Faced with accelerated reporting deadlines and unrelenting demands for higher quality information, accounting and finance teams are constantly challenged to increase operational efficiency and contribute more prominently to the strategic direction of the business. Managing these rising expectations, and often competing priorities, creates a delicate balancing act that requires greater agility from the office of finance to support evolving business needs, despite mounting resource constraints.

Finance and accounting teams today are facing very heavy challenges including a shift to real-time reporting, a demand to increase operational efficiency and fast-changing business conditions. As organizations become more complex, consolidating data from multiple divisions, systems and business units becomes increasingly arduous. 

In order to shorten the close cycle times and maximize productivity, Finance teams need to eliminate the manual, error-prone data collection, conflicting reports or long wait times for new data to be delivered by the IT organization. 

In Part 2 of our three-part series we discuss how an agile approach for reporting and analytics enables Finance teams to increase productivity and efficiency of the close process.

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. 

Transcript:

Ardeshir Ghanbarzadeh: Thank you for joining us today for part two of our three part series on driving agility with financial analytics transforming the clothes cycle with agile reporting and analytics.

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

Ardeshir Ghanbarzadeh: 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 I 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.

Ardeshir Ghanbarzadeh: My name is artist your cameras i'm in court as Director of product marketing, I will be your moderator and speaker today.

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

Ardeshir Ghanbarzadeh: 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.

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

Ardeshir Ghanbarzadeh: Brian’s a senior executive advisor at the Neil group located in New York City he's an expert in residence at E capital advisors out of me minneapolis Minnesota he's also an adjunct professor at Florida international University in Miami.

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

Ardeshir Ghanbarzadeh: Brian has over 25 years experience in finance if you're in a Treasury and investor relations and probably prior to joining FP he held a number of pressure and finance positions that F F F H lb Washington mutual JP Morgan and are you CFC fifth third and Fannie Mae.

Ardeshir Ghanbarzadeh: 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.

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

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

Ardeshir Ghanbarzadeh: 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 clothes process.

Ardeshir Ghanbarzadeh: will get some of the common challenges in analytics and reporting for finance.

Ardeshir Ghanbarzadeh: Another area we'll take a look, is how in quarter helps finance team to unified data and financial and operational analytics.

Ardeshir Ghanbarzadeh: Ryan, is going to give us a DEMO of the 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 Kalish to kick us off.

Brian Kalish: 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.

Brian Kalish: 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.

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

Brian Kalish: 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.

Brian Kalish: 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.

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

Brian Kalish: Other things and so then it's a question of do I have the right people with the right process and the right culture, I do, I have the capability, but if I have the capacity and I have the capability, then I can start collaborating with my business and that's really where the finance.

Ardeshir Ghanbarzadeh: You know.

Brian Kalish: it's really important where we're having our value add especially you know in this world of will be referred to as very high Volcker.

Brian Kalish: 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 then we're living in today.

Brian Kalish: 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.

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

Brian Kalish: 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.

Brian Kalish: And it's a matter of what I would call front loading or automating as many of the processes that we possibly have.

Brian Kalish: 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.

Brian Kalish: 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.

Brian Kalish: 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.

Brian Kalish: 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.

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

Ardeshir Ghanbarzadeh: i'll do we want to.

Brian Kalish: deploy you would do the whole sure.

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: Put yours into the.

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

Ardeshir Ghanbarzadeh: Okay, Brian so it looks like 60% of the people anywhere from one to five days 40% six to nine days.

Brian Kalish: Oh wow okay so we're starting with a very strong group so you can go to the next slide please.

Brian Kalish: 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.

Brian Kalish: 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.

Brian Kalish: Their General Accounting and reporting open standards benchmarking survey.

Brian Kalish: For this, it was an open ended question yours was closed, but again, we just ask people in general.

Brian Kalish: And the metric is defined as the clothes cycle and calendar days between running the trial balances.

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

Brian Kalish: 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 again, I may play off of Ryan, a little bit because he has a military background.

Brian Kalish: And the whole concept of hurry up and wait to get 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.

Brian Kalish: So we surveyed 2300 organizations, you know that answered the question the the bottom 10% of the bottom 25% was about the new 10 days or more, which nobody in the group had so nobody's in the bottom quintile.

