Two months ago I shipped a tiny AI tool that
looks at a CSV file and asks better questions
than I can on day two at a new job. It went way
further than I had expected and it showed me
what I had missed. So I rebuilt it. Today I'll
share what worked, what didn't and I'll give
you a short quiz, a discussion prompt and a checklist
that you can use. Hey, I'm Mukundan. This show
is about solving real problems with small, useful
AI. Things that you can actually use today. Each
week, we pick one problem, build a simple workflow
or tool, and talk through the decisions about
what to automate, how to check quality, and how
to make it reliable. If you're a builder, creator,
or just AI curious, you'll leave with the steps
that you can copy tonight. Hit follow and start
with the Startup Pack episodes. in the show notes.
Hey everyone, welcome back to the Data and AI
with Mukundan show. Today, we'll talk about how
I built an AI tool that helped look at CSV files
and Excel files and generate questions from it
and how I took it to the next level. This is
about that story. You join a new team. Here's
why this exists. I call it the day two problem.
You join a new team in a company and maybe let's
just say, or even you join a new company and
someone will send you this data set without any
context and no goals about what you want to achieve
with this data. It's just, just tell you with
pressure, right? Just pressure. That's all it
is. And version one of my tool, when I first
did this, you know, this project before. It helped
me by generating 10 smart exploratory data analysis
questions like the EDA questions, each with a
short why this matters. And it gave me like a
plan instead of me panicking about it, right?
So I built this tool before. I called this version
one, right? And what happened was I... created
this like exploratory data analysis tool what
basically it would take you take your csv file
and then it would generate questions from it
give you like 10 smart questions to ask ask this
data set right that will help you really to understand
the data and it will also give you like a why
this matters why this question matters statement
as well so it gives you like a plan right so
to speak like it give you like what what to do
gives you a plan instead of like you know you're
panicking about it um so when i did this when
i did the first version of this it went viral
a lot of people were able to resonate with it
uh some even you know some reacted to the blog
post i'd written it was a blog post with code
and i'll link to the blog post as well So I'll
tell you what worked, why it went viral and what
was missing in this version one. So people liked
three things. They liked that there was a fast
grip, right? I mean, you would be able to get
really useful questions from the data in under
a minute. And that's really quick, right? Like,
I mean, otherwise we're just spending hours and
hours, maybe days and days, just looking at the
data set without knowing what to answer. And
especially when you're looking at it fresh, you
don't know what's going on and you don't know
what question to ask. We don't know how to answer
that because A, you don't know what question
to ask really, right? You don't want to be doing
count distinct, right? It's a SQL query if you're
not familiar with it. Count distinct will basically...
in a way just count the unique records or something
right of whatever is the number of whatever is
the table you're looking at so let's say you
want to look at the number of you know let's
say you want to look at the number of cars and
you would do that by let's say car id right you
have something called as a car id field so you
would be using count distinct to see how many
you know unique cars there are in this showroom
for example right so that was something that
will not really help but we want to be asking
some more useful questions and I'll talk about
what questions those are and everything as we
go down but I just wanted to give you some background
about this so the other thing people liked was
like it acted like a partner gave you a vibe
of a partner where it nudges but not doesn't
doesn't give you like a dashboard dump right
it's not like dumping everything on you it's
just like being your partner right you support
your partner and so it's it's basically giving
you those questions and then once you have those
questions all you have to do is just uh you know
use your analytical skills to answer those questions
and there was also a respect for time that's
the third thing people liked because it was short
clear and it was human so to recap people like
three things because it was a fast b acted like
your partner and three respected your time So
I feel like I still missed three key pieces.
That's what people said. I mean, that's what
I believed it as well. I hadn't mentioned what
was the business objective up front and why am
I doing this exercise? Why am I doing this little
project, this AI tool? Why did I build that?
I didn't really mention any objective up front.
