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Incorta Named for First Time in Magic Quadrant™

CEO Scott Jones shares insights on Incorta being named in the 2022 Gartner® Magic Quadrant™ for Analytics & BI Platforms. "The data warehousing paradigm can no longer keep up with the demands of today’s world."

Read Blog

Incorta Named for First Time in Magic Quadrant™

CFO's Transformation: From Old School to New Digital


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Abboud Ghanem: Welcome to this live panel session and webinar my name is Abdul Kalam i'm the senior Vice President for in quarter here in India.



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Abboud Ghanem: And i'm really excited to really welcome our guest speakers so they're going to be doing, most of the talking today experts in the office of finance.



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Abboud Ghanem: The agenda is really how you drive you know digital transformation and move from old school and to the new digital Lisa David banner and owner key capital advisors joining us all the way from the US, a highly say good morning to you.



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Abboud Ghanem: and running.



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Abboud Ghanem: And Brian Kia CIO at in quarter as well, good morning to you as well, and thank you for joining us both appreciate your time looking forward to today exciting discussion today.



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Abboud Ghanem: Our agenda is really simple so i'll do a very quick welcome and introductions and most of the time is going to be spent around discussions between.



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Abboud Ghanem: Lisa and Brian around you know their experience in the field and how they're helping organizations drive digital transformation.



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Abboud Ghanem: and drive real reliability and accuracy around data and analytics within quarter and our partnership with e capitals and we're also going to have 20 minutes open Q amp a session.



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Abboud Ghanem: And as a as a note, there are two functionalities here in the chat you have a chat box, you can chat live as we go along and there's also a Q amp a option, where you can share your questions and i'll be you know you know challenge these questions to our to our speakers.



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Abboud Ghanem: Why we here today.



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Abboud Ghanem: You know i've been in this field for over 10 years and worked with a lot of finance leaders across EMEA globe and.



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Abboud Ghanem: You know, recent discussion with one of the CFO with a big pharmaceutical companies said, are you don't know what's happening my business right now.



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Abboud Ghanem: He said I don't know what happened in my business a week ago, and said why he said, because we cannot extract the data from source in real time.



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Abboud Ghanem: He said, by the time I get the data is probably one through 10 different transformations I don't know how many excel workbooks so we call the excel help.



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Abboud Ghanem: He said, by the time I get the result, one is to eight and to I don't know if it's an accurate number, so we are running a business based on gut feel what we think is correct.



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Abboud Ghanem: So the question is, how can you change that What are the options available to you and that's going to be the focus file discussion today with Lisa and Brian.



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Abboud Ghanem: Just before I hand over to the speakers.



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Abboud Ghanem: In preparation for this session today, you know, as you know, gartner survey.



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Abboud Ghanem: CFO is across the globe asking about priorities for 2021 obviously there's no surprise that advanced analytics our PA upscaling digital transformation Ai is top agenda.



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Abboud Ghanem: However, as you see here it's also perceived as really, really difficult So if you want to drive advanced analytics capabilities and the office of finance.



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Abboud Ghanem: it's really hard work, or is it so that's going to be the compensation for today i'm very excited to welcome our CIO and cloud general manager at in quarter Brian here.



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Abboud Ghanem: Brian obviously i'm going to hand over to you to who's also an ex customer and he loved the technology so much he decided to join the company so.



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Abboud Ghanem: Brian first of all, again Good morning, and thank you for your time today i'd love to hand over to you quick introduction introduction to yourself and we'll take it from here.



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Brian Keare: Great thanks so much a bird really appreciate being here today.



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Brian Keare: Good morning from us in the United States good afternoon for those of you who are in different parts of the world it's really great to be able to come together and talk about the challenges that.



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Brian Keare: Companies are facing with their data analytics and business systems.



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Brian Keare: As you mentioned, before I came to incorporate a I was on the enterprise side, so I ran business systems it and data analytics for nortel, which is a global.



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Brian Keare: manufacturer and we'll get into the story about that, but the long story short, is that.



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Brian Keare: In korda being brought into our business at nor tech transformed our business and allowed us to solve problems in a way that we never could before and to really turn.



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Brian Keare: From an organization that was in reactive mode and to one that was in proactive mode and really drove.



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Brian Keare: Strategic decisions using data and a lot of that data and a lot of those insights came from in quarter, and it was so transformative.



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Brian Keare: That when the CEO and in court said hey Brian if you ever want to make the jump from enterprise to.



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Brian Keare: Technology company and join us we'd love to have you, and so I have, and you know I now have the opportunity to work with customers and prospects, who are considering their digital transformation journey.



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Brian Keare: and trying to figure out how to leverage some of the modern tools to really allow them to transform and to make data driven decisions.



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Brian Keare: One of the points of focus that i'm going to bring here today is my partnership with our CFO back at nortel because we had a CFO.



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Brian Keare: Who was able to partner with me and to get out of you know we're a public company, we had to close our books every month, there was a lot of pressure on the CFO his office.



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Brian Keare: And what we were able to do in the CFO his office is to transform that function from one that was also reactive, and that was focused, you know that spent so much of their time on closing their books and looking.



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Brian Keare: at putting a looking backwards and looking at how the historical reports were make sure that they were correct for the business and instead.



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Brian Keare: be a strategic partner to every department in the business and really empower sales operation supply chain, the folks that were looking at customer profitability and trends and really allowed the CFO his office to be.



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Brian Keare: A strategic partner that was able to leverage tools to really empower everyone in the organization so i'm going to bring that lens today and we're going I think we'll have a fruitful discussion, but I want to turn to.



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Brian Keare: Our featured guest and and a company that has really become a critical strategic partner to in quarter and that's E capital advisors i'm thrilled to be joined today by e capital advisors in, and in particular their owner and partner Lisa David.



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Brian Keare: E capital has they were with us from the beginning, and one of the things that I think makes our partnership at encarta with the capital so fruitful is that Lisa David herself.



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Brian Keare: is indeed a visionary she was she comes from a financial background and at E capital they help customers solve some of the thorniest problems they have, including in the office of finance and as Lisa will explain.



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Brian Keare: One of those problems had to do with being able to get data to the CFO his office in a way that they can actually make more strategic decisions and be more productive and performance and.



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Brian Keare: Lisa saw the light and she started working with in court and said gosh you guys are offering a solution that we cannot solve otherwise, or we.



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Brian Keare: Or we can only solve with a lot of manual work and a lot of Labor involved and so so Lisa partnered with encarta to fit.



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Brian Keare: incorporate into the ecosystem and we'll talk a little bit about that, but more than that.



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Brian Keare: E capital really has differentiated themselves in the way that they partner with their customers to really understand problems and opportunities.



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Brian Keare: at those companies and how and how you can drive successful solutions and so.



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Brian Keare: Lisa is not obligated to us in court at all, she brings the best solutions, no matter where they come from, it so happens that a lot of them, including corridor.



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Brian Keare: And we're very fortunate that is the case, and so we're going to talk about that a little bit more today.



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Brian Keare: Let me turn it over to you Lisa so that you can give a more you know even a more a little bit more about your background, and then we can dive into some of the slides and your perspective on the office of finance.



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Lisa David: Thank you, Brian good morning everyone as Brian mentioned i'm a partner and owner be capital advisors, I actually started my career in public accounting so i'm a CPA and started my career as an auditor with Arthur Andersen.



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Lisa David: and spent eight years in public accounting, you know at the audit level really in the manufacturing high tech manufacturing and consumer product industries across North America.



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Lisa David: For the last one for the last 19 and a half years i've been at E capital any capital really started as one of those with a product offering for the finance teams to really expand their range from.



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Lisa David: A two dimensional excel to a multi dimensional budgeting and forecasting applications, we knew that our finance teams needed to be able to run scenarios on their business and be able to do that quickly and without having a.



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Lisa David: very laborious effort so we've been on that journey for almost 20 years now, and of course that transitioned back in the day 20 years ago into.



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Lisa David: Traditional data warehouse business intelligence platforms and really that has that has been still one of our key pillars of E capital.



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Lisa David: With that pillar though our finance teams for for almost all of those 20 years until that some of the innovations that have occurred in technology.



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Lisa David: You know, they still desired more than even because one of those things about those type of applications is that we were showing variances and we were showing.



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Lisa David: Our forecasting at the aggregated level of our data, so we were you know we were forecasting.



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Lisa David: Products at the major category level, maybe product family level, but the insights that were needed to really transition our finance teams to that agility that Brian describes was really below that aggregate level so.



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Lisa David: Today, you know that journey has expanded and we're proud to be partnered with in quarter to really provide that insight, both at the aggregate.



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Lisa David: level and then that underneath at the at the granular level so we'll be talking about how that works today, but our passion is to partner with our customers as Brian described understand the business model.



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Lisa David: i'm passionate about understanding, like the Needle, as I call it the needle movers that really drive that operating income to the business.



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Lisa David: what's going to improve gross margin so we're really focused today and in 2021 and beyond on the modern enabling the modern day CFO.



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Lisa David: Really delivering that speed insight helping them align any any any lovers on the cost management side and then give them that financial efficiency as they continue to be a better value to their to their companies.



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Lisa David: To do that, just as a as a next slide highlights, are we believe these are kind of the pillars are lanes that e capital helps our customer teams, as I mentioned our 20 year journey started in the forecasting system side.



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Lisa David: And, and the business intelligence side what we're going to talk about today within core data is really adding this middle layer of the unified data analytics platform really to give us that top level KPI down to the penny and down to like exactly the answer of.



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Lisa David: What exact product or skew caused that variance yesterday, obviously, another passion of ours is really to help once we've got that set up.



