Meeting Title: Brainforge Data Analyst Interview Date: 2025-11-26 Meeting participants: Tanay Parikh, Awaish Kumar, Awaish Kumar
WEBVTT
1 00:01:17.090 ⇒ 00:01:18.030 Awaish Kumar: Hello.
2 00:01:18.490 ⇒ 00:01:19.260 Tanay Parikh: Hello.
3 00:01:28.550 ⇒ 00:01:29.380 Tanay Parikh: Glow.
4 00:01:31.500 ⇒ 00:01:33.489 Awaish Kumar: Can you hear me?
5 00:01:33.490 ⇒ 00:01:35.169 Tanay Parikh: Yeah, I can hear you.
6 00:01:37.550 ⇒ 00:01:39.859 Awaish Kumar: Hello, my name is Avesh Kumar.
7 00:01:40.760 ⇒ 00:01:44.870 Awaish Kumar: I’m kind of data engineer, it’ll bring forward.
8 00:01:45.940 ⇒ 00:01:48.710 Awaish Kumar: I will be, like, kind of,
9 00:01:48.920 ⇒ 00:01:53.600 Awaish Kumar: I’ve been working as a data engineer for the last 8 years, and…
10 00:01:54.500 ⇒ 00:01:58.770 Awaish Kumar: Basically, building end-to-end pipelines and helping people.
11 00:01:59.040 ⇒ 00:02:06.020 Awaish Kumar: which companies, Helping companies build their data foundations, and the…
12 00:02:06.360 ⇒ 00:02:13.469 Awaish Kumar: the, the, like, the set of the infrastructure and build the end-to-end pipelines, including the…
13 00:02:13.580 ⇒ 00:02:16.130 Awaish Kumar: reporting. So…
14 00:02:17.050 ⇒ 00:02:25.700 Awaish Kumar: In this interview, like, you can start with your introduction, and then we can just deep dive into your experience, and how you… what you have been working on.
15 00:02:26.730 ⇒ 00:02:45.800 Tanay Parikh: So, I’m a guy who loves data and love numbers, so that’s what encouraged me to pursue my bachelor’s in computer science. And after that, I started working as a business intelligence analyst at Vodafone, where I was developing dashboards, creating a lot of Ado tasks using SQL and Python, and showcasing those insights to the stakeholders and with the senior leadership.
16 00:02:46.140 ⇒ 00:03:04.840 Tanay Parikh: And after that, I got a chance to do a master’s in data analytics. I did my master’s in data analytics from Northeastern University, and within the span of 2 years at Northeastern, I did an internship at Sumitomo Pharma as a data scientist intern, where I built an automated retail pipeline using SQL and Airflow.
17 00:03:05.150 ⇒ 00:03:13.010 Tanay Parikh: Python and Airflow, and at the same time, I was involved in a lot of dashboarding tasks using Tableau, and presenting those insights with the stakeholders.
18 00:03:13.350 ⇒ 00:03:14.540 Awaish Kumar: And.
19 00:03:14.540 ⇒ 00:03:21.230 Tanay Parikh: Yeah, so, and currently I’ve been with Rebecca Velin as a data analyst, where I’ve been optimizing SQL queries, and
20 00:03:21.860 ⇒ 00:03:26.069 Tanay Parikh: Doing a lot of reporting tasks and, dashboarding tasks.
21 00:03:26.550 ⇒ 00:03:28.560 Tanay Parikh: So, that’s a little bit about me.
22 00:03:30.760 ⇒ 00:03:41.890 Awaish Kumar: Okay, so… Like, your recent experience, like, currently you are working with which company?
23 00:03:41.890 ⇒ 00:03:46.030 Tanay Parikh: So it was a contract position, I was working with Rebecca Everly.
24 00:03:46.270 ⇒ 00:03:58.440 Tanay Parikh: So, there have been… I’ve been optimizing SQL queries, and doing a lot of reporting tasks, and using Excel, and showcasing those insights to the different stakeholders.
25 00:03:59.380 ⇒ 00:04:06.329 Awaish Kumar: You’ve been optimizing SQL queries, and, like, for what? Like, for analysis, or for… For.
26 00:04:06.330 ⇒ 00:04:06.960 Tanay Parikh: of…
27 00:04:08.620 ⇒ 00:04:25.869 Tanay Parikh: So, yeah, I’ve been optimizing SQL queries for, basically, for the analysis part. So, basically, the SQL queries were taking a lot of time to load into the… into the dashboard. So, basically, we were using Tableau, and it was taking a lot of time to load into the dashboard, so I optimized the SQL queries.
28 00:04:26.510 ⇒ 00:04:37.260 Awaish Kumar: in Tableau, like, you were running, like, Tableau, you basically use, like, the UI to build the dashboard, so… was that taking time to load, or…
29 00:04:38.330 ⇒ 00:04:43.629 Tanay Parikh: The data, the data loading part, it was taking time, so it was taking time to load the data.
30 00:04:44.230 ⇒ 00:04:45.960 Tanay Parikh: Basically, so…
31 00:04:46.450 ⇒ 00:04:50.400 Awaish Kumar: Load the data to this, or load the data to Tableau, that’s fine.
32 00:04:50.960 ⇒ 00:04:56.449 Tanay Parikh: Yeah, so the data was getting slow while it was also getting loaded to the tableau.
33 00:04:56.770 ⇒ 00:05:10.740 Tanay Parikh: So, it was taking a lot of time, so I created a… basically tried to optimize it by creating indexing across it, and tried to reduce the joints as much as I can, so that it will, run… it will try to load into the tabloid.
34 00:05:10.860 ⇒ 00:05:11.720 Tanay Parikh: A little faster.
35 00:05:11.720 ⇒ 00:05:16.590 Awaish Kumar: The… Were you using extracts, or were you using it live?
36 00:05:17.570 ⇒ 00:05:21.990 Tanay Parikh: So it was… basically, I was using extract here, again, in that form.
37 00:05:22.210 ⇒ 00:05:32.960 Tanay Parikh: So, I was trying to extract it because the data was not, data was not very… was not live to us. So, I was using extract function in the tableau to basically extract it.
38 00:05:33.870 ⇒ 00:05:36.989 Awaish Kumar: So you’re saying your XTAC was taking longer?
39 00:05:37.980 ⇒ 00:05:39.699 Tanay Parikh: Yeah, it was taking a lot of time.
40 00:05:41.340 ⇒ 00:05:43.990 Awaish Kumar: Okay, how much time it was taking?
41 00:05:44.580 ⇒ 00:05:59.999 Tanay Parikh: I think it took around, 1-2 minutes to load the data, and it was, again, not making sense to take that much time, so I tried to optimize it and, make sure that we are trying to load it in a better form.
42 00:06:00.920 ⇒ 00:06:04.789 Tanay Parikh: No, my… I’m just trying to understand, like, if it’s the next…
43 00:06:05.150 ⇒ 00:06:07.960 Awaish Kumar: It runs once a day, normally, or…
44 00:06:08.210 ⇒ 00:06:11.119 Awaish Kumar: How frequently you are refreshing your extract?
45 00:06:11.120 ⇒ 00:06:30.590 Tanay Parikh: So basically, the thing was, we wanted to, like, we wanted to automate the whole Tableau dashboard for this particular… because the concept was regarding the warehouse thing. So the warehouse, warehouse stakeholders and, other pickers, stakeholders, they wanted to make sure that we are loading the data consistently.
46 00:06:30.590 ⇒ 00:06:39.739 Tanay Parikh: And, they wanted to automate and try to refresh it according to the warehouse standards, so that they can basically make sure that the warehouse
47 00:06:39.820 ⇒ 00:06:48.350 Tanay Parikh: And all the other items in the warehouse, all the SQs are in a perfect manner for the supply chain demand. So, that’s why,
48 00:06:48.400 ⇒ 00:07:00.159 Tanay Parikh: while loading the dashboard also, while creating the charts and all, in their end also, it used to take a lot of time, so I tried to optimize it from my end so that they would get the better results.
49 00:07:00.760 ⇒ 00:07:04.860 Awaish Kumar: I get it. My question is, how frequently you are running extracts?
50 00:07:06.090 ⇒ 00:07:12.380 Tanay Parikh: It was not frequent, but yeah, again, like, round note.
51 00:07:13.100 ⇒ 00:07:17.400 Tanay Parikh: I think the extracts were run around 3 times a day, I guess.
52 00:07:18.500 ⇒ 00:07:23.560 Awaish Kumar: Okay, so if you are running your extracts, and then 3 times, like,
53 00:07:23.770 ⇒ 00:07:28.060 Awaish Kumar: Maybe, like, after every… every 8 hours, like, right?
54 00:07:28.200 ⇒ 00:07:33.520 Awaish Kumar: So… And then your extract will take 1-2 minutes.
55 00:07:33.700 ⇒ 00:07:36.930 Awaish Kumar: Was that really a problem that you tried to solve, or…
56 00:07:37.380 ⇒ 00:07:50.220 Tanay Parikh: The main problem here was that from the warehouse… from the warehouse stakeholders, the charts and graphs used to take a lot of time. So, basically, we didn’t want that to happen, because,
57 00:07:50.340 ⇒ 00:07:50.930 Tanay Parikh: Yeah.
58 00:07:50.930 ⇒ 00:08:00.490 Awaish Kumar: Yeah, I… I don’t… basically, I don’t know the whole picture, so if you can elaborate your end-to-end flow. I don’t know where… what do you mean by warehouse stakeholders?
59 00:08:00.490 ⇒ 00:08:01.260 Tanay Parikh: Okay.
60 00:08:01.680 ⇒ 00:08:09.599 Awaish Kumar: What are the, like, basically, the data comes from, like, the normal flow is data comes from some source, it goes to the warehouse.
61 00:08:09.770 ⇒ 00:08:20.530 Awaish Kumar: There, some modeling happens, then Tableau is connected to Warehouse, and then we run some extracts, and we refresh it maybe once a day, twice a day, thrice a day.
62 00:08:20.980 ⇒ 00:08:31.360 Awaish Kumar: And then, when the data at Tableau… data is in Tableau, then you can just keep your charts. So, what… what part of this pipeline was taking a long time?
63 00:08:32.240 ⇒ 00:08:46.140 Tanay Parikh: Okay, so basically what happens is we receive the data, so the data engineering all extracted the data. I received the data in a normal standardized form according to the data governance standards. I made sure that I do the data quality checks, then…
64 00:08:46.140 ⇒ 00:08:46.700 Awaish Kumar: I mean.
65 00:08:46.700 ⇒ 00:08:47.790 Tanay Parikh: I… yeah.
66 00:08:49.020 ⇒ 00:08:54.950 Awaish Kumar: You receive the data, From source, or you receive the data from warehouse?
