Building Resilience with Agile, Adaptive and Continuous FP&A

Transcript:

Ardeshir Ghanbarzadeh: Okay Hello everyone, and thank you for joining us today for Part One of our three part series on driving agility with financial analytics building resilience with agile and adaptive continuous fba.

Ardeshir Ghanbarzadeh: Before we get started today a few housekeeping items, if you do need to leave early, we will be making this webinar available on demand at and quarter.com and you'll receive a link to access it within a couple of days.

Ardeshir Ghanbarzadeh: shoot you have any questions, please feel free to type them into the chat we will be having a Q amp a session, towards the end of the webinar.

Ardeshir Ghanbarzadeh: My name is artistry gamblers out i'm director of product marketing here at in quarter joining us today is also Ryan Garrett senior sales engineer at the end quarter Ryan will be doing a DEMO of the analytics hub for finance later in the webinar.

Ardeshir Ghanbarzadeh: So a little bit about what we'll be covering today I will take a look at some of the challenges that the fema teams are facing for analytics and reporting.

Ardeshir Ghanbarzadeh: will compare the modern and agile approaches to data architecture next we'll take a look at the benefits of a unified approach to planning and operational data.

Ardeshir Ghanbarzadeh: Ryan, will give a DEMO and quarters analytics help for finance and, finally, we will answer your questions in the Q Q amp a session of the webinar.

Ardeshir Ghanbarzadeh: So we'll begin by taking a look at some of the key challenges facing fema teams on some of the things that we are seeing.

Ardeshir Ghanbarzadeh: In terms of trends around reporting requirements is there is a migration to real time insights and the demand by business to to drive.

Ardeshir Ghanbarzadeh: Data collection and reporting in that direction, there is a need to have insights now on a daily basis, or even sometimes an hourly basis.

Ardeshir Ghanbarzadeh: There is a desire to have answers for questions that are being immediately asked in a very short time and short window.

Ardeshir Ghanbarzadeh: For answers, this means that, having data readily available and easily consumable is going to become very critical to day to day operations and financial decision making.

Ardeshir Ghanbarzadeh: and other trends and another challenge that we see is visibility to not only top line data which is aggregations of data but also transactional level detail.

Ardeshir Ghanbarzadeh: While oftentimes there is visibility to the top line aggregations there is not a lot of times availability availability of data to be able to drill down and get into.

Ardeshir Ghanbarzadeh: a lower level transactional level details that are going to provide the level of granularity that is needed to be able to.

Ardeshir Ghanbarzadeh: accuracy to be able to accurately identify whether aggregations are correct, whether adjustments need to be made.

Ardeshir Ghanbarzadeh: oftentimes to find patterns or identify root causes that are creating variances, for example, and forecasts, especially in times, where there's volatility.

Ardeshir Ghanbarzadeh: And variances widen this becomes pretty important to have that level of granularity to identify what the drivers are behind those changes and variants so um this this level of.

Ardeshir Ghanbarzadeh: detail in transaction becomes pretty critical and also has a application when you look at things like advanced analytics use cases such as using machine learning to generate forward looking.

Ardeshir Ghanbarzadeh: Understanding with predictive or prescriptive analytics another key area of challenge is the the velocity and volume of data, so there is.

Ardeshir Ghanbarzadeh: exploding volumes of operational data right now happening in business and it's being collected, but not all of it is being leveraged.

Ardeshir Ghanbarzadeh: To help reduce rich analytics or do meaningful analysis to help with planning and forecasting.

Ardeshir Ghanbarzadeh: also add the in most businesses, there are multiple business applications your PS custom sources of data, all of which have you know valuable data in them, but not always accessible.

Ardeshir Ghanbarzadeh: And certainly not accessible at a granular level so that's another challenge, but the data is out there, but not always available to the fba teams to be able to use that and leverage it for decision making and insight generation.

