Welcome everyone to today's webinar with Zap. Our presenters today are Christopher Reeves and Tony Van Hoogden. A few housekeeping items to go over before we begin. First, all attendees are muted. This webinar is being recorded and you will receive the link within two business days of the conclusion of the webinar. Questions will be answered at the end of the webinar. Please enter them into the Q&A window. And now without further ado, I'll turn it over to Christopher. Christopher Reeves Hi everyone. I'm Chris and I love it when you talk data to me. Now I could be charged with keyword stuffing the title here and very honestly, there is a little bit of that going on. But there's also a theme. Our keywords are all better with the data warehouse and I hope to make that crystal clear. Very quickly, our agenda is exactly as the title would suggest and I'll round us out with a product demo. Now we like to keep things punchy at Zap, so I'll be targeting 30 minutes, which gives us time for some questions and hopefully that'll still get us out early. Please answer them as Julie's noted in the platform and get to those at the end. Now first, a quick note on AI. In any data talk, it's an elephant in the room if you don't touch on this topic. And equally, if you do, there's a balance in how deep you go and not falling for the height. So I'm going to try and walk that line very carefully. It's certainly not something to ignore. That is AI in the data and analytics space. It's extremely relevant and it may even be applicable in the short term, although the bulk of your results you would expect to see in the medium term. Whatever your role, hopefully your going to take something is going to take something away from this is going to take something away from this. And if that takeaway is an AI strategy, all the better. But very quickly, if a data warehouse is the common theme, what is a data warehouse? So start by imagining your ERP as a data factory. And in this case, Syspro creates the data. And please imagine or visualize that factory floor. Waltzing in and grabbing your product is never a good idea. You'll be hard pressed to find what you need. Depending on the factory floor, you could lose a limb. Worse than that, if your customers, like most of our businesses, have multiple such factories, you don't only risk getting the wrong product. You'll need to waltz or drive from factory to factory to find what you need. On the other hand, a physical warehouse is designed for distribution in volumes big and small. You have product from multiple factories all in one place. Shelves are well labeled and organized and limbs stay intact. So if you've got that in mind, if you can see that visual warehouse. Now imagine those shelves are stocked with data products. And right there, that's your data warehouse metaphor. There are a few technical terms as apt as a data warehouse. So we're very fortunate there to have something that's quite well captures what we do. So let's get to the AI part. And if this were a physical audience, I'd probably ask the question of whose mum hasn't heard of ChatTPT? And I ask that because it's a great way of making the point that at this point, pretty much everyone has heard of ChatTPT. Everyone knows that it makes stuff up. As I like to say, it talks with the confidence and fallibility of a teenager exposed to too much social media. It might be that you're thinking this should fix this bug by now. But the thing is, they don't code AI. So it's not a bug per se. Now, if you took, it did take a very large part of our marketing budget to pull the starlings together for this shot. So I very much hope you'll appreciate it. And if you took just one or two starlings, you can imagine you wouldn't see the pattern here. It's only at the scale of millions that we see these incredible waves. This is called emergence. It's the behavior or properties that emerge only when millions millions, billions, billions, billions, billions can interact. AI is an emergent phenomenon. It's not coded. And that's why it's inherently unpredictable. So how do they fix it? I don't think it's too much of a stretch of the metaphor to say they trained the starlings better. And my point is, it's not going to be easy, which leads to my public safety warnings. because ai is inherently unpredictable things can go wrong so i'll give you a few quick takeaways now these are my basic rules for ai in business firstly validate where the stakes are high it almost goes without saying an egregious example of this personal examples where my niece was put on academic report for apparently using chat gpt to write her essays fortunately they took her off when they realized their flaw i don't know of a better example of failing to validate in this case they didn't validate the arbitrator you can't always validate though so secondly expect errors where stakes are low or it's simply not possible to validate as i've said you must accept that there will be errors now sentiment analysis is a great example where you may accept errors in a large enough sample it's really not going to matter whether the net is that your sentiment score is 3.33 or 3.35 based on a few errant scores either way at that point your service is probably meh and finally use quality data ai works at unfathomable scale and it's too easy for errors to slip in so famously it took only one smart alec on reddit to suggest that you should add glue to your pizza sauce to stop the cheese sliding off the base so to reiterate validate expect errors and use quality data there's a sensible hook on