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okay today's webinar is what is data and our presenter is ravi Prince Nagorny Ravi it's all yours thank you so much all right hello and welcome everyone to the what is did a webinar if you're looking for fashion icon and burlesque performer did avanti's you're in the wrong place this is a webinar for everyone who's heard of the term data or wants to further the career in technical writing now before we start I just want to take a quick poll to see where people's knowledge of data is so you should be getting a list of questions from I have a good graphs bits and pieces No and I master the topic but I'm interested to learn more so we'll let people trickle in and answer this question right now I'm in cloudy Rochester in New York the temperature is a bit cold that is extremely damp so bear in mind I might have to mute myself a little bit that the blow my nose or something all right mary says stay warm thank you so much Mary all right we're getting some polls in progress it looks like we're at 89% so that's a considerable achievement and we'll just leave it up for six more seconds see if we get a bit more answers here they come in right 91 percent this is great all right that's good enough to get just I'm gonna close off the polls now now let's see what we have so it looks like 13% have a good grasp of it but not the whole picture we have most of you 70% which bits and pieces and then 18% have no idea and then only 2% that they've mastered the topic but they're interested in learning more about data so this is a good starting place because this that webinar will go into great detail about all the different terms and concepts related to data alright let's see I'm gonna hide the results here and let's get started so a little bit about myself my name again is Ravi Prince ed warning and I work at jour Seca as the marketing operations manager I've been there for about three and a half years and I love it so as a marketer I'm fascinated with content and the content metrics that I used to measure the kind of achievements in marketing that were obligated to prove now in technical writing there's a whole new realm of content metrics that need to be developed in order for technical writers to prove what they do to the rest of the company and to show their value so I've been pretty interested over the past couple of years and I did a webinar with the SCC gosh it has to be take a year and a half ago regarding this topic so while my role is not a technical writer I have worked alongside technical writers for almost four years now and you know it's exciting information so a little bit about my company jour SEC is the makers of easy data is a component content management system as everything from collaborative authoring single source content management and multiple channel publishing so enough about that let's get started here's a outline of what we'll be covering today feel free to sleep through any of the boring parts I'm just kidding it's all really super exciting stuff this is everything that I wish someone had told me when I started almost four years ago so we'll begin going over the common turns and constants that are associated with data structured content semantic tagging the definition of data the benefits of data did itself the topic types reuse and publications so I will leave some time at the end for questions so hopefully it won't take too much time to go through these ok so a lot of webinar start off with did it is a XML standard that's it congratulations you don't need to watch the rest of the webinar but I wanted to start off in all seriousness with why we want to know what data is in the first place and it all comes down to this it's pretty easy for humans to read and communicate and understand languages that we've developed over the millennia but not so much for machines remember at the very core level robots and computers communicate in ones and zeros there's been a major push to develop languages that allow us humans and machines to easily understand each other so that we could use machines to improve our daily lives I want you to think about all the latest trends and technologies that are coming about that require computers to understand and use content in ways never before required you have chat BOTS that are required to know the complexity of conversations and respond in context of the dialogues that they have you have augmented reality that requires computers to recognize their surroundings and display relevant and concise information to the you vert user so how do we get machines to understand what we want them to do with our content we do it by adding additional structure to the content so machines understand what we're trying to say did ah in a nutshell is one of these structured languages humans have developed to advance the way as computers can interpret and use content so I know terms structured content gets thrown around a lot and is used pretty loosely but most of us already create some semblance of structured content we have chapters in books recipes step by steps and instructions a fake use privacy statement email signatures and so on each with a common formatting type of information and related context by writing content in consistent forms is easy for us to understand at a glance think about a stop sign if every stop sign was a different shape and color it would be really hard to know where to stop without looking closely at your surroundings at each and every crossroad for Standardization of these kinds of content it makes it easy for us to understand well let's look at an example traditionally structure content meant databases in spreadsheet and text was quote/unquote unstructured but the text we create has structure it's just harder to translate into the ones and zeros that machines use we often indicate the difference in text visually in disturbing ways just as the titles being bigger short descriptions are italicized more important terms are in bold and so on as humans it's easy for us to see and interpret these visual differences but not so much machines because they can associate the meeting behind is styling here we have one of my favorite poems by Joyce Kilmer I think I'll never see a poem as lovely as a