Google PM Interview: Google Maps Korea Launch

Summary

In this mock product management interview, Hal, an engineering manager with previous experience at Google, discusses strategies for Google Maps' potential launch in South Korea. He evaluates factors such as data quality, cost, and integration challenges when choosing between local vendors for map datasets. Hal emphasizes the importance of precision and recall in business search results and suggests A/B testing and user-generated content to refine data accuracy. The conversation highlights a user-centric approach to product management, focusing on the ultimate goal of providing the best user experience.

Takeaways

• 😀 Hal, an engineering manager at Cressicore and former PM at Google, discusses choosing a map data vendor for Google Maps' launch in South Korea.
• 📍 The decision-making process should consider data quality, cost, and non-functional aspects like integration and updates.
• 🔍 Data quality is crucial for accuracy and user experience, including precision and recall of business listings and directions.
• 💰 Cost may not be a primary concern for Google, which prioritizes the best user experience over vendor pricing.
• 🔄 Non-functional considerations include the ease of integration, licensing, and the reliability of ongoing data updates.
• 📈 Hal suggests using A/B testing with user interaction metrics to evaluate different data sets' effectiveness.
• 📊 Metrics to monitor include interaction rates, search abandonments, and query refinements indicating user satisfaction.
• 🏢 Google's infrastructure and user engagement features, like reporting incorrect data, can compensate for initial data set deficiencies.
• 🔑 The ultimate goal of Google Maps should guide the trade-off decisions between precision and recall of data.
• 🤔 Hal reflects on the importance of clearly defining the mission and goals at the outset and tying them back to decision-making later.
• 🔑 The interview concludes with feedback emphasizing the structured and use-case driven approach Hal took in addressing the problem.

Q & A

What is the main topic of the mock interview in the video transcript?

The main topic of the mock interview is about Google Maps wanting to launch in South Korea and the decision-making process for choosing a local vendor for map data sets.

Who is Hal and what is his background according to the transcript?

Hal is an engineering manager at a startup called CressiCore. He was previously a product manager (PM) at Google for five years, working on Google Assistant.

What are the three factors Hal considers when choosing a map data vendor for Google Maps in South Korea?

Hal considers data quality, the business aspect (including cost and contract terms), and non-functional aspects such as integration ease, licensing, and data reliability.

Why does Hal believe that data quality is the most important factor in choosing a vendor?

Hal believes data quality is the most important factor because it directly affects the user experience and Google's ability to provide the best service to its users.

What does Hal mean by 'precision and recall' in the context of map data quality?

In the context of map data quality, 'precision' refers to the accuracy of the data, ensuring that the places listed are indeed what the user is searching for. 'Recall' refers to the coverage of the data, ensuring that the data set includes all relevant places.

How does Hal suggest measuring the difference between the map data and reality?

Hal suggests using an A/B test with different data sets for a portion of users and comparing metrics like interaction rates, abandonments, and refinements. He also mentions the possibility of using human evaluators to gather 'golden data' for comparison.

What are some of the metrics Hal suggests monitoring during an A/B test of map data?

Hal suggests monitoring interaction rates, abandonments, and refinements as indicators of how relevant the search results are to the users.

How does Hal define the success metric for Google Maps in the context of the interview?

Hal suggests that the success metric for Google Maps could be whether users actually visit the places they find through the app, as it indicates the relevance and usefulness of the map data.

What is Hal's approach to dealing with trade-offs between precision and recall of map data?

Hal's approach is to understand Google Maps' ultimate goal or 'north star' metric and make a decision based on what aligns best with that goal. He also mentions the possibility of merging multiple data sets to balance precision and recall.

What feedback does the interviewer give to Hal at the end of the mock interview?

The interviewer appreciates Hal's use-case driven approach and his structured method of outlining considerations and diving deep into data quality. The feedback also includes the suggestion to refine the mission and goals at the beginning and to tie back decisions to the initial vision.

Outlines

00:00

🗺️ Google Maps Expansion Strategy in South Korea

The video script begins with a hypothetical scenario where Google Maps is considering launching in South Korea and must choose between several local vendors for map data. The interviewee, Hal, introduces himself as an ex-Google PM and current engineering manager. The discussion focuses on the criteria for selecting a map data vendor, emphasizing the importance of data quality, cost, and integration feasibility. Hal suggests that for Google, data quality is paramount to ensure an excellent user experience, while cost and integration challenges are secondary concerns due to Google's resources and capabilities.