that last point so having provided the cautions if you're not using ai daily i strongly suggest you start integrating it into your workflow it's excellent at crafting excel formulas drafting emails to get you past the writer's block and if you're unsure on how ai can help in your role just ask it it's uh very good at answering that particular question as well now i'm actually here to talk data and analytics so we're going to talk to how ai can help in the data and analytics space and i'm going to provide the three quick categories starting with assistive ai these are co-pilots or chat bots and we'd all be very familiar with them at this point they help you do what you could already do but with your untrusty co-pilot you can do it faster and better at this stage i think most of you would be familiar with ai in this form and it's very similar in the data and analytics space equally applicable for operations such as design guidance around dashboards or models but just keep in mind he's an untrusty co-pilot secondly you have analytic ai which transforms data for analysis and alerts and i'm emphasizing transforms there so maybe the simplest example of this is again our sentiment analysis back again against crm tickets or support tickets that is taking the body of a ticket and transforming the text into a sentiment score so there's that emphasis of the transform another example is transforming the text into keywords to slice and dice that data and there are plenty more analytic ai use cases that we could talk to but the last point is predictive ai is predictive ai and this simply takes historical patterns and extends them forward true predictive ai works by turning the data patterns such as up tick down tick sideways movements into the language of gpt's and that is tokens this sounds great however the big catch is that it's ai and so it hallucinates as we've discussed frankly predictive ai is where i think the hype has gotten ahead of the hype has gotten ahead of the reality it's coming and yes as a data company we're staying very close but right at this moment i take my cfo's cash flow forecast any day of the week now having said that can data be used to predict and of course the answer is yes it can we call that data science and we've been doing it for decades but it's not ai and as you might imagine the first step is getting your data in a data warehouse now this represents a great jumping off point and segue to power bi with its assistive ai in its new q a feature very quickly for those unfamiliar with power bi it's a tool built by microsoft and one of the world's leading bi tools this screenshot includes some example questions you could ask of your data and you'll note they're in natural language or even a little cryptic natural language just imagine how much fun you could have playing around with this syspro data but i do need to caveat this microsoft's solution to avoiding hallucinations appears to have been to dramatically limit what questions you can ask so this screenshot might oversell it a little bit but my point is that it's coming moreover if you're interested in leveraging the ai and analytic capability capabilities of power bi because it is a fantastic analytic tool you can save yourself literally hundreds of hours by starting with a pre-built data model and analytics so quick note we have a mixed audience as usual so i will drop in a few technical terms but this shouldn't add upset the flow so please feel free to ignore them if they're not familiar model being one of those i do mean data model so pre-built data model or not if you're using finance data you simply must leverage a data warehouse for your analytics if you're doing external analytics power bi does not protect against data duplicates unless you explicitly tell it to do so on every single data pipeline which is to say it's not safe by default and duplicates do occur very naturally when extracting through dirty reads or custom transforms i don't know any cfo who would accept an opening closing balance being duplicated so again allow me to emphasize that if you're using power bi you'll want to have a data warehouse in place it is best practice as you can research yourself through the googles now power bi is a great bi tool and we're big proponents of it but it's not a financial reporting tool you can get a financial reporting tool you can get a p&l out of it if you strangle it but don't try balance sheet at home which leads to the question what's the difference between analytics and reporting and i usually tongue-in-cheek give the answer that analytics are so the human eye can detect patterns or trends and reports are for nitpicking or detail now if you're using excel for your reporting for your reporting financial reporting and you have a bi tool separately say power bi you now have potentially two sources of truth what's very important is that they draw from the same trusted source at your foundation this should be a data warehouse or for the more technical in the room a cube or semantic layer the semantic layer sits on top the data warehouse and makes pivot tables in excel a breeze in addition to offering listering speed as you're seeing here now back around to analytics and this is an accounts receivable aging over time chart enriched by industry information that we've brought in from a crm system i won't go deep into what we have here but very simply gray means not due and the darker colors are bad this is a great example of enriching data by combining the sources from erp and crm and because we've visualized the data that human