6 Big Ideas on Big Data

At GigaOM’s Structure Big Data event in New York City, we gathered big data disrupters from Bundle, Revolution Analytics, Tap11, Stanford and other organizations to talk about how they’ve found new business opportunities, created architectures to better analyze the data and discovered new applications for big data. We’ll update this post throughout the day with their insights.

var galleryData = [{“title”:”Ted Nitka, Spiceworks”,”caption”:”Ted Nitka, evangelist of Spiceworks \”Cool New Things,\” explained how businesses can plug into the collective intelligence of Spicework’s shared IT management software to change the way their businesses are run. And to make more money. “,”thumbnail”:”http:\/\/gigaom2.files.wordpress.com\/2011\/03\/1z5o9743.jpg?w=48&h=48&crop=1″}, {“title”:”Lee Edelfsen, Revolution Analytics”,”caption”:”Lee Edelfsen, chief scientist at Revolution Analytics, addressed the hardware restrictions on managing large quantities of data, saying, \”The rate of increase of the capacity of hard drives is much greater than the increase of the speed of other components: cores, RAM, the speed at which we can write data to hard drives, etc.\” “,”thumbnail”:”http:\/\/gigaom2.files.wordpress.com\/2011\/03\/1z5o0130.jpg?w=48&h=48&crop=1″}, {“title”:”Jaidev Shergill, Bundle”,”caption”:”Shergill talked about how Citigroup found a business opportunity in its data and created Bundle. \”We noted that you could see where people lived, how much they spent at each restaurant, as well as which other restaurants they frequented and the relationships between those pieces of data,\” he said.”,”thumbnail”:”http:\/\/gigaom2.files.wordpress.com\/2011\/03\/1z5o0249.jpg?w=48&h=48&crop=1″}, {“title”:”Bruce Church, Dataspora\/Via Science”,”caption”:”Church talked about building a causal model to determine why patients are on hormonal replacement therapy and the unexpected insights from that. \”It’s not enough to measure one or two things very accurately,\” he said. \” You have to measure many, many things very accurately.\””,”thumbnail”:”http:\/\/gigaom2.files.wordpress.com\/2011\/03\/1z5o0661.jpg?w=48&h=48&crop=1″}, {“title”:”Mayark Bawa, Aster Data”,”caption”:”\”Data by itself being stored in great amounts isn’t interesting,\” said Bawa. \”53 percent of companies struggle to analyze data that isn’t in a traditional DBMS or DW format.\””,”thumbnail”:”http:\/\/gigaom2.files.wordpress.com\/2011\/03\/1z5o0892.jpg?w=48&h=48&crop=1″}, {“title”:”Michael Linderman, Stanford University”,”caption”:”Linderman, a biosciences research assistant, said, \”Big data analysis allows us, a smaller lab, to complete large-scale research for not a lot of money.\””,”thumbnail”:”http:\/\/gigaom2.files.wordpress.com\/2011\/03\/1z5o1380.jpg?w=48&h=48&crop=1″}];