Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer for Data Diversity. I want to thank you for joining the latest in the monthly webinar series, Data Architecture Strategies with Donna Burbank. Today, Donna will discuss graph databases, benefits and risks sponsored today by StarDog. Do the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them by the Q&A panel or if you'd like to tweet, we encourage you to share highlights of questions via Twitter using hashtag DA strategies. And if you'd like to chat with us or with each other, we certainly encourage you to do so. And just to know the chat defaults to send to just the panelists, but you may absolutely change that to network with everyone. And to open the chat or 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 the recording of the session and any additional information requested throughout the webinar. Now, let me turn it over to Naveen for a brief word from our sponsor StarDog. Naveen, hello and welcome. Thank you very much for the introduction. Hopefully you guys can see my screen. I want a quick introduction. The VP of product here at StarDog. StarDog is an enterprise knowledge graph platform based out of Arlington, Virginia. We're a VC backed company and been in the business for a little over 10 years. Happy to be here real quick. I wanted to take some time to talk a little bit about, you know, how we think about this market space around enterprise knowledge graphs and our ability to democratize data for analytics and AI workloads. We all know Gartner's continuously evaluating the market's landscape and one of the areas that they've really honed in on this notion of how to tackle with this growing volume of data and distribution across the enterprise, making it very difficult for organizations to exploit their data assets efficiently and effectively. And what they recommend and we certainly believe that this is the right approach when you're thinking about your own data architecture strategies is to adopt a semantic approach to your enterprise data. Otherwise, they certainly fully believe and we certainly believe as well. You and the large enterprise organizations will continue to face this endless battle with data silos. And we kind of drill further into what does that really look like, you know, at the data architecture level. There's still points of friction that continue to remain when it comes to sharing data and knowledge broadly right you think about breaking down the data architecture in terms of the culture you're trying to create the data culture, data models that you need to build to in order to serve the enterprise, the integration that is required in order to bring all this data together. The interrogation of that data and eventually the intelligence with the underlying data and the metadata, and how do you turn transform what are what I would say are traditional challenges with current architectures to opportunities so when we think about data culture, the focus sort of shifts away from big data to wide data that means you're not necessarily just worrying about collecting data you're also worrying about connecting data. And you're not just looking at an approach that is purely centralizing all of your data assets but working in ways that allows you to federate data access. And importantly, you're not just leaving this in terms of control with the handful of specialists, but broadly working on data sharing to enable citizen data users. And when you think about it from a data model perspective current current architectures kind of data models are tightly coupled and shaped by the underlying data storage infrastructure. And where the opportunity lies is really in this notion of semantic models right the semantic model that is abstracted away from the underlying data structure, which and then represents itself as business meaning, which means it's sort of more consumer driven rather than data consumer driven rather than data producer driven. So you're looking at it from that lens. And that way you have a business meaning attached to it, which enables at the end of the day this notion of data harmonization reuse and interoperability. When we look at data integration. It's really in this notion of well everything has to be solved by making physical copies of data in order to serve the needs of the business right so you end up creating these complex data pipelines, either ETL or ELT. So today, there are opportunities here to look at ways to virtualize access to data that limits data sprawl. Yes, we will never ever move away from ETLing but there are opportunities where you may have to take the right strategy. So when it comes to, you know, enabling the business for ad hoc data analysis or working with data that's in a source system that requires real time access to that information so you're looking at the fresh, freshest data possible. And, and frankly limiting the amount of complex data pipeline development that needs to go on. The same thing around data interrogation a lot of times data interrogation is sort of centered around this notion of you got to define your queries up front and those queries are optimized for performance based on the underlying data repository, where really the opportunity is to create this notion of a search driven data exploration, enabling more complex query processing by citizen data users in ways that they can self serve. And lastly, not but at least is sort of this notion of data intelligence right so what do we know about the data and oftentimes again in traditional architectures it's sort of this collection of metadata that's catalog inventory for passive analytics. You know it sits on the side with no association to the underlying data itself, and the opportunities really how do you connect that metadata through that semantic model with the actual data so you can kind of infer new relationships and drive intelligent to the foundation so with that, obviously, we get into this notion of what enables an organization to take advantage of those opportunities. An enterprise knowledge graph is exactly what is needed, because at the end of the day what an enterprise knowledge graph does that enables this flexible semantic data layer for answering complex queries across a diverse but connected data landscape, and doing so in the way it does is of course harmonizes data metadata and rules with a business information model. It limits data sprawl with federated data access to heterogeneous sources and enable citizen data users to self serve and participate in the data process itself right at the end of the day, you want to serve the business users and being able to independently work with data and gain the insights and drive the decisions that need to make to function within their organization their operation, whatever it may be. And to that extent, Stardog really is about delivering this enterprise knowledge graph platform, connecting data based on business meaning and providing this flexible reusable semantic data layer. We have customers across many industries again really our goal is to empower data citizens to make knowledge informed decisions. And if you have, if you have an opportunity, and the inclination to embark on a journey, whether it's an enterprise or otherwise, we certainly have some assets here for you to get started for free, gives you an early hands on experience in both in terms of learning, trying using an enterprise knowledge graph platform for a whole host of use cases that might be of interest and we have pre fabricated knowledge kits by domains by industries that you may even leverage to as a starting point if you don't want to bring your own data. If you are Databricks users and inclined to work within that kind of an environment. We also have a established partnership with Databricks so within your Databricks environment, you can look up the category called semantic layer, which is Stardog, and then certainly you can connect to us through your Databricks environment directly as well. I know I kind of rushed through this very fast and I look forward to the presentation here shortly and then of course come back and take any questions as we go into the Q&A session so with that I'll turn this over to Donna I think we make good time and we'll move along accordingly. Thank you so much. And thanks for kicking us off if you have any questions for Naveen you may submit your questions in the Q&A and he'll be joining us in the Q&A portion of the webinar. And let me introduce 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. And with that, let me give the floor to Donna to begin her presentation. Hello and welcome. Hello, thanks so much. Always a pleasure to join this webinar always nice to see some familiar names in the chat and online. Thanks for the presentation from StarDog. Really, really helpful. So, for folks that are new to this webinar. This is a monthly series that we have been doing for several years now and we'll do again 2023. So, often is a question right at the beginning that I'm sure Shannon has been answering. Will this be recorded? Absolutely. And the nice thing about data diversity is all the previous topics are recorded and available, I think in perpetuity. So, if you missed any of the other topics earlier this year, we'll be touching on some of those like master data management and data quality as we talk about graph because they are all interrelated. And we do take advantage of that. In next month will be enterprise architecture and its relationship into data architecture and then person will be posting next year's lineup as well. So hopefully you can join us there. But what we are talking about today and that was a great introduction to kind of the topic and in ways you folks can get hands on. But yeah, you had to follow on from what Niveen was saying is that, you know, graph databases are cool because they do allow you to kind of quickly have that idea of an enterprise knowledge graph. We'll kind of talk about what that is. Understanding those key relationships between these different enterprise data sets. You know, you probably are using graph databases more often than you realize as a user. And things like recommendation and social networks, you know, they are kind of the driver of a lot of things we're kind of getting more used to. So we'll kind of go over what they are and how they can use and also what they're not right within a new technology. There's always that, you know, the penchant to sort of say, you know, we can do this for everything and tools are good for individual things. So I think a lot of folks that frequent this webinar tend to be sort of from that relational database background, which is, you know, as we've talked about in previous webinars that's still like 90% of the market if not more. There's nothing wrong with that, but there's also new tools. So, so hopefully for folks that have kind of grown up or live and doing a lot of work with that relational model. This presents kind of an alternative or augmenting of that relational model.  