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Lisa David: The business data science side we'll talk about that with some of our customer examples here.



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Lisa David: on how really once I have that data organized and that data platform I can enable those predictive and prescriptive algorithms to really drive even that enhanced business value.



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Lisa David: That our finance teams need in today's modern world and then of course throughout that entire process is the automation side.



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Lisa David: Which just helps us get data from where it's at in the source systems to enable these processes to occur.



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Lisa David: And, just like a dude mentioned on the gartner chart another recent study that just came out in August of 2021 work day did it 270% CFO.



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Lisa David: Inner interview survey process and published an indicator survey and in August of 2021 and in that data side.



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Lisa David: Really focused on what's important to the modern day CFO and if you see in the left here it's really kind of like what are they focusing on and the data foundation to drive these insights.



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Lisa David: And you know you'll see the list i'm sure it's familiar to some of you, in your organization, but really.



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Lisa David: Taking down those data source silos and really bringing that as a central view so I can make decisions comprehensively across the organization.



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Lisa David: on the right side you know the number one thing and it it that goes with the gartner charts that as well the number one thing that our CFO wants to invest in is to be able to achieve predictive analytics.



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Lisa David: And machine learning we'll talk about that as we get into some of the case studies on how adding a data hub really helps enable that and speed up the process to that predictive analytic side.



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Lisa David: This kind of presents are our ecosystem, today this this architecture, would have been very different 20 years ago.



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Lisa David: So as Brian mentioned, you know we started our journey with all of our customer teams had the left, the left side so everybody has their earpiece systems.



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Lisa David: Your CRM systems, some of you know that's obviously had a massive transformation now from on premise to cloud source data sources, you may be investing in.



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Lisa David: Data lakes, but that everybody's kind of got there, the left side of their our customers are unique to each and every one of the companies we work with we started as I talked about 20 years ago we started on the right side of this architecture slide really with those.



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Lisa David: Planning forecasting systems consolidation systems and business intelligence and reporting tools.



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Lisa David: What we found is there was still a need for deeper analytics between these between this area, and I mean.



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Lisa David: You know 15 years ago that conversation would have been okay we're going to.



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Lisa David: extract and transform data from the sources and build a data warehouse and basically design a star schema and then put a visualization layer on top.



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Lisa David: Well, within core data, we were able to go to a whole nother chapter, and that is in just our data at from the source systems, all the way down to the granularity level of.



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Lisa David: Every single transaction, and that that data source and then provide that analytical lens so that you can have that daily insight so it's not as you described Brian it's not too late to act on so that's really been transformative in our ecosystem of adding this capability in the middle.



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Lisa David: We spent a lot of time in that in that area today.



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Brian Keare: yeah I mean if I chime in there, I mean we've got a slide later that goes into a little bit more about exactly how encoded does that, but just as a perspective.



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Brian Keare: You know, one of the things that is different about in quarter compared to the atl and maybe a data warehouse or data lake process that you're talking about.



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Brian Keare: Is that encoded ingest the data, exactly as it is in the source system and there's no transformation involved in, as a result you don't have things that break you ingested into the source system, and you can begin doing the analytics.



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Brian Keare: immediately so we'll get we'll talk a little bit about that later, but I mean this is a fascinating slide so you're talking about the older version was you'd have your data sources, and then you figure out how to layer on the.



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Brian Keare: Reporting planning consolidation and counting tools, on top of those and integrate those into those let's talk about a few of them so tablo power bi if you have a customer that has investments and licenses in those how do they think about where inquisitive fits into that ecosystem.



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Lisa David: yeah and quarter fits as a great I call it the the the powerful engine underneath of tablo and power bi so one of the things with.



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Lisa David: tableau and power bi is, if you get to the large amounts of data that you want to analyze you could get it, it really struggles in that performance area so.



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Lisa David: In corner really provides the ability for our customer teams to have millions, if not billions of transactions in encarta and use power bi and tablo is that single version of the truth.



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Lisa David: And and look at that data through the lens of those visualization tools, but they're at the data is actually sitting in in quarter so it's really that powerful engine.



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Lisa David: underneath of it to really give you that stability of the other version of the data and then have that the speed of which it can process is unbelievable I mean we have customer teams.



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Lisa David: That you know, in the retail side might have 6 billion transactions processing in in quarter in like in point two seconds, and then they can see it through power bi or tablo.



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Brian Keare: So, give me a flavor you know I mean i've been in orgs I mean I know the answer, but I want to hear your perspective as our partner.



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Brian Keare: has been an orange where you're where somebody creating a power bi report says, you know either a it's running really slowly Can you help or be I do not see be I do not see.



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Brian Keare: The data that I need I can't pick it out of the list of fields, so and that's a problem that might take a really long time how does in court, a transform that equation for somebody who's used to instant gratification or who wants to reduce those cycle times.



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Lisa David: And that's what's really powerful that the person who's you know struggled with I call it the cup of coffee report as you're describing so they can go make a fresh pot of coffee why that report is running.



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Lisa David: and come back in it, hopefully, has has run by the time they get back to their desk.



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Lisa David: And corner really becomes like I said that powerful engine and the person using.



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Lisa David: That report through the lens of power bi doesn't even know that that they're actually looking at the data in in quarter, if that is.



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Lisa David: The engine is running at speed in in court up but i'm using i'm directly showing it through the lens of power bi so that I eliminate those pains that you're describing.



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Lisa David: But the for the casual user may not, they would just see it as it as a day in the life normal from a power behind tablo perspective.



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Brian Keare: Right, I mean I think one of the ways that we think about it is that your your source systems on the left are what are they built for they're built for processing transactions that's what they're optimized for.



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Brian Keare: Adding transactions one, at a time into the database, making sure that you can get them into the database.



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Brian Keare: They are not designed to be optimized for taking all of those rows and aggregating them and doing complex calculations on the fly so one of them what things and reasons why in quarter was formed, it was formed in founded.



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Brian Keare: From folks that came out of Oracle that is optimized for as a you know, a database or a transaction processing system and they said, we want to optimize the system.



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Brian Keare: built for analytics so we're going to take that exact same data and we're going to put it into in quarter and have it ready for instant analytics.



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Brian Keare: for end users and so that's really the reason why it can go through and pick out any field, you want.



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Brian Keare: Do aggregations over billions of records and not really have the kind of cup of coffee lag that you're talking about and so that's one way to think about it.



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Brian Keare: So going down, I mean I you know i've worked with our CFO I know we had adaptive insights and one stream in our organization.



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Brian Keare: I know what those do you know, especially on the consolidation level, can you can you paint me a picture of kind of a before and after.



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Brian Keare: With with those you know with those two tools, in particular, before you have incurred a how is a CFO his office using adaptive insights in one stream and then, how does incorporate a change the nature of how an office of finance would interact and leverage those those tools.



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Lisa David: yeah so like as I, as I described, you know this is how he capital started was really in this lane of helping our finance teams with these type of solutions.



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Lisa David: But the solution and again these solutions add great value at helping our customers run multiple you know monthly forecast quarterly forecast rolling forecasts.



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Lisa David: And can you know, consolidating our financials at the end of each month in order, but one thing about the foreign, especially in particularly the forecasting process is that.



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Lisa David: I would never advise you to we would never set up a forecasting system at that, like skew level at you know the product level, it would be.



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Lisa David: aggregated up a level so that you could understand it, because if you're doing a bottoms up forecasting process it's got to be something that the person can provide input to.



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Lisa David: At maybe the category level, and so, then actually what happened from the earpiece systems, and we would run variances to that to that forecast and again that's still a you know, a.



00:22:56.430 --> 00:23:07.980

Lisa David: needed process and an organization, however, one of the things that was missing is in what these eat you know performance management or up on platforms is our customer teams always wanted.



00:23:08.790 --> 00:23:22.620

Lisa David: I want to drill down all the way and see exactly which customer caused this issue, our customer profitability, which one all the questions operationally on your data at the agility level.



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Brian Keare: And maybe that number doesn't quite look right.



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Lisa David: or.



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Brian Keare: You know boy that jumped this month, why did it jump and you know the old process might be.



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Brian Keare: Oh, I only have the summary number somebody please run report give me an excel spreadsheet with everything and hopefully they reconcile to that number but, but what is in quarter to allow you to do there.



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Lisa David: And Clara encarta allows you to have that granularity because, again, as you described we've adjusted those source systems at at the lowest level those tables exist, and those are systems.



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Lisa David: And then i've met i've mapped to business schema and lens so that I can answer any of those questions at the granular level, so you know, one of the areas you know that our customers, want to see is like.



00:24:08.550 --> 00:24:19.590

Lisa David: Especially our retailers skew location day information both at their e commerce platforms, as well as their store level and, again, you could answer a question.



00:24:20.160 --> 00:24:24.300

Lisa David: In with a forecasting system or an end a reporting system traditionally of.



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Lisa David: Okay, what was the store revenue in this category yesterday, but I would not have gone down to the like.



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Lisa David: Oh, this is the exact color size style That was the revenue for that store yesterday so that's the granularity that we can have an encoder at that skew level.



00:24:44.430 --> 00:24:49.620

Lisa David: The supply chain level the inventory level and see it at that level of detail.



00:24:49.980 --> 00:24:59.070

Lisa David: And so that you can act on it faster the other The other thing our customer teams described as you mentioned the the former engineers, or the engineers.



00:24:59.310 --> 00:25:11.550

Lisa David: of import a really came from an oracle engineering background at the VIP level, so they couldn't like, for instance, some like sub ledger accounts receivable they may be, could run every every two weeks, because it was just took so long.



