Anthony Bellcourt 0:00:13.4: 

Welcome, everybody. Thank you for being here today. I'm Anthony Bellcourt. I'm managing partner at Fadmoor Consulting. 

 

Mazi Afshari 0:00:20.2: 

I'm Mazi Afshari. I'm a finance transformation lead at Magnit Global. 

 

Anthony Bellcourt 0:00:25.0: 

We are here today to discuss scalable finance transformation with Magnit Global. So before we get into the financial transformation in your role, in that, set the table by telling the folks here a little bit about who Magnit is as a company, as an organization. So Magnit is a global leader in managing the extended workforce. Helping organizations source, manage and optimize contingent talent at scale. Through a combination of an AI-powered, cloud-based platform and managed services, Magnit enables companies to gain full visibility to their workforce, including contractors, freelancers, service providers, while driving cost efficiency and faster access to critical skills. Today, Magnit supports close to one million workers, partners with more than 14,000 staffing suppliers, and services around 600 enterprise clients across 130 countries. Placing it among the top providers in the contingent workforce and vendor management space. What differentiates Magnit is its ability to combine technology, data and human expertise into a single integrated solution, helping organizations build more agile, scalable and intelligent workforce strategies. So that's a bit about your company. Why don't you tell us a little bit about yourself, your role at the organization, your responsibilities and how you see being a digital transformation leader at Magnit? 

 

Mazi Afshari 0:01:57.1: 

Yes, perfect. Thank you. That looks good on paper. I was just looking at it right now. So thank you for that introduction. So as my role as a finance transformation lead itself, my goal is to be able to drive strategy and execution to modernize the finance and the operation aspect within our company itself. Which means improving not only the planning and forecasting, and the reporting aspect, but also being able to providing better and faster insights for business leaders to make decisions. I think that's the hardest part of all of us working either in operations or finance, is how can we be able to deliver that in a digestible manner, in a way that the executives can be able to use that as an informed decision, to be able to make those decisions from itself? Being as a big company, as you guys saw, there is just a lot of pieces into that puzzle. So putting that together itself takes tremendous amount of time, and you need basically tools in order to implement that. Hence, the whole aspect of Anaplan, where it comes into that picture itself. So hopefully that was a good enough introduction, before we go into the next questions. 

 

Anthony Bellcourt 0:03:07.0:  

Yes. You have a history with digital transformation. Correct? So when you were hired at Magnit, I take it you had a vision of how to conduct digital transformation with Anaplan?  

 

Mazi Afshari 0:03:20.1: 

Definitely. I do have a background also in finance, as well. So what we tend to do as finance, and I think everyone here can attest, being in the finance world itself, we tend to be very reactive to the information, as far as what happened of doing so. So what I try to do is shifting that mindset of not only being reactive, but being more proactive. Which meant that being able to transforming how we think about it itself and getting it away from the cycle of chasing the numbers and driving more insightful decision-making aspect itself. In order to do so, we needed to basically be able to be influencers and we needed platforms in order for us to do so, in centralizing these aspects. That's where Anaplan basically came in, because it connected the data and centralized it for us, and could basically paint that picture, and Fadmoor, who is our solution, basically, team implementation partner, basically executed that vision that we have, and being able to put all of those cards into place. 

 

Anthony Bellcourt 0:04:19.1: 

So before execution, when you came in day one, though, what sort of challenges were you looking at? 

 

Mazi Afshari 0:04:26.5: 

Yes, like many organization itself, we saw fragmented systems and disconnected processes across regions and teams. Keep in mind, we're global, knowing the fact that this is a struggle, what a lot of global companies do, we're getting that information from different regions and teams where does it differently. There's different systems in place. When you have acquisitions itself, there's always a tough part of consolidating those aspects itself. So we ran into those aspects like, okay, how can we be able to get that, getting into an ecosystem, we're all looking at the same thing itself, which was our biggest struggle to be able to do so. Also, we know that we don't want to be always spending our time of reconciling the data. It loses our time of actually being able to do those insights and the influential aspect that finance teams should be doing. So instead of - and I know everyone here, within the finance sector, we know that our finance team always works hard but, for the lack of a better word, not always the smartest, because we lose the lack of synergy, at least in our cases itself, of having it with business partners to helping us driving the insights and predictability into it, and shifting it from what we always tend to doing is reconciling those numbers.  