67 00:08:56.060 ⇒ 00:09:04.809 Tanay Parikh: So, basically, the data has been received from warehouses, but it’s, all based on, like, basically, it’s…
68 00:09:05.030 ⇒ 00:09:21.600 Tanay Parikh: But again, it’s on a data format, but… so the warehouse people doesn’t sit on and type the data entry, so it’s all done using the machines they use, try to access the data. So on that… using that machine, we receive the data on our end.
69 00:09:21.620 ⇒ 00:09:30.949 Tanay Parikh: And obviously, after the standardizing and make sure the data is in a form… correct format, we received the data, and then we performed the analysis on it.
70 00:09:31.870 ⇒ 00:09:39.889 Awaish Kumar: Yeah, I’m just, confused now here with machines thing, like, what data warehouse you are using, for example?
71 00:09:40.440 ⇒ 00:09:46.339 Tanay Parikh: No, basically the warehouses I’m talking about is, like, the supply chain warehouses.
72 00:09:46.520 ⇒ 00:09:49.020 Tanay Parikh: I’m not talking about data warehouses. Oh, yeah.
73 00:09:49.020 ⇒ 00:09:50.349 Awaish Kumar: I’m aware, so that means.
74 00:09:50.350 ⇒ 00:10:10.349 Tanay Parikh: That is a planned warehouse, I’m talking about that warehouses. So we receive the data from their end, then we receive the data to the data engineering teams, and then they format the data according to the standards, and then we perform the analysis, and then we build out the whole supply chain analysis and the demand analysis on our end, and then showcase the insights, yeah.
75 00:10:10.820 ⇒ 00:10:16.949 Awaish Kumar: Yeah, I get it. Data is… that is the source, like, from that web systems. Yeah.
76 00:10:17.400 ⇒ 00:10:21.430 Awaish Kumar: It’s reading data, then it brings in data tools from warehouse.
77 00:10:21.530 ⇒ 00:10:22.719 Tanay Parikh: Whatever it is.
78 00:10:22.720 ⇒ 00:10:26.109 Awaish Kumar: Maybe they are bringing in Keri, or Snowflake, or whatever.
79 00:10:26.110 ⇒ 00:10:27.250 Tanay Parikh: Yeah.
80 00:10:27.250 ⇒ 00:10:42.919 Awaish Kumar: But where you… I’m trying to say where you are sitting in that diagram. Are you part of the data engineering team, which is bringing data from warehouse? Are you part of data analyst team, which is basically only reading the data which data engineering team already
81 00:10:43.520 ⇒ 00:10:52.139 Awaish Kumar: modeled, right? So, are you just… are you… at that point, right? You are reading the data, which data engineering team already modeled, right?
82 00:10:52.140 ⇒ 00:10:53.779 Tanay Parikh: Yeah, that’s gotta… that’s gotta…
83 00:10:54.730 ⇒ 00:10:58.440 Awaish Kumar: So you are reading that data, and then you are connecting Tableau
84 00:10:58.660 ⇒ 00:11:03.550 Awaish Kumar: with your warehouse, and what is that? Data warehouse? Like, what are you using?
85 00:11:03.550 ⇒ 00:11:06.170 Tanay Parikh: So, I’m connecting with MySQL here.
86 00:11:07.140 ⇒ 00:11:13.269 Awaish Kumar: you’re connecting with MySQL, and then you basically connect Tableau and run extracts.
87 00:11:13.560 ⇒ 00:11:14.510 Tanay Parikh: Yeah, that’s correct.
88 00:11:14.510 ⇒ 00:11:23.619 Awaish Kumar: So, when we are connecting Tableau with MySQL, and you are running your extracts once, maybe…
89 00:11:24.730 ⇒ 00:11:30.700 Awaish Kumar: Look at… twice a day. So, and then you said it takes only one to two minutes.
90 00:11:31.070 ⇒ 00:11:32.720 Awaish Kumar: To create that extract.
91 00:11:33.490 ⇒ 00:11:42.960 Tanay Parikh: Yeah, so basically, from my end, it used to take 1-2 minutes, but after we create charts and graphs, and then showcase the findings to the different stakeholders, and they want to
92 00:11:42.960 ⇒ 00:11:55.070 Tanay Parikh: The supply chain people, they want to see, live, and they try to automate it, but from their end, it used to take time to load the graphs and charts, which used to take around 10-15 minutes from their end to see.
93 00:11:55.070 ⇒ 00:12:08.450 Tanay Parikh: So that’s the whole… so there was this issue. So then I tried to optimize it so that basically tries to just query the data according to the… what… what the peoples, what the stakeholders need from my end, and then see.
94 00:12:08.450 ⇒ 00:12:08.850 Awaish Kumar: of that?
95 00:12:08.850 ⇒ 00:12:10.700 Tanay Parikh: Data only, which we need.
96 00:12:11.570 ⇒ 00:12:16.660 Awaish Kumar: That means that is not an extract, like, if it is extract, it’s already in Tableau.
97 00:12:16.870 ⇒ 00:12:20.539 Awaish Kumar: even if whatever you do in MySQL doesn’t matter anymore.
98 00:12:20.660 ⇒ 00:12:26.049 Awaish Kumar: Because the data is already being downloaded in… some Tableau’s backend system.
99 00:12:26.220 ⇒ 00:12:35.689 Awaish Kumar: If you’re using live, then it reads from your database, and then, obviously, optimization techniques can work.
100 00:12:36.990 ⇒ 00:12:44.149 Tanay Parikh: Yeah, so basically that’s what I tried to do here, like, try to reduce that time, and so that the data goes.
101 00:12:44.150 ⇒ 00:12:51.389 Awaish Kumar: People are using it in a live mode, like, so, like, your final stakeholders, they were directly
102 00:12:51.620 ⇒ 00:12:59.909 Awaish Kumar: Like, they were using your charts, or they were trying to create their own charts and their own data source.
103 00:13:00.560 ⇒ 00:13:15.860 Tanay Parikh: No, they were trying to basically see my charts, and trying to see, what, analysis that I’m doing, and what… how does the supply and demand is for this particular product, and then they try to analyze it, and then they perform their activities in… at their end.
104 00:13:16.990 ⇒ 00:13:26.970 Awaish Kumar: Okay, so if I… If I’m correct, then you are already creating an extract, And then,
105 00:13:27.540 ⇒ 00:13:29.500 Awaish Kumar: the data lives in the Tableau.
106 00:13:29.850 ⇒ 00:13:38.839 Awaish Kumar: And then, dual charts, basically, you create some charts, and those charts are basically being created from the extract itself.
107 00:13:42.020 ⇒ 00:13:56.229 Awaish Kumar: and somebody who’s loading uses those charts, they… the Tableau just uses the, like, the extract. If they want to use the MySQL directly, like, it won’t directly query the MySQL itself at that time.
108 00:13:56.470 ⇒ 00:14:02.519 Awaish Kumar: So why it was taking longer, and why your optimizations work… worked.
109 00:14:03.540 ⇒ 00:14:11.890 Tanay Parikh: So, basically, in this scenario, the data was very long here, so it used to take a lot of time to load the data into the tablet.
110 00:14:11.890 ⇒ 00:14:17.190 Awaish Kumar: I get it, so I get it. So my… my just… Even it was taking…
111 00:14:17.660 ⇒ 00:14:25.490 Awaish Kumar: like, as you mentioned, the XTEC was taking some time, maybe 2 minutes, 5 minutes, 10 minutes, That’s…
112 00:14:26.070 ⇒ 00:14:29.269 Awaish Kumar: okay, right? I’m, I’m, I’m more concerned.
113 00:14:29.400 ⇒ 00:14:32.330 Awaish Kumar: About, your optimizations.
114 00:14:32.440 ⇒ 00:14:36.939 Awaish Kumar: At the time of loading the charts, like, if I…
115 00:14:37.110 ⇒ 00:14:39.299 Awaish Kumar: So, for example, my data is in MySQL.
116 00:14:39.430 ⇒ 00:14:56.699 Awaish Kumar: And I’m directly from my computer, I run queries. If I run my query directly on the MySQ, and it’s slow, I can see it can be a traffic, it can be problem in network connection, it can be problem with my query itself, it is slow.
117 00:14:56.800 ⇒ 00:14:59.630 Awaish Kumar: Right? I can then optimize these.
118 00:15:00.060 ⇒ 00:15:10.919 Awaish Kumar: things. But then, for example, once I run the carry data downloads, and I have it in my cache here, for example, next time, if it takes longer.
119 00:15:11.080 ⇒ 00:15:13.670 Awaish Kumar: Then is the problem in my code, or something.
120 00:15:14.130 ⇒ 00:15:21.790 Awaish Kumar: in my way I’m using it, because data is already in my cache. It’s not… now it’s not reading from my screen.
121 00:15:22.500 ⇒ 00:15:40.630 Tanay Parikh: Okay, so basically in this scenario, we… we wanted to, like, they wanted to see the live feedings of the SQL data, so in this scenario, they have to see for every day the different supply chain people and different warehouse people, they want to see it for every 4 hours, 5… 5 hours.
122 00:15:40.630 ⇒ 00:15:43.950 Tanay Parikh: So that they can use supply chain demand. So…
123 00:15:44.820 ⇒ 00:15:52.410 Awaish Kumar: But they can’t, like, if they are utilizing your charts, which are built on X-Rank, they can’t bypass it, right?
124 00:15:52.720 ⇒ 00:15:59.730 Awaish Kumar: They can only do that once you change your data source. Once you change your data source from extract to live.
125 00:16:00.000 ⇒ 00:16:08.259 Awaish Kumar: And then you directly create a chart which uses your data share source, which is not a extract, which is the live connection with the MySQL.
126 00:16:08.360 ⇒ 00:16:13.279 Awaish Kumar: Then, only… it matters. Otherwise,
127 00:16:13.890 ⇒ 00:16:20.000 Awaish Kumar: Even if they come after 4 hours or 3 hours, whatever is in its extract, they can only see that.
128 00:16:22.100 ⇒ 00:16:25.700 Tanay Parikh: Yeah, so that’s what I tried to do, like, basically.
129 00:16:28.760 ⇒ 00:16:37.780 Awaish Kumar: I’m not sure why it worked, but, exec shouldn’t, like… exec doesn’t depend, but that’s okay. We can,
130 00:16:38.150 ⇒ 00:16:42.700 Awaish Kumar: Like, move on to the other parts of
131 00:16:43.140 ⇒ 00:16:54.040 Awaish Kumar: data analysis. So, one is the tooling, and tooling is… having experience with some kind of tools is good enough, but that’s…
132 00:16:54.160 ⇒ 00:16:56.649 Awaish Kumar: As a data analyst, like, that’s one of the…
133 00:16:57.090 ⇒ 00:17:00.100 Awaish Kumar: A kind of a motor leg, or something.
134 00:17:00.420 ⇒ 00:17:14.100 Awaish Kumar: Because it can change. You’re working on Tableau right now, maybe you joined Brain Forge, and maybe we don’t have Tableau. We may ask you to work on Power BI, we may ask you to work on Omni, or some other tools.