00:25:11.580 --> 00:25:23.460

Lisa David: To get that day out, they now can see it every day another example of that is like being able to see like days sales outstanding by department at a global level that was difficult to do.



00:25:23.820 --> 00:25:31.470

Lisa David: Any like you like you mentioned Brian it's nothing against these data sources, they were architected in a way to process transactions.



00:25:31.680 --> 00:25:44.160

Lisa David: They were architected intentionally in that way, not for us to do, analytical insights quickly and fast and that's what in quarter really brings and complements the investments that you've already made.



00:25:44.520 --> 00:25:54.960

Lisa David: In planning tools and reporting tools that's The other thing that I love about it, is it it it adds new value but it doesn't it doesn't take away from the investments that you've made.



00:25:55.290 --> 00:26:06.480

Lisa David: In in those other systems, our customer teams are comparing those bottoms up forecast with, then what what a machine forecast can tell us in in quarter.



00:26:06.930 --> 00:26:14.700

Brian Keare: gotcha so we're going to get to a couple case studies in a moment, but I have a couple more question just a couple quick questions on on this slide, but I think.



00:26:14.970 --> 00:26:22.530

Brian Keare: You know our case studies are going to bring some of the things that you've mentioned to life, I believe, but let's go back just just for a second.



00:26:23.490 --> 00:26:35.610

Brian Keare: If you go, so we just a couple more questions on on this, so I see robotic process automation we saw that in the gartner chart We saw it in your CFO survey.



00:26:35.970 --> 00:26:46.200

Brian Keare: You know we're hearing about that get tell me a little bit about what that is and how CFO and the office of finance should be thinking about that, and how that fits into this ecosystem.



00:26:48.600 --> 00:26:54.450

Lisa David: yeah I love I love robotic process automation as well, so what right, but what it really does, is it really.



00:26:54.870 --> 00:27:05.880

Lisa David: takes away that manual process of going to get data and moving it from where it exists into where I needed to be so from from as we're talking about with our data hub and encarta.



00:27:06.210 --> 00:27:12.690

Lisa David: I may, I may have data I might have you know 20 subsidiaries across the country that have you know.



00:27:13.170 --> 00:27:21.120

Lisa David: Their own operating systems and they're in different locations in different countries, I need data from all of those may be related to inventory.



00:27:21.390 --> 00:27:25.410

Lisa David: And I needed fat and it used to be a process where somebody as an analyst had to go.



00:27:25.830 --> 00:27:35.880

Lisa David: To go get excel files from every one of those systems and bring it together and aggregated to have that combination, I now can have a software robot go get.



00:27:36.330 --> 00:27:48.840

Lisa David: Go get that data and automate that process, so I take away that painful our hours that it took to do all those manual processes so it's like if anything can, if a data process can be mapped it can.



00:27:50.460 --> 00:28:00.180

Lisa David: In most cases, be automated and it just speeds up the efficiency of this entire data movement of here's where I needed it from and here's where I want it to be so.



00:28:00.630 --> 00:28:07.560

Lisa David: You know you see on the left over here external data sources that's become another huge opportunity for our customer teams is no longer.



00:28:07.860 --> 00:28:20.760

Lisa David: Some of our industries, we not just the internal data is necessary, I want to see external data, so I can automate wherever that external data is at to get it into my hub as well, so that I can use it from that analytics layer



00:28:21.210 --> 00:28:34.140

Brian Keare: Right so get get the people in the office of finance, out of the business of late nights and weekends wrangling data and a lot of manual work and more into functions.



00:28:34.380 --> 00:28:51.690

Brian Keare: That really use their value added and where the analysis and higher level functions can can happen so that's great okay last question before we go to the case studies is you know gartner your CFO slide talks a lot about machine learning and Ai.



00:28:52.710 --> 00:29:07.020

Brian Keare: Are you saying that we can have a system without having to turn to a different kind of tool we have a system here that can open up the pandora's box of some of that and allow us to play with some predictive analytics and we're going to hear about that.



00:29:07.590 --> 00:29:11.220

Lisa David: We are going to hear about that you'll see some more things coming up in our case studies, but.



00:29:12.090 --> 00:29:23.160

Lisa David: Our art Yes, our data science team loves partnering in quarter with the process of adding those use cases in predictive and data and machine learning, because as they describe it.



00:29:23.430 --> 00:29:32.880

Lisa David: It once I have the data in a court in a system like in quarter, I am one stop shopping for the data science team and it just speeds up the process so they're able to.



00:29:33.180 --> 00:29:40.950

Lisa David: tackle that data science use case so much faster, because the data is organized organized in that hub ready to go, it also has.



00:29:41.220 --> 00:29:54.330

Lisa David: That the hub has built in Python and spark capability so then depending on the algorithm our team needs to use they'll use that algorithm to provide that insight, so our customer teams, you know, for instance, we have.



00:29:55.560 --> 00:30:02.100

Lisa David: You know, a credit union that you leverage is the hub and their data science team, then, was able to them.



00:30:02.970 --> 00:30:14.160

Lisa David: A number, a number of private label credit cards, and they are able to do predictions on customer inactivity so that they get line their marketing costs correctly they're also.



00:30:14.610 --> 00:30:27.030

Lisa David: able to do predictions on credit card charge offs, so any of those data science use cases it just makes our teams just makes the team faster and enable that business data science for actions.



00:30:27.870 --> 00:30:31.320

Brian Keare: cool alright let's jump into the case studies.



00:30:32.430 --> 00:30:43.290

Abboud Ghanem: So I just want to maybe just follow up I know Lisa a common question here is around you know traditional route data warehouse you know it's in.



00:30:43.680 --> 00:30:57.270

Abboud Ghanem: In a project that never ends right, the other day of speaking CFO Oil and Gas Company said, our data warehouse projects been going on for for three years and me as a CFO I haven't got my data in one place so.



00:30:57.660 --> 00:31:01.350

Abboud Ghanem: And what you've been talking about is that whole transformation time to value.



00:31:01.710 --> 00:31:11.220

Abboud Ghanem: In your experience, you know how long these projects, taking you know when you go and deliver this kind of the smart data hub, as you sell within quarter, for your customers.



00:31:11.490 --> 00:31:17.250

Abboud Ghanem: Now, if you didn't maybe give a perspective around the timeline they didn't have any of these projects out that'll be fantastic.



00:31:17.760 --> 00:31:25.530

Brian Keare: yeah I mean Lisa Lisa has a broader view i'll tell you i'll tell you the Nordic view we were in the process of.



00:31:26.220 --> 00:31:44.130

Brian Keare: implementing what I will what we call our data warehouse version two point O project, and you know fancier more sophisticated etfs tools, a better data warehouse layering on some of these reporting tools, on top of it we budgeted.



00:31:45.270 --> 00:31:54.210

Brian Keare: More than a couple million dollars and eight to 24 months, you know scope creep we weren't sure if it was going to go for me to 24 months.



00:31:55.500 --> 00:32:11.250

Brian Keare: As an alternative we ended up you know in quarter ended up coming across our radar we proved out the value in a weekend and in fact we had our first beta of the quarter system live in.



00:32:11.910 --> 00:32:21.030

Brian Keare: At North tech in four weeks my CEO said when he saw the beta the iPad that I gave him with the recorded dashboards.



00:32:21.540 --> 00:32:32.760

Brian Keare: He said nope this isn't beta we're going with this, this is live, and I think that that's you know, that was a land speed record at nor tech for any business system enterprise wide.



00:32:33.450 --> 00:32:41.490

Brian Keare: To be able to implement it astounded everybody, and I think it's reflective of I mean, I think we were probably on the short end.



00:32:42.330 --> 00:32:50.190

Brian Keare: But you know one to two months is typically what we're seeing for getting started with a couple core use cases.



00:32:51.000 --> 00:33:06.870

Brian Keare: Within quarter and so it's it's pretty transformative and you know we don't find the kind of scope creep and the kind of like oh my gosh i'm 12 months in and I need to redesign it because so much has changed in my business in those 12 months so Lisa what's your perspective there.



00:33:07.860 --> 00:33:12.960

Lisa David: yeah i'm glad you asked a question of dude I kind of almost forget forget that to talk about that.



00:33:12.990 --> 00:33:13.740

Abboud Ghanem: that's why i'm here.



00:33:15.000 --> 00:33:15.690

Lisa David: Thank you for that.



00:33:15.960 --> 00:33:19.620

Lisa David: yeah that's one of the other things that I love is the speed.



00:33:19.680 --> 00:33:29.100

Lisa David: To Roi value, I have a major retailer that last year, wanted to do add this insight like I said at the skew location day information.



00:33:29.400 --> 00:33:43.140

Lisa David: And it's something that their bottoms up forecast what took them two months two weeks to produce in the current month to even have a lens of that forecast so in the holiday season last year and eight in eight weeks we help this retailer.



00:33:44.430 --> 00:33:51.180

Lisa David: ingest the data into and coordinate that skew location day information provide those outlier dashboards and produce 15 to 20.



00:33:52.200 --> 00:34:03.180

Lisa David: executive dashboards in in like I said eight weeks and that's that's The thing that I love about again compared to the old, traditional data warehousing projects are i'd say the modern data lake projects.



00:34:03.420 --> 00:34:11.610

Lisa David: That are like two to three years and they still don't have the value or how they're going to get data out fast that's the speed of which you're able to put this.



00:34:12.090 --> 00:34:28.170

Lisa David: get this hub set set up in your in connected to your data and start to drive insights that you can act on is tremendously different than traditional data warehouse systems and I love that because, if we can have that we can have the insight faster and be able to act on it.



