Transforming the Close Cycle with Agile Reporting & Analytics

Watch now to learn:

• Why the combination of unfeathered access to data with speed of insights can help shrink the close cycle and improve efficiency.
• How a unified data environment increases productivity, accuracy and trust in financial reporting and decision making.
• How to respond to changing business requirements for reporting and analysis without the need to involve IT. 

Today, for part two of our three part series on driving agility with financial analytics transforming the clothes cycle with agile reporting and analytics.

Before we get started today if you housekeeping items, if you do need to leave early.

Will we will be making the webinar available on demand and encoding calm and you'll receive a link to access it after a webinar should you have any questions, please, please feel free to type them into the chat, we will have a Q amp a session, towards the end of the webinar.

My name is artistry gamblers i'm in quarters director of product marketing, I will be your moderator and speaker today.

Our speakers today, besides me are Brian kailash Brian is a principal at kailash consulting in addition to his robust consulting and advisory practice, he is available as a public speaker addressing many and.

Almost all sorts of topical issues facing treasure and fema professionals today he has spoken all over the world, with audiences, large and small, including.

North America, South America, Europe, Asia and the Middle East, he continues to host fema roundtables and events in special occasions.

brian's a senior executive advisor at the Neil group look at it in New York City he's an expert in residence at E capital advisors out of me at minneapolis Minnesota he's also an adjunct professor at Florida international University in Miami.

and former Executive Director of global FP amp a practice at AFP.

Brian has over 25 years experience in finance if you're in a treasury an investor relations and prior prior to joining FP he held a number of pressure and finance positions at F F F H lb Washington mutual JP Morgan and are you CFC shift third and Fannie Mae.

Also, joining us today is Ryan Garrett Ryan, is the sales engineer manager at in quarter and he's responsible for North America, North America East strategic teams.

He joined in quarter in August of 2021 and has previously held roles at Pedro duty and IBM, where he worked primarily with fortune 500 companies on operations analytics and digital transformation projects.

he's based out of Toronto, Canada and has served as a signal operator in the Canadian forces from 2001 to 2009.

A little bit about what we will be covering today we'll take a look at some of the roadblocks that face the finance teams during the closing process.

will look at some of the common challenges in analytics and reporting for finance.

Another area will take a look, is how encoder helps finance team to unified data and financial and operational analytics.

Ryan, is going to give us a DEMO of importers analytics hub for finance and, finally, we will answer your questions in the end of the webinar at the Q amp a session so without further ado i'll hand it over to Mr Brian kailash to kick us off.

Great well it's a pleasure to be with everyone here today, you know, obviously, when we think about you know the clothes it's a very processed generated.

Activity it's required right if we're public companies, but you know, the challenge has always been, how do we make it faster right, and the reason.

Other than the fact from just from a pure efficiency standpoint is it's it really flows in the whole concept of the three c's.

Which is capacity capability and collaboration right So if I don't have capacity I can't do anything so I love the fact that we're talking about the clothes I use it.

was saying in teaching, but also in public speaking, which is you know the way I say it is if it takes you 30 days to do the clothes there's nothing else you do right, but if you leverage your people, your process technology and culture, and can get it down to let's say.

15 days, then all sudden you created 15 days of capacity okay Well now, I can start thinking about doing.

Other things and so then it's a question of do I have the right people with the right process in the right culture, I do, I have the capability.

But I have the capacity and I have the capability, then I can start collaborating with my business and that's really where the finance, you know.

is really important, where we're having our value and, especially, you know in this world of what we refer to as very high Volcker.

volatility uncertainty complexity and ambiguity, I truly hope that for everyone that's on the call today that we never see a period of higher volatility that we're living in today.

But when we think about you know the clothes and we're going to ask you a question, so I was like trying to set things I think about how long your clothes process we're going to do a quick poll on that.

But you know when we think about how we can improve the process.

Really, the three elements that we've seen from the research that we've done is it's it's a matter of quality control.

it's a matter of process and it's a matter of what I would call front loading or automating as many of the processes that we possibly have.

Because I think at the goal their true aspiration and I happen to be someone who's probably way way out the curve, as far as what I think technology can do and what's great is that we have technology people.

On the call today i'm not i'm a finance person, but i'm very intrigued with what technology can enable us to do.

And one of the things that people talk about is this concept of continuous clothes right, so the idea that again if we have the right people with the right systems, with the right processes.

We literally can be doing the doing the clothes, every day, so, in theory, in theory, and Ryan can probably add some insight to it.

Is that literally at the end of the month we push a button and basically the closest right it's almost zero so that's certainly the aspiration that we have so next one.

Do we want to pull you into the whole sure.

Alright folks, you will see a poll pop up Please go ahead and submit your answer, how long is your usual cycle time for your clothes process single choice answer please go ahead and.

Put yours in to the.

chat and we'll have the results for you in a few seconds.

Okay, Brian so it looks like 60% of the people anywhere from one to five days 40% 69 days oh wow okay so we're starting with a very strong group so you can go to the next slide please.

