Shivani Amar: Good to see you guys. How’s everything? Oh, thank you. Good. How about you guys? Uttam Kumaran: Good. , relaxed. , it’s been a relaxing two weeks. Shivani Amar: Good. Ush, how are you doing? Awaish Kumar: , all good. It has been nice. How about you? Shivani Amar: It’s really cold out, but otherwise, life is good. It’s the first winter… I don’t know, , we’ll see, because we still have time, but I’m , my spirits are pretty up relative to the weather outside, … Uttam Kumaran: Got a hammer of vitamin D, I feel . Shivani Amar: And… , I have the beautiful Urban Stems flowers in my apartment, it was very nice. Uttam Kumaran: Yay, nice. Shivani Amar: , there’s… I feel there… , in this January sprint, you have an agenda of things we can talk about, but, , one of the things I’ll just add that I was thinking about was, . … but then, , a time for me and you, or I don’t know, whatever, for us to, , really sit with what Source Medium has done, because I know if we say, , , theoretically, one day Source Medium is phased out, ? , let’s say that that’s the goal, but not, , pressing. And it’s … Is the way that they’re showing the metrics the most useful, or are there better ways to, , deliver. insights to people, and I don’t know if we want to try and do that in January, just a working session, , the three of us, or the two of us, or whatever, but I was staring at it yesterday, and I was , it’s, , got the data, , but it’s, , but it’s… it’s messy, and, , if I were to… if I were Carlos, , what would I want answered readily? And I’m , I feel that could be a nice thing to aim towards now that we’ve done the discovery with, , e-commerce that. Does that resonate with you? Uttam Kumaran: , and it’s also, there’s , , owner internally, clearly, ? . And… I feel it’s a lot of, , , it works, and, , we’re getting the data, but then nobody’s probably looked into, , the inner workings in a long time. Someone’s . We’re just gonna trust it, ? You can tell that’s happening, and it’s up to us to break that open and say, , are we comfortable with this? There is also gonna definitely be, , redundancy there versus what’s coming out of this system. Shivani Amar: that was just, , another thing in my mind. I was , what can we, , , , what are we digesting? What are we discovering? But then, , what are we , , generating? , again, it’s . I… hold me accountable, ? If I start thinking, , let’s generate some insights, and it’s , it’s… to be deliberate equals getting definitions laid out, and , there’s still a lot of work to do, , help me understand where we are in that journey, ? Because maybe my… maybe I was, , just thinking about it, I was , oh, let’s get wholesale, , a version of a dashboard, but, . if we still have a lot of cleanup we need to do, definitions we need to align, that’s, , where the good stuff is, ? , I just want to preface, it’s , I might start getting excited about things myself, and we all need to say, , what is the, , super methodical way of doing this, versus, , the racing to something… Some answer to one question that’s not gonna be, . , trusted long-term because we didn’t do our, , thorough work thing. Uttam Kumaran: , … that’s what we’ll talk about today, is, , what is our goal for Jan, , and start to see, , do we want to… continue to drive towards, , a BI tool? Is it more necessary to drive towards, , a decision on source medium, or, , a preliminary report? Because also, wholesale is getting, , support. ? , when we’re thinking about that, that’s, , something we’ll discuss today, is, , , should we solve their problems? If they’re, , off in the ether. Or should we focus on, , Carlos’ world, where he’s already getting some support from source medium? It may not be, , trusted and accurate, but at least they’re getting data, versus on wholesale, we saw that they’re very limited on, , their… What they’re able to get out of the system. And then I’m also curious on, … , I would to know more about the contract details on Source Medium, , how much are we paying? What’s, , what is our scope with them? , maybe they can support a small slice of things that… and we can phase them out on a longer cycle. Shivani Amar: phasing them out longer term is probably the move, ? , I would say that that’s… we don’t need, , multiple data consultants that, that’s aligned. I don’t even know what the contra… the cost is, , irrelevant to me. , it’s , that’ll go in. Uttam Kumaran: More is just I can… I can know about, , more of, , I just want to know what the details are, what you guys are paying them, we can see, , is there still some value? If you’re signing up for them for another year, then it’s , , maybe we see if we can get… they can still support some… and figure out the phased-out plan. Versus moving on to ours. Versus if they’re, , if they’re coming up on, , , they’re gonna be phased out by, , February. then it’s , , I need to think a little bit about, , the timing. Shivani Amar: I don’t think we have a timeline to phase them out. . , I also don’t… I haven’t heard anything about, , we’re locked in with them until X, nor have I heard… I’m there’s, , similar to you guys, there’s probably a period of notice. ? , which is, , for you guys, it’s 14 days. I don’t know what their contract says, but let’s say it’s 30 or 60, and it’s , then that’s fine. It’s , when we decide that we feel ready to, , phase that out, it’s us leading it versus, , their contract leading it. Cool. , … you had an agenda, or I had sent some topics, but do you want to go through, , I feel , how do you want to structure this time? Uttam Kumaran: , Awish, do you want to lead? Awaish Kumar: , I… I’ll just share the slides. , we, mainly just looking to discuss, , the… Gantt chart, and then they review it, and then review the ingestion blockers. Then what we are looking to deliver in, January. And then there are some topics to discuss. We can go over it in more details. I can just start with the slide congestion blockers. , , we started ingesting data on 22nd of December. We set up the Polytomic and Shopify and Recharge connections. Shopify data is flowing in for orders and most of this stuff, which we want to do analytics on, but there is some data regarding refunds and payments. that’s not flowing in because of some permission issues. we, , we need to ask tech team to increase permission and scope for that token, which is being used in Polytomic. we just get more permissions, and we can get refunds data. Which would be, . Relevant to where we are building a sales model. Then for immersion, we have access to data, but that’s on different Snowflake instance, now that we have our, , internal, Snowflake instance, where all the data is being landed on, we just want them, the immersion team, to… directly share it with the elements in the Snowflake instance, which we have created. instead the one they shared, and we can’t do that because of some, Because it’s already a share, we can’t share a share. Uttam Kumaran: this… for this item, it’s just… we’ll just draft the email. if Jason has the contact there, then I can… He can send that over, or if you’d us to send it directly, we can go direct. Shivani Amar: , . Have you been put on an email chain with them? Uttam Kumaran: I have not been on a chain with them. I just got forwarded the last… we were asking them just, , what are other ways to get the Emerson data? I just have. Shivani Amar: forwarded the reply. , that’s fine. then… draft it, and then when Jason comes… when we’re all back, we can figure out, , does it make sense for you to have a direct line with Emerson at some point? Awaish Kumar: But after immersion, we have Spins, we have been waiting on Matt Davis from Spins, we have this, , the email thread, we need to follow there. , what’s the status, and how long it’s going to take to get this data? Shivani Amar: Matt Davis. , Spins Access… Your signature, maybe I just didn’t review the documents, sorry. I thought I did. Let me… Let me send him a note, and… Say… Let me send him a note now. Awaish Kumar: , after that, we have Amazon, Where to Go, and Walmart. These are the sources, for which Polyatomic is building the connector. And they’re not working, , this week, they will be back on Gen 5, and then… They can start delivering these connectors. Shivani Amar: Out of curiosity, did, did Fivetran have connectors to this easily? Just out of… Uttam Kumaran: Fran just has… just has the Amazon one. Shivani Amar: But you’re not, you’re , this will happen quickly. Uttam Kumaran: , they would have had it by this week, it’s just Christmas week. It should happen in one week. Shivani Amar: , perfect. Awaish Kumar: , we… , we can. Uttam Kumaran: The Walmart connectors on Fivetran are not good at all, and then there’s nowhere to go in Fivetran, … Awaish Kumar: , on this, we don’t need… there’s action needed from our side. We will be just waiting on Polyatomic. that’s all for the condition stuff. Then we have these. Shivani Amar: , can I ask a question? , , there are blockers, but do you, , if I were just to say, , how do you feel about the data you’ve gotten far? You’re , we’ve gotten, . Rich amount of data, … … We’re feeling the blockers mean that we can’t make a nice transaction table. Uttam Kumaran: Can you go back