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  1. Elinor Ostrom, Winner of Nobel in Economics, Dies at 78

    Professor Ostrom inspired the Data Governance Council with her work demonstrating that people could effectively self-organize to govern common resources, such as fields, fish, lakes, rivers, and data. Her inspiration came not from text-books and formulas, but from field work with real people working together to govern the use of these common resources without government intervention or regulation. Self-Organization and Self-Governance are not just nice theories. They are solutions to common human problems that have existed for milenia. These ideas are the foundation for IT developments such as social networking and crowdsourcing, and therefore we would like to recognize the importance of her work and mourn her far too early passing.

    http://www.nytimes.com/2012/06/13/business/elinor-ostrom-winner-of-nobel-in-economics-dies-at-78.html?_r=1&ref=obituaries

    Elinor Ostrom, Winner of Nobel in Economics, Dies at 78
    By CATHERINE RAMPELL

    Elinor Ostrom, the only woman to win the Nobel Memorial Prize in Economic Science — an achievement all the more remarkable because she was not actually an economist — died on Tuesday in Bloomington, Ind. She was 78.

    The cause was cancer, according to Indiana University, where she taught for many years.

    Professor Ostrom’s work rebutted fundamental economic beliefs. But to say she was a dark horse for the 2009 economics Nobel is an understatement. Not because she was a woman — although women in the field are still rare — but because she was trained in political science.

    Professor Ostrom’s prizewinning work examined how people collaborate and organize themselves to manage common resources like forests or fisheries, even when governments are not involved. The research overturned the conventional wisdom about the need for government regulation of public resources.

    At least it did for the economists who knew who she was and had read her work.

    “The announcement of her prize caused amazement to several economists, including some prominent colleagues, who had never even heard of her,” Avinash Dixit, a Princeton economics professor, said when introducing Professor Ostrom’s work at a luncheon in 2011. Usually, he noted, Nobel laureates need no introduction.

    In fact, when the Nobel recipients were announced, some economists mistakenly thought the prize had gone to Bengt Holmstrom, an economist with a similar-sounding (and, to economists, much more recognizable) name. One prominent scholar acknowledged visiting Wikipedia to figure out who exactly she was.

    Surprise at Professor Ostrom’s honor, which she shared with Oliver E. Williamson, in some cases gave way to disdain and name-calling on economics blogs.

    “Some things said about her in blogs and other media were so ignorant and in such bad taste that I felt ashamed on behalf of the economics profession,” Mr. Dixit said.

    Professor Ostrom was not the first laureate to hail from outside the field. Previous recipients include Daniel Kahneman (psychologist), John Nash (a mathematician who was the subject of the book and movie “A Beautiful Mind”) and Leonid Hurwicz (trained in law).

    As with these other winners, the outsider perspective Professor Ostrom brought to the field contributed to what made her work so groundbreaking. But the unconventional nature of her studies also made it difficult for her to find a foothold in academia earlier in her career.

    “A lot of important questions are on the narrow borders between disciplines, but it is difficult to find a home for that kind of work,” said Marco Janssen, a mathematician at Arizona State University who collaborated with Professor Ostrom. “She had experienced many of these challenges over the years. Eventually she and her husband just created their own center for it.”

    In 1973, Professor Ostrom and her husband, Vincent, who survives her, founded the Workshop in Political Theory and Policy Analysis at Indiana University. It would become the first of several interdisciplinary institutions she helped shape, and a locus for her collaboration with scholars across academia, including ecologists, computer scientists and psychologists.

    Just as her academic habits emphasized collaboration and cooperation, so did the content of her study.

    Traditionally, economics taught that common ownership of resources results in excessive exploitation, as when fishermen overfish a common pond. This is the so-called tragedy of the commons, and it suggests that common resources must be managed either through privatization or government regulation, in the form of taxes, say, or limits on use.

    Professor Ostrom studied cases around the world in which communities successfully regulated resource use through cooperation. Her work has important applications for climate change policy today.

    Professor Ostrom’s research and Mr. Williamson’s related work on corporate oversight are part of a field known as institutional economics. Some economists still debate whether the field deserves a rightful place within the economics discipline.

