Meeting Title: Eden Tableau Walkthrough Date: 2025-09-04 Meeting participants: Tracie, Amber Lin, Mitesh Patel, Brad Messersmith, Demilade Agboola, Henry Zhao
WEBVTT
1 00:00:11.220 ⇒ 00:00:12.350 Amber Lin: Hi there!
2 00:00:16.970 ⇒ 00:00:18.630 Amber Lin: Hi, nice to meet you.
3 00:00:18.630 ⇒ 00:00:21.020 Tracie: Nice to meet you, as well!
4 00:00:22.100 ⇒ 00:00:23.380 Mitesh Patel: Hi, Amber, how are you?
5 00:00:23.380 ⇒ 00:00:29.529 Amber Lin: Hey, okay, we just had a stand-up, we have a question… a few questions that we’re gonna post out in the channel.
6 00:00:29.680 ⇒ 00:00:40.820 Amber Lin: But I know this one is about Tableau. Demolade should be joining. I saw a few other folks on the invite, should we wait for them to come as well?
7 00:00:40.940 ⇒ 00:00:44.099 Mitesh Patel: Yeah, let’s have, let’s wait for Brad, too.
8 00:00:44.100 ⇒ 00:00:44.960 Amber Lin: Alright.
9 00:00:47.020 ⇒ 00:00:56.719 Mitesh Patel: Amber, we’ve certainly exchanged some emails, but I think, you know, and I know we were in the data, one of the data stand-ups together, but I think this is the first time I’m seeing you on video.
10 00:00:56.900 ⇒ 00:00:58.070 Mitesh Patel: So, nice to meet you.
11 00:00:58.070 ⇒ 00:01:05.640 Amber Lin: Because every time it’s so early in the morning, I roll out of bed, because I’m on the West Coast, I roll out of bed, it’s like, nobody is gonna.
12 00:01:05.640 ⇒ 00:01:08.099 Mitesh Patel: Like, the video is staying off, I get it.
13 00:01:08.670 ⇒ 00:01:10.230 Amber Lin: Hi, Brad.
14 00:01:10.840 ⇒ 00:01:12.239 Brad Messersmith: Hey, how’s it going?
15 00:01:12.870 ⇒ 00:01:15.660 Amber Lin: Good! Are both of you new?
16 00:01:17.070 ⇒ 00:01:17.800 Amber Lin: Okay.
17 00:01:18.300 ⇒ 00:01:19.750 Brad Messersmith: Do quick intros.
18 00:01:21.830 ⇒ 00:01:29.339 Brad Messersmith: Yeah, I can start. Brad Messersmith, I, came on as VP of Telehealth Operations. This is my…
19 00:01:29.540 ⇒ 00:01:40.069 Brad Messersmith: Third day, I think? Second or third day? I have a background, a lot of operations, supply chain, manufacturing, large and small companies.
20 00:01:40.150 ⇒ 00:01:52.290 Brad Messersmith: I’ve done some digital and ERP integration-type work, so have some background in things like business analytics, for example, and Tableau and Power BI dashboards, or
21 00:01:52.400 ⇒ 00:01:57.449 Brad Messersmith: near and dear to my heart, I guess. So, really looking forward to the subject matter here.
22 00:01:59.360 ⇒ 00:02:00.690 Amber Lin: Okay, awesome.
23 00:02:00.980 ⇒ 00:02:02.539 Amber Lin: And Tracy.
24 00:02:02.540 ⇒ 00:02:15.950 Tracie: Tracy, I’m Tracy. Today’s day 7 for me, so I’m a little further. I… I’ve… I’ve loved learning about supply chain and operations, and done this for…
25 00:02:15.950 ⇒ 00:02:24.109 Tracie: gosh, a long time, a very long time, and I am really excited to kind of dig in and help see what I can solve, where I can solve.
26 00:02:24.170 ⇒ 00:02:32.609 Tracie: I’m pretty familiar with a lot of tech and data and analysis and Tableau and all that stuff, so I’m really excited to see what you guys do and how I can help.
27 00:02:34.010 ⇒ 00:02:51.170 Amber Lin: I see. So, I think first, before we introduce you to Tableau, I think, because we have quite a few dashboards, we want to understand, what area you would be working, and then we can point you that way, and I think your intro helps us. I’m looking at…
28 00:02:51.170 ⇒ 00:02:55.319 Amber Lin: So I… let me share this documentation with you.
29 00:02:55.440 ⇒ 00:02:59.880 Amber Lin: So here’s one that our analysts
30 00:02:59.980 ⇒ 00:03:14.259 Amber Lin: prepared for us, so we have some dashboards for the execs, some for marketing, and some for, member experience, pharmacy operations that we’ve done lastly for.
31 00:03:14.260 ⇒ 00:03:20.939 Amber Lin: a few for product and a few for finance. And I think when you guys work on the supply chain,
32 00:03:20.940 ⇒ 00:03:32.089 Amber Lin: that data should be feeding into the executive and the marketing, dashboards, and Demolotic, let me know
33 00:03:32.500 ⇒ 00:03:34.070 Amber Lin: what.
34 00:03:34.070 ⇒ 00:03:39.820 Demilade Agboola: Yeah, so, hi, my name is Dimladeh, basically…
35 00:03:40.050 ⇒ 00:03:48.799 Demilade Agboola: we have a number of dashboards, and we do… I could run you through a couple of them, but I think, most importantly.
36 00:03:51.770 ⇒ 00:04:02.799 Demilade Agboola: knowing what your, like, deliverables are and what your end goals are would be very helpful for us to know what dashboards you need. But we can… we can be able to say, hey, if you need to see the time it takes for
37 00:04:02.800 ⇒ 00:04:18.160 Demilade Agboola: you know, delivery, for instance, we can start to have dashboards specific about that. So being able to, like, tune into your OKRs will be very helpful, but, like, for now, we can just kind of walk you through, like, what we have in place for different teams. So I can share my screen.
