At Tableau, a key cultural value is to “eat our own dogfood”. Over the last several years, we’ve had an on-going internal project (“Project ALPO”) focused on connecting Tableau to our own operational data, ranging from SalesForce CRM data to Fogbugz defect data, to data from our Cisco router. Not only has this given us the typical benefits of improving the team’s familiarity with the product, getting usability feedback, and finding bugs, but it has also enabled us to reap the benefits of analyzing our business’s core metrics.

As a developer, usability fanatic, and Tableau co-founder, I’ve been fascinated by several aspects of our Project ALPO to use our own products. One of the most interesting elements has been to watch how each employee’s perspective on data and its utility to their day-to-day job changes as they use the product. There are two findings I’ve seen with most new users:

1. People don’t know what questions to ask of their data. Most tools for asking questions of data are difficult to use and require a heavy investment for each new question. As a result, people rarely venture to ask new questions and invest little time thinking about what questions they would ask if they could. People stumble a bit with the possibilities: The blank palette can be intimidating at first. But then, they start asking simple questions (“How many bugs do I have open?”). When the answers come easily, they venture to ask more sophisticated questions evolving into rapid-fire Q&A sessions with their data (“How rapidly are bugs verified and closed after being resolved?”, “What percentage of revenue this quarter is due to new business compared to previous quarters?”). It is an exciting process to witness.

2. Visual analytics? I’ll take a text table, thanks. The transition to visual analytics is incremental. Tableau is really two tools in one: (a) an easy to use tool for Q&A with your data and (b) a data visualization tool. Most people start using it for the former and then incrementally venture into using it for the latter. People start by simply recreating the reports they can already generate, simply gaining the benefits of quicker and easier answers. But for almost every use, they slowly venture into visual analytics. One day they take their traditional text table and drop a metric on color, highlighting the anomalies. A week later, they tweak the table, transforming it to a tabular bar chart, communicating the same data as before but new information starts to jump out. Then they create a new sheet with a whole new view of their data and soon they are experts in the process of visual analytics and seeing their data like never before.

Over the next several weeks, I’ll author several blog posts outlining how we’ve connected Tableau to numerous common data sources. For this post, I’m focusing on a data source that I care deeply about, our defect-tracking system Fogbugz. I’ll follow up soon with a posting about SalesForce.com and our evolving use of visualization in our analytics process.