# Lost in translation: how to get your data analysis + web-facing technologies to work together effectively

![](https://stillsfrmfilms.files.wordpress.com/2013/12/04.jpg)

Far too often, there lies a wall between what you use to grab customers' attention on the web and the analyses you perform that pique customers' interest. Analysis lives on someone's terminal before it is distributed as "answers" to a question, hiding the techhniques used, which can make it harder to reproduce. On the other hand, the maze of frameworks, competing web technologies, and changing user needs keeps analysis from being interactive, growing the divide between the analysis and your ultimate consumer. 

R has been a language typically reserved for statistics; yet in the past few years, it has been extended with a host of packages that extend its reach to a broader audience, becoming one of the most used languages in data science. Through a framework called Shiny, data analysis can be shared easily in a responsive environment where web technologies and the underlying data techniques can play well together. 

I'll show you an example of how a data analysis pipeline can be extended with a server and a user interaction model in mind, connecting the strengths that your teams already have. Beyond a business focus, I will show how such a technique can be useful in the public sector, where spatial context to the U.S.'s energy sources can strengthen the messaging of proposed rulemaking.

# Lost in translation: how to get your data analysis + web-facing technologies to work together effectively

![](https://stillsfrmfilms.files.wordpress.com/2013/12/04.jpg)

Far too often, there lies a wall between what you use to grab customers' attention on the web and what analyses you perform to pique customers' interest. Typically, analysis lives on someone's terminal before it is distributed as "answers" to a question, hiding the techniques used, which can make it harder to reproduce. On the other hand, the maze of frameworks, competing web technologies, and changing user needs keeps analysis from being interactive, growing the divide between the analysis and your ultimate consumer. 

R has been a language typically reserved for statistics; yet in the past few years, a host of packages have extended its reach to a broader audience, becoming one of the most used languages in data science. Through a framework called Shiny, data analysis can be shared easily in a responsive environment where web technologies and the underlying data techniques can play well together. 

I'll show you an example of how a data analysis pipeline can be extended with a server and a user interaction model in mind, connecting the strengths that your teams already have. Beyond a business focus, I will show how such a technique can be useful in the public sector, where spatial context to the U.S.'s energy sources can strengthen the messaging of proposed rulemaking.

Split into small sections and reword first line.
Split into small sections and reword first line.
