This is part one of a two-part series on building effective visualizations. In this post, we take a shallow dive into evaluating existing visualizations. In the next post, we’ll dive a little deeper as we explore techniques on how to improve them.
A few years ago we worked with the Alberta Government on a tool that would make Fish Consumption Advisories more accessible to the general public. And after working its way through the government’s approvals process that tool is finally here.
If you aspire to be a data scientist, you’re really aspiring to be a data wrangler. You see, 80% of your working hours will be spent wrangling the data. That’s on average. On some projects, you will spend more than 100% of your “working” hours with your lasso. I hope you enjoy that sort of thing.
If you’ve tried to visualize your data with a map, you know how time-consuming it can be. It shouldn’t take so long or be so difficult, so we built MapInSeconds.com, which takes your data and generates a map - in seconds.
An analytics project is really three projects: a change management project stacked on top of a software development project stacked on top of a research project. So, how does one prevent the whole thing from collapsing under its own weight?
You’ve assembled a team and they’re ready to transform your organization into a data-driven decision-making juggernaut. But where should they start? How do you coordinate this team so they’ll actually be effective?
People highly value style, even to their own detriment. That means we need to value style but execute without causing detriment. We need to find a way to ensure that we not only deliver data in a meaningful way but also seek to deliver it in a compelling/engaging style.