Our research group typically prototypes and iterates new ideas and algorithms in Python and then rewrites the algorithm in C or C++ once the algorithm is proven effective,” said Vijay Pande, professor of Chemistry and of Structural Biology and Computer Science at Stanford University. “CUDA support in Python enables us to write performance code while maintaining the productivity offered by Python.”

Support for CUDA parallel programming comes from NumbaPro, a Python compiler in the new Anaconda Accelerate product from Continuum Analytics. This support was made possible by Nvidia’s contribution of the CUDA compiler source code into the core and parallel thread execution backend of LLVM, a widely used open source compiler infrastructure. Read the Full Story.

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Analytics is not a descriptive exercise; it’s a predictive exercise. Therefore . . . there’s uncertainty. Maybe some of our focus should be on helping the organization understand the ‘bounds of uncertainty’ and the actions we can take within those ‘bounds of uncertainty’. We’re at a point where we can’t ignore any data telling us about the effectiveness of our business strategies. The stakes are just too high,” says David Kreutter, Pfizer VP of U.S. Commercial Operations. “We’re taking analytics from a planning perspective to a planning, execution and evolution perspective, so it becomes much more operational than it’s been in the past. The overall direction going forward is that analytics will be integrated, meaning that secondary market research, market analytics and management science will be integrated into a single analytical function. And it will be integrated on a global basis. The question isn’t ‘how much money do we spend on data and analytics.’ It’s ‘how much value are we getting from data and analytics.’

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Some people look at every interaction or project as a performance, a test of their intelligence. Others look at those same interactions as opportunities to learn, to engage, to gain more knowledge. I want to hire the latter. I prefer questions . . .  where there is not a single, universally-accepted method or technique. It gives [the applicant] the opportunity to take a stance, express an opinion, and defend it based on [their]  experience. I also want to hear if [the applicant] has experimented with different approaches and what  has [been] learned from those experiments.

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People offline are not necessarily who they say they are when they’re online. Social media user accounts are personas. Without a reliable and substantive signal in place—such as money changing hands—the relationship between an online persona’s social sentiment and an offline person’s commercial value is weak. For the vast majority of businesses, analytical resources are probably better deployed almost anywhere other than on social media analytics. Online, for most businesses, is just another sales channel and should be evaluated accordingly

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The system can suck data in from ERP systems as well as unstructured data such as emails or documents. “Whatever you want to analyze, you can compress and analyze,” Larry Ellison said, taking a jab at companies such Autonomy (soon to be acquired by HP for $10.3bn ) that sell software for analyzing unstructured data. The Exalytics appliances are also aimed at IBM’s own Smart Analytics Systems, which pair its InfoSphere data warehouse with its Cognos BI tools. Now, with Exadata and Exalytics, Oracle can sell one-on-one against Big Blue.

Our pal Timothy Prickett Morgan at The Register has the low down. Read the Full Story

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One might argue that because chief information officers are theoretically in charge of information, this task would fall under their purview. But there is a world of difference between managing data and understanding the business value of that data; hence the need for a new class of business data specialists.

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Nathan Yau

You can spend a lot of time with icons or fancy print, but the graphics are interesting because the data that the visuals represent is interesting.  It should always be data first.  Certain graphics get eyeballs because they show something that wouldn’t be seen in a table. Visualization is less about the individual values and more about the distribution of them over time and space.

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Digital photography transformed an industry by eliminating obsolete layers. In-memory analytics and related technologies will do the same. Data warehousing won’t vanish overnight, but it will inevitably be relegated to particular types of tasks as in-memory analytics becomes more robust and takes on larger volumes of data. If your job relies on your existing data warehousing skills, better get used to the new world, or move to another role.

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Thanks go out to David Smith of Revolution Analytics.  His blog post pointed us towards this piece.

Download the Whitepaper (PDF)

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Data Mining is not an unfocused search for anything interesting. It is a method for answering a specific question, meeting a particular need.  Data Mining equals deployment. Without [it], you have may have done something valuable . . .  but you have fallen short.

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