Meeting Title: Tableau Dashboarding Work Review Date: 2025-08-28 Meeting participants: Awaish Kumar, Annie Yu
WEBVTT
1 00:00:10.520 ⇒ 00:00:11.610 Annie Yu: Hello, Aish.
2 00:00:12.600 ⇒ 00:00:13.790 Awaish Kumar: Hi, Julie?
3 00:00:18.150 ⇒ 00:00:19.479 Awaish Kumar: Oh, how’d you doing?
4 00:00:22.150 ⇒ 00:00:27.919 Annie Yu: I’m, I’m good, I’m good. I’m, like, kind of eating my breakfast.
5 00:00:28.420 ⇒ 00:00:29.120 Awaish Kumar: Okay.
6 00:00:29.500 ⇒ 00:00:33.470 Awaish Kumar: Yeah, so… Duh.
7 00:00:34.330 ⇒ 00:00:39.449 Awaish Kumar: So, like, we have, like, we are moving… I thought, like, we are moving to…
8 00:00:39.640 ⇒ 00:00:43.209 Awaish Kumar: From rainfalls to somewhere else, maybe?
9 00:00:43.620 ⇒ 00:00:47.190 Awaish Kumar: So how…
10 00:00:50.060 ⇒ 00:00:58.110 Awaish Kumar: But… I would say, like, best of luck for your future endeavors, but, like, Yeah, like, it’s…
11 00:01:00.950 ⇒ 00:01:08.419 Awaish Kumar: Hard to hear, like, … yeah, so how… how do you feel, like….
12 00:01:11.590 ⇒ 00:01:14.959 Annie Yu: I… yeah, I actually haven’t…
13 00:01:15.390 ⇒ 00:01:32.009 Annie Yu: even, like, starting looking elsewhere, because I… I need some… I need to take some space to refocus and just focus on some personal things. So I’m kind of keeping things open-ended for now, but I think it’s… it’s, a right decision for me.
14 00:01:33.400 ⇒ 00:01:39.730 Awaish Kumar: Yeah, okay. I just wanted to… Understand, the… the…
15 00:01:40.600 ⇒ 00:01:43.269 Awaish Kumar: like, I just wanted to scope out the…
16 00:01:44.120 ⇒ 00:01:49.599 Awaish Kumar: the kind of work you have been doing. So, we know the dash… there is Tableau dashboarding work.
17 00:01:49.730 ⇒ 00:01:55.499 Awaish Kumar: And then there are some manual ad hoc requests that you were getting.
18 00:01:56.060 ⇒ 00:01:57.290 Annie Yu: column.
19 00:01:58.670 ⇒ 00:02:02.220 Awaish Kumar: But I don’t have much context into everything, ….
20 00:02:02.220 ⇒ 00:02:02.850 Annie Yu: Yeah.
21 00:02:02.850 ⇒ 00:02:08.730 Awaish Kumar: So, I would like to understand from your point of view what… if you could… maybe if you could divide your…
22 00:02:09.020 ⇒ 00:02:15.210 Awaish Kumar: Tableau work in some segments, or, like, what exactly we’re doing there, and also… what kind of…
23 00:02:15.700 ⇒ 00:02:25.720 Awaish Kumar: ad hoc requests we have been getting, and how… how much was your split? And, for example, last sprint, what do you think your split was? Like, how much we were spending on
24 00:02:25.910 ⇒ 00:02:32.819 Awaish Kumar: Dashboarding, how much on… you know, ad hoc requests, or… Something else, whatever it was.
25 00:02:33.620 ⇒ 00:02:34.830 Annie Yu: Okay, okay.
26 00:02:34.830 ⇒ 00:02:36.330 Awaish Kumar: Sugar just described those.
27 00:02:36.620 ⇒ 00:02:37.929 Awaish Kumar: That would be nice.
28 00:02:38.530 ⇒ 00:02:41.260 Annie Yu: Yeah, … I…
29 00:02:42.010 ⇒ 00:02:52.050 Annie Yu: I think I want to say majority of my time was spent on dashboarding, but also for the ad hoc
30 00:02:53.090 ⇒ 00:02:54.330 Annie Yu: …
31 00:02:54.850 ⇒ 00:03:11.190 Annie Yu: sometimes ad hocs are straightforward. They ask for a report, so what I needed to do is just query from across different tables, and then eventually give them a CSV. I think that’s one of the most straightforward ones, but…
32 00:03:11.320 ⇒ 00:03:15.689 Annie Yu: I remember other ad hocs, like the Jonas
33 00:03:15.900 ⇒ 00:03:25.220 Annie Yu: the Cox one. For that one, it was kind of complex, so for that one, I definitely spent more than 10 hours, because
34 00:03:25.760 ⇒ 00:03:32.399 Annie Yu: they… provided their invoice data in a PDF, so I had to
35 00:03:32.590 ⇒ 00:03:35.279 Annie Yu: Write a script to scrape out everything.
36 00:03:35.400 ⇒ 00:03:44.040 Annie Yu: and make sure I have the data into CSV format, and then from there, me and Demlade …
37 00:03:44.210 ⇒ 00:03:54.039 Annie Yu: try to map out those product names with Rebecca, and then… We have to fill the… Cox.
38 00:03:54.790 ⇒ 00:03:58.710 Annie Yu: to the invoice data. So, so, it was… Things of…
39 00:03:58.830 ⇒ 00:04:03.440 Annie Yu: Things like that, spent… like, took much more time.
40 00:04:03.600 ⇒ 00:04:08.830 Annie Yu: And then ad hoc, there was also one ad hoc where…
41 00:04:09.400 ⇒ 00:04:14.789 Annie Yu: Qatar and Mitesh was asking about the cancellation rate and churn rate.
42 00:04:15.010 ⇒ 00:04:29.669 Annie Yu: From the offer channel versus the other channels. So for that one, I think that was also kind of open-ended, so… but Cutter also did mention he wanted to see some kind of statistics
43 00:04:29.810 ⇒ 00:04:48.830 Annie Yu: proof there. So with that one, I first wrote a query like I always do, but then I put that query into Tableau. So I used Tableau for some data visualization to explore what I see, and then from there, I then moved those queries into
44 00:04:49.060 ⇒ 00:04:54.430 Annie Yu: Python, and then using Python to run the statistical
45 00:04:54.770 ⇒ 00:05:00.170 Annie Yu: tests. So, those are more, like, diverse and, …
46 00:05:00.880 ⇒ 00:05:07.850 Annie Yu: taking a bit more time than just your regular, like, report asking for a CS.
47 00:05:07.990 ⇒ 00:05:11.309 Awaish Kumar: That is, like, also an ad hoc request, right?
48 00:05:11.580 ⇒ 00:05:14.679 Annie Yu: Yep, yeah, all of those are ad hoc.
49 00:05:15.330 ⇒ 00:05:22.210 Annie Yu: So, like, that was… I think they started off as ad hocs, but then they would ask something else, and then we…
50 00:05:22.410 ⇒ 00:05:24.359 Annie Yu: Built on that.
51 00:05:25.070 ⇒ 00:05:28.870 Awaish Kumar: Okay, so tonight… Oh, God.
52 00:05:29.770 ⇒ 00:05:38.770 Awaish Kumar: So that was… so you get an ad hoc request sometimes, and then in the… at the end, it might be translated into a dashboard, right?
