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What makes leaders effective in an environment that is rapidly changing, volatile, and unpredictable? And what makes those effective leaders get the most out of life?

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What's a wiki? A catalyst for innovation.

Blog

March 27, 2014

You could say that innovating using Big Data requires looking for love in all the wrong places—and when you find that something to love, persevering through thick and thin to make it take hold for everyone’s mutual benefit.

 

Alfred Sommer is an eye doctor who, in his relentless review of research datasets, came across critical information that led him to forge a low-cost, highly efficacious, life-saving innovation in healthcare.Sommer’s simple medical intervention has likely saved millions of children from premature death. Of data, he says:

March 26, 2014

There is a general perception today that the typical entrepreneur is a 20-something tech wiz who builds a million-dollar social application out of his parent’s garage or college dorm room. The problem with this stereotype is that there is no such thing as a “typical” entrepreneur. Indeed, perhaps one of the few things common to all entrepreneurs is that they are not typical. They are risk takers and innovators, and often times, success means thinking in creative, untypical ways to satisfy an unaddressed consumer demand. This essential color-outside-the-lines quality is not limited to the college dropouts (of the Bill Gates, Steve Jobs and Mark Zuckerberg variety); it is found in people across all backgrounds and ages. 

March 25, 2014

Odds are, if you’re reading this, you live in a city or its metro area. More than 80% of Americans live in cities, and urban population growth is outpacing overall U.S. population growth. With so many people crowding into U.S. cities, government leaders, academics, scientists and others are always looking for innovative approaches for making urban living better. When it comes to crime prevention, data is giving police departments smarter ways to address crime and make more effective use of the public safety tools at their disposal. 

For decades, police departments used push-pin paper maps to show crime density by location. This rudimentary tracking method showed where and what kind of crime occurred, but it did not easily tell police departments much more. It required constant attention and yielded a limited amount of intelligence for better policing. 

March 24, 2014

The great need for science, technology, engineering and mathematics (STEM) education in this country has been clear for years. From the school house to the White House, America’s leaders, businesses, and teachers have emphasized the growing skills gap that leaves U.S. companies without the high-tech talent they need. With STEM jobs growing rapidly and STEM skills not growing fast enough, the implications for the Big Data revolution are concerning. 

Data does nothing on its own; it is the people who analyze it and put it to work who allow businesses and governments to realize the potential from it. This can only happen, however, if there are enough people with the right skills. Yet, the notion of STEM skills is abstract. Saying America needs more scientists and engineers is like saying we need regulatory and tax reform. Of course we do, but the specifics are unique to the many different challenges business and individuals face. When it comes to realizing the fruits of data-driven business and innovation, there are specific skillsets and educational backgrounds needed to put to work insights gleaned through massive amounts of information. 

March 20, 2014

Catherine Rampell's bitingly witty new WaPo column argues that public and private sectors should both "put data to good use." I agree with that point, but disagree when she says that this isn’t happening. The truth is that the private sector already uses data for good. And while Rampell doesn't see the value of odd ads crafted by richly rewarded Valley types, I see them as the long tail of our data-driven economy. 

I see 5 key values of data:

    • Power to transform - disruption of the status quo
    • Access to opportunity - opens up new fields
    • Potential for people - alters the way we live
    • Impact on the economy - creates value across all sectors
    • Serendipity of use - new uses for old data

 

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