{"id":139,"date":"2014-01-22T22:55:37","date_gmt":"2014-01-22T22:55:37","guid":{"rendered":"http:\/\/blogs.stthom.edu\/cameron\/?p=139"},"modified":"2017-04-13T20:54:52","modified_gmt":"2017-04-13T20:54:52","slug":"how-much-data-is-big-data","status":"publish","type":"post","link":"https:\/\/blogs.stthom.edu\/cameron\/how-much-data-is-big-data\/","title":{"rendered":"How Much Data is BIG Data?"},"content":{"rendered":"

A Fair Question.<\/span><\/b>\u00a0 Everyone in business is talking about Big Data, but the term is impenetrably, wonderfully vague in common use.\u00a0 Certainly, it means dealing with large amounts of data, but businesses have been struggling with processing large amounts of data for eons.\u00a0 The current emphasis on \u2018Big data\u2019 has to be more than just concern about \u2018lots of data\u2019. \u00a0It is instructive to recognize that our view of what constitutes large amounts of data has actually shifted over the years.\u00a0 Fifty years ago, I was working on a demographic research project for the Department of Agriculture forecasting population migrations across the United States.\u00a0 For that project, we had 24 punched cards for each county in the country for each of two US population censuses, 1950 and 1960.\u00a0 The cards contained census data about such things as births, deaths, and migrations associated with each county.\u00a0 For that day and age, this was very big data indeed.\u00a0 Doing the math, that was something over 12 megabytes of data.\u00a0 And backing up the data required a second full copy of all those punched cards!\u00a0 The first generation mainframe computer that we were using had no operating system and used vacuum tubes.\u00a0 It cost $2 million, but it only had 2k of main memory on board.\u00a0 There were no higher-level programming languages, only machine code.\u00a0 It took forever to process all of those racks of punched cards.\u00a0 Back then, this was a lot of data!\u00a0 This illustrates that, in a practical sense<\/i>, \u2018big data\u2019 tends to be whatever level of data stretches your capacity to process it.\u00a0 In the real world, big data is situational, relative to a firm\u2019s operational ability to deal with it effectively.<\/p>\n

Characteristics of Big Data.<\/span><\/b>\u00a0 With Big Data, the discussion always turns to the three V\u2019s<\/i> \u2013 VOLUME, VELOCITY, and VARIETY.[1]<\/a>\u00a0 We could add a fourth \u2013 V for VALUE, as well.<\/p>\n

Volume <\/i>is the most obvious aspect of Big Data.\u00a0 Today, businesses are preparing for what is being called The Age of Exabytes.\u00a0 An Exabyte is a billion billion bytes \u2013 and Zettabytes cannot be far behind (a trillion billion bytes).\u00a0 Driven by advances in storage methods and technologies, the quest for competitive advantage by harnessing data seems endless.\u00a0 Velocity<\/i> describes the pace at which data arrives for storage.\u00a0 Modern networking has had a profound impact on this rate of arrival, streamlining the connections to data sources and optimizing data flows everywhere.\u00a0 Internet apps, automatic sensors, and devices of very description are generating data at a prodigious rate.\u00a0 Variety<\/i> is probably the most perplexing aspect of big data today.\u00a0 This refers to the myriad formats in which modern data exist in corporate data warehouses.\u00a0 Most of the history of data management has dealt with managing well-defined, structured data.\u00a0 But today\u2019s relevant business data is mostly highly unstructured, often machine-generated, and frequently free formatted text.\u00a0 Organizing and evaluating it is complicated, to say the least.\u00a0 Value<\/i> deals with such factors as timeliness and accuracy.\u00a0 Data is obviously more valuable when made available in time to use it.\u00a0 And accuracy is crucial for the reliability and ultimately the trustworthiness of the data.\u00a0 But a good estimate achieved early is often better than a more exact answer received later.\u00a0 So, these factors interact importantly in the assessment of the value of the data.<\/p>\n

Charles K. Davis, Ph.D.<\/a>
\nProfessor; Cameron Endowed Chair of Management & Marketing<\/strong><\/p>\n

See more posts by this author<\/a><\/p>\n

\u00a9 Copyright by Charles K. Davis, November 2013<\/p>\n

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 <\/p>\n

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[1]<\/a> McAfee, A., & Brynjolfsson, E.\u00a0 (2012). Big Data: The Management Revolution. Harvard Business Review<\/i>, 90<\/i>(10), 78\u201383<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

A Fair Question.\u00a0 Everyone in business is talking about Big Data, but the term is impenetrably, wonderfully vague in common use.\u00a0 Certainly, it means dealing with large amounts of data, but businesses have been struggling with processing large amounts of data for eons.\u00a0 The current emphasis on \u2018Big data\u2019 has to be more than just…<\/p>\n","protected":false},"author":5,"featured_media":149,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[44,33,37,9,51],"tags":[55,15,27,54],"class_list":["post-139","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-critical-thinking","category-commentary","category-csb-faculty","category-management-2","category-managment-information-systems","tag-big-data","tag-charles-k-davis","tag-management","tag-management-information-systems"],"yoast_head":"\nHow Much Data is BIG Data? 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