Ardeshir Ghanbarzadeh: And finally, we often talk about a single source of truth and What this really means is building a common and trusted environment.

Ardeshir Ghanbarzadeh: That you can leverage to generate insights and then drive some of those incremental analysis across the business line.

Ardeshir Ghanbarzadeh: So we want to be able to bring data into a single place so that you enable cross functional teams to work with finance teams and fba teams and everyone in the ecosystem of finance.

Ardeshir Ghanbarzadeh: will do that using the same set of data for analysis and and then extending that beyond just those functions and pushing that out to the rest of the organization.

Ardeshir Ghanbarzadeh: So that there is there's a trusted accuracy behind.

Ardeshir Ghanbarzadeh: what's happening with planning what's happening with consolidation reporting and anything else that's used in the tools within the business So these are some of the key challenges that we find exist today for fema teams and in the ecosystem of office of finance.

Ardeshir Ghanbarzadeh: But one of the concepts that is out there today is the concept of the modern data architecture, but this is somewhat of a source of friction for for the office of finance, because of the flexibility and the limitations and it puts around data.

Ardeshir Ghanbarzadeh: and access to data, so in this process, the way it works is the first step is moving data from some of the business sources that are out there, for example, whether it be.

Ardeshir Ghanbarzadeh: Any rp Oracle database, or in any other business application of business system that houses data that is necessary for.

Ardeshir Ghanbarzadeh: Making business decisions extracting data from that source putting it into a raw data zone and then from that raw data zone, there is a.

Ardeshir Ghanbarzadeh: level of a lot of transformation that needs to happen to move this data out into a refined data zone.

Ardeshir Ghanbarzadeh: And then, this and this level of transformation actually starts to strip away at some of the details and the fidelity of.

Ardeshir Ghanbarzadeh: That data is coming from the from the source, furthermore, this data can is then more there's more reshaping and changing of this data.

Ardeshir Ghanbarzadeh: dawn throughout this process to land it into a business data is all where it can be used by some of the common tools and applications in the office of finance.

Ardeshir Ghanbarzadeh: Even some of the data discovery tools for example and and eventually the reaching the consumer of the data, whether it be the fema teams era P teams.

Ardeshir Ghanbarzadeh: or teams involved in the in the claws process.

Ardeshir Ghanbarzadeh: The disadvantage of this approach is that much of the transactional detail and the fidelity of the data is lost through this transformation process, so the very information that is needed to do things like root cause.

Ardeshir Ghanbarzadeh: or verifying accuracy of aggregations is no longer available to the end user, but simply stuck with those aggregations and the subset of data.

Ardeshir Ghanbarzadeh: That was transformed from the original source and delivered to them So how do you solve this problem of loss of fidelity of data, the lack of transactional details.

Ardeshir Ghanbarzadeh: To do that would cause analysis and the kind of friction that this creates when you're moving data from the raw source or different different sources but you want to be able to do this with with speed and agility so let's uh let's take a quick look at that.

Ardeshir Ghanbarzadeh: So in quarter actually kind of takes a different approach here by going directly to the source of the data in the business again, whether that be an aarp and operational management system or other business application.

Ardeshir Ghanbarzadeh: that's using the organization we're able to take all of that data and combined it across multiple sources into a central hub, in a way to add contacts and be able to.

Ardeshir Ghanbarzadeh: enable end users to generate actionable insights in this case, though, the difference is that there's not a lot of transformation being done here.

Ardeshir Ghanbarzadeh: Eliminating the need for these transformations that make you lose the fidelity of data that you had and the visibility to the transaction level details means that you're making 100% of that data from the source available.

Ardeshir Ghanbarzadeh: In to the end users and in a way that works with your existing tech stack and once that data is on the platform.

Ardeshir Ghanbarzadeh: One of the things that we can do is, we can use in quarters blueprints.

Ardeshir Ghanbarzadeh: To significantly accelerate implementing reports and dashboards that can also work in concert and side by side with other reporting tools that you have today.

