan and think about it everyone's human no one's like strictly negative against you but you're asking people the most likely start to do things differently what are going to be those big rocks or little rocks that you want to have to and get ahead of that because I can make or break your strategy so that I just wanted to bring in that change management aspect because that that again is really going to be a key indicator and you know love to have time to discuss them but that's not the format of this but something to just take away yourself and maybe think of when you're launching your data strategy what do you think the biggest fear of change might be and involving that and then what might excite people and it's probably bold you definitely want to start pointing towards the green and start to avoid some of the red but that might be just something you know later when you're brushing your teeth and thinking through your plan for strategy what's going to get people jazzed about this and what might people be nervous about for valid reasons and then start to address them in your plan so again data strategies all about the business and about the data that's what makes it tricky you know it's governance it's architecture and then get that proper roadmap so you're doing those quick wins so everyone understands where you're going and don't forget to bring the culture and the org along for the ride so I will pass it over to Janice for questions little fun for next month's March webinar on data mesh kind of demystify some of that a shameless plug but we do strategies for living it's in our name so if you need help with any of this don't hesitate to reach out so with that Shannon I will open it up to Q&A Donna thank you so much for this great presentation and if you have any questions for Donna feel free to put them in the Q&A portion of the screen and just answer the most commonly asked questions just a reminder I will send a follow-up email to all registrants by end of day Monday for this webinar with links to the slides the recording anything else requested so we've got a few minutes here diving in and Dan and Shane welcome you to join back in so Donna can you talk a little bit about mesh and fabric and where that fits in oh dear here we go I brought up last week too I will keep it quick because we have a whole session next month on data mesh I think that's one of your both architectural choices when you think of remember that slide I had with the strategy well for pillars right there's a strategy what do we want to do what's the right architecture is it more you know maybe virtualized or federated or more centralized it ties into your governance because you know is it a top-down governance is it a more federated governance and I promised before it's also kind of the theme you know theme of what your strategy is and how you your culture you know is it more you mesh the people have more you know individual responsibility is it more centralized so I'll I'll leave it there because I know we have a whole session next month but I'll open it up to the other speakers as well and get their thoughts oh yeah I'll just come in they see a lot of customers implementing data mesh at the moment and typically you know it starts with the four principles domain-oriented ownership of data moving towards a self-serve data platform and Monte Carlo is often part of that self-service platform to instill trust reliability in the data and then thinking of data as a product and finally federated computational governance but it is really a way to decentralize the ability to build and manage and distribute kind of data products at scale across an organization data fabric to my understanding is is a an architectural approach approach that's applied to to standardize data across clouds so it's more of a technical architecture and anything you want to add you're muted if you're oh that's pretty much covers it I love it so I mean continuing on here are data flows and data lineage meaning the same thing how detailed the data flow should a data flow diagram be for example column level schema level or database level um I'll take that a little granular for architecture but I think it ties I mean for strategy but I think it ties in I think there's at every level like at the strategy level you know I think a big way to show is is both your current state in future state so understanding at a very high level how data flows across the organization what the value is to me that's almost a solution architecture level at some point there's almost like your source of target mapping type level that it can go down to the column and that's more your data lineage I think that's definitely important and part of the implementation but I think at the strategy level I would keep it up at more that much you know higher higher level but in terms of the implementation that's when you're going to get down to those detailed data flows and data lineage yeah that's when to that yeah go ahead I would say the same right I think you know one of the things we look out for obviously in my line of work we have kind of for you know as much the consumers right we're looking into a lot of the same things I think as the folks that that Shane and Donna are helping are but you know one of the things we do caution people is like as you're out right doing it don't it's a balancing act between depth and breadth and I think just one of the things I would point out a lesson learned for us has been you know go as far as as you can but when you find that certain areas are making you're having better results right in one particular domain than another look at that as potentially a flag um it you know as I as Donna said at the strategy level it's not so important to have depth but if you find that you can't get a reasonable level of depth across all kind of the domains you might want to be looking at consider why that is one of the things I think is interesting is it's almost never really technical my experience has been it's almost always organizational so um and I think you covered that really Donna right is the concept of the organization hiding things inadvertently right and hopefully everybody who's here is well on the path to being part of the solution for that you know obviously data mesh is an interesting concept because the belief is that right there's some democratization of data there but I think again just getting asking folks you might not normally ask around your organization tends to shine light on on um you know things um because right a lot of data flows or right well not a lot all data flows stem from workflows and workflows themselves stem typically from business processes right so communication simple things like that can can really cause you grief well the perfect timing we are right out of the top of the hour I'm afraid there's so many great questions we didn't have time to get to but thanks to our attendees for being so engaged in everything we do and thanks to digital realty and to Monte Carlo for sponsoring today's webinar to help make these webinars happen again just a reminder I will send a follow-up email by end of day Monday with us for this webinar with links to the slides links to the recording thanks everybody I hope you all have a great day Shane and Dan thanks so much for joining us Donna thanks as always thank you thanks a lot thanks everyone