So what I just wanted to share a little background, so this is a survey that we did with a company that we work with ap QC.

And again, just to be formal i'll give you the formal definitions, the metric here is the cycle time to monthly close it comes from again ap QC.

To their general accounting and reporting open standards benchmarking survey.

For this, it was an open ended question yours was closed, but again, we just ask people in general and the metric is defined as the clothes cycle and calendar days between running the trial balances.


to completing the consolidated financial statements, so the cycle times the total time from the beginning of the process, the end, including time spent actually performing the process and what's really, really important.

The time spent waiting for us to be able to move forward, because oftentimes what we see is we do our activity, and then we make friends and so.

I may play off of Ryan, a little bit because he has a military background that's the whole concept of hurry up and wait, but it gets to the next step and then we're going to sit there and wait for a while, so i'm sorry if that becomes annoying Ryan just told me be quiet.

So we surveyed 2300 organizations, you know that answer the question the the bottom 10% the bottom 25% was about they need 10 days or more, which nobody in the group and so nobody's in the bottom quintile and then.

Sorry, the top performers and we had one thing 60% were in five days or less actually an average down to 4.8 days.

And then, in the middle it's the 6.4 days and so that's just kind of giving us a general idea.

And what's really great way when I love that we're able to add the post to understand where the group is right, because, and then we can actually tailor the conversation a little bit so we're not dealing with the situation everyone here on the call.

where everyone that answered the poll basically where we're mid to talk, but still again we're talking about four days or six days, and how can we improve that so the next slide please.

So obviously top priority for CFO is closing the books faster again, not just from an efficiency standpoint but actually to build build up that.

capacity of the ideas as we speed it up it's just going to free to free our teams to do more, what I would consider higher level activities right, I mean part of our goal.

is to help organizations move low IQ activities off the plate of high IQ people and again it's just the idea of what can we do I, like it, I got asked a question on a.

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different format recently live well if you can take you know if you can take your process from 10 days to five days.

aren't you worried that your job is going to go away and I tried to explain people in finance we really don't have that problem that there aren't more things to do, and so again as we're trying to create.

an organization that's more agile that's able to react to business what's happening, the business much faster.

Again, from an aspirational standpoint I love this comment that a CEO made to me she's from a fortune 100 company.

And she said her aspiration for the organization or finance team is that when something comes out that.

That is related to the business she wants the organization, the finance team to be able to react within 90 seconds.

Now that sounds extraordinary and she would agree it's extraordinary but it's aspirational but we see it in the real world and we saw it.

The last two days in the stock market right stock market spin up 1000 points down 500 points right news comes out.

And nobody says well i'm going to wait a day or a week and see how everything shakes out.

And then take an action right, it means literally when news comes out positive or negative, the market reacts very quickly.

And the aspiration is again if we can get to our data faster we can improve our processes make them faster, it will give us the ability to pivot much quicker to what's going on the business, so the next slide was.

So you know we think about well Actually, this will be a kind of a call out question for a second so again feel free to use the chat function, so my question to the group is obviously data is very important.

So much so that people were saying data is the new blank so if people just want to use the chat function for a second so just answering with a one one word or two words to you data is the new what.

So i'm sure I don't know if we're getting any responses or not, if not we can move.

Then you're on it yeah not as of yet, but waiting on it.

i've heard data be called all sorts of things yeah go ahead, no, I mean i've heard i've heard it's kind of the new paradigm i've heard it called.

The new gold, the new revenue stream, you know so it's coming i've heard a few different ways data has been described by company so.

I guess put that in a value on it, you know that kind of critical that you wanted So those are all interesting my the ones that i've heard someone somewhere so it's the new gold.

it's the new oil it's the new superpower my personal favorite someone shared was it's the new bacon right, because we all love making.

Ardeshir Ghanbarzadeh: The way that I would describe it as I think data is the new water, and I say water because water data like water, it needs to be clean and needs to be accessible and it's necessary for survival.

Ardeshir Ghanbarzadeh: And so you know when we think about what holds up the process when we're when we're doing the clothes, a big part of it is, is the data so.

Ardeshir Ghanbarzadeh: As the point that we're making here, you know, basically, you know it gets down to data quality again we've got poor data quality, it takes an inordinate amount of time.

Ardeshir Ghanbarzadeh: To scrub that data right we're still dealing with this challenge for the last 30 odd years you know that.

Ardeshir Ghanbarzadeh: Our finance people spend 80% of their time for data acquisition verification reconciliation and you go back to old copy old hard copies of CFO magazine that has that.

Ardeshir Ghanbarzadeh: It really hasn't changed that much I would say, up until what I affectionately refer to BC before coven.

Ardeshir Ghanbarzadeh: And it certainly has exploded in the sense of people are realizing that their processes have to be much different.

Ardeshir Ghanbarzadeh: The importance of clean data has has never been higher.

Ardeshir Ghanbarzadeh: And so, people are looking for improving their their people skills, the technology they're using the processes again so that as the third point, we have here the you know the data is coming into the system clean like water.