tree the tighters the larger than anything else the poems written written in italics and the author is indicated in bold and comes at the very end of the paragraph this is where it gets exciting here you could see that we've added structured bits of code to the content to help the computer identify what each of these elements represents these bits of code add semantic meaning to the text in a way that computers can be programmed to interpret we have title tags around the title the poem is labeled as a poem the date is separated as a date once these tags are added it's easy for machines to be programmed to use the content like data instead of being one gigantic block of unstructured quote/unquote text these bits of code are called semantic tags which is one of the common terms used to describe what data is it's a semantic markup language so tag your it with tags there's typically a open tag and closing tag leaving the content sandwich in between this moves the task from trying to get a computer to understand the complexity of linguistic rules to just understanding how to interpret the tags themselves think about it this way when you go to a grocery store there's tags on the produce that explained the price and information about that product is it organic what's the expiration date is it cheaper if I buy two of them and so on now it's not just text but images videos and other types of mediums can be tagged using the same methods as long as there's tags computers can no look at the tags and know what to do with it here we've identified a ball a book and a human however the problem arises when we try to agree on what to call each one of these objects but one person might see as a ball am I another might label as a circle as a book as pages a human as the cowboy this could quickly spiral out of hand if everyone can't agree on the same terms what I could call of all you could call a ring a hoop a wheel a sphere a globe a disc a hoop a Halo or orb which brings us to the need for Standardization of these tags one of the early coding languages for this very purpose of standardizing tags was sgml which stood for standardized general markup language go figure of course needs evolved from the 1960s when sgml was first created so there's been variations of their original language you should recognize HTML ads it's the standard tags for the web and there's XML which is also very common for web application but as much broader use cases there's actually more than over 250 different variations of XML standards and growing both HTML and XML led into further variations like CSS to style HTML for XML there's over hundreds of standards like doc book journal authoring tag sweets and prism publishing requirements for industry standard metadata now these particulars standards are all for technical publication data is one of these popular technical publication standards all right to recap we know why we care about data because it serves as a human machine relatable readable language and it allows us to create new technology and it's standardizing the semantic tags so we use the same terms when describing our content but what does di ta actually represent it stands for Darwin information type architecture so in a nutshell Darwin means higher hereditary attributes information represents data at content and knowledge typing is a classification of information an architecture which is the hierarchy of elements our so let's look at what Darwin means in the context of ditto at a high level every semantic tag rewrite is taken into context of the larger elements of content to make sure that people are on the same page a data element is something that we've semantically tagged like the picture of a ball or a poem of text let me use an example say we're writing about toast toast personally there's nothing better in the morning than some sliced bread toasted to perfection with a bit of jam or butter on top we straight we start by creating a document with the topic of toast we add information on how to create toast by using a toaster and what you will need in order to accomplish a task such as bread and butter now as humans we could easily make the connection from these prerequisites and tasks as related to the overall topic of toast and data many of these elements have hereditary information almost like humans have DNA did elements have their parent elements within the tag so machines understand that it's not just the prerequisite it's a prerequisite for the topic of toast now if you're ready to publish this and the computer didn't understand what a prerequisite was I would know that the text in the prerequisite was part of the topic of toast and treated just like the topic the same goes for any elements that we put inside the tasks or prerequisites for example making sure that they don't grab the toaster cord with a wet hand within a note the note would be taken in context with the prerequisite again if the computer didn't understand what I note was it would treat it the same as a prerequisite and then it would roll up to the topic the same hereditary attributes apply when if we were going to create a course syllabus instead the prerequisite for the course would be recognized as a course prerequisite and not a toaster topic prerequisite all right enough about Darwin the next we have I for information if you really want to debate about it information is everywhere but hopefully I won't need to go in too much in detail for what information stands for in this webinar but to be brief it's the content of text synthesis images videos that we create on a daily basis next up we have the T for typing now typing is not the process for writing on the keyboard but the process of classifying information everything in data is separated into topics instead of storing them into one document each of these topic containers if you will of information has a very specific purpose the biggest difference between data and what people are accustomed to when writing book oriented or narrative content is because data has everything as discrete pieces of information now in a four order for us to break down our current topics into these topic types or documents into these topic types there's some guidelines that we need to have to follow make sure