If we don’t understand the data, information, and content we’re working with, how can we attempt to explain it to others? Worse yet, how can we (and they) make informed decisions based on it if none …
If we don’t understand the data, information, and content we’re working with, how can we attempt to explain it to others? Worse yet, how can we (and they) make informed decisions based on it if none of us really understands? I’ve spent the past year and a half finding and experimenting with tools to help solve some gnarly enterprise information architecture problems. I was immersed in a world of information that is often duplicated, stale, or untrustworthy, and I was asked to make sense of it. Traditional IA tools couldn’t provide the answers. I wasn’t trying to design a website or app, or produce a deliverable for its own sake — I was trying to understand the information piling up around me, and help others make decisions based on it.
Using my own work as an example, I’ll help you answer and explore questions like:
- Should we choose our tools not for what they produce, but for what we can learn from them?
- Where can we even start when we’ve got a large amount of data to figure out?
- What can we learn by playing with information and transforming it in different ways?
- How might we better understand the scope, limits, relationships, patterns, and structures in our information?
Finding useful tools can be difficult, and they can be found in unlikely places. We’ll look at several uncommon tools that have been valuable in my own work, including Gephi, Google Refine, and Tiddlywiki. I’ll share my successes and failures and discuss how I’ve learned to evaluate these tools. If they are unfamiliar, I’ll show you enough to get started using them immediately. After brief overviews and basic examples, you’ll be armed with ways to improve your own work, and new directions to explore!