And I also did not, focus on any columns right
i treated basically every feature the same and
everyone knows that if you treat every column
in your table as the same how is that going to
you know really help you right um and i also
wanted to give an option to export the data set
or export the you know questions and everything
so that was missing as well so there's no export
feature to available no export feature which
was available to drop it into slack or even into
notion or your jira board right so that was uh
something i felt was missing so i wanted to take
that to the next level here were some lessons
that helped me rebuild this so i wanted to first
put the decision in the room first spend attention
like money attention in this case would be time
and time is money so focus on you know what i
wanted to do um what problem i wanted to solve
and like a little one page brief so that the
work actually moves here's how the rebuild actually
took place the three simple features now before
anything else the tool asks what outcome are
you trying to move so this is the objective input
the y box right so let's talk about that so just
to ask yourself like what outcome are you trying
to move so example is 30 do you want to example
as you want to reduce 30 day churn for monthly
plan users by 15 an optional mode would be risk
opportunity anomaly or quick wins to shape the
tone result questions became specific and useful
and then i wanted to also do a column focus the
second simple feature one was the objective input
so what outcome i'm trying to move right and
the second one more on the columns and this is
specifically how much columns i want five to
eight columns only is what i chose and up to
eight columns or except smart hints right ask
for that so you should want to be able to look
at the data quickly if you look at 30 columns
together like you want to pick out every feature
how is that going to help so that is not a time
saving tool or maybe it saves a little bit but
not as much as you would want right so the column
focus where we picked up to eight columns or
accept any smart hints and the tool concentrates
its thinking budget on what matters and warns
about any leakage or messy labels So that was
number two. And then number three was markdown
export. So ship the plan. So one click and you
get a clean brief. So the objective in your own
words, you'll mention top 10 questions and why
this matters. Those three that was already there.
And data health checks, quick analysis to run
risks and next decisions. And also paste it into
a Slack, Notion and Jira and go. right so before
anything else like i mentioned like these were
the three rebuild features simple features that
i've worked on the objective input where we're
trying to uh where you're trying to figure out
what outcome you're trying to move the column
focus where you're picking up to eight columns
or accepting any smart hints the markdown export
right where you're able to export the data set
and export the the questions really like objective
the 10 questions why this matters the data health
checks quick analysis to run and risks and any
next decisions all right so here's here's a fun
fun session for you lightning quiz that i have
for you here's a quick quiz that i just prepared
and i'll read the answers right after i'll give
you some time to think if you're if you're listening
um so that like you know um i don't want to directly
prompt the answers and maybe just give you a
few seconds to think. First, I'll just give you
the questions and then you can think about it
and then, you know, obviously give me the answers.
Well, I mean, the answers I will be giving you,
but you can also come up with your own. You can
also send me on chat. Just so that I know you're
listening and you're paying attention. So yeah,
just quick quiz. I'll read the answers right
after. So pause if you need more time. Question
one, true or false? Start exploratory data analysis
or EDA by uploading the data and exploring the
charts, right? So I'll just repeat this again.
True or false? Start EDA by uploading data and
exploring charts. So it's telling you that as
soon as you upload the data, and explores the
charts, you need to start EDA. Question 2. You
have 50 columns. What do you focus on? 5 to 8,
that's option A. Option B, 20 to 25. Option C,
all 50. What comes first? Writing a business
objective, option A. Cleaning every column, option
B. And option C, running 20 plots. Question 4.
If you were to ask, if you were asked, to predict
the 30 -day churn and you're asked what was the
leakage here would it be 10 -yard days on day
one would it be churned in 30 days or would it
be region so just to like reiterate leakage means
really the target variable right if you're asked
to be predicting the 30 -day churn what would
be the target variable would it be the 10 -yard
days on day one would it be churned in 30 days
or would it be So there's three options, right?
I'll just repeat the options again just so that
you guys are clear. Option A, 10 -year days on
day one. Option B, churned in 30 days. And option
C, region. And finally, question five. So just
to reiterate, what is the best artifact after
an exploratory data analysis? Is it option A,
dashboard gallery? Is it option B, a one -page
markdown brief? Or is it option C, a raw notebook?
Question 1, true or false? Start ADA. Would you
start ADA by uploading the data and exploring
charts? Question 2, when you have 50 columns,
what would you focus on? 5 to 8 columns, option
A. Option B, 20 to 25 columns. Or option C, all
50 columns. Question 3, what comes first? A,
writing a business objective. B, cleaning every
column. Or C, running 20 plots. And question
five, what would be the best artifact to give
after an EDA? Would it be a dashboard gallery?
