{"id":3426,"date":"2013-07-24T12:47:25","date_gmt":"2013-07-24T05:47:25","guid":{"rendered":"http:\/\/science-technology.vn\/?p=3426"},"modified":"2013-10-31T10:17:27","modified_gmt":"2013-10-31T03:17:27","slug":"vi-du-ve-big-data","status":"publish","type":"post","link":"https:\/\/science-technology.vn\/?p=3426","title":{"rendered":"V\u00ed d\u1ee5 v\u1ec1 Big Data"},"content":{"rendered":"<p><span style=\"font-size: 14px;\">M\u1ed9t sinh vi\u00ean khoa h\u1ecdc m\u00e1y t\u00ednh h\u1ecfi: \u201cB\u1ea1n em l\u00e0m vi\u1ec7c trong c\u00f4ng nghi\u1ec7p b\u1ea3o em r\u1eb1ng Big Data ch\u1ec9 l\u00e0 kh\u00e1i ni\u1ec7m s\u1ebd x\u1ea3y ra trong t\u01b0\u01a1ng lai v\u00e0 m\u1ed9t ng\u00e0y \u0111\u00f3 \u0111\u01b0\u1ee3c d\u00f9ng trong c\u00f4ng nghi\u1ec7p. Em b\u1ecb l\u1eabn l\u1ed9n, n\u00f3 l\u00e0 c\u00e1i g\u00ec \u0111\u00f3 \u0111\u00e3 \u0111\u01b0\u1ee3c d\u00f9ng r\u1ed3i hay n\u00f3 v\u1eabn l\u00e0 m\u1ed9t kh\u00e1i ni\u1ec7m m\u1edbi? Xin th\u1ea7y l\u1eddi khuy\u00ean?\u201d<\/span><\/p>\n<p>&nbsp;<\/p>\n<p>\u0110\u00e1p: Big Data KH\u00d4NG ph\u1ea3i l\u00e0 kh\u00e1i ni\u1ec7m m\u1edbi; n\u00f3 \u0111\u00e3 \u0111\u01b0\u1ee3c d\u00f9ng trong nhi\u1ec1u n\u0103m \u0111\u1eb7c bi\u1ec7t trong ph\u00e2n t\u00edch t\u00e0i ch\u00ednh v\u00e0 th\u01b0\u01a1ng m\u1ea1i th\u1ecb tr\u01b0\u1eddng ch\u1ee9ng kho\u00e1n. Ng\u00e0y nay, n\u00f3 \u0111\u01b0\u1ee3c d\u00f9ng r\u1ed9ng r\u00e3i trong nhi\u1ec1u ng\u00e0nh c\u00f4ng nghi\u1ec7p t\u1eeb b\u00e1n l\u1ebb, ng\u00e2n h\u00e0ng, t\u1edbi d\u01b0\u1ee3c ph\u1ea9m v\u00e0 c\u00f4ng ngh\u1ec7 sinh h\u1ecdc, v.v. Trong qu\u00e1 kh\u1ee9, vi\u1ec7c d\u00f9ng Big Data b\u1ecb gi\u1edbi h\u1ea1n v\u00e0o c\u00e1c c\u00f4ng ti l\u1edbn v\u00ec n\u00f3 r\u1ea5t \u0111\u1eaft khi n\u00f3 y\u00eau c\u1ea7u m\u00e1y t\u00ednh m\u1ea1nh v\u1edbi dung l\u01b0\u1ee3ng nh\u1edb l\u1edbn. Ng\u00e0y nay ti\u1ebfn b\u1ed9 c\u1ee7a c\u00f4ng ngh\u1ec7 m\u00e1y t\u00ednh v\u00e0 l\u01b0u gi\u1eef \u0111\u00e3 l\u00e0m cho Big Data th\u00e0nh \u0111\u1ea3m \u0111\u01b0\u01a1ng \u0111\u01b0\u1ee3c cho n\u00ean vi\u1ec7c d\u00f9ng n\u00f3 tr\u1edf n\u00ean ph\u1ed5 bi\u1ebfn h\u01a1n.