a wish? , refunds and payments are, , not gonna be the highest priority on Shopify. , payments… , payments is, . We get some, , credit card details, typically clients ask us for that if they’re, , interested in, . , are people who use Amex versus this, , better customers, stuff that, but it’s not really a priority. Refunds, refunds is important, but both of these will get cleared up, . Very, very. It’s just, , one press of a button in Shopify. The Emerson data we’ve already walked through, and then we’ll… the… I’m excited to see the spin data, in particular, and the where to go data. We’re already familiar with Amazon and Walmart, what you get, we’ll get everything we can from them. The Amazon will be, , really, really rich. Shivani Amar: And, , now that you already… now that you already have a decent amount of Shopify data, … I’m curious, , , there are blockers, but you’re , hey, these aren’t the highest priority, , things to have in the dataset away. , now that you have a wealth of Shopify data, which is, , what the source medium has in BigQuery or whatever, ? But what is that… What is that, … Generate for you in terms of excitement about, , , , the tables that we can produce and… that. Uttam Kumaran: , it’s… , go ahead, Awish. Awaish Kumar: , , it’s … we have… almost all the data we need to build the models, SalesMart, , we have all the orders information, the total revenue generated out of this, and the customer information, we can generate, , dim customer tables out of this. We can calculate metrics LTV on Shopify channel. And also, , it will have wholesale data for Shopify, he can generate, . metrics for wholesale, wholesale customers mod, and if we pair it with, , Google Sheets from… from Wholesale team, that can make it, , very rich, dim customer… customer smart for us, for Wholesale team. , , we have, . You can say we have 99% of the data we need. Shivani Amar: Awesome. That’s awesome. , the, … , I even think about how… , I just think about how people are, , downloading snapshots at a time, and I’m , the fact that we’re about to get, , the history of something is just exciting to me, even though I don’t . Uttam Kumaran: And also from the source, ? You’re , … There’s … there’s intermediary now. we don’t… the data we’re getting from Polyatomic is an exact reflection of what’s in the system, every step of the way we build on top of it is documented and, , understood. Versus now, we’re getting it from source medium, and as you saw from everybody we talked to, they’re , Source Medium is, , how we get stuff out of Shopify. But then, there is… there are models, as source media mentioned, between Shopify and their Looker dashboard, and there’s logic on the dashboard side that nobody… Really, , was, , articulated, or it was done maybe, , more than a year ago, and … That all gets removed, , in this process. And , for each of these, we’re gonna land as much as… as much as possible, and then, , you can go all the way back. To have a source of truth for, , order data and understand the transformations , what makes the sausage at the end is, , what this is solving. Awaish Kumar: , and then… Uttam Kumaran: I mentioned the stuff from Spins, Emerson, Where to Go, , Walmart, those are all, , net new. Awaish Kumar: , for Shopify, , the Polytomic is bringing in the historical data as , it will be… It will go back as much as they can. To bring in the data. , , that’s… that’s for January. We… what we want to achieve. In terms of deliverables? I’ve divided into 3 different… Categories? ingestion modeling and reporting. For the ingestion part, , we just want to solve immersion thing with Which will be… which is a quick thing, but I just mentioned it. But then we have Google Sheets for Wholesale team, the… all the Google Sheets that which the team is managing, and maybe using for their analysis, we are going to bring that in warehouse. And somehow also make a connection, if they update anything, we get those updates in our warehouse. Then we are going to… . Shivani Amar: , what does ERP mean here? Do we mean CRM? , , , . I was , I was just getting confused by the slide. , , , sorry, , Google Sheets for Wholesale team, the CRM that they have, that’s what you’re saying, let’s ingest it we can do the mapping. Awaish Kumar: You got him. Shivani Amar: , , I’m with you. Awaish Kumar: that CRM is, , they will have the customer’s , majority of the customer’s information is… lives in those sheets, and the Shopify is… is… just have… will have, , orders information. we are going to connect those both, and generate some sales marks, some customer. Uttam Kumaran: And the logic… their logic that they’re doing… there’s… there’s gonna be, , VLOOKUPs and joins in sheets that we will replicate in SQL, and then produce the same outputs. And then, of course, ideally, when NetSuite comes, there’s sheet, , there’s sheet. Source of truth for much, ? The source of truth. Shivani Amar: , networks. is… I’m getting confused when we’re talking about NetSuite versus… that’s why I’m , ERP versus CRM. This is happening. Uttam Kumaran: do with… , meaning, now, they’re … their actual source of truth for, . their customer information for the wholesale is that Google Sheet. they’re merging in Shopify data with some source data that only lived in that Google Sheet to produce their final reporting. Shivani Amar: , I’m getting thrown off. Why is NetSuite coming into this topic? Uttam Kumaran: Because after NetSuite is included, you will longer have a Google Sheet that is source of truth for customer, , information. Shivani Amar: But I don’t think we’re saying that NetSuite becomes the CRM. Uttam Kumaran: , but my point is that where, , where’s… where is the… Customer information for wholesale gonna end up living longer term? Shivani Amar: , that’s not NetSuite, ? , Phil and I said maybe Salesforce, HubSpot, , I don’t know what the… but, , let’s really… I’m , let’s… let’s just change the slide now to say CRM, because otherwise I’m, , I’m, , getting very… when we say NetSuite, and then in this topic, I’m , it feels separate to me. Unless NetSuite has a CRM functionality that I’m not aware of, but nobody has said that. Everybody’s , at some point, we gotta get the wholesale team to be, , working off of Salesforce or something that. Uttam Kumaran: , , … what… wherever the… more of what is, , the source of truth for… That spreadsheet will move into another system. Shivani Amar: At some point, . Uttam Kumaran: At some point, before that, we have to ingest the Google Sheet in order to replicate their reporting, . Shivani Amar: Perfect. . Cool. Aligned. Just ERP, NetSuite, separate, separate, separate. for you on the ingestion piece, ? This is continuing with the commercial team. Phil has asked if we could get Gorgeous ingested in Jan… or, , I don’t know if it’s ingested in January, but, , start conversations with Gorgeous, with the CX team. And , , where, , are you , hey, this is gonna keep our pipes busy, , all of this ingestion, and there’s not gonna be… our pipes are gonna be… have a cube. Or you’re , , we could be, , we could be ingesting gorgeous. Uttam Kumaran: I’m talking about it. , the bottleneck here is gonna be on the modeling side. On the ingestion side, there’s , . we don’t… we just turn it on. There’s, , , it’s… maybe that… there’s not an… I don’t know what the fair analogy is, but it doesn’t… it doesn’t… , there’s … we could just keep turning on more connectors. What’s gonna happen, though, is that our ability to model it into a finalized data mart you have, , you have things you can easily query, that’s where the bottleneck is gonna be. , in this, , maybe I wish, can we show, , the Gantt chart? I just want to show, … and this is where we’re gonna… we’ll loop in one more person on our team to support with modeling, next week. Because now that we have all the core data landed. Our… the core mission now is to start to build these , commercial marts. , that’s where… We can land the gorgeous data, Pretty quick, but… to start to build, , the data mart on top of it is going to be… the bottleneck. What is possible, though, is if someone on that team just wants to get raw data, and they want us to just run queries to pull that raw data out, or they want to do some modeling in Excel, we can easily give that to them. Shivani Amar: Cool. … I’m trying to figure out how to articulate what you just said in, , a… To say, , one… let me give you one more way of saying it. We’re… ultimately, we are driving towards… Uttam Kumaran: a data mart with our core company objects. DIM customer, DIM orders, fact transactions, ? the source of truth for these really particular objects, we didn’t… we didn’t factor in time for Modeling out a similar data mark for customer experience, what could be back tickets, ? , things about, , ticket health, ticket speed. NPS, ? Everything that goes into Customer CX, we just didn’t… playing for that. . Shivani Amar: That makes total sense. , , that was… that was wordy. Let me try to play it back, ? , I’m saying… Let me think about, … , if we think about who we’re doing discovery with, , in this upcoming month, we haven’t done discovery yet with retail, ? With retail stakeholders. Uttam Kumaran: , on our… on our… , the