38 00:04:18.230 ⇒ 00:04:22.119 Demilade Agboola: For that, it might not necessarily be…
39 00:04:23.890 ⇒ 00:04:29.849 Demilade Agboola: Give me one second. It might not necessarily be what you need right now, but it’s currently what we have in place.
40 00:04:30.760 ⇒ 00:04:34.340 Demilade Agboola: Boom.
41 00:04:39.400 ⇒ 00:04:40.949 Demilade Agboola: Okay, can you see my screen?
42 00:04:43.350 ⇒ 00:04:47.199 Demilade Agboola: Alright, so we have a couple of dashboards, here.
43 00:04:47.530 ⇒ 00:04:58.130 Demilade Agboola: Some are in progress, and some are… have been published to… to different teams. So for instance, this will be something Mitesh is quite familiar with.
44 00:04:58.310 ⇒ 00:05:02.960 Demilade Agboola: So here we have, like, the marketing dashboards where we’re able to see
45 00:05:04.210 ⇒ 00:05:22.600 Demilade Agboola: retention, and just, like, how much people are, like, when we break even, and how much, like, the different cohort sizes, and what’s happening. We can start to filter by products, and just have an idea of what’s going on, and how well we’re doing with our stickiness, which, again, this is very important to
46 00:05:22.760 ⇒ 00:05:23.880 Demilade Agboola: marketing.
47 00:05:24.140 ⇒ 00:05:29.189 Demilade Agboola: As supply chain, I don’t actually think that will be the most important thing to you right now.
48 00:05:29.480 ⇒ 00:05:36.150 Demilade Agboola: But I think things that could be really helpful to you right now, high level, would be…
49 00:05:36.200 ⇒ 00:05:37.959 Amber Lin: I know we have our…
50 00:05:38.280 ⇒ 00:05:40.350 Demilade Agboola: order joining dashboard.
51 00:05:41.500 ⇒ 00:05:49.480 Demilade Agboola: So, share… Let me second.
52 00:05:50.660 ⇒ 00:06:06.099 Demilade Agboola: So here we have, like, some of these metrics, so you can start tracking things like, how long it took from when it was ordered to when it was shipped, the order to pharmacy time, from pharmacy to shipped time, and from order to delivery time.
53 00:06:06.300 ⇒ 00:06:13.180 Demilade Agboola: So we can start to, you know, go a bit more granular, and start, like, looking at the individual, like.
54 00:06:13.430 ⇒ 00:06:14.230 Demilade Agboola: Fine.
55 00:06:14.610 ⇒ 00:06:25.979 Demilade Agboola: So over the last couple of weeks, the products, we can filter by products, the membership plans, the pharmacies, that sort of thing. We can start to dig deeper into, like, the turnaround time.
56 00:06:26.390 ⇒ 00:06:28.380 Demilade Agboola: For the different pharmacies.
57 00:06:28.620 ⇒ 00:06:32.799 Demilade Agboola: And then we have, like, risk signal.
58 00:06:32.960 ⇒ 00:06:34.479 Demilade Agboola: orders right now.
59 00:06:36.890 ⇒ 00:06:43.949 Demilade Agboola: Yeah, so we kind of have, like, these tables. I think that’s kind of why I said, like, I can show you things we have.
60 00:06:44.230 ⇒ 00:06:54.629 Demilade Agboola: But I think what’s really important is just knowing, like, what do you need to see on a daily basis to be able to say, hey, I’m productive, I’m able to hit my goals.
61 00:06:54.830 ⇒ 00:07:00.269 Demilade Agboola: And I’m able to understand what’s going on with, you know, our supply chain.
62 00:07:03.450 ⇒ 00:07:05.120 Tracie: That’s great, thank you.
63 00:07:06.890 ⇒ 00:07:07.850 Demilade Agboola: Removal.
64 00:07:08.480 ⇒ 00:07:11.230 Demilade Agboola: So, do you have some, like, numbers you would love to see?
65 00:07:13.940 ⇒ 00:07:32.830 Brad Messersmith: What you just showed, I think, was definitely relevant to what we’ve been discussing, at least recently. We’re both… Tracy and I are both brand new, so I’ll pass the baton to Mitesh in just a second, but ultimately, we’re both going to be focused in operations and supply chain in, I’ll call it telehealth.
66 00:07:32.960 ⇒ 00:07:40.780 Brad Messersmith: But what we’re really looking to do, I think, right now, is improve on the number of out-of-condition SLA orders.
67 00:07:40.940 ⇒ 00:07:55.999 Brad Messersmith: I mean, that’s the kind of code red that we’re on right now, is to try and get to 95%. That… so, if there’s one KPI, I would say that’s it, but if you can go back to the previous dashboard that you were just in, the order journey, I think is what it was called.
68 00:07:56.380 ⇒ 00:08:02.699 Brad Messersmith: Some of that looks pretty similar to some of the information that Tracy’s currently pulling into spreadsheets.
69 00:08:03.140 ⇒ 00:08:09.229 Brad Messersmith: And exporting from the system, so… from my view.
70 00:08:10.090 ⇒ 00:08:16.629 Brad Messersmith: it might be helpful, even just to free up some Tracy’s day-to-day and some of the team that she’s working with.
71 00:08:17.310 ⇒ 00:08:20.279 Brad Messersmith: If we can use some of this information
72 00:08:20.380 ⇒ 00:08:25.889 Brad Messersmith: Like, this, pharmacy turnaround… sorry, order to shift greater than 3 days, so…
73 00:08:26.570 ⇒ 00:08:36.859 Brad Messersmith: some of the questions I think I would have, maybe if we can get Tracy and myself access to this, would be, how does Tracy’s information compare to what’s listed out here? Because
74 00:08:37.330 ⇒ 00:08:44.429 Brad Messersmith: If I understand correctly, Tracy, you’re essentially pulling this same type of information into our daily meeting, right?
