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Posts tagged ‘teradata’

11
Jun

Data/BI/Analytics Evolution @ NetFlix


More data + Better models + More accurate metrics + Better approaches & architectures = Lots of room for improvement!

netflixIt’s amazing to watch how quickly the data engineering / analytics/ reporting/ modeling/ visualization toolset is evolving in the BI ecosytem.

There are clearly massive foundational shifts taking place around big data. I am not sure how large conventional Fortune 500 firms can innovate and keep up with what’s going on.  I have run into CIOs who have not heard of Hadoop in some cases.

It’s also fascinating to see how data-driven “bleeding” edge firms like NetFlix are pushing the envelope.  Netflix stats are amazing:  1/3+ Internet traffic (NA / peak);  100+ Million hours per day; 65+ Million members / 50+ countries; 500 Billion Events / Day.

NetFlix is clearly reinventing Television and targeting 90 million potential subs in the US market alone.  Binge-watching, cord-cutting are now part of our everyday lingo. What most people don’t realize is how data-driven Netflix is…. from “giving viewers what they want” to “leveraging data mining to boost subscriber base”.

Viewing -> Improved Personalization -> Better Experience is the virtuous circle.

Here is a glimpse at how their BI landscape has evolved in the past five years as they integrate 5 million to 6 million net adds for several years now.  The figures are from a presentation by Blake Irvine, Manager Data Science and Engineering.

BI tools @ NetFlix pre-Hadoop

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18
Mar

Making Money on Predictive Analytics – Tools, Consulting and Content


Here are just a few examples of analytics at work

  • Target predicts customer pregnancy from shopping behavior, thus identifying prospects to contact with offers related to the needs of a newborn’s parents.
  • Tesco (UK) annually issues 100 million personalized coupons at grocery cash registers across 13 countries. Predictive analytics increased redemption rates by a factor of 3.6.
  • Netflix predicts which movies you will like based on what you watched.
  • Life insurance companies can predicts the likelihood an elderly insurance policy holder will die within 18 months in order to trigger end-of-life counseling.
  • Con Edison predicts energy distribution cable failure, updating risk levels that are displayed on operators’ screens three times an hour in New York City.

Now you are interested.  So what about your organization. Do you have the right toolset, dataset, skillset and mindset for analytics? Do you want to enable end users to get access to their data without having to go through intermediaries?

The challenge facing managers in every industry is not trivial… how do you effectively derive insights from the deluge of data? How do you structure and execute analytics programs (Infrastructure + Applications + Business Insights) with limited budgets?

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