00:34:28.830 --> 00:34:36.990

Abboud Ghanem: yeah Thank you, thank you both you both mentioned something maybe it's worth elaborating on before we move to the next slide you mentioned that.



00:34:37.350 --> 00:34:53.040

Abboud Ghanem: Dealing with new business questions ad hoc questions new requirements tell us a bit more maybe you know, Brian in the pre pre in quarter life, how would you do that, you know as a core central function supporting your business and how do you change that within quarter.



00:34:53.400 --> 00:35:00.990

Brian Keare: Well let's let's let's go forward a couple of slides to the you know the quarter architecture slide and I think we can give a frame of reference there.



00:35:01.500 --> 00:35:01.830




00:35:08.010 --> 00:35:22.590

Brian Keare: So you know, this is very similar to what you know what Lisa showed right so you've got all your source systems on the left and you've got now you have encarta.



00:35:23.130 --> 00:35:36.390

Brian Keare: In the middle and you've got your users who either can use in quarter visualizations or as Lisa mentioned, they can use the visualization tool that they already are using.



00:35:37.860 --> 00:35:43.800

Brian Keare: What was you know so so what happens here as we talked a little bit about.



00:35:45.060 --> 00:35:58.680

Brian Keare: What incorporated and one of the value props have incurred a is show me your data in your ear rp or in your data lake and I will suck it in doing cordell replicated exactly as is, and I can start.



00:35:59.100 --> 00:36:16.470

Brian Keare: Doing analysis on it almost immediately, so you know we have customers that take data from netsuite they can put hundreds of millions of transactions in in an hour and immediately start doing operational analytics on top of it.



00:36:17.970 --> 00:36:23.880

Brian Keare: For me, when I first heard that that was the value prop of in court, I actually didn't believe it because i'd been living.



00:36:24.240 --> 00:36:33.930

Brian Keare: The pain before so to your question, what would I do before I had seen what you have to do to any rp data, you have to take it.



00:36:34.170 --> 00:36:41.790

Brian Keare: You can't just suck it in you need to actually write complex sequel queries on it, you need to create your star schema you need to flatten it.



00:36:42.120 --> 00:36:47.550

Brian Keare: You need to normalize it and then you need to put it into a data warehouse, then you need to go through the process of.



00:36:47.820 --> 00:36:56.580

Brian Keare: You know, oh, I have a rp data, and I want to join it with even as something as simple as a spreadsheet, then I need to figure out how that goes together, I need to flatten it again.



00:36:57.210 --> 00:37:03.450

Brian Keare: And that's not even good enough, because if you want to avoid the coffee time.



00:37:04.170 --> 00:37:14.460

Brian Keare: issue the coffee cup issue that Lisa was talking about, you need to build cubes or data marts or something that the visualization engine can.



00:37:14.970 --> 00:37:25.740

Brian Keare: can talk about or can relate to in a way that doesn't require you to wait for an hour or 12 hours overnight in order for that to happen so within korda.



00:37:26.520 --> 00:37:30.000

Brian Keare: That is transformed, and I think that you know, one of the things.



00:37:30.300 --> 00:37:41.730

Brian Keare: That we like to say to our customers is you really need to see it with your own data to believe it and the good news is that you can see it with your own data in a couple hours standing up a cloud trial of encarta.



00:37:42.450 --> 00:37:44.850

Brian Keare: Getting your data in there and and.



00:37:45.630 --> 00:37:55.830

Brian Keare: And then seeing how it works, but the fact of the matter is you've probably already replicated your source system data in court, so if you have a question that you haven't answered before.



00:37:56.100 --> 00:38:05.430

Brian Keare: You, it is probably already in in court and all you need to do is to drag it onto your onto your visualization tool.



00:38:06.060 --> 00:38:15.240

Brian Keare: In order to begin reporting against it, if it doesn't happen to be in encarta than the process is probably only a couple of minutes or maybe at most an hour.



00:38:15.450 --> 00:38:24.330

Brian Keare: To get it into encarta and you don't need to go through all those steps that I talked about in order for it to be ready for you to do your reporting on in the office of finance.



00:38:24.810 --> 00:38:28.740

Brian Keare: If you think about it, that transforms what you're doing instead of saying.



00:38:29.010 --> 00:38:43.350

Brian Keare: You know, instead of going to your it department, and you know and say Sally hey i've got a problem I can't see this, I need to report to my boss or up to an executive meeting about this, how quickly, can you do it and Sally says well our backlog is six weeks, Brian.



00:38:44.340 --> 00:38:56.220

Brian Keare: How important is this maybe I can get it down to a week, but our backlog is normally six weeks in quarter transforms all that, because it is probably there, and if it's not there it's a matter of.



00:38:56.610 --> 00:39:02.940

Brian Keare: An hour for you to be able to have that in your reporting system and so it's really, really transformative and so.



00:39:03.450 --> 00:39:15.150

Brian Keare: And so I think you get a little bit of an idea, this is similar to the slide that Lisa showed and it kind of sets up our case study we say anything you want to you want to add here before we go to the north tech case study.



00:39:16.740 --> 00:39:19.020

Lisa David: Now i'm doing it really just allows as you.



00:39:19.620 --> 00:39:29.070

Lisa David: As you asked originally our cut our finance teams to really transition their mindset and allow that data curiosities scale, so our customers described scenarios of like.



00:39:29.340 --> 00:39:36.660

Lisa David: Before they had this capability and one of their executives would ask a question, particularly maybe about an outlier or like a vendor.



00:39:37.020 --> 00:39:42.600

Lisa David: You know performance of give me a custom time dimension that it before having this.



00:39:42.930 --> 00:39:53.580

Lisa David: In your architecture that would have taken the the analysts teams, you know you know bunch of time jockeying spreadsheets together to answer that specific question.



00:39:53.910 --> 00:40:05.280

Lisa David: And that's how like if you, if you remember, like in the traditional business intelligence world we ended up with like customer teams having like 15,000 hard coded queries of one particular question.



00:40:05.640 --> 00:40:13.710

Lisa David: Well, that question changes all the time, so our customer teams now can answer that question in five minutes, with a platform like this, they don't have to have that.



00:40:14.070 --> 00:40:25.140

Lisa David: You know the days and then and then maybe only even the aggregated level to answer back to that key executive it's that's really exciting from a standpoint of you can see it fast and then act on it.



00:40:26.070 --> 00:40:33.120

Brian Keare: So one more point before we jump into the North case study is that you know this again shows that encarta is.



00:40:33.840 --> 00:40:43.860

Brian Keare: The one platform that upon which you can do both operational analytics as well as predictive analytics and using machine learning and Ai and as Lisa mentioned.



00:40:44.550 --> 00:40:48.600

Brian Keare: You know, the question is why well that data is already in.



00:40:48.870 --> 00:41:00.420

Brian Keare: The system you're using it may be primarily for operational analytics but the data that you use for operational analytics is already there you don't need to grab new data and put it into a data science tool.



00:41:00.720 --> 00:41:08.130

Brian Keare: And then build your model and then test it out against your operations throw it back over the wall to your operational people.



00:41:08.520 --> 00:41:19.770

Brian Keare: It is already in an analytics optimized engine, and so you can do your predictive models on top of that same data subset of that data test your models do your aggression fits.



00:41:20.040 --> 00:41:27.420

Brian Keare: And then put them into production without ever having to leave in court and that's really transformative will see a couple of examples of that.



00:41:29.130 --> 00:41:31.740

Brian Keare: In the case studies alright so let's go to North tech.



00:41:31.770 --> 00:41:36.000

Abboud Ghanem: Talk about what our Ryan, I think one of the questions coming through I wanted to ask you.



00:41:37.140 --> 00:41:41.850

Abboud Ghanem: You know quite often given your background within the it side right.



00:41:42.960 --> 00:41:53.220

Abboud Ghanem: And there's a big worry around self service around you know losing you know, governance and security, and you know, not knowing, you know how these dashboards are being built and.



00:41:53.520 --> 00:42:03.840

Abboud Ghanem: And even in my experience dove into a lot of customers in previous roles that we had before joining in quarter, we were saying we don't know where these dashboards coming from what's the underlying.



00:42:04.230 --> 00:42:13.200

Abboud Ghanem: Data source, how many excel files have been put together, whether you know the the excel workbook has been updated or not, we have thousands of those floating around so.



00:42:13.710 --> 00:42:20.910

Abboud Ghanem: You know, tell us from your experience how did you address that in in court and how incarnate customers addressing that today.



00:42:21.450 --> 00:42:30.930

Brian Keare: Well let's let mean there's a bunch of there's a bunch of aspects of that so when you think about the old way you're worried about stale data, as you said.



00:42:31.830 --> 00:42:45.330

Brian Keare: you're worried about matching it to the source system and do the numbers tie and you mentioned security and who can see what right all of those are potentially problems.



00:42:46.500 --> 00:42:58.950

Brian Keare: Because you know deal with the last one first if you've got your security once it's in a spreadsheet and once it's out of my solar system which might have permissions and roles and it's in an excel spreadsheet.



00:42:59.460 --> 00:43:08.700

Brian Keare: She how are we managing roles and permissions now well it's probably manual who gets to see the spreadsheet am I worried about putting it in a shared drive.



00:43:09.030 --> 00:43:19.770

Brian Keare: And figuring out, who has access to that or not, who gets on the distribution list of excel you know of your of your email if you're emailing it out those are problems right.



00:43:20.490 --> 00:43:26.070

Brian Keare: Including to solve that because in quarter was built with security row level security.