135 00:17:14.300 ⇒ 00:17:23.289 Awaish Kumar: My question is, As a data analyst, Whenever you are… Given a chance to…
136 00:17:23.609 ⇒ 00:17:36.040 Awaish Kumar: to work on something for a client, or for a… how would you basically think about it? Like, how you… so now, there are a few things, like, number one, somebody comes in and gives you all the requirements, like.
137 00:17:36.510 ⇒ 00:17:40.119 Awaish Kumar: I need this dashboard, these fields, things like that.
138 00:17:40.330 ⇒ 00:17:45.070 Awaish Kumar: Sometimes, as a CEO, I came in, I said, okay, I want to monitor my revenue.
139 00:17:45.870 ⇒ 00:17:48.179 Awaish Kumar: But I don’t know how to do that.
140 00:17:49.750 ⇒ 00:17:50.560 Awaish Kumar: What?
141 00:17:51.410 ⇒ 00:17:55.590 Awaish Kumar: what should I do now? Like, I want to monitor my revenue, my performance of the
142 00:17:56.190 ⇒ 00:17:58.600 Awaish Kumar: Of the company, and then…
143 00:17:58.740 ⇒ 00:18:02.570 Awaish Kumar: But what kind of product should I build? What kind of,
144 00:18:04.100 ⇒ 00:18:10.559 Awaish Kumar: dashboard should I create? What charts should I create? So, how would you go for that?
145 00:18:11.220 ⇒ 00:18:21.970 Tanay Parikh: So, basically in this case, I think asking the right questions is important whenever you are talking with a client or with the stakeholders. So, in this scenario, like Azure told me that
146 00:18:22.110 ⇒ 00:18:30.470 Tanay Parikh: if a CEO comes and asks about the revenue. So what I will try to do here is ask the right questions, like, what products do you want to see the revenue of?
147 00:18:30.630 ⇒ 00:18:36.080 Tanay Parikh: And what time frame do you want? Is it for a quarter, is it for a year, or is it for the days or weeks?
148 00:18:36.340 ⇒ 00:18:40.199 Tanay Parikh: And, obviously, if I’m trying to, create a…
149 00:18:40.350 ⇒ 00:18:55.189 Tanay Parikh: start regarding the weeks or quarters, I will try to create historical trends here, like, basically the time chart, basically, like, the line graph, so that we could better understand with the past weeks and with the weeks that we are currently going on.
150 00:18:55.400 ⇒ 00:18:56.660 Tanay Parikh: And,
151 00:18:57.170 ⇒ 00:19:16.120 Tanay Parikh: So that’s what… and, like, obviously, asking the right questions, like, what KPIs is the company use… does the company use, does the clients want to see? So, is it, they want to see, particular KPIs, like average order per revenue, or some, they want to see the total, or they want to see the average of their products, and then…
152 00:19:16.290 ⇒ 00:19:21.989 Tanay Parikh: Again, filter it by different products, filter it by different meth… or with different…
153 00:19:22.590 ⇒ 00:19:27.279 Tanay Parikh: things which are provided in the data, so that’s what I’ve tried… that’s what I’ve tried to do here.
154 00:19:33.410 ⇒ 00:19:40.580 Awaish Kumar: And so you asked the questions, you got the KPIs, you got the…
155 00:19:40.780 ⇒ 00:19:43.949 Awaish Kumar: Like, that is interview, that is done.
156 00:19:44.330 ⇒ 00:19:46.459 Awaish Kumar: What are the next steps?
157 00:19:46.890 ⇒ 00:19:53.100 Tanay Parikh: Our next steps is to basically present my dashboard to the client, or to make them understand how the dashboard looks like.
158 00:19:53.710 ⇒ 00:19:58.529 Awaish Kumar: Like, From interview, you got something, some information.
159 00:19:58.530 ⇒ 00:19:59.100 Tanay Parikh: Okay.
160 00:20:00.240 ⇒ 00:20:14.599 Tanay Parikh: Yeah, so after that… after that step, I will make sure that the data is in our… in our… in the data governance standards, make sure I do the data quality checks, according to the data, how… how the data looks like, and if the data looks good,
161 00:20:14.820 ⇒ 00:20:19.689 Tanay Parikh: And, I can go ahead and try to load the data into the dashboard.
162 00:20:20.420 ⇒ 00:20:22.330 Awaish Kumar: Yeah, one thing I said, like.
163 00:20:22.480 ⇒ 00:20:33.009 Awaish Kumar: as a CEO, I don’t know, like, I want to monitor my revenue, I want to monitor my performance. I’m not a data person, I just look at numbers, and
164 00:20:33.340 ⇒ 00:20:41.829 Awaish Kumar: I don’t know, like, some revenue, minus cost equal to profit, I see something good. My company has some profits.
165 00:20:41.990 ⇒ 00:20:47.479 Awaish Kumar: But I don’t know what… else? What other metrics I can add, what…
166 00:20:48.260 ⇒ 00:20:51.519 Awaish Kumar: what else I can… I should monitor? What should I check?
167 00:20:51.810 ⇒ 00:20:53.750 Awaish Kumar: So, like, what will you do, right?
168 00:20:53.750 ⇒ 00:21:01.430 Tanay Parikh: So, other things we can monitor is, basically understand the sales, like, how the sales are going, performing, in different months.
169 00:21:03.970 ⇒ 00:21:13.879 Tanay Parikh: Yeah. And next thing we can perform is try to understand, like, is there a specific month or a quarter where the sales have been getting dropped, so that we can work on… work on.
170 00:21:14.550 ⇒ 00:21:16.150 Awaish Kumar: Yeah, my… yeah, I’m…
171 00:21:17.140 ⇒ 00:21:22.520 Awaish Kumar: like, that’s what I want to see, like, how would you come up with those requirements? Not, like.
172 00:21:23.000 ⇒ 00:21:41.830 Awaish Kumar: I don’t want the requirements right now for a revenue. I’m not… I’m not interviewing for… as a CEO. I’m just trying to understand tomorrow, like, this is a revenue discussion. Maybe somebody else comes in. He’s not a revenue, he’s a marketing guy, so he wants to know his own things. He comes in, he says, I’m onto my… I’m spending millions of dollars
173 00:21:42.180 ⇒ 00:21:44.780 Awaish Kumar: Doing marketing for some of my…
174 00:21:45.030 ⇒ 00:22:02.389 Awaish Kumar: products on some channels. I don’t know how to monitor if I’m doing it correctly, I’m segmenting customer correctly, I’m… I’m running my campaigns on correct platforms. Where should I spend my money on? He doesn’t know any… he has these questions, but he doesn’t know how to answer them.
175 00:22:02.800 ⇒ 00:22:11.400 Awaish Kumar: And he can’t give you KPIs, because he’s not able to come up with something. He’s here, that’s why he’s here. So how would you…
176 00:22:12.120 ⇒ 00:22:21.020 Awaish Kumar: Come up with something that seems meaningful for that specific use case, then the building, the dashboard comes up.
177 00:22:22.280 ⇒ 00:22:35.279 Tanay Parikh: I think in this case, like, if the marketing guy comes in, I would try to understand what campaigns is he trying to run, and what products, is, is the campaign is going for, and try to understand, basically, if the campaign is getting enough
178 00:22:35.280 ⇒ 00:22:52.650 Tanay Parikh: profit according to the products that he is trying to sell, and if we don’t run the campaigns on those particular products. So, can we get the same revenue bid without running those campaigns or not? Try to understand with the marketing people as well, to make sure that
179 00:22:52.980 ⇒ 00:23:00.350 Tanay Parikh: after doing the marketing, what’s the impact of the product? How much revenue we are getting more after getting the campaigns running?
180 00:23:00.470 ⇒ 00:23:09.489 Tanay Parikh: And, also, like, doing vice versa as well. If we don’t run the campaign, what, how much revenue are we getting again with those campaigns running?
181 00:23:12.480 ⇒ 00:23:16.690 Awaish Kumar: Okay, so you won’t use the… Thank you.
182 00:23:17.550 ⇒ 00:23:23.249 Awaish Kumar: Maybe some Google search, some… use of ChatGPT,
183 00:23:23.620 ⇒ 00:23:26.029 Awaish Kumar: Yeah, to come up with some…
184 00:23:26.840 ⇒ 00:23:34.490 Awaish Kumar: Exec… like, there will be hundreds of people who already worked on the same problem.
185 00:23:34.960 ⇒ 00:23:45.289 Awaish Kumar: he… maybe the marketing guy of the company doesn’t know, or doesn’t have time, basically, to do that. That’s why he came to us. But now we can, basically.
186 00:23:45.700 ⇒ 00:23:53.149 Awaish Kumar: search on Google and come up with some, like, what other people are choosing. How, for example.
187 00:23:53.610 ⇒ 00:24:09.800 Awaish Kumar: some of the teams, or, like, are monitoring their campaigns. What are these industry best practices, or industry standard KPIs? So, basically, we can do some Google search, or we can do some AI tools.
188 00:24:09.930 ⇒ 00:24:13.020 Awaish Kumar: To come up with some kind of,
189 00:24:13.390 ⇒ 00:24:21.630 Awaish Kumar: metrics. Obviously, you can use your brain to sound like what looks good, what is…
190 00:24:22.030 ⇒ 00:24:25.379 Awaish Kumar: What is highly submitted, or what is the real thing, but…
191 00:24:25.680 ⇒ 00:24:27.950 Awaish Kumar: That’s what you can use those.
192 00:24:28.070 ⇒ 00:24:29.230 Awaish Kumar: Tools, too.
193 00:24:29.420 ⇒ 00:24:30.190 Awaish Kumar: We say, come on.
194 00:24:30.190 ⇒ 00:24:46.810 Tanay Parikh: Yeah, I think important here is to, like, we should have the data of that… of the trains, and try to understand. And obviously, after doing the Google search, and after trying to do the analysis on the data, I would be communicating with the marketing people again and again.
195 00:24:46.810 ⇒ 00:24:59.780 Tanay Parikh: try to understand that, is this what you want regarding the data? And if not, I can work through some other task and try to find a better analysis of it, or try to find a better way to solve the problem, basically.
196 00:25:01.190 ⇒ 00:25:01.790 Awaish Kumar: Nope.
197 00:25:01.790 ⇒ 00:25:04.060 Tanay Parikh: Ivanic… yeah, please wait.
198 00:25:04.060 ⇒ 00:25:04.639 Awaish Kumar: Go ahead.
199 00:25:04.980 ⇒ 00:25:10.680 Tanay Parikh: Yeah, so basically, I will just try to integrate, try to solve the problem, and
200 00:25:10.780 ⇒ 00:25:27.849 Tanay Parikh: even if the marketing people or the salespersons are not sure about the questions they have, I would try to find a question out of it, out of the data, and try to find or implement a solution out of it, and then present them in a… present them the findings with the people.
201 00:25:28.710 ⇒ 00:25:31.070 Awaish Kumar: Okay, how would you come up with questions?