Ardeshir Ghanbarzadeh: The fact that you know the there's so much data that we're dealing with now where we're into into pronto Bytes so just it's an increasing the end of the importance of having very high data standards and having very high and having very good data governance, the next slide.

So when you think about kind of the three actions that people can take as they're trying to move and again.

it's outstanding that the group is we're starting we're starting you know from you know the top 75 to the top 25%, but what we really see.

The actions you can take it involves the list of the chart that's using the standard chart of accounts using common financial data definitions and having good data governance so basically.

From the data that we've acquired, we see that organizations with widespread adoption of of a standard.

chart of accounts can shave almost two days off the time it takes to complete their monthly consolidation compared to organizations that don't.

The consistent use of names identification numbers, based on a standard chart of accounts means finance teams spend much less time guessing and kind of bridging gaps and can get their information to their decision makers much faster.

When we think about it, you know.

common financial data definitions, basically, the way that we're looking at it is.

For multi state excuse multi site companies with separate reporting entities finance has to ensure the chart of accounts naming and numbering conventions are closely matched as possible.

Organizations that strictly adhere to a common financial definition again not surprising need fewer days to complete their monthly consolidation.

And it's really where the benefit is that finance teams don't have to guess what data means where data data goes and financial statements and, of course, that saves time.

And you know we have this challenge, even something with what's a sale right, how does the sale, how does marketing look at a sale happens.

Sales look at the sale, how does finance looking at sales, so we need to have common definitions of what it means, so that again, so we can do the analysis, we can do the clothes faster.

And then we think about just good data governance good David dukes to the good data governance, because we are instead of excuse me standard that data definitions.

We want to create systems for accountability is that improved data quality and consistency and.

You know, really reduce the risk of regulatory repercussions, so no matter where the data comes from whether it's the vendor invoices emails.

You know the addition of new customer accounts or other sources, they should align with your organization's technology and financial processes.

and good data governance includes procedures for data accountability, finance and data.

Reliability and the reduction of unnecessary tasks in decreased efficiency so it's a great opportunity to look at something like rp a robotic process automation where we really can improve the efficiencies next slide.

So again, what we see so that that's the first big challenge we see it's usually about the data.

And really quickly you know when it's not you know if you've got good data usually what we see the problem is, is that data with excuse me with process.

Problems so again it's an opportunity, whether it's again as part of an automation project that you're on or just from a best practices.

You know you do want to establish and document monthly procedures for the clothes and then always you know be on the lookout scanning the horizon for where kind of log jams or or or stop points are sticking points are and be able to resolve this next one.

So then, when we think about you know how much can we front load and again kind of that that that aspirations moving towards the continuous close.

But again, not to go point by point, the ideas when you think about how the process works so obviously a lot of this is driven by process and a lot of it's about data.

it's how much of the work can be pre loaded how much of it, can we do it ahead of time how much can we automate.

All of these things, even if it's five or six or 10 minutes in aggregate can have a very powerful impact on how long the entire process takes so again, these are just a number of items that are good targets for either automating or front loading next line.

And again part of it, I love the first one, which is you know, is actually you know, is to actually use the the checklist that you create again.

it's a little bit more on the softer, but I think it's how we really think about.


Finance business partnering is about the communication right is to go out there and explain to the organization because oftentimes finances just seen as Dr know.

Or we just have these rules that come out of nowhere, the more that you can communicate to your business partners, why we're doing what we're doing the more.

Compliance you'll get from them and because, again everyone's trying to move in the right direction.


So, again we think about going out to all the different departments explaining you know you know when we're going to stop taking data and so that we can start doing our roles.

and obviously goes from you know from the sales teams to accounts payable and then.


Especially when you start thinking about the technology that's involved in what we were able to accomplish and getting to a higher level of granularity it's kind of this last point, which is.

We want to maximize the use of Sub ledger's to record the detail, but we only want to post the summarize changes changes to the general ledger next slide.

So, really, you know it, in conclusion, for my part, again, regardless of where you are cycle, time is now there's always room for improvement.

And so it's always you know when people say well how are we going to spend time, money and resources, you know, improving the process, the argument is really what can you do with that free time right.

A lot you know people would argue, even in the in the group that we have you know the difference between a four day close and the Six Day closes two days.

Right what could you be doing with that time and again, what are the best ways are probably the highest return activities, you can do it's focusing getting a better handle on both your data and your processes.

And again, the whole idea is that it's going to free up our finance teams to be able to move up that analytics data.

maturity curve right to get us out of the world of of the clothes and reporting and getting us out of descriptive and diagnostic to doing what I think finance is really interested in which is getting much more into predictive prescriptive and cognitive I think that's my last slide.


Yeah Thank you Brian that was very helpful coming up with some of those ways to eliminate these roadblocks that certainly getting the way of the other close process and trying to extract those efficiencies I think.

One of the things you mentioned was that bottom bottom line comes down to saving the time right the whole reason you want to go down from you know attend a close cycle to.

