OK, so data analysis usually starts messy, right?
You've got this mountain of raw data. Yeah, the
cleaning, the charting, all that tedious checking.
We know the drill. It just eats up hours, hours
of your life. And then there's AI promising this
like instant insight shortcut. But it's tricky
if you just kind of toss a random spreadsheet
at a large language model. You usually get fast
garbage back, don't you? The real power isn't
just the tool. No. It's having the right map,
the right approach. That's the actual shortcut.
Absolutely. So we've gathered this deep stack
of guides and tutorials for you, the listener.
Our mission today is really to boil this down
to two key frameworks. These are the things that
turn that boring data into, well, useful, actionable
insights. We're basically simplifying data science
here. So first up, you get achieve. Right. Achieve
tells you when AI is actually the best tool for
your specific job. And second, there's D. That
gives you the reliable step -by -step process,
how to do the analysis correctly every single
time. And you don't need a PhD in computer science
to get this. Let's dive deep into the sources.
So achieve. Think of AI as this incredibly fast.
Very obedient research assistant. Yeah, super
smart, super fast, but it needs crystal clear
instructions. Achieve lays out five situations
where AI really transforms your data work. Starting
with A, aiding human coordination. Ah, yes. We
humans, we can be messy collaborators, can't
we? We really can. AI helps clean up that noise.
So imagine uploading, say, a dense 30 -minute
meeting transcript. OK. Instead of listening
back or reading it all. Exactly. You just ask
the AI, summarize the decisions, list all the
action items, and this is key assign responsibility
and deadlines. Wow. Instant structure from just
conversation chaos. Pretty much. Or a classic
business headache. Comparing suppliers. Oh, yeah,
you've got maybe five different vendor emails
food sound whatever for an event, right? You
feed them all in ask for a simple table name
service price available date boom instant clarity
that saves what an hour of digging through emails
and attachments easily So next is C cutting out
tedious tasks the repetitive stuff the soul crushing
work That's where AI just shines data cleaning
is probably the biggest win here. Oh, definitely.
We've all seen that column, right? You should
just say But you get sales team, sales, maybe
with a capital S, maybe someone typed the department
name and Vietnamese. Like, can Don adept? Yeah.
AI just standardizes all of it, instantly turns
it all into just sales. That's huge. And it handles
basic data checks too, right? Like upload a CSV,
say 50 workshop signups. Yep. Instantly find
the number of columns. Or, find the top three
most popular topics from an interests column.
Stuff that used to mean fiddling with filters
for ages. Exactly. Okay, so AI gives speed, but
we need a safety net. Which brings us to H. Help
provide a safety net. Because humans make silly
errors. Especially with detailed stuff like compliance
or policy checks. You know, I have to admit,
I still get tangled up in confusing policy rules
sometimes. Or even just prompt definitions. It
happens. We all do. It's easy to miss things.
So let's take an expense report. Say I submit
a $160 dinner receipt. Three people and oh it
includes wine. Okay, so you upload that receipt
and the company is like 50 page expense policy
PDF right and the policy says maybe fifty dollars
per person max and Absolutely, no alcohol reimbursement.
That's a perfect test. Now. What if that policy
PDF is like Enormous. 100 pages. Does the AI
really read it all? Or does it just skim the
start and miss some crucial detail buried on
page 73? That's where its large context understanding
is so powerful. It doesn't just skim. It synthesizes
across the whole document. You ask it, check
for violations. It flags that $160 meal instantly.
It says, hold on. That's over the $150 limit
for three people. And there's wine. It's a perfect
second checker. The compliance safety net. Exactly.
OK, moving to I. Inspire better creativity. This
is about challenging our own assumptions, breaking
habits. Yeah. So upload your 10 -slide pitch
deck, or a big presentation, and ask the AI to
act like a very tough, skeptical investor. Ooh,
I like that. Don't ask for praise. Ask it to
find the holes. Right. Focus purely on risks,
hidden costs. It breaks your confirmation bias.
So it forces you to confront those tough questions
you might ignore. Totally. Like, what's your
current burn rate, and how many months of runway
do you actually have? Or, show me the one metric
that proves your model works at scale. Yeah,
it finds those conceptual gaps we tend to blind
ourselves to. Okay, and the last one in achieve.
E, enable great ideas to scale faster. This is
moving beyond just simple reporting. Think mass
personalization. Okay, like those workshop signups
again. with interests and experience level. Exactly.
You might have hundreds of entries. Use that
data to write unique personalized emails for
every single attendee. Whoa. So if someone's
interested in writing in their intermediate...
The email gives them a specific tip, maybe about
writing benefit -driven headlines. But if someone
else picked design and beginner... Their email
recommends focusing on the 60 -30 -10 color rule.
Completely different. Tailored advice. Wow. Imagine
scaling that level of personalized analysis,
that outreach, to thousands of users instantly.
Right. That used to take marketing teams days,
maybe weeks. That's a real shift in power. So
reflecting on achieve, what do you think is the
core lesson about using AI effectively here?