 

Mazi Afshari 0:05:35.6: 

Which doesn't leave a lot of room for us to going into the predicament aspect itself, or insightfulness of how can we get ahead of the issues we had on the previous Monday to next month? Because as soon as we finish understanding what happened in this month, we put the narrative down. We're already into the next itself. So we wanted to basically get away from that cycle and being able to creating something, an ecosystem that could be able to do those in a more autonomous manner itself, and leave us more to the critical thinking aspect. 

 

Anthony Bellcourt 0:06:05.1: 

What sort of tools were you working with at the time, before Anaplan, that weren't enabling those goals, and for you to conquer those challenges? Was it another platform? Was it Excel? Was it process-wise? The business was siloed with all of these acquisitions, what would that look like with your current suite, before Anaplan?  

 

Mazi Afshari 0:06:26.2: 

No, 100 per cent. I think overall, just process-wise itself, it was very siloed, right. We had fragmented systems itself, and disconnected processes across regions and teams. There was multiple systems that we were using. Then when we integrate and acquire teams, we tend to lead them as a more of a standalone, because the lack of having a more of a centralized platform that could put it together. So I think what we tried to do is to be able to see when the business comes out and saying, as business change with the needs and everything, we needed to be quickly adapting to saying, how can we get ahead of it? In order for us to do so, we needed to get better of being able to consolidate the information, which we're always - it's like a catch-22, when we have so many other companies to doing so, to getting it all together. We realized that there needs to be a better and safer way to doing so, or consolidating it. Whether it's going to be a manual intervention that we're using now, or more of an automated aspect to get those processes into a fashion where we can leave the finance team to start telling the future story, rather than always telling the past. 

 

Anthony Bellcourt 0:07:30.2: 

Yes, absolutely. So a lot of challenges. Not the right tools in your toolkit to solve those problems. Is it fair to say, though, that there was an organizational awareness on Magnit's part, that these were challenges and a desire to run more optimally, overcome these? Case in point, hiring someone with your skill set. Was the organization ready and had the energy behind taking on these initiatives of improvement? 

 

Mazi Afshari 0:07:58.1: 

I would say yes and no. I think the vision was that they wanted to do these fixes, but I think they didn't realize how hard it would be able to get to that point, right. Because as soon as you start uncovering rocks and seeing the difficulty aspect itself and realizing what it requires to fixing it, we definitely got some pushbacks. Because you also have to realize, you're working with an organization that it's not their necessarily day job to be able to transforming, right? So their day job is, if you go to accounting, they have to closing the books, they have to do journal entries. You go to FP&A, they have to do forecasting. So in addition to that implementing new tools and stuff, it's something additionally they're doing. Obviously, they're going to see the greatest value into it, but getting that threshold of getting them to committing the time and efforts to make sure that you're creating it in the right way, so they can see that value, is a difficult path to be able to go across. Sometimes it takes longer than expected because there's always going to be urgencies of whether it's going to be executives to be able to get these ad hoc reports, or board of directors have another path of growth, or in this case, hiring freezes. So there's always this scenario that plays into it, which kicks into my roadmap. Which I always have to be able to say, we can't do this based on this. It's a journey to getting there itself. Ultimately, I think we're all aligned what the value could add into the end itself. It's just that path could go up and down. 

 

Anthony Bellcourt 0:09:18.2: 

Yes. So given the current state of play, we kind of touched - the people, the energy, the ambition around it, the process, the platforms you had. When you entered the partnership with Anaplan, you started initial implementation, with that being the current state of play, what were your expectations, and how did it unfold from there? 