that the information is still coherent number one a topic should answer a single question or subject it shouldn't try to cover a multitude of ideas or insular multiple questions number two there should be sufficient information for the topic to stand alone or be what's commonly referred to as self-contained what this means is that you have enough information to describe what the specific topic is for but not this topic not that the topic explains everything related to it this is commonly referred to as minimalist writing an example for this is if we were to include information about all the great recipes for toast in a step-by-step instructions on how to use a toaster it's no bueno number three each topic should have discrete units of information that means it shouldn't rely on information before or after the topic to explain what it is I know this is just high-level concepts so let's look at very specific examples with a basic building blocks of data these topic types now everything in data standings stems from the basic topic type called a topic there's three common customizations of this basic building block concept tasks and references the concept topic presents essential conceptual or descriptive information so the reader can understand the background and context of a subject tasks topics provide procedural information on how to do something reference topics provide detailed facts or specifications often in the form of a table or a bulleted list let's take a look at some examples going back to our toaster scenario here we have a product description of the Lightning toaster it has a title short description and a main body of text the Lightning toaster comes with a timer dial and bagel mode have a perfect slice of delight with your favorite jam or butter easily achieve exquisite toasted bread with just a push of a button there is the the market Ernie anyways it looks like the short description is italicized and the tightest title is bigger than the rest of the text but let's look at the backend and see what is happening as far as what computers can read here we can see the semantic tags are added telling the computer what each of these elements of text represents we have a title tag a short description and a body tag I also want to hide highlight the con body of or the concept body this is a great illustration of the hereditary attributes of data now notice this nowhere in this concept is anything about styling because there isn't any applied to the raw text styling is applied when it gets published which means as a content creator we could focus on just producing the content I did a survey a couple years ago with the SDC that I mentioned and I found out the average respondent of this survey spends about 4.5 hours a week formatting content and considered it one of the least valuable tasks they could be spending their time on now formatting is really important it helps is how we easily understand and scan text think about the stop sign example that I mentioned earlier if each and every Scott stop sign was a unique color and shade and Texas and font size and serifs you know New Times New Roman it would be really hard to understand each and every time you came to a crossroad so what did allows us to do is separate the process of authoring and formatting so we could focus at one on a time one at a time the second building block is a task topic here we have instructions on how to clean a toaster we could see the title the short description we have prerequisites and we have steps and expected results and we have within the prerequisite note before using the toaster read the safety warnings these are just some of the elements that you could add for full lists you could go to the Oasis website for standard for data Oasis is the Organization for the Advancement of structured information standards there's a link in the resource section of this webinar so don't worry about googling it while we're talking again it's easy to understand and read this product description on how to clean a toaster or because this task topic is structured in a very similar way to the concept topic the short descriptions italicize and the title is bigger than the rest and the prominent texts are in bold when we look at the back end again there's no styling just semantic tags that make up the data elements we have the title tag short description and the hereditary in front where the body text is now a task body instead of a concept body we can see that the note is sandwiched in between the prerequisite tags and that the task body is enclosing both if the note was missing or this computer didn't understand what I know it was the text would be treated with the higher semantic tag in this case ap for general paragraph so why do we need all this granularity one of the benefits of data is that if we're reviewing the our data concept is so much easier to tell when something's missing because we could do a search for all anti short descriptions rather than all italicized text the third basic building block of data and keep in mind data is just a standard of XML is the which means that there's other ways of labeling and tagging this information all within the standard of XML sorry there was a question that just popped up I wanted to make sure we got it before they stay on the rest of the webinar you're welcome all right so the third basic topic type is a reference topic here we have some product specifications for a toaster we know that its Class A as the capability of toasting one to two slices and it's for non-commercial use now it's not just blocks of texts or a list that we could label with semantic tags we can also do it with tables looking at the back end we have a table properties such as value in description it gets pretty granule as far as the information we could tag which allows us to control exactly how we want to use and render these elements when publishing again if we look at the property head tag you can see they are passed down to the child element tags for property type value in description header tags additional granularity can be added via specialization of data remember that it's just one part of XML but remember the whole reason to use data and the reason it exists in the first place is to standardize