Well, I think it shows AI is really an amplifier.
Your clarity of instruction is way more vital
than raw technical skill. Okay, so achieve tells
us when AI is most valuable. Now, how do we actually
use it correctly? You know without getting bad
data or wrong answers? That's where the DIG framework
comes in describe introspect goal set. It's basically
exploratory data analysis EDA, but really optimized
for talking to an AI. Just that first crucial
step of checking your data for flaws and features
before you ask the big question. Exactly. And
step one, the D, describe your data is the absolute
most important. You cannot skip this. Do not
pass go. Do not collect $200. Huh, right. Don't
ask for the fancy charts immediately. First,
just make sure you and the AI are on the same
page. Upload your file, say customerfeedbackq1
.xlsx. And straight away, ask for three things.
List all the column names. Tell me their data
types. Text, number, date. And show me the first
three rows of data. Why those three things specifically?
It forces both of you, you and the AI, to see
potential problems right at the start. You need
to look for NAN. Which means not a number, basically
blank or missing data. Right, because missing
data causes huge mistakes later on. Then you
verify understanding. Ask the AI to explain what
each column means, but in simple language. Like,
okay, explain rating one to five. And you want
it to say something like... That's the customer
satisfaction score. Five means very happy. If
it gets that wrong or misunderstands a term,
correct it right there immediately. Read a sec,
though. If the AI is so smart, why do I have
to do this describe step? Feels like I'm hand
-holding it. Can't I just trust it to figure
out the columns? Think of it like this. The AI
is brilliant, but maybe a bit naive, like a genius
kid. It can interpret symbols, but it doesn't
feel the real world context behind your specific
data. Checking for missing data, making sure
the definitions are spot on up front that prevents
tiny errors from snowballing into massive problems
when you run complex analysis later. It saves
you pain down the road. Got it. So describe first,
then what's the I in DIG? Step two, introspect
the data. Now that you both understand the basic
structure, you start thinking about patterns,
relationships, potential red flags. How do you
do that with the AI? Ask it to suggest, say,
five interesting questions the data could answer
based on the columns it sees. OK. It might suggest.
Is there a connection between the support agent
column and the rating, one to five? That's a
good question the data probably can answer. This
is also where you catch its mistakes, right?
Crucial correction loop, yeah. If the AI suggests,
what are the sales figures in each country? But
you know, all your customers are in Vietnam.
You have to jump in and say, hold on, this data
is only for Vietnam. Exactly. Correct that assumption
immediately. This back and forth, this introspection,
it prevents you running a whole analysis based
on a totally false premise. It might feel a bit
slow initially. But it guarantees accuracy later.
Precisely. OK, final step. G set clear goals.
This is really about prompt engineering. Prompt
engineering. Just giving the AI clear instructions
on the output you want. Yeah, clear constraints.
The AI needs context. How should the final result
look? What's its purpose? So be specific. My
goal is to find out why customer satisfaction
dropped last quarter. And add detail. Focus only
on negative comments ratings one and two. I need
three summary bullet points and one pie chart,
formatted for a professional PowerPoint presentation.
Ah, OK. because that's totally different from
asking for. Fun facts for Twitter about customer
feedback. That implies a completely different
tone, analysis depth, and output format. The
goal dictates everything. So let's say someone's
impatient. Why shouldn't they trust the slow
described step when they just want speed? Because
skipping that early data description, it almost
guarantees expensive, painful mistakes later
on. It's just not worth the risk. All right,
now let's go beyond just cleaning up spreadsheet
tables. AI unlocks analysis that, honestly, used
to need dedicated data engineers, especially
with something called smart filtering. Smart
filtering? Yeah. You mean filtering based on
concepts, not just exact words in a column. Exactly.
That's a huge shift. Think about job hunting.
You're looking through a massive list. OK. You
want a salary between, say, $50k and $80. Easy
enough. And you want it located on the US East
Coast. And you want jobs involving keywords like
woodworking or maybe carpentry. OK, but wait.
My spreadsheet might only list cities Boston,
New York, Miami. It probably doesn't have an
East Coast column. Right. Traditional software
just fails there. Can't make the connection.
And it might not have a skills column with woodworking
either. Exactly. But the AI knows that Boston
is on the East Coast from its general knowledge.
It can also read the job description text. Ah,
so even if the title is Residential Project Manager,
if the description mentions carpentry. The AI
can conceptually match it to your carpentry keyword.
It applies its massive knowledge graph to your
specific data points. It's not magic, it's inference.
That makes sense. Okay, that's powerful filtering.
What else? Making your work reproducible. This
is crucial for any professional team, really
anyone doing serious analysis. Meaning you don't
just save the final chart or number. No, you
have to save the method. How did you get there?
Ask the AI to create a kind of recipe book. A
recipe book, like a log file. Sort of, but more
structured. A tracking document, it lists the
original file name you used, all the steps you
took, like standardized the department column,
and any limitations you found, like 20 % of customer
comments were blank. OK, so that's the roadmap
if someone else needs to rerun it, or if I need
to remember what I did three months later. Exactly.
