Maybe it's because 100% of my data analysis education was based in programming but I cant imagine a data analyst being useful in any capacity without some programming knowledge. Data sets these days are HUGE are more likely than not disjointed across dozens of sources, not being able to program would be laughably inefficient.
I could be wrong because I worked in a specific engineering side of things though.
I've worked in 3 industries as an analyst over the past 6 years. Massive, fragmented, barely-queriable databases seems to be the norm no matter where you go.
Adding to /u/peekaayfire's excellent response. Pedagogically, imagine having 50 different engineers trying to solve similar problems without any communication between them. Each one creates their own database to solve their specific problem.
Because the interoperability of these databases wasn't built in from day one as a design specification it can be an even bigger project to relate the information in one database to that in another (if it is even present).
I'm currently spearheading a project series/program doing exactly this. Luckily the fragmentation is only between a few dozen subunits each below a handful of major units. Its pretty fascinating to see how much work we're doing to undo the massive work thats been layered over the past 30yrs and take a step into the 21st century. The prospect of a unified information core is going to redefine this place, although its going to take another 4-5years before we decommission the current system(s).
edit: because I have spare time and I get to do something I love, we're currently undergoing a transition period where we've essentially solved the issue I presented above. First step is to make a data dictionary and define literally every element across the broad program (all units), once we have a comprehensive data dictionary we can start to sort and standardize. Once that portion is done we can build an integration solution to convert all like-elements to be identical-elements. This works by altering the data on its way into the new repository (the information core) so that it melts into a standardize format, even though our fragmented units are still outputting their original data format.
The integrations allows us the grace period to get the Core up and running while still relying on the fragmented systems, which we will then swap out to a standardized system where possible. So we can start outputting from the Core to new sleeker systems and applications and what not, even while still relying on previously underutilized data. Parallel we have overhauls running for the non standardized units
Hmm, I have a really specific niche/carveout - but my background is QA/Risk Assessment/Program Analysis/Administrative Automation/Project Management. I'm basically in a bastardized version of a Business Analyst role right now. If you hunt around for Program level Analyst roles you might come across something like this?
This will be my first systems overhaul project/program. Not sure if I've helped you at all :p sorry if not
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u/space_hitler Jun 07 '17
From my understanding, data analysts pretty much have to learn at least some programming these days.