<\/p>\n<p>N\u1ebfu b\u1ea1n nh\u00ecn l\u1ea1i trong l\u1ecbch s\u1eed, tr\u01b0\u1edbc khi ph\u00e1t minh ra m\u00e1y t\u00ednh c\u00e1 nh\u00e2n (PC), ch\u1ec9 c\u00e1c c\u00f4ng ti r\u1ea5t l\u1edbn m\u1edbi c\u00f3 th\u1ec3 \u0111\u1ea3m \u0111\u01b0\u01a1ng c\u00f3 c\u00e1c m\u00e1y t\u00ednh l\u1edbn nhi\u1ec1u tri\u1ec7u \u0111\u00f4 la. Apple v\u00e0 Microsoft \u0111\u00e3 l\u00e0m thay \u0111\u1ed5i \u0111i\u1ec1u \u0111\u00f3 b\u1eb1ng vi\u1ec7c \u0111\u01b0a m\u00e1y t\u00ednh v\u00e0o m\u1ecdi nh\u00e0. C\u00f9ng \u0111i\u1ec1u n\u00e0y \u0111ang x\u1ea3y ra ng\u00e0y nay v\u1edbi Big Data, khi gi\u00e1 c\u1ee7a x\u1eed l\u00ed t\u1ed1c \u0111\u1ed9 cao v\u00e0 b\u1ed9 nh\u1edb l\u1edbn gi\u1ea3m xu\u1ed1ng, c\u00e1c c\u00f4ng ti c\u00f3 th\u1ec3 \u0111\u1ea3m \u0111\u01b0\u01a1ng vi\u1ec7c d\u00f9ng ch\u00fang v\u00e0 Big Data \u0111ang tr\u1edf th\u00e0nh khu v\u1ef1c t\u0103ng tr\u01b0\u1edfng nhanh. C\u00e1c nh\u00e0 ph\u00e2n t\u00edch c\u00f4ng nghi\u1ec7p d\u1ef1 b\u00e1o r\u1eb1ng trong qu\u00e1 kh\u1ee9, ph\u1ea7n m\u1ec1m \u0111\u00e3 gi\u00fap h\u00e0ng ngh\u00ecn ng\u01b0\u1eddi ph\u00e1t tri\u1ec3n tr\u1edf th\u00e0nh tri\u1ec7u ph\u00fa v\u00e0 t\u1ec9 ph\u00fa th\u00ec v\u00f2ng xoay n\u00e0y s\u1ebd l\u1eb7p l\u1ea1i v\u1edbi Big Data v\u00e0 ch\u1eb3ng m\u1ea5y ch\u1ed1c nhi\u1ec1u nh\u00e0 khoa h\u1ecdc d\u1eef li\u1ec7u, ki\u1ebfn tr\u00fac s\u01b0 d\u1eef li\u1ec7u, nh\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u s\u1ebd tr\u1edf th\u00e0nh tri\u1ec7u ph\u00fa v\u00e0 t\u1ec9 ph\u00fa.<\/p>\n<p>Ng\u00e0y nay c\u00f4ng ti c\u00f3 th\u1ec3 truy nh\u1eadp v\u00e0o kh\u1ed1i l\u01b0\u1ee3ng bao la c\u00e1c d\u1eef li\u1ec7u c\u1ea3 b\u00ean trong v\u00e0 b\u00ean ngo\u00e0i c\u00f4ng ti v\u00e0 d\u1ef1a v\u00e0o ch\u00fang \u0111\u1ec3 c\u1ea3i ti\u1ebfn doanh nghi\u1ec7p. Kh\u00e1i ni\u1ec7m n\u00e0y