53 00:05:39.780 ⇒ 00:05:41.040 Annie Yu: ….
54 00:05:42.340 ⇒ 00:05:44.239 Awaish Kumar: Some of it, not everything.
55 00:05:45.400 ⇒ 00:05:53.110 Annie Yu: Not so… I wouldn’t say into a dashboard, not so much, but I would leverage
56 00:05:53.740 ⇒ 00:05:58.229 Annie Yu: tools like Tableau to help, understand the patterns.
57 00:05:58.540 ⇒ 00:06:01.340 Awaish Kumar: Okay, so that… but that was not for…
58 00:06:01.760 ⇒ 00:06:06.350 Awaish Kumar: Not for you to, like… not mandatory, because, like, Azure…
59 00:06:06.520 ⇒ 00:06:12.189 Awaish Kumar: Tries to, like, whatever, use whatever tools you can to answer their questions.
60 00:06:12.190 ⇒ 00:06:12.979 Annie Yu: The other question….
61 00:06:12.980 ⇒ 00:06:14.140 Awaish Kumar: still add on.
62 00:06:14.310 ⇒ 00:06:25.700 Awaish Kumar: And, obviously you use Tableau, somebody can use Notebook, or direct Convict queries, or whatever, and get the data, and then put it into Python.
63 00:06:26.100 ⇒ 00:06:28.839 Awaish Kumar: Descript, or whatever, and then start your…
64 00:06:30.580 ⇒ 00:06:37.930 Awaish Kumar: analysis, but yeah, I completely understand. So, I would categorize that as a… still a… Like, at all.
65 00:06:38.240 ⇒ 00:06:42.950 Awaish Kumar: I wanted to understand, like, so… We did, like.
66 00:06:51.770 ⇒ 00:06:55.689 Awaish Kumar: So we have… we have done, like, 29 story points.
67 00:06:57.030 ⇒ 00:06:59.530 Awaish Kumar: In the last, cycle.
68 00:07:01.450 ⇒ 00:07:03.660 Awaish Kumar: Not the current cycle, but the last cycle.
69 00:07:04.280 ⇒ 00:07:09.210 Awaish Kumar: I just… Want to, like, … I split them.
70 00:07:09.800 ⇒ 00:07:11.080 Awaish Kumar: Into one.
71 00:07:14.320 ⇒ 00:07:18.010 Awaish Kumar: ad hoc and, non-ad hoc.
72 00:07:18.780 ⇒ 00:07:20.280 Awaish Kumar: Like the timeline, so…
73 00:07:24.640 ⇒ 00:07:29.539 Awaish Kumar: Is there a way to add, … Some kind of lemon?
74 00:07:35.770 ⇒ 00:07:37.040 Awaish Kumar: We’re helping profit.
75 00:07:38.710 ⇒ 00:07:39.790 Awaish Kumar: Sonny?
76 00:07:39.790 ⇒ 00:07:43.590 Annie Yu: Under project, there is already that ad hoc, right?
77 00:07:45.280 ⇒ 00:07:46.220 Awaish Kumar: ….
78 00:07:46.220 ⇒ 00:07:48.720 Annie Yu: On the, on the right side, there’s labels.
79 00:07:48.720 ⇒ 00:07:53.149 Awaish Kumar: Just to give, for example… It does not have a project assigned.
80 00:07:56.600 ⇒ 00:08:00.139 Awaish Kumar: Okay, I don’t know now where to add this.
81 00:08:01.000 ⇒ 00:08:02.500 Awaish Kumar: Let’s call it this one. Okay.
82 00:08:02.500 ⇒ 00:08:03.589 Annie Yu: Yeah, yeah.
83 00:08:04.680 ⇒ 00:08:08.820 Awaish Kumar: So go back, and I can maybe then filter at any filter.
84 00:08:10.490 ⇒ 00:08:11.580 Awaish Kumar: God.
85 00:08:14.850 ⇒ 00:08:18.419 Awaish Kumar: Project… Feeding it at all.
86 00:08:20.170 ⇒ 00:08:23.519 Awaish Kumar: Okay, so we have, like, 6 story points on Adop.
87 00:08:26.970 ⇒ 00:08:35.329 Awaish Kumar: T456… And then this one… I don’t know why it’s… Well done.
88 00:08:36.190 ⇒ 00:08:39.360 Awaish Kumar: Is that an adult that it is talking about National.
89 00:08:39.580 ⇒ 00:08:42.960 Annie Yu: I think this is… This is…
90 00:08:43.470 ⇒ 00:08:50.469 Annie Yu: I don’t know if this is an ad hoc, so we… We, … They initially request
91 00:08:50.820 ⇒ 00:09:00.660 Annie Yu: Some charts, and then after they saw those charts, they said, we also want this metric to be in other existing charts.
92 00:09:01.030 ⇒ 00:09:09.589 Annie Yu: So I don’t… I think that’s probably not an ad hoc, it’s more, like, a follow-up of a dashboard request.
93 00:09:11.330 ⇒ 00:09:13.510 Awaish Kumar: But, okay, but the virus?
94 00:09:15.000 ⇒ 00:09:18.960 Awaish Kumar: Once this is done and this is done separate.
95 00:09:20.070 ⇒ 00:09:21.910 Awaish Kumar: gorgeous.
96 00:09:22.390 ⇒ 00:09:25.789 Awaish Kumar: Because this was moved to next cycle, and then it was done.
97 00:09:26.290 ⇒ 00:09:27.540 Annie Yu: Oh, okay.
98 00:09:28.530 ⇒ 00:09:30.819 Annie Yu: That’s… yeah, it’s probably….
99 00:09:30.820 ⇒ 00:09:34.410 Awaish Kumar: Okay, and then we have these… Tickets
100 00:10:37.330 ⇒ 00:10:38.080 Awaish Kumar: Okay.
101 00:10:49.370 ⇒ 00:10:54.609 Awaish Kumar: We have, like, kind of 20… story points on…
102 00:10:55.350 ⇒ 00:11:01.699 Awaish Kumar: Trevor, let’s verify if all of this is, … basically Tableau one.
103 00:11:04.120 ⇒ 00:11:07.580 Awaish Kumar: Gosh, we’ll move… national money.
104 00:11:11.020 ⇒ 00:11:13.749 Awaish Kumar: So, all of this is dashboard, right?
105 00:11:14.380 ⇒ 00:11:17.800 Annie Yu: … Let me make sure….
106 00:11:18.100 ⇒ 00:11:20.530 Awaish Kumar: Let’s just, like, read the titles and….
107 00:11:21.480 ⇒ 00:11:30.219 Annie Yu: I think other than the last two, 669 and 516, the other ones are all Tableau.
108 00:11:31.500 ⇒ 00:11:37.329 Annie Yu: Yeah, other than the tab… the tableau and the architecture. Yeah, yeah.
109 00:11:40.310 ⇒ 00:11:43.990 Awaish Kumar: We can… yeah, this is also part of Tableau, right?
110 00:11:43.990 ⇒ 00:11:46.259 Annie Yu: Yes, yes, you’re right.
111 00:11:51.570 ⇒ 00:11:54.740 Awaish Kumar: Okay, … I’m not good.