It stops you reinventing the wheel or getting
lost trying to trace back through 50 chat messages.
And for that analysis, like the pie chart of
complaints we talked about? Here's the really
cool part. You can ask the AI, generate a full
commented Python script, maybe call it complaintspy
.py, that does exactly what we just did. Whoa.
So it writes the code for the entire cleaning
and analysis process. Yep, and now that sequence
of steps. It's a reusable tool, which leads to
this idea of turning conversations into programs.
Okay, that sounds potentially complicated. Like,
is that Python script actually readable? Can
someone like me, who isn't really a coder, understand
and trust it? Yes, generally. Because you tell
the AI to make it commented. It doesn't just
generate code. It adds explanations in plain
English for what each chunk of code is doing.
So it makes the analysis transparent, even if
it's code. Right. Think about a complex sequence.
Maybe grabbing 10 frames from a movie file, resizing
them, converting them to grayscale, asking the
AI to generate descriptions for each, and then
saving all that info into a CSV file. That sounds
like a lot of manual steps or needing special
software. It was. But now you can walk the AI
through that process conversationally, then ask
it to bundle that entire sequence. Into a single
Python program you can just download and run
next time. Exactly. Download it, share it, run
on your local machine whenever you need it. So
thinking about that reproducible code generation,
what's the biggest barrier AI really removes
there? I'd say the biggest barrier removed is
needing that deep manual coding expertise just
to automate and share a specific analysis workflow.
Any sequence can potentially become a reusable
tool now. Now we've talked a lot about AI generally,
but we should probably touch on specific tools.
They aren't all the same, are they? No, definitely
not. Different models have different strengths.
Chat GPT, especially with its advanced data analysis
feature, is kind of the reliable, flexible, all
-around choice for many common data tasks. Good
place to start. OK. What about others? Claude
gets mentioned a lot. Yeah, Claude often gets
highlighted for a few things. Generating really
clean code, creating interactive dashboards maybe,
and especially its huge context window. Context
window. Right. Meaning how much information it
can handle at once. Exactly. That large context
window is a big deal when you're working with
truly massive or numerous documents. Imagine
uploading like a whole company's archive of legal
contracts. Or maybe 200 different annual reports
to compare them all at once. Right. For that
kind of huge -scale file analysis and synthesis,
Claude's ability to handle more information simultaneously
is a major advantage right now. Interesting.
And you also mentioned perplexity. Proplexity
shines for research and finding real -time information.
It's great at citing sources, and it has focus
modes. If you switch it to finance mode, for
instance, you can get current market analysis
layered right onto your data questions. So choose
the tool based on the specific job. Pretty much.
And remember, data analysis doesn't have to stop
at just creating a report or a chart. Right.
You can use these tools as building blocks for
actual applications. Totally. Think practical
stuff. You could build a basic traffic analysis
app that monitors real -time data feed and pings
you with alerts about incidents on your commute.
Okay, or what about video? You could build a
video privacy tool, something that automatically
scans through, say, thousands of hours of security
footage and blurs out faces or license plates.
Wow. Or maybe for finance, like an investment
research assistant. Yeah. Imagine a simple Q
&A interface like a chatbot that sits on top
of a massive private database of financial reports.
You just ask it questions in plain English. Going
back to the tools for a second, if you were tackling
really huge complex documents like that Library
of Legal Agreements example, which tool stands
out? Based on current capabilities, Claude's
large context window generally makes it superior
for that kind of large -scale multi -file document
analysis. Okay, let's try and bring this whole
deep dive together then. We started with Achieve.
That framework helps you define when AI really
adds the most value. Right. Aiding coordination,
cutting tedium, help with safety nets, inspiring
creativity, and enabling ideas to scale. Achieve.
And then we got DIG. That's the framework to
make sure your actual analysis process is sound.
Every single time. Describe the data meticulously
first. Introspect for patterns, relationships,
and flaws. And set clear goals for your output.
DIG. You know, this technology is not really
here to replace your strategic thinking, is it?
No, not at all. It's here to make you faster,
more accurate, to let you achieve data manipulation
and analysis that frankly was impossible for
most people before without years of dedicated
coding training. The power of complex data insight.
It's actually becoming available to you, the
listener. Yeah. So here's a call to action. Take
a spreadsheet you work with regularly, something
you know well, upload it to one of these tools.
And then just walk through the DIG framework,
step by step. Exactly. Describe, introspect,
goal set, just see what comes up, see what insights
surface that you might have missed before. Practice
and repetition, that seems key. So let's leave
everyone with a final thought, a provocative
one. Now that this power is more accessible,
what non -traditional data might you analyze
first? Maybe that folder full of messy voice
notes you've been meaning to transcribe. or an
archive of old marketing videos. What hidden
patterns could you unlock now? Lots to think
about. Until the next deep dive.