00:43:26.610 --> 00:43:39.060

Brian Keare: From the outset, and what we do is we mimic the roles and permissions security of this order system directly and in quarter, so if you are a sales REP and you're only supposed to see your accounts.



00:43:39.870 --> 00:43:50.340

Brian Keare: Then, when you go into encarta whatever the role and permission is of the source system gets replicated inside of in court, and we know that if the CFO is allowed to see.



00:43:50.700 --> 00:43:59.310

Brian Keare: a bunch of stuff with respect to you know, are you have payroll department and you want to do some analysis on that we can respect the same.



00:43:59.820 --> 00:44:06.360

Brian Keare: permissions and roles that are in the source system so that only the people who are logging in doing quarter to have permission to see.



00:44:06.600 --> 00:44:15.570

Brian Keare: payroll can actually see those numbers, and that is very different from putting them in an excel spreadsheet and having that out in the wild and having to worry about.



00:44:16.080 --> 00:44:23.880

Brian Keare: The security about that matching to the source system is really you know we've all had questions where did you get this number.



00:44:24.780 --> 00:44:29.910

Brian Keare: On this spreadsheet and you know i've been in meetings where the first half of the meeting is debating.



00:44:30.450 --> 00:44:39.150

Brian Keare: about whether the calculation is correct, whether it's up to date and my number from the CFO his office is different from the supply chain number.



00:44:39.450 --> 00:44:45.120

Brian Keare: or different from the sales guys number, and so you spend half of the meeting figuring out.



00:44:45.840 --> 00:44:52.590

Brian Keare: who's number is correct and where did you get the number and what cell in the excel spreadsheet what's the formula that you use and it's different than mine.



00:44:53.040 --> 00:44:58.200

Brian Keare: So in cordova really transformed the nature of all that, because.



00:44:58.980 --> 00:45:07.830

Brian Keare: You know, because of two things first the data is not has not gone through those transformations has not been subject to ad hoc calculations.



00:45:08.160 --> 00:45:18.120

Brian Keare: You don't have to bring in the guy who wrote the sequel code that created that number, because the number matches exactly to the source system and the second thing is.



00:45:18.360 --> 00:45:26.400

Brian Keare: As you agree on those common formulas and the common way of viewing the environment, you know you might have 10,000 fields in the.



00:45:27.030 --> 00:45:40.440

Brian Keare: In your source system, but in quarter allows you to create a semantic layer that says well supply chain, you only need to see these hundred 50 fields and so we're only serving up 150 fields for you to build your own reports.



00:45:40.710 --> 00:45:49.080

Brian Keare: The finance Office here are the fields that you care about, and you can even you can even segment it by role within that department.



00:45:49.440 --> 00:45:57.210

Brian Keare: And so, and on top of that, you can say once we have common definitions, for you know how are we really calculating.



00:45:57.420 --> 00:46:08.850

Brian Keare: Gross margin, you know, a gross margin calculation or something like that you put that inside of incorporate once you agree upon that everyone uses that field, the same calculation.



00:46:09.150 --> 00:46:17.580

Brian Keare: and puts it on to their dashboards or tables are insights and so all of those things mean the right people are seeing the right data.



00:46:17.910 --> 00:46:39.000

Brian Keare: The everyone is using common calculations and the numbers tie exactly back to the source system, and so are and that really changes the nature of self service once you do that, you can empower people to really do self service in a way that they hadn't before and that's really transformative.



00:46:39.450 --> 00:46:50.790

Lisa David: Up dude I said it's an auditors dream because, whether you're an internal auditor or an external auditor imagine that you actually can prove the top level KPI down to the past, so instead of this like.



00:46:51.060 --> 00:46:56.400

Lisa David: assumption that it's a national account that caused the problem you can be down to the transaction.



00:46:56.820 --> 00:47:09.180

Lisa David: To the penny, and so you it's like an auditors dream and then to the enablement of self service The other thing that I love is that it's a palatable design and build it's a it's a very easy it's easy to use.



00:47:09.900 --> 00:47:21.990

Lisa David: ui it doesn't take somebody that knows how to somebody with sophisticated coding skills to design a report, I mean I have customer teams, where the CEO asked a VP and finance a question.



00:47:22.380 --> 00:47:29.880

Lisa David: And they were able to in five minutes answer that question themselves with the self service capability and that's something that.



00:47:30.270 --> 00:47:41.430

Lisa David: Even though legacy business, and I mean it takes a skill set that is above a lot of people's finance skill set to answer to design in a lot of reporting tools, this is true self service as well.



00:47:42.090 --> 00:47:44.070

Abboud Ghanem: yeah let's jump into the case studies to bring this to.



00:47:44.100 --> 00:47:45.480

Abboud Ghanem: Bring bring some of these to life.



00:47:46.290 --> 00:47:49.230

Abboud Ghanem: And now you're able to jump on the case study Brian ego.



00:47:49.830 --> 00:47:54.570

Brian Keare: yeah there you go, so all right take security and control, this is where I ran data.



00:47:55.710 --> 00:48:02.280

Brian Keare: Data analytics and business systems for five plus years global manufacturer.



00:48:03.630 --> 00:48:18.900

Brian Keare: You know multi country multi currency, we have lots of different brands, we were very acquisitive, and so what were some of the challenges here, well, we acquire a company, it would have a different era P they'd come to me and they'd say Brian.



00:48:19.920 --> 00:48:35.430

Brian Keare: You know, we need to integrate it into netsuite which was our rp of choice, you know that's a three to six month effort you know CFO how do I get visibility into my acquired company and do some consolidated reporting in advance of that.



00:48:36.480 --> 00:48:48.630

Brian Keare: So how do I move beyond monthly close operations and actually become more strategic as a office of Finance in our organization.



00:48:49.680 --> 00:48:58.650

Brian Keare: Can I implement some of these predictive analytics that that you know gartner and Lisa were talking about in order to really drive.



00:48:59.550 --> 00:49:03.780

Brian Keare: Some better some better functionality in the office of finance.



00:49:04.080 --> 00:49:13.140

Brian Keare: And then we had kind of a crisis because we were implementing this during the China tariff trade war which had huge effects on our business, which was.



00:49:13.410 --> 00:49:17.610

Brian Keare: We had a supply chain that was focused on China, and so a lot of our skews were impacted.



00:49:18.060 --> 00:49:25.410

Brian Keare: So these were all in the back of our mind as we were designing data warehouse two point O, or what became in quarter.



00:49:25.740 --> 00:49:36.930

Brian Keare: So if you go to the next slide you talk about some of our business outcomes well you know what would have taken six months to integrate into netsuite.



00:49:37.830 --> 00:49:50.730

Brian Keare: We were able to do to only take two weeks and have consolidated reporting that was really granular in level for our CFO How did we do that, well, we sucked in the European data of the new of the.



00:49:51.000 --> 00:50:00.150

Brian Keare: New acquired company we created a mapping table between the chart of accounts of our master chart of accounts and the acquired company.



00:50:00.510 --> 00:50:06.390

Brian Keare: And only a few more things that we needed to do and we're able to all of a sudden have consolidated reporting.



00:50:07.320 --> 00:50:17.490

Brian Keare: My CFO his eyes popped out of his head, because he came to me, and he said, you know what you've done, I see some common customers here then i'm able to do historical reporting on.



00:50:17.760 --> 00:50:28.080

Brian Keare: Between the acquired company and the company that I have, and I can go back years and years, by the way, remind me when I integrate that acquired company into netsuite.



00:50:28.410 --> 00:50:38.670

Brian Keare: you're not going to be able to bring over the historical transactions are you and the answer is no you're not that's actually way too difficult to do you only kind of looked on a go forward basis.



00:50:39.510 --> 00:50:52.350

Brian Keare: And so, he said gosh I actually prefer this because I can do, common customers common items between our company and what we just acquired and I could do some real analysis that would have been really painful to do.



00:50:53.100 --> 00:51:03.420

Brian Keare: Otherwise, so huge win with our CFO be and, and that was because of the speed insight of encarta predictive analytics we were able to really quickly.



00:51:04.560 --> 00:51:18.270

Brian Keare: put to you know implement some use cases that that looked at churn risk of customers and also looked at, we predicted when they were going to pay us back based on past.



00:51:19.230 --> 00:51:23.430

Brian Keare: Past dates that they had submitted payment for invoices.



00:51:23.730 --> 00:51:37.290

Brian Keare: And what did it do it freed up the office of finance from having to send out those dunning letters or make those calls because we knew that yes, they were overdue by one day, but we always also knew that.



00:51:37.500 --> 00:51:45.270

Brian Keare: They paid on the 34th day like clockwork you know, to simplify what it was, and so we knew that we knew that.



00:51:45.900 --> 00:51:55.320

Brian Keare: The the check was coming in the mail the proverbial check was coming in the mail, so we were able to really use predictive analytics to streamline the function of the of the office of finance.



00:51:55.950 --> 00:52:07.380

Brian Keare: We were able to give analyst real self service because of what I was talking about you give the analyst who would spend their nights and weekends manually wrangling data.



00:52:08.220 --> 00:52:17.970

Brian Keare: And having to deal with the six week backlog, and you gave them access to encoder and they were able to really do some higher level functions, rather than their data wrangling.



00:52:19.860 --> 00:52:30.450

Brian Keare: we're able to combine salesforce and salesforce and netsuite data, so we gave sales and finance and integrated in sophisticated view into sales operations, they were able to.



00:52:30.720 --> 00:52:34.590

Brian Keare: drive sales operations in a way, they never could before before it was.



