Summary:

After nearly 18 months in relative stealth mode, ClearStory Data is finally available. It’s a pretty novel way of doing business analytics that tries to let lay users do more by automating much of the hard work.

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ClearStory Data has been teasing prospective users since it launched in March 2012, promising a next-generation analytics experience designed with business users in mind. The company has let out a few details since then — that it’s a cloud service, that it runs on Apache Spark to ensure low latency, and that it specializes in surfacing and “harmonizing” different datasets — but few people have actually got to see the product in action. On Monday, however, ClearStory finally took off the wraps and opened its service to the public.

I’m going to spare most of the details, because some of them you’ve heard before and others — like the company’s focus on business users versus skilled data analysts — are par for the course in the business intelligence world right now. Instead, here’s a collection of screenshots that help illustrate the ClearStory experience.

The user homepage, including all available “stories.” If stories include changing datasets, users will see the latest version when they open them.

Homepage

ClearStory suggests external data, including from premium partners like Factual, Dun & Bradstreet and Kantar, that might be helpful.

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Mapping internal data against business data from Factual. “Data harmonization” takes place on the Spark platform, meaning the common variables in datasets are recognized and modeled in real-time without user input.

Mapping data from two different sources.

Another view of that same data, this time with collaborators sharing comments. Each time something is shared or edited by a collaborator, it becomes a new document, which means previous work isn’t lost and users can actually track the progression of a story.

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More collaborators getting in on the act. This time, they’re looking at web video-viewing behavior data analyzed against analyzed against link domains.

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If a particular point in a graph is noteworthy, users can annotate it so others don’t miss it when they look at the graph.

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A couple other features worth noting are data inference engine that assists in properly preparing data once it’s connected to the ClearStory service (similar to an aspect of the Paxata service that also launched on Monday), and the ability for advanced user to create their own customized views using functions or SQL queries.

On the business front, ClearStory Co-founder and CEO Sharmila Mulligan said the company has had success winning early customers in areas such as consumer packaged goods and retail, including among some large enterprises. And the nice part for ClearStory is that customers don’t usually view it as a replacement for existing BI products, but rather as new tool that lets geographically distributed workers collaborate on data and also to let non-data-analysts (including store managers in some situations) work with data.

“We’re seeing a lot of white space in accounts where they’re looking for something they can put in the hands of average business users,” Mulligan said.

Right now, ClearStory is targeting enterprise users and priced with them in mind, although Mulligan said it does hope to offer versions more amenable to individual and small-business budgets and use cases at some point. Based on the demo I saw, there should be plenty of those smaller users lining up to use ClearStory, although it’s hard to blame a startup for focusing on large companies with deep pockets if they’re interested in paying real money for the product.

For a little more on the thinking behind ClearStory and its take on democratizing data, check out this presentation by Mulligan at our Structure: Data conference last March.

Update: Some of the original images in this post have been replaced with higher-resolution images provided by ClearStory Data.

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