Delivering Business Impact: Analytics Data Hub for Finance

Transcript:

Ardeshir Ghanbarzadeh: Welcome everyone thanks for joining us today before we get started, just a little bit of housekeeping if you have any questions during the presentation, please do type them into the.

Ardeshir Ghanbarzadeh: Q and A box at any time we're going to do a Q amp a session at the end of the webinar also if you do have to leave early, we will be making the webinar available on demand, and you will receive an email with a link to be able to access that post webinar.

Ardeshir Ghanbarzadeh: My name is artistic gamblers auto I am director of product marketing here at and quarter joining me today is Mike nadir VP of business analytics solutions.

Ardeshir Ghanbarzadeh: Mike is a highly experienced solutions architect consultant and educator he has more than 25 years of experience working with enterprise analytics and performance management.

Ardeshir Ghanbarzadeh: As the former global domain lead for analytics with Oracle he worked with strategic global clients enterprise analytic strategy.

Ardeshir Ghanbarzadeh: and worked on deployment of those strategies Mike has worked with numerous data visualization tools analytics engines and relational platforms further Mike also has an extensive background in data governance and analytics.

Ardeshir Ghanbarzadeh: A little bit about what we're going to talk about today, we will look at today's approach to financial analytics.

Ardeshir Ghanbarzadeh: Then we will talk a little bit more about the analytics data hub for Finance, I will talk about in quarters finance data Apps mike's going to take us through a demonstration of in quarter and and then we will take your questions, towards the end of the webinar.

Ardeshir Ghanbarzadeh: Now, when we look at what some of the The challenges are today with the approach that is taken to financial analytics we find that.

Ardeshir Ghanbarzadeh: What finance teams require and what's being really delivered to them from existing era P system and finance tools.

Ardeshir Ghanbarzadeh: That really brings in a gap that is forcing organizations to make these unacceptable trade offs between what's required on what's being delivered.

Ardeshir Ghanbarzadeh: For example, finance teams need to have a holistic view of financial and operational data in order to be able to support a variety of use cases, this could include things like FPA.

Ardeshir Ghanbarzadeh: reconciliation and close managing working capital accounts payables and receivables.

Ardeshir Ghanbarzadeh: they're also looking for the ability to have a real time analysis, so they do need the latest data available made available to them.

Ardeshir Ghanbarzadeh: And they need to have confidence that that data is free of errors and it is also highly accurate, that is going to be able to give the finance teams, the ability to provide the right amount of commentary and and put confidence behind the decision making from that data.

Ardeshir Ghanbarzadeh: Next time they need the ability to be able to drill into the data in practically any direction they want, so that they can identify trends.

Ardeshir Ghanbarzadeh: investigate anomalies, be able to do root cause analysis of discrepancies with for variances and they also need to be able to answer new questions, a lot of times data.

Ardeshir Ghanbarzadeh: models are designed to answer an existing or a question of the past, so getting the latest update to to an old question is not really a fit anymore in today's business climate, especially with.

Ardeshir Ghanbarzadeh: Some of the business challenges right now that are looming over organizations, including inflation and recession and and supply chain issues.

Ardeshir Ghanbarzadeh: They a lot of new questions are being asked by the CFO and those questions need to be answered.

Ardeshir Ghanbarzadeh: And, and to do that, they need to be able to have the flexibility to run an ad hoc queries and be able to get those get those new questions answered.

Ardeshir Ghanbarzadeh: Also finance teams have made quite a bit of investment in their existing finance tech stack and the finance tools that they have and they don't want to be able to get the most value out of the capabilities of these tools and also added the data that exists and resides in these tools.

Ardeshir Ghanbarzadeh: Unfortunately, what we see oftentimes is that the earpiece systems and finance tools that are out there, right now, are not able to really deliver on these requirements, for example.

Ardeshir Ghanbarzadeh: And this is very common data sitting in multiple your PS or business Apps databases or spreadsheets essentially siloed pen and limited access to that data.

Ardeshir Ghanbarzadeh: extractions that are required to pull that data from these different source systems are quite time consuming and they actually introduce a lot of delays into the finance operations processes.

Ardeshir Ghanbarzadeh: And that makes it very difficult to be able to be responsive or even proactive when it comes to answering business questions.

Ardeshir Ghanbarzadeh: they're often limited to aggregations and top line kpis so they are not able to do a deep dive drill into the data.

Ardeshir Ghanbarzadeh: To be able to do full analysis and come up with commentary about the data that is about the data is presented.

Ardeshir Ghanbarzadeh: It could take weeks, sometimes even months to stand up a new bi project or new dashboard or schema to answer a new question.

Ardeshir Ghanbarzadeh: And that is simply unacceptable when you have business requirements that are moving more and more towards real time analysis needs.

Ardeshir Ghanbarzadeh: And finally, some of the implementations of earpiece solutions or even data warehousing tools can be very long and very expensive.

Ardeshir Ghanbarzadeh: Essentially, by the time it reaches the point of being near production, it almost becomes obsolete.

Ardeshir Ghanbarzadeh: or not usable so we find that there's this gap between what finance teams require and what they're getting from existing tools out there and the trade offs that they're making and and essentially.