05:01

📊 Prioritizing Data Quality for Google Maps Launch

In this paragraph, Hal delves into the factors that determine data quality, particularly precision and recall of business listings. He explains that precision refers to the accuracy of the search results, ensuring that the places listed are indeed what the user is searching for. Recall, on the other hand, is about the comprehensiveness of the data, ensuring that all relevant businesses are included. Hal suggests that Google could potentially use A/B testing with different data sets to measure these metrics and make an informed decision on the vendor that provides the best data quality.

10:01

🔍 Measuring Data Quality Through User Interaction

Hal discusses how to measure the effectiveness of map data through user interaction metrics. He suggests monitoring how users engage with search results, including interaction rates, abandonments, and refinements. These metrics can indicate the relevance and usefulness of the data provided. Furthermore, Hal proposes tracking whether users actually visit the places they find on Google Maps, suggesting that this could be a strong indicator of the data's real-world value. He also considers the possibility of using user-generated content to fill gaps in the initial data set.

15:02

🤔 Balancing Precision and Recall in Data Selection

The script addresses the trade-off between precision and recall in selecting map data. Hal acknowledges that one data set might excel in precision while another might have better recall. He suggests that the decision on which to prioritize depends on Google Maps' ultimate goal or 'north star' metric. Hal also contemplates combining multiple data sets or starting with one and using user contributions to improve it, highlighting the importance of identifying and addressing gaps in the data.

20:03

💡 Reflecting on the Interview Process and Approach

In the final paragraph, Hal reflects on the mock interview process. He admits to jumping ahead at times and not clearly defining Google Maps' mission and goal at the outset. Hal believes that a more structured approach, starting with the mission and tying it back to decision-making later, would have been beneficial. The interviewer appreciates Hal's use-case driven strategy and his methodical dive into data quality, finding the interview valuable and indicating that Hal would be a good team member.

Keywords

💡Product Management

Product Management is the process of guiding a product from its inception to its launch and beyond. In the video, the interviewee, Hal, discusses the strategic decisions involved in product management, such as choosing the right map data vendor for Google Maps' launch in South Korea. This concept is central to the video's theme of decision-making in a product management scenario.

💡Google Maps

Google Maps is a widely used online mapping service developed by Google. The video's theme revolves around a hypothetical situation where Google Maps is considering launching in South Korea, and the interviewee must decide on the best local vendor for map data. Google Maps serves as the context for the discussion of product management strategies.

💡Vendor

In the context of the video, a vendor refers to a local provider of map data for Google Maps' South Korea launch. The interviewee discusses evaluating different vendors based on factors like data quality, cost, and integration feasibility, which is a critical part of the decision-making process in product management.

💡Data Quality

Data quality is a measure of how accurate, complete, and reliable the data is. In the video, Hal emphasizes the importance of data quality when choosing a vendor for Google Maps' map data in South Korea. It is a key factor because high-quality data leads to a better user experience.

💡Precision and Recall

Precision and recall are metrics used to evaluate the quality of search results. Precision refers to the accuracy of the results, while recall measures the completeness. In the script, Hal discusses using these metrics to assess the data quality provided by different vendors for the businesses and places listed in Google Maps.

💡A/B Testing

A/B testing is a method of comparing two versions (A and B) of a product or service to determine which performs better. In the video, Hal suggests using A/B testing to compare different map data sets from various vendors by giving different user groups access to each data set and measuring their interactions and satisfaction.

💡Integration

Integration in this context refers to the process of combining the chosen map data with Google Maps' existing infrastructure. Hal discusses the ease of integration as a non-functional aspect to consider, including licensing and the ability to receive ongoing updates from the vendor.

💡User Experience

User experience (UX) is the overall experience a user has while interacting with a product or service. The video's theme focuses on selecting the best map data to enhance the user experience on Google Maps in South Korea. Hal mentions that Google typically prioritizes providing the best experience to its users.

💡North Star Metric

A North Star Metric is a single, overarching metric that aligns a company's efforts towards its ultimate goal. In the video, Hal suggests that understanding Google Maps' North Star Metric would be crucial in making the right trade-offs between different data quality aspects and vendor choices.

💡Trade-offs

Trade-offs involve making decisions that involve balancing one desirable outcome against another. In the context of the video, Hal discusses the trade-offs between precision and recall in map data quality, as well as between urban and rural coverage, which must be considered when choosing a vendor.