202 00:25:31.340 ⇒ 00:25:34.499 Awaish Kumar: If only given the data, without any…
203 00:25:34.670 ⇒ 00:25:35.600 Tanay Parikh: So…
204 00:25:35.840 ⇒ 00:25:45.540 Tanay Parikh: if only data is given, I would try to understand what things they are trying to find us. So, if they’re trying to find us a particular
205 00:25:45.950 ⇒ 00:25:51.060 Tanay Parikh: Say, if they’re trying to find a particular, particular thing about the product, yeah.
206 00:25:51.060 ⇒ 00:25:57.869 Awaish Kumar: Yeah, I’m trying to see if my campaigns, the budget I’m spending, is worth spending.
207 00:25:58.450 ⇒ 00:26:02.770 Awaish Kumar: in those channels, or on those products.
208 00:26:03.300 ⇒ 00:26:06.590 Awaish Kumar: My only concern, as a marketing person.
209 00:26:06.990 ⇒ 00:26:09.220 Tanay Parikh: How I measured it, I don’t know.
210 00:26:09.590 ⇒ 00:26:13.449 Awaish Kumar: Right? I can’t give you anything. You go in the data.
211 00:26:13.910 ⇒ 00:26:17.360 Awaish Kumar: You explore it, maybe, and you come up with something.
212 00:26:17.720 ⇒ 00:26:25.390 Awaish Kumar: But will that be good? Like, in software engineering, like, we have some patterns, we call it, like.
213 00:26:25.610 ⇒ 00:26:29.000 Awaish Kumar: Right? Some, we call them,
214 00:26:29.990 ⇒ 00:26:38.830 Awaish Kumar: the… like, there are some standard patterns, right? So, like, singular pattern or something, if you are… if you are familiar with coding.
215 00:26:38.930 ⇒ 00:26:39.890 Awaish Kumar: So…
216 00:26:40.180 ⇒ 00:26:55.759 Awaish Kumar: those… what that means, like, we can, obviously, as a person, if you are a logical person, you can come up with similar solutions. What is the point of having those standard patterns? Is that, like, it’s easy to map? Like, if you are having a problem, you don’t have to think about it.
217 00:26:56.050 ⇒ 00:27:03.189 Awaish Kumar: you don’t have to read with the wheel, you don’t have to spend, like, 5 days exploring data to come up with some KPI.
218 00:27:03.420 ⇒ 00:27:04.440 Awaish Kumar: Bjour.
219 00:27:04.900 ⇒ 00:27:11.080 Awaish Kumar: We know this is the problem, this is the solution, so we know here. So, like, the basic step is that
220 00:27:11.710 ⇒ 00:27:13.920 Awaish Kumar: use the knowledge, which is…
221 00:27:14.540 ⇒ 00:27:15.810 Tanay Parikh: Yeah, that’s like…
222 00:27:16.180 ⇒ 00:27:18.180 Awaish Kumar: And come up with something.
223 00:27:18.530 ⇒ 00:27:24.199 Awaish Kumar: But while collecting the data, while collecting that information or knowledge, you can use your
224 00:27:24.750 ⇒ 00:27:39.380 Awaish Kumar: like, the rationale, or what… and, like, what you are including and excluding, and things like that. And obviously, once you have MVP, you are going to get the feedback. Then, when you show something to the marketing team.
225 00:27:39.510 ⇒ 00:27:44.889 Awaish Kumar: Marketing guy, and then he obviously have enough to talk about.
226 00:27:45.370 ⇒ 00:27:47.149 Tanay Parikh: Yeah, that’s correct, that makes sense.
227 00:27:48.560 ⇒ 00:27:55.369 Awaish Kumar: So… Okay, so when you have, so, like, we talked about that.
228 00:27:57.260 ⇒ 00:28:07.730 Awaish Kumar: how you, like, build, like, as a… like, as a data engineer, I’m… maybe I’m not directly talking with my client, but as a data analyst.
229 00:28:07.880 ⇒ 00:28:12.500 Awaish Kumar: Maybe you are… The person who is talking to client rightly.
230 00:28:12.710 ⇒ 00:28:18.609 Awaish Kumar: So… and… and then, obviously, client comes in, he will,
231 00:28:18.810 ⇒ 00:28:23.289 Awaish Kumar: he says, as a marketing person, as I said, I need this, this, and that.
232 00:28:23.410 ⇒ 00:28:31.759 Awaish Kumar: And you basically, how would you then, like, convert that?
233 00:28:31.870 ⇒ 00:28:33.300 Awaish Kumar: conversation.
234 00:28:33.440 ⇒ 00:28:35.569 Awaish Kumar: Into some kind of task.
235 00:28:35.980 ⇒ 00:28:44.040 Awaish Kumar: For me, as a data engineer, I’m data engineer, I’m for your support. We have analytics engineer, maybe that’s also for your support.
236 00:28:44.160 ⇒ 00:28:51.070 Awaish Kumar: But… and you are finally the person who will use the data, which we will bring in for you.
237 00:28:51.310 ⇒ 00:28:58.060 Awaish Kumar: And then, you know, we’ll get some dashboard and some story for the client. So, how…
238 00:28:59.530 ⇒ 00:29:04.009 Awaish Kumar: how will you create a ticket for me, for example? You’re a journalist, I’m a…
239 00:29:04.740 ⇒ 00:29:08.419 Tanay Parikh: So first… yeah, please go ahead, please go ahead.
240 00:29:09.140 ⇒ 00:29:16.799 Awaish Kumar: You… you got something from… some input from client. I was not there, so I don’t know anything. You will have to create a ticket for me.
241 00:29:17.310 ⇒ 00:29:19.610 Awaish Kumar: Think about, like, I need…
242 00:29:20.710 ⇒ 00:29:30.690 Awaish Kumar: these models, or I need a data model in some… this way, so I can show it to the client this way, and then it will share some story.
243 00:29:32.890 ⇒ 00:29:35.509 Tanay Parikh: So, I think in this scenario, like,
244 00:29:35.790 ⇒ 00:29:42.109 Tanay Parikh: if I want a help from data engineer, it would be regarding the data issues. Like, if the data is not.
245 00:29:43.340 ⇒ 00:29:46.760 Awaish Kumar: So, you are… you are asked for something.
246 00:29:46.890 ⇒ 00:29:51.900 Awaish Kumar: Right? You… for example, you looked into existing models.
247 00:29:52.060 ⇒ 00:29:53.210 Tanay Parikh: That’s nothing.
248 00:29:53.210 ⇒ 00:29:53.699 Awaish Kumar: It’s no…
249 00:29:53.700 ⇒ 00:29:54.340 Tanay Parikh: Okay.
250 00:29:54.510 ⇒ 00:29:55.360 Awaish Kumar: Let’s not good.
251 00:29:56.320 ⇒ 00:29:58.810 Awaish Kumar: There’s no data, no modeling, there’s nothing.
252 00:29:59.250 ⇒ 00:29:59.930 Tanay Parikh: Okay.
253 00:30:01.090 ⇒ 00:30:08.910 Tanay Parikh: So, I think the best person, again, to your approach is the data engineer, so that, like, if I’m not able to find a particular amount of data.
254 00:30:09.110 ⇒ 00:30:13.180 Awaish Kumar: I, I know. You will approach me, that’s okay, you will approach me and change.
255 00:30:13.350 ⇒ 00:30:23.849 Awaish Kumar: Yeah. But the thing is, how would you approach? How… what can, like, you will be the one who will be creating… because you talk to the client, you will be responsible for creating tickets.
256 00:30:23.970 ⇒ 00:30:28.939 Awaish Kumar: Maybe one, maybe two, maybe three different tickets for me, for a person.
257 00:30:29.380 ⇒ 00:30:34.199 Awaish Kumar: To… so that we… so, like, think of this, like.
258 00:30:34.730 ⇒ 00:30:38.329 Awaish Kumar: Jord, at the end of something, I… I need to…
259 00:30:38.590 ⇒ 00:30:40.909 Awaish Kumar: do something, like, I need to build a…
260 00:30:41.240 ⇒ 00:30:50.520 Awaish Kumar: billing. That’s my end goal. But for the billing, for, like, for that, I need help from, some,
261 00:30:50.930 ⇒ 00:30:55.650 Awaish Kumar: Like, suppliers, which are going to supply me some,
262 00:30:56.300 ⇒ 00:31:03.850 Awaish Kumar: like, the necessary items that I need help… labor… labors and things like that. So…
263 00:31:03.980 ⇒ 00:31:20.559 Awaish Kumar: you have to think of that entire flow. So, you will need maybe DE, you will get a ticket for him, what will be in the description, you will… maybe you need help from AE, what those models will be, what kind of description would you put in for AE?
264 00:31:20.770 ⇒ 00:31:27.329 Awaish Kumar: And you have to make sure that once the AE finishes their task, you have everything you need.
265 00:31:27.490 ⇒ 00:31:28.830 Awaish Kumar: Formate the dashboard.
266 00:31:29.370 ⇒ 00:31:43.499 Tanay Parikh: Okay, so in this scenario, what I will try to ask from the client is what type of data they are trying to… what type of analysis they are trying to find out from that particular case. So I’ll try to make, understand about the columns, about the data models.
267 00:31:43.550 ⇒ 00:32:02.380 Tanay Parikh: the relationships between the different tables, try to understand how that data looks like, and if they have a sample of the data, that would be, again, a good prospect for me as well, so that I can basically use that sample to explain the data engineers and analytics engineers about it.
268 00:32:02.610 ⇒ 00:32:06.060 Tanay Parikh: And obviously, find out the relationships between different tables.
269 00:32:06.220 ⇒ 00:32:13.479 Tanay Parikh: And then, use all the findings, all the things which I have, and then present, with… to the data engineers.
270 00:32:13.630 ⇒ 00:32:29.849 Tanay Parikh: And, again, if it’s my first time, I would try to ask the data engineers, analytics engineers, that, is there anything else which is missing? And then I can go back with the client and ask if there is something missing from their end or my end, I can finish that thing.
271 00:32:30.330 ⇒ 00:32:34.879 Awaish Kumar: Yeah, my thing is that, at the first instance, you create a ticket.
272 00:32:35.050 ⇒ 00:32:43.809 Awaish Kumar: what will be that ticket for… like, think of that revenue, or that marketing guy comes in. He can come up with something.
273 00:32:44.410 ⇒ 00:32:47.740 Awaish Kumar: Right? You can think, you can use it, like, you can take your time.
274 00:32:48.000 ⇒ 00:32:58.899 Awaish Kumar: Think the same scenario. Somebody comes in, he asks for a marketing campaign performance, right? I’m using multiple channels, Facebook, Google, TikTok, Google.
275 00:32:59.200 ⇒ 00:33:14.719 Awaish Kumar: And running my campaigns over there. I have products ABC. I’m running campaigns for those, right? I have some customers’ data as well with me. I’m using… I can even do target marketing as well, using those existing customers’ profiles I have.