the way that we tag content back on track we have two last letter of data Rome was there a for architecture now now that we took our documents and broke them down into topic types what do we do now we need some way of connecting the topics back together when we want to build manuals or other kinds of publications this is done by using data Maps data maps are like table of contents they link all the bits of information contained that can be contained in one publication think of data Maps like paper binders that allow you to insert reorder or remove pages simply by snapping open to class unlike paper biters data allows you to reuse the same topic in multiple locations get to inter the pallet exactly works later in this presentation here we can see the same topic types are used in both manuals or both data Maps alright so let's take a brick a break from all the nitty-gritty and look at the benefits on why we want to use data first off is the benefit of content management we've taken a large number of quote unquote unstructured text like mints and we've broken them down into little pieces of content so we know at a granule level what kind of content we have we could easily do searches for any kind of information or elements like missing steps short descriptions or even titles this allows us to make very consistent documents and to quality control when working in large teams you may also notice that all these two topics stem from a single structured topic did a promotes consistency across large team because of the inherent structure now just like it would be time-consuming to try to write webpages from scratch mainly entering the code as text people have built web content management systems that it makes it easy for people to create websites the same thing goes for managing data content these systems are called component content management systems and they make did a content management easier for non-programmers or non programmers and similar way by providing visual interfaces and automation for creating and editing data content sorry it's getting a little chilly in here next up we have the benefit of faster workflows it feels like with every passing year our customers are getting more and more demanding unless patient waiting for information who's gonna wait two weeks to get a brochure in the mail that describes what their product does no one they want to get the information now and they wanted to have available on every device that they have and personalized for them in data since everything is broken down into the self-contained topics people can work on all the different parts simultaneously without waiting on the rest of the team this means that documents can be developed and released much more quickly aligning to the agile methodologies that many companies are starting to use I mentioned this before but styling and formatting is separate from data content this allows Didache content to be automatically styled when it's published the many different media formats and devices with just a push of a button multiple studies have shown separating an automating formatting can reduce the cost of publication from anywhere from thirty to fifty percent another aspect is because content has been semantically tagged it's much more future proof proof since you can change the publishing configurations without touching the raw content in itself here is a big one many companies move to did just for the reuse benefits and just in a few slides I'll describe exactly how we reuse works and how you could apply it to your own data content but just know that it could save you a lot of money in time with reuse if you have a lot of overlapping content whether it's multiple products that are similar multiple audiences or publications that have overlapping or similar content mix-up interrupter Interop teror I can't even say it interoperability and scalability of your content is an open standard which means that content created in data can be transformed into other systems without a complex or can't costly conversion process everyone can look up the tags that are used in data if you use that proprietary tagging standard you know if someone had developed their own standard of XML that was you know personalized just for that company then the tags would be hidden from public knowledge and make it really difficult for any other organization or team to use your content with data broken down into little topic types you could send the translator the individual topics that haven't been translated yet instead of submitting entire publications over and over again which reduces the translation cost all right so enough about the benefits let's get into how we actually reuse in data I know I've been building enough for some time so let's get started reuse is a smart way of that allows you reuse is a smart content methodology that allows you to write content just once and reuse it everywhere else reuse works by creating content from one central location and then linking that content everywhere else that you want the content reference think about writing a paragraph on a piece of paper and then holding it up it in front of a mirror any changes you make to the paper are reflected in that mirror reuse works in somewhat a similar fashion you can see the content in the mirror but it's only a visual reference to the original content of course in the real world your text would be backwards in the mirror so it's not a perfect analogy okay so we're just covering three basics reuse types in data this webinar is designed just to give you a taste of how Reese works but there's other webinars out there they go into much more detail than I will have time for it today the first method that we'll be using is reusing entire topics so first we need to write the topics within our repository here we created some topic topics about safety installation maintenance and troubleshooting we get word from our managers on high that we have to create a manual for our maintenance curve so we need to create a data map and then either add relevant topics to it so we needed to have some introduction the maintenance information and any troubleshooting information that they might need and so it's pretty easy because we have developed these topics to create this