💡User-Generated Content

User-generated content refers to various forms of content, such as text, images, or data, that users create and share. Hal mentions leveraging user-generated content to fill gaps in the map data provided by vendors, such as reporting incorrect opening hours, which can enhance the overall data quality.

Highlights

Google Maps is considering launching in South Korea and evaluating local vendors for map data sets.

Interviewee Hal, an engineering manager with previous experience as a PM at Google, discusses the strategic decision-making process for choosing map data vendors.

Three key factors identified for decision-making: data quality, business terms (cost and contract), and integration feasibility.

Data quality is emphasized as the most critical factor due to its direct impact on user experience.

The importance of precision and recall in evaluating map data for business listings is discussed.

Coverage of map data, including its reflection of reality, is considered crucial for data quality.

Google's infrastructure and capabilities are highlighted as potential solutions for integration challenges.

The role of user-generated content in enhancing map data through reporting inaccuracies is mentioned.

A/B testing different data sets with user populations to measure effectiveness is proposed.

Interaction metrics, such as search refinements and abandonments, are suggested as indicators of data relevance.

The concept of 'golden data' collected manually to verify and ensure data accuracy is introduced.

The potential use of user engagement with map results as a proxy for measuring data value is discussed.

Trade-offs between precision and recall in map data are considered, with a focus on Google Maps' ultimate goal.

The idea of merging multiple data sets or complementing initial data with user-generated content is explored.

The interview concludes with feedback on Hal's structured approach and use-case driven strategy in evaluating data quality.

The importance of aligning decision-making with Google Maps' mission and vision is emphasized in the debrief.

The mock interview serves as a valuable example for viewers preparing for product management interviews.

Transcripts

let's imagine that google maps wants to

launch in south korea and they're you're

choosing between several local vendors

and each one has a different map data

set tell me which one you would choose

[Music]

hey everyone welcome back to another

exponent product management mock

interview my name is kevin and on

today's show we have how we're going to

be doing a product management analytical

or strategy type question but before we

jump into that how can you just tell the

audience a little bit about who you are

hey everyone uh so i'm hal i'm currently

an engineering manager in a startup

called cressicore but before that i was

a pm and 8pm at google for five years

working on the google assistant

sweet thanks for your time today so this

is what i like to ask you let's imagine

that google maps wants to launch in

south korea and they're you're choosing

between several local vendors and each

one has a different map data set tell me

which one you would choose

okay so google maps wants to expand

basically in a new country and i'm

choosing between

[Music]

um different vendors for for maps data

yes

okay

um

and then how do i

how do i decide basically

okay so uh maybe just just to just to

clarify but so when we say maps data i

assume that's kind of like you know the

data that i would see in you know the

google maps app and uh so like when i

when i open google maps in south korea

like whatever i see there with the

streets and the

the businesses that that's what i'm

that's what we're referring to here

right

yeah we can start with this assumption

and then if we have time we can expand

upon this definition cool okay yeah

and

um is this specifically like um like

like you mentioned south korea is there

a specific reason why why

google maps is not in south korea yet or

like

why why are we why why are we expanding

into south korea now yeah this is just a

strategic decision and you're the pm so

now we want the uh a good data set to be

chosen for the the team

okay cool uh do you mind if i just take

like 10 seconds just to like kind of

construct my phone a little bit here no

feel free

okay so i would say i mean i think off

the bat i think i'm definitely seeing at

least kind of like three different

factors that we should consider here um

the first one being kind of like just

you know data quality in the sense of

like you know how good is the data

meaning like how accurate is the data

um are we are we missing anything that

like

when we talk about businesses and

streets for instance like how how

closely does that map to reality

basically play it

um then we have kind of like i was

calling the business side of things

right like

how much are we paying for that um

[Music]

