July, 2012

article thumbnail

Web Analytics Consulting: A Simple Framework For Smarter Decisions

Occam's Razor

As I've gotten older I've come to appreciate the value of frameworks a lot more. When we are young, the answers to everything are simpler because, of course, we know everything. What metrics should I use? Use BR & CV. What digital marketing works? Definitely Y, do that. How can I improve my business? Simple, do A then B and you're done.

article thumbnail

Soda vs. Pop with Twitter

Edwin Chen

One of the great things about Twitter is that it’s a global conversation anyone can join anytime. Eavesdropping on the world, what what! Of course, it gets even better when you can mine all this chatter to study the way humans live and interact. For example, how do people in New York City differ from those in Silicon Valley? We tend to think they’re more financially driven and restless with the world – is this true, and if so, how much more ?

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Nutanix Complete Cluster-Product Updates

Nutanix

Nutanix is adding new product features at a rapid pace. I want to pause and take some time to talk about the advancements our engineering team has made.

20
article thumbnail

Wake Me Up When You Are Ready To Talk CapEx

Nutanix

The title of this entry is an actual quote from a recent meeting I had with a customer.

20
article thumbnail

Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

article thumbnail

Edge Prediction in a Social Graph: My Solution to Facebook's User Recommendation Contest on Kaggle

Edwin Chen

A couple weeks ago, Facebook launched a link prediction contest on Kaggle, with the goal of recommending missing edges in a social graph. I love investigating social networks , so I dug around a little, and since I did well enough to score one of the coveted prizes, I’ll share my approach here. (For some background, the contest provided a training dataset of edges, a test set of nodes, and contestants were asked to predict missing outbound edges on the test set, using mean average precisio

Metrics 81