The challenge with being a “data professional”

During PASS Summit, I wrote a post about the broadening data platform. I talked about the term Data Professional, and how I feel how it describes the changes going on the in SQL space. Here’s the problem: It’s a terrible guide. It’s a great description, it’s a wonderful attitude, it’s an ambitious goal; but it’s a terrible guide.

Being a data professional means being a jack of all trades. It means being a renaissance man (or woman). It’s means a career plan that looks like this:

And then you end up with Buck Woody telling you you are trying to do too much, cut it out kid.


So that’s the problem. Sometimes broadening your horizons is really a mask for being scared of commitment. Sometimes it’s a mask for being scared of an ever-changing future. You have to bet on a horse, you can’t bet on them all. Being a data “professional” is great in theory, but in practice it turns into majoring in the “universe” (see XKCD).

Major in the Universe

I’m not saying don’t learn Docker or Powershell. If you don’t learn those things, Kevin Feasel will warn you about becoming homeless. And who wants that.

What I am saying is that if someone asks you “Where do you want to be in 3 years?”, “everywhere” is not an answer. If someone asks you “What are you going to learn this week?”, “everything” is not an answer. So yes, generalize your skill set, who knows what you’ll be doing in 5 years. But right now you need a focus, it’s the only way to become an expert at anything.

Ultimately, I think it comes down to two quotes:

If you don’t know where you are going, any road will get you there.

-George Harrison, paraphrasing Lewis Carrol

and

Two roads diverged in a yellow wood,
And sorry I could not travel both
And be one traveler,

[…..]

I took the one less traveled by,
And that has made all the difference.
-Robert Frost, The Road Not Taken

For me, I’m looking into Data Science. The problem is I’m not sure what Data science actually is! What I know for sure is it involves R and pirate jokes. We’ll cover that in next week’s blog post.

6 thoughts on “The challenge with being a “data professional”

  1. Interesting thoughts Eugene – and yes agree for “data professionals” you cannot be an expert in all – but you need to have an awareness of it all, so I kind of think the term “Data Professional” is workable.
    I agree you need 1-2 SME skills that form your deep knowledge backbone and then continue to review if that skill is relevant/up-to-date. For the rest – get a gauge on broad industry shifts and ensure you understand why its occurring and its impact to your chosen SME skills, and adjust accordingly.
    Clearly themes are either beginning to form (or already formed) in our data related industry – such as Hyper-Scale IaaS/PaaS Cloud Services (ie Azure/AWS/Google), recognition of “Data as an Asset” and “Data Gravity” (ie Data Lake / Big Data, etc), and a new wave around “Intelligent Data” (ie ML/AI/Bots/Cognitive).
    Ex. If your SME skill is “SQL Server + Data Warehousing” then be conscious of how cloud PaaS services can help/hurt you, how to integrate DW with DL, and how to prepare data pipelines for downstream ML services.
    Review yearly and be prepared to change quickly!

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