Ardeshir Ghanbarzadeh: You know this is where, in quarter can come in to help and how incarnate helps here is.

Ardeshir Ghanbarzadeh: In quarter brings agility to the entire value chain of the office of finance and we do this by enabling the it teams, with very fast.

Ardeshir Ghanbarzadeh: Data pipelines that can acquire 100% of your original source data in its normal form in its original form, and make it available for analysis, without any heavy transformation or aggregation.

Ardeshir Ghanbarzadeh: Another approach that in court, it takes is that we have a a library of finance data Apps, and these are out of the box schemas and dashboards pre built and purpose built.

Ardeshir Ghanbarzadeh: For source systems such as Oracle SAP salesforce and other common bi solutions that exists in organizations, today this the data Apps they work as an analytics accelerator and mike's going to show you a DEMO of data, a little bit later and in the end the webinar today.

Ardeshir Ghanbarzadeh: But then what they do is they make it very quick and easy to add a new report for analysis for for finance use cases and as as as these teams.

Ardeshir Ghanbarzadeh: are looking to.

Ardeshir Ghanbarzadeh: leverage the what they can and the most likely can get out of their existing tools, what we are doing at in quarter, as we are expanding our technical partnerships.

Ardeshir Ghanbarzadeh: so that they can easily augments the analytics data hub for finance with existing finance solutions and tools that are out there and you'll be hearing about some of those and the coming days on what we're doing in terms of enabling that type of augmentation.

Ardeshir Ghanbarzadeh: So by by enabling the it teams to finance it teams to to.

Ardeshir Ghanbarzadeh: To have these kind of capabilities that they can then in turn empower the office of Finance to drive better, faster and more informed decisions.

Ardeshir Ghanbarzadeh: And more frankly more confidence and decision making on not only the data is data driven decisions but there's a high degree of confidence in that data, because one you have access and visibility to all the data.

Ardeshir Ghanbarzadeh: And to you, it is the latest data made available to you, so it increases the quality of the decisions and the and the confidence in those decisions.

Ardeshir Ghanbarzadeh: On the office of finance teams like I said get they get visibility to all the financial and operational records, because it teams are now able to.

Ardeshir Ghanbarzadeh: Essentially, get 100% of the data from the source system so we're not talking about an aggregation here or or a subset of the data.

Ardeshir Ghanbarzadeh: we're talking about 100% of the data from the source system.

Ardeshir Ghanbarzadeh: And this is by bringing that these capabilities to the office of finance this is going to open up a much needed operational capacity and improve the overall efficiency of finance teams when it comes to executing on their.

Ardeshir Ghanbarzadeh: finance operations i'm going to hand it over here to Mike was going to talk a little about a little bit about our unique approach to financial analytics Mike over to you.

Mike Nader: Go ahead yeah thanks so much sure if you would just go ahead and exciting, for me, and I appreciate very much the the introduction debbie travel with me.

Mike Nader: So thank you so much.

Mike Nader: yeah go ahead and, if you would just paint the the content and for me here.

Mike Nader: I mean when we talk about darkness just point the concept of a data hub it's really about, what are we adding to the process or how are we changing the process.

Mike Nader: of getting data to business teams over my career i've had a lot of opportunity to both work with you know existing environments, but also architect new systems.

Mike Nader: And it's been a fairly standard process that we follow, and for good reason there's a lot of value in a lot of what you will see in there and we'll talk about that process momentarily.

Mike Nader: But one thing I want to highlight today is the value of of aligning quarter with those those current data processes it doesn't have to be in instead of.

Mike Nader: And oftentimes it becomes that conversation if you put any technology in any business account or sales conversation, now it is.

Mike Nader: What do I have to throw away everything else to change things no in court is designed.

Mike Nader: Specifically, for a few key pieces of functionality, we are designed specifically as an operational analytics platform sitting directly on the sub ledger the ledger and supporting operational systems.

Mike Nader: With the goal of providing finance teams access to data very, very quickly, without having to interrupt everything else the it organizations are doing an artist you brought up the.

Mike Nader: The data Apps and eXtensible templates earlier with demonstrate something a little later, but we're going to talk about the process a little bit first are sure if you make the next one, and for me here.

Mike Nader: The other thing I don't want to get lost in here and it's a hard thing to demonstrate in a webcast is the concept of near real time or incremental feeds of data.

Mike Nader: It is not enough, very often, and it hasn't been in the solutions that i've developed as a as a consultant that you could say everything's going to be a nightly feed or the state is going to come in weekly my last two clients that I worked with.

Mike Nader: Short portions of the data nightly was bright, but a lot of your organization had to do daily analysis of key operational drivers had to have weekly.

Mike Nader: accrued panels and had to constantly re forecast based on market conditions commodity prices other activities so data can't be stale.

Mike Nader: it's got to be is near real time as possible in many respects to support decisions that are ultimately going to be perishable you have to make them with some of the data or all of the data we're going to prefer the latter are sure if you put the next one point at forgiveness and then.

Mike Nader: The other key thing here is being able to enrich the data in a way that is meaningful calculations matter.

Mike Nader: We have to be able to put those in, we have to build a take feeds from master data systems or more.

Mike Nader: play a little bit of a role there were necessary in organizations and may not have those fully deployed and.