112 00:11:55.080 ⇒ 00:11:56.760 Awaish Kumar: obligation.
113 00:11:58.740 ⇒ 00:11:59.740 Awaish Kumar: How would you…
114 00:12:07.780 ⇒ 00:12:09.280 Awaish Kumar: And, …
115 00:12:13.250 ⇒ 00:12:21.209 Awaish Kumar: So, like, you have been spending… kind of… 23… 20, …
116 00:12:43.220 ⇒ 00:12:45.889 Awaish Kumar: So, kind of 23 story points.
117 00:12:46.380 ⇒ 00:12:47.770 Awaish Kumar: On dashboard.
118 00:12:47.770 ⇒ 00:12:57.540 Annie Yu: I wouldn’t say those points are all accurate, though. I think some of those… weren’t assigned any points.
119 00:12:57.660 ⇒ 00:13:01.970 Annie Yu: But they definitely was not, like, a 5-minute task.
120 00:13:02.770 ⇒ 00:13:04.869 Awaish Kumar: Okay, so, like, for example, this one…
121 00:13:09.370 ⇒ 00:13:12.810 Awaish Kumar: Do you think it took you more than an hour?
122 00:13:13.880 ⇒ 00:13:16.890 Annie Yu: What’s this one? Let me see, …
123 00:13:18.870 ⇒ 00:13:21.530 Annie Yu: No, this one was probably 20 minutes.
124 00:13:23.050 ⇒ 00:13:30.500 Awaish Kumar: Like, that’s a… We don’t have 0.5 here, we might add it.
125 00:13:31.220 ⇒ 00:13:36.830 Awaish Kumar: But it’s just, like, under 1 hour, it’s… Because, like, …
126 00:13:38.340 ⇒ 00:13:41.860 Awaish Kumar: Right now, it just says 0, but it should be 0.5 at least.
127 00:13:42.100 ⇒ 00:13:45.199 Awaish Kumar: So we can quantify it somehow.
128 00:13:50.030 ⇒ 00:13:52.729 Awaish Kumar: And then we can see all of the zeros.
129 00:13:55.180 ⇒ 00:14:01.520 Awaish Kumar: Or if you can… you can review if you think, because we spent… more consummated…
130 00:14:02.160 ⇒ 00:14:04.880 Awaish Kumar: These three are, like, mop zero, I don’t….
131 00:14:05.680 ⇒ 00:14:06.490 Annie Yu: Hmm.
132 00:14:08.270 ⇒ 00:14:18.769 Annie Yu: And I do have something… so, I think a lot of my time actually spent on scoping and clarifying the ticket requirements, because…
133 00:14:19.130 ⇒ 00:14:21.880 Annie Yu: Some of the….
134 00:14:21.880 ⇒ 00:14:23.159 Awaish Kumar: Part of the ticket, right?
135 00:14:23.380 ⇒ 00:14:27.009 Awaish Kumar: For example, you gotta… that’s part of the ticket.
136 00:14:27.350 ⇒ 00:14:28.180 Annie Yu: Like, that….
137 00:14:28.180 ⇒ 00:14:29.560 Awaish Kumar: That is, like
138 00:14:29.950 ⇒ 00:14:38.640 Awaish Kumar: You know, if you wanted, you could have said that I need a separate ticket for Spike, that we call a spike, because you have unclear requirements.
139 00:14:38.780 ⇒ 00:14:47.879 Awaish Kumar: what you are going to do is that you say, okay, I don’t know the requirements, let’s have a spike, meet with… let me meet with the stakeholder, and…
140 00:14:48.390 ⇒ 00:14:53.160 Awaish Kumar: Get the clear requirements, and then split out between the dashboard and modeling requirements.
141 00:14:53.430 ⇒ 00:14:57.060 Awaish Kumar: General Conan. What if you have done it in a single ticket.
142 00:14:57.240 ⇒ 00:15:01.100 Awaish Kumar: then you can just assign all the coins to the single ticket. I’m not saying you
143 00:15:01.230 ⇒ 00:15:04.359 Awaish Kumar: Should not put the time you spend, but if
144 00:15:04.840 ⇒ 00:15:07.589 Awaish Kumar: Like, just for that time in the story parts.
145 00:15:09.710 ⇒ 00:15:14.510 Annie Yu: Oh, oh, so you said including those time….
146 00:15:14.810 ⇒ 00:15:18.100 Awaish Kumar: Yeah, for example, if I build a dashboard, I spend 3 time in building.
147 00:15:18.310 ⇒ 00:15:24.819 Awaish Kumar: 3 hours in building, and I spent maybe 1 hour in, clarifying the requirements, and maybe
148 00:15:25.210 ⇒ 00:15:28.780 Awaish Kumar: Half an hour more, To split between,
149 00:15:29.660 ⇒ 00:15:34.700 Awaish Kumar: to scope it out, and like, so I would say, like, maybe I spent 5 hours on this, right?
150 00:15:34.970 ⇒ 00:15:35.520 Annie Yu: Okay.
151 00:15:35.880 ⇒ 00:15:38.349 Awaish Kumar: Half of four and a half, or something like that.
152 00:15:40.170 ⇒ 00:15:43.639 Annie Yu: Yeah. Well, some of the ticket…
153 00:15:44.220 ⇒ 00:15:55.880 Awaish Kumar: there were two ways to handle that. Like, normally, if I don’t know technical things, I would say I will look for it, right? I need to spike. I can’t say yes or no or anything until I figure out.
154 00:15:56.160 ⇒ 00:16:05.510 Awaish Kumar: what’s gonna happen? Something new comes up, you just see first if it is even possible, right? So, that’s, like, spiked.
155 00:16:07.070 ⇒ 00:16:07.950 Annie Yu: Yeah.
156 00:16:08.920 ⇒ 00:16:22.989 Annie Yu: Yeah, that’s… yeah, that’s something I did not do, which I probably should have, because, yeah, some of the requests, I wouldn’t know if it’s doable within Tableau, or that requires modeling.
157 00:16:23.160 ⇒ 00:16:25.320 Annie Yu: … Help.
158 00:16:28.070 ⇒ 00:16:28.860 Awaish Kumar: Okay.
159 00:16:29.300 ⇒ 00:16:33.800 Awaish Kumar: But just right now, if we are correcting the story points, right?
160 00:16:33.920 ⇒ 00:16:35.920 Awaish Kumar: What would you think?
161 00:16:37.980 ⇒ 00:16:38.454 Annie Yu: …
162 00:16:44.250 ⇒ 00:16:48.090 Annie Yu: Do you mind, clicking into them?
163 00:16:49.700 ⇒ 00:16:52.399 Awaish Kumar: So, implement dashboard filters for day training now.
164 00:16:54.630 ⇒ 00:16:58.330 Awaish Kumar: If the fields are there, it should have been easier to implement.
165 00:17:01.140 ⇒ 00:17:06.460 Awaish Kumar: To the date field, pharmacy field, and product field are already in the model, or data source.
166 00:17:07.880 ⇒ 00:17:14.150 Annie Yu: Oh, yeah, this one, I think this one was… Probably less than an hour.
167 00:17:15.130 ⇒ 00:17:16.200 Annie Yu: ….
168 00:17:16.200 ⇒ 00:17:17.159 Awaish Kumar: Don’t a mess.