But with graph databases the way I think of it because there's so many different data based models or data approaches when I kind of almost have a mnemonic for each type and for me graph is sort of that thing relates to thing. The cartoons there, Dr. Seuss, but what does that really mean? I guess the fancy way might be some nodes and edges or vertices and relationships or whatever but literally it is kind of like your entity relationship diagram tables and relationships between tables but we'll talk more about that of how the relationships between data really help that, you know, there was a question in the chat about what that we mean by that semantic layer. And that really is sort of it and to get a little bit philosophical in this presentation because, you know, a lot of this is sort of the concept of what we're even trying to do with each type of database storage. One of the things that's nice about a graph database at least in my brain is it sort of mirrors the way we tend to think. We don't always think literally I know I don't.  The beauty of the human brain is that we do make those connections right because there's these interrelationship patterns and what's the difference here you have data right a boat is data, a squirrel is data and brother and family relationships but the difference here is those relationships between things if that makes any sense. And that's what's nice about graph because as was mentioned in the intro we don't always know those rules up front. And a lot of it is the discovering those patterns and overlaying different patterns on the data or that semantic layer mean one relationship here is those familial family relationships that Mary has a brother named john. Right, the other is might be you know, favorite hobbies, they like to ride boats and all the things around boats right the data is the same is still Mary and there's still a boat. But those relationships between them sort of was different if that makes any sense right the way I like to think of it I guess getting philosophical. There's different ways of looking at the world and this to me is almost more that relational database world right in the, you know, 1735 if you all view folks have to memorize this in school, kind of that idea of higher degrees or taxonomies. You know, the, you know, kind of memorizing the way we could create ways to group biological systems and we know I had to met what was a third grade or whatever kingdom phylum class order family genus species right and the idea back then was that if we could just put everything in the world in its box, we can understand the world and wouldn't that be nice everything seems so simple. And sort of as we learn more about the world and the world gets more complex, the kind of different theories, kind of around that are things like even chaos theory or right if you think of this idea of emergence, where if you follow this but in complex systems, the patterns arise out of the multiplicity of relatively simple interactions and that's kind of the Wikipedia approach may be a way of thinking of that is, you know, without getting religious or philosophical there's this idea of a snowflake, and there's certain patterns of snowflakes that sort of you can see all of those things in the lower left or snowflakes, but no one top down designed each snowflake and each one is different there's no sort of pattern of snowflake that's the same, where this theory is uses often a city planning, where I'm just thinking of my university where everything was built and very ordered squares and, you know, there was everything was right angles. And when people want to go to library that cut across that long because that was the fastest way was sitting planning they often look at those patterns of traffic music movement and build roads based off that. So to me, and you might be running one on earth are you getting out here Donna, to me that's sort of the difference between that relationship model relational database where it's still very valid I know I'm going to create a customer database with these fields. It's a customer can have more than one order, and we link customers orders through keys, and we've designed that absolutely still a place for that there are things and there are business rules in your organization that you must manage, just like Kingdom File and class order family gender species hasn't gone anywhere that's still very helpful we have the periodic table of elements that model is still valid. There's this new way of a bird's I might not know how all of my customers fit together. Are they families they have similar buying patterns. Are they committing fraud together. Right, and that's that new that flexible thing that an enterprise knowledge graph and that more that discovery or that emergence or discovering patterns in the data through those kind of first order relationships. In any sense I'm sort of feeling like I'm getting awfully philosophical but I think that is really a different way of looking at data that's different than that relational database world and I agree from the beginning ETL isn't going away. But there's also things we can add to that for different discovery patterns. In the graph database, it is more all about the relationships and their first order constraints that's kind of your semantic layer right kind of ironically in a way I'm going to quote, many of you may know Karen Lopez, you should think a relational database isn't about relationships is about constraints really, we call it relational database, but it's really about doing keys, you know a key that links a customer to an account. And also when you think of key value pairs that's not super, you know, that's not really what they are great at but at a graph database, you know, a customer is the owner of an account and kind of those verb phrases are sort of what it's all about.  The relationship are really key to linking your graph databases together and that's kind of what makes the graph so powerful. So, and again, I'm not going to get super technical here that the vendors, you know, as a star dog mentioned, you know, download it try it for yourself, lots of great training online, which this webinar isn't going to certainly not replace. But we're hopefully, you know, if this isn't in your, your, your paradigm or your way of working. It could be an interesting addition again it's not a cure all for everything I want to be careful of every new technology isn't the best fit for everything but it is a very powerful tool if you're not looking at it in your, in your company, or in your way of working it might be something you want to look at. So what are some of the use cases. Social networks, right we kind of we kind of hinted at that with Mary and her brother right so understanding we have a bunch of people in our database. You know who are the cool kids in the database people who are linked with Donna because they go on webinars and they talk about data. So if you've sad lonely people up in the corner who don't like data and they are not in my network is why would I have them in my network right, but that that is used all the time think of Facebook, a lot of that right how do we think of patterns or customer buying patterns. You know what are our patterns or what are think of it in insecurity and risk there's a terrorist and who are those people linked with that, maybe bad actor, right. So this idea of that social network approach which is all about the relationships is a really neat use case for sort of graph in that graph model. We're not all over the map in this presentation, but we were sort of used to that concept. If you remember maybe it's the old folks in the call the bacon number right what's here, how many degrees of separation are you from Kevin bacon. And, you know, that there's actually a website out there where you can kind of called the Oracle of bacon where you can actually see your bacon number and how far away you are from Kevin bacon. So here for example, Audrey Hepburn and famous actors, I've done a lot of great movies. You can say, okay, what's the, what's the bacon number for Audrey Hepburn. You'll see right away there's a bit of a problem. There's two Audrey Hepburns right this is more than one actress with the name Audrey Hepburn so which we're talking about So it kind of proves the point that data quality master data management metadata management is still important these graphs can be super helpful, but if you have the wrong name for Audrey Hepburn or can't can't kind of differentiate who's I mean, the power of the graph is you can kind of figure that out they're both in different movies. You know, and that sort of thing but you know you can't have these great powerful graphs when you don't have great data as the basis right but kind of maybe a fun example to kind of show both the power but also kind of some of the risks. Um, fraud detection, I kind of mentioned that so think of that in terms of you have online transactions that there's typically you know identifiers that you know I logged in at it with a certain user ID certain IP address geolocation credit card etc. And graph patterns can kind of help you detect some of that fraud right so I have one IP address that just suddenly had 17 or here seven of this example credit card transactions all within a few minutes seems a little strange. Is that a bot is that someone you know committing fraud. I mean, the every IP address that is more than one the one on the right just must be three people in the same family they're all shopping for Christmas at the same time, or the one next to that could be me with both my personal and my business credit card buying a couple things near each other, those might not flag anything weird but when you start to see massive, these kind of tightly knit, you know, graphs or kind of indicators of maybe we should take a deeper look into this right. So companies can kind of put some triggers in the place to say hey you know when this happens let's let's maybe do something about it. Recommendation engines, you know we're all sort of familiar with those I bought this you know would you like to also buy this when you're doing online shopping that can be powered by a graph database right because you know that this customer bought this product that does is a graph relationship to another product super powerful. You know again very different from maybe how you might use a relational database in terms of that power but that's definitely an interesting use case. As with everything though, there are, you know, it isn't a silver bullet you need good data quality. You also need good data volume so this was an example I was kind of going through. And then did, you know, I was on Amazon.com and then just pick something strange, an axe on the left, which actually followed me around on the internet because they thought I wanted to buy axes and it was sort of creepy. Different topic, but the recommendation engine I got was, you know, people who purchased this axe also bought coffee filters, which is a very strange recommendation it's really hard to see why on earth.  