Introducing Fizz

The response to our website and company launch on Monday has been great. We’re already hearing from people who are as excited about our vision for data expression as we are and we’re getting great feedback on our initial offerings, Fizz and Cartagram.

We’re also sensing a blend of curiosity and hope, especially from our friends at blogs like Infosthetics, Flowing Data and We’re working hard to fulfill that hope!

Our long-term plan is to build a product that offers many different visualizations that can be applied to a wide variety of data sources. We’re building the product one piece at a time, starting with Fizz.

Fizz shows recent updates from your network on Facebook or Twitter. Large circles are people, small circles are their updates. Typing in the search box highlights matching terms:

Fizz can connect to data from two places right now: Twitter and Facebook. Both of these are personalized to present recent updates from your own network of connections. We plan to add more data sources soon.

Designing Personal Data Visualizations

The personal nature of the data immediately presents an interesting design problem. How do we show you what Fizz is and does without knowing who you are and what data is relevant to you? We’ve introduced a wireframe mode to the visualization as one possible answer to this question.

Fizz is the first of many visualizations we’re building. It’s adaptated from a fairly common chart, the bubble chart (well implemented by our friends at Many Eyes and offered in open source libraries like Protovis) but we’ve adapted it to be more dynamic and playful. It’s a different way to look at textual information, like tweets, and as we develop it we’ll add extra layers of relationships and connections onto the foundation it provides. It’s also a nice stress test of the new features in browsers like Safari, Chrome and Firefox, and we’re using the Processing JS library to handle the drawing and animation.

As we add new data sources to Fizz we don’t want to be tied to a lowest common denominator treatment of that data. For example, if we add LinkedIn as a data source it might be easy to limit Fizz to showing people and status updates as it does for Facebook and Twitter but we might also want to represent people and companies instead. Ultimately, the Bloom platform will allow these choices to be made by anyone but for now we’re exploring them one by one. That’s why we began simply with Fizz for now, and it will gain in flexibility and expressiveness as we develop our tools.

If you have thoughts on features or inputs we should add to Fizz next then please let us know in the comments, on Twitter, or fill out or feedback form on Google Docs.


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