Would it be a one -page markdown brief? As option
B is one -page markdown and option A is dashboard
gallery. And option C, would it be a raw notebook?
here we go with the answers so the answer one
to true or false it's false so you would first
start with the decision right you wouldn't directly
just do eda that's what it means really upload
data and to ed that doesn't it's not how it works
you need to be understanding the business question
and everything so don't directly jump into exploring
charts um answer two is five to eight columns
so i asked what what does the you know how many
columns would you choose like if you have 50
columns which would you focus on the answer is
five to eight columns you don't want to be overburdening
yourself right so that's something you would
tell the ai to do as well answer three what comes
first between writing a business objective cleaning
every column or running 20 plots the objective
comes first so the options were writing a business
objective cleaning every column or running 20
plots. So objective, writing a business objective
comes first. Question four was predicting 30
-day churn. What was the output variable? Was
it 10 -year days on day one? Was it churned in
30 days or was it region? The answer is churned
in 30 days because that is the target variable.
Question five, what was the best artifact after
an exploratory data analysis? Is it a dashboard
gallery? one page markdown brief or raw notebook
answer is one page brief so those were the answers
so again just to reiterate answer one was false
so you don't directly start eda you first start
with the the business question really what you
decided to do so that is one answer two was if
you have 50 columns you focus on five to eight
columns first so question three what comes first
between writing a business objective, cleaning
every column and running 20 plots, the objective
comes first. And answer four, when you're trying
to predict the 30 -day churn, what is the output
variable? Is it tenure days on day one, churned
in 30 days or region? The answer is churned in
30 days. And answer five, best artifact after
EDA? A dashboard, gallery, one -page markdown
brief, or raw notebook? The answer is a one -page
brief is the best artifact after an EDA. So that
was the quiz. If you scored a 5 on 5, you're
ready. You're ready to be the expert data analyst
yourself. If you scored between 3, 2, 4, and
4 or 5, just use the checklist next. And if you
scored between 0 to 2 out of 5, three to four
if you scored then just use the checklist and
zero to two if you scored um don't worry this
checklist will help a lot all right so discussion
time i want to just like do a little discussion
with you and this is this is a discussion that
will be held in my sub stack so basically sub
stack is uh if you're not familiar with sub stack
is it's one of the great content creating websites
um it gives content creators a platform to be
heard uh through primarily i would say their
written content yeah sure you get like a lot
of audio and video uh podcast hosts or whatever
but i think primarily what i've seen what works
a lot on substack is written text so that's a
good platform for that a lot of creators uh are
getting their voices heard on substack but anyway
I wanted to also invite a discussion on Substack.
So here's what I want to discuss about. I want
to hear your story and your process. Let's say
you joined that new company, your new team on
day two or day three, maybe week one for that
matter. You were asked to look at dataset. And,
you know, they just asked you like, you know,
tell me. this is a messy data what will you come
up with that is a lot of stress um because you're
trying to make a new you know an an impression
on this team that you want to be like on top
right like you want to make a great impression
you want to feel that they made a good choice
in hiring you uh for their team so that's why
i say day two data set horror story so yeah just
I want to hear your story in this. How do you
handle a situation like this? The other thing
I wanted to share, I wanted to discuss is what
is one metric that you would move in 30 days,
in your first 30 days? And why? Which metric
you would move and why? And the third thing,
the five to eight columns that you would start
with right now. So let's just say you're looking
at maybe... a churn data set what would be five
to eight columns that you'd want to look at right
now anything what is your day two data set horror
story what is one metric that you would move
in your first 30 days and also in 30 days in
general and why would you move that and third
what are the five to eight columns that you would
start with right now and an example here Could
be anything that you would want to talk about
your organization or anything that you, you know,
are passionate about with this five to eight
columns. I would, you know, definitely appreciate
like if you would also add a comment in this
discussion with an okay to read, like a comment
in quotes saying okay to read. If I can feature
your reply the next episode or even anonymous
if you prefer, right? I read every comment. So
join the discussion and I'll link the discussion
in the show notes. So that was a discussion that
I wanted to have. Now I wanted to give a quick
walkthrough of how an app would work like this.
