I work with a wide variety of people with diverse cognitive and analytical skills, analytical tools, database experience, and programming backgrounds. One of the common themes used in talking about using Tableau is a variation on this post's title theme, e.g. :
- "Let's load that data into Tableau."
- "Why don't we load that data into Tableau and see what comes out."
- "Once we load that data into Tableau we'll be able to make sense out of it."
In many cases this is understandable given the history of BI, in which data had to be reaped, consolidated, and integrated into special databases before it could be analyzed. On the other end of the scale it's also almost universally true in people's Excel experience, where the data must be contained in worksheet rows and columns before it's available for examination.
While there are circumstances wherein this makes good sense, it's positioning as the one and only true way has been a real failing in the traditional BI paradigm. But it's an erroneous casting of Tableau's relationship to data, and completely misses one of the primary points that makes Tableau so wonderfully powerful, flexible, and useful—Tableau lets you look at your data where it lives.
That's important enough to be worth repeating.
Tableau lets you look at your data where it lives
(add to it:) instantly, simply, and effectively.
This simple fact has changed the relationship between people and the data they need to understand from a remote, distant, disconnected one with many barriers to one that's intimate and immediately rewarding. Communicating "load the data into Tableau" completely misses this essential point and consequently misses the chance to reorient the human-data relationship. Worse from a Tableau perspective is that it casts Tableau as just another tool and in this conception the natural inclination of people who don't understand its essential value is to dismiss it as another barrier for them to climb over with no clear corresponding benefit to reward the effort. Tableau's greatest gift is in lowering the friction involved in achieving data understanding and this doesn't come through.
It's particularly cringe-worthy to hear people who should know better use the bad phrasing. In my current environment there are client-facing people who are Tableau enthusiasts, most of them fairly new to it, and to hear and see them extol its virtues with this phrasing and urge their clients to embrace Tableau is to see opportunities lost. In many cases I can almost see an initial glimmer of hope get dimmed as people don't see the "this is going to be great for me" message they're hoping for in the fog of more empty promises.
But it's just semantics.
Is a common refrain from people who think that this sort of nit-picky language fussiness is somehow misplaced, wrong, or the mark of a language snob with nothing better to do than police language political correctness (as if that would be a bad thing).
Saying "it's just semantics" is lazy, sloppy, and unprofessional for someone whose responsibility is to help people understand anything. When it come to using language to communicate information semantics—the meaning of words—is everything. It's all there is and to not care what meaning one's conveying is a fault.
And that's all I have to say about that.