7 REASONS YOU SHOULD NOT BECOME A DATA ANALYST | LET'S BE REAL... | CAREER LIFE | Ohema Nae
Summary
TLDRThis video outlines seven reasons one might consider avoiding a career as a data analyst. The speaker, a data analyst herself, discusses the need for patience in coordinating with others for data, the frustration of coding, dealing with imperfect data, managing expectations and de-escalating conflicts, enjoying data and interpersonal skills, resourcefulness in problem-solving, and the necessity to be a quick learner in adapting to new tools and processes. It highlights the challenges of the role while candidly sharing personal experiences.
Takeaways
- 🧑💼 Data analysts often need to 'babysit' team members to ensure they receive necessary data on time, which can be frustrating and time-consuming.
- 🕒 Patience is crucial for data analysts, as they must deal with delays and issues in data collection and processing.
- 🔍 Data analysts must be detail-oriented and able to identify and address data discrepancies and issues in the data they work with.
- 🤝 Strong communication skills are essential for data analysts, as they need to interact with various teams and stakeholders to gather requirements and present findings.
- 😡 De-escalating tense situations is a skill data analysts should have, as they may encounter frustrated stakeholders due to data or dashboard issues.
- 💻 Data analysts should enjoy working with data and people, as their role involves both technical analysis and interpersonal communication.
- 🔑 Resourcefulness is key for data analysts, as they often need to find solutions and answers independently, especially when under time constraints.
- 👥 Being aware of and navigating corporate politics and 'tussles' is part of the data analyst's role, especially when dealing with multiple teams and data sources.
- 📈 Quick learning and adaptability are important for data analysts, as they need to keep up with changes in tools, processes, and data sources.
- 🛠️ Data analysts must be prepared for manual work and dealing with scattered data from various sources, which requires patience and problem-solving skills.
- 🚫 The video script serves as a cautionary guide, outlining reasons why someone might want to reconsider a career as a data analyst based on the challenges involved.
Q & A
What is the main topic of the video script?
-The main topic of the video script is the seven reasons why one should not become a data analyst, as explained by a data analyst.
Why does the speaker mention the need for patience in data analysis?
-The speaker mentions the need for patience because data analysis can involve dealing with imperfect data, manual work, and the time-consuming process of troubleshooting issues.
What does the speaker mean by 'babysitting' in the context of data analysis?
-In the context of data analysis, 'babysitting' refers to the need to constantly follow up and coordinate with other teams to receive the necessary data to perform one's job.
What challenges does the script highlight regarding the coordination of data from different teams?
-The script highlights the challenges of coordinating data from different teams, such as the need to wait for them to send data, the possibility of delays, and the potential for miscommunication or misunderstanding of priorities.
How does the speaker describe the frustration that can come with coding in data analysis?
-The speaker describes the frustration with coding as something that can be challenging and time-consuming, requiring patience and persistence to troubleshoot and resolve issues.
What is the importance of de-escalating situations in the context of data analysis?
-The importance of de-escalating situations in data analysis is to handle conflicts or frustrations that may arise from data discrepancies, dashboard issues, or miscommunications with stakeholders in a professional manner.
Why does the speaker suggest that enjoying working with data and people is important for a data analyst?
-The speaker suggests that enjoying working with data and people is important because a data analyst's role involves not only analyzing data but also communicating with teams, gathering requirements, and presenting findings.
What does the speaker mean by 'corporate tussle' in the context of data analysis?
-In the context of data analysis, 'corporate tussle' refers to professional conflicts or disagreements that may arise in a workplace setting, often due to misunderstandings or frustrations with data and dashboards.
How does the speaker emphasize the need for resourcefulness in data analysis?
-The speaker emphasizes the need for resourcefulness by explaining that data analysts often have to find answers independently, especially when dealing with new tools, processes, or time constraints.
What is the significance of being a quick learner in the role of a data analyst, according to the script?
-The significance of being a quick learner in the role of a data analyst is to adapt to new processes, changes in tools, and to quickly build and analyze data in response to the demands of the job.
Outlines
📊 Coordination Challenges in Data Analysis
The first paragraph discusses the challenges of coordinating with teams to obtain necessary data for analysis. As a data analyst, one often has to wait for others to prioritize and send data, which can be time-consuming and frustrating. The speaker describes having to follow up and 'babysit' individuals or teams to ensure data is received on time, which is crucial for meeting deadlines and building dashboards. This process can lead to delays and requires patience and persistence.