Ardeshir Ghanbarzadeh: Also visualization tools such as tablo excel power bi and even provide data to machine learning applications for training algorithms.

Ardeshir Ghanbarzadeh: That can be used and for predictive predictive analytics Ultimately, this is going to provide a lot more flexibility for fema teams and the entire ecosystem.

Ardeshir Ghanbarzadeh: or around the office of finance, but forecasting and planning bringing some agility to the ap and our process.

Ardeshir Ghanbarzadeh: To get better visibility on how to manage those dollars that are coming in and dollars that are going out and also help.

Ardeshir Ghanbarzadeh: The teams that are supporting the closed process shrink down that that's close cycle, making them more agile in in delivering what they need to do to the business by by having that visibility and access to all of the data.

Ardeshir Ghanbarzadeh: And now taking a closer look at it and finance teams and kind of the face kind of the pain points that these two teams face.

Ardeshir Ghanbarzadeh: And encounter on a daily basis when they're trying to accomplish what they need to enable the business.

Ardeshir Ghanbarzadeh: Finance teams are trying to do one thing really well built fast data pipeline, so that the business users can get the data they want when they want it.

Ardeshir Ghanbarzadeh: However, this is a pretty complex process, while we're in quarter comes into enable these teams is by simplifying the process.

Ardeshir Ghanbarzadeh: for getting that data from the source and making it available to the end users, so that they can generate insights and take action.

Ardeshir Ghanbarzadeh: while at the same time, the business users are often hampered by not having enough data or data they need, they are enabled by getting immediate access to to that data.

Ardeshir Ghanbarzadeh: At the detail level, and then they can operate in those very short windows, where they have to give give the business quick answers questions that are coming up and are not repetitive questions from the previous week or a previous month.

Ardeshir Ghanbarzadeh: Or the previous quarter the benefit here is that finance teams and business users, such as the fema teams are saving time they're becoming more efficient.

Ardeshir Ghanbarzadeh: At the delivery and consumption of business data, and they are able to provide commentary to the business to be able to drive decision making and move on move decisions forward.

Ardeshir Ghanbarzadeh: So with this consideration of these different pain points that both the the finance and it teams and the ecosystem of the often a finance are facing.

Ardeshir Ghanbarzadeh: here's some of the exciting quarters unique value propositions that that help strike essentially a balance between the organizations and reduce that level of friction.

Ardeshir Ghanbarzadeh: and bring benefit to both So how do we do that we provide unrivaled data access by simplifying how data is sourced from multiple business systems.

Ardeshir Ghanbarzadeh: and bringing a hard percent data hundred percent of that data to to the business for analysis and enabling those business users to explore that data ask questions.

Ardeshir Ghanbarzadeh: and determine by drilling into the details, whether the data is accurate and make adjustments to their forecasts or make adjustments to their.

Ardeshir Ghanbarzadeh: scenario plans according to 100% of the business data, and not just only aggregations will also provide an environment where there's a trusted secure an accurate level of.

Ardeshir Ghanbarzadeh: source data available to both teams, while giving the the business user.

Ardeshir Ghanbarzadeh: The complete fidelity to access all the data, but at the same time giving the it teams complete control over the security and data governance, which is really important, obviously, to them, but at the same time enabling the business users to still.

Ardeshir Ghanbarzadeh: explore that data and generate insights and drive decisions and, finally, one of the things that.

Ardeshir Ghanbarzadeh: That is key here is that eliminating the the transformation and reshaping of data and all the way from the source and bring it to the dashboard for reporting analysis, this gives you.

Ardeshir Ghanbarzadeh: far faster time to insights than other solutions, including solutions such as traditional and legacy bi solutions.

Ardeshir Ghanbarzadeh: One of the customers who has had great success with within quarter solution is a very commonly known name in the in the technology industry.