276 00:33:15.020 ⇒ 00:33:18.190 Awaish Kumar: Right? This is all the things you have.
277 00:33:19.990 ⇒ 00:33:35.439 Awaish Kumar: like, as a marketing team, this is all I’m… this is my tech stack, which I’m using to run campaigns, but I don’t have anything to monitor for reporting. I don’t have analyticals. All the systems are working.
278 00:33:36.640 ⇒ 00:33:38.869 Awaish Kumar: I’m spending money.
279 00:33:39.020 ⇒ 00:33:54.479 Awaish Kumar: But I don’t have a unified view of what’s happening if it is producing any meaningful output for me, or I’m spending $100,000 on Facebook, but is it really returning
280 00:33:54.830 ⇒ 00:33:59.419 Awaish Kumar: If there’s any ROI out of it, right? That’s what I’m… I want to understand.
281 00:33:59.930 ⇒ 00:34:05.440 Awaish Kumar: This is your… requirement. I can’t give you any other thing, because I don’t know.
282 00:34:05.630 ⇒ 00:34:09.960 Awaish Kumar: So usually, you can do a Google search if you want, like, now.
283 00:34:10.030 ⇒ 00:34:11.779 Tanay Parikh: Like, you can use it.
284 00:34:11.830 ⇒ 00:34:13.380 Awaish Kumar: ChatGP, or whatever.
285 00:34:13.659 ⇒ 00:34:30.879 Awaish Kumar: you don’t have to, like, come up with all the… like, I need… I will come up with 100 metrics. You can use those. You don’t have to basically come up with all the KPIs will be… will be needed, but just come up with something that, okay, I will…
286 00:34:31.310 ⇒ 00:34:37.770 Awaish Kumar: use maybe 2-3 metrics, I will build that for you first. And then,
287 00:34:38.710 ⇒ 00:34:41.939 Awaish Kumar: You can, say, like, for that.
288 00:34:42.170 ⇒ 00:34:44.699 Awaish Kumar: Warehouse is… there’s no warehouse right now.
289 00:34:46.040 ⇒ 00:34:46.889 Awaish Kumar: Right?
290 00:34:47.050 ⇒ 00:34:49.750 Awaish Kumar: Yeah, you can listen to me first, like.
291 00:34:49.750 ⇒ 00:34:50.170 Tanay Parikh: Okay.
292 00:34:50.170 ⇒ 00:34:50.899 Awaish Kumar: for the…
293 00:34:50.900 ⇒ 00:34:52.900 Tanay Parikh: Yeah, yeah, I’m listening, listening, yeah.
294 00:34:53.320 ⇒ 00:35:03.340 Awaish Kumar: So, there’s no warehouse, right? So, you will create tickets for me as a team, not me, I’m the marketing guy. You will create a ticket for your team member, like, data engineer.
295 00:35:03.630 ⇒ 00:35:07.279 Awaish Kumar: To bring in data from, like, how to create a ticket.
296 00:35:08.000 ⇒ 00:35:25.799 Awaish Kumar: Right? What kind of description you will provide. Obviously, your data engineer guy will come in, he will look at the ticket, he’s going to say, maybe this is missing, that is missing, that’s the natural process. He will not be able to finish the task if you haven’t provided enough information. So he will ask for that.
297 00:35:25.830 ⇒ 00:35:29.759 Awaish Kumar: That’s not the point. I want that, at the first step.
298 00:35:29.880 ⇒ 00:35:32.460 Awaish Kumar: like, the V1 of the ticket creation.
299 00:35:32.730 ⇒ 00:35:49.179 Awaish Kumar: how will you create a kind of a ticket? Like, it’s not about ticket creation, it’s about… I’m trying to understand, like, what diff… what… how deep you are… you know things, like, the flow of… which happens. Ticket creation is just a part, like, I’m just using it as an…
300 00:35:49.280 ⇒ 00:35:58.270 Awaish Kumar: like, to understand how would you create it. But my major point is that how would you… if you understand, like, how it flows from,
301 00:35:58.520 ⇒ 00:36:03.510 Awaish Kumar: From start to finish. So, you will create a ticket for a DE.
302 00:36:03.750 ⇒ 00:36:12.379 Awaish Kumar: what kind of description will it have? You mentioned I will have… I will ask the marketing person, he will give me everything, and I will put it here.
303 00:36:13.080 ⇒ 00:36:19.150 Awaish Kumar: what that will be, I don’t know. That’s what I’m curious about. What will be in the description?
304 00:36:19.260 ⇒ 00:36:21.160 Awaish Kumar: It doesn’t have to be, like.
305 00:36:21.530 ⇒ 00:36:26.200 Awaish Kumar: specific, like, I will put TikTok, like, TikTok doesn’t matter, like…
306 00:36:26.450 ⇒ 00:36:29.929 Awaish Kumar: like, you can say, I will list down the sources.
307 00:36:30.060 ⇒ 00:36:31.140 Awaish Kumar: This is…
308 00:36:31.380 ⇒ 00:36:43.230 Awaish Kumar: you can be generic, but still have some steps, like, I will be list out… I’ll list out all the sources which I need the data for, right? Then what? Then what?
309 00:36:43.240 ⇒ 00:36:51.470 Awaish Kumar: What will be in a ticket for D? Same for A. What kind of models you need? You don’t have to be 100% accurate here. You can…
310 00:36:51.490 ⇒ 00:36:54.569 Awaish Kumar: Imagine, like, some,
311 00:36:54.990 ⇒ 00:37:03.909 Awaish Kumar: artif… like, the… what could be the useful, like, what could be the fact table? What can be the summary table? What can be by dimensions?
312 00:37:04.130 ⇒ 00:37:09.860 Awaish Kumar: And then… Yeah, and then you just give me that information.
313 00:39:38.780 ⇒ 00:39:40.269 Tanay Parikh: So okay.
314 00:39:41.630 ⇒ 00:39:51.930 Tanay Parikh: So, basically, that’s how I will start. Like, I will first ask the marketing, marketing people to, like, basically understand what do they need from my site.
315 00:39:52.110 ⇒ 00:40:07.739 Tanay Parikh: understand their campaigns from on different social medias, whether it is Facebook, TikTok, or Meta. We’ll try to understand that first. Try to understand the data source as well, from the marketing people. If they don’t have an idea, I will try to look for the right data source.
316 00:40:07.850 ⇒ 00:40:10.560 Tanay Parikh: According to the marketing campaigns they are trying to do.
317 00:40:10.880 ⇒ 00:40:26.749 Tanay Parikh: And at the same time, I’ll try to ask, the marketing people, like, what type of, analysis they are trying to do. Is it, they are trying to compare different data sources, or to compare different, marketing campaigns for, like, Facebook versus Meta, or…
318 00:40:27.400 ⇒ 00:40:38.480 Tanay Parikh: TikTok, or they are trying to find out the revenue out of it. Like, what’s the revenue they are trying to spend, and what’s the output? Like, basically, what’s the profit they’re trying… they’re making out of it.
319 00:40:38.790 ⇒ 00:40:52.959 Tanay Parikh: And obviously, I will try to find out, so how does the profit, look for them, basically, whether it is a profit, or it’s the total number of products they are selling, or it’s the total revenue they are trying to generate.
320 00:40:53.360 ⇒ 00:40:55.990 Tanay Parikh: For, for the particular products.
321 00:40:56.340 ⇒ 00:41:14.300 Tanay Parikh: And that’s… that’s how I will try to figure it out. And, and talking a little bit about the data. So, again, I will try to understand the data sources, from, like, what the data sources could be for the Facebook for, for the Meta, for the TikTok, and, then try to,
322 00:41:14.700 ⇒ 00:41:22.620 Tanay Parikh: use that data, in the… in the ticket… in the ticketing software to make sure that I’m trying to use,
323 00:41:23.110 ⇒ 00:41:25.159 Tanay Parikh: Every aspect of the data.
324 00:41:25.540 ⇒ 00:41:27.780 Tanay Parikh: Try to find out, the…
325 00:41:28.530 ⇒ 00:41:31.339 Tanay Parikh: Dimensions and the fact stables, or…
326 00:41:31.730 ⇒ 00:41:35.189 Tanay Parikh: How the relationships are with the different tables.
327 00:41:35.400 ⇒ 00:41:36.100 Tanay Parikh: Boom.
328 00:41:36.510 ⇒ 00:41:39.209 Tanay Parikh: In… in the data, and then present.
329 00:41:39.210 ⇒ 00:41:39.540 Awaish Kumar: Okay.
330 00:41:39.540 ⇒ 00:41:40.870 Tanay Parikh: Or, yeah.
331 00:41:41.920 ⇒ 00:41:47.129 Awaish Kumar: Okay, you got this, all of this. How… that’s what I’m thinking now, that how would you generate those tickets?
332 00:41:47.430 ⇒ 00:41:51.860 Awaish Kumar: for DE and A, what will be the titles of those tickets?
333 00:41:52.000 ⇒ 00:42:01.270 Awaish Kumar: Like, my ticket, like, if I say, Like, as I said, ingest data from TikTok.
334 00:42:01.470 ⇒ 00:42:02.150 Awaish Kumar: is my…
335 00:42:02.150 ⇒ 00:42:02.470 Tanay Parikh: weekly.
336 00:42:02.470 ⇒ 00:42:03.920 Awaish Kumar: One of them, I would say.
337 00:42:04.510 ⇒ 00:42:18.600 Awaish Kumar: So, what are different tickets we’ll create for DE person? What kind of information they have? You don’t have to be, like, in each ticket, I will have this at this, like, there will be the same thing which will be in all the tickets, right? I can generalize it, and just tell me.
338 00:42:18.930 ⇒ 00:42:20.610 Awaish Kumar: What will be in those tickets?
339 00:42:21.170 ⇒ 00:42:21.940 Awaish Kumar: so that I.
340 00:42:21.940 ⇒ 00:42:23.130 Tanay Parikh: So basically…
341 00:42:23.620 ⇒ 00:42:33.830 Tanay Parikh: Okay, so basically, I will try to create a subject out of it, and try to present a summary, like, this is what Tickets tells you about.
342 00:42:34.050 ⇒ 00:42:44.139 Tanay Parikh: So, basically, there could be a lot of information in the tickets, and I will try to just create a subject and try to add a summary. So, my subject would be here, like.
343 00:42:44.870 ⇒ 00:42:54.130 Tanay Parikh: extracting data, or extracting data for this particular campaign name. The campaign name is, suppose, some campaign name is there, so I’ll try to use that.
344 00:42:54.460 ⇒ 00:42:57.370 Tanay Parikh: And, I’m working for this particular client.