75 00:08:46.790 ⇒ 00:08:51.749 Brad Messersmith: So this one would definitely be, I think, very high on our list in terms of access.
76 00:08:52.080 ⇒ 00:08:52.670 Brad Messersmith: Cause…
77 00:08:53.010 ⇒ 00:09:02.739 Brad Messersmith: maybe this… maybe this becomes our home base, Tracy, that really, you know, from what I’ve seen in the couple of meetings that I’ve been in, I want to really make sure that we’re
78 00:09:04.190 ⇒ 00:09:22.870 Brad Messersmith: fundamentally moving the needle on the SLA. What I understand is we have thousands, potentially, of orders that are out of condition, I’ll call it. I think of this as, like, a backlog in my world, but if we have this big backlog, we’re working through some errors, there’s a combination of different factors that are causing this.
79 00:09:23.230 ⇒ 00:09:32.900 Brad Messersmith: But we want to make sure, potentially with this dashboard or one like it, that we’re… we’re actually seeing the fruit of that labor.
80 00:09:33.080 ⇒ 00:09:36.839 Brad Messersmith: And then creating… You know, a sort of…
81 00:09:37.300 ⇒ 00:09:39.610 Brad Messersmith: I guess, baseline for being able to
82 00:09:39.780 ⇒ 00:09:43.399 Brad Messersmith: Maintain that long-term as we continue to grow.
83 00:09:44.070 ⇒ 00:09:48.010 Brad Messersmith: fill in for me here, Mitesh, if I’m missing anything, or Tracy as well.
84 00:09:48.910 ⇒ 00:09:51.219 Mitesh Patel: No, I think you got it. I think,
85 00:09:51.790 ⇒ 00:10:02.249 Mitesh Patel: So, so from a dashboard perspective, this is very helpful, and this is exactly what we need, you know, sort of, orders out of SLA at two levels, right?
86 00:10:02.680 ⇒ 00:10:15.210 Mitesh Patel: one… at, to the doctor, right, for a consultation. And the second one is to the pharmacy.
87 00:10:15.210 ⇒ 00:10:31.670 Mitesh Patel: And what we’ve done, and I think you have them all here, it… the statuses, you know, there’s, like, two status columns. There’s a order status, like in these reports we get from BASC. I assume you’re getting all this data out of BASC, right? But in BASC, there…
88 00:10:31.690 ⇒ 00:10:36.059 Mitesh Patel: Yeah. In Bass, there’s an order status, and there’s a pharmacy status.
89 00:10:36.100 ⇒ 00:10:46.269 Mitesh Patel: And the actual status status of the order is some combination of those two fields. And I’ll show you, you know, we did this… I did this to kind of…
90 00:10:46.340 ⇒ 00:10:50.850 Mitesh Patel: And it’s not completely refined. Let me share…
91 00:10:51.080 ⇒ 00:10:54.700 Mitesh Patel: my screen with you, if I can find the right term.
92 00:10:55.310 ⇒ 00:10:58.050 Mitesh Patel: Half the time, my day goes searching for the right tab.
93 00:10:59.860 ⇒ 00:11:04.820 Mitesh Patel: What do we call it?
94 00:11:05.160 ⇒ 00:11:06.230 Tracie: End of the day?
95 00:11:06.680 ⇒ 00:11:08.389 Mitesh Patel: End of day. Nice, thank you.
96 00:11:08.390 ⇒ 00:11:11.180 Tracie: You’re welcome. I have to search for it a lot.
97 00:11:22.110 ⇒ 00:11:30.660 Demilade Agboola: Also, just, while, like, Nitesh is looking for this, just a heads up that, while we can point you in the direction of the order journey dashboard.
98 00:11:30.750 ⇒ 00:11:45.759 Demilade Agboola: This was built out for Rebecca and her team, so it’s possible there are certain things that, A, you might want that are not in there, or B, might just be unnecessary to you, and you might just take it out so you have a more, you know, focused dashboard.
99 00:11:45.900 ⇒ 00:11:52.500 Demilade Agboola: So, that is also something, yeah, we could talk about and just help you, you know, be the most productive you can be.
100 00:11:52.950 ⇒ 00:11:59.759 Mitesh Patel: Sure, and we’ll work with you on that. Part of it is we’re kind of figuring that out ourselves, too, right?
101 00:12:02.160 ⇒ 00:12:17.360 Mitesh Patel: Okay, so what I did, I just did my old-fashioned, simple, you know, very simple Excel formulas or whatever. These are unique combinations of order status and pharmacy status that we could get.
102 00:12:17.500 ⇒ 00:12:37.349 Mitesh Patel: Okay? And I’ve had to add to this list. When I run a report, I’ll be like, oh, a few are missing, so we add to it. And then I got, because we’re starting, you know, trying to understand and learn what the different combos mean, what I did is… really, my goal was this.
103 00:12:37.350 ⇒ 00:12:53.020 Mitesh Patel: that, depending on different versions of order status, or different combinations, I should say, of order status and pharmacy status, what is the actual status that we want to report on, right? So, you know, this is sort of…
104 00:12:53.250 ⇒ 00:13:02.370 Mitesh Patel: My way, poor man’s way of doing just, you know, this kind of data modeling or data mapping here.
105 00:13:02.600 ⇒ 00:13:12.400 Mitesh Patel: And we do that to generate this report. This is the dashboard that you’ve created, right? To help us understand
106 00:13:12.400 ⇒ 00:13:25.429 Mitesh Patel: How many are in each status at each pharmacy, and then the other layer of this is not only how many are in each status, but then how many are out of SLA.