 

Mazi Afshari 0:09:39.4: 

Yes, so initially my expectations were, from the beginning, I kind of wanted to basically establish a few basic foundational stuff, like single source of truth, because I think the issues that we were running into, we wanted ultimately to make sure that everyone is looking at the same data itself, right? Then after that, having more of a standardized planning processes. As I mentioned before, being global, when you have multiple regions and teams, they tend to do it their own way itself, and consolidation makes it very difficult. Which hindsight would slow down the cycle of getting the reportings and the plannings on time, which again, we're like, it's a catch-22 because we always run out of that time to drive the insight into it. So we really needed to form it in a way that we can drive agility and insights very instantaneously, to keep up with the trend. Nothing is going to stop. It's the time never stops. So we needed to make sure how can we better, more accurately and faster get to that point, so we're not always running out of it where we can make impactful decision-makings before it happens. So that's always the issue itself. Even up to this day, itself, we haven't really figured it out, but we're getting better at it. I know what we would have to do, but again, we're always running into the issue, which I think a lot of people can attest to, is the resource capacity, right?  

 

Mazi Afshari 0:10:55.7: 

A lot of those things itself, you would need the resources to doing so, which is the hardest part with the business that we have. Because again, as I mentioned, it's not the day-to-day job itself. That's why the strategic partner itself, as I mentioned, having helped us to doing so by having an external team to emphasizing on the Anaplan aspect itself, and then being able to have the internal people focusing on the operational business aspects. 

 

Anthony Bellcourt 0:11:21.1: 

So after the initial implementation, if I can paint with broad brush strokes, that was a crawl, walk, run approach. You got some… 

 

Mazi Afshari 0:11:36.7: 

To say the least, yes.  

 

Anthony Bellcourt 0:11:37.4: 

Yes, very popular. Get some initial return on your investment. See value. Let the organization who, like you say, it's not their full-time job, to see how it makes their full-time jobs easier by participating in the process and then using Anaplan as a part of their day-to-day. So from there, after the organization started to see it, what became the expectations in your phase 1.5, phase 2 continuous improvement between larger initiatives? What was that vision coming out of initial implementation to start walking and then running, and how has that unfolded? 

 

Mazi Afshari 0:12:15.1: 

Hundred per cent, yes. So to your point, it's just we started with the crawling and the walking aspect itself. It's like once we get to that point itself of having good initial foundation set up of understanding what we want to doing, that's when we started to scale. Going into having connected aspect of OpEx, revenue, the workforce aspect, workforce planning aspect stuff and connecting it. We started to see how business decisions impact the flow on financially in Anaplan itself. It became very - it was a big game changer because then we started to realize, you know what, we have shifted from being the team that says what happened, but instead now it can be like, this is what we should do next. It was like a mindset of changing it, because it basically set our team, mostly our FP&A team up for success, because they could actually focus on we know that that's going to happen itself, and through our assumptions and drivers, our model can give us what we want to look at. So far, we've been pretty accurate at this point. We're still fine-tuning it, and I don't think we ever reached the perfection aspect, but we have been able to be able to reducing that significantly where we're along with the budget and the re-forecasting aspects that we weren't before, with the massive drivers and targets that we have into it. So there's huge progress. Still a work in process itself. But so far, right now, we're seeing the progress itself as it happened day-by-day. 

 

Anthony Bellcourt 0:13:41.1: 

That's great. I'm sure with [?Adam 0:13:42.8], we'll get to talking about the future and what functionality on the platform has. You're really excited. Before that, what lessons have you learned, to date, along the way, through multiple projects, continuous improvements that you're going to apply as you look forward to what Anaplan is rolling out, and how you'll take advantage of that as a business? 

