Introduction to Data Collection

Name: What is Data Collection? | Data Fundamentals for Beginners
Uploaded: 2026-02-28T17:05:37.398523+00:00
Channel: Alex The Analyst
Description: Summary and key takeaways on Introduction to Data Collection, covering to Data Collection Data collection is the process of gathering data from different data

Alex The Analyst

Feb 28, 2026

•

3 min read

YouTube video ID: cd_jj0IRmaA

Source: YouTube video by Alex The Analyst — Watch original video

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Data collection is the process of gathering data from different data sources to use in analysis decisionmaking and problem solving. It brings together raw data so that teams can analyze, make decisions, and solve problems based on information rather than guesswork. The process relies on knowing where data is created and how it will be used downstream.

Data Sources

Data sources are the places where data is created and stored, and they vary by context and system. Examples include EHR systems, bank accounts, APIs, websites, and CSV files. Knowing the specific sources is essential for designing how to collect and integrate the data.

Importance of Data Collection

Good data collection ensures the raw material needed for informed decisions and helps identify trends, patterns, and opportunities. It also lays the foundation for data quality. As the speaker emphasizes, "if you collect data poorly or if you do not process that data correctly that can lead to bad data which can give you incorrect results and then of course you're going to make bad decisions with bad data."

Data Collection Systems

Data collection does not just happen; it doesn't just magically appear—this is a very calculated and specific process that needs to happen in order for you to get that data. Systems are set up to collect and place data into databases for analysis, and those systems can be manually created or purchased as paid solutions. An example given is tracking customer cart additions versus purchases for an online shop to measure behavior and outcomes.

Data Pipelines (ETL)

A data pipeline automates the movement of data from one place to another while often transforming it along the way. ETL stands for Extract, Transform, Load and describes stages where raw data is pulled from sources, placed into a staging area, transformed to be more usable, and then loaded into a data warehouse, database, or even an Excel file. Data engineers, developers, analysts, and data scientists are among the roles that determine and perform the transformations needed for analysis.

ETL Stage	What happens
Extract	Raw data is taken from various sources such as parts of a website or other locations.
Transform	Data is placed in a staging area and then altered to become more usable for analysis.
Load	Transformed data is moved into a data warehouse, database, or Excel for use.

Continuous Data Collection

Data collection is not a one-time thing; data collection is always happening. ETL pipelines can break or need updates, and source data can change, requiring fixes to pipelines. This is a very active process that typically happens one time then is adapted or changed and often fixed over time.

Practical Roles and Experience

Data professionals are involved across all stages: extracting, staging, transforming, and loading data. The speaker noted experience on a data collection team for over 3 years and described participating in all steps, including helping clients understand how to collect new types of data. This hands-on involvement illustrates that collecting and maintaining data systems requires ongoing attention and collaboration.

Takeaways

Data collection is the process of gathering data from different data sources to use in analysis decisionmaking and problem solving.
Effective data collection supplies the raw material for informed decisions, helps identify trends and establishes data quality foundations.
Data collection systems are calculated processes that can be built manually or obtained as paid systems to place data into databases for analysis.
ETL pipelines automate data movement and transformation through Extract, Transform, and Load stages, involving engineers, developers, analysts, and scientists.
Data collection is continuous: pipelines can break, sources can change, and the process requires adaptation and maintenance over time.

Frequently Asked Questions

Who is Alex The Analyst on YouTube?

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Yes, the full transcript for this video is available on this page. Click 'Show transcript' in the sidebar to read it.

Helpful resources related to this video

If you want to practice or explore the concepts discussed in the video, these commonly used tools may help.

Data Warehousing Book Recommended

Reference material to understand storage and loading concepts, useful when deciding where to load transformed data.

Amazon →

Etl Reference Book

Practical guide to ETL patterns and practices that helps teams design reliable data pipelines and transformations.

