{"id":562,"date":"2017-12-14T04:18:30","date_gmt":"2017-12-14T04:18:30","guid":{"rendered":"http:\/\/kusuaks7\/?p=167"},"modified":"2025-04-17T13:10:28","modified_gmt":"2025-04-17T13:10:28","slug":"big-data-and-butterflies","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/big-data-and-butterflies\/","title":{"rendered":"Big Data and Butterflies"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tReady to learn Big Data Analytics? Browse courses<\/a>\u00a0like\u00a0Big Data – What Every Manager Needs to Know<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\u201cBig Data\u201d\u2013 we\u2019ve heard the term, but what does it mean exactly? According to Wikipedia, Big Data refers to \u201cdata sets<\/a>\u00a0that are so large or complex that traditional\u00a0data processing<\/a>\u00a0applications are inadequate,\u201d and that is correct, to a point. The reality is that Big Data means different things to different people and organizations. It\u2019s a term we all use, but not in the same way, and in every industry or area of endeavor it means something different.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe National Security Agency (NSA) has often made the news over their collection of recordings of phone calls or emails. They have billions of voice recordings and emails, and the tools to analyze this enormous amount of data to hopefully prevent terrible things from happening. We know that\u2019s Big Data. But what if you\u2019re not the NSA?\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Big Data Is Relative<\/h3><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTo me, Big Data simply refers to data sets that are bigger than you\u2019re accustomed to handling. I\u2019ll give some examples. One of our clients, The Optical Society of America, needed to convert approximately 750,000 pages of materials for their archives going back to 1917. That\u2019s Big Data in the technical publishing world. In the legal sector, a litigation firm might have tens of millions of pages in an eDiscovery production. The US Patent Office, also one of our clients, receives five million pages per month of filings, every month. Examples like these change the definition of big data on case by case basis, industry to industry. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAnother definition of Big Data emerges when you are dealing with compilations of content arriving from different sources. Take for instance Elsevier\u2019s\u00a0Scopus database (the largest abstract and citation database of peer-reviewed literature), with upwards of 60 million entries \u2013 multiply that by the pages per entry, and you\u2019re getting into fairly large numbers.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Big Data Project Plans Are a Big Help<\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe reality is that in every field of endeavor, data sets are getting bigger because we now have the technology and processes to deal with ever larger content collections, and have the means to better monetize them. No matter what your definition of Big Data is, when it comes to a conversion project the keys to success are having a project plan, and the right people to work with. You need to ask yourself:\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nWhat does the content look like? You need to develop an inventory of some sort, because for any large collection, the content is likely to be varied and be spread out over multiple locations.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWhat do you want out of it? Think about the value in your content, and how you envision people using it. What products will you be able to develop now that you have the data?\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tPlan a pathway to get there \u2013 you may not have all the answers upfront, and may need to build a flexible plan that will develop over time as you learn more about what\u2019s in your collections. Remember that agile development provides for flexibility, but doesn\u2019t mean \u201cwinging it\u201d.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe definition of Big Data will continue to change. And, \u201cBig\u201d today is very different than it was just a few years ago. As technology progresses, the definition of what is being collected as big data will change. Think about this: The Smithsonian National Museum of Natural History has a meticulously catalogued archive that includes 30 million dried insects, 4.5 million preserved plants, 7 million preserved fish in jars, birds, butterflies, seashells, minerals and so much more, all in drawers and cabinets. Much of these specimens are digitized and findable as collections on their website. Their collection for the Department of Botany alone boasts 1,424,662 records. That\u2019s their Big Data.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Ready to learn Big Data Analytics? Browse courses\u00a0like\u00a0Big Data – What Every Manager Needs to Know developed by industry thought leaders and Experfy in Harvard Innovation Lab.\u201cBig Data\u201d\u2013 we\u2019ve heard the term, but what does it mean exactly? According to Wikipedia, Big Data refers to \u201cdata sets\u00a0that are so large or complex that traditional\u00a0data processing\u00a0applications<\/p>\n","protected":false},"author":126,"featured_media":3109,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[122],"ppma_author":[1663],"class_list":["post-562","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-big-data"],"authors":[{"term_id":1663,"user_id":126,"is_guest":0,"slug":"mark-gross","display_name":"Mark Gross","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Gross","first_name":"Mark","job_title":"","description":"Mark Gross, <\/strong>President at Data Conversion Laboratory, Inc, is a recognized authority on XML implementation and document conversion. Prior to joining DCL, Mark was with the consulting practice of Arthur Young & Co. He has also taught at the New York University Graduate School of Business, the New School, and Pace University. He is a frequent speaker on the topic of automated conversions to XML and SGML."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/562","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/126"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=562"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/562\/revisions"}],"predecessor-version":[{"id":37639,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/562\/revisions\/37639"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3109"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=562"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=562"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=562"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=562"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}