Visualization startup Datahero opens its doors and delivers data analysis for the masses

When I first met Datahero Co-founder Chris Neumann a year ago, I was pretty excited about what he claimed his new company was going to do. Essentially, he told me, it was going to offer a simple, cloud-based data analysis and visualization service that anyone could use. About a month later, in late May, I got a demo of a very-early-stage Datahero and was impressed with the vision. On Tuesday, the company is officially opening its service to a public beta, and the more-finished product still strikes the right chord.

Before evaluating Datahero, though, it’s important to know what it’s not. Namely: it’s not enterprise software, it’s not even business intelligence software and it’s not designed for people who hope to run complex analyses. Neumann nicely summed up what Datahero is during a recent call: “We’re gonna make it usable by the masses,” he said, which means there are going to be some things advanced users want that the service doesn’t do.

By and large, what I wrote in May about the company’s vision and product holds true today (although the product is obviously more polished in terms of aesthetics and functionality). The service’s “data decoder” — which Neumann said received a majority of the development resources over the past year — does a good job characterizing data types and determining whether they’re quantities, dates, email addresses, currencies or whatever else they might be. As I explained in my “data for dummies” post in January, formatting data can be a pain, sometimes even after it’s uploaded to a service, so easing that burden is critical.

Now's your chance to correct the data decoder's choices.

Now’s your chance to correct the data decoder’s choices.

Probably the biggest difference since last May is the addition of an import feature from data sources that lots of Datahero’s core audience of individual developers, startup employees, and other resource- and analytics-skill-strapped professionals are likely to use. These include crm), Google(s goog) Drive, Stripe, Github, MailChimp, Box and Dropbox. Really, Neumann explained about the decision-making process, the Datahero team just asked “what were the ones [its users] were clicking ‘export to Excel’ on the most.” easily has the most difficult APIs to work with when it came to building an easy import process, Neumann said, but it was necessary to connect with “if only because so many people use Salesforce and so many people hate it.” It’s pretty much held together with duct tape, he joked, and the recommended process for building a report is essentially to dump everything into Excel and go from there.

Once you’re actually visualizing, though, it’s quite literally as simple as drag, drop and maybe a few mouse clicks to filter stuff. Using a process Neumann and co-founder Jeff Zabel call “chart magic,” Datahero suggests certain charts based on the data set or let users make their own. Then, the app automatically creates what it thinks is the best chart for visualizng those particular variables (although switching to other types is really easy). Neumann said this capability is the result of lots of research about best practices for visualization, as well as the company’s own user testing and a little common sense.

Charting the frequency of terrorism incidents in the U.S. by year and target.

Charting the frequency of terrorism incidents in the U.S. by year and target.

“Every time I would think something is simple and intuitive, [Zabel] would say, ‘You’re crazy, you don’t know what you’re talking about,'” Neumann joked. And, overall, Datahero’s Odd Couple approach to building an analytics service (Neumann is a data engineer who cut his teeth at Aster Data, while Zabel is a UI specialist formerly at BMW) seems to be working out.

Among planned features for later iterations is the ability to personalize chart selections based on a user’s past decisions and transitions from chart to chart.

But nothing’s perfect. After experimenting with it, other things that would be nice to have include the ability annotate points on a graph and perhaps to embed finished charts rather than just export as an image file (although perhaps that shortcoming is just in the pre-release version I have been using). And although Datahero isn’t trying to compete with Platfora in big data visualization or ClearStory in business analytics, it could take a page from their playbooks and render charts in HTML5 to make them easier to slice, dice, zoom in on and generally interact with.

Still, Datahero by and large does what it claims to do, and that’s important as we transition into a society consumed by data. But even with more and more data available, it will be more a dictatorship than a democracy if only a few people control the means to analyze it. I think Datahero, along with statistics-focused startup Statwing, fills a necessary place on the spectrum between Infogram and Tableau Public in terms of offering a simple product for doing analytics that does more than just make charts.

It’s not yet the easiest place to make a living, but that could change as more people get interested in digging into the piles of data they’re generating on their own terms.

You can check out the gallery below for a step-by-step process of the Datahero service and some example visualizations. I used two data sets from the Guardian’s DataBlog to experiment with — one on the 100 most-followed musicians on Twitter, and another on terrorism in the United States between 1970 and 2011.