Brian Kalish: And then.

Brian Kalish: Sorry, the top performers and we had 60% were in five days or less actually it averaged out the 4.8 days and then in the middle it's the 6.4 days.

Brian Kalish: And so that's this kind of giving us a general idea and what's really great way when I love.

Brian Kalish: That they were 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.

Brian Kalish: 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.

Brian Kalish: 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.

Brian Kalish: Capacity the ideas and 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.

Brian Kalish: 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.

Brian Kalish: different format recently live well if you can take you know if you could take your process from 10 days to five days aren't you worried that your job is going to go away.

Brian Kalish: And I tried to explain people in finance we really don't have that problem that there are more things to do, and so again as we're trying to create.

Brian Kalish: an organization that's more agile that's able to react to business what's happening in the business much faster.

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

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

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

Brian Kalish: 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.

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

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

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

Brian Kalish: 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 in the business, so the next slide please.

Brian Kalish: 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.

Brian Kalish: So much so that people are 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.

Brian Kalish: So true I don't know if we're getting any responses or not, if not we can move.

Ardeshir Ghanbarzadeh: In your other on it yeah not as of yet, but waiting on it.

Ardeshir Ghanbarzadeh: i've heard data be called all sorts of things new.

Brian Kalish: yeah good.

Ardeshir Ghanbarzadeh: Now I mean i've heard i've heard it's kind of the new paradigm i've heard it called.

Ardeshir Ghanbarzadeh: The new gold, the new revenue stream, you know so it's coming i've heard a few different ways that has been described by company, so I guess put that kind of value on it, you know that kind of critical that you want it.

Brian Kalish: So those are all interesting my the ones that i've heard it's somewhat similar 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.

Brian Kalish: 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, it needs to be accessible and it's necessary for survival.

Brian Kalish: 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 as the point that we're making here, you know, basically, you know.

Brian Kalish: It gets down to data quality again we've got poor data quality, it takes an inordinate amount of time to scrub that data right we're still dealing with this challenge for the last.

Brian Kalish: 30 odd years you know that our finance people spend 80% of their time on data acquisition verification and reconciliation and you go back to old copy old hard copies of CFO magazine that has that.

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

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

Brian Kalish: The importance of clean data has has never been higher and so people are looking for improving their their people skills, the technology they're using the processes.

Brian Kalish: Again, so that, as the third point, we have here is that you know the data is coming into the system clean like water.

Brian Kalish: The fact that you know the there's so much data that we're dealing with now we're into into bravo bites 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.

Brian Kalish: And so we think about a kind of the three actions that people can take as they're trying to move and again.

Brian Kalish: 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.

Brian Kalish: The chart that's using the standard chart of accounts using common financial data definitions and having good data governance so basically.

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

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

Brian Kalish: 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.

Brian Kalish: When we think about it, you know common financial data definitions, you know, basically, the way that we're looking at it is.

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

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

Brian Kalish: And I mean 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.

Brian Kalish: 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.

Brian Kalish: Sales look at the sale, how to 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.

Brian Kalish: And then we think about just good data governance good to ever do a good data governance, because we are instead of excuse me standard that data definitions.

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

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

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

Brian Kalish: and good data governance includes procedures for data accountability, finance and data.

Brian Kalish: Reliability and the reduction of unnecessary tasks and 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.

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

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

Brian Kalish: 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.

Brian Kalish: 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 were kind of log jams or or stop points are sticking points are and be able to resolve things next slide.

Brian Kalish: 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.

Brian Kalish: 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.

Brian Kalish: 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.

Brian Kalish: 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.

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

Brian Kalish: 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.

Brian Kalish: 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.

Brian Kalish: The more that you can communicate to your business partners, why we're doing what we're doing the more.

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

Brian Kalish: 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.

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

Brian Kalish: 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 its last point, which is.

Brian Kalish: 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.

Brian Kalish: 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.

Brian Kalish: 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.

Brian Kalish: A lot you know people would argue, even in the in the group that we have you know the difference between a four day closing the Six Day closes two days right what could you be doing with that time.

Brian Kalish: 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.