Why would that have any relationship to coffee filters right probably my guess is that that type of axe is used for camping. And those coffee filters are kind of some of the ones that people use when they go camping and kind of use that so you know maybe three people up in Alaska or Canada were kind of going camping and bought these and there wasn't enough of a data set to really the recommendation of here's other three kinds of camping axes that people might have bought or or a sharpener for that camping axe or something right. So in this case, the model is fine there's nothing wrong with the graph pattern, but the data set itself was flawed. So something to think about that of, you know, mall, both the data quality, or the being able to have that master data those those nodes be correct, but also having F volume to have this make sense, which leads me to master data management. Because I have whole rants about master data management often folks kind of talk about that 360 view, or really understanding all of that that's not master data master data of, you know, enables that master data is which Audrey Hepburn and I talking about, do we have her right name and do we have her right address right which enables the 360 view of all the great things about Audrey Hepburn, but it's not master data. So, this idea of a graph database is used in some of these solutions around master data management. But I just want to be a little careful that there's different ways to approach master data management. Refer you to the master data webinar we gave earlier this year and there's a lot of a lot of great resources on master data management, you know different models. So one model is kind of millions let's go for that centralized approach that typically is your relational database model. Say I want that hub and I want all my customers in the same place, and I want to create my matching rules, it's the same customer. If they have the same name and the same birthday and the same social insurance or Social Security number right. It's pretty important to get that right node. So if we're going to say push back to your operational systems right right once I've identified the correct Audrey Hepburn or Donna Burbank, I'm going to then go push back to all the systems. Once Donna changed her address and make sure all the systems are synced. You really want to get those matching rules and really get that, you know, down. But often kind of that idea that was touched on the intro, you know that virtualized approach, or maybe more of the analytical, you know, I'm not necessarily pushing back to all the source systems if I, if I just trying to see buying patterns of wealthy people. And I want to know, you know who Audrey Hepburn is related to. So we can target her friends to buy jewelry or whatever the rich people buy. And then you kind of want that that flexibility and it's fine to have that kind of virtualized layer across and it's more that discovery approach. I've seen customers kind of use both really well together of let's use something like a graph or that virtualization type approach to do discovery and see those patterns and even see you know who are top customers. And how do they link together, which kind of helps define some of those business rule patterns, and then they also went to more of that kind of relational mode of the, you know, the match rules and the match merge sync for the MDM. I'm not going to say one is right, one is wrong, they both have their, their benefits, but you know that MDM of trying to do those match merge how do you discover who's the who's the right Audrey Hepburn. That's kind of the hardest stuff that you have to get right before you want to get all of the benefits and stop you from doing a graph they can, again, it's a cycle, you can do the graph to do the discovery that might help you with some of the data quality, which is then going to start a graph so it doesn't mean you have to get everything perfect before you start a graph, you know, on the contrary, they can kind of be synergistic and help each other. So kind of, you know, in the title of this webinar we didn't want to just only sing the phrases of graphics to the benefits and risks. I think one of the risks that I touched on in the beginning is you know what's that phrase when you have a hammer, everything So data warehousing is, is, is something that's been around for a very long time. It's still a very valuable thing it's not a graph they have different use cases so data warehouses are still around they're not going anywhere. They're not the only tool in the toolkit. But if you are trying to aggregate and summarize data probably wouldn't use a graph database you're classic you know I want to report on product by sales by region by sales rep by whatever you're sort of describing a, a data warehouse. So that's a very different approach and they can work really well together for different use cases. You know I want to get my total sales by region by customer each month in 2017. You know, I think most folks in this call are kind of familiar with that approach sort of almost where diversity type webinars grew up and started and in the day because we've been around for a long time. We understand the source systems get the data quality from the source systems, do that ETL model it either relationally or dimensionally and then start to you know create your cubes and do the slicing and dicing do we even know how to calculate total sales. You know what do we mean by a region was a customer is a fiscal month or you know calendar month all of that is still super valid. Maybe I also want to add on a knowledge graph of who might be my most influential customers with the most connections, you know Audrey Hepburn bought a fancy watch. Now all her friends. I know she's not a lot of her friends would be buying that same watch. So maybe those are the folks I want to target or, you know, etc, etc. So, again, it's really a different approach and a really powerful approach to work together. It's really something to consider. This might be another odd odd thing. But I think data management and ballroom dancing what on earth is she talking about here. So I tried ballroom dancing a long time ago, and it's kind of what the teacher had said there was you know, first when you're just beginning dancing. You're just standing at your looking at your feet so you're not going to step across and step on your partner that's probably still where I am. And then when it kind of clicks.  Then when you're really good. In my life I got kind of knew what the person was talking about. Everyone is such a good dancer and they're all in sync and you feel like the whole room is kind of connecting and the music and you're, that's really where kind of that progression of data management again first you dance with yourself don't step on anyone, then you just with your partner, and then you're really it to me that sort of a graph database, maybe I'm the only person who would have put those together. But again, you want to get your data quality right I want to make sure it's the right Aubrey Hepburn or Donna Burbank's birthday is correct so you can all send me gifts. But then once you get that and then you can add things like the graph that's when you're starting to make those connections and really dance with the room right because there is so much data in these these various silos and different areas across the room and the more you can unlock that and do that idea of the enterprise knowledge graph. That's really getting to that next level where you're not hindered by having to, you know, step by step or link everything together with ETL. Maybe that didn't make sense but it sort of did to me and the thing of you know that opportunity of really getting that graph approaches that kind of enterprise dancing with the room. And it can present this holistic view of the organization through these relationships. So again, you can discover things this might be say I'm high net worth insurance company that ensures people like an Audrey Hepburn for her, you know her mansion in Aspen and her yacht and her, you know, she filed the claim because her windows on her yacht broke or something like that. And you probably really want to understand that customer what, you know, what assets they do on who they're related to all of that sort of those graph patterns and this might be something like Audrey Hepburn, you know, maybe I have a customer called Luca Dotti which was born in 1970, who's her son. And you might not have realized that without some of these these graph patterns or you know really again that's the, it's hard enough to get the right claims and the right dates of birth and all of that but really it's those those linkages, or that power of the graph. They can really help with some of that. So hopefully that made sense as well. I thought this would be helpful. If many of you know who have joined these webinars. We do global data strategy and diversity do a survey. It's been like five years now that we do this every year sort of trends and data management of who's using graph databases, unfortunately or fortunately the fact that there's sort of lower level of adoption than some of the others as I mentioned the number one and on the planet is still the good old fashioned relational database. Don't think that is going anywhere. What keeps me up at night is number two is spreadsheets so please stop doing that. Nothing wrong with a spreadsheet but really is not an enterprise data management tool. So again whether it's relational or on prem kind of relational, what you say on prem or cloud relational still kind of leads the way. But I think the idea one of the things I find interesting about this graph is the number of other options and again it's not an either or it's an and so yes you may be using an on prem or cloud based relational database doesn't mean you need to stop. But what can you use to augment that in a graph database, you know, back again to the beginning, you may have these silos of relational databases or data warehouses and kind of having that graph pattern to go across that and really create that enterprise knowledge graph the dancing with the room can really be helpful so but unfortunately right now it's only about like it's almost 14%. According to this this is kind of the diversity crowd are using graph databases today. So looking ahead and maybe kind of going into what people are looking at is definitely growing. So you'll see it jumped up much higher. So back to the top you know relational still atop I think more folks are looking to the cloud than on prem matches my experience, but you'll see graph database has jumped and over 21% of folks are looking at that so I'd be kind of curious anyone here in the call who might be here. So, or getting kind of some some thoughts on that in the discussion which I know folks are never shy. So interesting to hear kind of people's experience there I find what sort of interesting here spreadsheets did go down in the group I think that's just because people know and wants to admit that they're go forward strategies spreadsheets but some folks are honored honest here. Yeah, definitely not the, not the thing we want to see. In summer and we did kind of leave some good time for questions I guess I spoke even faster than I normally do. But again what's what's graph again with all the different things you need to keep in your very busy brain. How do I think of a graph, I often think it's that thing relates to thing and the idea that semantics or the relationships are kind of first class constructs in a graph database. So, interesting use cases for graph talked about a few of the idea of that that social network is kind of an easy one to think of in your brain because it is that thing related to thing or customer related to customer fraud detection recommendation. You know that that really that enterprise knowledge graph of linking everything together. And yeah, not as many people are using it.  