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Steven Sinofsky

It's been a little over a month since Windows and Windows Live president Steven Sinofsky abruptly left Microsoft due to apparent clashes with management, but now we know his next move. According to his Twitter account, Sinofsky will be teaching at the Harvard Business School this spring, something he did prior to joining the Windows team. A follow-up tweet indicated that he'll be teaching courses related to product development. It's not clear yet if this is a single-semester deal or whether Sinofsky plans to stick around Harvard, but his extensive experience at Microsoft developing Windows 8 (among other products) should make for an interesting class next semester, to say the least.

Excited to return to @harvardhbs to teach again this...

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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. Turn.com 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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Mark Zuckerberg and friend in Noe Valley

It's incredibly hard to start a company.

Fortunately, a lot of smart people have done it before, and they have sound advice to share with budding entrepreneurs.

We pulled the best quotes from recent blog posts, conferences, and interviews that can help startups at every phase, whether they're still deciding what to launch or figuring out how to scale.

Here's the truest, most timely startup advice from business stars like Pinterest's Ben Silbermann and Y Combinator's Paul Graham.

On deciding what to start: "Facebook, I didn’t start to ‘start a company.’ It was mostly just through wanting to build it and having it be this hobby and getting people around me excited. It eventually evolved into a company. But I never understood the psychology of wanting to start a company before deciding what you wanted to do. Explore what you want to do before committing." - Mark Zuckerberg, CEO and co-founder of Facebook

"If you are thinking of starting a non-transactional consumer startup, be aware that you are entering what is perhaps the most competitive sector in tech in the last decade….ten million users is the new one million users." -- Chris Dixon, Partner of Founder Collective and founder of Hunch

On the stress of running a company: "As a startup CEO, I slept like a baby. I woke up every two hours and cried." -- Ben Horowitz

See the rest of the story at Business Insider

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The Forest of Advocacy is a series of animations that explores the political contribution patterns among eight organizations, such as Bain Capital, Goldman Sachs, and Harvard Business School.

These visualizations provide a dynamic look at the partisan tilt of giving within organizations. For each organization, individuals are characterized as points sketching out a line over time. The X axis is time, and the Y axis represents the net partisan tilt of contributions over the preceding 6 months. Over the decades, one sees lines sketched out, reflecting the partisanship of individuals over time. For each organization, we also provide the net contributions of the entire organization, and the names of biggest Democratic, Republican, and "bipartisan" contributors (the individual with the highest product of Democratic and Republican contributions).

At the core, each animation is a time series chart, but the aesthetic and animation, which is narrated, provides for a more organic feel. In particular, the movements of people, represented by squares shifting straight across or up and down, makes it easy to see consistent and not so consistent contributions. [Thanks, Mauro]

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