next slide was more… we wanted to do a discovery call with finance, marketing. And it’s gonna be, depending on what our JAN deliverables are, we will need to do another call with wholesale, because we will have the… we’ll have the data landed, and we’ll be modeling. Primarily for their use case. this is where we can now decide who we wanna, , talk to. Shivani Amar: , marketing team… is… nebulous to me. , … . Uttam Kumaran: That’s what I was talking to Awash today, we just didn’t know whether… , we just didn’t know whether you wanted us to slot that in, or if there’s another. Shivani Amar: , marketing team equals, , , , Carlos. what ? Blake… And then, , retail is doing their own, , retail is , , we’re gonna have the ladder at Target that has, , the end… what is it called? The. Uttam Kumaran: End cards, . Shivani Amar: And cars, , , , we’re gonna do that, ? And, , we’ll trade spend that. what is, , really needed is that you need to get to know Russell on the retail side. Uttam Kumaran: , oh wait, what did you mention to me this morning? Why did we want to try to do another… why did we want to do the call with marketing? , what we discussed? Awaish Kumar: , , we had a call with Carlos, and that was more focused on, , e-com stuff, , what he’s measuring, how the revenue is being generated, what is in Shopify, what is in Amazon. , even though there’s separate marketing team, but what by marketing team discovery call is that I want to understand how they spend money, , what platforms are being used, how money is being spent, and how can we ingest that? , if someone is spending on meta, we can bring that… Shivani Amar: Oh, , , , perfect, perfect, perfect. That makes sense to me. that’s… when you say marketing team, you just mean another round with Carlos, . , gotcha, because I was, , I was just trying to share, I was , you’ve already. Uttam Kumaran: , I forgot, we just talked about this, , an hour ago, I was , what did… I feel , I was , we had the answer, I forgot, because I asked the same question? Alright, cool, great. Shivani Amar: , let’s say, , the discovery calls we want to do are with retail, finance. Carlos with the lens of marketing, more than, , more than Shopify, ? And then we want to do one… , we’ll do the discovery call with EX just to understand, , the questions they’re answering, noting that, note we can ingest gorgeous data But modeling is not prioritized for, this 3-month project with Brain Forge. , perfect. Then we’re saying we want to ingest… I’m just taking notes for myself to make I’m, , super clear on this, ? We want to… can you go back to the ingestion slide, I wish? , we want to ingest… ingest, Emerson data, wholesale Google Sheet, parentheses, CRM. Emerson, into, , into our snowflake, into our italicized snowflake instance, ? Awaish Kumar: Here’s that feel. Shivani Amar: Is it our Snowflake warehouse? Warehouse? Where… Uttam Kumaran: , warehouse. Warehouse. Shivani Amar: Welcome to our Snowflake warehouse. As opposed to… in a shared… warehouse, ? Where to go. And then spins… Walmart… Walmart.com. That’s what that is, ? Walmart.com. Walmart online. I’ll just put Walmart online, and then Amazon. Love it, that feels clear, and then I’ll say gorgeous. We’re gonna also ingest gorgeous, let’s just say that that’s… Uttam Kumaran: Did Phil mention, , what… What he wanted… what he was… wanted them out of that, or… Shivani Amar: he was, , he was , hey, by the way, did you… let me… let me just… gorgeous. Uttam Kumaran: Cause we talk… we talked about it… , maybe a month and a half ago, where they were debating between Gorgeous and, , a new system. Shivani Amar: he was , , regardless if they choose a different system, we’d want the gorgeous data. And he. Uttam Kumaran: He’s a kid. Shivani Amar: He was just , can you have the Brainforge and CX team start to get to know each other, ? I was , , , … and then we can make that clear. That’s, , there’s… we’re not going to be able to deliver a lot of insights for you, but if we want to just, , understand, , the purpose for that discovery call is, , lay of the land on, , the tools that. Uttam Kumaran: , how we do the disco calls already, we’ll just be… we’ll ask them 100 questions, and then… We’ll, we’ll have that, . Shivani Amar: I’m adding… , I’m adding just to your ingestion, ? Then we have modeling, ? I’m , , the… . modeling… , I feel complete on ingestion, , let’s get into modeling. Awaish Kumar: , from our side, what we are looking to do is, , for data modeling, we need to have an infrastructure ready to run the… , the queries we are… the models we are going to write, the SQL queries, they need to be run every day, or on some cadence, and also for the PR, , the… whenever we are creating new models, we need some place to validate them before it goes to production. , we need that infrastructure set up, and we are looking to do that by Gen 9. After that, we are… , we plan, , these mods for wholesale. If we are aligned, we can, , have… , we have Shopify wholesale data, we have… we can ingest Google Sheets for… Wholesale team, and then we can start to build customer mart and the sales mart, , for the wholesale. Shivani Amar: That sounds good. it’s , if I were to put that in, . plain language for me, it’s , maybe Customer Mart is, , the way, but I’m… of it as, , a really clean… , maybe you can explain it to me, because I’m , I … I’m , I see this, and I’m , we’re gonna get a really clean table, we’re gonna get a couple really clean tables for wholesale that can, , be the foundation of… of dashboards in the future, but then I’m , you have your customer Martin, your sales mart, and I’m , don’t know how to describe. Uttam Kumaran: this is where, , these are all semantic definitions of, , data objects, meaning customer mart is, . things about customers, sales is things about sales, you will get several clean tables that describe the customer, that describe sales, ? Tables that refresh on time, have all the necessary information, and are, , validated and trusted, ? And that’s, , what delivering the customer mart… the mart is more of, , a place to come get that information, meaning nobody should be going… directly into the Shopify raw data. Nobody should be, , writing complicated queries again. If they say, I have a question about orders, , check out DIM orders. Some people, that may look , , you can go into your dashboard, and any order-related information is powered by DIM orders. ? But dim orders is gonna be, , we have to join 5 sources together, Amazon is slightly different than Shopify, we’ll have to combine, combine IDs, do unions, , all of the SQL logic that goes into that. is the iceberg underneath this, , one dim orders table? Of course, . , , when you’re saying dim orders, I’m , that gets into our omni-channel of, , , all the orders, ? Shivani Amar: That’s not… that’s not slated for January, because… because we still need to do the ingestion of Emerson, blah blah blah blah blah. the February output. Uttam Kumaran: , that’s correct. Awaish Kumar: put a, , the string, , wholesale. , , for this, for the part of January, , we can have a table. showing all the orders for wholesale team, and… and data about all the customers who we are… who are making, , wholesale orders, ? And then we can continue to build this, then we can have, for e-com, similar tables, and then for retail. And when we have all of them, we can just, , have a one joint table, which combines them all into one. Uttam Kumaran: , wholesale customer mart, wholesale sales mart will be delivered. Shivani Amar: cool. I feel aligned on this, and , , when we think about… Uttam Kumaran: This is where… this is where our… our work is. , really the net… , . Shivani Amar: when we think about this, and, , you’re , hey, we’re gonna need more discovery time, ? , I’m trying to think about how do I flag to Laura that this is, . what is my two sentences I’m sharing with Laura, she can prepare in her sprint to say, hey. We’re going to, … … Let me think… Uttam Kumaran: When we go to replicate the logic that’s in her spreadsheet, we are going to most likely have some questions. about, , the way it’s replicated. There will be things we… we may find that, , , they’re legacy logic, should we be replicating this? Are there additional things that you couldn’t accomplish in your spreadsheet that you want to accomplish in the sales mart? Shivani Amar: , let’s say Brainforge’s goal is for January is to deliver some, , beyond clean tables, , , is to… because you’re saying, , it’s, , to … not have to go into Shopify to get the data, ? You’re , to be able to have . Uttam Kumaran: we should save them on the manual work that… some of the manual work that they’re doing now. That we should save them on that. Similarly, there is probably parts of the spreadsheet that they’re, . This is being held together by duct tape. we want to , , move that out of that type of environment. Third is there’s going to be types of analysis that they haven’t been able to do. And we want to show them that they can now accomplish those analyses, given these… clean tables. Shivani Amar: And the clean tables, , live in Snowflake, ? Uttam Kumaran: They live in Snowflake, and… Shivani Amar: Would it be, , how, , at Brave, I