I think you know, if you're like me. It's nice to kind of know in webinars like this what's out there, what the possibilities are, but it doesn't quite click until you've actually done it yourself and gotten your hands dirty so I would take the vendors with their star dog or anyone else that kind of offers either these more detailed training. Now again this wasn't the how to of how to build a graph. It was more the, the why to. So again, I take up the vendor and they'll be on the Q&A as well. For that so do want to give a bit of a plug. Again, everything in the past was on on this will be recorded. If you want to catch it again. It's on enterprise architecture if you want to kind of join us for that. Totally blatant plug if any of this is of interest for you, we do this for a living until hesitate to contact us at global data strategy. And then without further ado, I'm going to pass it over to Shannon to open it up for Q&A. Just me who's not hearing it. I'm here dollars in the vein. Shannon still on.  Maybe that whole whole fast. I will read them often. Great. I hope I get permission from Shannon on that later. So the first one is, you know, is the knowledge graph used or suited for a referential metadata, more than for statistical time series data. So I'll pass that to you Naveen first. What are your thoughts on that. Yeah, I mean, look, we, we talk about this sort of in the context of knowledge graph being a database. But really this knowledge graph is graph based technology. So the modeling the semantic modeling is graph oriented. But the data does not have to be materialized necessarily and inside of the, the storage layer of knowledge graph. One of the benefits of an enterprise knowledge graph platform is you can leave data in place. So typically what we see clients with that, that kind of collect time series data tend to do those in one of their preferred big data platforms and pull time series data in a sort of almost like a virtual access, virtualized access and connect that up with other meaningful business concepts that helps them derive some useful insight. So yes, while, while from a materialization standpoint, it's better suited for referential or metadata, or even unstructured text based, you know, entities that you're extracting using NLP pipelines. But the approach people take with time series data is not necessarily push everything into the materialized view of a knowledge graph. But the benefit of having a virtualized access is that you can pull in the relevant time series data to answer a specific question that's connected with other ideas or concepts within the knowledge graph. Yeah, no, I would agree with that. I mean, I do think that benefit of the knowledge graph is that referential aspect of it. I think it's a good example that data could be an input by, yeah, I think if there's better ways to store raw time series for what you're trying to do. But yeah, the knowledge graph is that layer above it that helps you do that connection so I think Shannon is. Sorry about that really fast today I guess. No worries and I was just having issues you know it's you know one of those days. So, thank you for diving in and. So, can you share more about success stories using graph databases. I'll start and then I'll definitely pass it over. I've seen more and more in terms of I gave some I'm not sure when this question came in. Some of the examples I'm seeing kind of pharma suitable doing making a lot of use of graph in that again what is farm it's kind of about some of that discovery and there's just a lot a lot of volume of data. I don't necessarily know the answer before you kind of go out so I think in terms of industries looking at that I know that's one that I've seen a lot of folks kind of using this idea of the power of the graph but I'll pass it back to start I can see see if you guys have any that you want to share. Yeah, so we see the application of knowledge graphs for a whole host of industry use cases. Everything from life sciences pharma which you mentioned around clinical preclinical R&D to, you know, all the way to the commercializations we call it sort of a molecule to market as part of the primary where you even looking at it from a safety recall perspective research perspective, understanding the interactions between compounds and molecules and proteins and the makeup of the particular drug interaction of the drugs right so so that's a primary use with large large pharmaceuticals we also see this in the context of financial services working around operational risk. IT asset management use cases customer 360 and manufacturing is another big industry that's that's looking at implementing knowledge graphs for things like digital twin digital thread for supply chain product 360 below materials or against top ship from a from a safety recall perspective, understanding that we also see this in terms of models based system engineering NASA big client of ours or, you know, they're, we're part of their moon rocket launch. The same thing with the Raytheon you know working on the F 35 understanding the entire supply chain of the parts and the components that make up and the quality associated with the components that make up the the fighter jets. Yeah, I wanted to add one more thing as you were talking it was getting my brain going I mean, I found interesting you know we do consulting for a living and one of the reasons we started looking more at graph. Often was a customers that were sort of saying hey I was at company acts, and we try to graph and one of the things you know in addition to the use cases that we both mentioned was kind of that speed to market. And again, it's not everything you see you still have to do the quality and all of that but in terms of, you know whether it's a POC or something that goes into production I think that is the power of this discovery way of discovery is that just in terms of time to getting some of this insight. There's been really some positive feedback on that approach. Not often I jump around and change the order of the questions, but this question I think it's very much related to the current the question that you were just talking about you know what are the most common use cases for using graph databases and especially for an industry like government. Yeah, I mean I think we touched on some of the common use cases, you know the farmer the pat you know when you're thinking if you're trying to discover patterns. Fraud detection is one that was mentioned for financial services I think in terms of which area of government one is understanding citizen and citizen relationships. There's a federal government, you know, some sort of bad actor threat detection in terms of terrorism and things like that is a use of, you know, graph because you're trying to understand some of the patterns. But yeah, I'll pass it over to start off and what do you think. Yeah, in terms of government certainly the ones you highlighted model model based system engineering is certainly big. As I talked about the NASA and the Raytheon use cases, we also see cybersecurity is another use case both in the government sector as well as, as well as financial services, again detecting patterns right. So if you look at, you know, more on the health side of the government sector.  Integrated criminal justice is another use case we see quite often. Depending on the part of the sector of the government civilian defense intelligence, you will see different applications ultimately you kind of think about this from the standpoint of your so you're doing this sort of wide data connectivity right so you have data and silos and how do you bring it together and presented in a way that's meaningful to someone that has to take certain action or decisions. And the best way to do enable that is through this, you know, this creation of the semantic model that ties the underlying data together against this model that's abstracted away from the underlying data infrastructure. So in regard to what appears to be a low use of graph databases any thoughts on why this is the case and increase. Yeah, I mean I'll jump in and then super interested in what jarbock has to say, um, you know, also this was a survey from data diversity again I think a lot of us at the diversity kind of grew up in that role and are probably busy enough with that. I think as with any technology I found that in certain industries, it's much more common and I think it will start to play out. And again, I've seen similar pharma, there's a lot financial services there's a lot. Good point. I think that aerospace engineering with got a few customers from there that are super interested in or have already implemented graph. So I think it will grow over time it's also just, you know, there are certain use cases where it's good for a certain where it's not another area I've seen it. But the other thing is, I think, you know, I wouldn't be surprised for people, not folks who are building it but maybe using it without realizing it. Some of the master data management solutions or this idea of customer 360 in terms of vendors that are providing kind of pre built solutions are using graph behind the scenes. And maybe a case also that is more ubiquitous and folks realize they may be, you know, not the folks building it if they're on a data diversity survey but they certainly kind of embedded in other things but back to you maybe and see what you think. Yeah, I mean certainly look at the, the application of graph knowledge graphs in general has really skyrocketed over the last few years and gardener says that 80% of all data analytics. You will have a knowledge graph component to it by 2025, you know, which is, you know, within this one to two year range that you described earlier Donna. More importantly, we're now seeing new patterns emerge from a reference architecture if you if you're following the whole data fabric versus data mesh, a reference architecture discussion that has a knowledge graph component to it, primarily for the reason that you know you want to create some ability to create these knowledge centric domains in a decentralized fashion that makes data as a product, but then be able to combine that across domains as well so that you can answer some more critical business questions in certain certain areas where there is a demand to connect across domains as well right so data fabric architectures data mesh architectures are beginning to be deployed within knowledge graph semantic layer underpinning. Once the knowledge graph is established, how do you add new information, aren't there refactoring issues. You're not a static just as you know any other sort of analytics isn't isn't static so you want to you know keep refactoring over time and keep adding and you know I like that reference to the kind of the data data mesh and data fabric you know that that is the beauty of these they are dynamic, but I'll pass that back to start off in terms of your approach to keeping things fresh. Let's look again it's it's now I know we the title says graph database and one of the things we really push this notion that it's, you don't have to materialize everything into a knowledge graph. And if you do have to do that that's fine, you know, you still employ things like change data capture and triggers to push new information rights to to the knowledge graph but the approach we find that's that's that works best for a lot of organizations leaving data in place right so you kind of limit the amount of data sprawl and at the query time, because you've created this mapping to a virtualized source, wherever the data lives. You're actually bringing the data together at query time so you're