Mike Nader: The analysis that you're going to see packaged in in court today and in hopefully in further conversations.

Mike Nader: will speak to all of these points you're going we're going to look at the templates we're going to talk through how quickly.

Mike Nader: And how efficiently, we run the process of getting users, information and we're going to connect to those existing systems and allow you to make use of the required this as your mystics into allow you to build in those.

Mike Nader: required calculations.

Mike Nader: Or to shift to go to the next slide forming.

Ardeshir Ghanbarzadeh: yeah Thank you.

Ardeshir Ghanbarzadeh: I can pick this on my.

Mike Nader: job.

Ardeshir Ghanbarzadeh: All right, thank you yeah so so some of the key benefits when it comes to the analytics data help or finance what the one thing to keep in mind is that.

Ardeshir Ghanbarzadeh: It what it is, is an end to end self service platform for all your financial and operational data, and what that means, or what that really enables is.

Ardeshir Ghanbarzadeh: Speed speed speed.

Ardeshir Ghanbarzadeh: and enabling better decisions faster, because you do get these highly highly agile data pipelines that are fast or fully flexible and are able to deliver the latest data to the end user, so they can use that to to make to make decisions.

Ardeshir Ghanbarzadeh: The the data Apps that we talked about and we're going to you're going to see and learn more about that a little bit further down.

Ardeshir Ghanbarzadeh: They are really there to expedite the business value because they're going to be quickly be able to bring the sub ledger details and operational data together in one view.

Ardeshir Ghanbarzadeh: for end users to be able to consume and and analyze and, finally, the analytics data hub allows.

Ardeshir Ghanbarzadeh: Finance teams and it teams to actually future proof their finance tech stack because it makes it very easy to integrate.

Ardeshir Ghanbarzadeh: New new and existing systems into the hub, while maintaining a consistent data model and complete governance controls.

Ardeshir Ghanbarzadeh: Over over security So these are key benefits that that can be realized by the implementation of the analytics data hub for finance and we talked a little bit about data Apps.

Ardeshir Ghanbarzadeh: I just want to get into a little bit more detail about them what data Apps do one they do quickly bring together.

Ardeshir Ghanbarzadeh: financial and operational data from existing source systems, they are pre built, they are customizable and they can significantly reduce the time that it takes to deploy new reports for analysis.

Ardeshir Ghanbarzadeh: The big benefit here is that they can.

Ardeshir Ghanbarzadeh: They can.

Ardeshir Ghanbarzadeh: They can increase the time to value because of how quickly you can get these baseline reports and kpis with the pre built schemas and dashboard templates that come with them.

Ardeshir Ghanbarzadeh: They give the end users that ability to have that self service analytics experience to essentially freely explore the data.

Ardeshir Ghanbarzadeh: drill in any direction quickly find answers to new questions without the need to have to ask for new data pipelines or or new reports.

Ardeshir Ghanbarzadeh: And it takes really a way that time consuming manual process of having to stitch data together from multiple.

Ardeshir Ghanbarzadeh: multiple sources and multiple reports and trying to reconcile discrepancies amongst them and these data Apps are available for a variety of.

Ardeshir Ghanbarzadeh: Finance use cases, including accounts payables receivables looking at the cash to cash cycle for for the general ledger for fixed assets.

Ardeshir Ghanbarzadeh: And and Just to give you an example of what you can what you can see from.

Ardeshir Ghanbarzadeh: A data Apps perspective here's an example of some of the questions that you can actually then click critical business questions you can actually answer with the.

Ardeshir Ghanbarzadeh: With the quarter data APP for accounts payable lonely so we're talking about being able to.

Ardeshir Ghanbarzadeh: understand whether key suppliers are being paid on time or that payments that are overdue what type of it discounts the businesses looking at from a payables perspective.

Ardeshir Ghanbarzadeh: What are some of the invoices that are outstanding and this is just a subset of the metrics and kpis that you're looking at on the screen there's quite a lot more.

Ardeshir Ghanbarzadeh: That are delivered through the accounts payable data APP and, as you can see these this data is available for a variety of different source systems, as you can see them listed below so.

Ardeshir Ghanbarzadeh: So you are able to actually take the data APP deployed very quickly and be able to get answers from your own data as it is in your in your source system like 100 back to you here a little bit to talk about the modern data architecture.

Mike Nader: Thank you, Sir, and i'm actually just answering one question so we had a question and i'll answer another one here in a moment.

Mike Nader: But we had a question specifically about whether or not we support just finance eight or other types of data so in court, a.

Mike Nader: supports transactional operational data that's what the engine is built around today we are talking about and showing quite.

Mike Nader: A lot around those sub ledger data Apps specifically discussing to finance, but I wouldn't limit it to that particular conversation, because in court is engine is adept at handling complex transactional data sets.

Mike Nader: Now we've talked a little bit about the process actually mentioned a lot about the process in.

Mike Nader: portion of this webcast we do have spoken with before especially myself here, there was talking about process process process.

Mike Nader: This is how from a consultant I we used to it and we still approach walking through building out of data, going from the sources going, but in Lake or warehouse seven of the data Mars getting access to information.

Mike Nader: And I don't want to discount the value in here there's a lot that comes from treating data as an asset.

