Top 10 disruptive technologies for the next 4 years
According to Gartner, the top 10 disruptive technologies in the next four years are:
* Multicore and hybrid processors
* Virtualisation and fabric computing
* Social networks and social software
* Cloud computing and cloud/Web platforms
* Web mashups
* User Interface
* Ubiquitous computing
* Contextual computing
* Augmented reality
* Semantics
I personally think that each of these technologies is already here or is in progress. The most interesting ones to follow are Semantics and Cloud Computing.
Visualizing Large Graphs
Pretty cool collection of large graphs presented here from the University of Florida Sparse Matrix collection.
Here are some samples.
Your Mobile Phone Data —> Your Habits
The whereabouts of more than 100,000 mobile phone users have been tracked in an attempt to build a comprehensive picture of human movements.
The study concludes that humans are creatures of habit, mostly visiting the same few spots time and time again.
Most people also move less than 10km on a regular basis, according to the study published in the journal Nature.
The results could be used to help prevent outbreaks of disease or forecast traffic, the scientists said.
“It would be wonderful if every [mobile] carrier could give universities access to their data because it’s so rich,” said Dr Marta Gonzalez of Northeastern University, Boston, US, and one of the authors of the paper.
Dr William Webb, head of research and development at the UK telecoms regulator, Ofcom, agreed that mobile phone data was still underexploited.
“This is just the tip of the iceberg,” he told BBC News.
More here from BBC
New marketing school by Drayton Bird
It is very interesting to learn that Drayton Bird, the man who according to David Ogilvy knows more about direct marketing than anyone else in the world, is starting a new marketing school in Europe.
Read more here.
SuperNap - The largest data center ever!
Drive a couple of blocks past the Loose Caboose and the Carburetor Shop on E. Sahara Avenue in Las Vegas, and you’ll find one of the world’s leading technology companies. The name of the company - Switch Communications - will go unrecognized by almost all of you. That’s because it has operated in near total secrecy for the last few years. Switch has preferred to keep its gold mine a need-to-know type of affair. “Pay no attention to the secure fortress in the strip mall.”
A few months ago, word of Switch’s apparently fantastic operations started to reach my in-box. Most of the people who visited the Switch facility were bound by non-disclosure agreements, but that failed to stop them from leaking out a few choice details. “This is the most advanced computing center in the world,” I was told. “It’s like the internet superhighway wrapped up in one package. All the heavies are there.”
Ever a cynic, I struggled to match these claims with the total lack of public information available on Switch. Companies fall all over themselves to issue press releases about things as a minor as cost-savings achieved by changing toilet paper suppliers. If a technology giant really existed in Las Vegas of all places, then it should be patting itself on the back and then letting city officials finish off the job with celebrations of their own.
As Switch’s CEO Rob Roy tells it, however, the company had good reason to avoid publicity.
Legend has it that the company managed to acquire what was once meant to be Enron’s broadband trading hub for a song. This gave Switch access to more than twenty of the primary carrier backbones in a single location. Switch tied this vast network to existing data center hosting facilities and attracted military clients, among others, to its Las Vegas shop.
Read more here.
Predictive Modeling 101
I read a very good article from marketingsherpa which explaines the basics of predictive modeling. A very good read for someone who is looking for an introduction to the art and science of predictive modeling. Enjoy the article!
How to Create a Predictive Model
A predictive model determines the probability of a certain outcome based on a target — what you want to predict. You use data-mining software to sift through your customer database.Every category of customer information — age or favorite color or buying frequency or how many times a customer visited your store in the past year — is a variable collected as a predictor of future behavior. A predictor is your model’s central building block.
For example, you want to predict which customers will visit your store at least five times in the next 12 months. Here’s a simplified version of what you need to do:
-> Step #1. Prepare your data
Preparing data is the most difficult and complicated step in the process. We’ll talk about why and what you can do about it later.
“It’s estimated that 70% to 80% of the time devoted to an analytical project is devoted to data preparation. It’s just getting the data in the one place in the right form to actually start building models,” says Richard Hren, Director Product Marketing, SPSS.
->Step #2. Set your target
Your target is the customers who will visit your store five times in the next year. For this example, the target is the same as one of the variables — customers who visited the store five times in the past year.
->Step #3. Determine the most important variables
Determine which variables are most relevant to your target. Some types of data mining software will dig through data and tell you. Other packages depend on your judgment to determine which variables matter most. Some software will do both: tell you what it likes and allow a statistician to tweak it.
->Step #4. Run program to get a model
The software weighs the importance of each variable and creates a model — think of it as an equation. You fill in each variable in the equation and then the model calculates and gives higher scores to customers with the greatest probability of visiting your store more times in the next year.
Usually, you don’t have to score one customer at a time. You can build a model to automatically score a database of these higher probability customers.
The next generation web and data mining
I truly believe the next battleground will be based on scaling the back end and more importantly mining all of that clickstream data to offer a better service to users. Those that can do it cheaply and effectively will win. The tools are getting more sophisticated, the data sizes are growing exponentially, and companies don’t want to break the bank nor wait for Godot to deliver results.
More here from Ed Sim from BeyondVC.
The art of logic
I came across this very cool website which has puzzles, when solved reveal the hidden pixel art pictures.
If interested, visit conceptispuzzles.
Implementing Google Analytics for a Successful Interactive Marketing Campaign
According to a recent Forrester Research report, interactive marketing is expected to reach $61 billion by 2012. This includes channels such as search engine marketing, online display advertising, e-mail marketing and other emerging media.
As more consumers are going online to research and purchase products and services, it’s essential to capitalize on the array of tools available to optimize the performance of interactive programs.
In order to gain insight into the effectiveness of your Web site (and, ultimately, your interactive campaigns), you must have some form of Web analytics in place. While there are numerous tools available, such as those from WebTrends, Omniture and Coremetrics — all of which will help you to track and monitor the traffic performance of your site — you must first evaluate what level of analytics you really need. For this reason, a good place to start is with Google Analytics.
Read more here.
Online TV ads no longer afterthought
Conscious that millions of people are now watching TV shows online, marketers are likely for the first time this year to make digital-ad buys a key part of their “upfront” ad-purchase negotiations with TV networks, media buyers say. “The digital ads aren’t a throw-in after the main conversation is over. It’s now part of the main conversation,” says Alan Schanzer, managing partner at MEC Interaction North America, part of WPP Group’s media-buying and planning unit Mediaedge:cia. Major TV networks sell about 75% of their ad inventory for the coming fall season during the upfront. Digital ads historically haven’t made up a significant portion of these buys. But marketers say they increasingly are looking to purchase digital ads as part of a package with their standard TV commercials so that they can reach the audiences watching a show regardless of whether it is on TV or the Web. Web audiences have become sizable enough that they can’t be ignored.
Source:Wall Street Journal