Sub five decor cycle and, interestingly enough, you know we looked at a survey by work day from Q4 of last year and and looked at what the CFO is.


How are they focusing on what kind of a fun a fun foundational elements were they looking at when it came to data for their organization and and looking at how they're spending their time today and.

It was some some very interesting results that came out a survey one was the also the Finance and the CFO teams are spending 28% of our times.

Doing data analysis and reporting they're spending 23% of the time it's almost a quarter or time sourcing and cleaning data so they're pulling data from the different sources that they have.

And, but they have to spend a lot of time actually cleansing that data so that they can be.

usable for reporting and and eliminating some of the noise that comes along when you just grab raw data, out of a out of a source another 23%.

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are trying to reconcile data that is coming from different sources that certainly does not add up or does not Jackson This is where you get into the multiple sources of truth.

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And the end and a difficulty being able to trust the the reporting and the results that you're looking at, because you have conflicting reports coming through.

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from different sources that are siloed and and the data is not being reconciled, a single location another 21% about a fifth of our time is.

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aggregating that data so essentially taking that data and then rolling it up into aggregations that can be used for analysis and reporting and and the most.

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I guess stunning piece of data that came out of this is only 5% of their time is being spent actually generating actionable insights so 95% of the time is being.

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Essentially, spent on trying to acquire the data trying to clean the data trying to analyze that and trying to reconcile that from multiple sources and only 5% and actually generating.

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hey What should we do with the information that we're getting out of this data, how can we act on this um so let's take a look at why that is.

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There, a few key challenges that are affecting the office of finance right now in today's world and we're looking at like pretty much the top five here so.

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What are what are they looking to do that, obviously, looking to maximize profitability and managing managed cast So how do we optimize cash and costs.

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Our costs position, how do we manage the relationship between every dollar that's coming in and versus every dollar that has to be paid out what can be optimized.

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Where can we maximize our profitability, where can we get detailed visibility to what are controllable cost drivers, for example, and detail visibility actually is pretty important.

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When you think about being able to identify down to the dollar, where where your costs are, how much they are and whether you can control them.

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Another element is that more and more reporting requirements are moving to real time insights so the data that was going to answer.

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The question for you that you need today can't be from two weeks ago, a month ago that's essentially still information that you're not going to be able to generate actionable insights on so with.

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Real time becoming kind of the de facto requirements that's a challenge, because you need to be able to access data sources and pull that information pull those data sets and for analysis just about immediately, so you can answer the question of now.

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The another another area where there are some challenge exists in the office of finance is actually having visibility to transactional level details, this is going down to the individual.

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records when it comes down to data so moving not just looking at top line aggregations but.

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But being able to drill down into those very details to verify the accuracy of the aggregation when you can make adjustments or you know you're looking for some kind of a root cause or.

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Or you want to look at some kind of a pattern i'm also you want to be able to leverage that transactional level of detail.

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In some cases for advanced analytics you know some some more progressive finance teams are using things like machine learning.

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To be able to do predictive forecasting or also prescriptive analysis, so that little transaction detail is necessary to be able to train those algorithms properly.

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um Then there is the issue that exists in just about every organization which is the complexity of source systems for pulling data out.

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So, in a lot of organizations or source systems, such as the RPS that have you know very rigid data architectures or data models and it's hard to get.

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Data out of them at scale and to do that quickly with speed also multiple sources so there could be business applications custom sources spreadsheets.

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CRM hcm systems, all of which have a lot of valuable data that the organization can use to do meaningful analysis.

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But obviously this data sitting in these different sources and getting them from these disparate sources can become a challenge, especially if you have to.

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reconcile them into one place and that kind of brings us to the last one, which you know you hear this term, a lot single source of truth.

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But the it's more about it's more about building a common trusted data environment.

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That is going to drive that incremental analysis across the business lines, so you want to be able to take the data from those source systems that we talked about but bring them into a single data hub.

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that's going to enable one a bunch of different teams like cross functional teams to work together with the finance team and use the same set of data for analysis and also extend that trust.

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In the data and and the information and the analytics that's coming out of the out of the source systems, because everyone is working off the same data set.

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So that's a that's another challenge that the these teams are looking to looking to work through, but it is not.

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it's not a challenge that is solely on the office of finance, because finance works very closely, obviously with it teams.

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And they're actually looking at two really different sets of challenges it teams are working their best on maintaining these finance the finance tech stack and.

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Looking to build these great pipelines, so that they can deliver that data that the finance teams are looking for when they're looking for it.

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But that's not an easy thing to do, there's quite a bit of complexity there and finance teams are.

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Essentially, in a position where they need to be able to answer questions like Brian said 90 seconds in 90 seconds and.

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You know that's that's that's not something you can sit down and wait on you know some kind of an extract to perform that might take three or four days to come through.

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When you have 90 seconds to actually answer that question so.

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So, while the final it teams want to build these fast pipelines that are quite complex.

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In quarters, helping them simplify that process by bringing that data directly from the source.