Mike Nader: But I also don't want to ignore what tends to happen on the side, I mean the most important one of the most important parts of the slot look at the numbers in the middle you're starting with everything and then you're going to parse that down because that's the normal.

Mike Nader: data flow data modeling process of what's happening in my experience with business teams in general finance teams, in particular, is what we're going to see next artist, or if you throw the next slide or.

Mike Nader: If I could just flip that on its side a little bit same process, but when I look at.

Mike Nader: My experience in.

Mike Nader: The space.

Mike Nader: Your leadership teams are elt your corporate cmt what whatever the right acronym for your organization as.

Mike Nader: they're looking at sets of reports and dashboards gentlemen they're going to have limited to to know drill down capability, so if they see a path the question a KPI.

Mike Nader: Maybe they've got a clicker to they can make remember that isn't a making a phone call so artists are just go ahead and fill up the next one, to you, depending on what they're looking at.

Mike Nader: That could be the controller subside that could be the FP amp a side.

Mike Nader: who are then going to push those questions out to their analysts teams and they're going to go to those business status i'm just publish data sets to allow you know as much to try to answer the questions best I can but to me it's sort of like.

Mike Nader: it's hit or miss, I would say 70 80% of the time you're not going to get everything you're looking for, because those business data zones are predicated on anticipated or known questions.

Mike Nader: Maybe you know very little when it comes to the unanticipated set so to resolve that.

Mike Nader: The analyst tends to engage a power user those power users go to the raw data zones, the refined it zones.

Mike Nader: They have more access and then they're going to build something on the side they're going to get it back to the analysts team an analyst team is then going to put that back out brought likely multiple days multiple person days later.

Mike Nader: And they tend to be rewarded with hey This is great, can I get this as a weekly report.

Mike Nader: And what that starts proliferating are these external secondary IT systems that are essentially.

Mike Nader: In conflict frankly with that more curated data strategy, but it really doesn't have to work that way, because if you think about that process still going on.

Mike Nader: You can have a data hub sitting in there that allows you to connect.

Mike Nader: and help the it teams connect and focus that transactional information and provide it out to the business teams and, frankly, the other downstream systems that you know.

Mike Nader: Like black line and i'll use black line in particular is because.

Mike Nader: In quarter announced a few weeks ago, a partnership the black line and that we are taking feeds from multiple geographies cross walking those together, which is.

Mike Nader: part and parcel of our data applications and then, providing that aggregate data set out from a reconciliation and close perspective.

Mike Nader: we're not performing the same function we're sitting in the middle, allowing for the analytics but also allowing for the requisite data to pass through in a simplified fashion and frankly cut the cost out of.

Mike Nader: Pulling that data together for those deployments that does not run counter to what's happening in your your data lake strategy or data warehousing strategy in fact it connects to it and sits in parallel to it.

Mike Nader: Or to share, if you would go to the next slide for me here.

Mike Nader: You know that process is valuable and it's going to take you years to go through this process happens every week and I promise you what happens every week, sometimes every day.

Mike Nader: What encoder really becomes is that bridge approach in between that can allow the user community to get to the information in the transactional fidelity we'll talk about the value of that a little more in a minute, but at the same time.

Mike Nader: insulating a lot of the requests and a lot of the.

Mike Nader: distraction that goes on what you're trying to run a more standardized data process.

Mike Nader: or shirt go the next slide from the place thanks, so much so, if I go back to what's the business process looks like after in quarter.

Mike Nader: And i'm basing this on a number of deployments that that I personally worked on with the organization it's that we're going to bring the data into the platform we're going to bring in it that transactional fidelity and then the user communities, whether they realize it or not.

Mike Nader: are going to have access up and down that data chain, now we can secure at the record level so you're not going to allow somebody to see something that couldn't previously.

Mike Nader: But what you're not going to have to do is go out and build scores and scores of secondary systems, you can very quickly as well we'll talk through this and show and demonstrate some of this.

Mike Nader: connect to and publish data out make it available to the business community within quarter in a very simplified fashion.

Mike Nader: So i'll speak to this one that our shelf throw up throw back to you and.

Mike Nader: In frankly, one of my favorite stories and in an organization that i've worked with quite heavily and forgive the anonymous nature of the slide will be more specific, with some customers in a second here.

Mike Nader: But in this particular deployment, we have an organization that is 16 plus billion dollars and their focus quite heavily on infrastructure and larger construction.

Mike Nader: Projects so you have more than 200 operating companies more than 30,000 active projects and more than 50,000 plus assets their entire world is about efficiency around use of those assets and in safety and frankly efficiency of their people on those projects that's how they drive.

Mike Nader: Revenue so they drive their cost savings in.

Mike Nader: Over the last year and a half, two years they have managed to deploy integrate more than 10 systems within quarter, but that's.

Mike Nader: an incremental add upon add on add the first one was 45 days.

Mike Nader: They put in deployed and tied to get the seven systems in 45 days to look at their work in progress and understand how their.

Mike Nader: Profitability drivers are trending on their jobs, and then to provide information directly to their financial bit of a CFO organization.

Mike Nader: And over the last year of this organization is save $1.2 million dollars and hard costs, specifically by removing redundant technologies.