what what does the kind of like

contract look like right this is kind of

like like are we committing for like 50

years uh this is a yearly renewal thing

um these kind of things

uh and then finally kind of like a

non-functional aspect of kind of like

how would the integration look like um

what about light licensing

can we expect kind of like you know

ongoing updates or is this kind of just

like a

revival data

one time and then we have to we have to

keep maintaining it so um so i think i

think

these are these are all kind of factors

to consider but um given given that we

are google in this scenario um

especially

google maps i think

i'm like i'm gonna make a bit of a

little bit of an assumption here but i

would imagine that like being google

like for instance the cost doesn't

really matter right like you know

google google typically really cares

about like just providing the best

experience to their users and i can't

imagine that they are gonna be very

price sensitive about you know

you know which vendor to pick i think

they always go with whoever provides the

best data um leading to the best user

experience here

similarly in this kind of like

non-functional thing like when we talk

about okay how easy is it to integrate

um

you know given that google is operating

at such a big scale like i think even if

there was a vendor that like you know

maybe provides the data in a slightly

different format than what the rest of

google maps does today

um i don't think that that would also be

a huge

kind of factor because google google can

probably just lift that thing and kind

of okay like convert it into whatever

format they needed and um and kind of

like provide it in a reliable way

one thing i didn't mention here for for

non-functional also reliability right

like um how like like if if we are

licensing this maps data

and um you're using it directly from the

vendor how reliable is it in terms of

uptime um etc but i think all these

things um google could just basically

provide that for the vendor instead

right like google google maps is pretty

reliable so i think they could just

switch all these infrastructure

um like all these factors regarding

infrastructure they could just switch it

over to google infrastructure um so i

think really the most important factor

here

would be data quality so i think in

terms of like you know choosing

choosing which which vendor we are going

with i think data quality should be the

single

biggest driver in terms of like you know

the value in that data because like i

said all the other factors i think

google can fairly easily or fairly

straightforward the

um you know solve these these problems

in in terms of big pillars that you're

looking at is this like some uh

unordered list that you just came up

with or is this a ranked list

um yeah i mean i i it started as an

unordered list um i think if if we were

to rank it by kind of like importance

and priorities like i said i think data

quality should be the biggest deciding

factor because that's gonna

um you know be the main driver for like

what the user experience in the end will

be

um and then i think the next one would

be this non-functional aspect but again

i think

um if that was something that a

particular vendor was very bad at i

think google could totally with the

infrastructure just just uh basically

um you know compromise on that so

um yeah and then you you mentioned that

there's the cost there so for the sake

of time let's just dive deeper into the

data quality here so can you tell me

more about what you would look for in

the data quality yeah yeah so um i think

what we should start with here is kind

of like

um just get crisp on like what the data

is actually going to be used for and

like what the what the user is expecting

as well and i think then we can talk

about okay what kind of what metrics and

what kind of characteristics about the

data we would be looking for um if that

makes sense to you so here i think the

one one key question to figure out

whether whether the data quality is good

or not is kind of like to think about

like what is this data used for right

um

so we um we kind of talked about

we kind of talked about it's kind of

like in google maps and um as in

as in kind of like

assumption here i'm just going to say i

think i think google maps kind of like

has has two major use cases maybe three

like a third one being smaller so the

first one i see is definitely kind of

like the typical

um i i need to go from a to b i need to

look up directions

um

whether that's driving or that's like

transit or like walking directions um i

just want to know how i get from a to b

right so

here i would just call that directions

and then i think the second one is kind

of like

um i'm looking for

for for for places for like businesses

like like i want to look up a restaurant

in my area and then like i'm kind of

looking for a list of all the places

that are better that are around me so

here

um i would say looking up

businesses

places or just like just like pois

basically

um

and then when i say looking our business

is probably also like information about

them right so like i'm i'm interested in

their opening hours i'm interested in

uh their phone number maybe because i

want to call them et cetera and then the

third one i was thinking about i don't i

would assume it's not a huge one but

it's something along the lines of like

more like fun things like you know i

think um especially google maps people

like to like oh this is my house and

this is my home address and they wanna

they wanna see that on google maps um

but i would say that's maybe that's

maybe more something for like

like street view and satellite imaging

which google would

uh you know

have have already or do on their own in

different ways so i think

it makes sense uh for us to focus on

these two use cases if you if you agree

yeah for sure um can you yeah feel free

to dive deeper into these if you can

sure yeah so um

let's let's start maybe with kind of

like um

the

the businesses part so i think here what

when we talk about data quality if i'm

looking at businesses i'm searching for

businesses um i would say there are a

few things that are important for us

right so i think off your bed i would

say

um

you you definitely have something along

the lines of like precision and recall

for the data right so like if i um

if i am looking for um a business so i

imagine i i'm in south korea i'm

searching for restaurants in my area

um precision in that case would be

you know the results that we are giving

you the places where we that we give you

based on the the vendor's maps data

are they are they actually restaurants

right like are we giving you the correct

answer

so um do we find or do we return the

right places

but then also um

recall here is kind of like more like a

coverage thing right like how like how

often are we able to actually give you

restaurants

right so i think i think here straight

away we kind of like already have two

metrics that we probably would want to

measure for all these different vendors

and compare against

that we that we want to maximize so

how often do we return

places or like results

um

let me think about other use cases but i

think

other high level things um

i mean i think i think coverage in

general is probably something that we

should um we should consider here as

well so um i mean just to take a step

back i think on a high level what we

want to do is basically some sort of

measurement between

how closely is

this maps data from this vendor

um

like how closely is that reflecting the

reality right like kind of like the

the ground truth um basically

um and then that's kind of what we want

to compare again so i think what what

what i'm trying to think think about now

is really

what is the best way to kind of like

measure that that difference between

this is the maps data and this is the

reality right

um

so here i already mentioned like

something like procession something like

recall

um uh kind of like helpful

um kind of starting points

um

i think

i think

let's just start with these maybe so um

if i wanted to figure out um

you know how how well is um

[Music]

