Occam's Razor

MARCH 12, 2012

Is there anything in the analytics space that is so full of promise and hype and sexiness and possible awesomeness than "big data?" I don't think so. So what is big data really? No one quite knows. As I interpret it, big data is the collection of massive databases of structured and unstructured data. The data sources include traditional (now considered puny) sources like corporate ERP / CRM systems and non-traditional (massive) sources like every technical ping from every human or

Big Data 127

Big Data Data-driven Unstructured Data Marketing 127

Edwin Chen

MARCH 4, 2012

It’s often easier to understand a chart than a table. So why is it still so hard to make a simple data graphic, and why am I still bombarded by mind-numbing reams of raw numbers ? (Yeah, I love ggplot2 to death. But sometimes I want a little more interaction, and sometimes all I want is to drag-and-drop and be done.). So I’ve been experimenting with a small, ggplot2-inspired d3 app.

Interactive 52

Interactive Visualization IT 52

Howard Dresner

MARCH 12, 2012

Hello Folks! We're only three weeks away from the end of data collection for the 3rd annual Wisdom of Crowds TM Business Intelligence Market Study. Data collection will end on April 2, 2012 at 5PM ET. So, if you have not yet completed a survey, please click on this link now! Here are some of the study demographics to date: Geographies : To date, the study has the largest contribution from North America with 56% and EMEA contributing 26%.

Business Intelligence 40

Business Intelligence Marketing Data Collection IT 40

Nutanix

MARCH 6, 2012

I've spent the last several years helping to implement cost-saving technologies, such as virtualization, at various Federal agencies.

Technology 20

Technology 20

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Visualization

Edwin Chen

MARCH 19, 2012

Imagine you’re a budding chef. A data-curious one, of course, so you start by taking a set of foods (pizza, salad, spaghetti, etc.) and ask 10 friends how much of each they ate in the past day. Your goal: to find natural groups of foodies, so that you can better cater to each cluster’s tastes. For example, your fratboy friends might love wings and beer , your anime friends might love soba and sushi, your hipster friends probably dig tofu, and so on.

Modeling 81

Modeling Statistics IT 81