169 00:17:20.770 ⇒ 00:17:22.389 Awaish Kumar: 30 minutes, right, maybe?
170 00:17:22.780 ⇒ 00:17:25.299 Annie Yu: Let me see… I think so.
171 00:17:26.010 ⇒ 00:17:27.140 Awaish Kumar: Yeah.
172 00:17:27.700 ⇒ 00:17:34.560 Awaish Kumar: Okay, I would suggest not, and… This one is also…
173 00:17:35.540 ⇒ 00:17:39.329 Awaish Kumar: Enabling the refresh is fine. Maybe 10 to 15 minutes.
174 00:17:42.280 ⇒ 00:17:44.260 Annie Yu: Tiny refresh…
175 00:17:48.250 ⇒ 00:17:50.960 Annie Yu: Oh, yeah, yeah, this one, this one was quick.
176 00:17:59.040 ⇒ 00:18:00.180 Awaish Kumar: This one?
177 00:18:00.510 ⇒ 00:18:06.659 Annie Yu: This one, I would say too, … I see it.
178 00:18:06.660 ⇒ 00:18:09.969 Awaish Kumar: This is adding an, like, export on something, right?
179 00:18:10.580 ⇒ 00:18:11.160 Annie Yu: Yes.
180 00:18:11.160 ⇒ 00:18:17.190 Awaish Kumar: So we, like, what I understand from this ticket is that we already have something, a dashboard.
181 00:18:17.380 ⇒ 00:18:22.570 Awaish Kumar: And this ticket is just… Adding a few… maybe some inability.
182 00:18:22.570 ⇒ 00:18:26.100 Annie Yu: No, this one, so for export.
183 00:18:26.200 ⇒ 00:18:44.200 Annie Yu: if we are just exporting whatever we have in Tableau, that one’s easier, because we just add a button. But for this one, they want the raw data, so that means I had to build out a separate worksheet that has the raw data.
184 00:18:44.350 ⇒ 00:18:50.049 Annie Yu: And then… so they can, they can export the raw data.
185 00:18:50.560 ⇒ 00:18:54.940 Awaish Kumar: Yeah, okay, but that is still, like, the data source is already built.
186 00:18:55.120 ⇒ 00:19:03.359 Awaish Kumar: You’ve created a worksheet with a, like, chart, type is a table, where you just get the… all the raw data there, right?
187 00:19:04.590 ⇒ 00:19:05.410 Annie Yu: Yes.
188 00:19:07.020 ⇒ 00:19:10.449 Awaish Kumar: I would say still, like, under… under 30, like, 30 minutes.
189 00:19:18.720 ⇒ 00:19:20.770 Annie Yu: Mmm, if you think so.
190 00:19:21.490 ⇒ 00:19:25.360 Awaish Kumar: Yeah, like, no, like, someone who’s experienced in Tableau.
191 00:19:25.600 ⇒ 00:19:29.340 Awaish Kumar: We can create a worksheet in a button stick and add it
192 00:19:29.800 ⇒ 00:19:33.930 Awaish Kumar: had a Tableau type of chart. I don’t know if there were more….
193 00:19:34.360 ⇒ 00:19:39.429 Annie Yu: Yeah, this… this was not a complex… ticket. So I always say….
194 00:19:39.430 ⇒ 00:19:52.209 Awaish Kumar: if there are, like, more filters you needed to add, you may need to make some, like, custom, like, some custom queries, or something, or aggregated, like, metric, then I would say, okay.
195 00:19:52.560 ⇒ 00:19:59.579 Awaish Kumar: It might have taken some time, like, but if you have the exact data you need in some kind of table already.
196 00:19:59.770 ⇒ 00:20:03.740 Awaish Kumar: We’re gonna plug that in a Tableau, and… destroyed that.
197 00:20:04.390 ⇒ 00:20:05.260 Awaish Kumar: Klein.
198 00:20:09.340 ⇒ 00:20:11.809 Awaish Kumar: Yeah, this one, this one’s straightforward.
199 00:20:15.430 ⇒ 00:20:20.879 Awaish Kumar: And also… If you connect this with,
200 00:20:22.790 ⇒ 00:20:26.369 Awaish Kumar: So, this was independent ticket, right? It was not like….
201 00:20:26.370 ⇒ 00:20:30.829 Annie Yu: That’s part of a dashboard, so I would say….
202 00:20:30.830 ⇒ 00:20:32.480 Awaish Kumar: dashboard, but I would say, like.
203 00:20:32.740 ⇒ 00:20:40.559 Awaish Kumar: This was a separate chart on the dashboard, so if you click on other chart, it doesn’t need to update this chart, right?
204 00:20:41.320 ⇒ 00:20:45.929 Annie Yu: I think I set up the filters S.
205 00:20:46.940 ⇒ 00:20:54.900 Annie Yu: So the filters for this raw data chart should be… Connected to the main dashboard.
206 00:20:55.540 ⇒ 00:20:56.350 Awaish Kumar: Okay.
207 00:20:57.940 ⇒ 00:21:01.139 Annie Yu: Yeah, but people can change the dashboard and then the.
208 00:21:01.140 ⇒ 00:21:09.549 Awaish Kumar: So, people can open for a dashboard. There are some filters. If they select the filter, then it is applied to all the charts, including this one.
209 00:21:11.560 ⇒ 00:21:16.569 Annie Yu: Within that dashboard, yes. Within that, this specific dashboard, yes.
210 00:21:16.570 ⇒ 00:21:17.210 Awaish Kumar: Hell yeah.
211 00:21:17.750 ⇒ 00:21:21.270 Awaish Kumar: They can only see one dashboard at the same… at a time, right?
212 00:21:22.130 ⇒ 00:21:23.289 Annie Yu: Yes, but…
213 00:21:24.010 ⇒ 00:21:32.260 Annie Yu: You could use multiple data sources for different sections within one dashboard, but for this one, I think we only use one data source.
214 00:21:34.570 ⇒ 00:21:35.570 Awaish Kumar: Okay.
215 00:21:36.940 ⇒ 00:21:37.770 Awaish Kumar: Okay.
216 00:21:39.830 ⇒ 00:21:45.679 Awaish Kumar: Okay, but if you had to do two different, like, for example, we have one dashboard.
217 00:21:46.100 ⇒ 00:21:48.390 Awaish Kumar: Inside of that dashboard, we have two charts.
218 00:21:48.540 ⇒ 00:21:51.620 Awaish Kumar: And both of these stars are coming from two different data sources.
219 00:21:52.350 ⇒ 00:21:56.249 Awaish Kumar: So would you need different filters for all of these, or same filtering?
220 00:21:56.250 ⇒ 00:22:04.040 Annie Yu: Yes, yes, you need to build different filters, because they might have different level of details and granularity.
221 00:22:06.330 ⇒ 00:22:12.029 Awaish Kumar: No, but for example, for example, product name fill, column.
222 00:22:12.140 ⇒ 00:22:13.950 Awaish Kumar: It’s available in both of them.
223 00:22:14.150 ⇒ 00:22:15.490 Awaish Kumar: both sources.
224 00:22:16.010 ⇒ 00:22:20.140 Awaish Kumar: Think of our sales data, and then I have a spend data.
225 00:22:20.450 ⇒ 00:22:23.770 Awaish Kumar: And both have a standardized product name, column.