345 00:42:57.520 ⇒ 00:42:58.480 Tanay Parikh: So…
346 00:42:58.860 ⇒ 00:43:06.850 Tanay Parikh: So that’s what I will try to do, and try to add a 3-4 line summary. That’s, this is, what I need, and this is,
347 00:43:07.570 ⇒ 00:43:15.640 Tanay Parikh: you need to do, and there will be a lot of… a big description, again, about it, but I will try to add a 3-4 line summary so that they will get a.
348 00:43:16.120 ⇒ 00:43:16.570 Awaish Kumar: for a day.
349 00:43:16.570 ⇒ 00:43:17.120 Tanay Parikh: up.
350 00:43:17.730 ⇒ 00:43:19.489 Awaish Kumar: what that is, I just…
351 00:43:20.080 ⇒ 00:43:25.320 Awaish Kumar: like, what I need for ingestion, for example, that I can perform the ingestion successfully.
352 00:43:27.000 ⇒ 00:43:29.700 Tanay Parikh: I think the right data source is important here.
353 00:43:30.910 ⇒ 00:43:38.630 Awaish Kumar: For example, I have to… if I have to ingest data, what do I need? I need list of sources. List of sources means TikTok, Facebook.
354 00:43:39.350 ⇒ 00:43:44.160 Awaish Kumar: our, or, you can say, LinkedIn, or…
355 00:43:45.480 ⇒ 00:43:51.459 Awaish Kumar: for example, any other source, right? These… this is the sources
356 00:43:51.670 ⇒ 00:43:58.039 Awaish Kumar: So, if I have to connect to TikTok, what do I need? I need authentication. If there is an API, I need API keys.
357 00:43:58.280 ⇒ 00:44:06.739 Awaish Kumar: without API key from the… like, that’s the client. Client can only give you… authorize you. Either they will authorize my Google account.
358 00:44:06.870 ⇒ 00:44:09.469 Awaish Kumar: or they will give me some API keys.
359 00:44:10.120 ⇒ 00:44:14.240 Awaish Kumar: What you have to do, the first step is, you can tell me.
360 00:44:14.830 ⇒ 00:44:27.199 Awaish Kumar: I mean, you need to ingest data from TikTok. Here’s the API key, or here is the authentication email that you can use, and the password, whatever, wherever it is stored, and…
361 00:44:27.910 ⇒ 00:44:33.749 Awaish Kumar: Then… What kind of data you’re targeting? Like, maybe TikTok, you have a…
362 00:44:33.860 ⇒ 00:44:48.029 Awaish Kumar: multiple data, you can say, okay, maybe I’m only concerned about ad spend data, so only the ads which are running on TikTok, how much we are spending on a granularity by day.
363 00:44:48.210 ⇒ 00:44:49.320 Awaish Kumar: That’s what I need.
364 00:44:50.580 ⇒ 00:44:53.390 Awaish Kumar: So, when you say this… Everything.
365 00:44:53.650 ⇒ 00:44:57.810 Awaish Kumar: from this ticket, to just go and execute. I don’t have to ask any question.
366 00:44:59.150 ⇒ 00:45:00.410 Tanay Parikh: Yeah, makes sense.
367 00:45:01.870 ⇒ 00:45:04.819 Awaish Kumar: So, still, I’m con… like, if you can give me some…
368 00:45:05.020 ⇒ 00:45:08.820 Awaish Kumar: ingestion part, I just give you something. But what else, right?
369 00:45:09.090 ⇒ 00:45:13.210 Awaish Kumar: You… from ingestion, you can’t directly go to building a dashboard.
370 00:45:13.580 ⇒ 00:45:16.010 Awaish Kumar: What will be the intermediate steps?
371 00:45:16.970 ⇒ 00:45:33.530 Tanay Parikh: So, I think the intermediate steps would be, to find out the relationships between the different tables here, so that, and obviously, a lot of doing a lot of data quality checks, like, because we have to basically find the end data, so we will do the…
372 00:45:34.700 ⇒ 00:45:42.120 Awaish Kumar: I understand, but still, I said, you have people in your team, you have data engineers, you have analytics engineer, who can help you with this.
373 00:45:42.370 ⇒ 00:45:53.950 Awaish Kumar: Can you think of some tickets which you can create, or you can deliver? Like, hello, DE guy, ingest data from TikTok. You can say, hello, AE person, do this for me.
374 00:45:54.150 ⇒ 00:45:56.149 Awaish Kumar: Create these tables.
375 00:45:56.520 ⇒ 00:45:57.760 Awaish Kumar: Yeah, it’s a…
376 00:45:57.760 ⇒ 00:46:12.960 Tanay Parikh: Data is not pure, it’s not in a consistent format. I will try to use that as well, like, obviously, like, if the data doesn’t make sense, or it has some null values, it has some duplicates values inside it, so I will try to use.
377 00:46:12.960 ⇒ 00:46:15.320 Awaish Kumar: Cleaning is one part of it.
378 00:46:15.860 ⇒ 00:46:17.649 Awaish Kumar: Added the claims, like, you added.
379 00:46:17.650 ⇒ 00:46:18.080 Tanay Parikh: Okay.
380 00:46:18.080 ⇒ 00:46:18.640 Awaish Kumar: description.
381 00:46:18.830 ⇒ 00:46:19.610 Tanay Parikh: Okay.
382 00:46:19.670 ⇒ 00:46:21.560 Awaish Kumar: This is one… one tick, like…
383 00:46:23.610 ⇒ 00:46:25.899 Awaish Kumar: Like, this is just part of the ticket, right?
384 00:46:25.900 ⇒ 00:46:26.640 Tanay Parikh: Okay.
385 00:46:28.100 ⇒ 00:46:37.460 Awaish Kumar: You can say, okay, deduplicate, Clean data, or handle nulls, or add tests. Okay, and what else?
386 00:46:37.980 ⇒ 00:46:41.910 Tanay Parikh: Creating dimension stables. In fact, stables after it.
387 00:46:41.910 ⇒ 00:46:49.050 Awaish Kumar: Yeah, what kind of… that’s… I’m… like, what kind of dimensions do we need? What kind of factor will you need… need for?
388 00:46:49.050 ⇒ 00:46:54.689 Tanay Parikh: Oh… So, basically, in this scenario, like, it should be…
389 00:46:55.590 ⇒ 00:46:59.179 Tanay Parikh: Like, there should be a lot of dimensions according to the…
390 00:46:59.900 ⇒ 00:47:10.640 Tanay Parikh: If you’re talking about, suppose meta, so there could be a lot of dimensions about a particular product, we can try to create a dimension model for a product. We can try to create a dimensional model.
391 00:47:11.790 ⇒ 00:47:18.690 Awaish Kumar: from beta, you… like, product is our internal information. We already have DIM product, right? That is…
392 00:47:19.420 ⇒ 00:47:21.040 Awaish Kumar: That we don’t get from…
393 00:47:21.270 ⇒ 00:47:22.020 Tanay Parikh: Okay.
394 00:47:23.780 ⇒ 00:47:28.870 Awaish Kumar: like, I have a database, so these are the platforms where I’m running marketing.
395 00:47:29.140 ⇒ 00:47:37.299 Awaish Kumar: I don’t… I will tell them the product name. Products I already have in my database. What product, as a company, I’m selling, I know.
396 00:47:38.430 ⇒ 00:47:39.590 Tanay Parikh: And… Yeah.
397 00:47:39.640 ⇒ 00:47:44.449 Awaish Kumar: We have Dim product for that, okay. What else? But, like…
398 00:47:44.790 ⇒ 00:47:51.970 Awaish Kumar: for this pipeline, A doesn’t have to implement DIM product, because DIM product already established It’s there.
399 00:47:52.120 ⇒ 00:47:57.180 Awaish Kumar: For this specific scenario, what new demand factors you will need?
400 00:47:58.250 ⇒ 00:48:03.170 Tanay Parikh: In this, I think, vegan…
401 00:48:04.970 ⇒ 00:48:10.339 Awaish Kumar: Can you think of something like, this is the information which we are only getting from… from this?
402 00:48:11.740 ⇒ 00:48:13.600 Tanay Parikh: connection.
403 00:48:13.610 ⇒ 00:48:18.880 Awaish Kumar: from Facebook ads, or from TikTok ads. This, I can’t get it from anywhere else.
404 00:48:19.390 ⇒ 00:48:20.930 Awaish Kumar: What could it be?
405 00:48:22.330 ⇒ 00:48:33.379 Tanay Parikh: So basically, the clicks, like, the number of clicks the user is trying to click on the Facebook or Meta, like, if they’re trying to purchase a product from Facebook or Meta, the number of clicks… That’s correct.
406 00:48:33.790 ⇒ 00:48:34.759 Awaish Kumar: Yeah, but that.
407 00:48:34.760 ⇒ 00:48:35.670 Tanay Parikh: of flex.
408 00:48:35.950 ⇒ 00:48:43.959 Awaish Kumar: get number of clicks from Facebook, but that is a metric, that is not a… that can’t be a time issue.
409 00:48:44.880 ⇒ 00:48:49.310 Tanay Parikh: Yeah, that’s a fact table, yeah, so that’s… that’s… that will be in a fact table.
410 00:48:49.700 ⇒ 00:48:50.670 Awaish Kumar: Yeah.
411 00:48:51.420 ⇒ 00:49:01.090 Tanay Parikh: So, like, try to understand the user, user perspective here, so that would be… the…
412 00:49:04.990 ⇒ 00:49:07.609 Tanay Parikh: Okay, depends on, yeah.
413 00:49:09.060 ⇒ 00:49:13.979 Awaish Kumar: Okay, yeah, we can, go ahead. What about storytelling? How…
414 00:49:14.190 ⇒ 00:49:19.139 Awaish Kumar: if something… how would you generally do, like, if I, as a client.
415 00:49:20.090 ⇒ 00:49:24.310 Awaish Kumar: came in, asked, gave you some tasks.
416 00:49:24.690 ⇒ 00:49:30.619 Awaish Kumar: You built some dashboard for me. Whatever you have to do. You did… you built a dashboard.
417 00:49:31.410 ⇒ 00:49:34.619 Awaish Kumar: Then what happens? How you deliver, basically?
418 00:49:35.360 ⇒ 00:49:44.020 Tanay Parikh: So, that’s how I try to, like, whenever I’m trying to deliver my dashboard to the client or to the different stakeholders, I try to,
419 00:49:44.020 ⇒ 00:49:55.499 Tanay Parikh: use as simple language as I can, try to basically understand the question first, and then try to implement a solution out of it, and then present them in a way which… in a very simple and easy language, so that they would understand.
420 00:49:55.560 ⇒ 00:50:00.809 Tanay Parikh: And make sure that I would understand, about the problems that they were trying to face.
421 00:50:00.890 ⇒ 00:50:02.030 Tanay Parikh: And,
422 00:50:02.180 ⇒ 00:50:11.129 Tanay Parikh: the solution, and what’s the impact of those problems that we’re currently facing. So there could be a case that, the sales have… the revenue has been getting dropped.