doing it, you're doing the integration that compute time rather than in a storage layer right so so that way you always have access to the freshest data, in terms of, you know the queries that that you execute and when you execute them using a graph database for business reporting lineage any suggestions. I'm sorry could you read that one again. I'm using a graph database for business reporting lineage. Any suggestions. Yeah, that's an interesting because that's definitely the thing that relates to thing right. Yeah, so that's an interesting use case. Start up what do you. Yeah, no absolutely we're saying increase the utilization of an OS graph platform to support lineage types of reporting and applications inside the enterprise. In fact, many organizations have invested in a data catalog, somewhere in the organization and in terms that's the basis of, you know, sucking in metadata about data. You know whether it's a calibre or unity catalog from Databricks or elation or purview from Microsoft rights, or even glue from AWS so that's a great way to pull in a lot of the metadata associated with information inside the enterprise about the data. But ultimately what you're trying to do is connect the metadata the actual data so you make it more queryable and you tie it to a more abstracted view of the information that has business meaning and representation to people that want to ask the question right so data scientists can ask from a linear perspective. What's the source of the data can I trust it. These are citizen data users, I'm sorry can ask those questions. Data scientists can even leverage that to say, I'm looking to do some feature engineering around the particular machine learning model and I need some geospatial data where in my enterprise data landscape can I find that information. Right so being able to use that as that sort of knowledge catalog that sits on top of your source systems or on top of your existing data catalog is a great use case. Yeah, and you bring a good point and I think that was touched on the initial question as well is that metadata connections and metadata is data at the end of the day right so you know there's that whole meta level of using the graph, as well as linking it to the data so so many use cases it's pretty exciting stuff. What else is on the list. Yeah, I think we can spend a whole, probably a whole year talking about this next question but certainly a webinar based on your experience to what degree is maintaining consistency of data a challenge and would you elaborate. Yeah, I'll jump on it first and I kind of touched on it in the webinar and you're right this is probably a series of, you know, of webinars, just because well probably even more so because you can link all of the data together. That data has to be a good quality right if I'm linking, you know, customers together or interactions together, you know, that only works well when you have a good data. And as I mentioned before, do you have to wait and get that benefit. It is a cycle and you can discover a whole lot of patterns that may help you understand more about the data which can lead to more discovery to help you, you know, and implement that data quality or, you know, the interaction but I think having visibility is a great first step so I think data quality is an evolution. You definitely want to, you know, work on that. I'll go back to Navin's point as well. It is necessarily that everything has to be in the same place all in one big database to be consistent that idea of kind of that, you know, graph approach where we're adding this, you know, virtualized, if you want to say you know layer on top of it can help with that consistency but I feel like I'm talking in circles but it doesn't obviate the need to really have that data quality so you know it is a bit of both of you still need to work on the data quality those nodes the data you're linking. If it doesn't make sense you're not going to get much from the graph, but it doesn't mean you have to stop everything and put it in one centralized place to start getting that value so you know that there's still a place for that in some cases but I think they can play nicely in with each other, but curious what startup wants to add to that. I think I see this question two ways one is sort of this common with the semantic and at a modeling level right this sort of how do you get alignment and agreement on, you know, a customer means a customer and then borrow the semantic analogy so you don't have to have the same definition you can actually have have a semantic relationship to the idea that a customer can be someone else you know can be a supplier, a customer can be, you know, maybe a partner, but that definition has that semantic relationship, because they're all kind of type type of people. And then that definition can also be segregated by different or different parts of the organization, and you can have multiple lenses, looking at that information in the context of their way and their definition right so there's consistency at that level. Yeah, in terms of consistency of the underlying data itself, you know, I think that's a function of a bit of quality a bit of defining the data quality constraints associated with the data itself. Those can be again tied to the semantic model they don't you don't have to do this in mass without any context right data has to be fit for purpose consistency of data. You really don't know what to do what what consistency means so defining it and it's sort of in terms of what is fit for purpose for a given use and enforcing that through these data quality constraints is something and all this graph can enforce as well so. Yeah, that's a great great point I mean we often say you know the definition of data quality is fit for purpose, you know there's no necessarily right or wrong so that ability in a graph to add those different semantic layers of what might be right in the context of one relationship may not be so much in the other right so yeah that's a great point of kind of really being a hone in on those different use cases is super important. Let's start to facilitate one's ability to writing graph queries. Yeah, so we have a, because we emphasize this notion of citizen data user participation we are one of our applications called explorer has this ability to allow users citizen data users to create graph queries work visually. So like a visual design experience of a query that doesn't require any knowledge of the underlying standard called sparkle. So that's certainly one way we facilitate writing graph queries. Alternatively, we obviously have an ID so if you are more of an advanced user and want to write your own sparkle queries. We have an ID workspace you kind of right right away and create create as many graph queries as you want. And I think we have time for a couple more questions here we got about five minutes. So what's the biggest challenge in implementing a successful graph. Okay, I'll just start with dog's opinion on that first. You want to take that one. Oh, yeah, sure. The biggest challenge in implementing a successful graph. I think it's, it's oftentimes the, the first set of conversations we have is, you know, the companies or users are trying to boil the ocean and trying to come up with this perfect semantic data model description that will help facilitate all the information that a business needs. And they get mired in terms of, you know, the typical blocking and tackling you know what's a common definition for me and what does it really mean.   Well, so let's start with a think glass and think and also be people and think and also be objects and thinking, you know, it has no relevance to financial services and the use case around, you know, IT asset management. So don't always think about this from a purist sense of approaching, you know, your journey on our knowledge graphs right just start with your minimalist aspects of entities and relationships that are needed in order to answer a question. And then you kind of build it's built from there right you can always add new classes and you not every class has to be mapped to the source so you can have classes with no instances of data, that's fine. You can add them over time you can add the mapping over time, but focus on on a given use case, and that will definitely yield the results that creates value for the business in short order, and eventually an implementation that's successful for the enterprise. Yeah, yeah, not agree that starting small I guess and one of my thoughts, especially for, you know, a lot of folks that grew up in a relational world that it is a different way of thinking and just kind of start with a great I mean I've had a lot of my clients have a lot of great experience with again that time to market and some value but it, it often seems like a new too big of a new thing to start with all the other things we've had. So maybe starting smaller, or I think the learning, I must say the learning curve is difficult, but the impression that it's yet another new thing to learn, I think stops a few folks but you know, again starting with the small use case is a great idea because I think it's a great thing to have in the toolkit. Yeah, and that shouldn't stop anyone because I was I was a little surprised at the low adoption as well because they are super powerful, probably something to add to your list this year of things to kind of explore. Right. Yeah. So, okay. I slip in one more question we've got less than two minutes here. How are controls built around arcs within a graph database and family relationships like you described in some examples if a is the mother of be is it possible to parse through the graph and reverse and say B is the son daughter of a what additional logic considerations are needed to formulate those modified relationships. And I'll pass that to start off first too as well. Yeah, and absolutely I mean they again the, they're, there's relationships you can infer so not everything within a knowledge graph has to be codified in in a in the semantic model right in terms of relationships so you can, you can have a relationship that says a is the mother of be, and then be perhaps has a certain age and so if a is a mother of be, then you can actually infer that be at a query time, you can infer the relationship between B and ASB being the son of a. So it doesn't have to be codified in terms of actually defining it in the model in sort of, you know, explicitly sorry, I was thinking about the word. So, so what that really means is a query time the notion of inferencing or reasoning, where you can apply on the graph, where you can say if, if a if there's an a relationship with a is mother be then you infer that be is the son of a, and you can create that relationship on the fly queries at query time. Great. I have nothing to add to that. Well, thank you both so much for this fantastic presentation but I'm afraid that is all the time we have for today's webinar. Thanks to start out for sponsoring today's webinar and helping to make these webinars happen thanks to all our attendees for being so engaged just again a reminder, I will send a follow up email by end of day Monday for this webinar with links to the slides links to the recording of this session as well. Thank you, everybody. Thank you Donna. Thank you, Naveen. Thank you. Appreciate the time and the opportunities so hopefully folks that have a good day. Thank you. Cheers.