226 00:22:24.060 ⇒ 00:22:25.440 Awaish Kumar: And, …
227 00:22:26.360 ⇒ 00:22:35.450 Awaish Kumar: That can be used, like, in both of them, both of the charts, so… but still, both the charts are using different data sources.
228 00:22:35.590 ⇒ 00:22:41.520 Awaish Kumar: So, would I have to use different filters, or can a single filter can be applied to both?
229 00:22:41.650 ⇒ 00:22:42.980 Awaish Kumar: Jobs.
230 00:22:43.380 ⇒ 00:22:54.570 Annie Yu: I think that depends on the case. I try to do that sometimes, but sometimes Tableau would tell you this is not the, right shared attribute.
231 00:22:54.760 ⇒ 00:23:05.389 Annie Yu: So whenever, if they do share something, and then I want to be safe, I would just join them, like, the fact transaction, and then…
232 00:23:05.580 ⇒ 00:23:12.400 Annie Yu: products. I… I wouldn’t necessarily just rely on the… the filters.
233 00:23:14.720 ⇒ 00:23:19.050 Awaish Kumar: Okay, so… That’s… good.
234 00:23:19.970 ⇒ 00:23:23.380 Awaish Kumar: Okay, apart from this, like, you know, for the…
235 00:23:24.040 ⇒ 00:23:35.750 Awaish Kumar: tickets-related discussion. So, apart from that, if you just say anything, right, your… work, for example.
236 00:23:36.380 ⇒ 00:23:40.480 Awaish Kumar: I would say, … Your scope of work, for example.
237 00:23:40.680 ⇒ 00:23:41.500 Awaish Kumar: Come on.
238 00:23:44.980 ⇒ 00:23:52.640 Awaish Kumar: In the Tableau, … Like… That could help, like, the next person who comes in.
239 00:23:54.200 ⇒ 00:23:55.010 Annie Yu: ….
240 00:23:55.010 ⇒ 00:23:58.430 Awaish Kumar: Kind of, the… if you want to talk…
241 00:23:58.820 ⇒ 00:24:02.819 Awaish Kumar: Talk… if you prefer talking, you can just let me, like.
242 00:24:03.190 ⇒ 00:24:08.630 Awaish Kumar: this meeting is recorded, I could use it as a…
243 00:24:09.040 ⇒ 00:24:13.230 Awaish Kumar: Or, like, to share knowledge to someone else, but otherwise, …
244 00:24:13.460 ⇒ 00:24:16.829 Awaish Kumar: If you prefer talking, you can. Otherwise, if you want to write something.
245 00:24:16.990 ⇒ 00:24:20.369 Awaish Kumar: An ocean dog, this is your choice right now.
246 00:24:20.550 ⇒ 00:24:24.249 Awaish Kumar: We haven’t thought of, like, how we are going to
247 00:24:24.640 ⇒ 00:24:32.560 Awaish Kumar: Have the knowledge sharing, but yeah, if you can… if you want to prepare a document, you can prepare it, like, giving all… any information about, like, …
248 00:24:33.240 ⇒ 00:24:43.139 Awaish Kumar: like, including, like, authentication to Tableau, like, I know that’s available in OnePass, but if you want to document it, that would be nice for the next person who comes in.
249 00:24:43.430 ⇒ 00:24:52.789 Awaish Kumar: And then, different work streams on the Tableau, or different data sources, how they are… refreshed, and …
250 00:24:53.710 ⇒ 00:24:58.359 Awaish Kumar: How are you setting up new data, so how, like, what, like, basically, you know all the best…
251 00:24:58.470 ⇒ 00:25:01.170 Awaish Kumar: Practices, and we want that to be possible.
252 00:25:01.300 ⇒ 00:25:04.240 Awaish Kumar: To be transferable to the next person.
253 00:25:05.720 ⇒ 00:25:10.650 Annie Yu: Yeah, I, we can, we can walk through those, here.
254 00:25:11.730 ⇒ 00:25:15.170 Annie Yu: I… yeah, we can walk through those, if….
255 00:25:15.170 ⇒ 00:25:15.840 Awaish Kumar: Okay.
256 00:25:16.280 ⇒ 00:25:23.229 Annie Yu: … So I… okay, I guess we can sign out first. So usually, I usually just save that…
257 00:25:24.110 ⇒ 00:25:36.239 Annie Yu: a link here, and then I… we’re signing… From here… … And you were saying… okay.
258 00:25:37.030 ⇒ 00:25:49.529 Annie Yu: Let me see… and we do categorize, all the dashboards into these categories, and they are all documented in the data platform documentation.
259 00:25:49.740 ⇒ 00:25:59.320 Annie Yu: So, I guess we can start with… so, if we want to see what data sources a dashboard is using, we can click into this.
260 00:25:59.540 ⇒ 00:26:03.419 Annie Yu: And then there’s a data source tab, so we…
261 00:26:03.540 ⇒ 00:26:11.250 Annie Yu: would clearly know, okay, this dashboard is using order summary. But this data source is also we… something we have to…
262 00:26:11.440 ⇒ 00:26:17.090 Annie Yu: upload. So there’s another section that’s published data sources.
263 00:26:17.360 ⇒ 00:26:21.509 Annie Yu: So these are all the data sources that we published. They are…
264 00:26:21.750 ⇒ 00:26:31.460 Annie Yu: pretty much just, like a daily refresh of our dbt models. And… Usually….
265 00:26:31.460 ⇒ 00:26:33.709 Awaish Kumar: to try to new, like, how can I do that?
266 00:26:34.420 ⇒ 00:26:35.020 Annie Yu: What?
267 00:26:35.470 ⇒ 00:26:37.599 Awaish Kumar: If I want to add a new data source here.
268 00:26:37.600 ⇒ 00:26:45.999 Annie Yu: Yeah, yeah. So, I… I don’t know if there’s a better way, but I always just go to a, … actually, let me open…
269 00:26:46.130 ⇒ 00:26:47.390 Annie Yu: Yes.
270 00:26:47.990 ⇒ 00:26:58.740 Annie Yu: … Yeah, and then there’s connected data, and this… we would go to BigQuery…
271 00:27:00.020 ⇒ 00:27:07.950 Annie Yu: And then I have this JSON file downloaded in my laptop, which I think Sahana has a walkthrough
272 00:27:08.110 ⇒ 00:27:12.490 Annie Yu: Video about how how to go about this.
273 00:27:13.240 ⇒ 00:27:14.060 Awaish Kumar: Okay.
274 00:27:15.310 ⇒ 00:27:21.600 Annie Yu: And then, just sign in, so now I’m connected, so it’s kind of very similar to…
275 00:27:22.490 ⇒ 00:27:33.420 Annie Yu: like, kind of like BigQuery. So here we will see all the data models we have, then we can find a… and do a fact transactions.
276 00:27:35.200 ⇒ 00:27:48.759 Annie Yu: So now it’s a live… we would see this is a live connection, and there’s also, like, more than one way to do this, but usually, okay, I know this is what I want, I would just first publish this.
277 00:27:50.390 ⇒ 00:27:51.960 Annie Yu: And then…
278 00:27:52.250 ⇒ 00:28:06.450 Annie Yu: I’m just gonna put in the staging for now, because we are testing. So in staging, there’s staging data sources, but this is a formal one, so I guess for a formal one, you would put it in the published data sources.