l\u00e0 \u0111\u01a1n gi\u1ea3n: Thu th\u1eadp nhi\u1ec1u d\u1eef li\u1ec7u, \u0111\u1ee7 m\u1ecdi lo\u1ea1i d\u1ea1ng th\u1ee9c t\u1eeb nhi\u1ec1u ngu\u1ed3n, ph\u00e2n t\u00edch v\u00e0 t\u1ed5 ch\u1ee9c ch\u00fang \u0111\u1ec3 cung c\u1ea5p th\u00f4ng tin b\u1ea3n ch\u1ea5t cho quy\u1ebft \u0111\u1ecbnh c\u1ee7a c\u1ea5p qu\u1ea3n l\u00ed. Tuy nhi\u00ean vi\u1ec7c th\u1ef1c hi\u1ec7n l\u1ea1i KH\u00d4NG \u0111\u01a1n gi\u1ea3n v\u1eady, n\u00f3 \u0111\u00f2i h\u1ecfi k\u0129 n\u0103ng \u0111\u1eb7c bi\u1ec7t. Big Data y\u00eau c\u1ea7u b\u1ed1n ki\u1ec3u k\u0129 n\u0103ng: K\u0129 n\u0103ng thu th\u1eadp d\u1eef li\u1ec7u (c\u00e1c k\u0129 n\u0103ng qu\u1ea3n tr\u1ecb c\u01a1 s\u1edf d\u1eef li\u1ec7u v\u00e0 khai ph\u00e1 d\u1eef li\u1ec7u) \u0111\u1ec3 thu th\u1eadp, \u0111\u1ecbnh d\u1ea1ng, t\u1ed5 ch\u1ee9c v\u00e0 l\u01b0u gi\u1eef ch\u00fang trong c\u01a1 s\u1edf d\u1eef li\u1ec7u hay nh\u00e0 kho d\u1eef li\u1ec7u. K\u0129 n\u0103ng ph\u00e2n t\u00edch (k\u0129 n\u0103ng to\u00e1n h\u1ecdc, th\u1ed1ng k\u1ebf v\u00e0 t\u00edch h\u1ee3p) \u0111\u1ec3 ph\u00e2n t\u00edch nh\u1eefng d\u1eef li\u1ec7u n\u00e0y v\u00e0 d\u1ecbch ch\u00fang th\u00e0nh th\u00f4ng tin h\u1eefu d\u1ee5ng. K\u0129 n\u0103ng di\u1ec5n gi\u1ea3i (k\u0129 n\u0103ng trinh s\u00e1t doanh nghi\u1ec7p, tr\u1ef1c quan ho\u00e1, v\u00e0 \u0111\u1ed3 ho\u1ea1) \u0111\u1ec3 bi\u1ec3u di\u1ec5n th\u00f4ng tin n\u00e0y trong c\u00e1c b\u00e0i tr\u00ecnh b\u00e0y, \u0111\u1ed3 th\u1ecb, s\u01a1 \u0111\u1ed3 hay \u0111\u1ed9 \u0111o \u0111\u01b0\u1ee3c d\u00f9ng trong OLAP. K\u0129 n\u0103ng th\u00fac b\u1ea9y (k\u0129 n\u0103ng to\u00e1n h\u1ecdc, th\u1ed1ng k\u00ea v\u00e0 d\u1ef1 b\u00e1o) \u0111\u1ec3 t\u1ea1o ra b\u00e1o c\u00e1o d\u1ef1 b\u00e1o, d\u1ef1 \u0111o\u00e1n.