Here’s everything about data analyst careers being just hype:
A lot of the hype around data analyst careers has to do with external investment.
Many businesses and organizations are increasingly relying on data analytics to help inform important decisions.
That can lead to great job satisfaction, but if you’re stuck crunching numbers without context, it might be unsatisfying.
So if you want to learn all about why exactly data analyst careers are just hype, then this article is for you.
Let’s jump right into it!
What Do Data Analysts Do? (5 Tasks)
We have a lot to cover to really answer this question correctly.
I’m going to walk you through the basic things that data analysts do.
Then, we can talk about why there’s so much hype around this particular career.
After that, I’ll talk about job satisfaction, and you’ll be able to determine for yourself if it’s all hype or not.
#1 Gather Data
This might sound obvious, but gathering data and analyzing data are very different things.
Methods to gather data are often automated.
Just look at the Internet of Things (IoT), which is a hardware approach to generating tons of data.
With IoT, every device in a system has the ability to collect and share data.
As an example, your refrigerator can have a Wi-Fi card that allows it to send information to the manufacturer that monitors temperatures and performance.
If millions of refrigerators are built in this way, then that’s a lot of data on refrigerators.
And, you can apply this to pretty much anything.
What’s the point of all of this?
Well, typically data analysts take those mountains of data and make sense of them, but the analysts are often involved in gathering data too.
The analysts need to review data harvesting to ensure that it’s reasonable, logically sound, and useful.
After all, the analytics are only as good as the data that drives them.
So, data analysts often have to review and even adapt the ways that a business or organization collects data in the first place.
#2 Crunch Numbers
After the data passes inspection (so to speak), then the analysts can do what you might be envisioning.
They run through the data, perform calculations, make some graphs, and otherwise try to produce meaningful results from the data.
This is a very different skill set when compared to gathering data, and this is the bulk of what a data analyst should expect to do as a professional.
Basically, organizations will come to you with their data.
You’ll check that the data is collected in a reliable way, and then you’ll really get to work.
The analyst is responsible for drawing conclusions and even identifying which conclusions are the most valuable.
From here, analysts can make strong recommendations, and that’s why they get hired.
Businesses and other groups want a data analyst to use their techniques to mathematically confirm which course of action will be the best.
#3 Manage Tools
But, running through data, analyzing things, making recommendations, and evaluating data collection are all tasks that require specialized tools.
It probably won’t surprise you to learn that there are millions of different software tools out there, all geared toward different aspects of data analysis.
You’ve probably heard of Excel.
It’s pretty popular and makes it easy to list data and then perform some levels of analysis on it.
You might not have heard of SAS (although it’s still fairly common).
This is a very different tool that still enables people to organize huge tables of information and draw meaning from all of it.
Overall, the number of tools out there is only growing.
Data analysts have to be able to review tools, identify which might be appropriate for the job at hand, and then use those tools effectively.
As a result, many analysts are proficient with mainstream tools like Apache Spark, Salesforce, Google Analytics, and many others.
On top of using tools, analysts also need to code from time to time.
One of the most powerful software tools for managing very large data tables is SQL.
It’s powerful, but it’s not the simplest software in the world.
As a result, many analysts have to learn how to code functions into SQL tools.
The amount of coding involved in data analytics will depend on the specific job, but light coding is often required.
For some types of work, heavy coding is necessary.
In fact, data analysts are often involved in software development when a company is trying to make the next great analytical software package.
The point is that coding comes up, but how much coding an analyst might have to do depends entirely on their specific job.
#5 Make Recommendations
I mentioned this briefly before, but analysts are expected to make recommendations.
That’s the real point behind data analytics.
All of the data collection, math, software tools, coding, and everything else ultimately aim at this result.
People hire analysts to make informed recommendations about what to do next.
This means that being an analyst often comes with a high degree of responsibility.
Businesses succeed or fail on the recommendations of data analysts, and that’s something that can be very appealing or unappealing to potential analysts, depending on the person.
What’s All the Hype About Data Analyst Careers? (4 Things)
That covers what they do.
Why is there so much hype around this field?