QuickBooks Online 2022. Enter transaction purchasing investments using bank feeds. Get ready because it's go time with QuickBooks Online 2022. Here we are in our bank feed practice file we set up with a 30 day free trial holding down control scrolling up a bit to get to the 125% currently in the home page otherwise known as the get things done page in the business view as compared to the accounting view if you want to change to the accounting view it's something you can do by going to the cog up top switch to accounting view on down below we will be toggling back and forth between the two views either here or by jumping over to the sample company file currently in the accounting view back on over to the bank feed practice file we're gonna open a few tabs to put reports in by going to the tab up top right clicking on it and duplicating that tab back to the tab to the left right clicking on it again and duplicating again back to the tab to the left right clicking one more time duplicating one more time as that is thinking let's see where the reports are located over in the in the accounting view and the sample company they're on the left hand side under reports if we jump back on over to the business view in the second tab the reports are going to be located on the left hand side in the business overview then we're gonna go into the reports then we're gonna close up the hamburger and we'll do the red do the balance sheet let's open the balance sheet up first the big balance sheet we'll do the range change up top from 0 1 0 1 2 1 to 12 31 2 1 and run and then we're gonna go to the tab to the right and do it again go into the business overview and into the reports close on the hamburger this time we're looking at the P and L profit and loss otherwise known as the income statement range change from 0 1 0 1 2 1 12 31 2 1 and run and then we'll go to the tab to the right opening up the trial balance less well-known but it's not fair that it's less well-known in the business overview the reports because it should be more well-known it's an excellent report trial balance called the trial balance I type it in because they put it way at the bottom of the list which again it should be in the faves we could add it to the favorites but I just type it in here trial balance is going to be 0 1 0 1 2 1 to 12 31 2 1 basically run is the balance sheet on top of the income statement good report for checking your work easy to scroll through and has all of the accounts that will be impacted on the balance sheet and income statement so you can have one tab open as you're doing the data input I would recommend having something open balance sheet income statement and or trial balance as you're doing data input so you could jump over and see what's happening as you enter the data so let's go to that the first tab here in prior presentations we set up our bank feed information which if you're in the business view is in the bookkeeping section on the left hand side transactions up top then in the banking we pulled this information in from our financial institution it's here in a bank feed limbo what I call bank feed limbo meaning these transactions have not yet been pulled in to create or support what has been created in the financial statements yet and we need to give them a little bit more information a little bit of help a little bit of guidance to bring them on through and that usually will include the account name that we have to add to them as well as of course the vendor and or the customer depending on if it is an outflow or inflow so the scenario we have here is going to be related to investments now the investments are going to be a little bit more unusual let's just think about them in general how they might work if I go to the tab to the right we're looking at the balance sheet now the investments if you have a business type of account are something that you might not have on personal investments you don't typically want your personal investments on the business side of things typically what you would like to do is generate revenue in the business side of things as you accumulate assets possibly cash you're either going to be choosing to put that cash back into the business or invest it if you're going to invest it then you typically want to take it out of the business distributed it to the owners if it was a sole proprietorship or partnership that being in the form of draws down below we would have draws taking money out other side recorded to equity draws and then the owner puts the money into say investment accounts to have some other side types of incomes or earnings that they can have if it was a corporation it would be the form of dividends or the outflows to the owners but you might be in a business that has some holdings in investments or you might be in a type of business that has investments as their type of business a financial type of company or you might be using quick books for personal investments and you can do that as well so let's just think about how the investments would be set up it would be similar for any of those kind of scenarios we have the same kinds of problems so that would be that we're going to take some of our money here we don't have any money right now in this file because we haven't put the beginning balance in or any of the deposits we'll talk about that later but if we had the investments we would be taking some of our money putting it into what we could call an other current asset or possibly long term assets that's one of the difficulties we need to think about is it current or long term to generate income in the form of typically capital gain interest and dividends so that's going to be the objective we're thinking here about stocks and bonds generally so how would we put those on the books how can the bank feeds fit into that type of scenario now first of all remember that quick books is not an investment type of software in that it's not used to track investments on a day-to-day basis you're not running graphs basically on a day-to-day basis to see and track what individual stocks are doing each day in quick books there are other softwares that you can use to do that if you want to do that and or use the information that's going to be available to you from your financial institution that you're purchasing the stocks from what quick books can do is help you to recap where the investments stand at certain points in time as part of the big picture with the rest of your financial investments to get an idea of where you stand on the balance sheet and your earnings on the income statement so that's going to be the basically the objective of it so you want to know what the software is actually kind of there or designed to do and how it might fit in or integrate with some other softwares note that there are other softwares that will pull in the investment information kind of like your balance sheet information and so that you can you can actually always have like your ending balance that would be pulling in from the financial institutions again that's not really what quick books is designed to do because quick books is designed for you to enter the financial transactions not just creating the balance sheet from the endpoint of the financial statements but also creating the income statement which is going to give you your performance numbers and if you were to just pull in your end numbers from financial institutions on your stocks and your bonds and your bank accounts and whatnot then you'd get a balance sheet that's that's kind of up to date on a current basis but you wouldn't have your creation of that your income statement numbers and you wouldn't be checking your financial transactions which is what the quick books is designed to do from an accounting standpoint okay so how could we set this up typically you're going to want to set up possibly like by financial institution is one way you could do it so you might have different financial institutions that you have stocks and bonds or investments with like a 401k plan possibly with your employer if it was a if it was a not if it was for your personal side of things or you might have stuff in like a vanguard or an e-trade or or you know your your actual physical bank bank of america or something like that that you then have your portfolio of stocks and bonds so that's one way that you might organize you might have see a drop down that would be your investment account and then organize your different types of investments by financial institution or you can organize by the types of things that you're investing in for example stocks versus bonds in general you could try to break out a little bit more detail than that like us stocks versus foreign stocks versus and so on but if you do that the more detail you put into your your accounting system here the more data input that you're going to have to do when you want to update your information so generally you want to keep it fairly general here when you're doing the data input into quick book so you can get an idea of where you stand at a particular point in time and get into all the details on the weeds in terms of the increases and decreases with other software and or your financial institution providing you that kind of supporting type of information the other way you might want to break it out is is a current portion versus long-term portion if it was an individual investment you might want to be saying that i'm going to put everything that's not under an umbrella of a retirement plan like a 401k plan or an IRA or some other retirement plan a 403 b plan those everything that's not under that umbrella might be a current asset everything that's under that umbrella would be long-term asset because that would be breaking out and indicating the fact that you can't get to those things under the umbrella as easily without restrictions penalties and whatnot you got to follow the rules under those you can access of course the the items that are in the current or outside of that type of umbrella so that's another way that you might want to set this up now then once you have those accounts set up then the question is well how do i track uh gains and increases and decreases in the stock so what if the investment goes up and down that can be a problem because you haven't realized the game so typically people will want to put the increases and decreases in the account because that's what the market value is saying we can track the market value because we actually have a financial institution or trading the stocks are trading on an exchange but since we haven't realized it there's kind of a question as to whether we should record the income as income on the other side so we'll talk a little bit about that and then of course the bank feeds come into play when cash takes effect so if you were to get interest and dividends that flow through from the investment to you then you're going to have cash transactions going into your checking account and you would be recording those oftentimes as income as they come through so let's just think about how this might be set up let's first set up a couple accounts in our chart of accounts to do that i'm going to right click on the tab on the left duplicate this tab and this second tab i would like to change this to the accounting view because i'm going to be dealing with the chart of accounts so i'm going to go up to the cog to the right and i'm going to switch this on over to the accounting view because i think the accounting view is nicer when you're adding accounts and dealing with the general ledger and i hope quickbooks will fix the business view so it will be easy as well there too because i think the business view from everything else works fine with it but i don't like the format of adding accounts on it currently so hopefully you know they fix that but listen hopefully someone else feels that way too possibly and we'll tell them that and they could fix it so we're going to go to the accounting down below and i'm going to say let's close up the hamburger and what i'm going to do is i'm going to put an account in here called investments and then i'll create a couple sub accounts which i'm going to imagine are my financial institutions that i have investments with and i want to do it that way because i'm going to get