Ardeshir Ghanbarzadeh: This company is called broadcom date design and manufacturer semiconductors and solutions around semiconductors they.

Ardeshir Ghanbarzadeh: They by implementing in quarter, they were able to actually see significant improvements and how their business intelligence and reporting.

Ardeshir Ghanbarzadeh: Improved they saw a 96% increase in the rows of available data, and they were able to access that within four seconds, which is incredibly fast and amazingly beneficial to their team for.

Ardeshir Ghanbarzadeh: visibility to data and speed of decision making, they they were able to expand from only a handful of refreshes a day to up to 96 refreshes per day.

Ardeshir Ghanbarzadeh: To ensure that they have the latest data available for their end users to consume do analysis to planning and and share that with the organization.

Ardeshir Ghanbarzadeh: And then, they were also able to see a significant reduction 50% reduction and bi staffing.

Ardeshir Ghanbarzadeh: And, which obviously helps their bottom line and and, in addition to that they were able to use this to de risk the business and protect something close in $12 billion in market CAP.

Ardeshir Ghanbarzadeh: For for broadcom so quite a lot of financial and operational benefits as a result of this implementation realized by by a broadcom, which is a which has been a quarter customer and has seen quite a lot of success over the years from this implementation.

Ardeshir Ghanbarzadeh: With that um I wanted to.

Ardeshir Ghanbarzadeh: hand over the the the realm to Ryan, to give you a DEMO of in quarter the analytics for finance so Ryan take it away.

Ryan Garrett: Thanks for sure, and what i'll do is i'll walk you through the quarter platform or i'll connect to a data source and this example will connect to an oracle data source typically we see customers connecting to complex.

Ryan Garrett: source systems such as Oracle SAP etc will bring that data into one quarter, we will apply a blueprint, so you can see how and quarter can help you.

Ryan Garrett: expedite the understanding of how those tables relate to each other how they're joined how to actually get usable data out of out of the Platform.

Ryan Garrett: And then we'll take a look at the semantic layer as well, so how do we kind of apply friendly names to it, how do we get that data ready.

Ryan Garrett: consumable for the business and then what i'll do is i'll walk you through that scenario where we have you know.

Ryan Garrett: High Level transaction detail is very high level details.

Ryan Garrett: Better accounts receivable and we can actually drill in there to artists, writers point is kind of drill down into transaction level detail to.

Ryan Garrett: Really uncover what's going on and really get those insights that we need as a as an FDA team to really make critical business decisions, so let me walk you through that platform now So the first thing is we're going to do is we're going to connect to a data.

Ryan Garrett: source so i'm going to connect to.

Ryan Garrett: an oracle data source, so in this case will connect Oracle it be rp a username and a password we can connect that connection, make sure that we're connected there.

Ryan Garrett: Once we've brought that you're connected to that data we can actually bring that data into the platform within in quarter we call that a schema.

Ryan Garrett: So you can bring that into a schema here and what i'll do is i'm going to take a look at this as counts receivable schema for us.

Ryan Garrett: So, as we bring that data into the platform, we can load that data, either as full or incremental bring it into importer.

Ryan Garrett: But some of the key things that we in quarter brain and really to shorten that time to value.

Ryan Garrett: For fema teams is, we can apply blueprints to this data so known data sources, you know all of the different modules within Oracle so you know you know general ledger accounts receivable accounts payable etc.

Ryan Garrett: In quarter has extensive blueprints for those as well as many more, and we can bring that data in apply those blueprints and now get data that is you know joined combined.

Ryan Garrett: across the different the different tables, so now we can actually take a look at this and say okay i've brought.

Ryan Garrett: You know accounts receivable data in we've been able to join those with the dates the transactions lines.

Ryan Garrett: This the payment schedules, the ar transactions so we've brought all of that data in we've created that metadata map and those joins within the quarter platform.

Ryan Garrett: Now this dramatically reduces the time it takes for customers to get into their data and start making.