279 00:28:07.240 ⇒ 00:28:11.230 Annie Yu: And then, fact transaction, I’m gonna do… first.
280 00:28:12.740 ⇒ 00:28:23.760 Annie Yu: And as for authentication, I… I’m not sure if there is a better way, but I usually just use this embed, this JSON service account.
281 00:28:24.730 ⇒ 00:28:31.280 Annie Yu: And then we’ll publish… So now we are in this…
282 00:28:31.400 ⇒ 00:28:45.209 Annie Yu: folder, and we have this. But again, this is a live connection. So live instance, real time, but when you build a dashboard, something could be, like, loading forever, because it’s a live connection, so we want to…
283 00:28:45.370 ⇒ 00:28:55.030 Annie Yu: Extract this… And then it usually takes some time to run, like, an extract refresh.
284 00:28:55.130 ⇒ 00:28:58.559 Annie Yu: So once that’s done.
285 00:28:58.780 ⇒ 00:29:14.230 Annie Yu: I can show you something faster, we can just pick one that we have here. So once that’s done… wait, this is also live. … yeah, I probably don’t extract everything in… in the staging, but…
286 00:29:15.520 ⇒ 00:29:16.870 Annie Yu: Let’s see…
287 00:29:19.580 ⇒ 00:29:32.550 Annie Yu: Because in staging, sometimes I just explore things. It’s not actually used for a dashboard or so, but we can pick one here. Cross-sale summary. So, once that’s extract…
288 00:29:32.990 ⇒ 00:29:44.080 Annie Yu: extracted, it will become extract here. And then we will come to this extract refreshes tab, and then you will set up the refresh cadence.
289 00:29:44.280 ⇒ 00:29:53.269 Annie Yu: And I know this also… I think Sahana also had, like, a video about this. We usually do every day, and then pick, like, a very…
290 00:29:53.590 ⇒ 00:29:54.520 Annie Yu: …
291 00:29:55.940 ⇒ 00:30:04.860 Annie Yu: like, sometime in the middle of the night, and this is based in New York time, so, I usually try to do after 3, because
292 00:30:05.670 ⇒ 00:30:11.320 Annie Yu: If it’s 12 New York time, it’s still 9 PM in my time.
293 00:30:11.510 ⇒ 00:30:19.379 Annie Yu: … So, yeah, that’s one thing, and then we would just create that refresh. That’s why it refreshes
294 00:30:19.820 ⇒ 00:30:37.109 Annie Yu: Every day. So yeah, now this is done, we will see this is extract, and you can set up the extract refresh here. And then, so from there, if we want to have a new dashboard using that data source, so we would do something very similar
295 00:30:38.490 ⇒ 00:30:46.350 Annie Yu: to what we did. We were… connecting… Okay.
296 00:30:46.840 ⇒ 00:30:53.290 Annie Yu: we were trying to connect to data, but then from here, we would select the Tableau server.
297 00:30:54.100 ⇒ 00:30:57.940 Annie Yu: And then you can just type in the fact, transaction.
298 00:30:58.450 ⇒ 00:31:03.060 Annie Yu: Just whatever data source you try to do, …
299 00:31:05.220 ⇒ 00:31:08.670 Annie Yu: Yeah, for this one, I joined these, all these.
300 00:31:08.910 ⇒ 00:31:10.260 Annie Yu: And then double click….
301 00:31:10.260 ⇒ 00:31:12.539 Awaish Kumar: Like, the one which we just settled?
302 00:31:12.710 ⇒ 00:31:14.930 Annie Yu: I don’t think so, let me see.
303 00:31:16.280 ⇒ 00:31:17.760 Annie Yu: ….
304 00:31:20.210 ⇒ 00:31:21.489 Awaish Kumar: Why is that option?
305 00:31:21.490 ⇒ 00:31:24.680 Annie Yu: Instructions, yeah, I don’t see that, let me…
306 00:31:24.900 ⇒ 00:31:29.740 Annie Yu: Oh, wait, wait, actually, no, I think we have just multiple here. Test.
307 00:31:30.250 ⇒ 00:31:30.970 Awaish Kumar: Okay.
308 00:31:31.930 ⇒ 00:31:40.909 Annie Yu: So we… yeah, we can close this one, so we will have all the, but, …
309 00:31:42.880 ⇒ 00:31:44.930 Annie Yu: Yeah, so that’s it, and then…
310 00:31:45.890 ⇒ 00:31:53.600 Annie Yu: we would, set up all the different metrics that we want, or the filters, if it’s not native to….
311 00:31:53.720 ⇒ 00:32:00.450 Awaish Kumar: I don’t know if you can just… Walk over… the…
312 00:32:00.810 ⇒ 00:32:06.010 Awaish Kumar: the video works teams, like, different… basically, you are building dashboards for different people.
313 00:32:08.050 ⇒ 00:32:17.060 Awaish Kumar: And, like, each… like, if you can just overview it, like, this is the work stream, these are the people, or what kind of data.
314 00:32:17.390 ⇒ 00:32:31.569 Awaish Kumar: like, not just… not specific models, but as a high level, like, what kind of data you’re dealing with here, and what are some business, like, contexts there, something like that. You can, like, look at your tickets, or if you want, or…
315 00:32:31.690 ⇒ 00:32:33.880 Awaish Kumar: Because you want to look at dashboards.
316 00:32:34.100 ⇒ 00:32:35.410 Awaish Kumar: Whatever.
317 00:32:36.160 ⇒ 00:32:43.020 Annie Yu: For that part, I think I’ll write something down, because there is, quite a few.
318 00:32:43.260 ⇒ 00:32:44.020 Awaish Kumar: Okay.
319 00:32:44.320 ⇒ 00:32:52.629 Awaish Kumar: Okay, just write a Notion document then, and yeah, just share it with, with me, or… or in the channel.
320 00:32:54.350 ⇒ 00:32:55.490 Annie Yu: Yeah, yeah.
321 00:32:55.640 ⇒ 00:32:58.119 Awaish Kumar: Okay, let me think….
322 00:33:00.660 ⇒ 00:33:06.929 Annie Yu: If there’s… Yeah, I think that’s… that’s, pretty much it. …
323 00:33:07.690 ⇒ 00:33:15.000 Annie Yu: And I would say, usually, when I look at the ticket, I would…
324 00:33:15.340 ⇒ 00:33:22.539 Annie Yu: just go through these, and then kind of think about what data sources I would use for each section, and then if it’s…
325 00:33:22.820 ⇒ 00:33:29.170 Annie Yu: … something I might already have in other dashboards that we can reuse.
326 00:33:29.500 ⇒ 00:33:33.599 Awaish Kumar: Yeah, like, I would love to have, you know, like.
327 00:33:33.740 ⇒ 00:33:42.990 Awaish Kumar: SOP on… if a new dashboarding ticket comes in, what is your process to, … To, like, bring…
328 00:33:43.310 ⇒ 00:33:49.539 Awaish Kumar: go from that, which is… which has, like, unclear requirements and some… something from time, right?
329 00:33:50.180 ⇒ 00:34:03.330 Awaish Kumar: So, what is your, process to basically convert that, uncertain requirements to a com- like, nicely,
330 00:34:04.310 ⇒ 00:34:05.620 Awaish Kumar: What’d you say?