map and we deliver it to our manager so within a couple of weeks they're so impressed that they asked us to immediately start the next task which is to create another manual this time for our internal engineers so we create a new map called the engineering manual we happen to know that these maps share some similarities because it's the same product only thing different is the audience so we can reuse the same content we pull the same topics from our content library into the engineering map now we've hit reuse gold the original content is in our content library but it's referenced in both manuals we can't just stop here because the engineering team needs more information to do their job now the engineers also need to know about safety installation and operation instructions so we could add those too you could see how easily once the topics are created it is to put together manuals and other publications as well it is to reuse existing information mark Baker wrote a book called ditto metrics saving trends with reusable master topics it's also linked in the resource section of this webinar Mark learned that using reusable topics you could drastically reduce the overall cost and time spent creating content for example with just three products you could save 40 percent of your time writing the test type topics alone with four products 49 percent and with five products 54% this is a trend forecast topics only when content inside the topics is similar between products non-identical now you're probably thinking how do I reuse safely there's two main scenarios when it comes to reusing entire topics the first is where the topics is identical from the different audiences or is where topics are identical for different audiences products or publication types a good example of this is content that contains copyright information that's required for you to provide your product or services reuse is also possible with entire topics when the content is identical with some exceptions for example different product names product specifications like dimensions or properties you can still reuse the topics by applying other reuse mechanisms to make sure that the right product name or information is appears in the right product that reuse type is a little too advanced for this webinar so we won't go into it today but know that it is possible with some slight variations to reuse whole topics okay so in data you could also reuse groups of topics you can do so by adding maps within other maps so let's take our maintenance manual that we previously created it contains a bunch of topics such as the introduction mate and instant troubleshooting a legal department has just reviewed the maintenance manual and recommended that we add a number of topics to meet our legal and service requirements now because this legal requirements applies to all deliverables that we create we need to make sure that it's an added to all other product manuals so we could manually add each and every one of these legal topics to each and every one of the maps that we have but this would be really time consuming instead we're going to put the legal top mix in their own map and reuse that inside all the other maps for our products as a result inside the maintenance manual we have the legal information now we save time in two ways first as the organization we've written and reviewed our legal topics just once and they can be reused in multiple Maps if there's any update the update is applied to each and every other place that the topics and maps is reused and secondly we've saved time because we didn't have to end manually add individual topics to each and every did a map we just needed to reuse the legal map what's more you could go one step further if you're using a component content management system you could create map templates that already come with the default topics or maps so that whenever you create a new map all the required Maps or topics are in the right place already created it sounds good too good to be true right now this practice is not part of data as itself this is called spaghetti reuse when you simply reuse topics from different Maps with that any thought on where they're coming from or whether used it gets a big mess this defeats the benefits of having all the content in a single place to manage and to update whenever you have content that's reusable separate it into its own folders so it's easy to find when you need to update something that's reused now in part from reusing entire topics in groups of topics you could also reuse parts of topics this is possible thanks to the reuse type called content references or shortened the con ref and many of the industry talks I mentioned before keeping separate folder for your topics is a good practice but you should also do the same when you were using parts of topics all the reusable topic content paragraphs should be kept in a single topic let's call it a topic warehouse for the sake of clarity but in reality is just a regular topic type with reusable paragraphs now this warehouse topic is the single source of truth for all reused content because it contains the original text alright so what you see here is a test topic steps that are used for most of our toasters going back to that toast example you could tell I might have a skip breakfast breakfast this morning so we get orders from our managers that we have to create instructions for two of our products of toasters the Lightning and the classic toaster so we need to create two tests topics for each of the toaster models and because we know that the process for toasting bread is the same for both models we get to reuse the steps so we create constant references or con raps in each one of these tasks topics and we link them to the centralized warehouse where we store all our reusable content related to toasters and presto change-o the steps are now referenced or reflected using the mirror analogy into our new topics now instead of using links we could have just copied and pasted the information from the warehouse but this would have been throwing away time and energy that we already spent creating content for reuse because once the content is updated we and our co-workers will still have to search for all the duplicate