Boosting your working capital by harnessing the power of your data

Transcript:

Ardeshir Ghanbarzadeh: hi everyone, welcome to our webinar today, boosting your working capital by harnessing the power of your data, this is the final installment of our three part series on driving agility with financial analytics.

Ardeshir Ghanbarzadeh: My name is artist here gamblers I am in quarters director of product marketing i'll be your moderator and speaker today.

Ardeshir Ghanbarzadeh: I have with me a colleague of mine today, Joe don't proceed to senior solutions architect here at in caught up a little bit about Jill he began his career in.

Ardeshir Ghanbarzadeh: Virtual call Center space as an IT analyst many moons ago.

Ardeshir Ghanbarzadeh: This is where he found that he has a knack for telling stories with data that sparked the immediate understanding of data to others his data stories help stakeholders, see the past the present and the future quite clearly.

Ardeshir Ghanbarzadeh: This helps them imagine what is possible and gives them actionable insights through visualizations where he takes them across the bridge from bits and Bytes to a compelling and comprehensible narrative Joe Thank you so much for being here with us today.

Joe DelPercio: thanks for having me on your show appreciate it.

Ardeshir Ghanbarzadeh: yeah right on alright let's get started um what we're going to cover today and in the webinar we'll talk a little bit about the data and analytics challenges.

Ardeshir Ghanbarzadeh: For finance teams, then we will get into how you can drive business agility within quarter.

Ardeshir Ghanbarzadeh: will touch upon the analytics data hub for finance and I will introduce you to the quarter finance data Apps.

Ardeshir Ghanbarzadeh: Towards the end of the webinar Joe will take you through a product demonstration of in quarter.

Ardeshir Ghanbarzadeh: This is specifically designed for finance teams and then we will take questions, towards the end of the webinar just as a as a bit of housekeeping here.

Ardeshir Ghanbarzadeh: If you do have to leave early, we will be sending a recorded version of the webinar out to you, within a couple of days, and if you do have questions for Joe or for myself, please type them into the Q amp a section of the of the chat we will answer your questions at the end of the webinar.

Ardeshir Ghanbarzadeh: So just taking a look at the the landscape of how data and analytics is delivered to the office of finance and really what finance teams require.

Ardeshir Ghanbarzadeh: From the data that an organization, has today finance teams are looking to have an end to end a comprehensive and holistic view of financial and data and.

Ardeshir Ghanbarzadeh: financial and operational data in one place, so what they really want is be able to go to a single location where they can look at.

Ardeshir Ghanbarzadeh: The the operational data that is coming from different parts of the business and be able to reconcile that with the financial data that they are looking at.

Ardeshir Ghanbarzadeh: To be able to then do analysis and provide some commentary to the business leaders and the other stakeholders so that decisions can be made to move the.

Ardeshir Ghanbarzadeh: Business forward and and and change the business outcome in a positive fashion.

Ardeshir Ghanbarzadeh: Another another area Another requirement really for the finance team, just to be able to get this data in it in a very timely and accurate.

Ardeshir Ghanbarzadeh: manner, in other words the data, and in fact the requirements for data analysis is moving closer and closer to real time so being able to get the.

Ardeshir Ghanbarzadeh: The latest data and being able to ensure that that data is reliable and the veracity of the data exists and it is accurate to be able to drive decisions becomes a critical factor in and data driven decision making.

Ardeshir Ghanbarzadeh: and other requirement that we often hear about is the ability to have visibility to the individual transactional level detail records at this becomes pretty important when.

Ardeshir Ghanbarzadeh: Data analysts and folks in the office of finance are responsible for and drilling into the data to say, for example, identify certain trends or look at the root cause of certain variances.

Ardeshir Ghanbarzadeh: So, having to oftentimes deal with aggregate data is not going to provide that capability to be able to do that type of analysis that deep dive analysis that is required.

Ardeshir Ghanbarzadeh: Another another area Another requirement that we also often hear about is the need to be able to answer the questions of today, so a lot of times with.

Ardeshir Ghanbarzadeh: Previous dashboards and analytics they are answering a question that's already been asked, because the data model has been structured in a way to answer that question, so you are really getting an update.

Ardeshir Ghanbarzadeh: To a previously asked question, but when you kind of look at the business climate today considering things like inflation and and the backdrop of the economy and.

Ardeshir Ghanbarzadeh: Potential looming recession, there are a lot of new questions that are arising, and the business leaders are asking.

Ardeshir Ghanbarzadeh: Asking the data teams and finance teams to answer those questions for them so to do that now, you need a new data set or you need a new data model or you need a new dashboard or certain new kpis or metrics sometimes and a lot of organizations, these new bi.

Ardeshir Ghanbarzadeh: Analyses can take weeks, sometimes months to stand up that's not going to be sufficient or acceptable when you have to answer the question of today.

Ardeshir Ghanbarzadeh: So so being able to get a new bi project or a new dashboard or a new set data sets that up quickly is also becoming a requirement, more and more these days as we look at some of the challenges in the office of finance and finally finance teams already have.

Ardeshir Ghanbarzadeh: Existing finance tools that they use for financial planning and analysis or for for managing the closed process so having the ability to integrate.

Ardeshir Ghanbarzadeh: Those solutions into an existing framework that supports financial and data analytics is also becoming a requirement, because they want to be able to have.

Ardeshir Ghanbarzadeh: visibility and access to the data that's available, also in the solutions to get the most value out of that data.

