Data Analytics vs. Analysis – What’s The Difference?
By Alan Hylands — 2 minute read
Forget the Python vs. R or Tableau vs. Power BI wars, there is nothing guaranteed to divide the data community quite as much as the Analytics vs. Analysis question.
What is the difference between Analysis and Analytics? Does one look at the past and one to the future? Is one of them reporting and the other data science?
So many questions, so many opinions and, unfortunately, no real concrete definitions that everyone subscribes to and agrees on.
Being controversial (for once…), I’ll say that if you have enough time to get so deep into the debate of what covers which area that you find yourself obsessing and arguing about it online, you probably aren’t giving the actual work the real focus it deserves.
It’s great to know exactly what someone means when they use a certain term but it’s not going to move the dial significantly on your results either way to know that it’s analysis or analytics you were supposed to be doing while you shot witty put-downs back and forward on Reddit or Hacker News.
David Kasik, the Senior Technical Fellow in Visualisation and Interactive Techniques at Boeing, defines the Analysis vs. Analytics divide as this:
“In my terminology, data analysis refers to hands-on data exploration and evaluation.
Data analytics is a broader term and includes data analysis as necessary subcomponent.
Analytics defines the science behind the analysis.”
If, by Kasik’s definitions, Analysis is a part of Analytics, what are we actually looking to distinguish here? I think it’s fair to say that the simple trope of Analysis looking at the past and Analytics looking at the Future isn’t quite correct.
Neither is being able to use the tools involved to split the two. If I use Excel for analysis, does that mean anything done in Excel can’t be Analytics? Of course not yet I’ve seen the argument used all over the web.