Mike Nader: And within the larger goal is there now providing access to information from and corridor out to all of their operating companies across all of their projects, they have a goal of reducing.

Mike Nader: inefficient use of asset and other revenue leaks by about one to 2% now that doesn't sound like a lot but you're talking 30,000 projects and $16 billion it's quite a bit of money it's a laudable goal.

Mike Nader: But it's, one that has to be taken by smart people actually that'd be take my smart people, but they need the data to do it and that's what we're doing in in quarter for them or to Charlotte to go in and talk to the next door.

Ardeshir Ghanbarzadeh: Oh sorry Thank you like that.

Ardeshir Ghanbarzadeh: So that was a good story another customer of important as had quite a bit of success with the analytics they have hope for finance.

Ardeshir Ghanbarzadeh: Is broadcom and you're surely you're familiar with the name they're like.

Ardeshir Ghanbarzadeh: conglomerate that are in a bunch of different businesses, including broadband infrastructure data centers electric motors cybersecurity and they recently.

Ardeshir Ghanbarzadeh: announced that they are going to get into virtualization and cloud computing with the acquisition of vmware.

Ardeshir Ghanbarzadeh: Their environment involve a whole variety of different data sources, including Oracle the rp workday model and.

Ardeshir Ghanbarzadeh: Even Microsoft excel and day the existing data warehouses and data marks now keep in mind that if they just acquired a $65 billion company they're going to be able to fairly quickly, I mean probably within under 30 days, be able to get consolidated financials.

Ardeshir Ghanbarzadeh: From vmware and broadcom because they can easily augment their data hub, with the with the financial and operational data that comes from.

Ardeshir Ghanbarzadeh: From vmware so a couple of key points that that they made in terms of our improvements that they saw was being able to actually not only make queries faster but going from 400 queries.

Ardeshir Ghanbarzadeh: A day up to 70,000 and and when you consider the scale of that that is, that is a massive massive improvement in terms of the.

Ardeshir Ghanbarzadeh: The velocity and the quantity of questions being answered when you're able to when you're able to scale up that way at that at that rate with with the questions that you can ask from the data and the answers that you can get.

Ardeshir Ghanbarzadeh: Now we're gonna do mike's gonna take it through a product demonstration, but just before that just set the table i'll talk a little bit about how those simple of how this simplifies the workflow.

Ardeshir Ghanbarzadeh: And Mike i'll give it back to you for for that.

Mike Nader: yeah so i'm going to walk through a demonstration in just a moment and i'm not ignoring any of the questions that have come up by their events or the number of them there's.

Mike Nader: One in particular i'm answering now, so I will make sure I finished answering that on towards the end here.

Mike Nader: But just to give you an idea of what I want to walk through from a demonstration perspective, today, I mean a lot of demonstrations, you see, especially whether it's an analytics tool bi or even finance side as you get a lot of things around.

Mike Nader: Corporate reports and dashboards or divisional reports or analysis, because that tends to be the more.

Mike Nader: exciting piece you get the the cool graphics and OPS II so some of that most definitely, but what is often ignored, are, the more on demand.

Mike Nader: Analysis requirements that come out and, frankly, the complex data process that feeds into all of that, and I, and in a 10 minute demonstration it's gonna be very difficult to do.

Mike Nader: But I do want to highlight forces that because I think it's important that you understand.

Mike Nader: How encoded changes the process and how it provides data out some of the questions that have been coming in, on the chat side have been.

Mike Nader: Around comparisons to lets you know other tools that do a modified atl process or something of that nature in court is a larger platform.

Mike Nader: It connects the data in our circle which causes the next slide for me here we're going to quote and connected to data we're going to pull that into the platform.

Mike Nader: we're going to ingest it we're going to enrich it and simplify it and i'm going to show you a quick process for doing that.

Mike Nader: And then i'm going to share what consumption looks like and, in fact, while i'm doing part of the part of the first two.

Mike Nader: we'll we'll we'll look at Point number three well what is key here is that it's not just about dealing with the data wants it's landed it's about how do I get and provide access to the information in a way that's simple.

Mike Nader: So with that, let me go ahead and jump into it, and then I know there are a couple questions out there that haven't been answered promise you, we will get to those momentarily me soon as we finish up here.

Mike Nader: So artists should let me know when you can see my screen i'm gonna go ahead and share that out now.

Mike Nader: You can see there we go there, we go now, as I look perfect all right, so you know, as I said earlier, a lot of times when we get into demonstrations, we tend to see things.

Mike Nader: like this, and this again this is wonderful, we can pull the data and at the transactional grain, we can show and calculate.

Mike Nader: Financial kpis and give you the ability to drill down simple example here, looking at revenue two bookings looking at a bookings trends from an external sales side breaking for quarterly appear multiple years down at the bottom, looking at the the current month's kpis.

Mike Nader: Even breaking it down by geography and providing the ability, then, if I wanted to drill in and start looking at my trends over you know those those eight quarters now that's a pretty standard type of output that we'd want to see on maybe a corporate view of the world.

Mike Nader: But if you get into the divisional do to get into the business unit view of it it's going to get a little more detailed I can get all the way down and quarter frankly to looking at what am I open orders and my open order lines.