 

Mazi Afshari 0:14:08.0: 

Hundred per cent. I think this has been mentioned in multiple times in different sessions, and I heard it this morning with both Nvidia and Synopsys, when they mentioned it. Change management is critical, right? It's one of the most important thing itself. The technology aspect itself is only a part of the equation. The adoption is really where you bring the value, where the team can actually leverage, and utilizing what you actually build out in real-time scenarios. Where you could actually see a team gaining that knowledge and making the decisions on the fly of understanding these are the repercussions and consequences this would have. Which in the past, having the reliance on heavy Excels and stuff, doing so, either you crash out on Excel for being too large itself, but you always run into version controls. Looking at the same source of data itself has been instrumental. Another lesson that we learned is because obviously, being a bigger organization itself, we have very complicated models itself. We realized, relatively quickly, to our partnership with that ourself of understanding, we need to start simple then scaling, because having a strong foundation earlier on in the beginning is the key, because where else you end up getting boggled down trying to solve too much too fast, of having bottlenecks and not really getting the progress that we would need it to move forward. 

 

Mazi Afshari 0:15:25.6: 

So we basically had the strong foundations of making sure the majority of which could apply to this model works, and then going back into iterating itself to making sure that everything else works itself. Another aspect is the data governance. I think we all know the model is only as good as the information that we actually feed into it. So it's very, very, extremely instrumental that there is controls on data validation and controls in the aspect of making sure that people can trust your data, or else you're going to be running into an issue. If you're deploying a product or something that you want to be solving for multiple business teams itself, where they're going to be seeing that if they lose the trust on itself, saying like, this is not working accurately, this is not what I expected. That's a huge uphill battle that you have to go, and then it makes it ten times harder to actually adopting it for other people within the team itself. So gaining the trust itself, originally, for them to be able to be happy with the outcomes is truly, truly, very important. Then finally, a lesson learned itself is going to be the ownership and accountability aspect. In order for the model to work the way we want it to doing so, it's very important to establish clear roles and responsibility. 

 

Mazi Afshari 0:16:37.2: 

Whether it's come to the finance, the administrative system task itself and other business stakeholders that will have a big play in making the model successful. Example could be revenue. We can put as many drivers as we want initially, or levers, but ultimately, if we don't have the insights where our salespeople would have, that could inherently not specifically be solved by an algorithm, that's going to be imperative for us to getting more accurate data at the end itself. So it's going to be a collaboration between not only have the technology in place, but also fostering a continuous improvement mentality of making sure that our model can actually address when everything - there's a screwball throwing into us at business aspect, we will be able to adapt through that, through making the model better. As we're getting better and better, eventually - and I know this sounds too idealistic - is going to take less time of us to doing the maintaining aspects of, and we can actually driving more predictive insights and understanding how can we do this better, and that's where the value truly from a platform like this will come into play. 

 

Anthony Bellcourt 0:17:41.3: 

Absolutely. So looking to the future, and this is a really exciting time to be asking you this question, because Anaplan has so much awesome stuff coming out on the roadmap, so you'll probably have to whittle it down. What things are you excited to explore and bring into your workspace, functionality-wise, to continually improve how you manage and plan your business? 

 

Mazi Afshari 0:18:05.2: 

Yes, I think to answer that in a more impactful way itself would be the main core things I'm looking into is speed and intelligence. How I would say that, we're moving towards an aspect where we want to get more of a real-life scenario planning aspects, so that we can get comfortable and confident about the numbers that we're getting in is so accurate, to the point we don't have to worry about it, and we could be able to look into the future more, right? Same way that the video this morning told us. We can't announce that we can predict the future, but yet that is the approach we want to get. We want to get as close as we can possibly into it. Changing the aspect, giving the confidence to the finance team, because the question that's always going to come in to finance executives is always going to be the same. They say, 'What happens if…?' Right. I feel like that's a very loaded question, and if you haven't set it up correctly, it's such a hard question to ask. But if it's structurally done where we can accommodate scenario planning to be able to be saying like, 'You know what, what if we do have a hiring freeze within this department, or we push out six months of this, or our growth projection is going to decrease by this much.' All of those simultaneously inputting it into the scenario and comparing it to the budget.  