had, coefficient reports? In Google Sheets? Is that the idea? Uttam Kumaran: , we will understand from them whether we need to bring that back into Google Sheets for their analysis, whether they’re equipped to come get that directly out of Snowflake. moment, we don’t have the BI tool, ? , in order to get value out of the modeling, A, they can come in to Snowflake and get that. B, we can also write that back to Google Sheets if they’d it there. Shivani Amar: It will operate very similarly to what you… Uttam Kumaran: felt with coefficient, where something’s writing to a Google Sheet and you’re pulling it out of there. Shivani Amar: , I’m still trying to think about the zoom out for myself, and sorry, I’m going a little slow on this, but it’s , e-commerce has source medium. Wholesale is , , we’re, , we’re aware that they’re doing a lot of manual data pulling. part of the reason we are, . making… I’m trying to articulate this for myself, , if Phil asks, or anybody asks, , why are you focusing much on wholesale? It’s , we have… we don’t have all the data ingested yet to jump to, , omni-channel overview, , , create the full order table and join everything together. We’re still in the process, but in the meantime, we want to deliver these stakeholders out of a high manual… high manual amount of work to do to, , glean insights. And to, , understand their data. We want to deliver them clean tables that can be their reference point for future analysis. And, , in order for us to continue doing… in order for us to achieve that this quarter, it means we’re gonna need some more FaceTime with them. Uttam Kumaran: And ideally, what I want to say, one more point, is not just replicating what they’re doing, this should ideally unlock More time and more flexibility to do more analysis, , versus just the things that they’re doing now. Awaish Kumar: , , , cleaning, standardizing the tags that she was talking about. Uttam Kumaran: , the tag work, , all the stuff Robert and her went into, which is a lot of the tag work, a lot of the, is this the way we’re modeling this data, , . … and also, this is just a… I would say, another way… why to focus on this. It’s… they’re… they’re… they’re really managing a lot in a Google Sheet, and the data is not as complicated as, , the e-commerce and the other data. it is… it is ripe for us to not only tackle that, but in this process, as , we’re using wholesale as a way to make we have ingestion set up, Snowflake set up, dbt set up. And… and really do the one pass at the end-to-end for, , one stakeholder. And this is really… every stakeholder after that, we’re not setting up dbt again, we’re not setting up Snowflake again, we’re not setting up, , an ingestion tool again. every next stakeholder benefits from us going all the way through this. And the next set of stakeholders, there’s a lot… there’s, , a lot more complexity in the… in the… , in the e-com business, And in the retail part, there’s a lot of complexity. Shivani Amar: And the e-commerce business is more complex because we’re also joining, because you’re joining… Shopify with Amazon. Uttam Kumaran: With the marketing, with some of the spend data. Shivani Amar: Walmart.com, and marketing spend. Uttam Kumaran: , there’s not only sources that the team knows, there’s also these news sources. And there’s, , there’s just… there’s a much larger set of metrics that we have to support. Awaish Kumar: We might also have to run shipping data, And all of that. In the… in the e-com sales model. Shivani Amar: , , with wholesale, it’s really just Shopify and the CRM. Uttam Kumaran: that’s… and that’s why I don’t want to say that it’s less complex in terms of the business, but in terms of the data, it’s one source, one Google Sheet, ? we can… we can test out every phase of the… … data modeling, . Awaish Kumar: Unless they are spending anything on marketing, I don’t know. Shivani Amar: , I also, , love the idea of, . deciding on a BI tool this month, that feels good, , momentum to, , keep the stack conversation. Uttam Kumaran: , Wish, you want to go back to the Gantt chart? Let’s… we could talk about that. This is also… I was … I just want to confirm, because this is what we had on our original plan. And this is what we would… I would want to pair with the source medium. Shivani, is, , we do the source medium decision. In addition, we can show source medium versus any other tool we’re deciding on, and, . Wrap that all into a decision by the end of the month. It’s this… but I would say… I would say this is still… I told a waste that I felt this was… aggressive, because I… I always worry that we may find more on the wholesale side than the team is currently aware of, because we’re going to be pulling directly from Shopify, . we just need to define a way, , what is the customer mart and what is the sales mart for wholesale? Shivani Amar: in terms of what tables we are delivering, and in case there’s, , a Phase 1 or a Phase 2. Uttam Kumaran: The BI tool decision, though, as soon as we have some clean tables, we can start to… Work on that. But, , the data… building data models and data mart is, . we will be doing that our whole time here at Element. , it’s gonna… it’s gonna be something we work on for a while, and these will… they’re… data marts mature, meaning we have a lot of clients, after, , 6 months to 8 months of working on a data mart, we’re longer making, . really big new tables or changes, but we are, , adding new columns or changing some logic as things develop. these data marks mature over time, but most of the next 6 months is gonna be doing these data marts, and then they’re gonna have to get displayed somewhere. that’s also what I’m interested to see, , out of the people we meet, are there people capable of coming into Snowflake? Or is everything gonna have to come through a BI tool or into Google Sheets? Let’s also… wrapping up the next set of discovery will help me hear more about that. Shivani Amar: , my instinct… but I don’t know. Uttam Kumaran: Is the mic. I agree with you, probably. Shivani Amar: , I’m just , I don’t know. Uttam Kumaran: People are BI tool. Shivani Amar: , it’s overwhelming, and it’s … . the instinct is clean up tables that you can, , pull into coefficient, and then. you can, , still do analysis in Google Sheets, , that’s my instinct. , sometimes… Uttam Kumaran: On the BI tool as , this is where we’ll do… , the BI tool is also where we’ll talk about all the AI stuff. Because… … Shivani Amar: That’s one thing that is gonna be a knock on source medium, is there’s … Is it aggressive? Uttam Kumaran: It’s aggressive. , sorry, sorry, go, you go ahead. Shivani Amar: Is it aggressive? It should be, , should we say that, , we’re gonna tee up the BI conversation, but not make the decision? , that’s, , where I’m , is this too much? , I would to say that. Because this is… the reason why is this… this is a very. Uttam Kumaran: it’s more subjective at this layer of the stack, and it’s really tuned to what you guys need, versus a customer data mart… , a whole, a Shopify customer data mart is not something… there will be some parts that are unique to Element, but it’s not… it’s very objective, , we’re driving toward the clean table. whether the team likes one BI tool or another. is… will take some time. Also, just, . Shivani Amar: Thank you. Uttam Kumaran: Those proof of concepts may take, , anywhere from 2 to 4 weeks to do. Shivani Amar: Dude, , it’s, , I don’t… it already… it feels. Uttam Kumaran: And it’s hard… this is hard to roll back, by the way. The BI tool is very hard to roll back, and I don’t know, also, if we’re gonna be able to get non-annual contracts. To be… to be quite honest. , I wanna, , , it’s just gonna be a… And this is also gonna be the area where Phil, , exec team, everybody is in and pulling data out. Shivani Amar: . , , getting their blessing, , nobody’s gonna… This feels high stakes to me, , versus Snowflake and pipes in the background thing, … I agree. I’m, , I feel very comfortable being , this is a decision for February, and you begin teeing up the conversation in January. , , to me… In parallel, I’m feeling it in my body, and that’s the signal. , at this business, it’s , if you’re feeling it in your body, that it’s, , feeling, , stressful, then it’s, , not the way to approach it. Uttam Kumaran: , , good to know. Then I would to put that to February. The benefit is, we will have the warehouse… we will have the wholesale, data marts ready for that proof of concept phase, for the BI tool proof of concept, and, , BI tool evaluation. And the last piece is, if there is value we can deliver to the wholesale team. prior to the… whatever the BI tool exists. I would to vote that we try to do that, because they’re… I could… they’re doing a lot of manual reporting, whether that is us pulling them a report out of Snowflake and giving it to them, whether that is us landing data in a Google Sheet, or there’s a third option, I’m not yet. I would to do that, and then all the tagging stuff that Robert highlighted. , more of my point here is, . we’re still continuing on our infrastructure path, but finally, we’re also deciding on a stakeholder to try to deliver some value from. My question is that, is that the