So let's just say that you're talking about a
subscription app. That's the scenario. The objective
type is reduce the 30 -day churn for monthly
planned users by 15%. The focus columns, the
columns you want to focus on would be planned
type. a signup channel, 10 -year days, region,
device, discount applied, and support tickets,
which is number of support tickets and first
week activity rate. So here are some of the questions
the tool generates with and why. But let me just
go over the columns again. And it may be the
scenario. So the scenario is let's say you want
to build a subscription app, right? And this
could be... let's say you're working for a subscription
-based company and their primary revenue is coming
from subscriptions. For example, you can take
your Netflix or whatever, they're subscription
-based, and your objective for them is you want
to reduce 30 -day churn for them for monthly
plan users by 15%, right? So that would be an
objective. the columns you want to focus on is
plan type sign up channel how did they sign up
on really like what is the plan type is it a
basic plan or whatever tenure days how many days
they've been a subscriber um which region where
in the world are they from which device are they
sign up on or which device are they using so
to to watch the shows in this case um and if
they had any discount applied so where i in the
united states at least i don't think netflix
has any discount anymore um but that being said
like you know it could be there anywhere else
in the world um so discount applied is another
column here and support tickets is number of
support tickets um that may be this this user
has created this user has requested right or
in the first week activity rate like They started
using it and how much time they've been watching
it in the first week. So this is, I'm just giving
Netflix as an example, but whatever, right? Could
be any kind of subscription app. The objective
really is to reduce the 30 -day churn for monthly
plan users by about 15%. So now let's look at
some of the questions that the tool has generated
with the why. The questions it came up with was,
are churn rates higher for paid social versus
organic search? And the why is because it's a
customer acquisition cost or a CAC payback risk,
right? Because organic search, you're not really
losing any money. Paid social, you're paying
for that, the visibility. So if a consumer who's
joined in through organic search and you're losing
that customer, quick versus you're losing that
customer who joined in through paid social so
that's a problem like i mean if you're losing
more on paid social versus on organic search
then um your paid social strategy is clearly
not working so that's going to be a big big uh
problem on your customer acquisition cost will
just rise higher right um no i mean customer
acquisition cost is one thing but i think what
what you're gonna face especially here in this
case that i'm thinking about it is well you spend
this money now you want to make money from this
customer so like a payback so to speak so that
would affect the payback that you get from the
customer because well their return is low that
is one question the second question is do sign
up discounts lead to lower first week activity
and a higher churn the why is a habit formation
so let's think about this question right so let's
just say you joined this streaming uh company
does the sign up discount that you got lead to
a lower first week activity and higher churn
so if they received a discount while joining
did they get um did they churn more So if they
got 40 % discount versus maybe 0 % discount,
did the 40 % discount person churn more than
the 0 % discount? Probably because the person
who's paid more wants to stay and try to make
his money's worth, right? Or yeah, and obviously
they probably engage with the app as well less.
I think usually that kind of behavior I engage
with, right? If I pay more, I'm inclined to stay
more because, well, I want to get my money's
worth. um so that is more of a habit formation
thing that's what the ai comes up with third
question do week two escalations predict churn
so this customer has been there on in week two
they try to do a lot of support ticket escalations
does that help predict churn so they create a
lot of support tickets and if those tickets have
been escalated does that mean that they are going
to churn Why? Because the early customers, they
are early experience pain. So that they want
to try to avoid. So that was the third question.
Now the fourth question is any region and device
combos with short tenure? So you want to see
if maybe people from Northeast United States,
for example, are they using mobile more but they
also stay on the platform less they use the streaming
device very less right i mean the streaming subscription
very less so that's something you'd want to see
so why the why behind this is it is a user interface
parity or localization right is it getting skewed
in one area or not is is uh the geographic area
playing a part that's what you want to see here
and uh so another question it came up with was
is the churn non -linear versus the first week
activity so let's just say you know you're trying
to churn right is it you know at all proportional
with how much you're interacting in the first
week so is the first week activity depending
on i mean is the first week activity affecting
churn like the more you interact meaning the
less you churn or something like that right or
is it not related as much not related alone with
the first week activity so maybe it's related
but like not in a linear way you know this will
help you uh the y here is like an activation
threshold so you can obviously simplify this
language and everything but that's how uh the
kind of questions the tool will help you generate
and it'll give you some data health notes also
give you negative 10 -year days, inconsistent
region labels, and 12 % missing data, missing
discount applied. So this is more of a data health
check for, maybe you can think of it like data
consistency or, you know, you want to have some
consistent data. So if you see some 10 -year
days with a negative, it means something is up,
right? You wouldn't expect these to be negative.