00:52:34.920 --> 00:52:45.360

Brian Keare: i've got salesforce to manage my leads, and my opportunities i've got netsuite to manage my transactions, how do I reconcile them and yeah we had integration between the two systems, but.



00:52:45.690 --> 00:52:52.080

Brian Keare: Nothing was you know included transformed that the nature of being able to combine multiple systems.



00:52:52.410 --> 00:52:59.640

Brian Keare: We with respect to the China tear of trade war, we were able to really optimize our supply chain, and we were able to.



00:53:00.030 --> 00:53:15.990

Brian Keare: reprice 10,000 skews across 2000 customers using in quarter, you know only 48 hours, I think this would have been a multi week 24 seven project with excel and a lot of late nights and weekends.



00:53:16.710 --> 00:53:28.530

Brian Keare: If we had had to do it manually and we probably would have gotten a lot of things wrong with it within quarter, we were able to have a consolidated view across multiple systems and we were able to really.



00:53:28.980 --> 00:53:42.240

Brian Keare: make strategic decisions nimbly and quickly and we were able to get the CFO and the CEO and our CEO involved, and we were able to be very precise and how we were able to react to.



00:53:42.870 --> 00:53:48.360

Brian Keare: The China tariff trade war, and it really you know was indicative of a use case that transformed our business.



00:53:48.750 --> 00:54:01.530

Brian Keare: So you know these are just the tip of the iceberg of how it transformed things that nor tech, and you know, again, one of the reasons why I came to incorporate it to help other companies, you know achieve similar types of transformation.



00:54:02.490 --> 00:54:07.230

Brian Keare: least I think you've got you've got a couple of examples from your E capital experience here to talk about.



00:54:08.520 --> 00:54:13.050

Lisa David: yeah the first customer i'm going to highlight is duluth trading it's a fun retailer.



00:54:13.590 --> 00:54:20.790

Lisa David: Lifestyle brand company headquartered out of the Midwest and North America, and you know they are very traditional company.



00:54:21.120 --> 00:54:29.220

Lisa David: From a standpoint of growing over time they're founded in 18 1989 and just over those years had you know basically.



00:54:29.670 --> 00:54:39.150

Lisa David: ended up with many legacy disparate data sources that they wanted to combine together to give insights to the executives of what was happening across.



00:54:39.390 --> 00:54:52.650

Lisa David: Both their store level their e commerce platforms their POs systems, so we we helped partner with their their team to really bring those disparate data sources together and now on a daily basis, their executives have.



00:54:53.040 --> 00:55:04.560

Lisa David: The insight to all of the sales across all of these different areas and can see it down to the granularity of basically Oh, especially as it related to.



00:55:05.790 --> 00:55:12.840

Lisa David: With the pandemic when the stores had to be shut down, they could immediately see what was by state.



00:55:13.320 --> 00:55:22.530

Lisa David: What was being ordered what all the women's line was being ordered higher than the men, women needed more retail therapy and ordering product online than men did.



00:55:22.800 --> 00:55:35.910

Lisa David: At first, and the pandemic, so the the the the women's line was trending higher than the men's line what fabrics what colors what size, so all of that insight can then effect where the inventory levels are at.



00:55:36.240 --> 00:55:39.630

Lisa David: And you know merchandising teams can obviously have that.



00:55:40.200 --> 00:55:48.930

Lisa David: Advanced inside as well that has continued to evolve over the last year and actually right now we're working with them they're they're opening a new were.



00:55:49.260 --> 00:55:58.710

Lisa David: warehouse location and so we're we're tackling a use case with ups data, so there were going to be showing them like which shipments are delayed and which.



00:55:59.070 --> 00:56:05.070

Lisa David: You know, again where's the product at either maximizing the new warehouse so it's really fascinating.



00:56:05.310 --> 00:56:13.890

Lisa David: What I love about a quarter, as well as I can start with those disparate data sources being combined to provide those executive dashboards on a daily basis.



00:56:14.160 --> 00:56:18.990

Lisa David: To those leaders at duluth trading and then, as new prop as new areas come up.



00:56:19.440 --> 00:56:28.440

Lisa David: In this case, that the new date, the new warehouse location that they're rolling out I can add that ups data into the platform and start to analyze that as well.



00:56:28.740 --> 00:56:33.660

Lisa David: So it's you know, helping them with the outliers of their inventory they're out there, out of stocks.



00:56:34.200 --> 00:56:47.070

Lisa David: Efficiency and doing that, and you know again it's once once they have information like this on a daily basis don't take it away it's because, once you can see, this and see all the trends that they could see by state very fascinating what were we were.



00:56:47.550 --> 00:56:55.140

Lisa David: able to like leveraging john Hopkins data during the pandemic and show them state by you know area by area.



00:56:56.190 --> 00:57:05.280

Lisa David: How things were being affected, based on their orders and really a fun a fun company to partner with and continue to add use cases to their platform.



00:57:05.550 --> 00:57:12.720

Brian Keare: yeah, and so I mean, I think we we got wind of that transition during the pandemic from.



00:57:13.260 --> 00:57:29.400

Brian Keare: Mostly focusing on or selling men's skews to selling women's skews and the idea, you know of not having to wait until a month closes and then doing the analysis and say Oh, here we are in October and in August.



00:57:29.760 --> 00:57:38.910

Brian Keare: We had a really interesting trend, where we went from women's demands, they were able to see that in real time right and like Oh, my goodness we actually need to rejigger our supply chain.



00:57:39.120 --> 00:57:40.650

Brian Keare: And ordering too much men.



00:57:40.800 --> 00:57:50.790

Brian Keare: We need to order you know more women's and re calibrate our product mix, and you know, the idea that dilute trading was able to do that and basically real time.



00:57:51.030 --> 00:57:59.250

Brian Keare: And rejigger their supply chain must have saved them, you know help help them re optimize you know and take advantage, putting it one way.



00:58:00.690 --> 00:58:05.520

Brian Keare: avoid a potential big huge inventory problem in another way, and do it in real time right.



00:58:06.690 --> 00:58:14.070

Lisa David: yeah and even even items like the fabric choices that people make made you know, obviously, the ones the pandemic it, we could the jeans.



00:58:14.580 --> 00:58:24.630

Lisa David: We wanted we wanted comfortable clothes on zoom I am so I mean even the fabric choices became became such a new level of insight that we saw on the retail level so yeah it's been a fun.



00:58:25.050 --> 00:58:31.170

Lisa David: it's definitely a very fun partnership and we love partnering with with training yeah cool.



00:58:31.980 --> 00:58:41.070

Lisa David: Our next customer to highlight is a is a major consumer electronics company, and you know what I love about this this story is.



00:58:41.730 --> 00:58:53.100

Lisa David: Just being when we talk about like even the gartner chart of the workday report saying the enabling predictive you know is how do we help finance teams enable that predictive capability.



00:58:53.490 --> 00:59:04.290

Lisa David: So, as I mentioned in this use case and this problem statement is that in the holiday season just having that faster insights so that they could make promotion disk disk decisions.



00:59:04.830 --> 00:59:09.780

Lisa David: During the holiday holiday season, obviously the pieces since season is very important to them.



00:59:10.140 --> 00:59:18.300

Lisa David: And so we were able to give show them at the daily level skew location day information across all retail stores and they're calm.



00:59:18.690 --> 00:59:28.110

Lisa David: And their e commerce business which provides them enormous insight on like I said promotions inventory outliers So where do I have.



00:59:28.980 --> 00:59:37.170

Lisa David: Low inventory high revenue, where do I have high inventory low revenue, so I can make decisions faster within that month and then.



00:59:37.830 --> 00:59:47.070

Lisa David: Another area for for the retailer is obviously vendor performance like can you show me the vendor performance for apple from this this date in October.



00:59:47.430 --> 00:59:54.660

Lisa David: Through this date in October, what was my vendor performance that was very difficult to see what taken a long time, and those questions can be answered.



00:59:55.020 --> 01:00:02.910

Lisa David: It you know we have a fun story from this customer from last year in the holiday season, where the the CEO called you know call it up the VP of.



01:00:03.390 --> 01:00:13.590

Lisa David: A night in finance and asked him one of those questions on you know, can you give me the vendor performance from Prime day, to this day and November and within five minutes.



01:00:14.070 --> 01:00:20.340

Lisa David: He could answer that question and get back to her that would have taken days like I said at the category level once.



01:00:21.180 --> 01:00:30.540

Lisa David: An area that really became fascinating was the fact that, once we had the data in there at that granular level, like, I said that skew location day information.



01:00:30.870 --> 01:00:43.470

Lisa David: We could turn on the predictive algorithm force for sales forecasting and like I said there they have the right side of the architecture slide they have a robust bottoms up forecasting system.



01:00:43.890 --> 01:00:57.450

Lisa David: And even and but but that's at the aggregated level because I put the prediction on what's happening at the skew location day information that predictive revenue forecast is already achieving a 50%.



01:00:57.990 --> 01:01:06.690

Lisa David: Higher accuracy than that bottoms up forecast and that's that's enormously important in what gartner and, like the other.



01:01:07.020 --> 01:01:11.130

Lisa David: Industry analysts are saying and that's really what i'm fascinated by is because I.



01:01:11.430 --> 01:01:26.520

Lisa David: I have this granularity it makes the predictive capability and then we're able to even take it one step further to the prescriptive area but that predictive capability, I think, adds tremendous value, because I have that granular details in the platform.



01:01:27.720 --> 01:01:33.210

Lisa David: But it's been a fun it's fun to partner with this organization and provide them insight.