statements on a monthly basis quarterly basis or possibly only a yearly basis that i can use to update my quickbooks accounts to uh the current activity so i'm going to go up top and say let's make a new account and i'm going to say this is going to be an other current asset type of account and investments so i'm going to say investment let's just say other investment and i'll just call it investments here now you might want to put like a short term investment versus a long term investment anything that would be under an umbrella like an ira or a 401k if it was a personal business or quickbooks on the personal side might go into into other assets down below which will be kind of long term assets you can think of as opposed to current assets so these are going to be the investments i'm going to say let's let's save that and then i'm going to put a couple sub accounts under that investment account i'm going to say let's add another one and i'm going to call one of them a this is going to be once again an other current asset and it's going to be let's say investments other and then i'll call this one a bank of america investments so that's going to be investments i'm imagining in stocks and bonds with bank of america so this is not the check-in account this is our investment account we're imagining through that financial institution we'll make it a sub account of the investment accounts so i'm going to put the investment accounts and then it's going to be the sub account don't get it mixed up with our inventory accounts okay so i'm going to save that and then if i scroll down if i scroll down there's our investments there's our sub account let's do it again i'm going to say let's do a new one again ultra vase another time we're going to say this is going to be the other current asset other current asset and we'll just call it investments other again this time this one's going to be prime america prime america i think i did i spell that right no i clearly did not prime america is that even a word i don't even know if that's a word okay i think i got it now i got it and then we're going to hit the we're going to hit the plus button and this is going to be investment again investments so there we have it and so i'm going to say let's save that and close it so now we've got our investments that's one of the difficulties that we have to do this now the next thing is that we might have beginning balances in our investments before we had entered the information into our quick book system so we might want to be putting our beginning balances in how could you do that you might look at the prior statement before the cutoff most likely if you could do this before january january first being the cutoff that's not in this practice problem but that's often a good practice to do so you can have a whole years of information in the current accounting system you might then go into the register for example and enter that beginning balance in place the beginning balance would not be going through your checking account as of this point in time because it was something that happened prior to when you started the quick books bookkeeping system and remember what quick books does not do is just pull in the ending balance on your financial institutions on the bank accounts or on the on your investment accounts you have other software that can do that for you but what quick books does is have you enter the transactions the detail to get to the end result because that records the activity given you the income statement so let's just enter this i'm going to hit the drop down right here and i'm going to enter a transaction i'm just going to call it a journal entry let's just call it well we can call it deposit maybe i guess we'll deposit it into there now let's go with the journal entry i'm going to go with the journal entry this is going to be on let's say 01 01 2 1 and so the beginning of the year would be best if you can enter these ads at the beginning of the year or start your books at the beginning of the year and then memo this is going to be to record initial or let's say beginning beginning balance and then i misspelled beginning i misspelled beginning i spelled it right the dictionary has it wrong and so this is going to be then 10 000 and then the other side needs to go into some kind of equity account so it's going to go into some kind of equity account so we got opening balance and then they gave us retained earnings so we could put it basically into the retained earnings that's what the accounts are going to roll into you don't typically post to retained earnings but when you're doing the beginning balances that's what you you might post it to the retained earnings the point being it needs to go to the equity account because it's not going to the income statement in the current time period it's part of the beginning balances which had already rolled into equity that's why it would generally be appropriate on the beginning balances to put it into the retained earnings so i'm going to go ahead and save it and close it so now if we check that out if i go back to my balance sheet and hold down control i'm going to scroll up i'm going to run it run it i was running and then we're going to go down here so now we got our investments dropped down we only have something in the primarica at this point in time if i opened that up we've got our beginning balance journal entry that we entered into it so that looks good if i go back up we're going to go back to our balance sheet and then scrolling down to the equity section if we go into the retained earnings note they don't let you drill down on retained earnings which is kind of annoying because that's the account that you roll into so if you want to see the detailed and retained earnings and i do you have to run a report i'm going to go to the tab to the right just to show that right click on the tab and duplicate it and then let's see if we can run a report which i'm going to call a general ledger report so i'm in now the accounting view where the reports are on the left hand side and then we can close up the handbook and let's call this the general ledger the general ledger the gl sometimes referred to as let's change the range shall we i'm just going to put that one day 0101 2 1 2 0101 2 1 and then run it and i'm looking for the for the retained earnings account which is a balance sheet account so there's retained earnings there's the activity there's the journal entry activity for it okay so back then to the balance sheet so now let's say that we're going to put more money in at this point in time that's when it's going to hit the bank feeds right so now i got some money in primarica let's say we're going to put some money out of our checking account into the primarica and wait till it clears the bank before we record it that's when we'll see it on the bank feeds that's one thing we'll see on the bank feeds so if i go to the first tab then and i'm going i'm now i'm in my bank feeds again note that you might need to refresh the screen up top because we added some accounts and we're going to be using some of those new accounts as we do the as we do the data input so as you go back and forth between those screens you might need to refresh them i'm going to go into this item and pretend this is another investment that we're putting into place so there's the investment now note that i could put it in this way and change this category to the investment investment if it allows me there it is investment for primarica right there but if i do that and it would be fine for me to do that but note what's going to happen it's going to show then this as expense type of form but i'm really going kind of from one financial institution to another so it might be easier or it might be better for me to say what if i do it with a transfer so if it was a wire transfer you might want to transfer because then when uh when it goes into when it goes into the investment side it looks like a transfer going from your checking account to the other side as opposed to an expense form that's actually increasing your investment account that's kind of what the transfer form does you don't really need the transfer form because you can you can do the same functions with an expense form or sometimes a deposit form if it's going from one checking account to another for example but it makes it a little bit more clear that this is going to two of your your financial accounts if it's a transfer form so that's gonna so let's do that it's going to be a transfer form here i'm not going to save the rule or anything of course and if you were doing this often you could save then the rule that you're putting into place and this might be typical if you had an investment that you put money in on a steady basis if you're basically saying okay i have it set up so i put another whatever it is in i think it was 25 dollars this time a month goes into the investment each time then it would be a standard transaction we can memorize it we could put a rule to it and then it would be increasing the investment account rather than going to basically an expense account so i could then add that and that would just be a normal type of transaction but it's not hitting an expense then if i go back on over to my balance sheet and run it again then i'm going to say now the checking account should be going down it's still negative because we have to do that beginning balance thing kind of like we did with the investment account to it we'll talk about that later but there's the transfer notice that's in there as a transfer that gives you an indication of oh it didn't go to an expense i didn't and this indication by the by the way that it's an expense form doesn't necessarily mean you did an expense to it because you could have bought equipment for example but it tells you hey i didn't buy anything it didn't go to a third party it went to me right i put i went out of this account and back to me in some other account that's what it's saying so if i go back in here it doesn't go to an expense form it doesn't go into the bank feeds when i drill down on the source document it goes to a transfer form so i'm going to close this back out and go back up top and say we're going to go back to the other side and this one's going to be in the investments so in investments down here in primarica now we have our beginning balance which we put into place by putting it putting that into place and then the ending balance that we have here this is the money that we're saying that we're imagining that we kind of put in there on a monthly type of basis back on over now while we're here just let's think about the other kind of problem you'll typically have with these investment accounts and and it's not a bank feed type of issue because it's not going to be money that's going through your bank feeds it's going to be the fact that the investments just went up in value so if these were stocks and bonds that were representing here and they went up in value or they went down in value how do i reflect that periodically so we're going to we want to reflect it typically periodically it's different than say an investment in equipment where we usually put it on the books at cost and we just leave it there at cost and then depreciate it because with the equipment the equipment is unique in nature i don't know what the value of is if it is changing this is one reason we've recorded at depreciated cost as opposed to the investments where those are trading on the stock market and they're not unique in nature they're exactly the same they're exactly the same stocks that are currently trading on the stock exchange and i can see what they're trading for therefore i at least have a real-time you know market value of what the stocks are at any given time i haven't realized the value until i sell it but i can see that change in what's going on in real time because i own something that is exactly the same as something else in theory and it's trading for a current current price therefore it becomes more reasonable for us really to kind of do a fair market value calculation on our stocks so we might do that in quickbooks periodically we can't do it we're not going to have the software that's going to just put the put the market value on the books because for