Or the region labels. Where the person is from,
you would not want to see different spellings,
for example, different labels. So that is affecting
something. And the other thing is discount applied.
If there's any missing data or something, that
is what it is telling you. And then finally,
an export of one page brief into paste into Slack
and align the team. So these are the three steps
for that export. So here are some choices. the
product or engineering can make put the objective
first focus second and then the questions come
in so business objective goes first what do you
want to focus on columns wise and stuff and then
what questions you want to ask then pre -compute
small statistics so that the model reasons more
and you know rummage is less what does rummage
mean really it means that it's not it's not going
systematically, right? So you want the model
to go systematically. So you want it to reason
more. So small statistic needs to be computed,
but also monitor these guardrails. Guardrails
are like, you know, one thing's going up, you
don't want other things to go down and stuff.
So guardrail is one thing's going up, you don't
want other things going down. So guardrail is
like, you matrix like churn, bounce rate. So
any kind of, uh fly any kind of leakage is there
or uh data leakage or messy labels are there
those should be you know addressed keep the copy
simple and reduce any blank box anxiety with
examples so what does this mean people freeze
basically when they uh let's look at what does
keep copy simple mean so when you're writing
the text the users see that the you know which
includes labels instructions button or button
text don't get any don't get clever or verbose
the instead of articulate your analytical objective
here just say what's your goal what do you want
to find out right just simple words less thinking
and it's a smoother flow so that's simple uh
copy and blank box anxiety means like people
freeze when they see a blank input field with
no guidance so example here would be if the tool
asks what's your objective and leaves a giant
empty box a user might not know what to type
this is called blank box anxiety the stress basically
you're starting from nothing and with examples
here means so you remove that anxiety by showing
an example or two right in the box or next to
it yeah some example placeholders example it
would be explain the dip in the week of 7 .06
to 7 .12 right that was a week or explain like
a march sales dip or something that or it could
be fine churn drivers for monthly plan now basically
the user sees how how to phrase their input and
they can just copy the style to summarize it
basically means use short and plain words in
your ui copy And whenever you ask users to type
something into a box, show them an example so
that they don't freeze up. Always prompt them
so they're not left guessing. And to now go over
guardrails, what it means is like when you're
building or using an AI EDA helper, like an exploratory
data analysis helper for AI, you don't want it
to silently accept everything. You want it to
flag potential issues that it sees in a data
set, but in a friendly guiding way. but not in
a scary error message. Here are some two common
issues, right? One is on the leakage side. That
could mean that the data that cheats by including
information, you wouldn't realistically know
at the prediction time. So example here is, let's
say you're trying to predict churn at a signup,
right? But your dataset includes a column called
churn in 30 days. That's leakage. It gives away
the answer. the guardrail is a to the tool that
the tool can warn um with the warning heads up
column churned in 30 days looks like an outcome
variable using it might cause leakage so that's
what is a leakage right you don't want um your
output variable to be essentially included in
the input which is called leakage which would
mean your dataset is overtrained or overfitted
in machine learning terms. Now, messy labels
is the other thing which happens. Definition
here would be, let's just say your categorical
values that are inconsistent, redundant, or even
unclear. So example, let's take a region column
with values of US, US. let's say you have a region
column with values us and then another one with
u .s. and another one with usa and another one
with north america and all are mixed together
right the tool can say as a guardrail notice
region has overlapping categories us u .s. and
usa you may want to clean this and it will give
you like friendly tips right like that so if
the tool says Error invalidator, that's a very
vague message and users can freeze about it.
They'll be like, what does that invalidator mean,
right? Users can just give helpful tips, be helpful,
right? Or if it says quick tip, you might want
to check X. Users might feel supported instead
of judged. You might want to check column X or
value X in the column or whatever. Quick tip.