content and update it manually because it'll be out of date so let's say that Engineering has retrofitted our product and now they have buttons on the latest models the steps get updated using the content reference so when you change the original source it's automatically applied everywhere else it's reuse now just to give you an idea and how we can impact your effectiveness let me throw a few statistics please note that the numbers are only for con ref content references no other reuse types is taken into account based on the calculation of mark Lewis and his white paper did a called did at metrics reviewing strategies and saving trends with warehouse topics which is also linked in the webinar slides it becomes clear that using parts of topics part of topic content references alone in sign excuse me using parts of topics content references alone inside multiple similar products you could save about 50% of your time so introducing content references for one product takes 50% more of time that it would be writing the topic without content references however however when you have two products you could save 11% of your time due through that due to that reuse content or creating the second topic with three products it's at 32% and with the fifth product it's almost half the time it took you to write the first one now these numbers will obviously vary depending on the nature of your content and could be higher or lower this is just to give you an idea on how much time you could potentially save with content references or con refs alone so we covered over how to create data topics and how data elements within topics work and how to connect topics using maps also how to reuse maps topics and parts of topics next we're going to talk about how to publish content because there's no styling applied to the raw text all formatting is done through a publishing engine writers develop rules and how they want to display each and every data element that they create once they set up their rules set up the rules they create scenarios for each type of publication format that they want to produce let's look at a very simplistic version of these rules what these rules could look like let's say we're tasked by management to deliver our content to PDF web and mobile devices for PDF we know that they're going to be printed so we have larger font sizes because they can't change the screen however we know that people are viewing the same content on web and on their phone so we want to reduce the font sizes so they've fit for the smaller screen size now it's not just about the devices or the formats you could also create publishing scenarios for different audiences or versions of content you can we do this by adding even more semantic tags to our content identify if the audience type so when we publish to specific audiences only the relevant content is actually shown this makes it easy for us to manage multiple variations of content within a single map or topic without needing to create duplicates here's the kicker once you've set up these rules in scenarios to format content the whole process can be automated which means that if you have a style guide it's finally enforceable since all your content will be published consistently obviously you need to capture keep track of changes to the rules and scenarios and there might be new elements that you haven't been accounted for so I've used the term publishing for creating deliverables we could also publish to all the technologies and software for example you could publish content to chatbots since they require granular content pieces so we've covered most of what you need to know about how to get started with data let's talk about how you could apply data to your own current content first look at your content and see if there's some consistency within the types of documents that you write if there is you can start identifying which parts you can classify into the basic building blocks called data topic types second if you have a lot of similar similarities between products audiences or versions or even publications try to find out where the content overlaps and see how you could centralize those reusable bits to make it easier to find an update third do you have consistent styles do you agree on the styles do you have the same styles for multiple meanings if you have consistent styles start thinking the rules that you could create to publish the content across different mediums forth or a driver like improving customer communication with chatbots globalization or simply managing content at a much larger volume as there is start thinking about how you could change your content from an asset into a revenue stream for your company how you can leverage your existing content in new ways if you control every element of it at a large scale remember the driver is at the beginning of the webinar these are just some of the possibilities your organization could be moving towards in the next few years and using data puts you in the position to implement these technologies sooner rather than later alright so here are some of the resources that you could use the further your education I know the SCC has a lot of great resources or ready so I won't mention those in this webinar but there's also learning dido comm which has supplemental free courses on data there's the Oasis standard website which I mentioned before which has all the lists of data elements that you could potentially use and of course we published articles monthly on different various topics about data here's the resources I also mentioned during the webinar if you're curious about where the statistics I got and I referenced as well as more information about if you're doing localization or reuse now last but not least the SCC is hosting another webinar in a month with my colleague John Baker who will be doing a webinar on the business implications of data and how do we value we did up for your organization this see if the benefits outweighed the investment now I might be biased but this is a great webinar to attend if you want to learn how to build a business case to advance your content your department and your career you've been a lovely audience so I like to take some time for any Christian questions that might