    Elinor Awan was born on Aug. 7, 1933, in Los Angeles, an only child. She often spoke about how growing up in the Depression had influenced her interest in cooperative institutions. She recalled helping her family grow food in a large garden and knitting scarves for soldiers. She received her bachelor’s, master’s and doctoral degrees — all in political science — at the University of California, Los Angeles.

    As a researcher she was notable for conducting fieldwork, an unusual method that is admired by some economists but scorned by others. In 1964, when she was working on her dissertation, fieldwork was considered the province of anthropologists, not academics trying to answer economic questions.

    “She would go and actually talk to Indonesian fisherman, or Maine lobstermen, and ask, ‘How did you come to establish this limit on the fish catch? How did you deal with the fact that people might try to get around it?’ ” said Nancy Folbre, an economics professor at the University of Massachusetts, Amherst, and a contributor to The New York Times’s Economix blog.

    “In economics, every successive cohort of economists is trained to put greater emphasis on the arsenal of mathematical and econometric expertise,” Professor Folbre said. “That was just not what her work was about.”

  2. Big Data isn’t just More Data

    For the past two months, we’ve been having calls in the Information Governance Community about Big Data. We’ve reviewed use cases, keynote presentations, Velocity, Volume, Variety, Veracity and lately Vulnerability. We’ve added a category to the Maturity Model, and we’ve stimulated lots of discussion. Separately, I’ve been traveling in the USA and Europe and talking with audiences and clients about Big Data. For many of us in the IT industry, this term “Big Data” is charged with meaning. Most outside this elite community have never heard of “Big Data.”

    This week I am in Vienna, Austria – Best City in the World to Live in according to a recent Mercer Study – attending the Major Cities of Europe Conference at the Wiener Rathaus. Big Data ist nichts zu finden hier in dieser toller stadt. In discussion after discussion, few if any of the 200 CIO’s and staff attending this event have any idea about Big Data. “Is it More Data” one CIO asked.

    This question stuck with me, because I have heard it many times in the information governance community calls. Inevitably when confronting new ideas and trends, people take what they know, what they’ve done before, and cut and paste it forward. They bring old frameworks and understandings to the new disciplines in a well intentioned effort to add value.

    But no, Big Data isn’t More Data. It isn’t bigger databases, more datawarehouses, or larger spreadsheets. It isn’t the apothesis of the burgoning data management trends we in IT have been warning about for a decade. It isn’t Data Armageddon.

    Big Data is a completely new way of approaching data collection, use, and re-use. It isn’t a purpose-built repository of large data sets. Rather, it is a small to large to huge cluster of cheap servers with storage, RAM, and processors and specialized software; that allows an organization to consume vast amounts of structured and unstructured information regardless of origin, classification, or type; that replicates this information across these servers who analyze these quantities in parallel, at high speed; and when we are done with this process, we discard the raw data and start again with a different set.

    Simply put, Big Data is a high-speed analytical engine for any problem. That’s its purpose. We don’t store information here long term. We get it, use it, keep the result, and discard the input. And we do this over and over again for different purposes each time.

    The speed of analysis redefines the need for Governance. Councils, standards, ontologies, and rigid protocols will not scale to the needs of Big Data because we don’t always know what kind of content we are looking at. We don’t know the structure, provenance, integrity, or ultimate purpose. Councils, Stewards, and other Data Governance mechanisms will be outmoded and outmatched by the Volume, Velocity, and Vareity of information. Old Security Architectures designed to protect applications will also feel outdated and inadequate.

    But still, we need to detect and understand what we are looking at. Is it Personally Identifiable Information? Do sensor readings reveal details about people, organizations, and trade secrets? Do the images and videos contain malware and disinformation? Is our sample deliberately polluted by competitors?

    Councils, Frameworks, Maturity Models, Architectures – these artifacts of highly structured data management processes will make many feel good, especially their authors. But they will not scale to meet the challenges of high-speed analysis of petabytes/day.

    To Govern in the world of Big Data, you need Big Data. Construct one cluster to analyze the data and information. Construct another to analyze the threats. Program the second one to find, mask, and neutralize PII, copyrights, patents, trade secrets, malware, disinformation. You need this layer to protect you and your customers from threats to data and threats from data. Build analytical lenses to keep your threat detection current.

    We can’t apply low speed solutions to high-speed change. We must meet change with change.

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