Business Data Analyst — the reality of the role and its demands
And it might not be exactly what they tell you it is
Since you were interested enough to click on this link, you deserve to know right away why my opinion carries some credibility.
I have been involved with data in some way or the other in my ~11 years of work experience — handling financial & business projections as an equities analyst, setting up the first business analytics unit in a now $6bn Foodtech startup and then as a product manager with two giant tech organizations. I learnt a lot on-the-job and got a bunch of myths busted in the process.
And this is my attempt at putting these learnings together and paint a more day-to-day picture of what such roles usually look like.
But first, let’s talk about nomenclature
Business analysts, data analysts, data scientists and data engineers — that is the more standard bunch of roles you’ll hear about. The definitions are so fluid that it is advisable to dig deep into role description and requirements before applying to any role.
For this post, I will be limiting myself to the business analyst and data analyst roles (referring to them as BA) — mostly interchangeable titles indicating an individual whose role is to analyze data to inform business decisions.
When I started out, the word ‘analyst’ itself brought up images of a geek sporting large glasses, huddled in a corner, keying in boring excel formulae. I was asked to hire a team very early on in my career and I remember my bar being quick excel skills and penchant for solitary work.
I was naïve.
Business Analysis is NOT a boring job
It is tough, as I will expound later, but it is not at all boring.
It is actually a lot of fun, if done right. Cliched as it may sound, finding a hitherto unknown behavior or trend is an adrenaline rush like nothing else.
Moreover, the BA holds the vantage point to know the ins-and-outs of the business better than even the CEO. So, they wield a lot of power. A sharp analyst in a meeting is the sword that can pare all the high-flying execs to size, regardless of the hierarchy.
A sword is needed by those looking to slay, and feared by the ones under attack. So, armed with their knowledge, analysts are respected across the organization. That’s a fun place to be.
This, however, is a reciprocal relationship. To become ‘great’, analysts need this network within the company. Here’s more on that.
Business Analysis is NOT for boring people
Many folks will tell you finding an insight from a trove of data is like finding a needle in a haystack.
They are wrong.
One can place really big magnet and find a needle in a haystack. Finding insights is like finding a slightly bent pin in a pile of needles.
More often than not, there are a number of such needle piles. Sometimes, after looking endlessly, it is realized it wasn’t a bent needle but a twig that needed to be found.
There is only one way to find the right answer — find the right problem.
And there is only one way to find the right problem — talk to the relevant people.
Great analysts know the relevant people, don’t hesitate in reaching out and then ask the right questions to extract the information they need.
It is a heavy on people and conversations role.
Analysts must have incredible amounts of patience
‘Clean data’ is like a unicorn. It does not exist.
Regardless of size or stage of evolution of the organization, all analysis needs to start with absolutely garbled data.
Analyst: “But this is a list of phone numbers. Why does it have text?”
Developer: ¯\_(ツ)_/¯
Analyst: “And what about duplicate phone numbers? Isn’t this the primary ID for logging in?”
Developer: *reconnecting*
While data analysis is more of a science, cleaning data is an art. And it requires the same amount of patient, tact and finesse — this Forbes article says data scientists (not the same as a BA, but similar use-case) spend 60% of their time cleaning data.
Needless to say, here again, talking to people helps the analyst know what to sift.
This art requires the nuances of the product that is capturing the data and, more often than not, fixing the source to fix the data. That’s why, IMO, BAs can be PMs in disguise, or in-waiting, if they desire that growth track.
BAs need to be awesome storytellers
An insight is pointless if it doesn’t get picked up by the relevant stakeholder for action. The BA needs to convince them, or convince a guy who can convince them.
They say, every job is eventually a sales job. A BA’s job is no different.
Many gainfully employed BAs fare miserably in one or more of these avenues. Many survive despite, but usually stagnate.
For every crazy AI innovation or brilliant machine learning algorithm, I have seen tens of inane data analyses, horribly formatted data files and poorly judged business decisions built on the wrong metric or a wrong way to look at the right metric.
The misconceptions around this role have trickled down to (mis)inform the hiring lens.
For entry-level up to 5 years, recruiters place way more focus on puzzle solving, knowledge of the likes of SQL/R and math scores to shortlist and select analysts. Short-term goals are sometimes met by these folks too.
But, over time, the gaps in problem solving and softer traits start overcoming the deftness in tools.
A better understanding of the daily nuances of a BA’s job needs to inform job seeker’s choice to be in this role — loving numbers doesn’t cut it, you must like insights and love the work that goes into getting to one.
More importantly, this understanding needs to better inform the hiring process. However, testing for a requisite combination of these skills isn’t easy. To paraphrase Tolstoy,
“All great analysts resemble one another, but each poor analyst is poor in their own way.”