331 00:34:06.440 ⇒ 00:34:19.430 Awaish Kumar: Technical… like, convert that into technical requirements, and then what is your process to split them between modeling requirements and dashboarding, and then what… when you… when it comes to dashboarding, how you go about building something
332 00:34:19.530 ⇒ 00:34:21.989 Awaish Kumar: My new, like, virtual room, like.
333 00:34:22.090 ⇒ 00:34:34.439 Awaish Kumar: You don’t have context, like, you haven’t worked on that data, for example, and you are working for the first time on, for example, finance data, so how would you search for it, search for context, look at…
334 00:34:34.630 ⇒ 00:34:38.879 Awaish Kumar: Google, AI, whatever you do, so kind of an SOP on
335 00:34:39.110 ⇒ 00:34:43.029 Awaish Kumar: If something like that comes in, how would you just…
336 00:34:43.199 ⇒ 00:34:45.999 Awaish Kumar: Get it from there until the finish line.
337 00:34:48.150 ⇒ 00:34:51.120 Annie Yu: Yeah, yeah, yeah, I can document those.
338 00:34:51.750 ⇒ 00:34:54.819 Awaish Kumar: So, yeah, I’m expecting, like, two kind of…
339 00:34:55.980 ⇒ 00:35:01.819 Awaish Kumar: two different types of documents, like, one is where you are just writing down about different work streams, and you’re
340 00:35:02.680 ⇒ 00:35:10.609 Awaish Kumar: scope and their context, whatever that is. Second one is SOP on the operating process, and that’s all.
341 00:35:11.230 ⇒ 00:35:12.060 Annie Yu: Can you hear me?
342 00:35:12.470 ⇒ 00:35:15.859 Annie Yu: What’s the difference between the two? So, ….
343 00:35:15.860 ⇒ 00:35:27.409 Awaish Kumar: SOP is just defining the steps, like, I would say, like, if I asked you, like, how would you go to, to grocery store and buy and come, like, come back with grocery stores, you would say, like.
344 00:35:27.810 ⇒ 00:35:32.850 Awaish Kumar: I… I use the stairs, go into my car, like…
345 00:35:33.070 ⇒ 00:35:40.810 Awaish Kumar: Travel to some grocery store, buy something, pay… like, these kind of… these are different steps you would perform to actually make that.
346 00:35:41.060 ⇒ 00:35:45.100 Awaish Kumar: happen, right? Similarly, if someone comes in.
347 00:35:45.400 ⇒ 00:35:51.480 Awaish Kumar: Jonah comes in with a finance model requirement, like, I want to have something like this, and…
348 00:35:51.620 ⇒ 00:35:54.909 Awaish Kumar: Then, what are your steps to con… like, basically what he…
349 00:35:55.020 ⇒ 00:35:59.520 Awaish Kumar: asks you is not completely, maybe, doable, or…
350 00:35:59.740 ⇒ 00:36:04.810 Awaish Kumar: uncertain, or whatever it is. It’s not, like, technical requirements, just some…
351 00:36:05.020 ⇒ 00:36:13.530 Awaish Kumar: context from a business perspective. So how… then, like, you are the one, basically, what… taking that on, converting into a technical…
352 00:36:13.680 ⇒ 00:36:21.909 Awaish Kumar: Requirements, then talking, like, splitting it between dashboard requirements and then modeling requirements, and then…
353 00:36:22.050 ⇒ 00:36:26.130 Awaish Kumar: Finally, building the model and delivering it back to client.
354 00:36:26.390 ⇒ 00:36:31.839 Awaish Kumar: And, like, communicating to that client. So these are different steps you might perform. So I have just roughly
355 00:36:32.070 ⇒ 00:36:39.120 Awaish Kumar: said what would you do, but you can, like, you know better what to do. So, if you can just write those steps in detail.
356 00:36:40.230 ⇒ 00:36:49.379 Annie Yu: Yeah, yeah. And one thing, though, I… I think I actually spent a lot of time on this, but I don’t think this should be, like, an analyst job, which is…
357 00:36:49.530 ⇒ 00:36:52.430 Annie Yu: like… …
358 00:36:53.940 ⇒ 00:37:02.229 Annie Yu: So these are all written by me, but lots of the time, I just have, like, a screenshot or, …
359 00:37:03.500 ⇒ 00:37:15.520 Annie Yu: Like, the breakdown of what the stakeholder typed without, like, actual, kind of, at least some categorization of the requirements.
360 00:37:15.520 ⇒ 00:37:25.130 Annie Yu: And that part, I don’t know if I want to document those, because I think that should be, like, a PM’s job, but I think I actually spent a lot of time on those things.
361 00:37:26.670 ⇒ 00:37:32.589 Awaish Kumar: Yeah, but that’s what I said, like, getting the requirements from the client, right?
362 00:37:33.580 ⇒ 00:37:38.729 Awaish Kumar: That’s part of getting the client’s requirement, and sometimes you have
363 00:37:38.940 ⇒ 00:37:42.759 Awaish Kumar: Like, in maybe product company, where you are…
364 00:37:43.050 ⇒ 00:37:51.800 Awaish Kumar: you have a PM, and then you are as an analyst, and you are all working on the same product. It becomes easier,
365 00:37:52.050 ⇒ 00:37:53.060 Awaish Kumar: to…
366 00:37:53.660 ⇒ 00:38:12.220 Awaish Kumar: for everybody to get the requirements, because everybody knows that… about the product, like, what we are working for, right? Like, in the previous company, I was working for a game… gaming company, and we know, like, we have this game, and we are working around it. It was easier to get the requirements.
367 00:38:12.520 ⇒ 00:38:16.119 Awaish Kumar: In a consultancy, it becomes harder.
368 00:38:16.870 ⇒ 00:38:21.609 Awaish Kumar: When you are on multiple cries, and also liking multiple people,
369 00:38:24.080 ⇒ 00:38:29.570 Awaish Kumar: Like, we are getting, like, multiple stakeholders involved, so you, you… get…
370 00:38:29.770 ⇒ 00:38:40.080 Awaish Kumar: requirement from there, and it’s not just you working on multiple clients, it’s like, PM is also working on multiple clients, so he… how she is also, like, …
371 00:38:40.570 ⇒ 00:38:47.839 Awaish Kumar: have, like, enough time to spend on one client, and, like, she can’t, like, move…
372 00:38:47.980 ⇒ 00:38:56.660 Awaish Kumar: basically groomed as much as we… a PM should, right? So it’s just, … kind of…
373 00:38:58.350 ⇒ 00:39:06.779 Awaish Kumar: just the kind of work that people has to do in constituency. Normally, like, I’m from… I’m in Pakistan, so normally we work
374 00:39:07.020 ⇒ 00:39:11.050 Awaish Kumar: all the Pakistani software companies, they provide services to the
375 00:39:11.410 ⇒ 00:39:23.850 Awaish Kumar: other companies in EU, US, whatever. So, I have experience working in service companies, and all of them are like that, so they can… they just pair you with a client, and then you are the PM, you are the developer.
376 00:39:24.070 ⇒ 00:39:25.430 Awaish Kumar: Not everything.