have been thought of during this webinar thank you so much one question from Lauren Conrad I'm still confused on what the differences between XML and data there the same thing are they so Judah uh let me pop back up to that slide right here so we can look at it here we go so did uh is one of the xml standards out there so did it is part of xml and it just contains fewer tags than the entire language of xml so you could think about a abbreviated version of English or another language okay yeah she already specified that you answered it already that's another question what is what is the type of tool that you use to create the map the tool so there are many different tools that you could use so just like I reference that with web content there's WYSIWYG editors that allow you to drag and drop webpages in the create content box the same tools can be applied to dit up so there's component content management systems which work in the same way they have visual editors that allow you to create new topics to drag and drop into maps and you know to reorder the maps there's also text editors which allow you to do it via writing in text similar how they used to create websites by writing it out so I let's see here I don't think I put it in the resources there are a lot of different now we provide free trials and I know a lot of the other C CMS is out there for find free trials so if you're interested in getting your hands dirty you do that um I can't think of any off the top my head that are free so hopefully they they answers your question another question that came in is did I use mostly in technical writing because it's best suited to technical content or because technical industries are more likely to explore automation and code compared to other publishing genres for examples of publishing journalism content marketing that's a good question so I come from marketing and so Zita is one of the standards related to technical publication so there's over 250 different kinds of XML standards each with their own kinds of content so you know there's various versions of XML so there could be XML for marketing there could be XML for accounting finance no human resources and in any given industry so but did it was created for technical publication in particular and technical publication works really well because oftentimes there's very structured information you know with marketing it could be you know a very loose blog post that don't really have much of a formatting or very graphical information that varies from every single publication so it doesn't really fit into contain structures like data but things like writing steps and tasks and concepts and concise bits of information that's consistent across all your content works well for data because it's easier to recognize and read and also to reuse all right the standard isn't updated regularly okay so the data standard is created let's see air boom yes so the Oasis crates and monitors they fit a standard so it did Oh was actually let's see I want to get straight I want to say Microsoft came up with it so it was originally created by Microsoft and don't quote me if I'm wrong and so Microsoft realized that this would be much more helpful for them and the rest of the industries if they publicize it so they handed it over to the Oasis committee and they managed it from there on out so I believe already added a 1.3 but you could find the 1.2 elements listed in the link for this webinar all right IBM thank you so much I should have my slide note the next question what are the types of document authoring tools used to create ditto content okay so there are content component management systems then there's this top publishing tools and then there are the due date there's a federal on XML authoring systems so XML authoring systems could not only just edit data content but XML in general the component content management systems usually stick to a very specific standard whether it's data or one of the other technical publication standards so and then there's text editors where you could simply you know open a text document and type out data all right those are all the questions that I'm seeing that came in perfect so you've been a lovely audience oh yeah one more one more one more how do you assess the cost of investing in data okay so that is a great question for the next webinar that we'll be doing in a month so this is going to go into all details about the business aspects of data and how you can look at it from a cost perspective or a revenue perspective and see if it fits for your organization so I know on our own website we have a short brochure it's just like three pages long and on the back of that brochure for is a a checklist to evaluate whether or not your content or organizational needs so so what is did a brochure on our site I think I could get the SCC to post that in the comments section for this webinar afterwards all right any other questions those are all the ones that I saw all right perfect I just want to follow up with one last poll you know as a organization we're working very hard to advance technical writers in their careers companies departments and organizations so a lot of the content that we produce is driven to make sure that you are successful at your jobs and your departments are successful and growing your company so we'd be happy if you're interested in getting more information we obviously don't want to send you any information that you're not interested in so and I won't be offended if you say now I totally understand we get way too many emails at it as it is alright it looks like we have gave you two percent of the votes which is still a great number to have all right that looks about 97 thousand votes 98 we're up there and a hundred percent perfect thank you so much for attending this webinar and I look forward to I see you next time thank you very much Rafi that was a great webinar and thank you to everyone who attended we do have more webinars coming up so please check out our education page on our website at SCC org and remember an email is coming away shortly and that will have a link to the recording of today's webinar as those link to an evaluation thank you again everyone for joining us and have a great day thank you