423 00:50:11.410 ⇒ 00:50:22.740 Tanay Parikh: And I would try to, go down with… go down with the analysis and try to find out what’s the impact of it. So, the sales have been going down. The main impact here is basically
424 00:50:23.130 ⇒ 00:50:42.899 Tanay Parikh: the revenue has been going down, the sales have been going down, and then find out the bottleneck of it. So, is it, is it a particular product which is responsible for it, or is it a particular month or a quarter that we have been facing a lot of, less revenue, less sales, according to the…
425 00:50:43.310 ⇒ 00:50:52.199 Tanay Parikh: In the company, or, is it, different category, different categories, like, the wholesale or the retail market is going down?
426 00:50:53.360 ⇒ 00:51:05.160 Tanay Parikh: Specifically for the quarter, and then, come up with the… with the recommendation technique as well, like, if we try to, if the product is going down for a particular
427 00:51:05.240 ⇒ 00:51:19.110 Tanay Parikh: product is not working as well, so I’ll try to find, like, talk with the sales and marketing people that if we can try to increase our sales campaigns or marketing campaigns for this particular product, we can maybe try to increase our sales in the next quarter.
428 00:51:22.520 ⇒ 00:51:27.080 Awaish Kumar: Okay, and yeah, how do you generally,
429 00:51:27.290 ⇒ 00:51:31.759 Awaish Kumar: Give that feedback to clients, like, what is the output formula, like.
430 00:51:34.150 ⇒ 00:51:38.440 Tanay Parikh: It should be in a very easy language, so I don’t have to go deep, yeah.
431 00:51:38.910 ⇒ 00:51:41.750 Awaish Kumar: Easy language is okay, but.
432 00:51:41.750 ⇒ 00:51:42.260 Tanay Parikh: Yeah.
433 00:51:43.300 ⇒ 00:51:46.230 Awaish Kumar: Like, one thing is that your deliverable is a dashboard.
434 00:51:46.640 ⇒ 00:51:46.970 Tanay Parikh: Yeah.
435 00:51:46.970 ⇒ 00:51:54.130 Awaish Kumar: Right? As, like, dashboard is a deliverable, but that is really cheap, and that’s not really,
436 00:51:54.500 ⇒ 00:52:04.020 Awaish Kumar: communicative in a human-readable thing, right? It’s still a dashboard. People have to filter, people have to check, understand. So…
437 00:52:04.660 ⇒ 00:52:07.130 Awaish Kumar: What you described, like, you did an analysis.
438 00:52:07.590 ⇒ 00:52:20.190 Awaish Kumar: you did some discovery, you come up with some… you did some investigation, you came up with some results, recommendation, then what is your deliverable? How you deliver that information to the client?
439 00:52:20.870 ⇒ 00:52:23.420 Tanay Parikh: It’s using, presentations.
440 00:52:23.880 ⇒ 00:52:37.900 Tanay Parikh: using that bottleneck, if the particular product is going down, I will try to create a versus chart here, like, all product versus this particular product, or if there is a difference between the retail and wholesale sector.
441 00:52:37.920 ⇒ 00:52:48.039 Tanay Parikh: If the retail market is going very down for the particular products, or for different products, I’ll try to create a different chart, and then try to,
442 00:52:48.530 ⇒ 00:52:49.280 Tanay Parikh: demos.
443 00:52:50.260 ⇒ 00:52:56.160 Awaish Kumar: Have you already created some presentations and shared with… Stakeholders, or, like…
444 00:52:56.860 ⇒ 00:53:00.249 Awaish Kumar: Have you been to some, like, meetings where you…
445 00:53:00.930 ⇒ 00:53:05.539 Awaish Kumar: Describing your solution to stakeholder in the presentation.
446 00:53:06.330 ⇒ 00:53:16.340 Tanay Parikh: I’ve been explaining the dashboard, while I was working with Sumitova Pharma and, currently at Rebecca as well. So I’ve been, explaining the dashboards.
447 00:53:17.050 ⇒ 00:53:22.229 Tanay Parikh: I haven’t got a chance to explain a particular presentation to the particular stakeholders.
448 00:53:24.280 ⇒ 00:53:28.800 Tanay Parikh: But yeah, like, I can obviously do it, so it’s, like, creating the… yeah.
449 00:53:29.440 ⇒ 00:53:34.549 Awaish Kumar: Okay, for example, if you come at Brainforge, I assign you with some tasks.
450 00:53:35.080 ⇒ 00:53:42.470 Awaish Kumar: And maybe, like, I could say, okay, this is your investigation part. You are new, obviously, you will…
451 00:53:42.810 ⇒ 00:53:45.060 Awaish Kumar: That’s what I’m asking. One new…
452 00:53:45.500 ⇒ 00:53:54.660 Awaish Kumar: to one of my clients I will be working on, I will add you as my team member as data analyst, and you are basically then responsible for
453 00:53:54.800 ⇒ 00:53:55.900 Awaish Kumar: I…
454 00:53:56.070 ⇒ 00:54:04.809 Awaish Kumar: basically, I… we… we asked you for some discovery, for inventory, for example, or for sales. Like, let’s go in.
455 00:54:04.980 ⇒ 00:54:19.740 Awaish Kumar: do some discovery, come up with something that we can share with client, which really is meaningful for the client, right, and useful. Which is, like, not just something, okay, like, this happened.
456 00:54:19.920 ⇒ 00:54:23.330 Awaish Kumar: But there’s no way to action on it.
457 00:54:23.810 ⇒ 00:54:27.120 Awaish Kumar: Okay, like, sales are down on this day.
458 00:54:28.770 ⇒ 00:54:35.099 Awaish Kumar: But we don’t know why, and… or we… sometimes we know why, but we can’t recommend anything.
459 00:54:35.440 ⇒ 00:54:42.730 Awaish Kumar: So that’s… not useful, right? That’s the natural… that will give us… put us in a bad spot, like…
460 00:54:43.160 ⇒ 00:54:48.570 Awaish Kumar: You came up with something, you showed that this is a problem, and…
461 00:54:50.010 ⇒ 00:54:54.010 Awaish Kumar: And, and it’s… this is why this is a problem.
462 00:54:54.560 ⇒ 00:55:09.289 Awaish Kumar: if… but, like, then client will say, like, I know this is a problem, but how can I solve it? If we don’t have an answer, then we are basically in a bad spot, like, then you have to give them recommendations. How to solve it, like, that should be actionable.
463 00:55:09.450 ⇒ 00:55:13.230 Awaish Kumar: Anything we come up with should be finally actionable.
464 00:55:13.360 ⇒ 00:55:19.820 Awaish Kumar: For the client to basically go and execute and chain things, and then it can improve their,
465 00:55:20.200 ⇒ 00:55:21.849 Awaish Kumar: Indian Avenue, for example.
466 00:55:21.990 ⇒ 00:55:23.170 Awaish Kumar: So…
467 00:55:25.040 ⇒ 00:55:33.630 Awaish Kumar: For example, if you come in a week’s timeline, we… maybe if you consider it a deadline, or whatever.
468 00:55:34.130 ⇒ 00:55:39.800 Awaish Kumar: Can you come on, on a new client, do this discovery in a week, and come up with some real
469 00:55:39.970 ⇒ 00:55:46.100 Awaish Kumar: Useful traits, and then, put up a presentation.
470 00:55:47.320 ⇒ 00:55:51.939 Awaish Kumar: And, and basically… go and, present to the client.
471 00:55:53.140 ⇒ 00:55:57.079 Tanay Parikh: Yes, like, I can surely do that, because, like, I think,
472 00:55:57.260 ⇒ 00:56:05.490 Tanay Parikh: important here is to, like, if I got a whole aspect of what’s the problem here, I will try to go deep down with the data, try to.
473 00:56:06.000 ⇒ 00:56:09.160 Awaish Kumar: I’m saying, you are going to… I…
474 00:56:09.300 ⇒ 00:56:13.109 Awaish Kumar: You are going to get the data, access to data.
475 00:56:13.400 ⇒ 00:56:16.249 Awaish Kumar: And then you’ll… you have to do discovery.
476 00:56:16.710 ⇒ 00:56:18.169 Tanay Parikh: Yeah, I can do it.
477 00:56:18.350 ⇒ 00:56:33.040 Awaish Kumar: There’s a problem, if it… even if the… like, we don’t see it, like, on a… if it appears on a top level, like, if you just look at revenue and see, okay, my revenue, is half, then, like, 50% lower than yesterday.
478 00:56:33.420 ⇒ 00:56:37.470 Awaish Kumar: everybody can see it, right? Everybody will say, this is a problem, like…
479 00:56:38.110 ⇒ 00:56:46.049 Awaish Kumar: We’re going to get pinged by client, okay, let’s go and look into it while it’s down. Is it just your data is bad, or is it really some problem?
480 00:56:46.200 ⇒ 00:56:56.499 Awaish Kumar: this is normally you would just get it, right? We… we are in a… like, you can say, for example, we are with the… in some with… we are with some client, which basically
481 00:56:56.640 ⇒ 00:57:01.949 Awaish Kumar: From the top, you see things are running smooth, data is going found, revenue is looking good.
482 00:57:02.090 ⇒ 00:57:06.470 Awaish Kumar: Like, same as it has been. So, there’s…
483 00:57:07.010 ⇒ 00:57:10.279 Awaish Kumar: Through the, like… let’s say, through the, like.
484 00:57:10.280 ⇒ 00:57:11.500 Tanay Parikh: If you just…
485 00:57:11.510 ⇒ 00:57:13.269 Awaish Kumar: Like, naked eye, it just looks…
486 00:57:13.270 ⇒ 00:57:13.980 Tanay Parikh: Yeah.
487 00:57:15.320 ⇒ 00:57:17.440 Awaish Kumar: But then you have to go discover.
488 00:57:17.800 ⇒ 00:57:19.750 Awaish Kumar: Things, patterns, trends.
489 00:57:20.360 ⇒ 00:57:36.080 Tanay Parikh: Yeah, so basically, when I was even working with Sumito Pharma, the same thing happened with me as well. So when I was working there, so the revenue looked good, but again, if I… the product was very new to us, so when I got, deep down to the data, I found out
490 00:57:36.080 ⇒ 00:57:54.359 Tanay Parikh: that the retail sector, retail market is not going as well it should be, according to the forecasting that we have done for the retail market. So then I explained to the sales and marketing people about the retail market. The retail market of this particular drug product is not working as well as it should be according to the forecasting times we have.
491 00:57:54.480 ⇒ 00:58:04.470 Tanay Parikh: And then I will give them the recommendation about the retail, that if we try to increase our retail, sector, retail, hospitals across our, of particular drugs.
492 00:58:04.570 ⇒ 00:58:10.970 Tanay Parikh: We can try to increase ourselves overall as well, and so that’s one of the approaches I did.
493 00:58:11.800 ⇒ 00:58:15.620 Awaish Kumar: Okay, so have you worked with Shopify data or something?