107 00:13:26.070 ⇒ 00:13:44.629 Mitesh Patel: And again, there could be SLA… sorry, two types of being out of SLA, and I’ll just show you today’s report. This is something the Farm MedOps team collects manually, right? It’s how many are out of SLA?
108 00:13:44.710 ⇒ 00:13:46.170 Mitesh Patel: by pharmacy.
109 00:13:46.420 ⇒ 00:13:50.480 Mitesh Patel: And these are out of, if you will, out of SLA.
110 00:13:52.930 ⇒ 00:13:57.269 Mitesh Patel: sorry, these are not out of SLA. These are what the current status is
111 00:13:57.460 ⇒ 00:14:16.000 Mitesh Patel: in terms of the different orders, right? This number, the 2,500, so 154 of the 26,000 orders have errors in it. And what our teams do is they look at each one of those and try to work through the errors, and you know, the details are here by pharmacy.
112 00:14:16.940 ⇒ 00:14:32.189 Mitesh Patel: Pending on hold means it’s somewhere between the checkout, right, the customer says, here’s your treatment plan, you know, I’m eligible for, and I’d like to be considered for, and the consult… or the prescription being written.
113 00:14:32.630 ⇒ 00:14:40.870 Mitesh Patel: Now, some of these are because we’re waiting on the doctor, the provider, to review the prescription information.
114 00:14:41.620 ⇒ 00:14:48.319 Mitesh Patel: And, you know, like, a few weeks ago, they got behind, so we went out of SLA.
115 00:14:48.620 ⇒ 00:14:54.089 Mitesh Patel: or the number of orders out of SLA in this stage got, we fell behind.
116 00:14:54.260 ⇒ 00:15:11.730 Mitesh Patel: Many of these are just… most of these, I would say, are just waiting for the customer to follow up. Either they didn’t complete all the information, or the provider is asking them a clarifying question before they can approve the prescription, and still waiting on their…
117 00:15:12.060 ⇒ 00:15:28.020 Mitesh Patel: response. So, what the teams do, the med ops teams do, is say, hey, this number is too high. Which ones can we follow up proactively with the customers to move them, you know, through the prescription stage, right?
118 00:15:28.350 ⇒ 00:15:35.189 Mitesh Patel: There’s different reasons orders get canceled. These are the ones sent to pharmacy, not yet shipped.
119 00:15:35.840 ⇒ 00:15:52.619 Mitesh Patel: Right? And so these numbers and these, before they’re shipped, are the two that can… they’re the two levels of SLAs that I’m… that I’m describing. I think these, the ones that… these 1,300 that are out of
120 00:15:52.860 ⇒ 00:16:00.400 Mitesh Patel: Fulfillment SLA, that’s the report you already showed us. And I think it’s exactly what we have.
121 00:16:00.680 ⇒ 00:16:13.139 Mitesh Patel: as long as we’ve updated the statuses… we did the status mapping correctly or completely. That’s it, right? Where’s the other data mapping sheet?
122 00:16:13.600 ⇒ 00:16:16.310 Tracie: The farm map product map, it’s one right next to it.
123 00:16:16.310 ⇒ 00:16:23.520 Mitesh Patel: That one, yep, okay. So we do the pharmacy mapping, because in the BASC reports, like.
124 00:16:23.690 ⇒ 00:16:37.690 Mitesh Patel: precision will show up different ways. I mean, you guys have that figured out. And then the product group mapping, because we also… here’s a layer that I didn’t see in your dashboard, and I’m not sure if we need it in your dashboard yet.
125 00:16:37.820 ⇒ 00:16:50.549 Mitesh Patel: Brad and Tracy will decide and, you know, maybe request that as one of these changes. All these different product names, there’s, like, almost 400 of them, you know, map into
126 00:16:50.730 ⇒ 00:16:52.100 Mitesh Patel: a product group.
127 00:16:52.290 ⇒ 00:17:04.879 Mitesh Patel: Because the other thing we added, it seems to have not been added to this pivot, but what we can add to this is this breakdown. I’m not going to do it now, because I think it’ll write over this, which I don’t…
128 00:17:04.880 ⇒ 00:17:07.990 Tracie: It shows up on mine, I don’t know why it shows up, doesn’t show up on yours.
129 00:17:09.540 ⇒ 00:17:11.459 Tracie: Does it need to be refreshed?
130 00:17:11.460 ⇒ 00:17:12.939 Mitesh Patel: Maybe. There… oh, there it is, yeah.
131 00:17:12.940 ⇒ 00:17:14.110 Tracie: Okay, good.
132 00:17:14.119 ⇒ 00:17:19.089 Mitesh Patel: So what this allows us to do is, It’s thinking.
133 00:17:20.200 ⇒ 00:17:21.180 Tracie: It’s huge.
134 00:17:21.819 ⇒ 00:17:22.529 Mitesh Patel: Yeah.
135 00:17:24.129 ⇒ 00:17:25.009 Mitesh Patel: Really?
136 00:17:30.009 ⇒ 00:17:32.039 Mitesh Patel: I… -Oh.
137 00:17:33.209 ⇒ 00:17:37.539 Mitesh Patel: Regardless, we can tell… within a pharmacy.
138 00:17:38.169 ⇒ 00:17:40.619 Mitesh Patel: Yeah, I’m… I don’t know what I did, Tracy.
139 00:17:40.620 ⇒ 00:17:41.040 Tracie: That’s alright.
140 00:17:41.040 ⇒ 00:17:48.569 Mitesh Patel: You can undo it, sorry. You all saw, I was just trying to expand the column, or the rows.