That's a very nice way to say it. basically the
tone matters small emojis or short wording let
your ai be your friend so it should be no jargon
really so what this essentially means is like
you know you're building soft card rails into
the tool that gently warns users about common
pitfalls like leakage or messy category labels
without breaking their flow now i just wanted
to again reiterate about this other point about
pre -computing small stats so the model reasons
more and rummages less. So this one's about helping
the AI spend its brain power wisely. So let's
break it down. So the problem is when you don't
pre -compute, if you just throw the data set,
raw data set into a model and say, ask 10 smart
EDA questions, the model has to pass every column
from scratch. It may miss obvious things like
columns which are numeric versus categorical
and it wastes tokens and time. ai tokens and
obviously time to rummage the data so to speak
through the raw csv it sometimes gives generic
or irrelevant questions and rummage just means
like it goes about in a messy way very generic
or relevant questions so you don't want that
to happen it's like asking a human analyst to
read a 500 page log file line by line instead
of giving them a one page summary first so give
some context context is very important essentially
right one page summary is essentially a context
And I'll talk about context engineering in another
episode, but brief thing is just like to give
context, right? If you want somebody to do the
job correctly, help them understand what they're
trying to do. Here's a simple fix to pre -compute
small stats. Before asking your model to generate
questions, you or your app should run a quick
Pandas summary. For each column, compute things
like type numeric. categorical text date, count
of unique values, percent missing values. So
get a count of unique values, see how much percent
of missing values are there, what are the data
types are there, what is the mean, median, and
standard deviation for the numeric columns. You
can use a lot of Pandas functions available.
So I think describe or something does it. And
info will give you the type. Or a Pandas profiling
report also. is another library which will give
you all these answers and in this and and there's
a couple of other ones like top three most common
values for categoricals or a min max date for
time columns so instead of dumping the full csv
you give the model this compact cheat sheet and
i'll link uh and i'll give you that in the show
notes as well why it helps is because basically
the model can reason about the data and plan
type it would be like hmm plant type only has
three categories maybe i can compare churn across
them so this kind of context will enable the
model to think smarter right it'll say like oh
i only have three categories in the plant type
maybe i can compare churn so it doesn't really
waste tokens or hallucinate right ai models are
known to hallucinate a lot So you don't want
to waste your AI tokens there. Because AI tokens
is obviously very expensive. So just use it wisely.
Let it come up with smarter questions. Let it
say, maybe this is a text column. Basically what
happens in this case is output gets sharper,
faster and cheaper. So essentially this whole
thing means like you'd want to do a lightweight
summary of the data set first. Then pass that
summary to AI. That way the AI spends more energy
on reasoning. and less on digging through ROROs.
So this is essentially like a message for product
and engineering. And finally, one last message
for product and engineering is nothing leaves
your session unless you opt in. So what does
that mean? Here's the concern, right? It's a
trust signal line. Here's what it means in plain
words. The concern is basically when people upload
a dataset into your AI and EDA tool, they immediately
wonder, Is this being stored somewhere? Is my
client's data safe? Will this end up draining
someone's model? That's a very valid concern.
So you need to make sure that you are not opted
in if you don't want to share your data. So there's
a way to check that in different tools. In chat,
GPT is pretty easy to do that. People have very
valid reasons to have this kind of privacy concerns,
what we call as a privacy anxiety. You don't
want the data to be used by other people because
maybe it's confidential. You don't want to end
up trading someone's model, obviously. Is this
being stored somewhere? So that are very valid
concerns, right? So just make sure you're not
opted in. Here's something you can watch out
for is you can calm that by setting like a clear
privacy boundary. By default, all your processing
happens locally or within the active session.
But when the user closes the tab, the data is
gone. Nothing is logged, saved or even reused
unless the user chooses to export, save or share
that. Here's how I would frame the copy. So in
app or in notes, this is how it would go. So
your file is processed only inside your session.
Nothing leaves or nothing is stored. This is
a more privacy -first approach, where if you
want to save or share your results, that's only
opting in. So to summarize, here's what my message
to product and engineering is. I'll start with
the last one first. Your tool won't quietly send
data to your servers, store logs or retrain models
with their data. Users will stay in control.
Because sharing is a choice and it's not the
default. Building soft guardrails into the tool
that gently warn users about common pitfalls
like leakage or messy category labels without
breaking their flow. So build those guardrails
into the tool that warns users about pitfalls
like leakage or messy category labels. Be supportive
about it as well. Next point. Use short and plain
words in your user interface copy. And whenever
you ask users to type something into a box, show
them an example of how to do it so that they
don't freeze up. We want to make everything easy.