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To the end user, so that they can generate the insights and at the same time, the business users in the office of finance can now have that immediate access to the data, so they can operate within those short windows of.

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Time that, for example, make up that close process so let's get a little bit into the tricky part of this and you count on that a little bit so, how does it work.

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Well, the way the way the way it works today and the way the reason that you saw the challenges we pointed to a couple of slides ago.

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Is that there are a whole bunch of data sources within your organization and you know I think I saw at the last I was reading something a couple of days ago that said.

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Add minimum and midsize company will have somewhere between eight and 12 data sources that are commonly use.

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In addition to that, there could be data sources that are just sitting there with dark data nobody's using them but they're still could be valuable data in there.

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But initially initially what happens is that data sitting in some kind of any rp or some other kind of business application system.

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Like ASAP or Oracle or salesforce and that data needs to move be moved moved from there into what typically is a deployment of a data lake.

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So all of that data gets moved into the data lake but then goes through a transformation process where the data is clean and and the data is reconciled and a subset of that data.

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something close to like half or less than half of that data will end find its way into some kind of a data warehouse now in different organizations, you can have.

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A whole bunch of different data warehouses because of the different functions that are involved, but ultimately that data needs to.

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reach the end user, the business user, so that they can analyze it and start making decisions, based upon it.

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And to do that, they typically move that data out of data warehouse another subset of that data.

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into a data Mart or what what you see here defined as the business owner and the business owner will eventually feed that data into some kind of a front end.

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visualization tool, or some other downstream system that is used in the office of finance for, for example, if DNA or to close process, and so on and so forth, so what happens is after all of this effort, and all this transformation and the aggregation of data.

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What do you end up with you end up with data that lacks accuracy, because that could be one multiple copies.

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made throughout that transformation process and it makes it difficult to validate the accuracy of data when you only have a subset and you don't have the transactional.

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level details to timeliness the these this type of activity is quite time consuming so getting to that requirement of near real time.

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data and do real time analytics for for finance becomes a challenge if you have to sit there and wait on that the the quality of the insights starts to.

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starts to lack of value because there's limited number of questions you can actually answer against data that's simply aggregated where you don't have visibility, or you can drill down into the details.

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And then finally somebody governance is this heart because through all this transformation you've lost that lineage and to be able to.

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That data lineage and you really might have a difficult time enforcing application level security So what can we do about this and how can we give.

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Application teams and business users and finance teams visibility to all the data that exists in the organization.

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Well, this is where, in quarters analytics pub for finance can really help on like the one a modern approach to.

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To analytics what encarta does is actually taste 100% of the data from the source systems, and this could be multiple source systems and and brings it into a single hop but through this process, there is no time consuming transformation.

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or any kind of or or aggregation and this way in quarters able to map data from different sources 100% raw data and provide 100% of that data to the end users for analysis and that.

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means that that data cannot go into visualization tools such as power bi or tablo or some other data discovery tool.

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It can be used with in quarters blueprints which we'll talk a little bit about later, but that are added a box.

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of capabilities to get you up and running very quickly with business using dashboards it can feed downstream systems.

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Or it can be applied to things like data science for machine learning so when you have all that data, you can actually.

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You can actually use that for when we talked about a little bit earlier being able to train algorithms for predictive and prescriptive analytics.

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The nice thing about it is that it works with your existing finance tech stack, which means that you're going to end up getting more out of your investment in your financial systems, but the key takeaways are you have now full fidelity of data.

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Because in quarter has connectors to something like 240 plus source systems for finance on operational reporting.

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And you're able to connect to multiple systems and bring that data together in a single location, you have full fidelity of access to data.

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You can make all of this data usable and you eliminate that data latency they're having to wait a long time, sometimes days or weeks for the data to be made available so that you can use it to do, analysis and generate insights.

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So you know what makes what's included as a unique value proposition here what's really important is to understand that the there's.

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there's certain things that obviously the office of finance is looking for from data and there's certain things that the it team supporting the office of finance are looking for with respect to data, but ultimately it comes down to three key things.

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Within quarter, you are able to access 100% of your data so you're not dealing with aggregations and you're not dealing with.

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a subset of data as a result of transformations so this means that you're combining and centralizing data from multiple sources in a single location and you're doing your analysis reporting based on that.

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Having having that level of data access is is critical, because this is where you can validate the veracity and the and the, and.

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Being able to drill down and look at the details and get away from this process of having to manually stitch together data from different spreadsheets in order to be able to do.

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analysis, you can go directly from source of visualization for analysis and reporting without any issues.

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nexus you can trust the data, you know you can improve the accuracy of the reporting, because you have that level of granularity of insights.

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If going from top line down all the way down to the individual transactional level details and that takes a lot of the guesswork out of the.

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Out of the decision making process, because you do have this single verifiable copy of your data.

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to drive the decisions and you're able to empower those business users to drill in any direction they want.

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so that they can identify in consistencies find root cause and ultimately take action, and that is going to have an impact on business performance.

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And the third being again comes down to saving time and having an speed have access to data and basically you're improving that efficiency and.