141 00:17:49.300 ⇒ 00:17:54.570 Mitesh Patel: This… what this does is tells us what drugs are…
142 00:17:55.130 ⇒ 00:17:57.749 Mitesh Patel: At each pharmacy, waiting to be shipped.
143 00:17:57.750 ⇒ 00:17:58.580 Tracie: Right?
144 00:17:58.580 ⇒ 00:18:16.650 Mitesh Patel: And that’s important, because we can say, possibly say, hey, maybe if these guys are gonna take too long, we can reroute them to another provider, given that somebody else has them, and can ship them to that state, and so on. I don’t know if we need that in the… in the dashboard, but I’m just showing you, sort of.
145 00:18:16.860 ⇒ 00:18:19.400 Mitesh Patel: some of the data we’re looking at, that we’re…
146 00:18:19.400 ⇒ 00:18:21.830 Tracie: It’s on my screen, if you want me to show it.
147 00:18:22.170 ⇒ 00:18:23.569 Mitesh Patel: Sure.
148 00:18:25.050 ⇒ 00:18:27.550 Mitesh Patel: I will reopen that, I’m not sure what I did.
149 00:18:28.270 ⇒ 00:18:35.219 Tracie: I did a duplicate copy, so I could make sure that anytime anything happened or broke, I had extra copies.
150 00:18:36.130 ⇒ 00:18:37.789 Mitesh Patel: Yeah, you learned your lesson, I’m sure.
151 00:18:37.790 ⇒ 00:18:39.620 Tracie: I sure did!
152 00:18:41.210 ⇒ 00:18:53.340 Mitesh Patel: But yeah, Amber Dimulatti, to answer your question, we’re kind of working through this, and as Brad and Tracy look at, you know.
153 00:18:53.440 ⇒ 00:18:55.750 Mitesh Patel: Have you… have we added them to Tableau yet?
154 00:18:57.030 ⇒ 00:19:03.889 Amber Lin: Last time I checked, Robert was going to add them. Are you guys able to go into… go in there?
155 00:19:04.360 ⇒ 00:19:06.089 Amber Lin: It’s only proper hospitality.
156 00:19:06.090 ⇒ 00:19:08.290 Tracie: I don’t know what I have access to.
157 00:19:08.730 ⇒ 00:19:09.880 Amber Lin: Mmm, okay.
158 00:19:09.880 ⇒ 00:19:17.670 Demilade Agboola: Let me share… let me share a URL right now, and let me see if you… if you’re, like, if you’re able to sign in. I think that’ll be the first step.
159 00:19:18.360 ⇒ 00:19:23.140 Demilade Agboola: One second…
160 00:19:29.960 ⇒ 00:19:35.610 Amber Lin: I know we wanted to add Tracy. Brad, do you also have access to Tableau?
161 00:19:36.420 ⇒ 00:19:43.939 Brad Messersmith: I don’t think I do. I made a request this morning through the IT portal. I’m not sure if that’s the right place to request.
162 00:19:45.010 ⇒ 00:19:50.419 Amber Lin: Sounds good. Let me check with Robert on both of your accesses.
163 00:19:53.350 ⇒ 00:19:58.800 Demilade Agboola: Alright, so, Tracy, I just sent the URL to Tableau in the chat.
164 00:19:59.030 ⇒ 00:20:00.210 Tracie: Oh, thank you.
165 00:20:04.580 ⇒ 00:20:11.509 Demilade Agboola: It’s also be helpful to… I think it’s a pin message in the analytics channel, but, like, you can always just save it somewhere else.
166 00:20:11.780 ⇒ 00:20:13.010 Demilade Agboola: So you can…
167 00:20:16.420 ⇒ 00:20:17.980 Demilade Agboola: Easily access it.
168 00:20:41.640 ⇒ 00:20:49.860 Henry Zhao: Also, I have a question, is Demolade, could we do the status mapping in dbt so that Mitesh doesn’t have to manually maintain this mapping?
169 00:20:51.900 ⇒ 00:20:54.560 Demilade Agboola: Oh yeah, we could definitely do it,
170 00:20:55.060 ⇒ 00:20:58.189 Demilade Agboola: the social that we… like, this is…
171 00:20:58.410 ⇒ 00:21:00.619 Demilade Agboola: Like, yeah, it can definitely be done in dbt.
172 00:21:02.180 ⇒ 00:21:07.819 Henry Zhao: Yeah, so Mitesh, let us know if you want us to eventually take this manual mapping off your hands and work together to just automate that.
173 00:21:08.180 ⇒ 00:21:10.960 Mitesh Patel: Yes, definitely will want that.
174 00:21:11.290 ⇒ 00:21:13.009 Mitesh Patel: Tracy will want that more than me.
175 00:21:13.010 ⇒ 00:21:13.930 Brad Messersmith: Definitely.
176 00:21:13.930 ⇒ 00:21:15.420 Demilade Agboola: They want it!
177 00:21:15.420 ⇒ 00:21:19.809 Brad Messersmith: And the people Tracy’s working with, probably, based on what I’ve heard.
178 00:21:20.900 ⇒ 00:21:26.540 Henry Zhao: Yeah, Amber, if you can create a ticket for that, we’ll just need to know the rules on how you guys classify it, and then we should be good.
179 00:21:27.190 ⇒ 00:21:28.590 Amber Lin: Yeah, sounds good.
180 00:21:29.980 ⇒ 00:21:30.530 Mitesh Patel: Sorry.
181 00:21:30.530 ⇒ 00:21:33.620 Amber Lin: share that sheet, Amber, with you. Oh, absolutely.
182 00:21:33.720 ⇒ 00:21:36.159 Mitesh Patel: And, and, and the two mapping tabs.
183 00:21:36.160 ⇒ 00:21:36.500 Tracie: Hmm.
184 00:21:36.500 ⇒ 00:21:40.250 Mitesh Patel: Have the… that… all of those, how we’re mapping today.