Ardeshir Ghanbarzadeh: Unfortunately, what we are seeing is that some of the existing Dr P systems and financial tools that are out there, right now, are failing to meet this.

Ardeshir Ghanbarzadeh: Mid meet these requirements that the finance teams have, and this is, this is really the reason that we are.

Ardeshir Ghanbarzadeh: Finding that today's approach to delivering data and analytics into the office of finance is really forcing that organizations to make these unacceptable trade off, for example.

Ardeshir Ghanbarzadeh: A lot of times data is essentially siloed or sitting in multiple sources that are either inaccessible, or you have to pull data.

Ardeshir Ghanbarzadeh: From these different sources and then try and stitch it together to basically get that end to end view that comprehensive view of the health of the business and a view of the financial operational analytics also.

Ardeshir Ghanbarzadeh: In a lot of certain scenarios, there is a a time consuming ETF process involved in actually being able to capture the latest data.

Ardeshir Ghanbarzadeh: And this could be you know hours to sometimes even days long to be able to run that process, and certainly the process doesn't actually time out and has to be restarted.

Ardeshir Ghanbarzadeh: And what this does is this actually introduces delays into the into the processes that the finance teams use to be able to to execute the analysis that is needed and a commentary that they need to provide to the stakeholders in the business.

Ardeshir Ghanbarzadeh: A third area where these solutions come short is that oftentimes they will deliver to you top line aggregations or kpis that.

Ardeshir Ghanbarzadeh: are going to prevent the deep analysis or that deep dive analysis needed to go many, many layers down.

Ardeshir Ghanbarzadeh: To to investigate some kind of a data anomaly or look at a root cause for, for example, for a specific KPI that's maybe outside of the acceptable threshold for the business and other times.

Ardeshir Ghanbarzadeh: The, as I mentioned earlier, could be it could be a really long wait time to get a new VI project modeled out and stood up for for analysis and.

Ardeshir Ghanbarzadeh: Again, the today data and analytics really needs to move at the speed of business and be able to answer the answer to questions that the businesses asking today, so that also becomes a challenge.

Ardeshir Ghanbarzadeh: For for folks in the in the office of finance and then, finally, the implementation of some of the solutions can be quite costly and and and and could take a very, very long time, sometimes in excess of one year, and that that is going to essentially.

Ardeshir Ghanbarzadeh: inhibits how quickly you know finance teams can leverage the data that they have to be able to drive decision making within within the organization.

Ardeshir Ghanbarzadeh: So the really the what we're looking at here is what finance teams to require for data and analytics and some of the shortcomings that has come up, that is.

Ardeshir Ghanbarzadeh: Out there, right now, from your P and finance tools and let's get into a little bit more more detail about what we are seeing today intercom from from that perspective so.

Ardeshir Ghanbarzadeh: What we are seeing is that it the the so called modern data architecture typically results in a pretty significant loss of data integrity.

Ardeshir Ghanbarzadeh: As a result of this complex set of steps that cause upwards of, for example, 90% of that data being lost, and creating multiple data copies in silos across the organization so here's why.

Ardeshir Ghanbarzadeh: If you kind of take a look at this diagram at the very left you'll see that data is copied from business data sources such as let's say netsuite or Oracle EBS into some kind of a raw data zone.

Ardeshir Ghanbarzadeh: Which is typically a data lake, the main objective here is to ingest that data as quickly and as efficiently as possible.

Ardeshir Ghanbarzadeh: But that requires keeping the data, and you know, keeping that data and its native for.

Ardeshir Ghanbarzadeh: know a transformation really happens at this stage, you still have 100% of the data and it's raw format, but it's really not ready for business consumption at this stage.

Ardeshir Ghanbarzadeh: In the next stage, the the raw data or this three NF data as it is called, and is refined, so it is combined harmonize filter and then it's transformed into so.

Ardeshir Ghanbarzadeh: You got pretty much as simple data table structure, through this transformation process and other copies made of the data and it's stored, you know, in a data warehouse.

Ardeshir Ghanbarzadeh: In some cases, each functional area that a business can have their own data warehouse for example of finance wanna marketing one.

Ardeshir Ghanbarzadeh: So you often end up with different copies of the data in different silos and the important key here is that the data warehouse has to be.

Ardeshir Ghanbarzadeh: designed with a purpose and a requirement ahead of time, so this is kind of where the agility starts to get lost This is also where as much as 75% of the data detail is last.

Ardeshir Ghanbarzadeh: Including the ability to be able to trace out the lineage you know or enforce granular security on that data.

Ardeshir Ghanbarzadeh: And then the next step is to further aggregate this data for business consumption typically into a star schema or a dimensional model, and it may be.

Ardeshir Ghanbarzadeh: fractured into many smaller data marts for performance and security reasons, on the by this point, you now have a multitude of data copies.

Ardeshir Ghanbarzadeh: With only about 10% of your original data details remaining what happens is if there's a problem here with the data that requires the business users to drill down into the next level of data.

Ardeshir Ghanbarzadeh: The link to that original data is lost, so you can't really easily drill down and validate the.

Ardeshir Ghanbarzadeh: The accuracy of those aggregates um what if, and what, if you like, for example, you wanted to perform some deeper analysis to say pose a question.

Ardeshir Ghanbarzadeh: To that aggregate model, you really simply just cannot cannot answer that question so after all this effort you end up with a set of data that.

Ardeshir Ghanbarzadeh: lacks accuracy, because the data is sitting in multiple copies and it's been transformed, so it makes it difficult to validate.

