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Top 10 Big Data Analytics Blog Posts

Feb 21

Written by:
2/21/2013 5:41 AM  RssIcon

We’d like to share the ten Big Data & Analytics blog posts which we believe are the most insightful, entertaining and useful posts of 2012. These posts received–and still receive–a lot of reader interest and cover a range of big data and predictive analytics topics from specific business uses to the future of data warehouses in the Hadoop world to futuristic thinking of where predictive analytics may be leading us (the good and bad)–and more.

Dig in!

Darth Big Data Darth Vader, Big Data, and Predictive Analytics
Jim Harris
Remember that Darth Vader did not intentionally become a cyborg (i.e., a fusion of humanity and technology). You needn’t lose your limbs in lightsaber battles with big data, or get burned in a lava flow of information overload, or literally become a cyborg in order to accept that humans and computers must work together to bring balance to the Analytical Force.

Sampling is Senseless –Where Predictive Analytics Fail
Paige Roberts
If you’re trying to get crucial business insights from your data, and you have to throw 90% of it away before you start because the insights would come too slow or not at all if you didn’t, that’s a big data problem.

Face It: Your Analytics Projects and Models Are Energy Guzzlers
David Inbar
Isn’t it time that we challenged the data science profession to measure itself by the efficiency of its products? We all know that MapReduce is often very slow, even on large Hadoop clusters, but we seem to accept it as a necessary evil when, for example, we wouldn’t tolerate the idea of driving an 18-wheeler for a short trip to the grocery store.

Big Data and Marketing: Creativity Meets Science
Julie Hunt
For marketing groups and their companies to get the most from the insights generated by big data analytics, they will have to become ‘analytics-focused’ organizations and abandon old ways for developing marketing programs. This requires decision-makers in the marketing organization to change to a culture of using marketing analytics to help formulate answers to important questions. This also means a big change from the long traditional focus on brand marketing (‘inside-out’ marketing) to now embrace customer-responsive marketing, with big data analytics fueling new marketing processes.

Is DW before BI going Bye-Bye?
Jim Harris
The Rumble in the Data Jungle coming from the so-called “Big Data Revolution,” which as Mike Hoskins recently explained, “is exposing how technically obsolete the existing data warehousing infrastructure really is. Relational databases were invented for transactional workloads, but they eventually came to be used for analytical workloads as well. Having a standard database for all workloads, whether they were transactional or analytical, made some sense until now. Relational technology is not well suited to large-scale analytical workloads. Big data analytics is going to occur on more modern technology infrastructure, such as Hadoop.”

Machine Learning Does Machine Learning + Predictive Analytics = Asimov’s Psychohistory?
Paige Roberts
It’s the concept of analyzing data to accurately predict human behavior that seems particularly timely, and a little creepy. Without venturing into the realm of science fiction, I find a bunch of examples in daily life where this is already happening. I don’t have to wait a thousand years for a galactic empire to form for machine learning to predict the course of my life. But I think Asimov, and his imaginary counterpart, Hari Seldon, had it completely wrong in one way: Individual human behavior is highly predictable.

Retailers: Compete in the Holiday Shopping ‘Omnichannel’ with Big Data Insights
Richard Maddox
So what is the optimal lead time to sales when so many consumers are using smart phones, tablets and other technology to aid their holiday purchases?  Well, throw conventional marketing notions away: Time to conversion is significantly shorter–increasingly real or near real time–and dependent on lots of data.

It’s Not About Being Data-Driven
Jim Harris
 Is your organization ignoring valuable information that could give it a competitive advantage? I don’t just mean the myriad of new data sources created by our increasingly data-constructed world. Is your organization also leveraging the intuition of your business leaders and subject matter experts? In the era of big data, it’s not about being data-driven—because your organization has always been data-driven. It’s about what data your organization is being driven by—and whether that data is driving your organization to make better decisions.

Transportation Analytics Big Data Analytics and Smart Transportation–Part 1
Julie Hunt
An efficient and effective platform for mining big data, preparing it for analytics, and integrating disparate sources and data formats is at the heart of handling urban transportation as a ‘system of systems’. The next step is to embed analytics and intelligence processes directly in the transportation infrastructure for real-time operational responsiveness, and for interacting with citizens during transportation experiences.

Big Data Analytics and Smart Transportation–Part 2
Julie Hunt
A lot of the data related to transportation networks is actually linked by spatial locations, and, in fact, most transportation data needs to be temporally and spatially linked for use in analytics. Using a GIS framework for data sharing and data integration processes helps connect socio-economic activity data to locations, enabling analytics for such activities as travel incident handling, routing, and demand forecasts.

Our Archive
Pervasive Big Data & Analytics bloggers have been at it for a while now, and there’s a lot of good information in our blog archive. We hope you’ll take some time and read some of our older blogs.
We’d also love to hear from you–tell us what topics you’d like to see us blog about.


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