Ryan Garrett: You know, decisions based on what's their data what we hear most often is people spend 80 plus percent of their times just trying to get data.

Ryan Garrett: In a usable fashion, how do I join this to that, how do I, you know extract data out of this space, how do I bring this over into to this other tool and there's a lot of manual process that goes to even to getting an understanding of workable data sets.

Ryan Garrett: Now from there, we actually take this a step further and we say Okay, now that we've got your your model within and quarter and we've joined all of those data, and you can.

Ryan Garrett: You know, continue to join additional as well, so if you want to create your own joins you can do that, within a couple of clicks.

Ryan Garrett: And then the next piece is okay let's get that data, you know consumable and ready for the business so within every quarter we call it a schema layer but think of that as your semantic layer

Ryan Garrett: And for us we're going to go take a look at an accounts receivable schema that we've already created here.

Ryan Garrett: So now, you can see the the the data in it's much more business consumable fashion, so you can see the Nice friendly names here.

Ryan Garrett: We can actually see the source column, where this comes from, so this actually helps improve the the trust for your data.

Ryan Garrett: One of the things we hear a lot from customers is that they they don't trust the data they don't know where it comes from.

Ryan Garrett: In a lot of cases it's come through three, four or five different tools before it's gotten to the hands of an F DNA profile professional.

Ryan Garrett: So here, you can actually see very quickly, where those have come from now, I can also go and add additional.

Ryan Garrett: fields to this, so the the business schema here, so I can go take an add other columns from other data sources, I can add fields I can add calculated fields I can bring all of that data into the schema.

Ryan Garrett: And with a couple of clicks I can make all the adjustments that I need So if I wanted to say, you know, this is my account type and i'm going to create a new account type name.

Ryan Garrett: I can do that within within a quarter or I can go create the formulas, I need So if you need a quick average four month formula if I need like a you know.

Ryan Garrett: I can create a quick average formula and bringing that on drag that onto the canvas again without.

Ryan Garrett: Creating a whole bunch of code or relying on multiple teams and tools and technologies we're now getting this data, to the point where it's you know ready consumable for analysis for visualizations.

Ryan Garrett: For predictive analytics etc now when I go and take a look at this, I can now explore the data within in quarter.

Ryan Garrett: I can explore that data, and I can see all of that uh you know nice friendly names easy for me to consume and I can start to build out.

Ryan Garrett: You know the the insights that I want to see so in this example, let me bring on in the organization ID let me bring on the quantity invoice if we're trying to see.

Ryan Garrett: You know our accounts receivable so I can go do that within in quarter, and you can see very quickly within a couple of clicks i've now brought in, you know these organizations and the amount that they've influenced.

Ryan Garrett: So that's the first path is you can explore that, and you know extend your data curiosity very quickly simply very easily or the other path as well is, we can take.

Ryan Garrett: Pre built content pre built reports that in court has that maps to you know these modules within Oracle to really drive that efficiencies and getting you to insights a lot faster, so let me take a look at the.

Ryan Garrett: The content here, so you can kind of see there's some examples here of pre built content that we're looking at and if I go into the EBS side.

Ryan Garrett: You can actually see we have quite a pre build a lot of pre built content now if I look at this and for the example here, we want to walk through is you know.

Ryan Garrett: i'm a financial analyst and i'm looking at.

Ryan Garrett: You know what's going on quarter and close and I need to be able to you know figure out what what transactions are how they've made you know what's missing etc so let's walk through that cycle now so when I look at this, I can actually go into.

Ryan Garrett: You know, a cash cycle summary and analysis again, these are some of the out of the box content that in quarter provides as part of our blueprint.

Ryan Garrett: And you can see a lot of really great information there receivables to revenue inventory to revenue.

Ryan Garrett: etc, you can see a lot of the trends that we're looking at as well, but if I go down here and I take a look at your cash flow.

