7 REASONS YOU SHOULD NOT BECOME A DATA ANALYST | LET'S BE REAL... | CAREER LIFE | Ohema Nae

Summary

This 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

00:00

📊 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.

05:01

🔍 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.

10:02

🤝 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.

Keywords

💡Data Analyst

A data analyst is a professional who collects, processes, and interprets data to help businesses make decisions. In the video, the speaker, who is a data analyst, discusses the challenges they face, such as coordinating with teams to receive necessary data and dealing with the imperfections in data. The role is central to the video's theme of understanding the less glamorous aspects of data analysis.

💡Dashboard

A dashboard in the context of data analysis refers to a user interface that displays key performance indicators and data visualizations for easy monitoring and decision-making. The script mentions building and pushing out dashboards by deadlines, illustrating the time-sensitive nature of data analyst work and the importance of these tools for teams to continue their work effectively.

💡Excel Files

Excel files are spreadsheet documents created using Microsoft Excel, often used for organizing and analyzing data. The video script describes how data analysts sometimes rely on Excel files sent by teams to perform their tasks, highlighting the common use of this tool in data handling and analysis.

💡Data Coordination

Data coordination involves the process of managing and aligning data from various sources to ensure its usability for analysis. The speaker mentions the need to coordinate with teams to receive data, emphasizing the collaborative aspect of a data analyst's job and the challenges that come with dependency on others for data access.

💡Patience

Patience is the ability to withstand delays without becoming annoyed or upset. In the script, the data analyst talks about the necessity of patience when dealing with coding frustrations and data discrepancies, showing that patience is a key trait for handling the unpredictable nature of data work.

💡Coding

Coding refers to the process of writing computer programs or scripts. The video mentions the frustrations that can come with coding, indicating that data analysts must be adept at writing and troubleshooting code to manipulate and analyze data effectively.

💡Data Discrepancies

Data discrepancies occur when there are inconsistencies or inaccuracies in data. The script discusses the need for patience when dealing with such issues, as they can arise from imperfect data sources or errors in data entry, requiring careful examination and correction.

💡De-escalate

To de-escalate means to make a tense or difficult situation less intense or serious. The video script talks about the importance of knowing how to de-escalate situations with stakeholders who may become frustrated with the data or dashboards, highlighting the interpersonal skills needed in data analysis.

💡Resourcefulness

Resourcefulness is the ability to find quick and clever ways to overcome difficulties. The speaker mentions the need for resourcefulness in a data analyst's role, especially when seeking answers or solutions on tight deadlines, indicating the importance of being able to think on one's feet and find creative solutions.

💡Corporate Tussle

A corporate tussle refers to conflicts or disagreements in a professional setting, often characterized by passive-aggressive behavior or unprofessional conduct. The script uses this term to describe the potential for conflict in data analysis, particularly when stakeholders are unhappy with the data or dashboards, emphasizing the need for professionalism and conflict resolution skills.

💡Quick Learner

A quick learner is someone who can rapidly acquire new knowledge or skills. The video emphasizes the importance of being a quick learner in data analysis, as the field is constantly evolving with new tools and processes. The ability to adapt quickly is crucial for staying current and effective in the role.

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