marine ecosystems, the intertidal zone, first the contents, the definition of intertidal zones, environmental fluctuations in intertidal zone, and plants and animal life present in intertidal zone. So, what are the intertidal zones are the intertidal we can say it is the ecosystem. So, it extends from splashes zone of ocean to the low tide zone. Simply, we can say that if we are talking about water body, so in a water body that position where the water splashes are coming, waves are coming from there to low tide zone. If we look at it, in oceans, you know there are tides, i.e. because of the moon there are tides. In that, when a tide is high tide, the water level is up and it goes to a particular position. Then, when it goes to low tide, the water level will go back and low tide zone will be where the minimum position is. Now, from low tide to the position where the water waves are going, all the areas that is called the intertidal zone, we will call it intertidal zone, this is the area where water is permanently not present, but for timing is present, for timing is not present. So, this is actually the intertidal zone. So, if we talk about environmental fluctuation here, if we talk about environmental fluctuation here, then this is the harshest environment, as you can say it is the harshest aquatic environment. So, the harshest aquatic environment is the intertidal zone, the harshest reference is that if the impact of the terrestrial environment is directly happening here, like in the day time if the temperature is very high, then ultimately the water here will be warm, when the temperature is high. Similarly, here water is not constant, water is coming, water is going back, so again the water availability is not available all the time, but along all these constraints there is the highest number of animal species present here, if we look here, the richest number of species we will get here, the highest number of species we will get on this position. So, if we look at the environmental constraints, there is the temperature fluctuation, surfing. Surfing is where the foaming is formed, so this is also a problem for aquatic animals. Then the effect of rain is there, erosion is there. Similarly, sedimentation is there, that when rain water comes, it will bring a lot of sediment which will settle down the soil particles, so the animals will be buried in the sediments. This is also the major issue, temperature fluctuation is also the major issue for the intertidal zone life. In all the different types of constraints, if we look here, there is the highest number of plants and animals present in this zone, now from this, why there is the highest number of plants and animals, number one, that nutrients are abundant, in this area we get nutrients or they are abundant because here the terrestrial environment is interacting, because of which the terrestrial environment nutrients are available in this zone. Next we will talk about different types of algae are there, different types of large plants are there, mangroves. If we look at the coastline of mangroves, there are mangroves present, one of them was too much, now they are very reduced, so mangroves are basically here, we get them here. Red algae is there, red algae is sea grasses, different types of grasses that we will get on the intertidal zone. For the animal life, if we look at the animal life, different types of fishes and different types of invertebrates are reported from here, if we look at it as fishes or the motiles like crabs or watertides will come up, then they will come in the intertidal zone, like the water will move backward, they will go back with the water, so in this sense they are coming to the intertidal zone for transit time. There are different types of invertebrates, there are a lot of invertebrates here, and the most of the invertebrates are when the tides will go back, they will go back, they will go back to their homes, they will bury themselves in the sand or mud, so if we have a small amount of intertidal zone soil, then there is a huge number of animals present in that soil, so in the soil we will get a lot of animals, so it means most of the animals in the intertidal zone are burying themselves in the soil. The third option that the animals have is that there are some specific areas where the water for the time being comes and stops, so where the water for the time being comes and stops, stands, then what will happen is that the stack of water that will be made, a storage water, some water will be collected with a rock, there are different types of earth reports, even different types of acino drones are also reported from the water, where the water goes, some places are locked up, the fourth option for the animals in the intertidal zone, the fourth option that the animals have is that when the water is not available, then those are all shelled animals, they close their shell, they close their shells, because of which they go to the drought or to the dryness or to the danger, so it means that the animals have different options. Number one, in the intertidal zone, the animals, they may move back along with the tides, they go back along with the tides, in the main water body, fishes and all those animals, they do this job. Second one, they may bury themselves into the mud, you bury yourself in the mud or sand, those invertebrates do this job. Third one, there are some isolated water, there are some waters that are temporarily stored, these temporarily stored regions, specific water it is having also enriched life, it has both types of insects, earth reports, arachnids, different types, even some types of the acino drones are also there, and the fourth option, they are for molluscs, molluscs shelled, so when water tides are not available, they close their shell and save themselves from the drought conditions.