example if this was an increased market value by five hundred dollars if quick books was just to put that on your books then it wouldn't record the activity it wouldn't record the income statement other software does that kind of thing and you can get a balance sheet but what quickbooks does is try to allow you to enter the data to record the activity that is going to be happening to get to the ending balance therefore you might do that periodically you might get the statement from primarica if the statement says hey this thing is now five hundred dollars more it's worth in other words ten thousand five twenty five fifty for example then you might increase the primary account here and you could do so by going directly to this account increasing it with basically a journal entry the question is what did i what do i do with the other side then should it be income because it's not really realized but the other the other where place you can put it is is into equity but most people don't on at least on the individual side because that's kind of confusing i think other income is a typical place that it will go so let me just show you how that might work so let's imagine that we got our statement at the end from primarica uh you could you could then say if it was increased by five hundred dollars let's say what did it go it was increased by five hundred dollars i could put it directly to this account i could try to create another account that is a sub account of primarica that would be tracking my unrealized gains and losses too so then i can break out my cost versus my unrealized gains and losses on it that could be a useful bit of information i'll add that little bit of detail just for the practice problems to see so there's a lot of different ways you can do it but let's go back to the register here i'm going to go back to my chart of accounts so i'm going to go into the chart of accounts and i'm going to add another account account up top which i'm going to say let's make another one and i'm going to say that this is going to be another current asset it's going to be an investment other and i'm going to say this is uh unrealized gains and losses for primarica for primarica primarica okay and then i'm going to make that a subcategory of the primarica account so we'll have this the unrealized gains and losses kind of connected to it so i'm going to say save it and close it and let's say i looked at my statement now and it was the value had going up by $500 so here's my sub account i'm not going to put that directly into here because i'm going to try to keep that at the cost and then i'm going to go down here and put my unrealized gains and losses and go into here and say this is going to be a journal entry and let's say this happened at let's say the end of the month like like we'll just put 103121 for our practice problem and then i'll say that this is going to be gains for october statement and this is going to be an increase of $500 and the other side now where does the other side go should it go to income not just normal income i usually would put it to other income so it's on the bottom of the income statement because this isn't part of our normal income so i'll possibly say this is going to be unrealized now i got i want to make another account here not the same unrealized gain add a new one let's add a new one and this one's going to be an other income not just income but other income so it goes on the bottom of the income statement because it's not part of normal operations and i'm going to say this is let's say miscellaneous income let's say it's unrealized income and you could say gains and losses but i'll say one income or loss just to differentiate it and let's save that and close it so there we have it and i'm gonna say save it so now when you have those those unrealized gains and losses i can go back on over to my balance sheet i can refresh it let's run it again and take a look at it so now i can say that we had our investment in primarica this is the amount we originally put in and then we put an unrealized gain or loss it went up we didn't realize that we didn't sell it but it went up by that much according to the bank statement and that means that the ending balance here that 10525 would tie in to what my bank statement says from primarica or my investment statement would say the unrealized gain then is going to be on the income statement and it's something i'm very dubious about i'm skeptical about that gain i don't put it in income up top because i haven't really realized it for number one and number two it possibly is not part of my normal business because the stock market could go down the next day i haven't locked in that game by selling it and therefore i put it on the bottom at the other income so that i can see my net income before that other stuff and then here's the other stuff which i would put the unrealized gains and losses your other option is to put your unrealized gains and losses and just not record them as income at all but put another account on the equity section of the balance sheet but that's usually a little bit more confusing for people to do so i think the bottom of the income statement is a typical way to go now if you're doing gap generally accepted accounting principal accounting then of course there's there's more standardized rules on when you use unrealized gains and so on and so forth i'm not trying to get into that in detail here just giving you some ideas on how you can record this stuff in quickbooks and use the bank feeds in order to do so so also note that you might have your your dividends and incomes that you have the income rolling in and then it gets fed back into the investments that could be a little bit tricky to when you want to try to break out these unrealized gains and losses too because because you've got you've got to you know figure out the way to break it out into two accounts when you have your income dividends and interest rolling back in to the investment in other words the last thing you could you could think about with these investments that i'll talk about now is the dividends and interest which is income to you if you were to receive the dividends and income then it would roll in through the bank fees because you would have deposits from them on the bank feeds and you can then record them as income to dividend and interest income however if you roll those income back into the into the system then they're not going to show up on you roll it back into the investment they're not going to show up on your checking account because you didn't actually receive them they're going to show up on your primary statements as reinvestments back in to to the account here so that means that when you see the new balance that you're looking at some of that new balances unrealized gain and losses and some of that balance is because you rolled in the dividends and interest into the and back into the investment okay i think we've gone way beyond the the bank feed portion here so we'll stop it there


A poem titled Fatima cries. She went before her father's grave and collapsed upon her knees. She clenched her heart and wept aloud. Oh father, do you see? You left this world and took with you the good that once was known. You left behind my broken heart and now my broken bones.  I miss your voice, your blessed face, the angels that once were here. I miss the comfort of your arms which used to draw me near. Oh father, if you only see how they've attacked me all as one, abandoned us then took from me my newly unborn son. Oh father, all these lonely nights, they're hard to suffer through. The pain has brought me to my knees as I now complain to you. Oh father, if you'd only see how they've attacked me all as one, abandoned us and took from me my newly unborn son. Oh father, all these lonely nights, they're hard to suffer through. This pain has brought me to my knees as I now complain to you. Oh father, if you only see how they've turned on our Ali, abandoned what you left behind and returned to disbelief. Oh father, who will hold me now when my days have turned to nights. Oh father, now your orphan cries, like Yaqub, I'll lose my sight.  You came to them and tried to save the dwellers of the Nahr. They lied to you and made an oath and forgot their judge is God. They took my rights and burned my door, my frightened children saw.  Oh father of all the broken parts of me that now remain. My heart is what cannot be fixed, nor Hasan, nor Hussain. We are all you left behind and look what they have done. My eyes are red, my bones are broke and it's only just begun.


Here they are guys, these are the pieces that we created for our very first giveaway to say thank you to all of you wonderful viewers for liking, subscribing, commenting, donating, sponsoring our channel. So all you have to do to be a part of this giveaway is you have to obviously be subscribed and then go watch both videos, like them, if you like them, and then comment on the one that you would like to win if your name is drawn. And then we are going to go and draw a name on March 17th. We'll try to do a live video of the drawing process. It won't be a long one, it'll probably be 10 minute videos so don't worry about wasting your life on a live feed and we'll draw for the winner and we'll contact you to let you know if you've won. If you don't let us know that you want to claim the piece within one week of the drawing date we'll have to do a redraw. The contest is going to run from February 16th to March 16th, 2018 and we'll probably do the drawing around like 7 p.m. central time on March 17th, 2018. Make sure you tune in to that to see if you've won, we'll ship it to whomever wins. Unfortunately, this contest is only for our United States viewers because YouTube doesn't really allow for international giveaways because of laws, rules, and regulations for other countries. So we'll probably do a more international style giveaway on our other social media outlets so make sure you follow us on Instagram and Facebook and Snapchat. All of those are in the description box below as well as the contest rules and the YouTube guidelines for every contest and giveaway apparently. It's super serious. So yeah, let us know which one you would like if your name is drawn. There's this one which is B, it's more ocean-scapey, it's got glitter in it, very lovely piece. I'm kind of upset that we're doing both of these for giveaway, I'd love to keep them but we really wanted to say thank you to you guys. So this is B and this is A, mermaid beach-scapey thing as well. They're both, they're kind of similar in some ways, super unique in other ways. You won't be disappointed on either of them. They're both on heavy-duty gallery rep canvases. So thank you guys for tuning in, for watching, and for participating in our giveaway very first one.


It's been a busy and successful week for the U.S. and the Royal Moroccan Air Force working together in Morocco during Exercise African Lion 21. During the exercise, multiple U.S. Air Force C-130J Super Hercules aircraft are flying alongside our Moroccan partners to conduct critical airdrop, airlift, and aeromedical evacuation training. Major Ty Payne, commander of the C-130 Operations for African Lion, says that there is more than one benefit that comes from training so closely with our partners. So working with the Moroccans has been great. Today on the flight, we got to have a pilot who's familiar with the airspace, knows what's going on, and can keep us out of trouble. But aside from that, just working with them and understanding the logistical procedures they go through makes it so that in the future we can really work effectively together. The C-130's also quickly and safely delivered 200 Moroccan, British, and U.S. Army paratroopers to an austere location via an airborne assault. Major Payne assures that the lessons learned here in Morocco today will have a lasting impact on how the Air Force accomplishes the mission with our partners and allies in the future. I think the things that we're going to take away from that is what enables us to be able to operate in the future in any kind of environment and conflict, whether it's here in Morocco or up in Europe, back in the States, as we can work together with other people and take those lessons to war.