Well, it’s receiving a lot of attention and investment from a lot of different sources.
Let’s look at some of those sources of hype to better understand how people seem to perceive data analytics as a career.
#1 Commercial Hype
A lot of industries are hiring data analysts to look at specific aspects of businesses.
Advertisers want to see how much revenue they generate with each investment.
Refrigerator manufacturers want to see which individual components are the best for maintaining temperatures in adverse conditions.
The list is endless.
As a result, analysts are seen as a bit of a savior class for many businesses.
The customers who hire these analysts expect a lot from them, and that generates a bit of hype.
Businesses are often excited when the new analyst arrives, hoping that they can shed light on some problems and help the business grow and succeed.
#2 Being Smart
On the other hand, data analysts tend to command respect when it comes to careers.
If someone tells you that they are a doctor, you know that they went through a lot of schooling and have a high level of expertise.
The same can go for engineers and professionals in a number of other fields.
Data analysts certainly fit into this.
In order to succeed as an analyst, you need sufficient math and technology skills.
In general, people will assume that you’re pretty smart.
That can feel good, and it contributes to the generic hype that currently surrounds data analytics as an industry.
#3 Big Projects
Sometimes, the hype has little to do with the analytics and a lot to do with what they’re attached to.
It’s possible that you could be an analyst working on a major project for a big player.
You might be analyzing things for Google or DARPA or other major organizations.
You might feel some of the hype if you’re analyzing data to help a project that will make self-driving cars, invisible jackets, cure diseases, or do something else amazing.
Simply being attached to such projects can feel invigorating, and it certainly adds to the hype.
Of course, there’s also pay.
Data analysts are in a group that tends to make good money.
According to Glassdoor, data analysts regularly break the six-figure barrier, meaning that this is a professional with excellent income potential.
Pay certainly isn’t the only reason to consider a career, but it can definitely add to the hype.
Is There Satisfaction in Data Analyst Careers? (2 Scenarios)
We’ve talked about the hype.
Is there any job satisfaction for data analysts?
Some report excellent job satisfaction.
Others are miserable.
Obviously, these kinds of things depend on the person, but I’m going to take you through two common scenarios to help you think about where you might or might not find satisfaction in this career.
#1 Meaningless Numbers
I talked a lot about external factors that can increase the hype around data analytics.
If you’re working on an invigorating project, that often comes with more satisfaction in the work.
But at the root of things, data analytics is all about working the numbers.
If you enjoy the raw application of it all, then you can find great job satisfaction in this field.
If you don’t, then consider this example to see if the job might be more hype than satisfaction in your case.
In medicinal studies, the standard is to do double-blind studies.
This means that the people involved in the study don’t know what the numbers are.
As an example, a drug trial might put one group of people on the new drug and compare them to a group taking a placebo.
The thing is, no individual working the study knows who is in which group.
It’s all randomized.
If you’re the analyst working on something like this, you also don’t get to know the details.
You see streams of numbers, and you often don’t even know what they’re attached to.
You might not know what drug is in the trial or even how the trial works.
Instead, the idea is to minimize any type of internal biases by keeping you in the dark as much as possible.
As a result, you’re crunching numbers with little to no context.
You have no idea what your analytics will ultimately mean for anyone, and you’re deprived of any type of satisfaction that can come from the external results of your work.
If you like crunching the numbers, you’ll be happy.
If you need to know what comes of your analysis, then you might go crazy.
Now, not all data analysis works like this, but there are plenty of cases where blind analysis has value.
So, it’s not unreasonable to think that you might be crunching numbers without much meaning.
Whether or not that’s satisfying really depends on the purpose, but it can help you think about the potential of a career in data analytics for yourself.
#2 Transforming Businesses
On the other hand, when you do get to see all of the context surrounding your analytics, then you often do have a chance for excellent satisfaction.
Seeing a business thrive because you were able to steer them in the right direction can be very gratifying.
If the project at hand is doing something you really believe in, then you get that much more satisfaction.
Ultimately, satisfaction as a data analyst depends on the work you do and how you feel about doing the analytics in its raw form.