Brian Kalish: 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.

Brian Kalish: 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 finances really interested in which is getting much more into predictive prescriptive and cognitive I think that's my last slide.

Ardeshir Ghanbarzadeh: yeah Thank you Brian that was very helpful coming up with some of those ways to.

Ardeshir Ghanbarzadeh: eliminate these roadblocks that certainly getting the way of the underclothes process and trying to extract those efficiencies I think.

Ardeshir Ghanbarzadeh: Are the things you mentioned was the bottom bottom line comes down to saving you time right the whole reason you want to go down from you know attend a flow cycle to.

Ardeshir Ghanbarzadeh: 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 those.

Ardeshir Ghanbarzadeh: 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.

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

Ardeshir Ghanbarzadeh: Doing data analysis and reporting they're spending 23% of the time it's almost a quarter our time sourcing and cleaning data so they're pulling data from different sources that they have and but they have to spend a lot of time actually cleansing that data so that they can be.

Ardeshir Ghanbarzadeh: 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%.

Ardeshir Ghanbarzadeh: are trying to reconcile data that is coming from different sources that certainly does not add up or does not Johnson, this is where you get into the multiple sources of truth.

Ardeshir Ghanbarzadeh: And and 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.

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: 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.

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

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

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

Ardeshir Ghanbarzadeh: What are what are they looking to do that are obviously looking to maximize.

Ardeshir Ghanbarzadeh: profitability and managing manage cash So how do we optimize cash and costs, our costs position, how do we manage the relationship between every dollar that's coming in.

Ardeshir Ghanbarzadeh: And versus every dollar that has to be paid out what can be optimized where can we maximize our profitability.

Ardeshir Ghanbarzadeh: Where can we get detailed visibility to what are controllable cost drivers, for example, and detail visibility actually is pretty important.

Ardeshir Ghanbarzadeh: 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.

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

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

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: The another another area where there are some challenge exist and the office of finance is actually having visibility to transactional level details, this is going down to the individual.

Ardeshir Ghanbarzadeh: records when it comes down to data so move it not just looking at top line aggregations but.

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: Or you want to look at some kind of a pattern also you want to be able to leverage that transactional level detail.

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

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

Ardeshir Ghanbarzadeh: Then there is the issue that exist in just about every organization which is the complexity of source systems for pulling data out.

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

Ardeshir Ghanbarzadeh: 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.

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

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: 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.

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

Ardeshir Ghanbarzadeh: 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 him into a single data hub.

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: 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.

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

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

Ardeshir Ghanbarzadeh: And they're actually looking at two really different sets of challenges it teams are working their best on maintaining these finance.

Ardeshir Ghanbarzadeh: Finance tech stack and 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.

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

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

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

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

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

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

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

Ardeshir Ghanbarzadeh: Time that, for example, makeup 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.

Ardeshir Ghanbarzadeh: 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.

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

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

Ardeshir Ghanbarzadeh: On the 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.

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

Ardeshir Ghanbarzadeh: 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.

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

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

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

Ardeshir Ghanbarzadeh: reach the end user, the business user, so that they can analyze it and start making decisions, based upon it, and to do that, they typically move that data out of data warehouse another subset of that data.

Ardeshir Ghanbarzadeh: into a data Mart or what would you see here to find that the business song and the business on will eventually feed that data into some kind of a front end.

Ardeshir Ghanbarzadeh: 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 enrolled as transformation and the aggregation of data.

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

Ardeshir Ghanbarzadeh: made through 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.

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

Ardeshir Ghanbarzadeh: 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.

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

Ardeshir Ghanbarzadeh: And then finally somebody governance is hurt because through the all this transformation you've lost that lineage and to be able to.

Ardeshir Ghanbarzadeh: 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.

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

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

Ardeshir Ghanbarzadeh: To analytics What it does is it actually tastes 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.

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

Ardeshir Ghanbarzadeh: means that that data can now go into visualization tools such as power bi or tablo or some other data discovery tool um it can be used with in quarters blueprints which we'll talk a little bit about later.

Ardeshir Ghanbarzadeh: But that are out of a box of capabilities to get you up and running very quickly with business using dashboards it can feed downstream systems or it can be applied to things like data science for machine learning so when you have all that data, you can actually.

