مشخصات مقاله | |
انتشار | مقاله سال 2018 |
تعداد صفحات مقاله انگلیسی | 36 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
منتشر شده در | نشریه الزویر |
نوع مقاله | ISI |
عنوان انگلیسی مقاله | An application of the dynamic knowledge creation model in big data |
ترجمه عنوان مقاله | برنامه کاربردی مدل خلق دانش پویا در داده های بزرگ |
فرمت مقاله انگلیسی | |
رشته های مرتبط | مدیریت |
گرایش های مرتبط | مدیریت دانش |
مجله | فناوری در جامعه – Technology in Society |
دانشگاه | Business Leadership Building – University of North Texas – USA |
کلمات کلیدی | داده های بزرگ، دانش، فرآیند ایجاد دانش، حس کردن، تصرف |
کلمات کلیدی انگلیسی | Big data, Knowledge, Knowledge creation process, Sensing, Seizing |
کد محصول | E6523 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
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1. INTRODUCTION
Big Data. Data Analytics. Data Mining. These are some common buzzwords we have all been hearing recently. In the past few years, there has been a huge emphasis on big data and business analytics. In simple words, ‘big data’ refers to the vast volumes and types of data that companies can collect from a variety of sources – both internal and external – such as employees, customers, suppliers, and the market (Harvard Business Review, 2014) [64]. Big data can be both structured such as employee performance data or customer sales data and unstructured such as internet clicks or social media content. Data is generated every second by both humans and machines alike; about 90% of the world’s data were generated just between 2010 and 2013 (SINTEF, 2013) [57]. Organizations are now trying to analyze these huge amounts of data so that they can be successfully interpreted to make smarter strategic decisions for positive value creation (Hargrave, 2013 [26]; McKinsey Global Institute, 2011 [42]). Erik Brynjolfsson, an economist at MIT’s Sloan School of Management, explains how companies will increasingly make strategic decisions based on data and analytics in the coming years rather than on experience. His study of 179 firms found that companies that adopted datadriven decision-making were able to achieve 5% higher productivity and profitability than their competitors (Brynjolfsson, Hitt, & Kim, 2011) [9]. Numerous business case studies have also indicated that data-driven companies make better decisions, are able to improve performance, and thus able to generate more value (McAfee & Brynjolfsson, 2012) [41]. Big data can enable organizations to accurately foresee potential business problems. Organizations can now perform ‘predictive’ and ‘prescriptive’ analyses, through which potential issues could be mitigated or even entirely avoided. Whereas predictive analytics can forecast future trends by performing statistical modelling on past data to help answer the question, “what could happen?” (Grillo & Hackett, 2015) [24], prescriptive analytics can help determine cause and effect among business processes for model optimizations (Bihani & Patil, 2014) [6], provide the foresight to ask, “what should we do?”, and offer prescriptions for moving forward. |