Ardeshir Ghanbarzadeh: You have not data latency because the timeliness of the data as it goes through all these transformation and aggregation processes is is introducing delays into your process.

Ardeshir Ghanbarzadeh: The the kind of washes down really the level of insights that you get from that data because.

Ardeshir Ghanbarzadeh: The now through these aggregations there's a limited number of questions that you can really truly answer.

Ardeshir Ghanbarzadeh: With the with the smaller data set that you have, and also the a lot of the governance is lost because you've lost data lineage and you really cannot enforce that application level security.

Ardeshir Ghanbarzadeh: This is where, in quarter steps in and actually simplifies.

Ardeshir Ghanbarzadeh: This process to be able to deliver that kind of business agility to the business so If so, if you kind of take a look at this diagram and how in quarter approaches this problem you still have your data sources on the left side of the diagram and you are still.

Ardeshir Ghanbarzadeh: ingesting 100% of that data in its raw original form into into n quarter, but in quarter does not actually go through that transformation and aggregation process we make 100% of that data available or for analysis to the end user.

Ardeshir Ghanbarzadeh: So what does that mean.

Ardeshir Ghanbarzadeh: We use a technology called direct data mapping, which essentially takes the data in its original form from source to visualization.

Ardeshir Ghanbarzadeh: With this, we are able to deliver full data fidelity so with with something close to 250 data connector so for data sources to be able to acquire.

Ardeshir Ghanbarzadeh: Finance and operational data for reporting from multiple sources, we can bring all that data into a single heart.

Ardeshir Ghanbarzadeh: Of that delivers top line aggregations all the way down to transactional the level details we make all the data usable and we leverage all the available data.

Ardeshir Ghanbarzadeh: For analytics with visualization layer that delivers that transactional details to finance teams for data discovery and for analysis or even to be able to feed.

Ardeshir Ghanbarzadeh: Certain data science and machine learning algorithms to generate forward looking insights and we completely eliminate data latency so instead of waiting days or weeks for data, the latest data is readily available, so you can start to look for insights within within minutes.

Ardeshir Ghanbarzadeh: um the some of the technical benefits and the way that the direct data mapping is work it works is that, through parallel loading of the data we are able to deliver a full and incremental data without any transformation and make that near real time reporting.

Ardeshir Ghanbarzadeh: available, the day the data is enriched with data map so every point of the data is aware out and about how it relates to every other point of data.

Ardeshir Ghanbarzadeh: And with our smart query routing we can automatically recognize joint pass so there's really no need to plan a query at runtime, so this is what.

Ardeshir Ghanbarzadeh: provides that unprecedented level of agility that enables in quarter to bring a business intelligence to the data, rather than bring data to business intelligence like we see in some data warehousing models.

Ardeshir Ghanbarzadeh: So now, I want to talk a little bit about the analytics data help for finance and when you when you think about the analytics data help or finance is something that we actually announced its launch, on Tuesday of this week i'm.

Ardeshir Ghanbarzadeh: Trying to imagine it as an end to end self service platform for all your financial and operational data and what I mean all is 100% of your.

Ardeshir Ghanbarzadeh: financial and operational data and what is the what are some of the benefits and thought about that.

Ardeshir Ghanbarzadeh: of using this kind of analytics data hub model for your data well one you are able to drive.

Ardeshir Ghanbarzadeh: Faster decisions on better decisions in your organization, because the data is delivered through these a highly flexible and very fast data pipelines.

Ardeshir Ghanbarzadeh: That bring that near real time data to the end user and enables you know immediate analysis analysis of that data it's important to also understand that this data can be coming from multiple data sources like Dr P systems CRM.

Ardeshir Ghanbarzadeh: Business applications databases file servers to spreadsheets all of this data is coming together into this into the single heart.

Ardeshir Ghanbarzadeh: When you put that in the context, so, for example, the office of finance.

Ardeshir Ghanbarzadeh: Think about how that can empower our financial planning and analysis teams because it's giving him that holistic end to end view of financial operational data, so they can then quickly adjust their forecasts.

Ardeshir Ghanbarzadeh: Do timely analysis even run some predictive models and get some forward looking insights.

Ardeshir Ghanbarzadeh: In addition, it can also help with the close process by shortening that consolidation and reconciliation cycle for accounting teams, because they have that granular visibility to all the transaction level details to validate the metrics and perform the root cause analysis that is required.

Ardeshir Ghanbarzadeh: Again, having that immediate access to the latest relevant financial and operational data without having to wait for it.

Ardeshir Ghanbarzadeh: Because there is no need to wait for that time consuming extract or the manual effort of stitching that data together for reports makes a huge difference in in the.

Ardeshir Ghanbarzadeh: In the efficiency of analyzing data.

Ardeshir Ghanbarzadeh: And also you're kind of taking the guesswork and out of decision making, because you're not dealing with different copies of the data you're not seeing the dealing with conflicting reports or multiple versions of the truth.

Ardeshir Ghanbarzadeh: And you have that single copy it's in its original form and it's 100% identical to the source, so a big benefit there in terms of being able to have that clarity.

Ardeshir Ghanbarzadeh: From data and and being able to drive analysis that way second is that in quarter in expedites business value of because we are able to bring together sub ledger details and operational data.

Ardeshir Ghanbarzadeh: With what we call a finance data Apps and what's what's interesting about finance data Apps is they are essentially built in and out of the box pre built.