494 00:58:15.960 ⇒ 00:58:17.200 Tanay Parikh: Amazon,
495 00:58:17.650 ⇒ 00:58:23.730 Awaish Kumar: Have you worked on, like, e-com… tool platforms like Shopify, Amazon.
496 00:58:24.460 ⇒ 00:58:28.780 Tanay Parikh: No, I haven’t worked with it. But again, like, I would love to get a chance to work on those data.
497 00:58:29.000 ⇒ 00:58:33.779 Tanay Parikh: Understand the data terms, understand the data terminologies…
498 00:58:34.070 ⇒ 00:58:37.989 Awaish Kumar: That’s my point, like, that’s why I said one week timeline, like, that means…
499 00:58:38.160 ⇒ 00:58:42.489 Awaish Kumar: Like, we want someone who’s, like, quick learner, get on to it.
500 00:58:42.640 ⇒ 00:58:48.900 Awaish Kumar: build something, come up with… maybe even you don’t have… maybe… I don’t know how… which tools you’ve worked on.
501 00:58:49.010 ⇒ 00:58:55.649 Awaish Kumar: Previously, but maybe you got a chance to work on some new BI tools, which haven’t worked before.
502 00:58:55.830 ⇒ 00:59:02.430 Awaish Kumar: So… Like, it will be really, fast-pacing, and I’m really…
503 00:59:02.970 ⇒ 00:59:08.650 Awaish Kumar: Or something, you get a chance to work on different tools, so how… how comfortable you are with that?
504 00:59:09.150 ⇒ 00:59:28.719 Tanay Parikh: So, like, talking about me, even right now, I’m in an early phase of my career, so that’s what I really want, to work in a company where I can learn new things quickly, and at the same time, showcase my findings and results in a better way, to the clients or to the senior leadership. So that’s what I’m trying to do right now, that’s what I’m willing to do.
505 00:59:28.720 ⇒ 00:59:39.690 Tanay Parikh: like, work, work… try to work in a fast-pacing environment, and try to learn new tools if I get a chance. So, that’s what I would try… love to do.
506 00:59:42.120 ⇒ 00:59:47.010 Awaish Kumar: Okay, I think that’s, it from my side. If you have any other questions…
507 00:59:48.480 ⇒ 01:00:06.840 Tanay Parikh: Like, if there’s a case, we could basically try to… I can try to do a case study in 3 to 4 days, like, or whatever timelines the company demands, or you demands from me, so I can try to work on that case study, showcase my findings and the results about that case study, and showcase it to you.
508 01:00:08.300 ⇒ 01:00:11.619 Awaish Kumar: No, no, like, we’ll decide on…
509 01:00:12.190 ⇒ 01:00:12.710 Tanay Parikh: Yeah.
510 01:00:12.710 ⇒ 01:00:15.169 Awaish Kumar: I would ca… if you want to have a case study.
511 01:00:15.440 ⇒ 01:00:19.350 Awaish Kumar: Where do you want to go? That’s okay, that next steps, like, we are…
512 01:00:19.820 ⇒ 01:00:22.939 Awaish Kumar: discuss and decide on that, but I’m just…
513 01:00:23.490 ⇒ 01:00:27.369 Awaish Kumar: If you have any other… any questions for us, for brain food.
514 01:00:28.540 ⇒ 01:00:34.620 Tanay Parikh: Yeah, so I have a question, basically, so, what challenges, is the current team facing?
515 01:00:35.060 ⇒ 01:00:39.250 Tanay Parikh: Like, what are challenges other data engineers or the analytics engineers facing right now?
516 01:00:40.120 ⇒ 01:00:43.009 Awaish Kumar: Yeah, there are a lot of…
517 01:00:43.570 ⇒ 01:00:48.330 Awaish Kumar: Challenges, like, could be, like, data quality monitoring,
518 01:00:48.510 ⇒ 01:00:52.399 Awaish Kumar: We are facing, like, we have some reliability tools we’re using.
519 01:00:52.660 ⇒ 01:00:57.170 Awaish Kumar: But we are still getting a lot of notifications, or sometimes we are not… Yeah.
520 01:00:58.010 ⇒ 01:01:05.560 Awaish Kumar: Like, a lot of… sometimes you get a lot of alerts, which can spam you, and sometimes you just miss, the real…
521 01:01:07.020 ⇒ 01:01:07.810 Awaish Kumar: Problem?
522 01:01:08.500 ⇒ 01:01:14.589 Awaish Kumar: So what, like, have you used any reliability tools?
523 01:01:14.840 ⇒ 01:01:19.540 Awaish Kumar: Any, like, data… how… How do you normally measure the data quality?
524 01:01:20.320 ⇒ 01:01:35.919 Tanay Parikh: So, whatever, if I’m collaborating with data engineer, I try to make… I try to understand with them, like, what’s the data quality checks I need to do before performing any analysis on it. So, that’s what I try to do, perform the data quality checks, in…
525 01:01:35.960 ⇒ 01:01:49.409 Tanay Parikh: in SQL or Python, and try to make sure that the data is in the right data governance standards. And if I have a documentation about that particular data, or about that particular data source, I will try to use that as well.
526 01:01:50.630 ⇒ 01:01:55.979 Awaish Kumar: From the data analysis, you… yeah, you are okay with…
527 01:01:57.280 ⇒ 01:02:11.380 Awaish Kumar: Yeah, are you okay with communicating with clients and context switching? So, we, as a Brain Forge, maybe one person would be working on multiple clients, so maybe you are working…
528 01:02:11.750 ⇒ 01:02:20.380 Awaish Kumar: 3, 4, like, in a day, maybe, or sometimes you just work on one client, and for 3-4 hours, and next 3-4 hours, you’re going to work on some other client, or…
529 01:02:20.810 ⇒ 01:02:28.069 Awaish Kumar: Or maybe something like that. So you are… maybe you are working on something, and some urgent thing comes up for another client.
530 01:02:28.190 ⇒ 01:02:34.900 Awaish Kumar: And then you have to support the team. So, are you okay with this setup?
531 01:02:34.900 ⇒ 01:02:36.670 Tanay Parikh: Yeah, I’m okay. Yeah, I’m okay with that.
532 01:02:39.200 ⇒ 01:02:46.309 Awaish Kumar: How good are you at? Like, how would you rate yourself when you… in the con… in the context switching terms, like.
533 01:02:46.740 ⇒ 01:02:53.310 Awaish Kumar: If you have to context switch, are you still the same productive… In terms of productivity.
534 01:02:55.680 ⇒ 01:03:12.600 Tanay Parikh: Yeah, like, I can work on multiple projects at a single time, and I’ve done that in past as well. Try to utilize my time in a right way, try to strategize it, and work on, with the clients or with the people who have,
535 01:03:12.850 ⇒ 01:03:17.249 Tanay Parikh: Whoever demand of the particular analysis.
536 01:03:17.550 ⇒ 01:03:19.580 Tanay Parikh: And, worked on it.
537 01:03:21.380 ⇒ 01:03:27.370 Awaish Kumar: Okay, yeah, that’s… Sorry, what was your question? I…
538 01:03:27.700 ⇒ 01:03:32.699 Tanay Parikh: So basically, what challenges is the company facing according, like… Right.
539 01:03:32.810 ⇒ 01:03:41.179 Awaish Kumar: Explaining that we have data quality monitoring checks, which we are really working on, can be challenging.
540 01:03:41.410 ⇒ 01:03:48.019 Awaish Kumar: Secondly, on the analysis side, yeah, we are… we need help in doing some analysis. We are building
541 01:03:48.250 ⇒ 01:04:00.829 Awaish Kumar: infrastructure for… for company. Companies, we are building some good data pipelines, but then when data ends up in a data warehouse, we maybe create some dashboards, but that goes
542 01:04:01.100 ⇒ 01:04:08.470 Awaish Kumar: That is all happening silently. So what we need help with, someone as a data analyst comes in. He not just
543 01:04:09.570 ⇒ 01:04:20.929 Awaish Kumar: There’s the dashboard, which is responsible for discovery, looking at dashboard, analyzing it, looking at data, diving deeper into it, coming up with some insights,
544 01:04:21.000 ⇒ 01:04:34.600 Awaish Kumar: convert into a presentation, and then talk to the client, like, regarding what we are figuring out, and why we are able to do it. Why we are able to do it, like, you have to justify your team, right?
545 01:04:35.140 ⇒ 01:04:49.370 Awaish Kumar: You are able to do it, because you have done it, this is the thing, this is the problem, how you can solve it, and why you are able to find out the problem and the solution, because of this.
546 01:04:49.610 ⇒ 01:04:53.520 Awaish Kumar: Data, there’s infrastructure, this team, and things like that.
547 01:04:53.650 ⇒ 01:04:55.230 Awaish Kumar: So, kind of this…
548 01:04:55.370 ⇒ 01:05:05.540 Awaish Kumar: flow. So we… we, like, I can say, we are… we need more resources to do that. Right now, we have, like, we have reached the limits of our
549 01:05:05.760 ⇒ 01:05:08.949 Awaish Kumar: Capabilities, so we just need more people in our team to do that.
550 01:05:09.620 ⇒ 01:05:10.350 Tanay Parikh: Okay.
551 01:05:10.490 ⇒ 01:05:12.040 Tanay Parikh: Yeah, sounds good.
552 01:05:13.950 ⇒ 01:05:19.830 Tanay Parikh: Yeah, like, that’s it. And also, like, what other tools, is the team using currently?
553 01:05:21.090 ⇒ 01:05:39.440 Awaish Kumar: Depends on the client, like, the Brainforge is a consulting company, so… depends on what… on what client you are working on. You might be working on Tableau, you might be working on Power BI, might be working on Omni Analytics Tool. You can be… might be working on,
554 01:05:39.610 ⇒ 01:05:43.050 Awaish Kumar: Rail is one of the platforms, and also you can
555 01:05:43.250 ⇒ 01:05:52.200 Awaish Kumar: Mixpanel is something. Looker Studio is one of the tools. These are the tools which can… which are common in the industry, yeah.
556 01:05:52.390 ⇒ 01:05:53.830 Tanay Parikh: Okay, got it, got it.
557 01:05:55.350 ⇒ 01:05:57.839 Tanay Parikh: Yeah, like, that sounds good, yeah.
558 01:05:58.690 ⇒ 01:06:06.089 Awaish Kumar: Thank you. I’m going to send my feedback to the team, and then Rico from our team is going to…
559 01:06:07.030 ⇒ 01:06:12.209 Awaish Kumar: Like, again, come up with some next steps.
560 01:06:12.570 ⇒ 01:06:14.840 Tanay Parikh: Okay, sure, sure, sounds good.
561 01:06:14.880 ⇒ 01:06:15.355 Awaish Kumar: Nope.
562 01:06:16.650 ⇒ 01:06:18.450 Awaish Kumar: Thank you for your time.
563 01:06:18.880 ⇒ 01:06:19.900 Tanay Parikh: Yeah, again.