🔍 Patience and Problem-Solving in Data Analysis
The second paragraph emphasizes the importance of patience when dealing with coding issues and data discrepancies. Data analysts must be able to troubleshoot and resolve problems without losing their cool, as coding can be frustrating and data is rarely perfect. The speaker shares personal experiences and stresses the need for patience and persistence in learning and overcoming challenges, including dealing with scattered data from various sources.
🤝 De-escalating Tensions in a Data-Driven Environment
The third paragraph highlights the need for de-escalation skills when dealing with frustrated stakeholders in a data analyst's role. Stakeholders may become upset with dashboards showing incorrect data, which is often due to underlying data issues. The speaker recounts a specific situation where a stakeholder was angry about incorrect data on a dashboard, which was traced back to an Excel file they provided. The importance of clear communication and managing expectations is underscored.
🗣️ Communication and Interpersonal Skills for Data Analysts
The fourth paragraph stresses the importance of enjoying and being skilled at working with both data and people. Data analysts must gather requirements, communicate effectively, and present their findings, which involves a significant amount of interaction with others. The speaker points out that if one does not enjoy talking to people, this aspect of the job can be challenging.
🛠️ Resourcefulness and Self-Reliance in Data Analysis
The fifth paragraph discusses the necessity of being resourceful and self-reliant as a data analyst. Often, analysts must find answers to problems on their own, especially when working under time constraints. The speaker mentions the importance of asking questions to ensure clarity in requirements and the need to be proactive in problem-solving.
💼 Navigating Corporate Tussles and Professionalism
The sixth paragraph addresses the potential for 'corporate tussles,' or professional conflicts, that a data analyst may encounter. These can arise from miscommunication, frustration with dashboards, or data issues. The speaker shares a story of a manager who was upset with a data analyst over the use of multiple data sources, which was actually a decision made by his own team. The importance of professionalism and handling such situations with grace is highlighted.
🧠 Adaptability and Quick Learning for Data Analysts
The final paragraph emphasizes the need for adaptability and quick learning in the role of a data analyst. Analysts must be able to pivot and adapt to new processes, changes, and tools quickly. The speaker reflects on their own experiences of having to switch between different data visualization tools and processes, noting that the ability to learn and apply new skills rapidly is essential.
Mindmap
Keywords
💡Data Analyst
💡Dashboard
💡Excel Files
💡Data Coordination
💡Patience
💡Coding
💡Data Discrepancies
💡De-escalate
💡Resourcefulness
💡Corporate Tussle
💡Quick Learner
Highlights
Data analysts often have to coordinate with teams and individuals to receive necessary data, which can sometimes be a frustrating process due to prioritization issues.
Data is frequently received in various formats like Excel files, requiring analysts to follow up and wait for data delivery, which can delay project timelines.
Data analysts must possess patience to deal with the complexities of coordinating data from different sources and teams.
Coding can be frustrating, and data analysts need to remain patient and persistent when facing coding challenges.
Data analysts should be prepared to deal with imperfect data and perform manual work to clean and organize scattered data.
Handling data discrepancies and issues requires patience and a methodical approach to identify root causes.
Data analysts may need to de-escalate situations with stakeholders who are frustrated with data or dashboards.
Understanding and managing data quality is crucial, as incorrect data can lead to conflicts with stakeholders.
Data analysts should enjoy working with both data and people, as the role involves significant communication and presentation.
Resourcefulness is key for data analysts, who often need to find answers independently and adapt to tight deadlines.
Corporate tussles can arise, and data analysts must be prepared to handle professional conflicts with tact and professionalism.
Quick learning is essential for data analysts, who must adapt to new processes, tools, and changes in the workplace.
Data analysts may need to switch between different data visualization tools and sources, requiring adaptability and speed.
The importance of clear communication and understanding requirements to avoid misunderstandings and rework.
The video creator shares personal experiences to illustrate the challenges and realities of being a data analyst.
The video provides a candid view on the less glamorous aspects of data analysis, offering a balanced perspective on the profession.