01:01:33.630 --> 01:01:45.450

Lisa David: That they can act on and there's there's many more layers to come in this journey as well and it's it'll be fun to see through this holiday season, that is just that actually officially I think kicks off on Saturday of this week so.



01:01:46.380 --> 01:01:50.700

Lisa David: it'll be fun to see the insights that they're able to gain through this process.



01:01:53.790 --> 01:02:05.190

Lisa David: And then they'll the last company that I wanted to highlight is Smith medical it's a multinational MED device company headquartered out of the UK and.



01:02:06.240 --> 01:02:16.080

Lisa David: This is an oracle EBS environment company and their finance team really wanted granular detail on accounts receivable and accounts payable and that's kind of where they started.



01:02:16.470 --> 01:02:26.910

Lisa David: And again, their their HR teams, you know basically got to see the sub ledger twice a month, it took a long time to see and how you act on it, what you do on a daily basis.



01:02:27.510 --> 01:02:43.200

Lisa David: With now that I have the data in this data hub easy to see once I connect from the EBS environments their HR teams can now see daily where should I focus my time and and and on from accounts receivable and really hone in.



01:02:43.830 --> 01:02:56.460

Lisa David: That level of granularity also a lot of our earpiece systems, as you know, our setup very traditional it's like aging is done one to 30 days 30 to 6060 to 90 very structured.



01:02:56.880 --> 01:03:06.120

Lisa David: Well, within quarter, I can adjust all of those attributes, what if I want to just car one to 15 days, today I want a car from 15 to 45.



01:03:06.450 --> 01:03:19.080

Lisa David: that's easy to do and and again it just liberates that day in the life of these ar and accounts payable teams to see it, and again the if we if we if you have if we have the customer on with us today talking about the.



01:03:19.350 --> 01:03:27.570

Lisa David: The previous state to the current state, I mean it was a timely process that took many steps manually and hours to get.



01:03:27.930 --> 01:03:37.290

Lisa David: get to see even one lens of this, and now I can see a daily so it's it's been a fun company to partner with globally, we hear this a lot because again.



01:03:37.860 --> 01:03:44.340

Lisa David: Many of our customer teams even, no matter what earpiece system they've invested in they might have multiple instances across the globe.



01:03:44.640 --> 01:03:50.970

Lisa David: And and those instances are not connected together, so this data hub really provides that ability for us to connect.



01:03:51.300 --> 01:03:57.600

Lisa David: Any of those source systems together and, as far as we've talked about this, the RPS were.



01:03:58.020 --> 01:04:10.980

Lisa David: Were architected intentionally to process transactions, they weren't architected to make it easy and fast for us to do analytics and that's why there's nothing wrong with what earpiece system you chose it's just that, if we add this type of.



01:04:12.270 --> 01:04:20.400

Lisa David: You know data hub, with it, I can achieve that fast results to action, and again I don't think there's any finance team in the world that doesn't want to.



01:04:20.730 --> 01:04:28.980

Lisa David: improve working capital so anything that can hone in on accounts receivable and accounts payable is a bonus to our to our finance teams.



01:04:30.210 --> 01:04:37.230

Abboud Ghanem: So somebody said, especially on the see in real time right, so I think what you and brian's been you know touching on is the.



01:04:37.500 --> 01:04:45.330

Abboud Ghanem: Really understanding what's happening in your business right now being able to take you know the right actions, right now, and trust in the actual.



01:04:45.750 --> 01:05:03.060

Abboud Ghanem: You know, decisions and the numbers that you see in front of you and that's that's that's game changing, for you know for the industry for across all industries, so thank you for for these use cases I know, Brian Thank you you've been answering some of those questions Q amp a.



01:05:04.320 --> 01:05:09.450

Abboud Ghanem: You know I don't know if there are, I think, maybe we can talk about it, maybe save you a bit of typing Brian.



01:05:09.810 --> 01:05:15.360

Abboud Ghanem: I think this is this could be you know, an interesting one it's not specifically a buying quarter, but one of the.



01:05:15.810 --> 01:05:29.730

Abboud Ghanem: Questions coming in from a finance director, with an MBA and he's asking, would you recommend a degree in applied statistics or data science to gain the skill set to bring into an organization and also.



01:05:30.540 --> 01:05:39.180

Abboud Ghanem: You know what we're going to be sharing the slides later so i'll answer that quickly so so yeah do you need what what's your advice for finance.



01:05:39.180 --> 01:05:41.370

Abboud Ghanem: Leaders doing a science degree.



01:05:41.760 --> 01:05:53.160

Brian Keare: I mean the Nice, the nice thing is that you don't need to spend you know the proverbial nine months, and you know hiring a team in order to get started.



01:05:54.810 --> 01:06:03.930

Brian Keare: For example, our predictive analytics analysis about customer churn and customer payment.



01:06:05.100 --> 01:06:16.350

Brian Keare: You know timelines we did with smart analytical people, with the help of uncoordinated and Cordis designed to allow you to begin to dip your toe.



01:06:16.710 --> 01:06:28.620

Brian Keare: without having to have the huge lead time in investment in people to do that, I think, once you dip your toe in the water, however, you will get you will quickly realize.



01:06:28.950 --> 01:06:46.830

Brian Keare: That wow there is a lot here, and I am only scratching the surface and I might benefit from somebody who's got some experience in understanding how to create a model that is easily operational operationalize double sorry.



01:06:48.750 --> 01:07:00.690

Brian Keare: And you can do that, but you don't need to start from zero have the budget and have the long lead times to begin dipping your toe in in the water, and so I would say.



01:07:00.990 --> 01:07:12.930

Brian Keare: Dave you, you can begin with your team to understand what the potential is and then make your own decision about how important this is to your organization and how much expertise, you want to bring in.



01:07:13.260 --> 01:07:18.780

Brian Keare: To turbocharge that journey along the advanced analytics and data science Lisa what's your answer.



01:07:20.790 --> 01:07:22.440

Lisa David: yeah I I mean.



01:07:23.970 --> 01:07:35.490

Lisa David: I love the question because I think the change management for our finance teams to your point of dude and the question is how do we start to turn on like that data curiosity, because I think our our teams have have have been.



01:07:36.390 --> 01:07:43.440

Lisa David: they've been stuck with the data that they've been given for so many years that it is a challenge at times to turn on like well what, if you could.



01:07:43.890 --> 01:07:53.220

Lisa David: If you could see anything about your data, what would you want to see, and why, and if you if you can get people thinking like that, then I don't you know it's again, can you open up.



01:07:53.520 --> 01:08:06.960

Lisa David: A new way of thinking, I don't believe you need that advanced because of the innovation and the technology like we're describing here I don't think you need to have the degree to achieve this in the in the organization.



01:08:07.290 --> 01:08:21.720

Brian Keare: Right, so what you're saying is well i've only had 100 different columns in this report that I get every week before you know, like what question would I ask and you're right once you once you say, actually, the world is your oyster.



01:08:21.810 --> 01:08:23.310

Brian Keare: All the data is already there.



01:08:23.610 --> 01:08:37.290

Brian Keare: In important if you wanted to correlate you know, we had companies that said oh i'm seeing these sales trends, is there any correlation between coronavirus case rates in certain zip codes.



01:08:37.890 --> 01:08:45.810

Brian Keare: certain States certain geographies and this well, you can load your coronavirus case rates into.



01:08:46.500 --> 01:08:53.340

Brian Keare: into the model and quickly answer that question, you know, in the old days it was does, how does weather affect it, but you know.



01:08:53.700 --> 01:09:03.360

Brian Keare: In the pandemic era, you know there's a lot more complicated questions, and so, and so, if you are curious about that, then the world becomes your.



01:09:04.080 --> 01:09:10.980

Brian Keare: oyster in this journey, and so you know, I think it just requires the data curiosity that you're talking about first.



01:09:11.820 --> 01:09:24.900

Brian Keare: Second, I think, if you want to get on the advanced analytics or in some of the data science, we can allow you to dip your toe in the water, because you don't need to have new systems and new methodologies for bringing different data into play.



01:09:25.620 --> 01:09:31.770

Brian Keare: But you know, eventually, you might get there, but you can start your journey, you can start with an nba.



01:09:32.190 --> 01:09:32.970

Lisa David: I have a fun.



01:09:33.360 --> 01:09:42.210

Lisa David: of it, and again we talked about the supply chain, you know, in the world today, you now can also start to prioritize where that inventory goes, I have a I have a.



01:09:42.690 --> 01:09:49.140

Lisa David: tequila company that of course can't keep you know everything they produce the soul, and so they can look at what zip code that that.



01:09:49.530 --> 01:09:58.140

Lisa David: Product is going, because they want to prioritize you know the brand in a premium way so it's like it, they don't want the product going just to every.



01:09:58.410 --> 01:10:05.130

Lisa David: liquor store they want to, they want to prioritize where that goes so that it that it actually has the brand improvement.



01:10:05.400 --> 01:10:19.830

Lisa David: To the to the product so again, it can get very fun on the data cure it's just that way of thinking of that data what I you say, the world is your oyster I said it's like imagine having an onion you peel back any layer and and see if any aspects of your data.



01:10:20.700 --> 01:10:24.330

Brian Keare: that's that's a better metaphor, I love that i'm gonna steal that from you Lisa.



01:10:25.020 --> 01:10:32.550

Brian Keare: um so yeah just to reiterate what you know, a couple a couple of the other answers Brian asked.



01:10:34.140 --> 01:10:47.280

Brian Keare: How does one get started on their journey we offer pre collect crowd cloud trials to customers that cloud encoding calm, so you can see it yourself if you want to poke around yourself.