185 00:21:41.350 ⇒ 00:21:45.370 Henry Zhao: And we’ll just have to figure out, like, the new ones that come in, how we map those.
186 00:21:45.740 ⇒ 00:22:03.699 Mitesh Patel: Yeah, if there… so what I did in one of my VLOOPs, right, I just said, well, if it doesn’t show up, add a big missing in all caps. So then I have to go, you know, talk to the farm with KDK, and she’ll help, say, oh, that’s what this mapping is. Because, yeah, a bunch still show up.
187 00:22:05.740 ⇒ 00:22:06.310 Henry Zhao: Doc?
188 00:22:14.610 ⇒ 00:22:29.800 Mitesh Patel: See, here’s the thing, like, this out-of-SLA report and the dashboard are very helpful for us, for Brad, Tracy, and I to be able to communicate very quickly with the ELT, you know, here’s how it’s looking, right?
189 00:22:30.140 ⇒ 00:22:38.420 Mitesh Patel: The farm ops team, the farm med ops team, they still need each of the orders, so then they can go in and say, okay.
190 00:22:38.590 ⇒ 00:22:57.599 Mitesh Patel: These are the ones that we need to go and address, right? And some of them they can address, some of them they have to get bass to address, some of them they have to send the pharmacy to address. So, it’s kind of like we need some way of combination of saying, okay, 10 orders are out of SLA, but then here are, you know, these are the 10.
191 00:22:57.750 ⇒ 00:23:00.549 Mitesh Patel: And I know that’s not the purpose of a dashboard.
192 00:23:00.710 ⇒ 00:23:06.980 Mitesh Patel: But that would really help not only us, but the teams actually addressing these.
193 00:23:08.860 ⇒ 00:23:10.710 Mitesh Patel: Can you help there?
194 00:23:13.010 ⇒ 00:23:14.009 Amber Lin: I think…
195 00:23:14.440 ⇒ 00:23:25.030 Amber Lin: in order for us to compile the number of, say, items that’s outside of SLA, we’ll need to have records of the individual items. So, maybe this is something that
196 00:23:25.030 ⇒ 00:23:46.730 Amber Lin: is more like an export, and I know when we build a dashboard for Danny, we have an export function built into the dashboard, so I think, that he can use it to download, okay, who are the ones who’s actually out of SLA? So we’ll look into that. Let me know if you think it’s possible, but we did build an export function for Danny, and…
197 00:23:46.730 ⇒ 00:23:53.980 Mitesh Patel: Yeah, that’s exactly what we would need. For example, and this is… I’ll share my screen again.
198 00:23:54.560 ⇒ 00:23:59.539 Mitesh Patel: If… so what they do every morning is this.
199 00:23:59.720 ⇒ 00:24:02.469 Mitesh Patel: Right? Here’s the total orders.
200 00:24:02.590 ⇒ 00:24:07.530 Mitesh Patel: This is the total orders, you can see they’re filtered based on some statuses, which we’d have to define for you.
201 00:24:07.680 ⇒ 00:24:10.770 Mitesh Patel: And then, they filter by each pharmacy.
202 00:24:11.210 ⇒ 00:24:25.120 Mitesh Patel: and copy them, here are all the Optio ones, here are all the Absolute ones, here are the… you know, and so on, right? So each pharmacy… so the orders are split out, all of the columns are split out into different tabs by pharmacy.
203 00:24:26.370 ⇒ 00:24:29.229 Mitesh Patel: And that way, they can, sort of.
204 00:24:29.340 ⇒ 00:24:31.880 Mitesh Patel: Review them in batch by pharmacy.
205 00:24:32.920 ⇒ 00:24:37.880 Mitesh Patel: So that’s really what we would need the export Functionalities to do.
206 00:24:41.430 ⇒ 00:24:42.570 Amber Lin: Gotcha.
207 00:24:43.970 ⇒ 00:24:47.499 Demilade Agboola: I mean, yeah, we’ll definitely look into this, and then,
208 00:24:47.710 ⇒ 00:24:52.610 Demilade Agboola: We’ll try a couple of things and see… see what works best for your use case.
209 00:24:53.080 ⇒ 00:24:54.200 Mitesh Patel: That’ll be great.
210 00:24:57.420 ⇒ 00:25:10.530 Mitesh Patel: Yeah, because I don’t want the… obviously, right? I don’t want the teams to have to do this manual copy and paste. I want them to focus on fixing the issues, right? Pushing the stuck orders through, or whatever. That’s what their time should be spent on.
211 00:25:14.270 ⇒ 00:25:17.470 Tracie: Yeah, that would be really helpful if you guys helped us out with that.
212 00:25:18.180 ⇒ 00:25:22.989 Amber Lin: Gotcha, and this is, this is based on the dashboard currently we have for Danny, right?
213 00:25:24.720 ⇒ 00:25:25.660 Mitesh Patel: Yes.
214 00:25:25.660 ⇒ 00:25:26.410 Amber Lin: Okay, gotcha.
215 00:25:26.410 ⇒ 00:25:32.450 Mitesh Patel: Yeah, so that dashboard you’ve kind of had with Danny, now it’s Brad and Tracy’s dashboard.
216 00:25:33.210 ⇒ 00:25:39.849 Mitesh Patel: Right? Okay. Danny got it started, and so… and Brad and Tracy joined now.
217 00:25:39.880 ⇒ 00:25:57.250 Mitesh Patel: you know, not only to… like, right now, as you know, we’re in this firefighting mode, because so many orders are out of SLA at the two different phases, right, that I described earlier. But once we do the… once we get everything back, you know, within, sort of, our thresholds, then they’re gonna…
218 00:25:57.260 ⇒ 00:26:00.089 Mitesh Patel: Still manage it day-to-day to day, right?
