Building Resilience with Agile, Adaptive and Continuous FP&A

Transcript

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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 fpa.

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Before we get started today on a few housekeeping items, if you do need to leave early, we will be making this webinar available on demand at and quarter.com.

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And you'll receive a link to access it within a couple of days I should 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.

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

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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.

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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.

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

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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.

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

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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.

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

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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.

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Another trend and another challenge that we see is visibility to not only top line data which is aggregations of data but also transactional level detail.

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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.

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

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

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

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And variances Why then this becomes pretty important to have that level of granularity to identify what the drivers are behind those changes and variance so um this this level of.

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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.

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

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

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To help produce rich analytics or do meaningful analysis to help with planning and forecasting.

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

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

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And finally, we often talk about a single source of truth and What this really means is i'm building a common and trusted environment.

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

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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.

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But 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.

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So that there is there's a trusted accuracy behind.

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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.

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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.

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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.

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

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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.

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

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

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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.

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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.

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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.

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or teams involved in the in the claws process.

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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.

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

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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.

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To do that would cause analysis and kind of frictions 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 kind of a quick look at that.

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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.

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That 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.

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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.

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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.

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In 2d and users and in a way that works with your existing tech stack and once that data is on the platform.

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

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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.

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

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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.

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

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To get better visibility on how to manage those dollars that are coming in and dollars that are going out and also help the teams that are supporting the closed process shrink down.

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That that's close cycle, making them more agile in in delivering what they need to the to the business by by having that visibility and access to all of the data.

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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.

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

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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.

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

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

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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.

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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 they're not repetitive questions from the previous week or a previous month.

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

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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.

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So with this consideration of these different pain points that both the the finance and it teams and the ecosystem of the office of finance are facing.

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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.

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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.

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and bringing a hard percent data hundred percent of that data to to to business for analysis and enabling those business users to explore that data asked questions.

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

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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 and accurate level of.

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

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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, and obviously to them, but at the same time enabling the business users to still.

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

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

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

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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.

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

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

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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.

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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.

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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.

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

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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 to $12 billion in market CAP.

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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.

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With that um I wanted to.

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

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Thank Sarah 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.

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

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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.

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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.

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

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High Level transaction detail and sorry high level details.

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Better accounts receivable and we can actually drill in there to artists or his point is kind of drill down into transaction level detail to.

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Really uncover what's going on and really get those insights that we need as a as an FP amp a team to really make critical business decisions.

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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 source.

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So i'm going to connect to an oracle data source, so in this case will connect Oracle it up a username and a password we can connect that connection, make sure that we're connected there.

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Once we 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.

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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.

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

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But some of the key things that we incorporating and really to shorten that time to value.

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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.

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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.

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

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

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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.

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

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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.

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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.

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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.

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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.

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

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

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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.

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

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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.

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In a lot of cases it's come through three, four or five different tools before it's gotten to the hands of an FP amp a professional.

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

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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.

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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.

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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.

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

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Creating a whole bunch of code are 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.

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

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

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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.

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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.

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You know these organizations and the amount that they've influenced 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.

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Pre built content pre built reports that include a 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.

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

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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.

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i'm a financial analyst and i'm looking at.

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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.

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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.

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

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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 you know our cash flow.

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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.

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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.

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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 accounts receivable, so now we can actually take a look at at collections.

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You here as well, so the outstanding balances overdue all that good stuff.

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But I drill into this and I take a look, and I can see, you know our top customers what's going on, but I also see some of these anomalous transactions that are going here.

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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.

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

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

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

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

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

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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.

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

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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 filtered down from.

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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.

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

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And then drill 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.

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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 fema professionals.

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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 join that data.

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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 to transaction level detail within a few clicks with a single platform.

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And that is in quarters approach to a finance analytics hub.

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harder sure back to you.

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All right, Ryan Thank you so much for that great DEMO 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.

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

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One having access to all the available data, and having it in an experience 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.

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

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

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

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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.

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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.

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

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

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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.

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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.

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and be able to apply that into a packaged.

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easy to deploy.

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analytics application so we bring that data bring that into in quarter.

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The joins the complex joins all that great stuff comes in, as well as the business sorry the the the schema layer as I showed you the joins all that good stuff the business layer

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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.

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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.

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

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

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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.

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

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awesome, thank you for that explanation another question here is more of a process question how do we compile it teams.

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To spend more time gathering data for for FP amp a so, so this is where this is the area where in quarter kind of creates that balance between it and finance where.

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We are able to enable the it teams to collect data extract data from the different sources within the within the business.

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bring them all into the analytics top and centralized them in a common data environments.

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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.

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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.

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

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or or make it or make a change to how a report is updated or delivered.

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

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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.

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In order to be able to collect data from different business sources, whether it er 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.

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Ardeshir Ghanbarzadeh: Alright folks just wanted to answer one more question here and there was a question here, along the lines of SOx compliance.

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Ardeshir Ghanbarzadeh: And and using Oracle as a data source, so one thing I just wanted to reinforce was the fact that.

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Ardeshir Ghanbarzadeh: With it within quarter you're able to pull in data from multiple sources so bringing in data from your peers and other.

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Ardeshir Ghanbarzadeh: Other operational systems is something that you can bring into that that finance data hub.

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Ardeshir Ghanbarzadeh: The than the question around the SOx compliance i'll have a better question after the team to see if the was implemented in quarter in a SOx compliant environments.

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Ardeshir Ghanbarzadeh: But I can tell you that the fact that in quarter allows you to bring 100% of your rp and operational data into the.

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Ardeshir Ghanbarzadeh: into the analytics data help or finance that provides the end users, the ability to be able to drill down.

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Ardeshir Ghanbarzadeh: To the transactional level details that essentially is the way to be able to support the audits and verifications of accuracy.

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Ardeshir Ghanbarzadeh: That is needed when it comes to drilling into aggregated data to make sure that the underlying.

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Ardeshir Ghanbarzadeh: Underlying accounts are are accurate, in terms of the in terms of the rolling up of the data so um so you know we'll get back to you on the on the implementation around SOx compliance, but I can tell you that.

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Ardeshir Ghanbarzadeh: The fact that that in quarter brings 100% of data in provide that level of granular visibility that most auditors are looking for when it comes to ensuring accuracy and compliance.

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Ardeshir Ghanbarzadeh: That is all the questions we have today folks be sure to join us on the second of August at the at 12pm utc for the second part of our three part series on driving agility with operational and financial analytics Thank you again for being here today and have a great rest of the day.