{"id":17515,"date":"2017-08-17T00:00:00","date_gmt":"2017-08-17T00:00:00","guid":{"rendered":"https:\/\/www.bbs.unibo.it\/big-data-revolution-challenges-and-perspectives-in-data-analytics-in-2017\/"},"modified":"2020-02-28T14:23:24","modified_gmt":"2020-02-28T14:23:24","slug":"big-data-revolution-challenges-and-perspectives-in-data-analytics-in-2017","status":"publish","type":"post","link":"https:\/\/www.bbs.unibo.it\/en\/big-data-revolution-challenges-and-perspectives-in-data-analytics-in-2017\/","title":{"rendered":"Big Data Revolution: challenges and perspectives in data analytics in 2017"},"content":{"rendered":"<p>The Economist has recently defined the <strong>Data Scientist<\/strong> as \u201c<strong>the \u201csexiest\u201d job of the 21<sup>st<\/sup> century<\/strong>, combining the skills of the IT expert, of the statistician and of the narrator to extract the golden nuggets hidden under mountains of data\u201d.<\/p>\n<p><!--more--><\/p>\n<p>Undoubtedly, <strong>data collection and analytics<\/strong> have become two of the key processes in the world of <strong>digital business, <\/strong>functional to the <strong>digital transformation of companies.<\/strong><\/p>\n<p>Nonetheless <strong>until now Big Data <\/strong>(collections of miscellaneous data, structured and non- structured, defined in terms of volume, speed, variety and truthfulness) <strong>have been mainly accumulated without being converted into an available asset<\/strong>. Their storage and preservation are indeed just the first tile in a complex process: Big Data work like a large artificial lake \u2013 first you build the dam, then you wait for it to fill up with water and, once full, you start using the &#8216;raw material&#8217; as a source to produce electricity, to irrigate, to drink. The digital transformation is letting several fields of application of data analytics emerge, the aim being to improve the business. A case in point is advertising: the collection of data concerning customers implies an increasingly precise definition of the reference target on which to construct <em>ad hoc<\/em> advertising campaigns.<\/p>\n<p>As for storage too, there is still room for development: it&#8217;s important for original data to be filed using criteria that make their use easy. The exponential increase of Open Source databases in 2016 is the direct consequence of this need. Next to the pioneering <strong>Hadoop,<\/strong> faster and more flexible frameworks were created, like <strong>Exasol <\/strong>and<strong> MemSQL<\/strong>, able to meet both the \u201c<strong>need for speed<\/strong>\u201d characterizing the digital revolution, and the need for a <strong>single storage space to file<\/strong> <strong>data<\/strong> from different sources.<\/p>\n<p>There are three main growth objectives in the world of Big Data for <strong>2017<\/strong>, i.e. the <strong>development of security guarantees<\/strong>, the <strong>improvement of usage <\/strong>and the proliferation<strong> of<\/strong> <strong>digital catalogs<\/strong>.<\/p>\n<p>The need for more security guarantees is determined by the relevance that technology has in the management of information: the web is the natural habitat of information flows that become more accessible, and as a consequence more exposed to risks, like data thefts. It is therefore necessary to pay attention <strong>to access to data archives,<\/strong> not to be the victims of attacks or intrusions. The authorization to access databases must be managed by professionals, and systems dedicated to these operations \u2013 like <strong>Apache Sentry<\/strong> or <strong>Apache Ranger \u2013<\/strong> are destined to increase in number and to be improved in terms of efficiency.<\/p>\n<p>The second objective of this year is an as easy as possible use of Big Data, both for companies and individuals. Even though the <strong>access and understanding <\/strong>of data is still a real obstacle, <strong>\u201cself service\u201d analytics platforms<\/strong> are being developed, allowing end users to move easily within databases to extract the information required. In parallel, to lighten archives, <strong>Data Preparation devices <\/strong>are proliferating, to collect, select and merge data.<\/p>\n<p>The last frontier of development in the use of Big Data is the <strong>growth of catalogs <\/strong>that simplify data search: <strong>Alation<\/strong> and <strong>Waterline <\/strong>are just two of the companies devoted to this activity, using \u201cmachine learning\u201d to automate data search in Hadoop.<\/p>\n<p>The world of data analytics is already a key industry of the digital world but it&#8217;s still highly complex, and it requires an <strong>increasingly targeted and in-depth training<\/strong>. Companies look for <strong>Data Scientists<\/strong>, professionals able to lead them through the digital transformation and to identify new business opportunities. <strong>Bologna Business School <\/strong>proposes a <a href=\"\/hp\/?p=9104\"><strong>full-time Data Science<\/strong><\/a><strong><a href=\"\/hp\/?p=9104\"> Master<\/a>&#8216;s course<\/strong> for newly graduated students: the Master&#8217;s provides a sound IT <strong>preparation<\/strong>, the <strong>understanding of technological aspects <\/strong>of the digital business, the <strong>awareness of corporate dynamics <\/strong>and it aims at preparing students destined to be employed in the \u201csexiest job of the 21<sup>st<\/sup> century\u201d. The general aspects of the digital transformation, with a specific focus on <strong>Cyber Security<\/strong>, are dealt with in the <a href=\"\/hp\/?p=36210\"><strong>Digital Technology Management\/Cyber Security<\/strong><\/a><strong><a href=\"\/hp\/?p=36210\"> Master<\/a>&#8216;s course<\/strong><strong>.<\/strong><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Economist has recently defined the Data Scientist as \u201cthe \u201csexiest\u201d job of the 21st century, combining the skills of the IT expert, of the statistician and of the narrator to extract the golden nuggets hidden under mountains of data\u201d.<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[90],"tags":[],"rubrica":[],"class_list":["post-17515","post","type-post","status-publish","format-standard","hentry","category-uncategorized-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Big Data Revolution: challenges and perspectives in 2017<\/title>\n<meta name=\"description\" content=\"The Economist has recently defined the Data Scientist as \u201cthe \u201csexiest\u201d job of the 21st century,\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.bbs.unibo.it\/en\/big-data-revolution-challenges-and-perspectives-in-data-analytics-in-2017\/\" 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