Mike Nader: And then, looking at what my inventory lead times are and what my backlog is sitting here, and I can tie that all together that's a pretty normal process in accordance with our clients do our customers do every day, but what I wanted to also do changing.

Mike Nader: Is the instances for you is, I wanted to show you an environment of the different, and I wanted to show you that process i'm going to go to step one.

Mike Nader: In that three step process, not just focus on the end of it, and I want to connect to some data and look at what that process looks like we talked about the data Apps.

Mike Nader: Data Apps in quarter are a combination of connections out to data source the pull of that data again calculations of critical analytics of critical metrics.

Mike Nader: Simplification for the business community and packaged content to go around it and that can be things that that import it provides.

Mike Nader: That can be things that we have partners to provide one question that came up was about.

Mike Nader: Do we have an example for do we have connectivity to people suck absolutely we have partners that have built data Apps on peoplesoft.

Mike Nader: On top of the quarter platform, but today i'll show you one just on on Oracle and and as much as i'd love to run all of these will take about half an hour we don't have that much time, so i'm gonna run one of these for you.

Mike Nader: So i'm going to connect in i'm going to go ahead and install an application that is going to pull in data from accounts payable for peoplesoft or excuse me for Oracle EBS like to give you an idea.

Mike Nader: that's going to bring in a whole series of extra content which i've got built that right here.

Mike Nader: But coming back over i'm gonna go ahead and just use the sample data set now if you already had a connection you connect out to your own data source, but I provide us our sample data set right this minute, and it is fairly complete.

Mike Nader: And so what's what's happening right now is just seeing what we're going to pull in we're going to pull in data from a variety of places out inside of era P environment.

Mike Nader: And we're going to set that up automatically for an incremental fee of data.

Mike Nader: so simple example we're going to bring in a bunch of information across these tables we're going to simplify it for the user Community day to.

Mike Nader: access and every will throw out a couple sample dashboards out but that's not the limit of it, meaning that.

Mike Nader: If the dashboards don't meet your needs if you have on demand analysis will look at that momentarily you can build something very quickly from scratch and there is a question about on demand, analysis and.

Mike Nader: And programming versus not we'll get to that in a moment, promise you we're not gonna ignore any questions.

Mike Nader: But i'm not having to go out and start loading up the data for us here, and this is going to take four or five minutes, so, while this is going we'll come back to it, we will we'll take a look at sort of what the output looks like.

Mike Nader: From this environment, but just so you can see the steps that in court is going to go through it is going to extract that data in but it's not going to change it.

Mike Nader: Meaning that if I have a billion transactions and those want to stream in every 15 minutes so i'm going to bring in.

Mike Nader: and coordinate at the engine and our data Apps is going to enrich that information it's going to make that available.

Mike Nader: into the into memory side, so that users can connect to it and then it's going to set it up, so we have.

Mike Nader: baseline reports and dashboards but, more importantly, from my perspective, you had that on demand analysis capability, but i'm gonna let this run for a minute, and when it come back over here now.

Mike Nader: i'm going to kick back over to the content.

Mike Nader: Just to give you an idea again once the data is in doing things like I want to look at the cash conversion cycle, or I want to look at what my profitability is and start drilling through to.

Mike Nader: From a top line number down into my operating expenses and seeing how that looks like at the cost Center level.

Mike Nader: Even them wanting to break down from the the the most.

Mike Nader: dependable cost Center for the customer and spending the most information even all the way down into expense report lines, and I think, in this case, not a lot but 71,000 in here but.

Mike Nader: Overall there's probably a billion half a billion records in here and what we're doing by this workflow was going really from a top line number.

Mike Nader: on the opposite side, all the way down to the individual expense report lines to understand let's say I want to see what the impact of spending what on gas, let me see everything here it's focused on that and you're the 1560 lines that I have anything to do with gas.

Mike Nader: On the expense report side, so the concept of import is going from that top line number, all the way down to the supporting transactions in a single platform.

Mike Nader: And not having to just use those fixed analyses, but also having the ability to come in from more of an on demand perspective.

Mike Nader: If I look at something like I want to connect into a look at my journal entry lines, and I want to look at various portions of my GL well, we can do something very simply.

Mike Nader: i'll duplicate that.

Mike Nader: Let me just clear some things out here.

Mike Nader: And so, right now, what i'm actually looking at.

Mike Nader: This simply.

Mike Nader: set of information and and I don't expect every user to do this, but when you talk about how many days does it take that power user in the previous scenario to go build something.

Mike Nader: and your your analyst Community not being able to get an answer questions efficiently the ability to come in and Sarah like I want to look at net amount or I want to look at my.

Mike Nader: My GL information i'm gonna look at my my balances here.

Mike Nader: And I want to do that.

Mike Nader: By my.

Mike Nader: My accounts.

Mike Nader: And maybe I want to do that across my periods.

Mike Nader: That becomes a very.

Mike Nader: Good just use period number here MAC my life really simple.

Mike Nader: That becomes a very simple very regular type of analysis that people doing and said, I want to see.

Mike Nader: Now Okay, but i've got an account descriptions, maybe i'm missing one there so let's go with.

Mike Nader: I want to filter out anything that isn't know.