stakeholder? And, , are we comfortable with, , those… , is wholesale the stakeholder to try to deliver for our… In this next, , Month or . Shivani Amar: , , you’re pressure testing the thing that we’re talking about, ? And, . Uttam Kumaran: , we’ve met a lot of other stakeholders as . Shivani Amar: what I… my notes, can I share my screen for a second? Bomp boom, boom! , wait, give me, give me… I’m , I’m trying to, , deliver it in a way that makes sense to me, give me one second. The other data ingestion… Gorgeous. One second… , I’m , , in my base, . I’m just, , showing you guys what I’m doing, ? it’s , I do this, , sprint setting document, which is just, , anybody at, Element can, , pop into my sprint and be , what’s going on with, , the data project, ? Because I’m not expecting them to look at your slides. I can link your slides, whatever, but… or the Gantt chart or something. But I’m , look. , the discovery is… , with retail, finance, , whatever. CX with the lens of… Carlos with the lens of marketing, CX, although, , note that this thing is out of scope. And then it’s, , continued… continued exploration with wholesale, and then I want to articulate that it’s, . the, , first team that’s going to be, , getting something. . that’s where I’m , , trying to put. Uttam Kumaran: I would… I would say. Shivani Amar: That’s helpful, ? , I’m , I’m, , I keep being, , we’re doing three things at once in January. End-to-end delivery for wholesale, not , because it’s not BI, but whatever. We’re doing parallel ingestion of other critical data sources, parallel discovery with other stakeholders, ? , I’m trying to figure out… do you see how what I’m doing? I’m, , trying to figure out how to, , make it, . Uttam Kumaran: , I would say we are doing some… , let me think about the wording. Shivani Amar: Think this. Wholesale is intentionally selected as the first stakeholder to go fully end-to-end because fewer data sources, high manual overhead today, tagging reconciliation, lower modeling complexity, and fast path to trust and usability. Uttam Kumaran: , , and then the last. Shivani Amar: I’m , I’m trying to, , take notes and, . Uttam Kumaran: , , that’s… that’s good. The one thing I would mention, though, is that it’s also a great use case for us to do the BI evaluation on top of that data, because there’s not gonna be many other sources. , we couldn’t do… End-to-end e-com modeling. , 3, 4 weeks. There’s, , too much there. But I feel pretty confident we can deliver the wholesale modeling. And use the wholesale data to… measured that to evaluate the BI tool. And , and then this is also where it’s gonna be up to us to figure out how many… , ultimately. how many parallel paths do we want to do? And that’s how, , I’ll… we’ll scale up our team. ? Because we have been discovered… Shivani Amar: … I’m , , the parallel ingestion… I’m… parallel ingestion… Uttam Kumaran: I would say the parallel ingestion is not the risk, because even if they’re , , ingest the next nth connector. We will request it, or we will add it. There’s , , in terms of bandwidth risk on us, that’s not where it’s gonna be coming from. there’s an… the other… , do… for example, if you were , , I wanna… I think we should… start modeling for CX? then I’m , , I need to… ? Or, , I need to start modeling for finance, , I’m just trying to get that we’re gonna agree on, , trying to deliver something. Shivani Amar: The thing that we’re holding is omni-channel modeling. Uttam Kumaran: , , then wholesale is something we have to do. We’ll quickly move into, , e-com and retail after that. Shivani Amar: , to me, it’s , , we’ve got January, then February output is really, the order… dim order table, ? , it’s … Uttam Kumaran: , but also for those, we need to finish the metrics dictionary, we need to think about how we’re displaying the KPIs, ? those are… on the Gantt chart, there’s still, . At the bottom of the game, there’s line items for, . The metrics dictionary being signed off. How are we gonna report on this data, , how does your team versus other teams report? That’s all fed, , Feb stuff. Shivani Amar: , here is where… , perfect. , with wholesale, I liked the… Uttam Kumaran: I can also give you this meeting transcript, by the way, if you want to use it. Shivani Amar: It’s . I’m … We’re doing it live, and I’m , are you not gonna rework my notes after? , wholesale is in, , I’m gonna say, oops, not here. I was trying to show you that marketing is sitting wherever, but I was gonna say that I can, . to Laura specifically, , , I can, , , this is the way this thing works, is, . Context for Laura… ? Wholesale is in… we are intentionally selecting Wholesale. Can you bold in the comment note? As the first stakeholder to go… end-to-end is weird language here. Uttam Kumaran: Not end-to-end, it’s , , to deliver… It’s , to deliver clean… data models for. It’s from what it is from our perspective. , and this is also the thing, , we… the data team overall needs to start building trust with the organization, and we will find out more ways about how people report on things. But this is a stakeholder that now starts to have requirements for us, ? Every few weeks, and we start to build stuff for them. we will also get a sense of, , what is the… , how much time needs to be dedicated now to… Wholesale per week, in addition to continuing to build, continue to do larger things, … it’s, again, just helping us understand, , bandwidth, and really, , this is, , when… if we… if we’re planning after February, we will at least by then know how much time it takes for us to support wholesale, can use that to project a little bit about how much time it’s gonna take to support retail. And… e-comm. And omni-channel support doesn’t really, , exist, maybe consider that, , a third stakeholder, and then you can get a sense of, , , how much time is needed? , that’s what I want to, , land at by 4 Feb, you can also get a sense of, , what is the… what’s the… scope, , that we’re gonna take on. Because ultimately, the worst thing we can make as a data team is to go and support a A team, say we’re gonna deliver something, then just kick it until, , 6 months. Which we haven’t done yet, but … Shivani Amar: I genuinely think Laura is very respected in the org, and… people are aware that, … , she flies to Bozeman when she has to present things about, , Shopify business versus Shopify e-commerce, , , , I feel she’s… she’s… Delivering first. Uttam Kumaran: Or could it be a great win for this team, then we. Shivani Amar: I don’t even feel, , murky on that. , I’m , if Phil was , why are you delivering for wholesale? I would be , here’s the why. It’s fewer data sources, we want to show that we can deliver some, , we can… it’s not even deliver a dashboard, it’s just, , this is where we’re starting off with the clean tables, and then the master clean table for omnichannel is still going to take some, , work on, , making we’re all super aligned on definitions that. . Uttam Kumaran: We may find that other teams we may also be able to deliver for before the BI tool, ? And that’s a good win for others that we can figure. Shivani Amar: And, , , , very tactically on this wholesale piece. Would you say that it’s, , it would be nice for me to have an hour a week scheduled with Laura and Madison. Or is that too much? Are you, , 30 minutes a week just for me to ask questions? , think about how you’re , we want to get to this clean table place, , what would be your flow for working with these stakeholders in the span of January? Uttam Kumaran: , Awish, what do you think? Awaish Kumar: , we can meet with them weekly. Once a week. Uttam Kumaran: And then… once a week in Jan, and then we will wind it down. We will wind it to something more… Infrequent, plus Slack, . Shivani Amar: , and , for how long would you want? Uttam Kumaran: I would ask for an hour a week. And , you can let them know that over time, most of our stuff will move into Slack, and then we can think about Which parts of the org need what type of reporting support? And then another thing that… Shivani Amar: I this as, , a flow that’s , when we’re in the zone of delivering your team clean tables, it’s gonna mean that we’re, , having a weekly. The discovery is a kickoff, but if I’m in the zone of, . , I’m, , really trying to deliver something for this team, it’s a weekly touchpoint, and , , come February, it might mean that you’re weekly meeting with Carlos, or Russell, or something on retail, ? But you’re not switching to the weekly with CX, because we’re not trying to deliver them, , cleaned up tables, we’re just doing the ingestion now. Uttam Kumaran: Correct, correct. And I could show you different ways that, data team , there’s a lot of different support models we can consider. , as we start to set the first data marts, and then those start to mature. Because they will already have… they will… as soon as they get new stuff, it’s . oh, , now that I know we can do this, , they’ll get… we’ll get more requests. then we’ll start to understand our bandwidth. Shivani Amar: Gotcha. then, , , another way that… I’m still figuring out the rhythms, but this is just, , you’re just getting a peek into. Uttam Kumaran: It’s also interesting, because its tech team