Ryan Garrett: And I see one of the things that I see as I see there's this change and receivables and I look at that and I say okay that's.

Ryan Garrett: that's not what we're expecting right there's a there's a transaction here that's not aligning we're not able to close those books until we can account for that.

Ryan Garrett: So now, I can take a look at this and I can actually drill into this and I can go into how we're actually accounting for that counts receivable, so now we can actually take a look at collections.

Ryan Garrett: You here as well, so the outstanding balances overdue all that good stuff but I drill into this and I take a look, and I can see, you know our top customers what's going on.

Ryan Garrett: But I also see some of these anomalous transactions that are going here.

Ryan Garrett: When I look at these these are kind of standing out for me so i'm looking at this and saying okay we've you know done business with Amazon and for whatever reason.

Ryan Garrett: we're not getting the correct balance here, so we want to drill into that a little further now in the traditional sense.

Ryan Garrett: A lot of cases this is, you know extracts and manual process and dumping stuff into excel and you know, trying to get.

Ryan Garrett: Data from multiple different tools to go and get that transaction level detail.

Ryan Garrett: Now, within a quarter this becomes a very simple process, because we can actually drill into this and drill into the transaction level detail.

Ryan Garrett: And i'll go over here to the accounts receivable transaction level detail for us to take a look at this.

Ryan Garrett: And so, now we can take a look at across all of our environment across all of the transactions that we've made and we're looking at you know, almost a half billion records worth of data.

Ryan Garrett: So when I look at that I can actually drill into this and we remember from the previous thing.

Ryan Garrett: We were looking at Amazon, and so I can now drill into all of the transactions that we've done with Amazon, and you can see, we filter down from.

Ryan Garrett: You know, half a billion records to just under 404,000 records in a matter of a couple of seconds, so now we can actually take a look at the transaction level detail.

Ryan Garrett: From you know that high level summary metrics, if you remember, we started with you know the the accounts receivable we started with the collections.

Ryan Garrett: And then drill the all the way through to the transaction level detail we've done that, all in a single platform we've done that, without writing extensive you know code we've done that in a point click environment no code low code and we've done that very quickly and efficiently.

Ryan Garrett: within a single platform, and this is really the value that we see that in quarter can provide our customers is being able to provide that finance and data analytics hub for FPA professionals.

Ryan Garrett: And so, let me just recap here and remind you what we've done we connected to a data source We brought the data in we applied the blueprints to easily combine that data and join that data.

Ryan Garrett: We we made that data available in a semantic layer, and then we also made that data available for visualization and then from the visualization we went all the way from top line revenue, all the way through to transaction level detail within a few clicks within a single platform.

Ryan Garrett: And that is in court as approach to a finance analytics hub.

Ryan Garrett: Or to sure back to you.

Ardeshir Ghanbarzadeh: All alright Ryan Thank you so much for that great DEMO.

Ardeshir Ghanbarzadeh: much appreciate it up so folks just you know and wrap it up, you know kind of the the takeaways from this webinar today the the three things that are really important when you are working.

Ardeshir Ghanbarzadeh: Around the data for analytics for financial planning and analysis for reporting is.

Ardeshir Ghanbarzadeh: One having access to all the available data, and having it in an expedient fashion, is going to be critical to developing accurate forecasts and and being able to plan out for the organization, the short and long term.

Ardeshir Ghanbarzadeh: Why, we need to go beyond just aggregations and have visibility like Ryan showed in the DEMO.

Ardeshir Ghanbarzadeh: To detailed data detail transaction data being able to drill down at the granular level, to make sure that the aggregations are accurate, or to.

Ardeshir Ghanbarzadeh: Do to study the the data to understand root cause and identify problem areas and, finally, with the the current.

Ardeshir Ghanbarzadeh: and accurate data in hand, you can actually generate insights that are going to really help your organization make better decisions and navigate the the what we have today, which are quite volatile business and economic conditions.