377 00:39:26.000 ⇒ 00:39:31.219 Annie Yu: Yeah, I mean, it’s easier, yeah, it’s easier when it’s just one client, one stakeholder.
378 00:39:31.320 ⇒ 00:39:36.030 Annie Yu: Which… Where you don’t even need a PM at that point, I think.
379 00:39:38.340 ⇒ 00:39:39.100 Awaish Kumar: Yeah.
380 00:39:39.570 ⇒ 00:39:41.000 Awaish Kumar: So, yeah.
381 00:39:41.320 ⇒ 00:39:44.389 Awaish Kumar: Thank you, like, for Slice, …
382 00:39:44.550 ⇒ 00:39:47.559 Awaish Kumar: Talking to you, and also nice talking with you here.
383 00:39:50.300 ⇒ 00:40:08.620 Annie Yu: Yeah, I wish I… I want to say thank you, too. I think you are obviously very talented, and I think you are very patient, with… because you are very knowledgeable around all of… all the things, but sometimes I just… I don’t get some of the things, and you are always, like, very patient, and…
384 00:40:08.620 ⇒ 00:40:17.109 Annie Yu: Explaining things. So… so thank you so much for that, and I really enjoyed the time working with you. And I would love to stay in touch.
385 00:40:18.470 ⇒ 00:40:19.699 Awaish Kumar: Yeah, sure.
386 00:40:22.750 ⇒ 00:40:30.779 Annie Yu: Okay, wait, can you help me, okay, just write the title for these two, so I… so I can make sure I focus.
387 00:40:30.780 ⇒ 00:40:33.970 Awaish Kumar: One is… one is, like, … Bob.
388 00:40:35.160 ⇒ 00:40:43.140 Awaish Kumar: like… Creating… creating a new dashboard in Tableau, like, SOP for creating a new dashboard.
389 00:40:43.770 ⇒ 00:40:45.000 Annie Yu: Hmm, yeah.
390 00:40:47.210 ⇒ 00:40:53.670 Awaish Kumar: Tableau dashboard, for example, for just the dashboard, because it can be any tool, right? Yeah. Tableau, PowerPI.
391 00:40:54.350 ⇒ 00:40:55.230 Annie Yu: And then….
392 00:40:55.230 ⇒ 00:41:00.170 Awaish Kumar: One is, like, respond, like, Eden….
393 00:41:01.430 ⇒ 00:41:03.209 Annie Yu: Eden, okay, Eden’s….
394 00:41:03.210 ⇒ 00:41:03.569 Awaish Kumar: I see you.
395 00:41:03.570 ⇒ 00:41:05.059 Annie Yu: excitement, I think.
396 00:41:06.010 ⇒ 00:41:07.700 Annie Yu: Eden’s workshop.
397 00:41:08.180 ⇒ 00:41:09.420 Awaish Kumar: different work streams.
398 00:41:09.670 ⇒ 00:41:11.670 Awaish Kumar: Like, and a business context.
399 00:41:13.660 ⇒ 00:41:15.990 Annie Yu: Mmm… okay.
400 00:41:16.760 ⇒ 00:41:22.480 Annie Yu: For business context, I probably won’t be able to give that much, but I think I’m planning to….
401 00:41:22.480 ⇒ 00:41:23.150 Awaish Kumar: Or, you know.
402 00:41:23.500 ⇒ 00:41:24.500 Annie Yu: Who, ….
403 00:41:24.500 ⇒ 00:41:35.699 Awaish Kumar: Because you have been talking to… directly talking with stakeholders, or whatever you know about those functions, about those people, you type it, like, that’s all mine. And then maybe we can…
404 00:41:35.810 ⇒ 00:41:44.430 Awaish Kumar: We are going to enhance it by, like, input… giving input from my side, from Demolati, from everyone, and we are going to make it more…
405 00:41:44.780 ⇒ 00:41:47.300 Awaish Kumar: enrich it more, like, but we, …
406 00:41:48.010 ⇒ 00:41:54.939 Awaish Kumar: We are going to start with your… Like, baseball, best practices.
407 00:41:56.130 ⇒ 00:42:04.270 Annie Yu: Yeah, yeah. Yeah, I think I’ll split this by their team, like, finance, marketing, and… Pharma.
408 00:42:05.800 ⇒ 00:42:07.349 Awaish Kumar: Okay, yeah, sure, sure.
409 00:42:10.370 ⇒ 00:42:15.050 Annie Yu: Okay, let me make sure I’m not missing anything. Okay.
410 00:42:19.870 ⇒ 00:42:20.420 Annie Yu: Okay.
411 00:42:20.420 ⇒ 00:42:26.790 Awaish Kumar: You can use tools like, for example, Whisper or something to So, basically.
412 00:42:27.910 ⇒ 00:42:30.239 Awaish Kumar: You can just talk, and they will…
413 00:42:30.620 ⇒ 00:42:33.259 Awaish Kumar: Basically, create a transcript for you, and then…
414 00:42:33.690 ⇒ 00:42:37.610 Awaish Kumar: you can, like, enhance it using AI and whatever.
415 00:42:38.350 ⇒ 00:42:39.210 Annie Yu: Okay.
416 00:42:39.600 ⇒ 00:42:41.870 Annie Yu: Yeah, no, I think for some of the.
417 00:42:41.870 ⇒ 00:42:43.770 Awaish Kumar: I’m going to save your time for writing.
418 00:42:44.800 ⇒ 00:42:45.790 Annie Yu: Yeah.
419 00:42:45.900 ⇒ 00:42:47.280 Annie Yu: …
420 00:42:50.380 ⇒ 00:42:57.109 Annie Yu: Yeah, I will… I think for this part, I will have to, kind of walk through
421 00:43:00.490 ⇒ 00:43:08.580 Annie Yu: Just… yeah. Try, try to walk through, like, a new ticket, and then… what I usually do.
422 00:43:09.760 ⇒ 00:43:11.320 Awaish Kumar: Okay, yeah, sure.
423 00:43:12.310 ⇒ 00:43:15.360 Awaish Kumar: Yeah, like, that’s it from my side.
424 00:43:17.400 ⇒ 00:43:20.250 Awaish Kumar: I’ll get back to you if I…
425 00:43:21.080 ⇒ 00:43:25.360 Awaish Kumar: if I had to… like, if I get something that needs to be
426 00:43:25.590 ⇒ 00:43:29.659 Awaish Kumar: Like, if you need any kind of, like, knowledge transfer, or…
427 00:43:31.660 ⇒ 00:43:38.799 Awaish Kumar: anything, like, if it is… if I miss something, like today, we are going to maybe ask.
428 00:43:38.950 ⇒ 00:43:48.300 Awaish Kumar: Again, for it, but otherwise… yeah, I think after those documents, We’ll have, enough information.
429 00:43:49.760 ⇒ 00:43:51.819 Annie Yu: Yeah, yeah, yeah, that sounds good.
430 00:43:52.990 ⇒ 00:43:53.770 Awaish Kumar: Okay.
431 00:43:53.960 ⇒ 00:44:02.720 Awaish Kumar: Thank you, … And best of luck for the future endeavors, whatever you’re gonna choose for you.
432 00:44:04.560 ⇒ 00:44:06.510 Annie Yu: Yeah, thank you, Awash.
433 00:44:08.130 ⇒ 00:44:08.769 Annie Yu: If I….