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Productivity that is going to help shrink down that cross close cycle, because you get the latest data in minutes you're not sitting there waiting hours of days to to be able to shrink down that data latency and reporting time cycles.

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And you're doing a lot of this in the office of finance really without having to burden the it teams.

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Who who you know, obviously have enough on their plate and they're trying their best, obviously, to give you the data that that you want, but because you already have access to 100% of data you're not waiting around for new reports you're not waiting for a new data model to be built.

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On the other hand, if you are in the IT team and you're supporting the the office of finance, you can leverage.

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In quarter, because what it does, is it actually makes it really simple for you to be able to pull data.

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You to connect directly to the source and quarter technology of direct data mapping actually maps all the data together so that you can easily.

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deliver that data without having to go through an ETF process 2d end users with the large list of a portfolio of.

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Data connectors you can practically connect to any.

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Any data source that you have in your ecosystem and ultimately you're going to improve the productivity of the IT team, because they are not spending a ton of time trying to.

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understand the requirements from from finance to be able to create new reports for the data that they're looking for the.

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The nice thing about it is that it will maintain 100% complete control over data security and access, because in quarter works with existing.

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Security frameworks, so you don't have to change, security models or having to maintain security in multiple places that will create a redundant overhead.

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And, and again, you are delivering all the data to the end user, so they are able to take take action on that data do their own analysis again find the finding consistencies and.

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Again, the the toughest probably the biggest complaint is that we hear from the it teams is that yeah we have these long time consuming inefficient.

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hcl processes and data aggregations that really slows down within quarter, they can actually eliminated all that and accelerate that time to value, so these capabilities, have been.

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very helpful and customers in quarter customers that have successfully deployed and i've been using a quarter over the years and and, that being recognized in fact.

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Just just this year a couple of months ago, or so i'm in quarter was recognized in gartner's magic quadrant for the first time.

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and much of much of this recognition, and this is, you know we're very, very proud of this recognition, obviously.

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Because there are only 20 analytics and bi vendors that are recognized in the magic quadrant every year, but it was.

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It came down to ultimately delivering that high power operational analytics with unlimited access at speed and scale and eliminating the need for that transformation and we shaping of the data which is part of that holy.

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yellowtail process that slows things down and and being able to analyze all the data, instead of just a subset of data, he key elements that help.

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In quarter enter the magic quadrant as a niche player in 22 and we are you're obviously quite proud of that, if you're interested in looking at the report in detail, you can.

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Go to encoding calm and download the download the report from from the resources section so with that i'm going to hand things over to Ryan, who is going to walk us through a DEMO of in quarters.

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Ardeshir Ghanbarzadeh: analytics data hub for finance and Ryan, please take it away awesome thanks aren't sure hey team Ryan Garrett here.

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Ardeshir Ghanbarzadeh: first thing before we kind of jumped into the the inquiry DEMO I just want to kind of think through a traditional clothes process.

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Ardeshir Ghanbarzadeh: If you think about the traditional way that teams have go about that closed process there's a highly high manual effort that goes in there, so if you think about even trying to figure out the variance.

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Ardeshir Ghanbarzadeh: You know, we get to the numbers and the numbers don't match what do we have to do well, we have to go back and we have to kind of maybe run an overnight report, or you know pull some additional data.

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Ardeshir Ghanbarzadeh: we're going to pull it into maybe two or three different tools we're going to pull you know, maybe into excel crates and pivot tables.

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Ardeshir Ghanbarzadeh: we're going to try and visualize that maybe a visualization layer

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Ardeshir Ghanbarzadeh: If we just don't have kind of all of that information all that detailed information we kind of have to go back to the back to the well, so to speak, to kind of.

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Ardeshir Ghanbarzadeh: Re re re evaluate maybe bring more data forward get that in the hands of our analysts so they're spending a lot of this time in that close process and kind of brian's point is.

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Ardeshir Ghanbarzadeh: You know there's a wide range there you know from you know, a short period of four days to kind of long period of 10 plus days.

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Ardeshir Ghanbarzadeh: And a lot of the actions that are happening in that period are really relying on you know high IQ people.

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Ardeshir Ghanbarzadeh: doing a lot of kind of manual mundane tasks to try and find how we balance the books, how we.

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Ardeshir Ghanbarzadeh: You know, bring data together so that we can kind of you know definitively say you know this is this is, these are our numbers for the end of the quarter, so we can close those books.

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Ardeshir Ghanbarzadeh: And what we'll do is we're going to walk through an example within the quarter platform of really that in that entire process and starting with very high level data so kind of looking at general ledger data.

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Ardeshir Ghanbarzadeh: And then going through and being able to drill through the different levels of deep detail to really under understand you know why we have a variance.

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Ardeshir Ghanbarzadeh: and actually drill all the way down to the transaction level detail.

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Ardeshir Ghanbarzadeh: So not only we can understand why we have an appearance, but more importantly, we can take an action on that variance so that we can come to closure of those books in very short order.