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

Ardeshir Ghanbarzadeh: 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.

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

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

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: there's certain things that obviously the office of finance is looking for from data, and there are 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.

Ardeshir Ghanbarzadeh: Within Cora you are able to access 100% of your data so you're not dealing with aggregations and you're not dealing with.

Ardeshir Ghanbarzadeh: 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 and reporting based on that.

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

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: analysis, you can go directly from source to visualization for analysis and reporting without any issues.

Ardeshir Ghanbarzadeh: Next is you can trust the data, you know you can improve the accuracy of the reporting, because you have that level of granularity of insights.

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: Out of the decision making process, because you do have this single verifiable copy of your data to drive the decisions.

Ardeshir Ghanbarzadeh: And you're able to empower those business users to drill in any direction they want, so that they can identify inconsistency is fine root cause and ultimately take action.

Ardeshir Ghanbarzadeh: That is going to have an impact on business performance and and the third being again comes down to saving time and having.

Ardeshir Ghanbarzadeh: An speed of access to data and basically you're improving that efficiency and productivity, that is going to help shrink down that cross close cycle, because.

Ardeshir Ghanbarzadeh: 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.

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

Ardeshir Ghanbarzadeh: 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.

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

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

Ardeshir Ghanbarzadeh: You to connect directly to the source in core technology of direct data mapping actually maps all that data together so that you can easily.

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

Ardeshir Ghanbarzadeh: Data connectors you can practically connect to any.

Ardeshir Ghanbarzadeh: 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.

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

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

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: hcl processes and data aggregations that really slow us down well within quarter, they can actually eliminated all that and accelerate that time to value, so these capabilities, have been.

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

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

Ardeshir Ghanbarzadeh: and much of much of this recognition, and this is, you know we're very, very proud of this recognition, obviously, because there are only 20 analytics.

Ardeshir Ghanbarzadeh: Bi vendors that are recognized in the magic quadrant every year, but it was it came down to ultimately delivering that high power operational analytics with unlimited access at speed and scale.

Ardeshir Ghanbarzadeh: Eliminating the need for that transformation and reshaping of the data which is part of that whole it yellow T to process that slows things down.

Ardeshir Ghanbarzadeh: And and being able to analyze all the data, instead of just a subset of data, he key elements that help.

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

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: analytics data hub for finance and Ryan, please take it away.

Ryan Garrett: awesome thanks aren't sure hey team Ryan Garrett here first thing before we kind of jumped into the the quarter DEMO I just want to kind of think through a traditional clothes process.

Ryan Garrett: If you think about the traditional way that teams are 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.

Ryan Garrett: 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.

Ryan Garrett: 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.

Ryan Garrett: we're going to try and visualize that maybe a visualization layer

Ryan Garrett: If we just don't have kind of all of that information all that detail information we kind of have to go back to the back to the well, so to speak, to kind of.

Ryan Garrett: 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 closed process and kind of Brian's point is.

Ryan Garrett: 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.

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

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

Ryan Garrett: You know, bring data together, so 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.

Ryan Garrett: 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.

Ryan Garrett: 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.

Ryan Garrett: and actually drill all the way down to the transaction level detail.

Ryan Garrett: 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.

Ryan Garrett: 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.

Ryan Garrett: So as artistry I mentioned part of the part of the value of in Korea is actually being able to do.

Ryan Garrett: All of this workflow in a single platform being able to bring that data in and being able to you know not just bring the data in in a unified way, but actually visualize that in a unified way as well.

Ryan Garrett: And we can do that, 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.

Ryan Garrett: 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.

Ryan Garrett: 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.

Ryan Garrett: 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.

Ryan Garrett: 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.

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

Ryan Garrett: 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 with that cash conversion cycle.

Ryan Garrett: So I drill into this and so now, I can actually see what's happening.

Ryan Garrett: 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.

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

Ryan Garrett: 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.

Ryan Garrett: 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 here so So why is this happening.

Ryan Garrett: So if we drill down a little bit further here and we can see.

Ryan Garrett: Again, according to you know what we've set up, you know our our 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.

