Every time the subject of data science comes up in informed company, the same argument begins - what’s the difference between a data scientist and a data analyst? Good question and one that should have a simple answer. The problem is that the distinction gets blurred by the claims being made by one group - data analysts - to the status of the other - data scientists. It never happens the other way around.
So why shoud a set of highly in-demand, respected practitioners whose deliverables now sit at the heart of any succesful business not be happy with their status? Are there advantages - real or imagined - in the status of those who they perceive as sitting above them in some hierarchy, like angels aspiring to be archangels?
Here are three reasons why data analysts should settle for what they have, not aspire to be something they are not.
You don’t have to be a unicorn
Data scientists are a rare - some would say mythical - breed. Which makes you wonder why anybody who isn’t one should want to become one. After all, mythical beasts seldom run in company, but exist in a singular state. They may be venerated, but they can also be hunted.
Join a commercial organisation with unicorn status and you will be expected to deliver miracles. Halo status may give you an initial grace period - let’s say three months - but after that, if you have not found the pot of gold at the end of the rainbow, your gleaming horn might soon be looking like a better trophy than a weapon.
By contrast, data analysts can fit right into a company’s development cycles, neither disrupting or simply conforming, but delivering uplift on a regular basis. How many senior executives would not prefer to have a champion every three weeks than a hero every once on a while? Analysts are the petrol stations of business, delivering the refracted oil of data in a form that can be burnt to get the organisation where it wants to go. That may seem prosaic, but it has impact in the real world, rather than in fantasy.
You don’t have to be a scientist
Domain expertise is strongly associated with data analysts. Theoretical expertise is the preserve of the data scientist. So if your preference is for applied practice, stay within that realm. Otherwise, you will have to venture into the territory where test-and-learn, hypothesising and fail-fast are the norm.
Most analysts are uncomfortable with that kind of environment. For them, continual innovation on proven models is preferable to trying to invent a paradigm shift. Meaurable outcomes on a regular basis give more satisfaction than long-term “moon shots” which only rarely come off.
In exchange for lower peaks, there are fewer troughs. Unless you can cope with the slow build up of pressure that comes with extensive experimentation and the increasing risk of disappointment - scientists look to disprove their ideas as often as to prove them - then don’t aspire to this status.
You don’t get a £30k salary hike…
Ok, so there is a downside to “settling” for being a data analyst - salaries are lower. But that is not the same as low salaries. In fact, analysts earn well, have a good career path and are pretty much guaranteed a long-term career.
By contrast, data scientists arrive with a high price ticket, but can quickly be the victim of mis-matched expectations and delivery cycles. Unless you want to be constantly managing your career, watching out for bear traps and navigating towards the next big placement, you might find it more comfortable to hold down a steady position as an analyst, then senior analyst, then head of analytics and, ultimately, chief analytics officer.
Doesn’t that sound like a career progression with less risk, more stability and a constant string of positive outcomes? If so, what’s wrong with that?