Amazon →

External Hard Drive

Physical storage for backing up raw extracts or staging files, supporting recovery when pipelines break or change.

Amazon →

Links may be affiliate links. We only include resources that are genuinely relevant to the topic.

Summarize another video

Full Transcript YouTube

what's going on everybody welcome back
to another video today we're going to be
talking all about data
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collection now data collection is an
extremely part of getting data in fact I
was on a data collection team for over 3
years I absolutely love data collection
and so I'm really excited to talk about
this topic let's not waste any time
let's jump onto my screen and take a
look let's take a look at data
collection now what exactly is data
collection just as a definition data
ction is the process of gathering data
from different data sources to use in
analysis decisionmaking and problem
solving a data source means where the
data is actually being created and data
is created all around us it could be in
a hospital EHR system it could be in
your bank account it could be through
apis it could be through a website or it
could be in a CSV file so why is data
collection important and what I really
should say is why is it really important
one it ensures that you have raw
material or the raw data needed to make
informed decision
you can then use that data to identify
Trends patterns and opportunities within
that data and it also lays the
foundation for data quality this is
something that we're going to cover
actually in our next lesson when we look
at data cleaning so if you collect data
poorly or if you do not process that
data correctly that can lead to bad data
which can give you incorrect results and
then of course you're going to make bad
decisions with bad data now data
collection does not just happen it
doesn't just magically appear this is a
very calculated and specific process
that needs to happen in order for you to
get that data let's take just a really
quick example you are actually running
an online shop you have a website you're
selling stuff on the right you're
selling T-shirts and jackets and other
clothes I'm assuming now you want to
track how many customers actually put
something into their cart you might be
able to compare how many customers put
something into their cart versus how
many customers actually bought something
and that could be useful information to
you that data just doesn't appear a data
collection system is set up to collect
that data and place it into something
like a database so it can be analyzed
and used now these data collection
systems can be something that you
manually create and we'll talk about
that in a little bit or it could be
something that you just pay for so it
could be a system that you say hey I
want you to collect this data they go
and do it for you and you don't have to
actually do the work but there is a data
collection system in place whether it's
you or some system you paid for all this
data is going to be processed via a data
pipeline now this is a system that's
going to automate the movement of data
from one place to another while often
transforming it along the way let's take
a look at this ETL pipeline ETL stands
for extract transform and load and you
can see that through this process on the
left hand side we have all these
different data sources it could be
different parts of your website or it
could be different websites or it could
be different locations of the data
entirely we're going to extract that raw
data and we're going to put it into a
staging area this staging area is kind
of like a temporary hold for your data
where then you can work with data
professionals like data Engineers
database developers data analyst data
scientists who can then determine how we
want to transform that data now
transforming data is something we'll
cover again in the next lesson when we
talk about data cleaning but
transforming the data makes that raw
data more usable for whatever you're
trying to use it for after we transform
that data we're going to load that data
into something like a data warehouse a
database maybe it could even be an Excel
file be as simple as that but then from
there we can use that to analyze our
data that's really what data collection
is in a nutshell now I worked on a data
collection team for many years and I was
a data analyst and I would often work at
every single step of this process I
would have to go and talk to the client
and see exactly what data they had
available and sometimes they didn't even
have data available and so they said hey
we want to collect this new type of data
so as part of my job is I would help
them understand how to start collecting
that data so then we could create these
data pipelines to then transfer it into
a database to use data collection is not
a one-time thing either data collection
is always happening the ETL pipelines
they break or they need new ones or the
source data changed on the left hand
side of this diagram The Source data
changed from one part of the website to
another and so you need to go back and
you need to fix that data pipeline to
correctly collect all the data that
you're wanting and so this is a very
active process this typically doesn't
just happen one time and then it's done
it typically happens one time then you
adapt it and you keep changing or maybe
fixing it over time that is data
collection in a nutshell and so if you
like this video be sure to like And
subscribe and I'll see you the next
video
[Music]