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Ardeshir Ghanbarzadeh: So let me walk you through that platform or that process here now and we'll we'll start, as I said, we'll start at the very high level will start you know general ledger type information.

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Ardeshir Ghanbarzadeh: So as artistry I mentioned part of the part of the value of in quarters actually being able to do.

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Ardeshir Ghanbarzadeh: All of this workflow in a single platform being able to bring that data in and being able to you know not just.

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Ardeshir Ghanbarzadeh: Bring the data in in a unified way, but actually visualize that in a unified way as well, and we can do that.

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Ardeshir Ghanbarzadeh: You know, you know, out of the box, you know a lot of the kind of the GL in general ledger you know accounts receivable accounts payable in quarter has actually purpose built blueprints to be able to show you.

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Ardeshir Ghanbarzadeh: You know this data, you don't have to go build it yourself right, so you can kind of leverage our engineering expertise in this space to be able to you know, produce a visualization much like we're showing you here.

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Ardeshir Ghanbarzadeh: And so, if i'm a you know, a CFO type or working in the office finance, you know I may look and start at this type of.

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type of view of my my business what's going on as we go to close the books, we started look and say Okay, we want to close the book, but we actually have a bit of a variance here.

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and based on our parameters we've actually kind of identified a potential parameter here we're looking at this cash conversion cycle, and this is outside of our normal.

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behavior so we want to kind of highlight this and we want to actually you know start to drill into this like what's happening here what's going on, so.

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If I take a quick look here, and I can go and say okay that's standing out to me, I want to actually drill into that understand what's going on.

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With that cash conversion cycle, so I drill into this and so now, I can actually see what's happening.

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So I can see, you know the transactions have all been summed up for me, I can see this the cycle, you can actually see you know it's taken us a little bit longer time now to go to convert you know transactions into revenue.

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we're actually able to see the receivables rate associated to revenue.

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So we got everything looks kind of like okay here, but as we start to kind of drill into things there's a few things that we can kind of call your color attention to so as we drill into this, you can see.

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You know hey the the trend here is we're not maybe not getting the same revenue at the time that we thought we might hear, so why is this happening, so if we drill down in a little bit further here and we can see.

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Again, according to you know what we've set up, you know are variances actually in that receivables so you know we've now gone from you know very high level detail and now we're really going to focus on that receivables.

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So if I drill in a little bit further to just understand what's going on with that receivables I want to get a little bit more information about what's going on.

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So here we can actually start to see like you know we've supplied, you know our good detail or our good inventory off to our customers.

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But have our customers actually received the goods and have they paid us back for for those goods, and so, when we look at this, we can see a few things start to kind of stand out.

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there's actually a seems to be a fairly high amount of overdue payments.

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We actually have a high amount of payments that are that are aging out, and so we really want to kind of drill into that to understand okay what what is actually making up those payments.

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What are the accounts that we're providing services to you know where do we stand with receiving those payments.

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So, as we you know kind of drilling a little bit further here again we're called to our attention here this gentleman Herman another he's actually.

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You know, representing the bulk of our overdue revenue, you can see, this is in representing a very substantial amount and a very large amount is actually overdue.

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So we want to drill into this a little bit further here to kind of again kind of get the next picture here so we'll just focus in on Herman.

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And all of the data that he you know he's responsible for the accounts that he's responsible for.

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And so what we'll see here is actually Herman is actually has one account, and it has costco.

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So we can see what's going on here, you know looks like costco place the large order, however, as we drill into this there's actually a fairly substantial amount of data that's.

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hasn't been sort of revenue that hasn't been collected, so we actually have a fairly high amount of overdue revenue that we haven't been able to to collect from our customer.

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Now we've gone a step further, though, and as we drilled into this we actually drilled into all of the specific accounts receivable transactions.

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that make up that variance so we can actually now have drilled all the way down into the individual purchase order records.

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Of what's outstanding for for our customer and I can even really focus on a little bit further here to say like okay like just tell me what's.

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what's you know, greater than our net 30 terms that we have with this customer so now we've actually drill down into a very set very actionable set.

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of purchase orders that we can focus on to take an action on so is that something that we know, make a business decision, do we go back to our customer and say hey like.

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You know, you need to pay us you're exceeding your terms, do we have another thing when we have to write this off as a potential loss or we make a financial statement in order to close the books.

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But what we've done in to brian's point is we've gone from you know very you know high level, you know, a company level general ledger detailed.

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And we've actually drilled all the way through through a single platform and got down to the individual transactions.

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That are making up those that accounts receivables and we've gone through that you know, in a matter of a handful of clicks.

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within a single platform within you know very short order without a whole lot of manual steps and extracts and file tables and pivot tables.

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But we're able now to to actually transverse all the way through to an actionable insight, so that we can take an action and really shorten that whole manual process that happens during the the clothes cycle.

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And really focus on kind of leverage leveraging technology in order to to be a ride those actionable insights.

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So just to kind of close things off here just to kind of reiterate the the process that we just walked through.