eye can detect some very obvious patterns most of our ar is under control but there's a meaningful trend and we really shouldn't be giving our insurance customers christmas presents now i love this chart because not only does it require a data warehouse to require a data warehouse to combine the data between erp and crm it's also demonstrating the power that is possible with behind the scenes calculations only delivered in a data warehouse so i absolutely love talking data and i would keep going were i not to have justification to pull up but allow me at this point to allow me at this point to talk to zap and you may certainly infer that we specialize in erp data warehouses specifically data warehouse automation with data warehouse automation we enable a complete erp data platform for you or your customers business including analytics in the browser or power bi reporting in excel and collaborations in team given we've touched on a team given we've touched on ai i will say that this month we're releasing our first analytic ai capabilities allowing you to transform data using open ai with the complete flexibility of a free form prompt but i'm conscious not to overhype this rather at this point i hope i've made the case that a data warehouse is not only an immediate solution for your data and analytics requirements but because ai needs quality data a data warehouse can also represent the first step in an ai strategy given the immense importance of that trustable quality data so trust your data now get value today and be ready for tomorrow let me preempt a few challenges though you might be concerned that data data warehouses are costly our warehouses are costly our automation and pre-built models make data warehousing a reality for small to medium and enterprise and we've helped over a thousand customers achieve justice you might be concerned that refreshes are slow with our latest version of zap we've incrementally updated a syspro customer with over a billion rows in less than 20 minutes the hardware used for the most hardware used for that was less than my two-year-old iphone you might be concerned that you don't have power bi skills we have our own integrated no code analytic alternative and no code literally means unlike some other offerings it's all drag and drop it's a very native and powerful experience and finally you might be concerned that it's difficult to learn a new tool and i would say as with any tool there's a skill up requirement but we have a built-in in-app training experience and naturally we have an ai bot i look forward to showing you these items in just a minute let me turn it around and allow me to put the challenge back to you as the audience if you or your customers are losing countless hours at month end or through the month to manual exports manual reconciliation and custom reports manual reconciliation and spread out data you need a data warehouse to automate centralize and structure your source of truth and that's a data warehouse but very quickly how does that fit in with embedded analytics it's the natural question my key takeaway from this slide will hopefully be that they are complementary maybe you or your customer starts with zap if they're on v7 or maybe they start with embedded analytics but again let me make the case and hopefully this slide makes the case that they're very complementary let's start with the most obvious point embedded analytics is the out of the box analytic offering from v8 r22 and it's fully integrated within the syspro interface providing dashboards and analytics right where the user wants to be the user's the out of the user's and that they're very specific to the user's and that's how it works in contrast zap offers its analytics through a web browser or power bi giving the users flexibility to access insights from anywhere embedded analytics runs directly against the operational data so it's real-time zap is not real-time rather a data warehouse by its nature is a trusted copy of the data you can however schedule it down to the 15-minute mark and refresh on demand at month and close and close and close and when it comes to customizing your own alex naturally we specialize in this our powerful analytic engine has a no-code design experience as i've talked to but again you have the option to customize your analytics in power bi it's the breadth and power of our data warehouse powered analytics in our browser or through power bi that really sets us apart now having said that embedded analytics also offers a customization experience so the sensible approach is to start with embedded analytics and turn to zap when you exceed the native customization experience data hub also provides an extremely flexible built-in financial reporting capability which we'll get to see shortly it's either in the browser or coming next month live in excel and you can distribute these financial reports or your analytics on schedule or data triggers embedded analytics doesn't have its own financial reporting but sensibly its analytics syspro does so again the sensible approach here is to start with the native syspro financial reporting and let your requirements take you from there at which point they may very well evolve into a zap requirement and finally data hub is to start with the native syspro financial reporting and finally data hub is naturally a full data warehouse automation tool allowing you to centralize and structure from multiple sources for a single trusted source of truth so if you find yourself regularly exporting or manually reconciling then you have