 

Mazi Afshari 0:19:19.0: 

Those are the things that the business leaders want to make informative decisions. Whether we pull the trigger on this, how do we do it as a re-forecasting model? As we all know, budget is done at a point in time earlier in the year. So re-forecasting, it's imperative to making sure that we keep ourselves aligned with it itself and make sure we don't go over it. Things happen. Sometimes we're in the lucky front itself, we're doing better than accepted on the top line, and unfortunately, it could also happen the other way around, where we're not. So we need to be able to be very agile and drive those insightful decision-making as we go along. So being here as of yesterday itself, with all of the sessions that we had with AI, make me really hopeful, because I think one missing key link into the whole Anaplan thing itself is sometimes you need to be more of an understanding how the model actually works. The UI aspect itself is a little bit lacking to get the involvement of our executive teams that we didn't get before. By looking at how the OWP and the other stuff is actually working, where it's more of a drag-and-drop functionality, I'm glad that there's functionality that we're improving in that aspect, where we could encourage leaderships, as far as execs and stuff, to be a part of it. 

 

Mazi Afshari 0:20:27.2: 

Historically, the end users of Anaplan, besides the reporting aspect, has been people like director, senior analyst, senior manager and stuff, to doing so and dragging those [?layers 0:20:37.9] because they know how it works. It basically left out the execs and stuff being the end users of that, rather than being able to making the changes. I really like the self-surfacing aspect of you being able to, if you wanted to, make simple scenarios yourself. You could doing so. Which was always a struggle because there's limitation. It was very to the point of gritty, for the lack of better words, we've been doing these changes, which they're striving away from, and utilizing such as the agents itself, that they have, being able to use a prompt-based thing itself. I'm hoping that can close that bridge, the gap, bridge that gap, basically, that in the past has always been that we weren't able to do so. So hopefully that answers the question. 

 

Anthony Bellcourt 0:21:18.4: 

Yes, absolutely. Answered a lot of questions fantastically, with all that great content. Before we move to Q&A, would you mind giving maybe a handful of key takeaways, the nuggets you think were in there that you hope the audience, yes, they can pass along? 

 

Mazi Afshari 0:21:36.5: 

I think we do have a slide on it, to be able to make it simpler. There we go. Perfect. So one of the things that itself, a lot of companies might be seeing the finance transformation might be like a program or a project aspect itself, but it truly is a continuous journey, right? Iteratively making it better to be able to be getting ahead of stuff that we didn't think of it before. Like anything itself, it's just a continuous working to making sure that everything is working. I think Anaplan enables that journey because it connects the people and the teams, the data itself with the projections, and being able to tell that story that wasn't being able to do it, which enhances the agility and adapting as the people using that insight to make informed decision-making aspect itself. So there's one part, a part that, as I mentioned before, the technology enables that behavior. Adoption is really the one that actually is going to be triggered of actually utilizing it from the business point of perspective itself. It's going to come to a point, if you focus on the governance aspect of making sure that it's done correctly, and all of the change management and making sure that we iteratively maintain this, it's going to come to a point where the impact goes beyond the finance team itself, right? 

 

Mazi Afshari 0:22:52.3: 

It's going to be you elevate from how the entire organization is going to be making the decision-making itself. Where it's going to be insightful decision-making at real-time, which is going to be saving us. But we'll also be able to get ahead of these decisions before they actually happen. Ultimately, when we get to a point where finance is not going to be a bottleneck, but instead it's going to be a competitive advantage of actually having that team itself, that will be the ultimate goal itself of how can we actually get financial transformation at a scale? So that's the ultimate goal that I'm seeing into. Which it's still a long road to go, but it's a very, very interesting journey along the way. 

 

Anthony Bellcourt 0:23:31.0: 

Thank you. 

 

Mazi Afshari 0:23:32.1: 

Thank you. So, I think we'll open it up for Q&A. Is it…? 

 

Anthony Bellcourt 0:23:37.1: 

I believe so.  

 

Mazi Afshari 0:23:57.2: 

[Pause] One, two, three, oh, there we go.  

