DBTB INT Katherine Ahern r

my name is Catherine a Hearn and I my name is Catherine a Hearn and I managed the visualization and analytics managed the visualization and analytics Department at clear story data there are Department at clear story data there are two things one is it gave me a deadline two things one is it gave me a deadline to accomplish some things that I to accomplish some things that I promised for the talk which was very promised for the talk which was very helpful for me professionally and the helpful for me professionally and the other is that I came early and I'm other is that I came early and I'm getting to hear some real industry getting to hear some real industry leaders just amazing talks particularly leaders just amazing talks particularly on machine learning and I've been on machine learning and I've been interested in predictive analytics and interested in predictive analytics and UX on large data sets for a while so UX on large data sets for a while so seeing both how young the industry is is seeing both how young the industry is is very validating because when clear story very validating because when clear story handles some very challenging projects handles some very challenging projects you sort of sometimes I feel like it the you sort of sometimes I feel like it the answer should be obvious but it's answer should be obvious but it's actually a pretty young industry so actually a pretty young industry so we're all working on pretty new we're all working on pretty new technologies and using new metaphors technologies and using new metaphors using new techniques I've always thought data was cool and I've always thought data was cool and exciting um you know when I started grad exciting um you know when I started grad school we were talking about the data school we were talking about the data explosion and I was in 2004 and it was explosion and I was in 2004 and it was amazing that you could have terabytes of amazing that you could have terabytes of data that's just psychotically large was data that's just psychotically large was like more than in history and there was like more than in history and there was this hockey stick curve to just sheer this hockey stick curve to just sheer quantity and now my my own dad is quantity and now my my own dad is talking about needing a bigger computer talking about needing a bigger computer because he needs a terabyte to store his because he needs a terabyte to store his vacation photos just the the scale is vacation photos just the the scale is really I didn't think it would get really I didn't think it would get bigger but it has by many many multiples I know it's yeah I mean there was the I know it's yeah I mean there was the perception that things couldn't get perception that things couldn't get faster that there was gonna be some sort faster that there was gonna be some sort of plateau but that was a decade ago you of plateau but that was a decade ago you know 15 years ago pay attention to user intention clarity pay attention to user intention clarity of communication availability of of communication availability of workflows and whether that will yield workflows and whether that will yield correct analytics results because correct analytics results because they're there ways to mislead people they're there ways to mislead people that can be subtle and but true so ways that can be subtle and but true so ways to sort of bring in tension ality to to sort of bring in tension ality to user selection of data I'm speaking user selection of data I'm speaking specifically of aggregation levels of specifically of aggregation levels of aggregation and granularity and so ways aggregation and granularity and so ways of bringing user attention to that but of bringing user attention to that but not overwhelming a user with workflows not overwhelming a user with workflows or selection opportunities that are or selection opportunities that are irrelevant because that is a tough irrelevant because that is a tough balance in developing a analytics balance in developing a analytics product uh well I you should learn d 3 and then uh well I you should learn d 3 and then come work at clear story if you want to come work at clear story if you want to become great if you want to become good become great if you want to become good enough you can um work for other enough you can um work for other companies