Ardeshir Ghanbarzadeh: Apps that provide business schemas and dashboards that can easily overlay on top of your existing data sets and i'll get into that data Apps and a couple of slides further down.

Ardeshir Ghanbarzadeh: in more detail but well the benefit here is that you are significantly increasing time to value because you're reducing the time it takes to deploy.

Ardeshir Ghanbarzadeh: baseline reports that have those kpis i'm pre built schemas and dashboards and them with the templates that the data Apps provide.

Ardeshir Ghanbarzadeh: The built in dashboards provide that instant visibility to key metrics and the business drivers that finance teams are looking for.

Ardeshir Ghanbarzadeh: For a variety of processes and use cases, including planning reconciliation payables receivables even more around fixed assets.

Ardeshir Ghanbarzadeh: You have the finance data Apps and and the quarter data hub also empower the end users with the self service analytics experience.

Ardeshir Ghanbarzadeh: so that they can actually fully explore the data in any way they want, they can drill in any direction they want.

Ardeshir Ghanbarzadeh: And that way they're able to quickly find answers to new questions without the need to have to actually go and build a new report or establishing new data pipeline.

Ardeshir Ghanbarzadeh: And also you're eliminating that a manual process of bringing data together by running a report from system, a getting a second report from system be not necessarily at the same time that could be a.

Ardeshir Ghanbarzadeh: lag between when you get access to these reports, but essentially you have to bring them all together at some points to be able to get that end to end view so that also becomes a challenge and.

Ardeshir Ghanbarzadeh: That challenge gets eliminated when you use in quarter as a finance data help for your operational financial data.

Ardeshir Ghanbarzadeh: And then finally within quarter, you are able to future proof your finance investments so if you already have certain finance solutions in your finance tech stack.

Ardeshir Ghanbarzadeh: And you can easily augment those the finance data hub with those solutions with easy integrations that are already available natively within quarter.

Ardeshir Ghanbarzadeh: You can again take advantage of the 250 or so existing connectors that we have for the variety of data sources to being able to pull data in.

Ardeshir Ghanbarzadeh: On top of that, you are also defining a common data model and also a common set of governance controls that's pretty critical to financial and operational data.

Ardeshir Ghanbarzadeh: When you're when you're trying to move data between source systems and destinations across the enterprise and then over time, this is going to help significant significantly reduce your operational costs.

Ardeshir Ghanbarzadeh: As you as you are now managing everything through a centralized centralized hub for your finance and operational data.

Ardeshir Ghanbarzadeh: Important to finance it teams is always security and and within quarter they maintain complete control over a user access and data governance so.

Ardeshir Ghanbarzadeh: they're ensuring that the right people are seeing the right data at the right time and an overall they're reducing the security risk of the business and also eliminating overhead.

Ardeshir Ghanbarzadeh: by retaining all the application level security parameters, including row level security and and and column level security so so you so you don't have that extra overhead of having to manage security in multiple places.

Ardeshir Ghanbarzadeh: So a little bit about the.

Ardeshir Ghanbarzadeh: The quarter finance data Apps so what what data Apps, as I mentioned, are are pre built applications that are specifically designed for some of the common and popular data sources in the rp systems that are out there today so.

Ardeshir Ghanbarzadeh: We have these data Apps for finance available for a variety of use cases everything, ranging from fixed assets management to receivables and payables.

Ardeshir Ghanbarzadeh: To general ledger analytics being able to manage your cash to cash cycle, which obviously is becomes quite important.

Ardeshir Ghanbarzadeh: Especially on the topic of this webinar which is around managing working and boosting really are working capital as much as possible and.

Ardeshir Ghanbarzadeh: Also for revenue and billing and an employee expenses, we have these available out of the box, where you can overlay it on top of the data that you have from your Oracle EBS from your SAP from your netsuite solution.

Ardeshir Ghanbarzadeh: Your your P cloud even your CRM platforms like salesforce so what what the data Apps do is they really act as a analytics accelerator.

Ardeshir Ghanbarzadeh: So with them you're able to eliminate some 80 90% of the time that it takes to deploy new analytics for the business.

Ardeshir Ghanbarzadeh: And and be able to give visibility to the end users and finance teams on on the metrics and the kpis from the data that's available in the source systems.

Ardeshir Ghanbarzadeh: i'll i'll step you through a couple examples of our data Apps so, for example in quarter has a data APP finance data for accounts payable.

Ardeshir Ghanbarzadeh: What that's what that provides is answers to a certain series of questions that would commonly be asked when you are when you're looking at.

Ardeshir Ghanbarzadeh: You know accounts payable data, so what are some of the questions that could possibly be asked, well, for example.

Ardeshir Ghanbarzadeh: What our payments to key suppliers and what payments are overdue that's one example of a question that gets asked what and metrics that come from the accounts payable data Apps.

Ardeshir Ghanbarzadeh: what's the what's the discount lost or what was the discount loss, for example, what are.

Ardeshir Ghanbarzadeh: The payable invoices holds by buyer, how can I, you know essentially that analyze that by individual purchaser, and how does that relate to my purchase orders.

Ardeshir Ghanbarzadeh: What are some of the top invoices and what type of discounting is involved with those So these are just a few examples of, say, you know.

Ardeshir Ghanbarzadeh: 20 different questions that can you can actually ask and get answered by the by the native data APP for accounts payable.

Ardeshir Ghanbarzadeh: With with in quarter and and Just to give you an example of a handful of the metrics that are.