a data challenge and data warehouse automation is the solution with that data warehouse with that data warehouse as a trusted foundation we then enable a platform as i've said within browser analytics or in power bi excel for financial reporting optionally financial reporting in the browser as well and teams for collaboration so at this point i'm itching to demo and that should get us out early depending on questions but let me talk to a quick quick success story we work with syspro companies from family owned businesses through to some very large multinationals naturally with the data volumes i've just talked but we love talking to aj wells because of just how far they've gone on their data journey although i'll be very brief this this time around because we want to get to the demo aj wells is a family owned business and they're from the isle of white off england known for their eco-friendly wood burning stoves and enamel products and they built a reputation around craftsmanship and practical innovation from their manufacturing they are big fans of syspro for their erp but like many growing manufacturers they had data flying around in all directions and they struggled to get the analytics and reporting they needed from the data silos leading to multiple hours of unnecessary manual work and plenty of data headaches so we were able to bring it all together for them so we were able to bring it all together for them we took their manual spreadsheets and their endless hours of reconciliation to an automated flow for regular updates of dava insights now they're not just looking at yesterday's numbers they're actually seeing what's happening right now on the shop floor production efficiency check financial reporting sorted and the leadership is now making decisions on freshly squeezed data instead of yesterday's stale or last month's stale month end reports so with that let's go on to a demo give me a minute transition so as i've talked to zap is browser-based so you can access it anywhere but it is also available on-prem we're hitting up our cloud instance here nonetheless and the first thing you'll note anytime you go into zap as a new user is the built-in in-app training experience so this is training at the point of using the application and it's a journey unique to you and your role so as you navigate through the product you'll see new little video notification icons telling you hey you haven't been here before here's something new for you to watch there's also a full learning platform there's also a full learning platform with tutorials cookbooks and our full training material will be coming here this month we've also got zappy which is our ai bot on the right and he's trained against our entire help documentation so we've previously talked to the benefits of data warehouse but let me just turn around another way and say it this way zap exists because humans shouldn't run manual exports humans shouldn't manually reconcile systems and data shouldn't be spread out like seagulls on a beach so let me paint a picture you're a finance controller it's friday afternoon and you're rushing to finish you've run this export a hundred times you know the sequence you know the checkboxes and then your mother-in-law calls and she's around this weekend and you've been promising to mow the lawn for so long that you're losing track of your baby dash your miniature dash found when you play fetch you click export rush home and it's not until three days later after spending a few hours that you realize you stuffed the export now imagine instead of updating a dowdy excel report you instead had something like this scheduled to drop into your stakeholder you in the box each month so you're going to drop into your closing box each month of course this is customizable to your company branding and scheduled or on data trigger that's automation for you now you're working for you now you're working for you now you're working for many months i am very excited to be showing you our new excel add-on so this is this week is the first time we're talking about it publicly and we're looking at general availability very early next year depending on our approval process we're targeting January so this feature will be available on all plans and we have a design experience coming in March April it's very important to note though it will be cloud first with on premise dates to be announced later in the year we believe with the expand and collapse capabilities that we're just about to see here that we are on the front line of excel add-ons and we're only going to build and make this experience richer because it it complements the analytic tool both your reporting and analytics then are from that same trusted source the truth moreover the excel experience of truth moreover the excel experience that you're seeing here is not just for finance data it's for your whole data ecosystem so you can use this tool against your crm data your payroll data the combination of that data your inventory data and of course with report packs this excel capability your analytics your dashboards data all reconciled and consolidated that is us completing our mission of avoiding manual reconciliation manual manual manual manual manual manual exports and spread out data so back to zap and unless your sales team is fine with oh i apologize back to zap and it's um what we're going to look at is our built-in role-based dashboards and so we're going to look at the role -based dashboards and so i always like to emphasize the importance of analytics being actionable visible