{"id":444,"date":"2015-08-28T07:28:42","date_gmt":"2015-08-28T04:28:42","guid":{"rendered":"http:\/\/kusuaks7\/?p=49"},"modified":"2024-09-24T13:40:25","modified_gmt":"2024-09-24T13:40:25","slug":"big-data-survey-reflects-organizational-faith-analytics","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/big-data-survey-reflects-organizational-faith-analytics\/","title":{"rendered":"Big Data Survey Reflects Organizational Faith in Analytics"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tThe Economist Intelligence Unit conducted a poll on behalf of CSC and EMC around March 2013 to collect the prevalent views on the role of big data analytics in organizational decision making. To complete their survey, The Economist distributed questionnaires to 212 selected executives of global companies with revenues exceeding US$1bn. The poll respondents represent 36% North Americans, 26% Asia Pacific, 37% Western Europeans, and only 1% Eastern Europeans. These represented 19 industry sectors and 16 distinct functional units.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tSome significant points to note in this white paper are:\n
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  • The majority of organizations (nearly 70%) in Asia-Pacific claim primitive ability of using big data analytics to drive executive decisions. Interestingly, 42% of North American and 43% of European organizations too consider themselves in this category.<\/li>\n \t
  • Organizations in Asia-Pacific claim the lack of accurate, timely, or relevant data from business units as serious impediment in conducting enterprise big data analytics.<\/li>\n \t
  • Organizations in Asia Pacific cited inconsistent reporting of information among business units to be a big roadblock to data-driven decision-making more often than companies in other regions.<\/li>\n \t
  • 15% respondents in Asia-Pacific, 34% in North America, and 33% in Europe\u0097 cited the volume and speed of the data in organization being overwhelming.<\/li>\n \t
  • 1% of the survey respondents clearly showed skepticism about the perceived value of big data analytics in organizations.<\/li>\n<\/ul>\n
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    • GE is one of the frontrunners in establishing that big data analytics has the potential to save billions of business dollars.\u00a0<\/strong><\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      \n\t\t\t\t\t\t\t\t\tBased on current practices, survey respondents provided details of data-driven decision making scenarios. The following graph has distilled the collective responses:\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      Current inhibitors to proper use of big data analytics<\/strong>\u00a0<\/strong><\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      1. Other obstacles to successful data-based decision-making include inconsistent reporting of data among business units, geographies or functions.<\/li>\n \t
      2. Inadequate tools for collecting, integrating or analyzing operational information.<\/li>\n \t
      3. Lack of accurate, timely or relevant data from across the business is also a major concern among companies with primitive or basic capabilities.<\/li>\n \t
      4. Lack of skilled personnel<\/li>\n<\/ol>\nThe engaging content\u00a0presented in\u00a0the white paper\u00a0<\/em><\/strong>provides helpful\u00a0insight into\u00a0the organizational practices involving the current and future roles of big data and analytics.\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":"

        The Economist Intelligence Unit conducted a poll on behalf of CSC and EMC around March 2013 to collect the<\/p>\n","protected":false},"author":11,"featured_media":2575,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[122],"ppma_author":[1606],"class_list":["post-444","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-big-data"],"authors":[{"term_id":1606,"user_id":11,"is_guest":0,"slug":"cameron-turner","display_name":"Cameron Turner","avatar_url":{"url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2024\/09\/cameron.jpeg","url2x":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2024\/09\/cameron.jpeg"},"user_url":"","last_name":"Turner","first_name":"Cameron","job_title":"","description":""}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/444","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\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=444"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/444\/revisions"}],"predecessor-version":[{"id":37056,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/444\/revisions\/37056"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/2575"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=444"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=444"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=444"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=444"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}