Ardeshir Ghanbarzadeh: Out of the box available this could include total number of holds average miles per invoice the percentage of manual invoices average days to pay an invoice.

Ardeshir Ghanbarzadeh: These are just some examples of metrics that you will immediately be able to see when you deploy the accounts payable data APP within quarter.

Ardeshir Ghanbarzadeh: Another example of a data APP is the is the cache to cache data at which you know essentially again becomes important for for managing working capital so.

Ardeshir Ghanbarzadeh: You can start asking questions like all right, what does my cash conversion cycle look like what is my average you know DSL.

Ardeshir Ghanbarzadeh: What is my average days of inventory and and you can you can do that by analyzing the metrics, such as the cash conversion cycle days payable outstanding they sales outstanding.

Ardeshir Ghanbarzadeh: You can look at trends around receivables or your aging aging details again the metrics that are going to be able to help you answer you know where caches coming in, where cash is going out and and how much of that you're going to have on hand to be able to operate the business.

Ardeshir Ghanbarzadeh: So again, like, I mentioned that there's a whole collection of finance data Apps for a variety of use cases just wanted to show you a couple of examples of those and what's available out of the box within quarter today.

Ardeshir Ghanbarzadeh: So much to talk about was a little bit of you know, so why in quarter why, why should finance teams using quarter as a.

Ardeshir Ghanbarzadeh: finance data hub, and why should they implement that well, one of the one of the one of the key values is that in quarter provides access to.

Ardeshir Ghanbarzadeh: The data on your organization that is unrivaled and you know we are able to pull data from all these different sources combine that data in the single hub.

Ardeshir Ghanbarzadeh: Create that single data model and overall make it very easy.

Ardeshir Ghanbarzadeh: To improve on one the quality and the static quality for those reports and and financial and operational analytics and also give you visibility to 100% of the data so that you can.

Ardeshir Ghanbarzadeh: You can analyze that all the way down to the transactional level detail again the the fast data pipelines, is the reason that.

Ardeshir Ghanbarzadeh: you're able to very quickly access that data and and be able to do analysis in real time on the data Apps here well provide quite a bit of.

Ardeshir Ghanbarzadeh: quite a bit of acceleration when it comes to standing up those new analytics so really shrinking the data latency time and the time it takes to to create new.

Ardeshir Ghanbarzadeh: financial and operational analytics reports, and then the third is that you can actually trust the veracity and the quality of that data, so it increases the.

Ardeshir Ghanbarzadeh: Your confidence in the decisions that you're making because you know you are your data is fully analyzed double and you're not.

Ardeshir Ghanbarzadeh: You know you're not being misled with any any any kind of a top line KPI you're able to go and investigate that whether a particular metric is is a false positive or a false negative.

Ardeshir Ghanbarzadeh: For it teams have been caught up offers very similar value on the fastest flexible pipelines to make it quite easy to acquire data from all these different sources within the organization and making that available to the end users, so you have that.

Ardeshir Ghanbarzadeh: very highly agile business experience with data, the second is that the it teams don't have to spend a whole lot of time now trying to build new reports, every time a new question is being asked by.

Ardeshir Ghanbarzadeh: By the office of finance so eliminating that time consuming ETF process and also reducing the time it takes to create new reports becomes a huge benefit and a big amount of efficiency.

Ardeshir Ghanbarzadeh: For for the the the folks in the in the IT teams that are supporting the office of finance and also they want to be able to make finance teams self sufficient.

Ardeshir Ghanbarzadeh: efficient and more productive so so being able to empower that office of finance with that self service capability to drill in any direction that they want.

Ardeshir Ghanbarzadeh: While still maintaining that control over security and data governance, which is pretty key becomes another benefit and that directly for the it organizations that are supporting the office of finance and then, finally, I just wanted to.

Ardeshir Ghanbarzadeh: As I talked about you know, being able to connect to a variety of different sources, you know you just can't you should keep in mind that in quarter.

Ardeshir Ghanbarzadeh: You know, as a fully eXtensible so that you can get a rich data analytics from an ecosystem of different sources that.

Ardeshir Ghanbarzadeh: Let you integrate all of this data into a single environments and really close that gap between complex technologies and how you.

Ardeshir Ghanbarzadeh: How, you are, how you are able to deliver analytics for financial analysis.

Ardeshir Ghanbarzadeh: So all of these solutions that we can actually connect to and and and pull data from or or send data to as a destination, it can include the data acquisition sources data transformational sources on some some of the.

Ardeshir Ghanbarzadeh: cleaning and data preparation systems out there even warehouses and lakes and and also, more importantly, and one of the fastest growing areas right now in the office of finance is.

Ardeshir Ghanbarzadeh: Enabling data science by by by using machine learning algorithms to create that predictable.

Ardeshir Ghanbarzadeh: That predictive and and forward looking analysis of data so being able to make all that data available to those algorithms is what is going to increase the accuracy.

Ardeshir Ghanbarzadeh: Of the predictive that comes from those types of solutions and so with that and just to just to kind of step you into the next piece of this and show you a little bit about.

Ardeshir Ghanbarzadeh: How, in court is able to deliver data to dashboards and how end users within the office of Finance to consume that data to.

Ardeshir Ghanbarzadeh: to drive those decisions and perform the analysis that that help that data driven decision making i'm going to hand it to my colleague, Joe who's going to step you through a demonstration of.