and dependable and to that end we have a whole suite of role-based dashboards from the chief sales officer to the inventory manager so what we're looking at here is the chief financial officer dashboard and because it's role-based it's eminently actionable again and again we have a full suite of financial reporting capabilities which we can see here and of course you've naturally got the drill through capabilities you can get right down to the detail and take it from there but if we just want to jump back actually we'll track down that 45 that's the purpose of the detail report if we go back to our chief financial officer dashboard if we go back to our chief financial officer dashboard there's a question maybe naturally about where's the ceo dashboard and the reason we don't have a ceo dashboard is that the role tends to be much more varied which is where customization comes in so i'll just take the bottom right chart right click to edit that as you can see right click to edit that as you can see and what we have here is a very familiar design experience to the excel pivot tables we've just renamed the placeholders because this is a chart but what we can do is move that and i'll swap in i'm going to use fiscal fiscal quarters and as you can see it's a drag and drop experience but equally if i double click on this you've got that no code experience that i talked to and again you've got all the power of a rich financial calculation engine and analytic engine but with that drag and drop experience that you'd be familiar with from excel now what you can see on the right is a very clean model of your data now because i'm using integrated analytics what i can actually do is right click and go straight back from the data model of course you've got the same analytics in power bi power bi is a fantastic rich and what um experience that many people would be familiar with and so we recommend that they use people use power bi where the skills are in power bi where the skills are in place but the option is to use our own built -in integrated analytic experience and then you get to perform that action as you've just seen what i'm showing you here is an analytic dimension and it appears trivial but i do want to emphasize that it's non-trivial analytic dimensions take a lot of modeling on the back and the reason it appears so simple in this is because data hub is erp specific and it's doing the magic and i do want to announce that equally we have next quarter not only our excel add-on capability but syspro analytical dimension support now we're discussing modeling and it's important to understand that in any analytic project 80% of the entire effort is data modeling data hub pre-built data models embed thousands of hours of expertise and validation and it's hard to overstate the time savings and risk reduction that you get from the data mappings relationships hierarchies advanced advanced date functions friendly names descriptions and more we invest in our syspro model for hundreds of customers and this means that we can deliver a measure of testing that wouldn't other and investment that wouldn't otherwise be justified and it's hard to overstate the time savings again that risk reduction that you get from starting with a pre-built model of course you get from starting with a pre-built model of course you might start to choose to start with a pre-built model but then you want to enrich your data and so that's where our connections come in we've got access to over 60 what we call smart connections but then you can also extend with just about any data source be it database or cloud that you can imagine with our database and rest connectors so at this point now pull up and return to our challenges again so they are manual export manual reconciliation and Vegemite spread data and there's a whole spectrum of tools to solve these challenges with more acronyms that you can poke a stick at but if you have more than one of these challenges and it's very likely you have two to three then you're suffering from a deeper challenge and it's a data challenge so it at this point it should be very clear that the magic ingredient behind our application is an erp data warehouse automation platform and again we provide the analytics in the browser power bi reporting excel or collaboration in teams you give me a few more minutes i'll just talk to two additional analytics but at this point i've really only just got to touch on some specifics around data warehousing we haven't even talked to the natural capabilities such as data governance scalability and more but let me just quickly show you our 13 week cash flow forecast and so this is an example of a customized report it's not one of our out of the box ones but it just demonstrates our ability to bring together two different modules using our calculation engine engine engine to produce a cash flow forecast it's also a great looking report and again recall that we talked to erp specifics one great example of that is that is a natural ability to consolidate company data where it's spread across multiple databases so with the engine in the data layer delivering this we then also have the capabilities in our financial reporting to consolidate and so what you're seeing here sensibly a consolidated parent child but uh with that again i'm going to hark back to our key points which is don't lose time on manual exports don't spend time on manual reconciliation and don't risk spread out data with zap you will have automation saving you time centralization for easy and secure access to easy and