how precise is my data in the sense that

like

um you know

all the places that i have in my ins

that i'm getting from my vendor are

these correct right so obviously are

these uh in the right location are we uh

our opening hours correct um uh uh you

know is the category of the place

correct like am i labeling a restaurant

as a bar and vice versa things like

these

how how could i measure this um or like

how could i find out

what the word difference here is

so i think

i think what we

what we could definitely do

is something where we could kind of like

just try to basically

um

almost run and run an ap test right so

like what i would imagine is kind of

like you could um i mean

on the implementation side it's maybe

maybe a lot of overhead but like just

conceptually what we could do is like we

take two different um you know data sets

from different vendors

and then um you know just just give

give a small percentage of users one one

set of data and another population

doesn't have a user population another

set of data but we can compare metrics

um so

i would say here we can basically do

like an

a b test

using

different

data sets

um

before i dive dive further into that i

think another higher level approach

could also be something like more

eval based or like human radar space so

what i'm imagining here could be

something like

um can we just kind of like

can can we just get get some golden data

where we know for whatever reason that

this is this this is reality right and

then we can kind of see okay like what

what does that data set say about this

place right so um here what i'm

imagining almost is kind of like maybe

we could have

operators or like human evolves um

just like gather some golden data so you

could basically go out there and say hey

like this business is here that's its

name these are the opening hours and

it's kind of manually um you collect

that golden data um

it's not very scalable but we we we just

need a bit kind of like statistically

significant

a sample size basically and then we

could go into the data set and then say

what does the data set say uh to tell us

about this particular place right does

it even exist and then

is the data is the data matching what we

collected um yeah thanks for flagging

that um approach but yeah let's continue

with the a

a b test here okay yeah so um let's say

we run an a b test right so i think here

the questions are really kind of like

what are the metrics that we would want

to um

um

like kind of monitor right

um

so i think um

i think the basically here um the way i

mentioned this av test is kind of like

users would would uh you know go to

google maps do do their thing and then

we we showed them that data set and like

i said in the beginning the the typical

things that people would do is kind of

looking at businesses right so here i

think uh like the it's pretty much like

a search quality problem right so like i

am looking for restaurants in my area

i'm getting a set of uh results for that

and now we just need to figure out how

relevant were these results right and i

think one way to do that is certainly

looking at interaction metrics so how

how often are people you know

interacting with the results that that

we are giving them

um how often are they

kind of like

abandoning that search and maybe try

something new like issuing another

search or like maybe doing like a

refinement to their to their query um

i feel like these are all indicators

that like the results that we gave to

them were not really

relevant or not really helpful right so

i think off the bat that's that these

are definitely things that i would i

would measure here so like interaction

rates

um as well as like abandonments

and um kind of refinements etc

so how would you uh understand if a user

really got value from the

their uh interaction that's that's a

really that's a really good question so

i think um

i mean if you think about it what the

user really wants is they want to find

relevant places um that that because

it's also in the real world that they

would want to visit as well right so um

i think here like i'm not sure what what

google maps kind of like success metric

is uh exactly but um i could imagine

that we would want something like

um did you actually go to that place

afterwards for instance right like you

could imagine that that might be

something like like a real conversion

basically right so like it's one thing

to like actually open a result and maybe

um

you know look at it and potentially

like interacting with it um but then

ultimately i think people use google

maps because they want to find places in

the real world that they go to right so

um it could definitely be a measurement

of like how often do you actually go to

the place that we showed you here right

um and then a proxy to that could be um

like in google maps how often do you

kind of ask for directions there um how

often do you call that place how often

do you like

like basically interact with it or

engage with with that place um so you

can you can probably also like save

places right as you can you can put a

star on them so i think these these kind

of engagements um could also be

uh be a proxy towards that but i think

ultimately

like i said maybe we can we can really

figure out okay

if you search for a place how often do

you actually go end up going to that

place afterwards yeah that makes sense

how often you go there or how often you

take a screenshot how often you share

share a link yeah something like that so

uh let's talk quickly about trade-offs

here so let's say that as you're

evaluating these different metrics one