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

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

Ryan Garrett: 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.

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

Ryan Garrett: 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.

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

Ryan Garrett: 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.

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

Ryan Garrett: 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.

Ryan Garrett: And all of the data that he you know he's responsible for and the accounts that he's responsible for.

Ryan Garrett: And so what we'll see here is actually Herman is actually has one account and has costco.

Ryan Garrett: 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.

Ryan Garrett: 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.

Ryan Garrett: 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.

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

Ryan Garrett: 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.

Ryan Garrett: 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.

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

Ryan Garrett: 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 statements in order to close the books.

Ryan Garrett: 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 details.

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

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

Ryan Garrett: Within you know very short order without a whole lot of manual steps and extracts and file tables and pivot tables but we're able now to to actually transverse all the way through to an actionable insight.

Ryan Garrett: So that we can take an action and really shorten that whole manual process that happens during the course the clothes cycle.

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

Ryan Garrett: So just to kind of close things off here just to kind of reiterate the the process that we just walked through We walked through from you know, a you know high level detail general ledger.

Ryan Garrett: 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.

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

Ryan Garrett: And i'll pause there and and say.

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

Ardeshir Ghanbarzadeh: Thank you, thank you very much Ram write them.

Ardeshir Ghanbarzadeh: All right, folks.

Ardeshir Ghanbarzadeh: Alright, thanks again i'm just like why I mentioned will start looking at how we can slowly start to wrap things up i'm hopefully you know after Brian's great the.

Ardeshir Ghanbarzadeh: 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.

Ardeshir Ghanbarzadeh: One day, hopefully, will be the goal the inspiration or aspirational goal for the for the clothes process.

Ardeshir Ghanbarzadeh: And and and 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.

Ardeshir Ghanbarzadeh: That you have in your 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.

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

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

Ardeshir Ghanbarzadeh: More high IQ tasks and and initiatives within the organization um with that will let's let's go to a Q amp a will open up the Q amp a now if you have questions for Ryan for Brian or for myself.

Ardeshir Ghanbarzadeh: Please do type them into the Q&A we'll do our best to answer those questions for you.

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

Brian Kalish: 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.

Brian Kalish: Or to automate that system that you can do, 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.

Brian Kalish: 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.

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

Brian Kalish: It just slows down the whole process right, so the idea that either through moving towards more of a continuous close against.

Brian Kalish: You know front loading the activity as much as possible once the once you actually kick off the closing process.

Brian Kalish: 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.

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

Ardeshir Ghanbarzadeh: next question for you Ryan.

Ardeshir Ghanbarzadeh: You mentioned blueprints in during your DEMO up, can you elaborate exactly how they're used.

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

Ryan Garrett: I think that is the fruit is the question which ones that we have available or how do they work.

Ryan Garrett: Is I can answer both.

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

Ryan Garrett: 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.

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

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

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

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

Ryan Garrett: 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.

Ryan Garrett: 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.

Ryan Garrett: 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.

Ryan Garrett: 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.

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

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

Ryan Garrett: 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.

Ardeshir Ghanbarzadeh: awesome, thank you for that um next question is where can I can you get the the gartner magic quadrant report, the one that has been quarter was included in the 2022 you absolutely can.

Ardeshir Ghanbarzadeh: Please go to.

Ardeshir Ghanbarzadeh: In quarter calm and go to the resources.

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

Ardeshir Ghanbarzadeh: Well folks that brings us toward the end of today's session, please do join us for part.

Ardeshir Ghanbarzadeh: Three of our three part series, the final.

Ardeshir Ghanbarzadeh: The final section, which is about boosting working capital by harnessing the power of your data data will be on June 2 at 9am Pacific time 12pm.

Ardeshir Ghanbarzadeh: Eastern time, thank you for joining us today on today's webinar, thank you to our guests, Brian Kaish and Ryan Garrett for the presentation and the DEMO and we look forward to seeing you on our next session on our next webinar have a great day goodbye.

Presented by:

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Ardeshir Ghanbarzadeh

Director of Product Marketing

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Brian.Kalish-Headshot-V3

Brian Kalish

Principal at Kalish Consulting

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Ryan Garrett

Sales Engineering Manager

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