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We walked through from you know, a you know high level detail general ledger.

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click through into you know accounts receivables and then all the way down to the purchase order number in a single platform and it's with a handful of clicks, and this is how we see in quarter.

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starting to help shave days, and you know, in some cases weeks, out of a close cycle for some of our customers.

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And i'll pause there and say.

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pass it back to our show, and we can kind of close up the conversation.

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Thank you, thank you very much, right right them.

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All right, folks.

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Ryan thanks again i'm just like why mentioned will start looking at the African slowly start to wrap things up um hopefully you know after brian's great the.

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overview of what can be done to improve the processes and efficiency and productivity of Finance to shrink down that close cycle and get as close to that.

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One day, hopefully, would be the goal the inspiration or aspirational goal for the for the clothes process.

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And and Ryan ryan's a great DEMO on how important can actually deliver that those types of insights through its dashboards and this capabilities and overall why it's so important to have access to all the data.

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That you have in the organization, be able to trust that data and be able to get visibility to it in just about real time to be able to answer the questions of today.

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Hopefully you've taken away from this, you know how important and get to thinking about how and quarter could help you potentially in your organization.

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improve the the the post process and save time, both for your it teams and your finance teams and leverage that time to do.

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More high IQ tasks and initiatives within within the organization um with that will let's let's go to a Q amp a.

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will open up the Q amp a now if you have questions for Ryan for Brian or for myself, please do type them into the Q amp a and we'll do our best to answer those questions for you.

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Brian our question for you, is there a specific part of the closed process that most commonly introduces inefficiencies into the workflow or finance teams.

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I would say it's going back to the to the clean data aspect of it right you're getting the data and it just doesn't reconcile, and so the idea that you can either move from do to get to a single source of the truth.

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Or to automate that system that you can once you identify a variance like as Ryan was going through once we see it, how quickly, can you get to it, so I I see the biggest challenge is conflicting sources.

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Part of it again is you know, is, I think Archer was talking about, or maybe Ryan was talking to you know, the average company has 1314 different applications.

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I was talking to someone last week, they have 104 finance applications happy again the challenges you're bringing all these pieces in and.

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It just slows down the whole process right, so the idea that either through moving towards more of a continuous close against fripp front loading the activity as much as possible.

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Once the once you actually kick off the closing process how quickly, can you identify the variances and then, once you can identify the variances how quickly, can you can you reconcile those I hope that, if not we didn't address, and please feel free to follow up on that.

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Okay now great now that's a must have address that because there is no follow up.

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next question for you Ryan.

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You mentioned blueprints in during your DEMO can you elaborate exactly how they're used.

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yeah absolutely and i've actually put a link to our blueprints in the chat there as well.

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I think the quote is a phrase, the question which ones that we have available or how do they work.

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Is I can answer both yeah.

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Why don't we will start with the kind of the which ones are available so.

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Really, if you think about you know complex crp systems that's where in quarter has invested our the bulk of our engineering, so the oracles the SAP of the world.

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The salesforce etc we've involved in invested our engineering there we've actually deployed or invested in building blueprints that cover off.

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The vast majority of those complex earpiece systems, specifically when it comes to things like accounts payable accounts receivable general ledger.

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You know, etc, etc we've actually purpose built those blueprints and and the blueprints really are.

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The your fast way to value so it's leveraging our engineering expertise to kind of.

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You know, make sense of you know how to how to all the different tables within you know the general ledger module within say Oracle.

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How do they actually map to each other, how do they join with each other, how do you combine those to actually get actionable data out of that.

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that's kind of the first part of the blueprint, and then the second part of the blueprint is actually the some of the pre built dashboards that I showed you.

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To be able to kind of very quickly say hey like you know we're leveraging leveraging you know general ledger within Oracle or SAP or what have you and we want to kind of get common you know common things right, so you know.

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accounts receivable you know how much is age out so kind of standard reports that we can deliver.

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And kind of the The end result of this is what we see is is customers are really able to get up and running.

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Within a quarter, with a with a fraction of the time, money and effort that it takes for them to do it no an absence of in quarter or the traditional kind of very manual way as well.

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awesome, thank you for that um next question is where can I, where, can you get the the gartner magic quadrant reports.

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The one that ain't been quarter was included in the 2022 you absolutely can please go to in quarter calm and go to the resources section, and you can download.

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The report from there, and I think our in quarter admin also put a direct link in the chat you can click on to to access the report.

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Ardeshir Ghanbarzadeh: That about wraps it up for q&a folks Thank you so much want to remind you that the part three of our series on driving agility.

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Ardeshir Ghanbarzadeh: For financial analytics are will be about boosting your working capital by harnessing the power of your data that will be on August 30th at 1pm London time, please be sure to register for that.

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Ardeshir Ghanbarzadeh: For that webinar will be talking about how to get the most out of your working capital and how to get the most extracted out of your data to support that particular use case for the office of finance.

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Ardeshir Ghanbarzadeh: Again, thank you for joining us today and have a great rest of the week and weekend thanks again bye bye.