01:10:48.270 --> 01:11:02.820

Brian Keare: You can interact with the import, export via chat once you stand up your trial or you can contact Lisa you can contact myself if you want some help on the or a bird if you want some help on this journey, so when can.



01:11:02.820 --> 01:11:04.200

Brian Keare: We do that.



01:11:04.440 --> 01:11:07.140

Lisa David: We really help our customers teams do what we call as a.



01:11:07.470 --> 01:11:21.030

Lisa David: You know, a pilot to production, so we we basically kind of hone in on a use case that they want to see insight in and we actually connect their data to the to the platform and show them that insight and I it really does, because it is.



01:11:21.720 --> 01:11:28.470

Lisa David: It is an innovation that is fun to just uncover it so that's really what we help our customer teams do, and then we start to define.



01:11:28.710 --> 01:11:42.360

Lisa David: The production, use cases that they will want to take on, but it's it's a really it's a you get inside fast and we tackle like a specific use case in a pilot capacity and then and then start to frame up that roadmap and journey yeah.



01:11:43.980 --> 01:11:45.720

Abboud Ghanem: saying is that you make for.



01:11:46.440 --> 01:11:55.140

Abboud Ghanem: For any potential customer out there, there you know the opportunity here is to work on actual real data, because you know, in the traditional way you know we.



01:11:55.590 --> 01:12:05.490

Abboud Ghanem: You know the proof of concept work of dummy data and then you know we know it takes months a month and you put into production, you know hit through law of issues while you're saying Lisa brian's that.



01:12:05.790 --> 01:12:18.330

Abboud Ghanem: You can get up and running very quickly an actual real data so real use case you can prove value, I think, Brian you mentioned in a previous life 24 months to two weeks so that's that's a game changer.



01:12:20.070 --> 01:12:30.480

Lisa David: Again it's not a platform to your point of do that i'm going to get an extract the data and show you basically the custom DEMO that that is you know that's kind of the old world.



01:12:30.720 --> 01:12:39.360

Lisa David: This is actually to prove you the value we're going to actually connect to your data sources so that you can see, this inside and answer these questions, because otherwise.



01:12:40.020 --> 01:12:53.370

Lisa David: Again, the platform is just not based on dummy like the extract the data is that they it's based on the connection to that data so that then I can design that business layer of lens that the business team needs.



01:12:53.640 --> 01:13:00.420

Lisa David: And to add that insight so very different and other kind of traditional business, you know reporting technology.



01:13:00.630 --> 01:13:01.230

or analytics.



01:13:02.250 --> 01:13:05.340

Abboud Ghanem: Well, they say it over the funding is in the eating right so.



01:13:06.990 --> 01:13:10.020

Abboud Ghanem: You got real takes so if your data I guess within quarter.



01:13:11.190 --> 01:13:14.850

Abboud Ghanem: I know we have maybe a couple of minutes left.



01:13:15.210 --> 01:13:15.600

Abboud Ghanem: I don't know.



01:13:15.720 --> 01:13:26.970

Brian Keare: I think for josh josh had an interesting question like you know we've got reporting i've got a data store, you know how does it fit into my ecosystem, so we had that.



01:13:27.390 --> 01:13:36.750

Brian Keare: And my answer is if you've got you know if you have a defined process that has.



01:13:37.710 --> 01:13:48.090

Brian Keare: Established reporting, you should continue using that I think the challenge is something happens you get thrown a curveball coronavirus happens you're having you know you have to answer a new question.



01:13:48.960 --> 01:13:59.400

Brian Keare: Or you have a new challenge you know, have we thought about you know how we thought about how these trends that we're seeing in our current reporting correlate to whether.



01:14:00.030 --> 01:14:11.430

Brian Keare: Well, what do you do with that rajesh right that maybe you haven't allowed yourself the data curiosity, to ask those questions, but once you do, that your current data store is going to probably either be brittle.



01:14:11.640 --> 01:14:18.330

Brian Keare: or relatively inflexible, so I think what we did it nor tech was we put it, side by side with the current.



01:14:18.780 --> 01:14:32.700

Brian Keare: mature reporting and we threw new use cases that were hard to answer, or that were that the analysts in the office of finance we're spending the proverbial nights and weekends always preparing for that Monday morning meeting.



01:14:33.510 --> 01:14:45.030

Brian Keare: And we said, can we automate this can we make this easier to use, can we give you operational reporting that actually is modern bi with with with modern interactive bi.



01:14:45.840 --> 01:14:54.120

Brian Keare: If you can't drill down to the source level transaction, and that is of interest to you can we put up a use case like that so.



01:14:54.720 --> 01:15:05.520

Brian Keare: when somebody wanted something more different or a new use case we said we put in court up and gradually CEO said I prefer this one.



01:15:06.270 --> 01:15:17.280

Brian Keare: Because I can drill down to the transaction the CFO said I prefer this one, because I can see that the numbers tie out to my general ledger and you've proven it to me, I can drill down to the individual transaction.



01:15:17.610 --> 01:15:34.920

Brian Keare: And I can also see the summary analytics, and so we eventually started migrating towards in court and people said I prefer this one, the encarta and bit by bit, we started with a couple use cases and mushrooms and that's the way we typically see our customers journey going.



01:15:35.580 --> 01:15:41.910

Lisa David: And one of the things I forgot to mention that dude that I think is really important to that question is the fact that I, you know.



01:15:42.390 --> 01:15:47.550

Lisa David: In court and i'm the one who doesn't work for in Korea but i'm and get to choose to partner with important but.



01:15:48.000 --> 01:15:56.070

Lisa David: What I love about the platform of well as well as it's affordable enhancement to incite, this is a palette, this is not like the.



01:15:56.430 --> 01:16:05.430

Lisa David: The price tags that you hear on data lakes and and data warehousing efforts, this is an affordable insight and again that's.



01:16:05.730 --> 01:16:11.010

Lisa David: Very important to our finance teams, I mean we again, this is not like an investment that.



01:16:11.340 --> 01:16:19.560

Lisa David: In Roi is pays for itself, I mean this is an affordable, you know insight into enhance those investments you've already made.



01:16:19.860 --> 01:16:32.430

Lisa David: I mean, some of our customer teams if they're if they're sun setting something, it would be legacy on premise either databases data, you know, over time, but again it's really just an enhancement to those investments they've already made.



01:16:33.330 --> 01:16:43.230

Abboud Ghanem: yeah I think you know I remember Lee so one of the calls with your customer joint customer they say that they made the money back by one single inventory decision.



01:16:43.470 --> 01:16:46.290

Abboud Ghanem: Right it's all about you know, making the right decisions and.



01:16:46.650 --> 01:16:55.290

Abboud Ghanem: You know, making sure that they're doing the right things for the business so so I guess you know we're wrapping up I don't know if there are any final key messages.



01:16:56.010 --> 01:17:04.200

Abboud Ghanem: from you, to the attendees and later for those who are going to be listening to recording the finance leaders any final messages from you.



01:17:05.850 --> 01:17:17.280

Lisa David: You know I tell my prospect teams and customer teams of dude you know i've been in i've been in the space of technology now for almost 20 years and it's really I have a lot of passion to bring this innovation.



01:17:17.550 --> 01:17:23.910

Lisa David: To our to our our finance to organizations it's fun to build a talk about something of new value.



01:17:24.180 --> 01:17:29.760

Lisa David: For the with the other investments they've already made and give them that daily insight that they're looking for.



01:17:29.970 --> 01:17:36.420

Lisa David: But it really you know it's an exciting time we have a lot of passionate about this product to talk about it for way longer than an hour and a half.



01:17:37.140 --> 01:17:44.010

Lisa David: But if you if for anyone who would like further information i'm happy to dive deeper in particular to that specific industry that they're in.



01:17:45.000 --> 01:17:47.610

Abboud Ghanem: Thank you Lisa Brian any final comments from you.



01:17:49.050 --> 01:17:53.940

Brian Keare: um I would say that my final comment is that.



01:17:54.300 --> 01:18:04.710

Brian Keare: When I first heard about encarta I said gosh I know the pain of what i'm currently in sounds too good to be true The good news is the proof and proof is in the pudding and you get to eat it and it only takes you know.



01:18:05.010 --> 01:18:18.450

Brian Keare: A day or two to be able to go through that journey to prove out the initial concept, and then we can help you with authority use case and see your own data and see what you know and and and plant, the Aha.



01:18:19.980 --> 01:18:26.940

Brian Keare: Inside of your heads, as you take a look at in quarter, and so we invite you to go on that journey with us and try it out for yourself.



01:18:28.290 --> 01:18:34.650

Abboud Ghanem: brilliant you know in in feel like it's an hour and 20 minutes so definitely enjoyed the discussion.



01:18:35.130 --> 01:18:41.220

Abboud Ghanem: I want to say thank you Lisa Thank you Brian and also thank you for the for the attendees, thank you for.



01:18:41.490 --> 01:18:46.800

Abboud Ghanem: The wider team at the back end that helped us put this together and or this big team effort, so I want to say thanks for everybody.



01:18:47.220 --> 01:18:52.950

Abboud Ghanem: really looking forward to helping a lot of the organizations, you know get real value in some value from the.



01:18:53.370 --> 01:19:04.170

Abboud Ghanem: finance data, so they can make better decisions around the business, so I wish everybody a great day ahead, and thank you so much work was of this panel discussion, thank you and goodbye.



01:19:05.250 --> 01:19:06.570

Abboud Ghanem: cheers thanks everyone bye bye.