set gives you really good precision but

then another set gives you really good

recall so how would you choose the

the trade-off here yeah that's that's a

really good question i think i think um

at the end of the day i think it comes

down to kind of like what uh what is

google maps um ultimate goal or like

success looks like like north star

metric basically

um

i think there will be definitely

trade-offs um especially like the

precision versus recall trade-off is a

very typical one i think what we didn't

talk about much is kind of like also

things like you know

um big cities versus rural right like

you might have data sets where like it's

it's really great in the big cities but

it's terrible and kind of rural areas

um

so in terms of like how i would make the

trade-off here i think really at the end

of the day um

i would imagine like you know google

maps has a nostalgic that we're trying

to optimize and um

like we would probably have to have to

make a call there and saying okay like

um this this is closer to the

um to the to the ultimate goal of google

maps and the success metric like

nostalgia metric of google maps um that

being said um

i wonder if there's something where we

can almost make a compromise of like you

know can we kind of merge the multiple

sets together or can we actually start

with one one vendor or like one set data

set and then kind of like add things

that will uh kind of fill the gaps

almost right so i think on google maps

is very typical that like for instance

users can actually report problems right

um so like um like opening up for hours

for instance like i i can go to google

maps and tell hey like peace opening

hours are wrong so um potentially

if we if we can identify those gaps we

can actually

complement like you know the data set

that we get from the vendor with like

more initiatives in this kind of like

user-generated content space um

or or you know any any other ways um how

we could fill those gaps but i think

just just even knowing where the gaps

are in the data set that we bought or

that we that we decided with i think

that's going to be super valuable makes

sense yeah figuring out what sort of

data is hard for user to generate what

sort of data is easy for user generate

picking the set that is hard for users

to generate and then allowing users to

generate the the data

yeah opening closing times um yeah great

yeah some sort of research study that

would be helpful

cool uh thanks hal for your your time i

think this wraps up our interview for

today and i i have some feedback here

that i'll i'll share real quick and also

love for you to tell me how you might

answer the question differently if you

got the question again

yeah that's that's uh i think i think in

hindsight it's always easier right um so

i think definitely what i what i noticed

as i was going through it um sometimes i

was just kind of like jumping ahead a

little bit you know like um with

precision recall thing it's just like i

think it's something that you are

trained off and like you want to just

say it right but then it wasn't really

the right time to say it i feel like i

would have been more structured if i

uh you know really started with this

kind of like okay on a high level what

do we want to figure out right like what

we really want to figure out is the

difference of reality to that to that

data set so in in this abstract way

um really like even just

even just to have have communicated that

this is how i feel think about it would

have been helpful

um

as well as like i think in the end we

kind of ran more towards okay actually

it would be good to know what the what

the north star metric or like what the

goal of google maps is what that kind of

mission is right um so i think that that

might have been also helpful to spend

some time

in the beginning i feel like i feel like

i kind of touched on that a little bit

in the beginning where i said okay let's

think about what this data is going to

be used for i mean i think in that part

might have been useful to say

what is the what is the goal of google

maps what is the vision and then also

then being able to later tie that back

to

um what i said earlier right i think i

think now in hindsight i feel like it

was it was not very

um

like like there was not really a kind of

like way to wrap it up right like you

didn't close a circle of like hey like

in in the beginning i talked about this

and that's why that's why um that's why

i'm making this decision here um because

remember in the beginning i said this is

the goal this is the vision i feel like

that was definitely missing

yeah for sure i think definitely um

refining the mission and the goal in the

beginning would help um but overall this

was a in my opinion a great mock

interview because i was able to at least

get the signals that you'd be a good

person to work with if you were on my

team and just zooming out and thinking

about your approach to this i really

liked how you were really use case

driven when you were diving into the

data quality about looking at what you

would look for in data quality and you

the structure you had was kind of like

breadth and then depth so you had this

breadth of outlining things that you

would consider and then you did a really

good job diving deep and drilling down

into uh certain parts of the data

quality factor that you wanted to

investigate

so overall really enjoyed this mock

interview with you hal thanks for your

time and for the viewers i hope you got

some value from this video and good luck

with your upcoming pm interview

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