Transcripts
[Music]
welcome back to my channel if you are
new here welcome so today i am going to
be talking about seven reasons why you
should not become a data analyst and
this is coming from a data analyst so
let's jump right into it reason number
one
you do not like babysitting people to do
their job and do what they need to do
sometimes you need to coordinate with
people and you need to receive data that
you need to do your job from someone or
specific team and to be honest a lot of
the times they have a lot of other stuff
going on so you are not their
priority you are not top priority for
them and the data that i am talking
about that the teams have to send me are
usually excel files are usually
different reports that the team pulled
and they usually only have access to
that specific tool that they pulled that
data from so i'm unable to actually pull
that data myself so of course the data
is on their end so they need to
physically send it to me
to actually get started of course there
are a bunch of different data sources
that are not like that but i have worked
with a couple of teams who do it that
way and that's the only way at the time
that they can get the data you have to
constantly ask them and follow up with
them about sending you the data or
sending you a report or sending you
whatever you need to
build the dashboard sometimes i have to
wait for them even after i follow up
with them they don't usually reply back
right away of course because like i said
they have other stuff that is higher
priority for them compared to sending me
what i need to continue to do my job and
continue to build the dashboard that
they need to continue to do their job
it's like a cycle it's crazy it's all
connected so in a way i have to wait for
them or put important things on hold or
babysit them to
get what i need from them with sending
me data and providing feedback on the
dashboard that i created for them so i
can make the needed changes for them
before the deadline because you can't be
working on a dashboard for months and
months up to a year at a time and the
team needs that dashboard to continue
the work that they're doing so
you need to create that dashboard and
push it out by that deadline or before
that deadline is always preferred as
well because some teams do need
dashboards like yesterday any delay with
not receiving that data that's another
day that's another week that i am behind
schedule in building that dashboard for
that team so as a data analyst sometimes
you do have to babysit that specific
individual or that team and ensure that
they send you what you need to continue
your job another reason you should not
become a data analyst is if you are not
patient say for instance the code is not
working
in the way that you expected it to
are you going to slam your laptop or are
you going to take the time to actually
put in the work and try to figure out
the issue
which person are you because
coding can be frustrating it can and
when i first started coding i was
frustrated but i never slammed my laptop
i never gave up
and i'm just not that type of person
everybody is capable of learning
in your worst subject you're still
capable of learning it as long as you
work hard and you're dedicated so i
believe we're all capable of learning
any and everything so i gave myself
grace and i gave myself patience and
even though i had no idea where to start
with coding right when i first started i
did not give up on that challenge
because i knew i could overcome it i
knew it and i did not saying that i'm
perfect with coding mistakes happen and
sometimes there are issues that are
harder to spot than others a lot of
times the data that data analysts
receive is not perfect and no data is
perfect so
sometimes you have to do manual work
with scattered data it would be great if
the data was all in one place but a lot
of times the data comes from different
data sources it can come from a database
it can come from an excel sheet it can
come from another tool that the team is
using and they prefer the data can come
from anywhere that does make it more
difficult with that you do have to have
patience because sometimes i work on
issues all day long and i look at the
clock and it's like the day is already
over time flies by when you're actually
working when you have your head down and
you're focused that's what i've
personally experienced you do have to
have patience and not lose your cool
when things don't go your way because of
course no job is perfect but as a data
analyst data discrepancies can happen
issues in the data can happen connecting
that data source to that visualization
tool can happen anything can happen so
you have to have that patience to
actually sit down and look deeper into
those issues to find the root cause
another reason
is that you do not know how to
de-escalate situations
and the reason why i added in this
reason to a reason why you should not
become a data analyst is because some
people do not know how to do that but of
course you can learn but there are
situations where people get frustrated
with their data they get frustrated with
the data set they get frustrated with
the actual visualization they get
frustrated with the dashboard because
maybe it's not working the way they had
hoped i have had a situation where i was
working with a stakeholder who got
extremely frustrated because
they thought the dashboard was showing
incorrect data usually 99 of the time
when the dashboard is showing incorrect
data it's usually the underlying data
that's incorrect because that dashboard
is pulling that data from that data
source so if a team sends me an excel
file that dashboard is reading the data
in that excel file so if that data in
that excel file was incorrect guess what
the data on the dashboard is going to be
incorrect i have had a stakeholder who
was so angry and he didn't cursor
anything like that i could tell he was
very very upset and
he was basically saying how can we
depend on your team if your dashboard is
showing this
data i asked him to pull up the excel
file that he sent me he pulled it up i
said go to column k
he went to column k
and i said well you see this number on
the dashboard right
that's the same number in your excel
file
he didn't know what to say it was his
data that was incorrect so as a data
analyst sometimes we do have to look at
the data quality as well but in regards
to that situation that team
had a person who was supposed to look at
the data quality so it wasn't an extra
step for me that was the agreement that
the data quality was good because they
had someone on their team who focused on
data quality issues so basically long
story short he went back to that person
and that's their business but he did
apologize
because he was just so adamant that he