is the only, . engineering team, we’re… I’m trying to understand also, , how people are meeting, . Shivani Amar: , , if I go to Laura’s, ? I’ll just show you guys how this stuff works. Laura… where’s her assessment brief? I just searched Laura. Laura Putnam, is hers. , she started planning Jan 26th. She’s, , already in here, ? She’s in here now. she’s trying to draft and present a 3-year wholesale vision, she’s , , she’s… Or that was what she was trying to do, reviewing December. Then she’s writing reflections. Then she’s planning her sprint ahead. Refine the 3-year vision. Finalized specialty retail. and then custom fridge designs, ? And then here, I can say, . , , it’s , she has to think, , am I already slammed with, , trying to make these fridges happen, ? , is the data work? , do I have time to dedicate how we make the data work? it’s , that makes sense. I’m gonna show you how Element works, because it’s, , very, , you can go into somebody else’s document and be , what is their thing for this, … Four weeks ahead. Uttam Kumaran: If she ends up being , there’s way I can do that, then we can think about what’s the minimum viable time , that we would need with her? Which, which at minimum, if we can at least get one hour meeting this next month, we can still deliver a lot. We learned a lot in the last one. And also it’s… I know, I forgot who the other woman on her team was, but maybe it’s working directly with her. Shivani Amar: Madison, ? … Uttam Kumaran: , if Madison is the primary person putting stuff together, then… I don’t… I’d rather just work with her directly, because… Shivani Amar: More of my job’s gonna be to empower her. Uttam Kumaran: And it doesn’t break up their sinks at all, ? Shivani Amar: And ultimately. Uttam Kumaran: Definitely, , we’re… the way I try to do this is we just layer it on, and then we can take it all back later, ? If we don’t need that frequent meetings, or we find it’s not, . We could do a lot async. Shivani Amar: , goal is to get wholesale a couple of clean tables that exist within Snowflake that will be a foundational source of truth. Regarding… Regarding wholesale customers and sales. Awaish Kumar: I don’t know, to get a couple… to wholesale a couple of clean tables that exist. Shivani Amar: within Snowflake, there’ll be a foundational source of truth regarding Customers and sales. And, , the nice thing is, , it will go back to the beginning of time, , ? , my work, , this week, if I, , think about what I’m doing, is, , tee up the things with the stakeholders, everybody knows what to expect. It’s a blessing that we have these weeks to, , do this, because otherwise it just gets very, , jumbled and frazzled. And , , the other person that I want to message is Esther. And she’s the head of, , she’s a leader in the CX org. And… I had pinged her, , in November, saying, with your Brainforge Discovery proposal, and, , … or, sorry, , Brain Forge Discovery Project Summary, , whatever your project summary was. And I said, action needed from CX now, but when we get to your team in the discovery phase, I’ll let , and we can set up time with the teammates. , I can say, . Hey, , … modeling… , it’s . we’ve started to ingest data across the business, ? And I’m not where you’ve landed with the gorgeous decision, but we figure we can at least, , start capturing the data in our warehouse this sprint. And , , who on your team would you recommend we have Brainforge connect with? Does that sound ? , perfect. Uttam Kumaran: And anything we learn will end up being valuable if we end up working with them 3 months or 6 months. , all the context is gonna be useful there, … Shivani Amar: And then I will tee up I will tee up… discovery with retail, and then the one that I’m feeling nebulous on is, , why… discovery with finance, talk me through why… , that’s the one that I’m, , from an omnichannel… is it definitions? Is it, , going into, , how they define things? . . it’s less about finance data systems, it’s more about vernacular. Uttam Kumaran: Unless that’s… unless that’s… relevant? If not, then… They’re gonna be the… accounting and finance stakeholder for, , metric sign-off. I’m gonna go through and understand their perspective on All of the most common revenue metrics, and… see, , if they… if they have an opinion on operational ones, too, . Because there are gonna be some metrics that they’re , if you use this metric, it needs to be defined this, . full stop. There’s gonna be other things that are, , those are operational, , they’re not… they don’t have to be, , GAP, ? that’s what I want to understand. Shivani Amar: Totally. , the person for that one is Jacob. Maybe Dan, also. Uttam Kumaran: I know we talked to Dan in the, , when we were just starting to chat. And if we talk to Jacob, then I can… I’ll have some stuff, at least, I can send to Dan async for him to review, because we’ll be working through a larger document, , on metrics, , definitions we’re seeing. Shivani Amar: Jacob and Dan in that meeting together would be good, I can… I can just, , look at… when would you want to do that? Because I can look at calendars and just, , make that happen also. Uttam Kumaran: , I wanna do, , there… I wanna do, There’s two conversations I want to have, there’s one on… Metric 62. , probably, , second or third week of Jan. Because after I get their input, I can start working on They have the entire metrics. dictionary and, , KPI standardization process. Shivani Amar: Do they have their one-on-one… They have a one-on-one, on Jan 12th. 1.30 to 2 p.m. Eastern, and it’d be interesting if, . , I feel if we talk to them after their one-on-one at 2pm Eastern on the 12th, that could be good, because… If you’ve teed up something with them before, they can even, , get aligned on it. Awish, does that time work for you? 2PM Eastern? . , I’m gonna just send that invitation now, one second, … Brainforge metric… Brainforge X Element Metrics Discussion. ? And then, let me add you both. But then, , if you have, , a… two-sentence thing you want to Slack me that I can put in the description of that, that will help. Just , , we want to go through which metrics are GAAP, not GAAP, , whatever you just said after this meeting. . I can add it to the… , I’ll just send the invite now to hold it, but, if you can do that, then I’ll add it to the description later. … Let’s see… . Uttam Kumaran: And then one other item, if that’s good, we… I told Awash that maybe it’d be great for us to meet with the tech team every two weeks to , . Just show them, , what we’ve been doing. that they’re just aware of, , what the setup is that we did in Polyatomic, we’re gonna set up dbt here shortly, Snowflake. I thought it would just be good to hold time with them. , bi-weekly. Shivani Amar: And then me, you, and Jason already talk weekly, … Uttam Kumaran: That way, I don’t have to cover as much of that there. Shivani Amar: Jason, Andy, Steve… , let me look at calendars. , if we go… maybe we’d want something Feb… Jan 7th, let’s say, because you want to talk about the blockers, or whatever, , you want something early, ? Uttam Kumaran: , , we would talk… if Jan 7, we can clear up the Shopify and the Emerson stuff, then that would be great. Ideally, we can do that… we can do that on… if we could do it on Wednesdays, that… that way, our calls are on Thursdays, and … Shivani Amar: How about 2PM Eastern that day? On the Wednesday. , that works for me. , … The thing is, I’m … , , there’s a note on all this that I’m gonna be off some days here and there in January, because I’m going on vacation with my family. , Can you send the invitation to Wish, it’s from your Zoom, for 2PM on the 7th, and then , the 21st, if you want to do it bi-weekly, because I don’t want to be on point to start a Zoom if I’m not able to join. And I can join this Wednesday, but I’m looking at it, I’m , I might not be able to join on the 21st. Awaish Kumar: , , obviously. Shivani Amar: Let’s see… Uttam Kumaran: And then we can also maybe plan on doing, , a source medium… , 2 hours, , , Deep dive sometime next month. Shivani Amar: You wanna do that me and you? , the three of us. that’s just, , a workshop for the three of us, because, , I don’t… Uttam Kumaran: That’s… , , , just us, . we can go through, and then I can, We can start to put together, . Shivani Amar: How about. Uttam Kumaran: At least we’ll have. Shivani Amar: It’s very open for me, the 9th. Uttam Kumaran: , I can do the 9th. Shivani Amar: Meaningful, , chunk with you guys. Uttam Kumaran: , , I’m … I can do, , 2PM Eastern. 