Mike Nader: And then the goal is to make this simple and to make it fast to to interact and do something on demand and if I want to come back over to where we're sitting right here.

Mike Nader: So we have extracted data let's say Emily rose hundred 45,000 there but 900,000 rounding up there i'm going to estimated about.

Mike Nader: 1.2 million so it's a four or 5 million rows all and.

Mike Nader: And now, in this environment, I could connect our bi to it, I could connect tablo to what I can I can excel plug into it, I can use it and in quarter, one of the questions that were in it was in the.

Mike Nader: chat was well, a lot of our you know we talked to customer maybe they have to use power bi on top of important it's not a half to it's generally a desire to.

Mike Nader: If you have a corporate strategy, the power bi is your go forward, then use it, but simplify the process of getting data over to a quarter if tablo is your corporate standard then use it.

Mike Nader: But I personally liked our ui very much and that's not to discount those tools really cool tech but it's about what can you do when you have the data quickly, and so I can connect those environments directly to.

Mike Nader: This business schema that we just put out there.

Mike Nader: So this looks very dimensional nature.

Mike Nader: But here is what we loaded in the about four to 5 million records, while we were talking.

Mike Nader: And now you've got that content available and then, as you run those those loads you set this up to say okay go ahead and load this incrementally I want to see every 15 minutes things feeding in.

Mike Nader: What are those key drivers those key activities that you want to pay attention to, so you can pull some levers.

Mike Nader: To stop revenue leakage is to make yourself more profitable before a pattern hits a report because, once it gets a report we're all familiar with this is not unless you can do after the fact, you can do something next month.

Mike Nader: But you can't do something in a month in court, it looks to change that paradigm that's really the focus of it so artistry i'm going to stop there just for sake of time and get some questions but i'll throw it back to you awesome.

Ardeshir Ghanbarzadeh: Thank you.

Ardeshir Ghanbarzadeh: Alright folks um yeah so Mike Thank you so much for a great DEMO we have some questions that came in during the dental that are in the QA.

Ardeshir Ghanbarzadeh: folks um basically the key takeaways from today's presentation when it comes to the analytics data for finance is i'm thinking about it in the context of being able to.

Ardeshir Ghanbarzadeh: centralize your financial operational data and be able to apply that common data model and and spend governance controls.

Ardeshir Ghanbarzadeh: Between your source systems and your destination systems and be able to do that, using these fast and highly flexible data pipelines that are going to make that data available in just about near real time.

Ardeshir Ghanbarzadeh: To the end users for consumption leveraging those out of the box data X, to be able to expedite.

Ardeshir Ghanbarzadeh: How quickly you put up the business schemas and dashboards and reduce the deployment time so that so that you know new reports and kpis and.

Ardeshir Ghanbarzadeh: metrics can be made available to answer new questions that are being asked from finance teams and also the ease of integration, when it comes to.

Ardeshir Ghanbarzadeh: Existing finance solutions that are out there and being able to being able to add an augment the analytics data hub.

Ardeshir Ghanbarzadeh: very easily and and make those sister get more really more either those systems leveraging those systems to get more out of them from their data and from their capabilities will answer a couple of questions here that are coming up.

Ardeshir Ghanbarzadeh: One question here.

Ardeshir Ghanbarzadeh: Mike is all these dashboards views changed.

Ardeshir Ghanbarzadeh: Change based on real time data or do we need to schedule to update the workflow back end data set which is pulled from various or original data sets.

Mike Nader: Somebody is.

Mike Nader: You know it's a great question, and there was the next one, I was getting to actually so in writing, but.

Mike Nader: No, you don't have to do, once the data is ingested into the platform, and when I said we incrementally feed as frequently as every 15 minutes, and we have people that are that are very aggressive like that, and some that are.

Mike Nader: A little less frequent and, frankly, you can vary it on data set by data set your your ar transactions or your orders, you might want to see more frequently, whereas you're.

Mike Nader: going to do certain pieces of fixed asset analysis, maybe nine please inadequate to interval for that it's not required, but once the data lands and and quarter, whether it's from.

Mike Nader: One E rp or 42 and I have a particular customer mind where it's exactly what they do take 42 yards and pull it together across versions and types.

Mike Nader: To provide analysis out the data is now available for the reports dashboards there's no secondary step beyond that.

Mike Nader: We keep that data in that transactional fidelity intentionally and then the value of that.

Mike Nader: For, especially for finance teams goes beyond just data access also looks to compliance, so if I start thinking about how do you how did this number get on a given report now we have built in lineage analysis it'll show you once it hits the platform, how it runs.

Mike Nader: all the way up to the numbers you're seeing on the screen, but just as importantly, and this holds true but, if i'm having multiple earpiece pulled together.

Mike Nader: I can, in a single click dis aggregate that and the components go down to the individual transactions.

Mike Nader: So i've got an auditor compliance team that wants to understand okay have this number get here can you give me assurance that I don't have something screwy going on.

Mike Nader: i'm showing you the raw transactions i'm showing you the source of truth, at your organization because that's what you're generating your revenue on that's how you're you're processing your expenses.

Mike Nader: And once you've done the your your allocations and adjustments and closed out that the data finds its way back to your ear P it's shown an encoded as well, so I can tie that all together.