Ardeshir Ghanbarzadeh: Often quarter and after the after joe's demonstration we'd be happy to take some of your some of your questions, towards the end of the webinar Joe it is over to you.

Joe DelPercio: Thanks artist year and, thanks to appreciate it i'm going to share my screen now and we're going to talk a little bit about and quarter.

Joe DelPercio: visual visually and I think this will give a lot of folks the best understanding of the tool and how the tool works, so I was a previous customer who became an accordion.

Joe DelPercio: About one a while back and i'm and I can tell you this is one of the big things that really drove.

Joe DelPercio: For me in the office of finance, when I was working with the finance team, what was the value of bringing in and quarter.

Joe DelPercio: When you have multiple platforms, you have multiple ways of truth it's very hard to get the you know finance outputs enough in a proper consistent manner.

Joe DelPercio: So what encoding allowed me to do and when a court is going to allow you to do is bring all the data into one spot.

Joe DelPercio: As artists your said, we can bring it in directly from source so sources one we're bringing in one.

Joe DelPercio: Then we can also then do business transformation, on top of that, because we also all do know that there's some nomenclature, we find in backend systems.

Joe DelPercio: That might not be as appealing to the end user and that's where we are able to come in and help bring that valuable raw data into something that is obtainable and understandable.

Joe DelPercio: What you can see here on my screen is, I have a financial overview going on right.

Joe DelPercio: So there's over a company and it's talking about the cash to debt, you know it's looking pretty good here we've got the current ratio, we have the debt to equity it's a little I like that.

Joe DelPercio: We have the return on equity is in green, as you can see me, you can color code these things so that.

Joe DelPercio: quickly draws the eye for anybody looking at this, to understand where we need to go and where we need to look.

Joe DelPercio: We can also add in conditional format now everything's pretty interactive if I start touching things are hovering over things you can see that I can start getting information.

Joe DelPercio: what's also really nice about this, as well as is that you have the ability to share this, you have the ability to also send this via PDF or take an image.

Joe DelPercio: I know a lot of times, sometimes when we're in financing, we have to close the books or we have to.

Joe DelPercio: Do different end of the month or different projects right sometimes folks need a really nice clean crisp picture so to be able to get this into a PDF or be able to take an actual.

Joe DelPercio: Drought to be able to download it as an actual image is really going to help drive the conversation.

Joe DelPercio: Now what also helps here, too, is is that you see all these numbers and you're going well okay i've got this operating income over here it's at 958 million.

Joe DelPercio: we're a little bit different from what we forecasted right and what we budgeted for, so if I click on this, we have the ability to filter.

Joe DelPercio: In this dashboard by the line item by quarter or you know what I want to understand a little bit more of what's going on, so we have something called a drill functionality so i'm going to click here.

Joe DelPercio: And it's going to drill me into a new, more in depth part around my operating expenses summary and now I can start to really look through what is going on.

Joe DelPercio: i've got some cost centers here that are a lot higher than I expected O T any we always know travel and expense, sometimes can be one of the highest.

Joe DelPercio: that's very important, but we have to make sure we're spending our dollars correctly, but we can understand that by kind of walking through this different dashboard.

Joe DelPercio: i'm going to drill a little bit more in just a moment, but what I want to be able to show is some of the power.

Joe DelPercio: of things that you can get at your fingertips, this is journal details right, we have the ability to go down and grab these journals you can see where the accounts are going, you can see, the period names, we can bring in as much data as you want.

Joe DelPercio: For for my examples right it's only seven to 10 years depending on compliance and socks and audits.

Joe DelPercio: You can also do some of that information as well, and here i've done that, as well on a previous life.

Joe DelPercio: And then you can also come down here and look at the invoice so here's that sub ledger detail that is sometimes so hard to get after.

Joe DelPercio: And right we're only showing 212,000 records, because we drilled in on one thing.

Joe DelPercio: Sometimes when you're drilling you could drill into 100 million records, sometimes even billion records and you're going to get them all back.

Joe DelPercio: So it's very powerful here and what you can do to with within quarter is see this P O number or this buyer number or even an invoice or an account number.

Joe DelPercio: You can use hyperlink functionality that will allow you, for example, if you want to go know what's on this to.

Joe DelPercio: You can put it on a hyperlink functionality that you click on that PO and it will bring you.

Joe DelPercio: right back to the to the p O, and you can see it and be able to look at it's very, very powerful.

Joe DelPercio: To be able to understand what the customers are seeing right because we're seeing it like this and we're only as good as what we know and what we see.

Joe DelPercio: The ability to to be able to see this information and then drill into it a little bit more can also have a more meaningful conversation, so if I come over here right i'm automatically looking at this big guy.

Joe DelPercio: What is going on in this cost Center right, we know that this cost centers tied over DNA, because you can see it on the colors but if I drill into this right, I can go down another level.

Joe DelPercio: Now i'm down into expense reasons i'm down into expense categories, this is very powerful for somebody to understand as they're the analysts trying to answer a question for their.

Joe DelPercio: Their their leader, their executive someone's asking why are we spending so much, why are we not.

Joe DelPercio: You can easily start to see it here, this is a different type of graph that allows you the ability as it's bigger that means there's more cost tied to.

Joe DelPercio: So if I come down, I can start looking at my spenders I can look at my categories right it's all interchangeable.

Joe DelPercio: And what's really awesome is, I can see the expense reports, and I can get all the justifications emergent needs.

Joe DelPercio: Now you're talking about a dream for an ap clerk or an ap manager trying to understand what happened and accounts payable or.

