Why data analysts are becoming more popular than data scientists

It seems like it would be hard to beat the data scientist, the so-called “sexiest job of the 21st century,” but it looks as if the humble data analyst may have just done that. What do I mean? And why?

The age of the data analyst has come

“Data scientist” is a flashy new title. It’s not all hype, although there’s plenty of that too. Salaries are high. Praise is high. But all the meanwhile, “data analyst” has been in the trenches, doing the “dirty data jobs.” Fixing workbook links. Standardizing data definitions. Getting reports out to the business.

Perhaps the data analyst’s time for glory has come. Take the Google Trends, for example:

To be fair, “data analyst” never actually did lag behind “data scientist…” but it was never much far ahead, either. So what changed? Is the age of data scientists over? Will data analyst be the sexiest job of the mid-21st century?

What explains this?

I’ve got a few ideas for what might be happening here in the data analyst/data scientist search trends. What do you think explains it? Or am I mangling this altogether? Let us know in the comments.

Job candidates are seeing the hiring data

Anecdotally, I’ve heard it’s very hard to get a vanilla entry-level data scientist job these days. Thus, the decision to search for a data analyst job or data scientist job becomes a cost-benefit analysis.

It’s true, data scientist salaries are higher. But would you rather sink your efforts into a job with a $120,000 salary that you have 25% of getting (data scientist), or look toward the $80,000 job that you have 50% chance of getting (data analyst)? Going with a straight expected value, the data analyst role wins out.

Google’s certification made it cool

Hat tip to Matt Brattin of TMB Analytics for this possible explanation. Matt commented on my LinkedIn post about the topic:

The Google Data Analytics professional certificate launched in March of 21 and I’m wondering how much that has influenced this trend.

I pulled the trends for “data analyst certificate” versus “data scientist certificate” and we may have something there:

Sure, the data scientist profession is glitzy. But where is glitzier to be associated with than Google? It’s possible that Google’s data analyst certificate on Coursera did add some flair to the field. Or maybe we have this in the reverse direction, and Google put out the program by following industry, well, trends…

Business intelligence is gaining ground

As an analyst, I often find it helpful to understand what’s happening in the data by “rolling up” a level. For example, rather than focusing on the data scientist and data analyst professions in particular, what if we look at entire adjacent fields?

To be fair, “data science” as a search term still trumps “data analytics,” although these are both “search terms.” Instead, let’s compare the topics (offering a roll-up of terms) of “business intelligence” versus “machine learning:”

Interesting stuff, right? Perhaps as a whole, organizations are starting to see that starting their data journey by hiring a bunch of data scientists to run flashy machine learning models just isn’t working. Rather, by getting the right data to the right people at the right time, they’ll be able to make more informed choices.

This is not a broadside toward data scientists

This is not meant to be an attack on data scientists, or a claim that they are useless parasites on the backs of data analysts. We all have our roles to play in the data game. The issue is that most organizations have placed all their resources in the data scientist role, without supporting the adjacent cast such as data analysts.

What does all this mean for data teams?

Let’s say that these trends mean anything and they are data that should be used to make choices 🙂. Not what? A few things:

1. Data job-seekers: Don’t feel like it’s “data scientist or the highway” for your job search. Data analysts can be just as useful for building up an organization’s data capabilities. They are needed to help the organization walk with data before they can run.
   Consider using “data analyst” as your gateway into the data profession. From there, you can tell if data scientist is the right next step for you, or something else like data engineer… or just stick in data analytics, because it’s so important too.
2. Data analysts: Don’t sell yourself short! This is your time to improve how organizations work with data. Data analysts have historically been paid way less than data scientists. But if they’re producing the actual results in the organization… maybe it’s time to change that?
3. Employers: Don’t use “data scientist” as bait to attract high-value candidates. No one’s falling for it anymore. Also, don’t treat your data analysts as second class data citizens.

Let’s build your analytics pathway together

Helping data analysts, their teams and their organizations advance in the competencies they need is at the core of what I do. To learn more about how I do it, be sure to subscribe to my newsletter for access to my analytics learning library. I also consult with organizations on these topics.

To close… let’s hear it for the data analysts.