2 to 4, or I can do… We have… we… we have a Friday, , all hands at 1 o’clock Eastern, if we can do two, that would be… Shivani Amar: why don’t we do… why don’t we just do an… , I can go. Uttam Kumaran: Or just do an hour. Shivani Amar: Let’s do an hour, because otherwise we might be, , eyes glaze over on a Friday, I don’t know, . Uttam Kumaran: I just want to click around with it with you and give you our commentary, and then… Shivani Amar: We’ll start to put our memo in there. , I’m sending that to you guys, source medium review for 2 to 3 p.m. on Friday. This is great. Hope you’re having a wonderful RNA period and holidays with your family. Wanted to give you a data project update. We’ve started to ingest some of our data from our commercial side of the business, Shopify Retail, etc, and Phil suggested we started ingesting Gorgeous data as . While we don’t… In this 3-month project. with brain words. Don’t have… data modeling… Slated for CX data. Kicking off. Setting up. data pipes. , perfect. I will send Esther a note. , I’m saying… hope… I’m, , running everything through ChatGPT today, because I’m still getting my brain activated. Hope you’re having a wonderful RNA period and enjoying the holidays with family. Wanted to share a quick update. We’ve started ingesting some of the commercial side data, and Phil… Bill, also, why did… why did Chad keep T safe? And Phil also suggested that we begin ingesting gorgeous data as part of this effort. We’re not planning to prioritize CX data modeling, but I do think it makes sense to kick off the discovery. to get the data pipeline set up in parallel we’re -positioned for future work. Let me know who on your team you’d to include on a discovery call with BrainForge, and I’m happy to take a look at colors to get something scheduled. Bus, that’s it. Does that sound good? Because I’m , this feels a little bit, , I’m , I don’t want to over-commit anything, , to CX. , if they’re, … , but you… but a connection point that’s interesting with them? It’s , the… Do I have it? Let’s see if I have it. saved. There’s, , this wholesale a CX collab. Let’s see if I can find it. Wholesale CX. There’s, , this document that… Uttam Kumaran: , that’s interesting for me to just hear about, , what the CX roadmap is, because… Oftentimes, it just becomes, , a black hole for data teams, because the business sometimes doesn’t care, but the CX… there’s a lot… Of data involved? Shivani Amar: The team probably wants to optimize, optimize, optimize, and, , everybody else is , you gotta be doing what you need to be doing, . Uttam Kumaran: that’s why I want to know, , what is it that they’re missing? Shivani Amar: I’ll read through their briefs also, and get a feel for it, and can follow up with you. Uttam Kumaran: People may be , oh, we need this… we need to make that we use a CX Insights-informed product, and there’s link, and you need data to try to… can we use data to find out what customers are saying? , … It’s, , one thing, I’m just trying to understand that, . Shivani Amar: what I was gonna say is, , there’s a file that… will I be able to find now? . But there’s a file where CX copies and pastes wholesale-specific the ex-concert. Laura to review. I see. That’s what I’m trying to get at. I’m , there’s a world in which Laura in particular is, , when does CX get inbound from my customers? Because she’s trying to make that her customers are happy. And that’s the thing people are, , manually putting together, that’s what I’m, , trying to find now, and I’m … I don’t know… I don’t know, , maybe, , if I look at what Laura has shared with me, maybe I’ll find it, but, , , , I’m , I would need to re-find that document, but, , there was a moment in time where There was a moment in time where Laura rolled out a change to some wholesale partners, saying, , , we’re not gonna sell you 30-count sticks anymore, we’re gonna sell you 12-count sticks or something, and she got, , a surge of inbound of people messaging CX, being , I’m pretty upset about this, because, , that’s where the CRM stuff gets a little complicated, ? . Because then you’re , who’s a… who’s a wholesale partner that I need to flag this to wholesale that this came in via CX? if we’re trying to deliver something for CX, and we end up ingesting data, gorgeous data, that might be, , a link that we can aspire to. That’s all I’m saying. . Long-winded way of saying that, but I was , there’s something here that’s beneficial. For wholesale in the end. Uttam Kumaran: next week, too, I can follow up with this directly on Monday, but we’ll… The spins follow up? Shivani Amar: I sent them an email being , did I send them the thing? Uttam Kumaran: , cool. I didn’t get anything, , , and then the Emerson follow-up, we’ll talk to Jason on Wednesday. We have our source medium call, we have a tech team call. we’ve made a decision on, , still going after the BI tool, but more in February. There’s, , there’s a lot I will tee up, , as much as I can in Jamf for that. Shivani Amar: Is your instinct Omni, on BI? Uttam Kumaran: I hate to say… I just don’t saying things that, because I just don’t… this… I have to really… Shivani Amar: Tell me! I’m not… turn off your recorder, , whatever. Uttam Kumaran: , , , it’s not about recording, it’s more about, … It’s just… it depends, , Omni is a really good one, Sigma is a really good one, they’re… they’re all… Shivani Amar: What’s your instinct? Uttam Kumaran: Aisha, what do you think? Awaish Kumar: , it’s pretty… , we are exploring the AI part, , we have been previously using multiple tools, Tableau, Power BI, Omni, and also some tools which you… which provide, , BI as a code. But then, … It depends on the client, , some people… are comfortable with Tableau, they just want to also explore themselves, come in the Tableau, try to build a chart themselves, they’re really good at it, and they just want us to use Tableau. We don’t have. Shivani Amar: anybody in the business is, , really good at making their own Tableau charts, , you’re not coming in… , we use Tableau at Brave, but, , I also thought it had… , I would love to be able to query data, and, , if it’s… what ? … Uttam Kumaran: I have a feeling it’ll be Omni or Sigma. Shivani Amar: But, , also, , I’m a weirdo, and I don’t even … Data visualization for me is, . If I have a clean table. that then I can, , see trends, or, , I can, … I’m more of the, put it back into Google Sheets I can mess around with it type of person. Probably. And, , there are, , some things that I, , really, … if we just do this very quickly, which then I’ll let you guys go, because I’m just taking up a lot of your time, but if… if we look at this really quickly, and I’m , this is… It’s chaotic looking, and I’m , what are the timeframes that we’re using here? , it’s month to date, ? I don’t… I’m , why isn’t it December 1st? , I’m just confused when I look at this. Uttam Kumaran: there’s a lot of dashboard design here that seems… the reason why these guys… because these guys will give you what they’re giving to everybody. Shivani Amar: And I don’t it. Uttam Kumaran: , I’m not… Shivani Amar: , what are the questions that we’re trying to ask ourselves regularly? And if it is, how are orders progressing weekly? Then give me a data table that shows that, ? , what do I want to understand? And it’s , I want to understand how orders evolve weekly over the business. And, , relative to what I thought it was gonna be, or whatever, , what is the question I’m trying to answer? Because this is just a hot mess to me. It’s not bad data, but I’m , I don’t know how to get one insight out of this when I look at this. Not one, because it’s daily data. Daily data does nothing for my brain. Uttam Kumaran: It’s , , it doesn’t. Shivani Amar: Why would I ever want data daily in this scenario? , and , with little graphs, I’m , I want to know… I feel I’m yelling, but I’m . Uttam Kumaran: , , , I agree with you, it’s not… Shivani Amar: I want to know, , weekly trends and whatever, and then I’m, , able to be , , that’s seasonality, that’s what we expected, that’s not blah blah blah. Uttam Kumaran: this is also where it’s , if our… if our team… if, , the data team is going to be modeling and developing the dashboards, then, , it’s gonna be Tableau, Sigma, or Omni. The other thing is I just drew… there are some new tools. The problem is that you may select them, and they may go out of business. And I can’t recommend one of the new… some of these new tools. Because, , they may be, , really flashy in AI, but… Some of them will go out of business, and … Shivani Amar: I want a demo of Omni. Can you just… Can we just do a demo of Omni? . Uttam Kumaran: Totally, I can demo you our Omni… we have an Omni instance for BainProach, I can show you everything. how the AI stuff works. Shivani Amar: Let’s do a mini omni… how much… , 30 minutes, an hour? How much would an Omni demo be? Uttam Kumaran: , we could do 30 minutes, and then… , , I can even give you access to ours to play around, because we have sample data in there, sample e-com data, you can go in there and mess around and do stuff. Shivani Amar: Can we do… Uttam Kumaran: , we should just do sometime, , next week. , we could do it during… if we’re gonna talk Friday, we can just do it… Part of our Friday call. Shivani Amar: . Or, , immediately after the Thursday call, , the three of us stay on for a little… , , we have the call with Jason, and then we just tack on 30 minutes to look at Omni. Does that work? Uttam Kumaran: I have a… I want to bring in, Awesu, on our team. is, , doing a lot of Omni stuff now, Demolade? I kinda would want… Awaish Kumar: Beautiful. Uttam Kumaran: , I kinda want him to come and do the demo. Shivani Amar: Does 3 to 3.30 work for, , 3 to 3.30 Eastern work after our Thursday call with Jason? Uttam Kumaran: I have a 3-330. Shivani Amar: Or, or 3.30 to 4, or 4 to 4, , just that day. We could do… Uttam Kumaran: , we can do 