219 00:26:00.330 ⇒ 00:26:03.349 Mitesh Patel: Once we get to that point, now I’m gonna be…
220 00:26:03.410 ⇒ 00:26:06.859 Brad Messersmith: The next request that’s going to be coming is…
221 00:26:07.570 ⇒ 00:26:15.550 Mitesh Patel: We don’t have too many orders out of SLA, you know, we’re down to, like, 0.1% out of SLA. That’s the goal, Brad and Tracy, right?
222 00:26:15.970 ⇒ 00:26:21.650 Mitesh Patel: Once we have that, then I want a little magic button that says, here are orders.
223 00:26:21.830 ⇒ 00:26:26.700 Mitesh Patel: that are gonna be… potentially be out of SLA by tomorrow.
224 00:26:26.880 ⇒ 00:26:28.170 Mitesh Patel: Or the next day.
225 00:26:29.380 ⇒ 00:26:42.790 Mitesh Patel: then what we want the teams to do. Right now, they’re firefighting, right? Fixing these, but now I want to get ahead of it, and to say, these are the ones that’ll be out of SLA tomorrow if we don’t do something about it today. So now we start addressing them proactively.
226 00:26:44.780 ⇒ 00:26:46.280 Mitesh Patel: Okay, that’s sort of the next step.
227 00:26:47.880 ⇒ 00:26:56.560 Amber Lin: So that would be… that would be items that has been in status for X days approaching SLA. I think that’s…
228 00:26:56.560 ⇒ 00:27:02.410 Mitesh Patel: I mean, it’s pretty… yeah, I would just say, like, for example, if currently out of SLA is 3 business days, right?
229 00:27:03.080 ⇒ 00:27:05.569 Mitesh Patel: We, we, we, those would be orders that are…
230 00:27:05.860 ⇒ 00:27:15.140 Mitesh Patel: currently near out of SLA, 2 business days, for example, you know, and we just say these will be tomorrow, if you don’t take care of it now.
231 00:27:16.650 ⇒ 00:27:19.369 Amber Lin: Awesome. Okay.
232 00:27:20.570 ⇒ 00:27:37.150 Amber Lin: I think this is a fix on the dashboard we can do. I think what we need is what status you want to see it in, and then we’ll group by pharmacy so you can see, what it is. And,
233 00:27:37.270 ⇒ 00:27:42.070 Amber Lin: do you guys know the statuses now, or I can… we can coordinate?
234 00:27:42.070 ⇒ 00:27:43.849 Mitesh Patel: For this export?
235 00:27:43.850 ⇒ 00:27:44.450 Amber Lin: Yeah.
236 00:27:44.600 ⇒ 00:27:51.370 Mitesh Patel: Yeah, so… here, let me just do this, right? And Tracy, correct me if I’m wrong. Oops.
237 00:27:54.010 ⇒ 00:28:00.929 Mitesh Patel: So, for order… for order status, They should include pending, sent.
238 00:28:03.120 ⇒ 00:28:05.579 Mitesh Patel: And maybe that’s all, but we’re double checking.
239 00:28:12.490 ⇒ 00:28:13.840 Mitesh Patel: I think that’s it.
240 00:28:14.110 ⇒ 00:28:16.309 Mitesh Patel: Yeah. Pending and sent.
241 00:28:17.380 ⇒ 00:28:18.020 Amber Lin: Okay.
242 00:28:18.020 ⇒ 00:28:24.050 Mitesh Patel: And that’s all you see here, pending and sent. And for pharmacy, Status.
243 00:28:24.340 ⇒ 00:28:29.560 Mitesh Patel: It should be all of them, other than… Archived?
244 00:28:29.930 ⇒ 00:28:31.070 Mitesh Patel: canceled.
245 00:28:31.250 ⇒ 00:28:34.430 Mitesh Patel: Completed, delivered, or shipped.
246 00:28:38.810 ⇒ 00:28:43.549 Amber Lin: Factors, so anything that’s still active, essentially.
247 00:28:43.550 ⇒ 00:28:48.649 Mitesh Patel: Yeah, I mean, you know, there’s, like, these funky ones. It’s completed, but sent to pharmacy, so…
248 00:28:49.310 ⇒ 00:28:51.389 Amber Lin: That should be included here.
249 00:28:52.170 ⇒ 00:28:54.400 Mitesh Patel: Yeah. I think anything other than these.
250 00:28:56.380 ⇒ 00:28:57.200 Mitesh Patel: Five.
251 00:28:57.200 ⇒ 00:28:57.820 Amber Lin: Gotcha.
252 00:28:59.030 ⇒ 00:29:10.530 Amber Lin: Sorry, I hear order status is pending and sent, pharmacy status is exclude, archived, canceled, completed, shipped, and what’s the fifth one?
253 00:29:10.530 ⇒ 00:29:11.690 Mitesh Patel: Delivered. Delivered.
254 00:29:11.690 ⇒ 00:29:12.780 Amber Lin: Gotcha, okay.
255 00:29:14.770 ⇒ 00:29:20.910 Mitesh Patel: Oh, I’m pointing it at my screen, but I’m not sharing my screen. That’s why I’m like, it’s right here.
256 00:29:24.320 ⇒ 00:29:42.999 Amber Lin: Gotcha, okay. I’ll ask the team to check on it. We’ll see if there’s any modeling tasks that needs to be done. If not, we’ll change it in Tableau. I’ve asked Robert in our channel to give you guys access. I think he’ll get back probably later today.
257 00:29:43.380 ⇒ 00:29:50.779 Henry Zhao: He might not have the ability to because of where he is, but if you can just give me the instructions on how to, whether it’s buy a new license or whatever, I can execute it.
