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Lavastorm Adds Transparency, Collaboration to Big Data Analytics

In a somewhat different take on democratizing Big Data, Lavastorm Analytics yesterday announced it was adding transparency and collaboration to analytics projects.

The idea is for non-technical consumers of analytics reports to have access to the logic that produced the reports -- the transparency aspect -- and then be able to adjust parameters to explore the data further with the project producer -- the collaboration aspect.

Instead of presenting a business analyst with a static report based on data analysis, for example, the project author provides a Web link to an analytic application. The analyst can view the resulting report or execute the application while viewing data flow logic, inspecting interim data and rerunning app scenarios with new dynamic logic parameters. In addition to the final report, the analyst gets insight into the "how" and "what" of the results, increasing trust in their veracity.

The company said its Lavastorm Analytics Engine 6.0 is meant to fix the typical current "broken" process of Big Data analytics.

The Lavastorm Analytics Engine
[Click on image for larger view.] The Lavastorm Analytics Engine (source: Lavastorm Analytics)

"This broken process provides non-technical consumers with only a static, end-result report that is segregated from the source data used to develop it," Lavastorm said in a news release. "Without transparency showing how results were obtained and what data were used, non-technical consumers often lack trust in the results to make key business decisions. Meanwhile, authors, in their attempts to maintain data governance, end up as bottlenecks and barriers, preventing transparency and trust."

The company claimed its Analytics Engine, using a visual data flow interface, lets users create analytic apps 10 times faster than traditional products. The flow moves from data acquisition to preparation to enrichment to analysis and finally to publication.

Now, the company said in a blog post authored just before the announcement, "We want to share how the analysis was constructed along with the underlying data being analyzed. This way, other members of the Big Data village inside a company can not only test and debug analytics -- to work on and assure the trust side of the equation -- but also see new questions or discover new insights. This also allows them to enter into a data-driven conversation with the 'author' or 'authors,' and in doing so, become meaningful contributors themselves"

The Boston-based Lavastorm in February announced enhancements to its Analytics Engine to democratize access to Big Data analytics by providing "an easy-to-use, drag-and-drop data preparation environment to provide business analysts a self-serve predictive analytics solution that gives them more power and a step-by-step validation for their visualization tools."

About the Author

David Ramel is the editor of Visual Studio Magazine.

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