3.30 to 4. Shivani Amar: Let’s do that. And then I’ll have someone from… You wanna send it? Uttam Kumaran: Doing a lot of… Shivani Amar: you can include your person. Perfect. Guys, we’re cruising. Uttam Kumaran: And we’ll add you, we’ll add you to our, our… Omni instance. And you can… you can mess around and see, , what a fake data set in there, . try to create dashboards and check out the AI features. Shivani Amar: , , perfect. , look, , Laura just updated her document, and she wrote. BI onboarding and set up with volume and brain functions. I’m , I don’t know if that’s the way to articulate that. , at least you have it on your, on your list. I don’t really know what you just wrote. Uttam Kumaran: They’re , I’m , I’m , please set us up for success here with VI onboarding. She’s , she’s , oh , great, they crushed it over Christmas, they got everything going, we’re gonna be good. Shivani Amar: We’ll let it… we’ll let it stay as she has it. Cool. Do you want, Jason to be on that? Uttam Kumaran: I can also… , we can also… we can send the recording, too, after. Shivani Amar: That’s fine, . How do you guys feel? Are you , cool, it’s gonna be a packed sprint, but it feels doable, we’ve punted the BI decision, that felt some breathing room, we’re committing to, we’re not doing any data modeling on CX, we’re just doing a discovery call with them, and… , are you, , cool, , this feels, , doable, manageable, the things that we’ve aligned on? Uttam Kumaran: , I feel it. , the biggest thing for you is to just keep pressure on Polytomic the next two weeks. And then we’re gonna loop in one more person to start helping with modeling Shivani next week. We’re just deciding on, . Our allocations for… Next quarter. That way, it’ll be me, Awaish, and one other person. Usually, we staff all of our teams with, , 3 anyways, but… That way, we’ll… we’ll be able to continue, because now, we’ll have, . Roughly 2 or 3 different parallel paths, … . Shivani Amar: For… that sounds great. For the… retail discovery. you want to draft the Emerson email, but you have retail data, ? , when you think about, , let’s say Russell is the guy who’s , , over retail now, and he’s the one… when about his role… , let me just look at something for a second, because I’m … There’s, mmm, let me show you something. , what he’s doing, , if we look at what he’s up to, , planning January, he’s, , securing the test… he’s, , the one talking to all the buyers. , that’s, , his role. then it’s , he’s the one talking to all the buyers. Now, is he the one looking at retail data velocity performance, ? , let’s look back at the org chart for a second. you’ve got… Commercial. Uttam Kumaran: Can I get, , a copy of this? Shivani Amar: I, I… Or … I hope . . Uttam Kumaran: Or even, , an image export, whatever. Shivani Amar: , I’m … Let’s do this. And I’m … … Uttam Kumaran: You’re multi-talented, pick what you do most often. Shivani Amar: I’m , what? I’m … Data analytics. One moment. Leave me alone, Figma. I’m just trying to do something. . , maybe visible to other people. Now, can I share it with you? Can edit? , can view. Uttam Kumaran: I don’t need edit access, . But it looks it’s global anyways. , it looks it’s. Shivani Amar: If I just send it to you… Uttam Kumaran: Anyone can access it now, just to… even if you just send me the… Shivani Amar: I wasn’t logged in, ? … Did that link work that I just sent you? But anyway, , let’s just look at revenue for a second on my screen. Uttam Kumaran: , it’s working. Shivani Amar: You got Will. ? And he’s, , overseeing all of these humans. Then you’ve got retail, and it just says Russell, Key Accounts. Now, he’s owning the accounts, is he the best person to say, how’s our drink mix , how is our sparkling velocity relative to what we thought it was gonna be? That’s ultimate… Uttam Kumaran: may not have a Madison, or who is… , if it’s gonna be Will. Shivani Amar: That’s ultimately Will, ? , please don’t make groups or artboards, but the revenue team makes it feel… , whatever, people are adding notes. , I’m , Will… Will is the person, but, . He is the person that is the busiest. ? I’m trying to think about, . , you should have a touchpoint with Russell, and just, , talk to him, and be , what things are you looking at? But it’s… I’m… what I’m trying to flag to you is that it might not be the most fruitful from a data perspective, and that might ultimately be me. That’s, , trying to help us craft it. And then if we look, look at the, oKRs… deliverables, ? , it’s … What is he tracking? commercial, will e-commerce, retail. they’re, , they’re putting in, . , what are our drink mix point of sales? Drink sales? This is, , probably, , Russell and Will just, , adding the data in. Drink makes point of sales, drink mixed sales, drink makes trade spend, sparkling point of sale, sparkling sales, drink makes point of sales in Target, , this was overall, then you’ve got Target, then you’ve got Walmart. Costco Canada. Costco US, Sam’s Club, Vitamin Shop, and you’re , , building off of the, , retailers that you’re in. , , going through this sheet with… with Russell is fine, ? But… I’m just floating. Uttam Kumaran: , , if one’s gonna end up being… , then it may just be this team. Shivani Amar: , … let’s look at this quickly. , if we look at Russell’s calendar, if you want to do the discovery call with Russell and just be , hey, we’d love to, , go through your OKRs, ask some questions , he’s available on the Thursday, if you guys are. Uttam Kumaran: , we can do before our call. Shivani Amar: Noon your time. , Brain Forge, X Element Retail. Discovery. Cool. And then added… that’s good. This was a good planning session. Uttam Kumaran: How do you feel? Shivani Amar: I forgot that I have a massage next Sunday, that made me feel better when I looked at my own calendar. I was , oh, that was smart of me to book the day before work really starts again. I was , oh god. That was some forward-looking thinking, girl. Uttam Kumaran: , me and… me and my girlfriend, we’re going to Boston tomorrow. We’re going to Maine, , for New Year’s. Spent some time with some friends, that’ll be nice. Shivani Amar: Awish, what are you gonna do for New Year’s? Awaish Kumar: , I will be visiting some places here in Karachi. We have, , a place where there’s an event for fireworks and all. Shivani Amar: That sounds fun. That’s awesome. I’m gonna be in Delhi, in… early March. Uttam Kumaran: During our Rest and Assess week, it’s … Shivani Amar: I wish I’ll be more in your time zone, and I might do a little bit of work that week, because I’m, I got engaged over the holidays. Uttam Kumaran: Oh, congrats! Shivani Amar: It was funny, because you guys sent the flowers, and that was the day that he proposed, and I was , this is perfect, and a lot of other people. And then… and , I’m going to India to, , shop. Uttam Kumaran: Hell , let’s go. Shivani Amar: , that’s… that’s the flow, … Uttam Kumaran: Oh, I’m glad the flowers were timely then. , the flowers worked out. I was , the vibes are good. Shivani Amar: , I’ll be in India and in your time zone for a little while. The rest and assess week, the beginning of March. And… we might do some… we’ll figure out a… I really liked what we did today, which is … , if… by the beginning of March, your contract is done, ? . Technically. I imagine it will continue, and then if we… that’s what I imagine, we’ll talk to Phil , but if we’re continuing, then we could do some call this, maybe before I leave for a year, to say, , how do we want to prep the next sprint? Because this was super helpful. Obviously, we’ll… Uttam Kumaran: you’ll start to… Shivani Amar: something… We’ll do the how to be. Uttam Kumaran: to get a sense of our speed, . , that’s why in this first two months, you’re gonna see, , what it’s for us to support one customer, we set up all this stuff, and then we can start to use that as a unit to be . , , if we’re gonna support 3… do we need to support 3 people? Do we support 5 people? How should… how does Brain Forge’s resources scale to hit that? And that… that’s all… that’s all we need to look to for the next contract, … we’ll be in a good spot by then. But then, I’m, , really hopeful we can get BI set up, And, , I really want us to start using some of the AI tools within the… within some of these BIs that we select, because that’s really, , some of the innovation that’s happening now, … Shivani Amar: Dude, , great session. I was , we could do 45 minutes. , it was an hour and a half. problem, it’s a… it’s a cho-. Uttam Kumaran: , a lot of clients are off now, and some of our team’s off, we’re just also planning our, , Q1 this week, … Shivani Amar: . It’s good, I’m happy to spend time. Thank you guys much, have a happy, safe entry to the new year, and then we have some great sessions set up, and if I have questions on, , how I’m describing things, I’ll just ping you async, , but I… Perfect. Uttam Kumaran: And I’ll send you a little blurb for the… for the, What was it for the finance meeting? , . Shivani Amar: , and at least I’m feeling more clear within myself on what we’re trying to do this month, that’s really nice. Uttam Kumaran: , perfect. Shivani Amar: , thank you! Uttam Kumaran: Alright, thank you. Talk to you soon.