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Data mining

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hal380The advent of Salesforce Marketing Cloud and Adobe Marketing Cloud demonstrates the need for enterprises to develop new ways of harnessing the vast potential of big data. Yet these marketing clouds beg the question of who will help marketers, the frontline of businesses, maximize marketing spending and ROI and help their brands win in the end. Simply moving software from onsite to hosted servers does not change the capabilities marketers require — real competitive advantage stems from intelligent use of big data.

Marc Benioff, who is famous for declaring that “Software Is Dead,” may face a similar fate with his recent bets on Buddy Media and Radian6. These applications provide data to people who must then analyze, prioritize and act — often at a pace much slower than the digital world. Data, content and platform insights are too massive for mere mortals to handle without costing a fortune. Solutions that leverage big data are poised to win — freeing up people to do the strategy and content creation that is best done by humans, not machines.

Big data is too big for humans to work with, at least in the all-important analytical construct of responding to opportunities in real time — formulating efficient and timely responses to opportunities generated from your marketing cloud, or pursuing the never-ending quest for perfecting search engine optimization (SEO) and search engine marketing (SEM). The volume, velocity and veracity of raw, unstructured data is overwhelming. Big data pioneers such as Facebook and eBay have moved to massive Hadoop clusters to process their petabytes of information.

In recent years, we’ve gone from analyzing megabytes of data to working with gigabytes, and then terabytes, and then petabytes and exabytes, and beyond. Two years ago, James Rogers, writing in The Street, wrote: “It’s estimated that 1 Petabyte is equal to 20 million four-door filing cabinets full of text.” We’ve become jaded to seeing such figures. But 20 million filing cabinets? If those filing cabinets were a standard 15 inches wide, you could line them up, side by side, all the way from Seattle to New York — and back again. One would need a lot of coffee to peruse so much information, one cabinet at a time. And, a lot of marketing staff.

Of course, we have computers that do the perusing for us, but as big data gets bigger, and as analysts, marketers and others seek to do more with the massive intelligence that can be pulled from big data, we risk running into a human bottleneck. Just how much can one person — or a cubicle farm of persons — accomplish in a timely manner from the dashboard of their marketing cloud? While marketing clouds do a fine job of gathering data, it still comes down to expecting analysts and marketers to interpret and act on it — often with data that has gone out of date by the time they work with it.

Hence, big data solutions leveraging machine learning, language models and prediction, in the form of self-learning solutions that go from using algorithms for harvesting information from big data, to using algorithms to initiate actions based on the data.

Yes, this may sound a bit frightful: Removing the human from the loop. Marketers indeed need to automate some decision-making. But the human touch will still be there, doing what only people can do — creating great content that evokes emotions from consumers — and then monitoring and fine-tuning the overall performance of a system designed to take actions on the basis of big data.

This isn’t a radical idea. Programmed trading algorithms already drive significant activity across stock markets. And, of course, Amazon, eBay and Facebook have become generators of — and consummate users of — big data. Others are jumping on the bandwagon as well. RocketFuel uses big data about consumers, sites, ads and prior ad performance to optimize display advertising. uses big data from consumer Web behavior, on-site behaviors and publisher content to create, optimize and buy advertising across the Web for display advertisers.

The big data revolution is just beginning as it moves beyond analytics. If we were building CRM again, we wouldn’t just track sales-force productivity; we’d recommend how you’re doing versus your competitors based on data across the industry. If we were building marketing automation software, we wouldn’t just capture and nurture leads generated by our clients, we’d find and attract more leads for them from across the Web. If we were building a financial application, it wouldn’t just track the financials of your company, it would compare them to public filings in your category so you could benchmark yourself and act on best practices.

Benioff is correct that there’s an undeniable trend that most marketing budgets today are betting more on social and mobile. The ability to manage social, mobile and Web analysis for better marketing has quickly become a real focus — and a big data marketing cloud is needed to do it. However, the real value and ROI comes from the ability to turn big data analysis into action, automatically. There’s clearly big value in big data, but it’s not cost-effective for any company to interpret and act on it before the trend changes or is over. Some reports find that 70 percent of marketers are concerned with making sense of the data and more than 91 percent are concerned with extracting marketing ROI from it. Incorporating big data technologies that create action means that your organization’s marketing can get smarter even while you sleep.

Raj De Datta founded BloomReach with 10 years of enterprise and entrepreneurial experience behind him. Most recently, he was an Entrepreneur-In-Residence at Mohr-Davidow Ventures. Previously, he was a Director of Product Marketing at Cisco. Raj also worked in technology investment banking at Lazard Freres. He holds a BSE in Electrical Engineering from Princeton and an MBA from Harvard Business School.

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google x research project

Google has developed a virtual "neural network" that taught itself what a cat looks like by viewing images from YouTube. Developed at Google X, the research and development lab best known for Project Glass and self-driving cars, the neural network is a cluster of 1,000 computers with 16,000 cores between them. Google fed the cluster 200 x 200 pixel thumbnails taken from 10 million randomly selected YouTube videos and had it look for recurring features. Not only was its creation able to detect faces, but also "high-level concepts" such as cat faces and human bodies.

Google's machine was not taught, or given any data on what a face, body, or cat looks like before it started its analysis. Once it had discovered a recurring object, the...

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Jer Thorp, a data artist in residence at The New York Times, shows off some of his work (like this and this) and speaks about the connection between the real world and the mechanical bits we know as data. Worth your 17 minutes.

People often miss this point about data — that it's a representation of the physical world — and because of that, things like uncertainty and complexity come attached to the numbers. There are also actual human beings associated with a lot of data. So while optimization, maximization, and efficiency are well and good, stories, ethics, and lessons are pretty good takeaways, too.

Update: Don't miss the unexpected discussion around data and capitalism.

[Jer Thorp]

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You knew this was coming, right? The New York Times describes the point guard fundamentals — dribble penetration, ball screen, and isolation — of Jeremy Lin in this animated Linfographic. For each play, the players of interest are outlined, and the frame shifts so that you can see where the players have been, relative to where they currently are. It's a simple concept executed well.

I'm familiar with this stuff already, but I imagine this being pretty useful for people just tuning into the game, due to their sudden case of Linsanity. Today's game against Dallas is gonna be a hot ticket.

[New York Times]

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