you know he thought he was right and i
get it but sometimes you do have to know
how to handle certain situations and
talk to people even when you can tell
that they're visibly frustrated with the
dashboard or their data right or the
situation at hand you have to know how
to de-escalate and i told him to make
sure that he
checks with his data quality specialists
before he comes to me
because
i told him that i don't want a situation
to happen like that again so just go
check with that person first and then
come to me
and that was that
remember to stay
brought to you by a humane
girl
this next reason may be a no-brainer but
i did want to add it in because it is
very very true
so you should not become a data analyst
if you do not somewhat enjoy working
with data and people as a data analyst
of course you're working with data on a
daily basis that's a given but you're
also working on the people side because
you're gathering requirements for the
dashboard so you have to communicate you
have to talk to people you have to have
meetings you have to present the
dashboard you have to do a lot of
front-facing sometimes and it does
depend on your actual team and your
actual company your organization and so
on and so forth but you do have to talk
to people so if you do not somewhat
enjoy talking to people
i don't know to tell you
it's a part of the job for sure another
reason you should not become a data
analyst is if you are not resourceful a
lot of the times you have to look for
answers yourself and depending on what
you're looking for sometimes it can be
tedious sometimes it can it can be hard
it can be very difficult to locate and
answer when you're on a time limit in a
sense of having to roll out that
dashboard you can ask questions of
course to your manager your team whoever
you're working with when i get
requirements for a new dashboard i like
to ask as many questions as i can just
to make sure me and that team me and
that individual are on the same page
because the last thing you want to do is
build a dashboard and the team comes
back and they're like
what is this this is not what we wanted
beware
of the corporate tussle
now i'm not talking about an actual fist
fight i know those happen i hope not in
the corporate setting but they happen
right i'm not talking about those
tussles i'm talking about that mental
tussle that pettiness have you ever had
a meeting with someone and they're very
passive aggressive or somebody who just
loses their pool or somebody that just
doesn't know how to communicate and talk
to people and try to to make it seem
like you're in the wrong all the time
and they don't have etiquette they
probably shouldn't be in the corporate
setting because they don't know how to
act basically the people who do not know
how to act anywhere so corporate tussle
is basically fighting in a professional
way
basically you're trying to sugarcoat it
and cover it in a professional manner
because you're in a professional setting
but if you were out there in the street
and somebody yelled at you you're
probably going to yell back so that's
the difference in the office that
shouldn't happen i know it does but we
want professionalism in the office
because it's our place of work you know
when people say per my last email
like i don't think that's a corporate
tussle but in a way it wants to try to
get there because it's just like didn't
you see my last email like but it's a
more professional way to say it like as
you can see from my previous email
corporate tussles do happen i've seen
them happen and i've heard stories one
that i've heard of and i wasn't on this
team but my friend told me what happened
because she was on this team but she
wasn't a data analyst there was a data
analyst that she worked closely with to
pull and gather the data because she was
the only one who had access to that
specific tool to get the data so her in
the data analyst would work very closely
in regards to that so she said that she
was at a meeting one time
and there was this manager who
got irritated with the data analyst
because there were six different data
sources that the dashboard was using and
that was a dashboard for his team but
the crazy thing about it is his team
decided to have the six different data
sources because they didn't have the
data all in one file all in one report
all in one database they just didn't
they were working towards that but in
the meantime they did need a dashboard
at the moment so it was kind of like a
interim dashboard he got mad at the data
analyst when it was really his team he
should have handled it a different way
it was kind of like he wanted to tussle
she was telling me that he was yelling
and just basically losing it and i get
people can get to a point where their
mental just can't take it anymore but to
me that's unacceptable he should have
gathered all of the facts first instead
of just going in there yelling at
people who he shouldn't have been
yelling at and he shouldn't be yelling
at anybody but my point is you need to
check your team all in all he was trying
to tussle not not physically but he was
raising his voice and it's just like you
want problems then that's just
unacceptable and as a data analyst
people do get frustrated with dashboards
and with data so be prepared for a
little bit of you know mental pettiness
or you know a little corporate tussle
here and there but from my experience
they do not happen often but that is a
con with being a data analyst
[Music]
another reason why you should not become
a data analyst is if you are not a quick
learner you do have to be able to pivot
and adapt to new processes changes tools
you need to have a quick turnaround time
with building your dashboards and
analyzing that data that you receive
because they want output they want you
to execute and come back and bring
something back to them something that
they can utilize throughout my years as
a data analyst i have had to move from
different data visualization tools
different data sources just different
processes on the team that i'm on
because
every team is not the same every team at
the same company is not going to have
the same processes they're not going to
utilize the same tools they're not going
to utilize the same data sources they're
going to have different ways that they
work basically they're going to have
different tools that they work with
sometimes the tool that you have been
working with for a year or so
changes to a new tool because the team
sees greater value in a new tool you
have to be able to pick up on things
quickly so you do have to be a quick
learner so those are my seven reasons
why you should not become a data analyst
if you like this video please like this
video and i'll see you guys next time
bye guys
you
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