Joe DelPercio: Also, in your travel and expense, if you have that segregated out, you can be looking at that as well and you're traveling a teeny company on.

Joe DelPercio: Part of your company's looking at stuff going, my goodness now what's really, really cool, though, and this is a big feature that sold me, as a previous customer is this search functionality.

Joe DelPercio: So I have the availability to come up and filter very simply, I have the ability to come, look at ledger the period names, the line items, but.

Joe DelPercio: I can also start typing right so, for example, I know that sometimes with with the how inflation is maybe gas has been the bigger issue here, why cost drone when so up.

Joe DelPercio: So I can type gas, for example, right it starts to bring up anything that has it but what's really cool is if I go here, I can find any record with gas, that means that, on the fly.

Joe DelPercio: I can click on this button and it's going to read all the data and find me gas, so if I click on this here.

Joe DelPercio: You can see that change that's likely change, but what really changed was down here here's the 1500 and 60 records that all have something with a justification of gas.

Joe DelPercio: that's really powerful, because now, I can drill I can start looking into things I can start seeing what is going on right, I can start clicking into different items and start playing with different data points so there's a lot of power in all of us.

Joe DelPercio: Now the other important part is oh my gosh I went all the way down I don't know how to get back up meaning, I went all the way down to this finite detail.

Joe DelPercio: what's really nice is here we have a button that shows you your drill path so we went three levels down so if you're thinking about it from a parent to child type of look.

Joe DelPercio: You started out your parents right you started at the highest level be drilled a little bit more.

Joe DelPercio: You go i'm interested I don't want to know what's going on with this data, because I have to answer a question.

Joe DelPercio: Well, it looks like the operating expenses really had the issues that travel expenses went up.

Joe DelPercio: So now, not only are we able to get down to this level of detail, say that you have another person who's interested in this data, but maybe they don't have access to them quarter yet.

Joe DelPercio: They can download this data into excel and send it to them, or they can also share it to them so there's a couple different ways, you can be able to do this what's also really nice here is when you're down at this level.

Joe DelPercio: So you want to keep this right you don't you don't want to forget it, you can add a bookmark so now this bookmark is really powerful because you can set it to make it public.

Joe DelPercio: which will then allow other folks to see it, or you can just send it to yourself if you want to and that keeps it perfectly private.

Joe DelPercio: However, though, when you come back and you're in this dashboard you're like oh gee what did I hit you come back and click on this on this bookmark.

Joe DelPercio: that's very powerful when you are an analyst of a type that's working multiple different questions and analysis, so you can bring this as well, you can actually things up here and, as you see.

Joe DelPercio: Of I start accessing stuff out we start bringing more data in and it starts to get even bigger what we just brought 2.7 million records in the click of a button and it's great because I can start looking through this data and really understand.

Joe DelPercio: If I go back up, I can go all the way back up, it takes away everything and now now i'm starting fresh.

Joe DelPercio: So, from a from a financial perspective right to be able to go and drill all the way into my income expense understand my travel expense like that at that granular level that I can literally say Bob Smith and.

Joe DelPercio: $400,000 last month on travel expense versus sallie Mae who only said, maybe did 20,000 but then I go and look at the sales and what which either one made to see if that makes sense, why they spend that money.

Joe DelPercio: And it's also it's a good conversation starter as you're talking with your with your teams because they're like wait, I want to see the detail, no problem.

Joe DelPercio: drill back down click on it so there's a lot of functionality and flexibility within the tool we have a lot of good things happening here.

Joe DelPercio: So this is the DEMO, I just wanted to be able to show you guys a little bit of the power of in quarter, where we can go.

Joe DelPercio: Where we can show we can show one through the operating income, you have the ability to even bring in cash conversion cycle and drill down into cash conversion cycle as well.

Joe DelPercio: We have formatting that will allow you to easily detect where you need to go look and then you also have your return on asset and capital so we're able to do this now what what everyone's saying is is okay, how long did this take to build.

Joe DelPercio: This took a couple hours.

Joe DelPercio: Why did it take a couple hours because of when artists year was talking about the data, think of data Apps as a business accelerator.

Joe DelPercio: And I can go read your earpiece complexity in 60% of its standard and 40% of its customer I have in three clicks within the back end of this tool in the ui no atl no code, I have brought all that data I have mapped all that.

Joe DelPercio: it's very important to understand the mapping of this data, because it's complex in the background.

Joe DelPercio: And what we're doing here is making it very simple So yes, this is a couple hours of work, and then you have so much functionality that, then you can also share and explain.

Joe DelPercio: Thank you very much for the time if anybody has any more questions on the functionality of in quarter anybody hasn't functionality questions in general on what you're seeing here and how we did this, please feel free to ask questions are sharing higher here to take those questions.

Joe DelPercio: Back to you, Mr sure.

Ardeshir Ghanbarzadeh: Thank you, Joe for that insightful down great DEMO again thanks thanks a lot folks up yeah like I mentioned earlier, the beginning of the webinar if you have questions for Joe and myself, please do type them into.

Ardeshir Ghanbarzadeh: into the chat we will be answering those questions now i'm Joe I think the first question is for you and that's great so So the question is, why is this so hard to get to the level of data that you just showed in your DEMO.

Ardeshir Ghanbarzadeh: from some of these other tools that it doesn't specify which other tools, but I think I can imagine what those could be or the second hypothesis of what they could create.

Joe DelPercio: These certain tools bring in complexity, they bring in timing issues, what happens is is in the normal transitional transactional life cycle of data.