The managerial accounting, however, is not limited in the same way. Why? Because we're not given these reports to anybody outside the company. They're internal reports. They're us in or working trying to make the internal decisions. So we might be making reports about a small group, a small unit. We might be making reports about some type of product or something like that, one product out of many. In order to do make those types of decisions, we could format the reports in any way we want because we're not giving them to external users who are then relying on them to make decisions. So we have a lot more flexibility for that reason for managerial accounting to run reports, to make reports, to think about what can we do in order to make the best decisions and what kind of data do we need in order to do that. The availability of the data, financial accounting is typically going to be if it's a publicly traded company in particular, it's going to go through an audit. We're not going to have the data possibly quarterly or yearly. We have to wait through that audit process to take place for them to really be finalized, to have those financial statements. So it could be a very set process on when we're going to get those numbers. And the numbers might not be finalized, of course, for a little bit of time after the period has ended. Whereas managerial accounting, and that of course deals with regulations. We have to go through the regulations. We got to make sure that everything is set up in accordance with rules and regulations, especially if it's a publicly traded company. Do these financial statements align with generally accepted accounting principles and whatnot. That's going to have that process is going to take time for financial accounting and it's very regulated. For managerial accounting, we don't have that same kind of regulation. We have limits, of course, but those limits are typically going to be, what can we do? So for managerial accounting, the question is often a cost benefit analysis. You know, we could get this more information. The question is, is it worth it for us to get the more information? There's always a balancing question of, is it worth us to put in the time to get more detailed information? Or is the better decision that we can probably make with that more detailed information not worth the added cost? That's typically the type of decision making restraints that we have on managerial accounting, as opposed to the types of regulatory restraints. So if we want to put numbers together, if we want to put reports together, if we want to analyze whatever we want to analyze internally, no problem, except that it costs money to do so, and we want to make a cost benefit analysis to decide whether or not the time spent is worth the effort, as opposed to conforming to regulations, as is typically going to be what we're going to do with financial accounting. The time focus for the financial accounting is typically going to be passed. We're looking into the past. In other words, we're looking at historical data. Now, of course, investors and are going to be looking to project into the future as well. But all the financial statement preparation is geared towards what happened in the past from our side, from putting the stuff together from a company side. We're trying to just say, hey, what happened and put it together as accurately as possible. And then the investors could make future projections based on that managerial accounting. When we put this information together, it usually has a future focus. So we're trying to focus on future decision making. So when we put together reports, internal reports, whatever we need to do from an internal standpoint, we're typically thinking about what can we do for the future in this place so that we can increase production. If we look at the information focus, it's going to be on the entire organization for financial accounting. We're looking for information that's going to affect the entire organization. And that's by design. The financial statements are there for the entire organization because investors and creditors are typically worried about the entire organization. They're not really looking into the detail but not looking at segments of the organization. Some will. But for the most part, the reporting is going to give a whole, as a whole, what do things look like as an entire. On a managerial accounting, we're typically going to go more into detail. We're going to look at the department by department. We're going to look at product by product and try to make those decisions. So typically, financial accounting is looking big picture. How do we stand overall? Whereas managerial accounting is going to go down into more of the nitty gritty and try to figure out what can we do from a piece by piece standpoint to improve operations. Nature of information. Financial accounting is going to be purely monetary. So remember that, of course, the balance sheet is going to have things like equipment on it and this stuff and stuff that's not money in and of itself. Equipment is, it could be a forklift or something. But when we put on the financial statement, we reported in terms of dollars. Everything is reported in terms of dollars. And of course that same information will be used in managerial accounting. But it's not the only type of information. We recognize that that's only one set of information. If we can put numbers to something, we typically will because that makes things a lot easier to compare and contrast if we have numbers to them. But oftentimes we may not have numbers. The numbers may not be representing monetary amounts. We might be having to compare other types of things time or other types of things. And we could have other things that are completely non numeric that we're comparing. We might have to actually compare different types of data actually just reviews or something like that and try to make managerial decision making processes based on that. So again, we typically will break things down to a monetary basis if possible, break things down to a numeric basis if possible, because that's better for making comparisons or easier to make comparisons. But we're also going to use in any type of data that we can to increase decision making processes.


And speaking of women's hockey, BSU senior goalie Brittany Mollett has been named the WCHA Defensive Player of the Week. Mollett led the Beavers to a series split with 2nd ranked Minnesota totaling 57 saves on the weekend including 31 and a 2-0 shutout victory on Saturday. She tallied a one and a half goals against average with a 9-50 safe percentage for the weekend. WCHA Defensive Player of the Week