 

Audience 0:24:12.2: 

[Pause] Hey there. I appreciated the presentation. This is awesome. We're about to start our journey. So this is well-timed for what we're about to see. My question is, how did you go about building that crawl, walk, run? What was of most importance at the beginning? I've heard process. I've heard OCM. I've heard data. How did you go about identifying what was most important up front? 

 

Mazi Afshari 0:24:38.1: 

Definitely. I think there's - we reiterated. I think one thing that helped this, we had a strategic partner that we work with, Fadmoor, that I mentioned before. We roadmap what it would be looking like, right. Again, it's very preliminary. Once we started to execute that itself, we realized there's much more broken process than we thought of. So we went back. So it wasn't like really a one-time plan itself fixing. It literally, we went back and looking to see what fix it, what we have to go upstream aspect and fixing root cause issues. One of the issues with the finance transformation itself is they never explain it to you. One, change management is extremely difficult. I realized, by talking into it with a lot of people that went through it, they kind of oversimplified it. But if you keep in mind that you have multiple teams, that could be hundreds of thousands of people utilizing this thing itself, making sure there's enough communication ahead of time, which sometimes could take months of doing so, getting commitment from teams and stuff to doing so. That process aspect is always understated. I did it, for sure, as well, going into it. So how I envision that we can create the technology aspect relatively quickly. Especially if you have help with a partner.  

 

Mazi Afshari 0:25:47.1: 

The hardest part will be able to accommodating that and adopting it within the team. So to your point, we went back redrawing the map, making sure our processes would be able to fit this, and when we molded something, we ended up changing some of them to make it more simpler and then less complex to be able to adhere to that. Then what we did is, later we made it a little bit back into the complicated one, because the adoption thing itself, if people knows it's easier a lift of going from what they were doing before questioning the status quo into this, then it's easier for reiterating it later. So we took two different approaches. I think the simple aspect, which I brought up in the lessons learned, proved to be much more powerful. Start off simple going into it itself, or else you might be losing the end users actually starting to adopting it. So it all depends on the in end case users, as far as how it is. I would say there's not a one-glove-fits-all scenario. You just have to play along with it, and hopefully, if you don't have a lot of root cause issues related to data and stuff, it's going to be an easier path to doing so. Our case, there was. We just had a lot of acquisitions. We had fragmented systems. It made it harder for us.  

 

Mazi Afshari 0:26:55.4: 

So our roadmap, potentially, quite frankly, telling us within a six month or twelve months that we had a plan to doing so, we're actually on to like eighteen or two years right now, because we went upstream and we really wanted to fix it. I don't want to deploy something that's, for the lack of a better word, half-assing it. So I really wanted to make sure that we did have something that is solid, because I wanted to have the trust that the business stakeholders and leadership team, that this is something they could rely on. But it takes time if there's trickling upstream issues itself. So, I would suggest adopting technology stuff is only one part of it, but make sure that you address the root cause issues that relates to data, because ultimately, if you don't have clean data coming into it, you're going to go back anyway. So that would be the ultimate advice, I would say is make sure clean data, trustworthy data and transformation goes into Anaplan first, or else you're just going to be working yourself backwards. I hope that answers your question. 

 

Audience 0:27:47.1: 

Yes, it does. Appreciate your insight. Thank you so much. 

 

Mazi Afshari 0:27:49.5: 

Very welcome. 

 

Audience 0:27:58.2:  

Hey, guys. I know we've talked a lot about the partnership between Fadmoor and Magnit here, but in your opinion, what are some things that make it a good partnership and that you leverage more than others, when you think about evolving your Anaplan practice and how you work together? 