258 00:29:51.110 ⇒ 00:30:02.229 Mitesh Patel: Yeah, and typically Robert tells… asks us if, you know, I need to buy two more seats or whatever, two more licenses, is that approved? Henry, it’s approved. If you need me to put it in the channel, just let me know.
259 00:30:02.870 ⇒ 00:30:03.440 Henry Zhao: Okay.
260 00:30:07.790 ⇒ 00:30:09.779 Amber Lin: New license…
261 00:30:10.850 ⇒ 00:30:22.959 Tracie: And then, Amber, I just shared with you the SOP process that I wrote with our team yesterday on the exact step-by-step that we did to get to the right filters, so that contains all the filters and statuses for you.
262 00:30:23.580 ⇒ 00:30:26.940 Amber Lin: Gotcha. Awesome.
263 00:30:27.130 ⇒ 00:30:33.159 Amber Lin: Checking… and I got the spreadsheet, and yes, I got the daily SOP.
264 00:30:35.040 ⇒ 00:30:37.050 Amber Lin: Okay, sounds good.
265 00:30:38.000 ⇒ 00:30:59.389 Amber Lin: Awesome. I think this is something that we’ll do next week, because this week we are finishing up some tasks for finance. This week we’ll scope it out, and we’ll let you know next week. On Monday, we do our planning on how long this will take, when we can expect it to be done. We’ll sync with you guys on, how the dashboard is working.
266 00:30:59.870 ⇒ 00:31:06.460 Mitesh Patel: Okay. Amber, I think you said from the stand-up you had some other questions?
267 00:31:06.510 ⇒ 00:31:14.430 Amber Lin: Yes. I think the first one is answered by Cutter, the second one is about QuickBooks.
268 00:31:14.650 ⇒ 00:31:21.620 Amber Lin: we need admin access to pull the data from QuickBooks, so I don’t know if you will be able to give us the answer. I tagged.
269 00:31:21.620 ⇒ 00:31:24.960 Mitesh Patel: Jonah has to enable that. Jonah and Zelle.
270 00:31:25.410 ⇒ 00:31:26.980 Amber Lin: Yeah.
271 00:31:27.110 ⇒ 00:31:28.950 Mitesh Patel: Do you want me to message them?
272 00:31:28.950 ⇒ 00:31:44.470 Amber Lin: I already messaged them. We have regular access, we just need higher access, and I understand that sometimes we don’t want to give QuickBook access as admins, so I just clarified with them if they’re able to give that to us. If not, we’ll try to download the data manually.
273 00:31:44.770 ⇒ 00:31:48.220 Mitesh Patel: Alright, I think it should be okay, but, you know, well, let them respond.
274 00:31:48.570 ⇒ 00:31:49.040 Amber Lin: Yeah.
275 00:31:49.040 ⇒ 00:31:53.220 Mitesh Patel: Jonah’s probably asking Danny to eat, and then… get it done.
276 00:31:53.470 ⇒ 00:31:54.010 Amber Lin: Yeah.
277 00:31:54.630 ⇒ 00:31:56.310 Amber Lin: Okay.
278 00:31:56.450 ⇒ 00:32:04.929 Amber Lin: That’s all. Any other… I mean, since we’re here, any other priorities on the marketing side, or any risks that you see? Just want to do a pulse check.
279 00:32:06.830 ⇒ 00:32:07.460 Mitesh Patel: I was paper.
280 00:32:07.460 ⇒ 00:32:13.570 Brad Messersmith: On my end, it’d be helpful to have a trend chart of the SLA as a percentage of all of the orders.
281 00:32:13.800 ⇒ 00:32:19.060 Brad Messersmith: I don’t think I saw that in there. We looked at it really briefly, but…
282 00:32:19.920 ⇒ 00:32:32.700 Brad Messersmith: being that that’s our main goal, the data, getting the data out that we just talked about and filtering it properly is a huge step forward from saving a bunch of people’s time. But if we can have, you know, a sort of overall snapshot
283 00:32:32.780 ⇒ 00:32:42.279 Brad Messersmith: Because that’s going to lead to what Mitesh is asking for in terms of the long term, right? How do we make sure that there’s no future, SLA issues
284 00:32:42.470 ⇒ 00:32:53.199 Brad Messersmith: we want to kind of see that as a percentage of all the orders, so whether it’s 95% on time to SLA or 5% out of condition, if you will,
285 00:32:53.320 ⇒ 00:32:56.799 Brad Messersmith: One or the other would be super helpful to see as kind of a trend chart.
286 00:32:58.400 ⇒ 00:33:06.900 Amber Lin: Gotcha, okay. I will clarify more details on that. I have to hop for another meeting. I’ll keep that noted.
287 00:33:06.900 ⇒ 00:33:07.430 Brad Messersmith: from…
288 00:33:07.430 ⇒ 00:33:12.700 Amber Lin: And once you guys have access, you can explore it to see if, is this what you wanted?
289 00:33:13.490 ⇒ 00:33:13.960 Amber Lin: Okay.
290 00:33:13.960 ⇒ 00:33:17.430 Brad Messersmith: Yeah, that’s great. So, can we follow up with you guys next week, then? Late next week?
291 00:33:17.430 ⇒ 00:33:25.710 Amber Lin: Yeah, totally. You are in the… I think in Eden Analytics channel. If you have any requests, just drop it there and at me.
292 00:33:26.070 ⇒ 00:33:26.500 Tracie: Awesome.
293 00:33:26.500 ⇒ 00:33:28.979 Brad Messersmith: Okay, perfect. Thank you. Appreciate you guys’ time.
294 00:33:29.390 ⇒ 00:33:30.229 Brad Messersmith: Thank you so much, Ian.
295 00:33:30.230 ⇒ 00:33:32.119 Demilade Agboola: Thank you. Bye. Bye.