I used to work in finance (esp Market Data) and now I've ended up in Employee Analytics.
One thing I've observed is that a user's requirements always flip, and they're usually not aware of it. The two biggest bits of feedback I've heard are "too much data" and "not enough data" -- often from the same person, and occasionally on the same dashboard.
People just want the highlights, except when they don't, in which case they want everything. The former is really hard because highlights require a lot of rule-based context. The latter is also hard because "everything" is often meaningless. Too much flexibility in your data exploration tool and people just go on random fishing expeditions.
Now I try and structure data in terms of a conversation, with an information "needs" to determine the priority order. Start with the hurdle requirements (e.g. something key like sample size), move through the highlights then present the bare minimum to guide further exploration. If you make data exploration easy from that point it this seems to work well, but I'm still learning.
I must admit, I took one glance at the "ideal dashboard" and was a bit bewildered. I had no idea what I was meant to be looking at, or the relative importance of things. Perhaps there is some critical, specific domain knowledge that I'm missing. Either way, will definitely get this book to find out more.
One thing I've observed is that a user's requirements always flip, and they're usually not aware of it. The two biggest bits of feedback I've heard are "too much data" and "not enough data" -- often from the same person, and occasionally on the same dashboard.
People just want the highlights, except when they don't, in which case they want everything. The former is really hard because highlights require a lot of rule-based context. The latter is also hard because "everything" is often meaningless. Too much flexibility in your data exploration tool and people just go on random fishing expeditions.
Now I try and structure data in terms of a conversation, with an information "needs" to determine the priority order. Start with the hurdle requirements (e.g. something key like sample size), move through the highlights then present the bare minimum to guide further exploration. If you make data exploration easy from that point it this seems to work well, but I'm still learning.
I must admit, I took one glance at the "ideal dashboard" and was a bit bewildered. I had no idea what I was meant to be looking at, or the relative importance of things. Perhaps there is some critical, specific domain knowledge that I'm missing. Either way, will definitely get this book to find out more.