I was part of team working on an analytics project that sought strong reasons to back up a major business decision, but found myself contributing much less than I’d have wanted to.
It wasn’t technical ability that I was lacking in. Rather, it was a distinct lack of industry/business knowledge that bottlenecked my value.
I’m not too sure if it’s just me, or if it’s something all those involved in professional analytical work go through, but at the start of my career I thought that technical knowledge was the main thing that would propel me to ever greater professional heights. I had never thought that domain knowledge would be that important, much less vital to doing good analytical work.
Technical skills look good on the resume, and are probably the first thing employers look for when seeking a suitable candidate to fill their analytical roles. But once in, technical skills are often secondary.
Sure, you may know your way around a MS Excel, Access, or SQL. But it’s really what you do with it that counts. And if you don’t know your business, your industry, you can’t really do much.
But let’s not discount the importance of technical skills. I find that the most useful technical skills to an analyst are those that allow you to perform automation and ETL (extract, transform and load) activities (especially when datasets are large or not primed/ready for proper data analysis), and spreadsheet or statistical software. The former helps tremendously in preparing data for easy exploration and analysis, and the latter for the exploration and analysis themselves.
But after that, it’s all domain knowledge, baby.
When I was working on the analytics project, people were bouncing ideas off each other. But being relatively new and not quite as well-versed in the concepts, lingo and accents(!), much of what they were saying were lost on me. It took me a while, with lots of emails and question-asking, before most of the project started falling into place.
So, I’m just saying that you can’t discount business knowledge when it comes to data analysis, and just as much effort should be spent improving that knowledge as that which is spent on technical knowledge.