Ardeshir Ghanbarzadeh: With that said we'd love to take some of your questions, so if you have a question finally type that question into the chat and we'll do our will do our best to answer them for you.

Ardeshir Ghanbarzadeh: Ryan question for you, we we alluded to blueprints and the presentation one part where I was talking about it and you mentioned the out of the box blueprints that we have in the core platform, can you explain a little bit what they are.

Ryan Garrett: yeah absolutely um so blueprints is in quarters approach for pre packaged analytics applications.

Ryan Garrett: What this means is we're spending, you know our engineering effort to go and understand those really complex or systems, you know the oracles of the world, the SAP systems and being able to.

Ryan Garrett: You know figure out what that known good state is, you know how those tables relate to each other how they combine with each other.

Ryan Garrett: and be able to apply that into a packaged.

Ryan Garrett: easy to deploy.

Ryan Garrett: analytics application so we bring that data bring that into in quarter.

Ryan Garrett: The joins the complex joins all that great stuff comes in, as well as the business sort of the the the schema layer as I showed you the joins all that good stuff the business layer

Ryan Garrett: You know, friendly names usable columns great as well as the visualizations so we take all of that, and we combine that into a single application that our customers can deploy very easily.

Ryan Garrett: And what we're seeing with customers is this helps take out many months, out of the oven analytics journey we have one of the large.

Ryan Garrett: Coffee retailers that's an importer customer and they've been working with in quarter now for several several several years.

Ryan Garrett: But when they originally had thought about kind of modernizing their data architecture, they had planned a 12 to 18 months process.

Ryan Garrett: Within quarter and with the leveraging our blueprints and leveraging the quarter technology we're able to take that down from 12 to 18 months into 10 weeks.

Ryan Garrett: So, think about those blueprints is really that shortcut into getting data, out of a complex system and into into the hands of your FPA people for for for rapid analysis and rapid insight.

Ardeshir Ghanbarzadeh: awesome, thank you for that explanation another question here is.

Ardeshir Ghanbarzadeh: more of a process question how do we compel it teams to spend more time gathering data for for FP amp a so, so this is where this is the area where.

Ardeshir Ghanbarzadeh: In quarter kind of creates that balance between it and finance, where we are able to enable the it teams.

Ardeshir Ghanbarzadeh: to collect data extract data from the different sources within the within the business bring them all into the analytics hub and centralize them in a common data environments.

Ardeshir Ghanbarzadeh: and give access to the business users in that single data hub, and to do that at a granular level without the transformation and reshaping of data so.

Ardeshir Ghanbarzadeh: So this makes the process a lot easier for them, it brings the users to the data empowers the users to actually make changes to.

Ardeshir Ghanbarzadeh: The reports that they want to make without having to go through the queue of it to make say, for example, add a column.

Ardeshir Ghanbarzadeh: or or make it or make a change to how a report is updated are delivered.

Ardeshir Ghanbarzadeh: Empowering the end users and enabling it to be able to deliver all of the data to them Is this how.

Ardeshir Ghanbarzadeh: We how in court is able to kind of create that right balance between the needs of the finance teams and the complexities that it has to go through.

Ardeshir Ghanbarzadeh: In order to be able to collect data from different business sources, whether it be our peas or other business applications and and centralized a secure that and a single location, so that the business users can have immediate access to them.

Ardeshir Ghanbarzadeh: and

Ardeshir Ghanbarzadeh: That is all the questions i'm seeing right now folks, thank you for joining us on the webinar today just wanted to remind you that, part two of this series will be on may 5.

Ardeshir Ghanbarzadeh: At 9am Pacific 12pm Eastern time we'll be looking at ways to take the chaos out of your close process, this will be part two of the driving agility with financial analytics webinar series thanks again for joining us today and have a great rest of your week.

Ryan Garrett: Thanks folks have a great day.

Ryan Garrett: All right.