secure access to all your data and structure ensuring you can trust it with that thank you for spending your time with us and i'm very interested if there are any questions so tony what do we have hi chris yeah um we do have a couple of questions that have come through so the first one uh does zap work with the excel um we can even slice and dice that live data and even slice and dice that live data thank you um the second question we have over here um is how long does zap normally take to implement um so i'll talk to that one essentially um when we sell our pre-built syspro solution we offer a jumpstart implementation so that is a bundle of 40 hours that we'll implement um 40 hours can be delivered over three to four weeks uh and uh traditionally once we've uh and uh traditionally once we've connected a customer's data set we'll then go through a number of activities one of those includes some validation around the uh chart of accounts so that the trial balance profit and loss um are happy you know balancing and and and customers feel confident that the the data is accurate um once those activities are underway we'll host a number of sessions to uh orientate clients on how to set up user security and and really get them started um and really get them started with that pre-built solution so from connecting a customer's data to up and running uh with those 40 hours we can be three to four weeks let's have a look if there's any um one more question that's popped into the chat here um in terms of our formulas in zap do those live in um the application itself or in the data replication side meaning if if a customer were to use power bi are those values within the data columns um within the data columns do you want to take that using power bi yep if they're using power bi we have our own dax editor per pipeline so that the data model itself becomes a semantic source of truth and so just expand on that a bit more because it's a very important point every time you add a column or up table or up table or change a name in your data warehouse you want it to be reflected in the power bi data model at the same time so we synchronize that we don't only build the warehouse we build and synchronize the power bi model and processing involves as well that synchronization so the reason we do keep the formula in data hub in our own the dax editor is so that you have that single source again of semantic truth not just data truth excellent thank you just having a look here um there's another question that's popped in how do you get to the zap add-in um this question might be related to zap as a whole as we mentioned it's a it's a separate web browser application so a customer will be provisioned a customer will be provisioned a new instance and then we'll step through connecting their data um don't know if you wanted to add anything to that one chris it might also be a question about the excel adding which i would say uh is next month where we're waiting on microsoft to publish it so i expect to make a lot of announcements as soon as that's live excellent excellent so no other questions at the stage in the chat there there's and there is one more so tony do you want to read that i'll pick that one yeah um so we've got a question around um getting a list of all those out of the box reports absolutely um we we'll be able to share that um after the session today um after the session today um with anybody interested so it's uh all the dashboards that chris was mentioning across the cfo uh you know purchasing manager dashboard inventory manager dashboard and then the underlying reports that are part of the pre-built solution so yes we can we can provide that list fantastic so popping in um um so are there demos available with customer data um yeah great question i think um i can talk to this one around what we offer sometimes customers want to see their data inside all of these pre-built reports and dashboards so um we would call that a short proof of concept and so this is available where we connect to a customer's data and then provide a one or two month experience where customer can trial out that solution um using their data as opposed to some pre-built um standard sample data yeah so that is available simplest path for that is there's a 14 day free trial straight from the website or better would be you get in touch with tony and i think as you said 30 to 60. some options there absolutely you have to use a tony reference number or something um um so we have another customer with a question popping in here um currently using spreadsheet server does the zap add in interfere with that spreadsheet server if not um can both be added into a single workbook i cannot imagine that there'll be a challenge but please allow us to do the testing and naturally we should test first but it's not very real um there's no reason to believe that there'll be any challenge with that i'll make sure we note it great thank you very much that's uh some great questions there some really good engagement so we'll give it maybe another 10 seconds last call and then we can get another 10 seconds last call and then we can get another coffee i really appreciate you all but it is awfully early here and so we're only two coffees in we're coming up on 7am in australia so we have a great team over there in the us and um we don't you're not usually dependent on us australians you can you've got you americans so i think at that point fantastic great to have you um julie back to you great great great thank you everyone and we hope you all have a nice day thank you