<\/p>\n<p>Walmart, c\u1eeda h\u00e0ng b\u00e1n l\u1ebb l\u1edbn nh\u1ea5t tr\u00ean th\u1ebf gi\u1edbi \u0111\u00e3 d\u00f9ng Big Data \u0111\u1ec3 c\u1ea3i ti\u1ebfn hi\u1ec7u qu\u1ea3 v\u1eadn h\u00e0nh c\u1ee7a h\u1ecd t\u1eeb nh\u1eefng n\u0103m 1990. Ng\u01b0\u1eddi qu\u1ea3n l\u00ed c\u1ee7a h\u1ecd hi\u1ec3u r\u1eb1ng b\u1eb1ng vi\u1ec7c c\u00f3 m\u1ecdi ki\u1ec3u d\u1eef li\u1ec7u, h\u1ecd c\u00f3 th\u1ec3 c\u1ea3i ti\u1ebfn d\u00e2y chuy\u1ec1n cung c\u1ea5p c\u1ee7a n\u00f3, gi\u1edbi h\u1ea1n v\u01b0\u1ee3t qu\u00e1 kho, gi\u1ea3n chi ph\u00ed v\u1eadn h\u00e0nh, v\u00e0 h\u1ea1 th\u1ea5p gi\u00e1 th\u00e0nh c\u1ee7a n\u00f3. Trong m\u1ed9t th\u1eddi gian ng\u1eafn, Walmart \u0111\u00e3 lo\u1ea1i b\u1ecf nhi\u1ec1u \u0111\u1ed1i th\u1ee7 c\u1ea1nh tranh c\u1ee7a n\u00f3 b\u1eb1ng vi\u1ec7c c\u00f3 gi\u00e1 th\u1ea5p h\u01a1n do hi\u1ec7u qu\u1ea3 v\u1eadn h\u00e0nh c\u1ee7a n\u00f3. Tr\u01b0\u1edbc khi Walmart d\u00f9ng Big Data, \u0111\u00e3 c\u00f3 h\u00e0ng ngh\u00ecn c\u00f4ng ti b\u00e1n l\u1ebb l\u1edbn tr\u00ean th\u1ebf gi\u1edbi, ng\u00e0y nay con s\u1ed1 r\u1edbt xu\u1ed1ng \u00edt h\u01a1n m\u1ed9t tr\u0103m. Walmart b\u00e2y gi\u1edd l\u00e0 c\u00f4ng ti b\u00e1n l\u1ebb l\u1edbn nh\u1ea5t tr\u00ean th\u1ebf gi\u1edbi v\u1edbi h\u00e0ng ngh\u00ecn c\u1eeda h\u00e0ng v\u00e0 h\u00e0ng tri\u1ec7u nh\u00e2n vi\u00ean. \u01afu th\u1ebf m\u00e0 Walmart c\u00f3 tr\u00ean c\u00e1c \u0111\u1ed1i th\u1ee7 c\u1ea1nh tranh c\u1ee7a n\u00f3 l\u00e0 \u1edf h\u1ec7 th\u00f4ng tin ph\u1ee9c t\u1ea1p v\u00e0 d\u00f9ng Big Data. \u0110\u00e2y l\u00e0 v\u00e0i v\u00ed d\u1ee5:<\/p>\n<ul>\n<li>T\u1ed1i \u01b0u gi\u00e1 \u0111\u1ed9ng: B\u1eb1ng vi\u1ec7c d\u00f9ng Big Data v\u00e0 k\u0129 thu\u1eadt trinh s\u00e1t doanh nghi\u1ec7p, Walmart c\u00f3 th\u1ec3 \u0111\u1ed5i gi\u00e1 s\u1ea3n ph\u1ea9m c\u1ee7a n\u00f3 trong t\u1eebng c\u1eeda h\u00e0ng m\u1ed9t c\u00e1ch t\u1ef1 \u0111\u1ed9ng. B\u1eb1ng vi\u1ec7c thu th\u1eadp d\u1eef li\u1ec7u th\u1ecb tr\u01b0\u1eddng t\u1eeb kh\u1eafp tr\u00ean th\u1ebf gi\u1edbi, k\u1ec3 c\u1ea3 gi\u00e1 c\u1ee7a \u0111\u1ed1i th\u1ee7 c\u1ea1nh tranh, d\u00e2y chuy\u1ec1n cung c\u1ea5p, d\u1eef li\u1ec7u kho, v\u00e0 d\u1eef li\u1ec7u h\u00e0nh vi kh\u00e1ch h\u00e0ng, Walmart c\u00f3 