 

Mazi Afshari 0:28:15.3: 

I like that question. I think it's the fact that as you start seeing them less as a partner, more of a part of the external of the team, right. I think in order for you to actually trust them to doing the decision-making, because there's an extreme amount of trust you put into the hands of these partners to be able to do these models. It's like they're in your house, right, making the changes itself. So you really want to trust them that they're doing it with the long-term vision in hand. I think this is what we establish a relatively early day. They understood what we needed before we set it, which made a huge difference, because in a lot of the partnerships actually with itself, we needed to explicitly get to that point to be able to say like, this is what we want to do this. Then we had to do more of the handholding. I think a strong partnership is when the handholding shifts of them doing it to us, versus we doing it to them. That was basically the big differentiator. We saw it as more of crutches for us to be able to rely on them. Also, financially, as far as anyone being on the finance aspects itself, of making a below-the-line investment on the thing itself, not hitting it the same way financially that it does if we're using internal workforces, was a night and day aspect, especially when you want to be operationally within the financial aspect itself.  

 

Mazi Afshari 0:29:25.8: 

So it helped us on that aspect, but also the fact that we could basically give prompts of saying, 'Hey, we need this and done, can you leverage that? Can you validate the data?' They go and quite frankly, swiftly coming back after that itself, saying, 'We did this.' And we can basically focus on the validation aspect. It was really like a night and day impact that us team, we didn't have to spend the time of doing it, but also it would have probably taken us three to five times longer, because we're just getting new training on these itself. So us doing that would take this much longer time itself. In addition, they already saw a lot of pitfalls that we would run into it. So when we brought up bringing - POC or saying, 'This is what we wanted to do.' I think right off the bat, in a lot of those cases, said, 'Well, we can't do this, this, and this because this will run into issues.' So we basically learned a lot of things right off the bat of, okay, let's not go down that path, and we shifted. Which would have saved us tremendous amount of time if we ended up doing it ourselves. So I think in that aspect, it showed us not only value on the money aspects of which can only be quantified, but it's the time aspect that you never get back.  

 

Mazi Afshari 0:30:31.1: 

I think we would have been able to closing it much faster and getting to our point much quicker, if we use someone that has the more experience itself. So that's the biggest value that we've seen using our strategic partner. Did that answer your question? Perfect. Thank you. Sorry, I can't see some of you guys because the spotlight is pretty strong. So I don't know where the question came from. Oh, there you go. I can see him. Hi. 

 

Audience 0:31:00.2: 

Hey. Is there anything you would have done differently prior to starting the project? Data prep or process, mapping or just understanding things a little bit better, or getting your organization really in tune with what a true transformation looks like. 

 

Mazi Afshari 0:31:19.1: 

Hundred per cent. I think what I would have done is - first of all, I would have had more of a validation step with the requirements. When you do acquisitions, I think there should be integration teams are doing so. I think we underestimated the aspect of getting requirements, basically running them as its own entity itself. We didn't consider the fact that what the consolidation would pull a piece into it, when we're going to be doing a more streamlined planning approach. If I would have done it differently, to your point, I would have mapped out all of the data sources, all of the different systems itself, and being able to understanding how can we make sure that clean data goes into the model before. Quite frankly, between all of you guys, I think the solution of inputting the platform itself came before doing so. That's why we kind of go backwards itself. So one lesson that I would teach on it before going into something like that, take a look at how data streams comes, because trust in the data, as I mentioned, data governance is a huge deal. Especially when you're dealing with a platform that all it is data. Make sure that it all does it correctly, because what we realize is also is not only does it come out correctly in some cases integration, it was wrong to begin with how it was processed.  

 

Mazi Afshari 0:32:30.0: 

So when you're actually combining it with other entities or other country, basically it stands out like a sore thumb. So you really want to make sure, because Anaplan does what it does as far as calculation aspect. But if the data you can't really rely on, you have to do those. I think instead of taking one step forward, we ended up taking two or three steps back. I'd rather have taken those two to three steps back to making sure that it runs through correctly, then getting a solution itself that's going to be based on that data. So data is going to be the king, as you're going to be hearing it more and more into it. I would prep the data flow itself. The data integration, data validation and integrity would be my main key and fix up, if I had to redo in itself. Focus on that one before going into any implementations. I hope that answers your question. All right. Thanks. No? I think that's it then. Thank you, everyone. Thank you so much for joining me. 

 

Anthony Bellcourt 0:33:32.7: 

Thank you, Mazi.