And finally, put your objective first, focus
second, and then question. That's really the
first point here. Objective first, focus second,
and questions last. Here's the numbers I track
which are, you know, more useful than cool. percent
of sessions with an objective like 80 percent
greater than 80 percent of the target variable
right percent of useful questions time to brief
like ideally under three minutes seven day return
so more on the habit side like the user activity
side and the real world use is this is how i
usually try to work with data set i prefer using
churn also as like a Churn rate over a period
of 30 days. That's my useful metric. That's why
I kept talking about it. Retention over a three
-month window, a six -month window, a yearly
window. They're very common metrics to look at,
but these are very useful also. Better than your
vanity metrics like followers or subscribers.
That's very vanity metrics. Number of users,
for example. Those are not going to help. user
growth is what really helps. And finally, let's
come to the listener checklist. I'll be also
adding this in the show notes, but here's what
I just want to read out first. This is a follow
-up to the quiz you listened to earlier. So let's
go with this checklist here. This is the third
key item I wanted to discuss. First, you write
the objective in one line. Like example would
be reduce x by y in z time frame for whatever
the segment is um pick five to eight focus columns
or accept the tools hints generate 10 questions
plus one line of why it matters so this is like
a prompt you are using essentially four run quick
data health checks nulls odd values naming export
a one page markdown brief number five number
six share it in slack notion jira and tag owners
number seven Run two to three quick analysis
today, not 10. Log what you learned, plus the
next decision. Nine, repeat tomorrow with tighter
focus. And tell me which step saved you the most
time in the sub stack comments. So I'll list
this checklist in the show notes so you can have
it. All you need to do is just don't overthink
it. Just run through these nine steps and you'll
move faster than 90 % of people who dropped into
a data set. right i'd love to hear from you which
step actually saved you the most time so again
just drop your answer in the comment section
i'll link the thread for you in substack and
i read them all like i said i'll share a few
next week if you'd want so we can all learn from
each other so what to steal from this show and
maybe think of it like an action plan for you
points start every analysis with the decision
limiting yourself to five to eight columns Always
ship a one -page brief. Track usefulness and
not vanity metrics. And finally, make the first
90 seconds feel like progress. All right? Before
you go, I just wanted to let you know that I
record with Riverside. I'm an affiliate partner
of Riverside. They helped me record this episode.
And they have amazing audio quality. So that's
something I just wanted to recommend if you're
ever, you know, deciding to do your own podcast.
This is a great platform to record because their
editing features are amazing. Their AI editing
is, I think, next level. Helps you remove all
awkward pauses and gives your sound a strong
voice. It gives you a strong voice. So I would
highly recommend Riverside as the reason why
I chose to... be an affiliate partner because
I actually use the product and I love it. So
link to join their platform is in the show notes.
And again, I'm an affiliate partner. So if you
make any purchase, I may make a small commission
from it. I also host an rss .com. Also, I'm an
affiliate partner of RSS because they have an
amazing distribution channel. They are able to
publish your episodes from RSS into Spotify,
Apple Podcasts, Amazon Music, Audible, Deezer,
Pandora, iHeartRadio, and even lets you publish
your episode with one button into YouTube as
well. Oh, did I forget to mention that you can
start making money by hosting on RSS .com even
if you have just 10 downloads a month. So that's
all you need to make money. 10 downloads a month.
It's a new feature they started and it's amazing.
So again, link to joinrss .com will be in the
show notes. I'm also using Cider .ai to help
with my research. So if you are interested in
any research for your content or for in general,
you want to learn about anything, want to read
research papers or any kind of thing to improve
your knowledge, cider .ai is your platform to
use. So again, I'm an affiliate partner and I'd
love if you're able to support the show by clicking
on my affiliate link and joining cider .ai. So
that's all I have, but take the quiz, drop your
story in the sub stack comments, grab the checklist,
share this with your friend. With that one friend
who's drowning in CSVs or Excels. Show notes
have everything. Substack discussion link is
also included. And a thread is also there for
you to join on Substack. So see you next week.
Thanks again. And this is Mukundan from the Data
and AI with Mukundan show. Hey, it's Mukundan.
If this episode helped you, two tiny favors that
make a huge difference. Rate the show five stars.
On Spotify, you can just open the show page and
tap the star button. On Apple Podcasts, scroll
to the bottom of the show page and tap five stars.
Also, leave a one -to -line review on Apple Podcasts.
Tell me one takeaway or which checklist step
saved you time. I read every single one and it
helps more people find the show. Thank you. Your
rating and review genuinely help this reach the
next analyst who's staring at a messy CSV. See
you next week.