Ardeshir Ghanbarzadeh: Thank you, thanks Mike um another question, this is from ashish he asked does in quarter have EPL embedded.

Mike Nader: In quarter.

Mike Nader: And the reason i'm pausing is that I don't want to.

Mike Nader: Making coordinating Alto in quarter extracts data it loads data and then you can transform that data if you need to.

Mike Nader: But what we would recommend is you replicate the information and and let it incrementally feed you apply whatever heuristics for those those are calculations.

Mike Nader: Time master data from another system in apply, you know filtering that that is appropriate for your business community and your security.

Mike Nader: So that is a level of transformation any calculations and transformation, but it is not intended to just be an atl environment it's an environment designed for analytics and to allow users to have that data, so you can more readily answer questions and to then push that information.

Mike Nader: out to let's say a reconciliation, a consolidation, even an FP amp a environment, one thing I didn't show in the DEMO and no need to jump back to an elbow happy to is.

Mike Nader: What those feeds look like because they're built in, and those can those data destinations can be scheduled as an output of the work that's being done here.

Ardeshir Ghanbarzadeh: awesome Thank you like one other question business do with your DEMO, the question is what you just showed in the DEMO how easy, is it to set up these reports or drill downs, and is it out of the box.

Ardeshir Ghanbarzadeh: thing this isn't a to ask question.

Mike Nader: yeah it really It really is, and in fact so i'm going to make sure i've got to get the right screen up and i'm gonna go back and show you something again because.

Mike Nader: it's important to me that that the that become sort of part and parcel of the process here, out of the box, we absolutely tied together.

Mike Nader: There we go out of the box, we absolutely tied together all of that in that type of information and, in fact, if I come into i'm going to back out right here and let's go back in there are some of our EBS blueprints.

Ardeshir Ghanbarzadeh: Let me know if you want to share my.

Mike Nader: Oh sorry about that.

Mike Nader: thought I was already doing appreciate that there we go so i'm just going back in a couple of radius blueprints but just to give you an idea.

Mike Nader: These drill Downs are out of the box right here, and the fact that we bring the information in, and then we have it interrelated for you i'll show you what I mean by interrelated.

Mike Nader: I added on the screen earlier but maybe I didn't to highlight that enough.

Mike Nader: Is we're going to provide the ability to let's say I want to drill in and understand automatically what's happening all the way down.

Mike Nader: To like it's ap account mismatches but it's not just the concept of of time dashboards together because that's actually pretty straightforward to do.

Mike Nader: But if I wanted to look at you know okay here's your drill reports, I want to look at my GL details my journal details to ar things of that nature, but when i'm in the environment and and quarter if I and i'll use a P, as an example again.

Mike Nader: In court it.

Mike Nader: ties together.

Mike Nader: let's just paint in.

Mike Nader: data, and I know this is a little weird on the spring.

Mike Nader: But my point and even bringing it up is to do real ap or ar you pick your data set analysis it's not about just having.

Mike Nader: A 10 dimensional data set with a billion rows in it it's about the ability in ap to be able to look at what fields are to look at who's buying things from the HR side.

Mike Nader: To look at what are the common dimensionality and common definitions, I have in my in my financial common area, what are the sub ledger man my excellent transactions what am I expense lines look like.

Mike Nader: So in quarter, because we have the innate understanding of that down to cell by cell basis, supported by we call our direct data mapping engine that concept of a drill from that top line number, all the way down is built into the platform it's just part of it out of the box.

Mike Nader: awesome.

Ardeshir Ghanbarzadeh: Great question, thank you, my.

Ardeshir Ghanbarzadeh: folks were going to.

Ardeshir Ghanbarzadeh: start to wrap it up here if we didn't get to your question, please hang on we will we will reach out to you to answer the questions that were not answered here today.

Ardeshir Ghanbarzadeh: i'm just a couple of events coming up that you might be find interesting, there will be two virtual hands on labs that will be about the analytics data help for finance.

Ardeshir Ghanbarzadeh: The first being about accelerating financial analytics with multi source transactional data that would be that will be on July 21 at 9am PT 12pm Eastern.

Ardeshir Ghanbarzadeh: And then the second virtual hands on lab will be on delivering financial and operational analytics.

Ardeshir Ghanbarzadeh: Through a truly agile data pipeline within caught up that will be a week later on July 28 at 9am PT and 12pm Eastern so please go to the link you see on the screen and quarter.com slash virtual dash hands dash one dash labs that series.

Ardeshir Ghanbarzadeh: And register for those virtual hands on labs you get up, you will get hands on experience within quarter there and it will be a guided experience, because we will have in quarter expert stepping through it.

Ardeshir Ghanbarzadeh: If you're interested in trying and quarter for yourself on your own you can always try.

Ardeshir Ghanbarzadeh: Signing up 14 quarter cloud environment that encoding calm it's a free 30 day trial, please, please take advantage of it and we'd love to hear your feedback on that.

Ardeshir Ghanbarzadeh: With that I want to thank you all for attending today thanks Mike for his participation and the great demonstration and we will see you all at our next webinar.

Ardeshir Ghanbarzadeh: Please check our events page to to see when our next webinars are going to be made available. Thank you again and have a great rest of the week.