Joe DelPercio: In this is from my experience of being in this for many, many years is you bring data in you capture your data and your your rp.

Joe DelPercio: Then, your data has to go through business logic business transformation, then your data has to then go into probably a data movement type pipeline.

Joe DelPercio: we've talked about informatica size, for example, that then has to go into a raw form into normally what we would call and what people will know, the data warehouse.

Joe DelPercio: In a data warehouse You then have to curate the data again in two dimensions and facts.

Joe DelPercio: which then is translated back into something meaningful for the end person to be able to consume, on the other side, so you just heard me talk through all those steps and you're like Oh, my goodness i'm lost.

Joe DelPercio: Within encarta you've logged you've moved all those steps the step is I connect to your source I pull all your data in as is.

Joe DelPercio: And then I can already start diagramming and exploring or I can then start adding in more business functionality, so what we did an encoder was is we took away.

Joe DelPercio: The pipeline and we took away the data warehouse functionality and brought you straight to your data so that's really how I would answer that question.

Ardeshir Ghanbarzadeh: Excellent Thank you, the second question is, I think I can take this one, it says the the speaker talks about shrinking the deploy cycle.

Ardeshir Ghanbarzadeh: You know how do, how do we streamline for that so actually you know i'm going to kind of adopted a little bit of joe's response here the the process that you go through.

Ardeshir Ghanbarzadeh: From data acquisition transformation aggregation and delivery when you think about the amount of time and effort that has to go into that.

Ardeshir Ghanbarzadeh: You know, essentially you're at the mercy of when the data comes to you, and you have to design your process around that.

Ardeshir Ghanbarzadeh: But when the data is available to you anytime you want at your fingertips, then you don't this is really not doesn't become the long you know polling tense right, so you can actually design a very efficient process, knowing.

Ardeshir Ghanbarzadeh: That that data is going to be there and it's not going to inject any delays in your process, so if you have a 15 day 12 day close process today.

Ardeshir Ghanbarzadeh: If that data is available to you now, you can create a create that more strict streamlined and efficient process.

Ardeshir Ghanbarzadeh: And and use that data when you need it so you can actually start shaving some days off of that that close that close cycle, I think we did a survey.

Ardeshir Ghanbarzadeh: Last webinar and and about 60% of the respondents said that their full cycle times are about 10 days with only about 20% saying that it was five days or less so there's definitely a lot of room for improvement there if you take five days off or cut your full cycle in half.

Ardeshir Ghanbarzadeh: that's a lot of time your resources can spend doing other things.

Ardeshir Ghanbarzadeh: So I would say that you know that's the way to go about shrinking that process down is by making data readily available to the folks that are responsible for that process and let them design their process around efficiency, not so much data availability.

Joe DelPercio: I.

Ardeshir Ghanbarzadeh: The The next question is Joe probably it's best for you to take this one does this only work for Oracle or other systems out there that can be represented.

Joe DelPercio: Great question so when I was a customer I was actually able to connect to about 40 I had about 40 different sources going through this platform.

Joe DelPercio: This is a full service platform, so we can bring in Oracle we can bring in SAP Hana and ECC we do have some data APP accelerations being built through those.

Joe DelPercio: We also can tap into other parts of oracle's hcm models for human capital management we've also been able to tap into espn.

Joe DelPercio: we've also worked with work day i've personally brought in as 400 as well.

Joe DelPercio: There and Microsoft sequel server, of course, you have the ability to pull in multiple different things into this platform i'd say the two biggest, though, we see a lot is Oracle and SAP.

Joe DelPercio: But those are from any rp standpoint that doesn't mean we can still bring in and have other types of CRM i'm in a previous life I had dynamics division as 400 Oracle EBS salesforce.

Joe DelPercio: Might not Microsoft dynamics CRM all coming into the same place all be and work day I apologize and work, they all coming samplers.

Ardeshir Ghanbarzadeh: Excellent yeah Thank you yeah and Joe you're you're spot on you know, the ability to to get that data from the different sources definitely a very good way to speed things up.

Ardeshir Ghanbarzadeh: The last question is a great way for us to actually wrap up our webinar today is is there every way I can try it and quarter.

Ardeshir Ghanbarzadeh: And yes, there absolutely so you can actually go to cloud that in quarter comm slash sign up and actually take in quarter for a test drive.

Ardeshir Ghanbarzadeh: for free, there is a there's a link, also in the chat that that you can you can use to reach that reach that URL or you can sign up and actually it all.

Ardeshir Ghanbarzadeh: set up a an encoder instance for you there's going to be some you know training material in there as well, some out of the box blueprints and data Apps available for you to use.

Ardeshir Ghanbarzadeh: So please do try that we'd love to hear your feedback on the trial, so if you do go through that process, please do.

Ardeshir Ghanbarzadeh: Please, do you know send us a note or go to Community that encoding calm, where a lot of your peers will be there.

Ardeshir Ghanbarzadeh: kind of talking about their experiences and some of the some of the success that they've had with in corner and somebody applications that they're using.

Ardeshir Ghanbarzadeh: In quarter floor in their organizations with that we will, we are going to wrap it up today Thank you so much for joining us for this final.

Ardeshir Ghanbarzadeh: Part of our three part series on driving agility with financial analytics i'm sure gamba Rosada with Giotto co signing off for today Thank you everyone for joining us and have a great rest of the week.

Joe DelPercio: Thank you team.