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By Mayank Bawa in Analytics, Blogroll, Frontline data warehouse on November 6, 2008
   

I was at Defrag 2008 yesterday and it was a wonderful, refreshing experience. A diverse group of Web 2.0 veterans and newcomers came together to accelerate the “Aha!” moment in today’s online world. The conference was very well organized and there were interesting conversations on and off the stage.

The key observation was that individuals, groups and organizations are struggling to discover, assemble, organize, act on, and gather feedback from data. Data itself is growing and fragmenting at an exponential pace. We as individuals feel overwhelmed by the slew of data (messages, emails, news, posts) in the microcosm, and we as organizations feel overwhelmed in the macrocosm.

The very real danger is that an individual or organization’s feeling of being constantly overwhelmed could result in the reduction of their “Aha!” moments – our resources will be so focused on merely keeping pace with new information that we won’t have the time or energy to connect the dots.

The goal then is to find tools and best practices to enable the “Aha!” moments – to connect the dots even as information piles up on our fingertips.

My thought going into the conference was that we need to understand what causes these “Aha!” moments. If we understand the cause, we can accelerate the “Aha!” even at scale.

Earlier this year, Janet Rae-Dupree published an insightful piece in the International Herald Tribune on Reassessing the Aha! Moment. Her thesis is that creativity and innovation – “Aha! Moments” – do not come in flashes of pure brilliance. Rather, innovation is a slow process of accretion, building small insight upon interesting fact upon tried-and-true process.

Building on this thesis, I focused my talk on using frontline data warehousing as an infrastructure piece that allows organizations to collect, store, analyze and act on market events. The incremental fresh data loads in a frontline data warehouse add up over time to build a stable historical context. At the same time, applications can contrast fresh data with historical data to build the small contrasts gradually until the contrasts become meaningful to act upon.

I’d love to hear back from you on how massive data can accelerate, rather than impede, the “Aha!” moment.

Aster Defrag 2008 97
View SlideShare presentation or Upload your own. (tags: systems data)

Comments:
Datawarehouse Admin on November 9th, 2008 at 12:35 pm #

How can we download your product?

Mayank Bawa on November 9th, 2008 at 2:21 pm #

If you would like to try out Aster nCluster, please send an e-mail to info@asterdata.com and we’ll get you set up. Thanks for your interest!

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