th\u1ec3 \u0111i\u1ec1u ch\u1ec9nh b\u1ea5t k\u00ec gi\u00e1 n\u00e0o \u0111\u1ec3 l\u00e0m c\u1ef1c \u0111\u1ea1i s\u1ed1 b\u00e1n v\u00e0 t\u0103ng l\u1ee3i nhu\u1eadn. Khi m\u1ed9t \u0111\u1ed1i th\u1ee7 c\u1ea1nh tranh c\u00f3 gi\u00e1 th\u1ea5p h\u01a1n, m\u00e1y t\u00ednh c\u1ee7a Walmart l\u1eadp t\u1ee9c h\u1ea1 gi\u00e1 c\u1ee7a n\u00f3 xu\u1ed1ng v\u00e0i ph\u1ea7n tr\u0103m \u0111\u1ec3 \u0111\u1ea3m b\u1ea3o r\u1eb1ng n\u00f3 l\u00e0 gi\u00e1 th\u1ea5p nh\u1ea5t.<\/li>\n<li>Ph\u00e2n t\u00edch v\u00e0 h\u1ed7 tr\u1ee3 b\u00e1n<b>:<\/b>\u00a0Walmart thu th\u1eadp m\u1ecdi th\u00f4ng tin b\u00e1n h\u00e0ng. N\u00f3 kh\u00f4ng gi\u1eef kho l\u1edbn nh\u01b0ng khi kho\u1ea3n m\u1ee5c n\u00e0o \u0111\u00f3 t\u1ee5t xu\u1ed1ng th\u1ea5p m\u1ed9t l\u01b0\u1ee3ng t\u1ed1i thi\u1ec3u, h\u1ec7 th\u1ed1ng m\u00e1y t\u00ednh t\u1ef1 \u0111\u1ed9ng \u0111\u1eb7t \u0111\u01a1n h\u00e0ng cho nh\u00e0 ch\u1ebf t\u1ea1o chuy\u1ec3n s\u1ea3n ph\u1ea9m ph\u1ee5 th\u00eam t\u1edbi c\u1eeda h\u00e0ng x\u00e1c \u0111\u1ecbnh. B\u1eb1ng vi\u1ec7c d\u00f9ng ph\u00e2n t\u00edch Big Data, c\u00f4ng ti bi\u1ebft chi ti\u1ebft xu h\u01b0\u1edbng th\u1ecb tr\u01b0\u1eddng \u1edf c\u00e1c khu v\u1ef1c \u0111\u1ecba l\u00ed r\u1ea3i r\u00e1c \u0111\u1ec3 t\u1ed1i \u01b0u s\u1ea3n ph\u1ea9m trong kho c\u1ee7a n\u00f3.<\/li>\n<li>Ph\u00e2n t\u00edch th\u00f3i quen mua s\u1ea3n ph\u1ea9m<b>:<\/b>\u00a0M\u1ecdi c\u1eeda h\u00e0ng Walmart \u0111\u1ec1u c\u00f3 m\u00e1y quay video \u0111\u1ec3 thu th\u1eadp h\u00e0nh vi c\u1ee7a kh\u00e1ch h\u00e0ng khi h\u1ecd mua h\u00e0ng. B\u1eb1ng vi\u1ec7c ph\u00e2n t\u00edch d\u1eef li\u1ec7u video v\u00e0 c\u00e1ch b\u1ed1 tr\u00ed c\u1eeda h\u00e0ng, Walmart bi\u1ebft ch\u1ed7 \u0111\u1ec3 b\u00e0y s\u1ea3n ph\u1ea9m \u0111\u1ec3 khuy\u1ebfn kh\u00edch h\u00e0nh vi mua nhi\u1ec1u h\u01a1n. M\u1ed9t s\u1ea3n ph\u1ea9m gi\u00e1 r\u1ea5t th\u1ea5p \u0111\u1eb7t \u1edf cu\u1ed1i \u0111\u01b0\u1eddng \u0111i s\u1ebd bu\u1ed9c kh\u00e1ch h\u00e0ng ph\u1ea3i \u0111i su\u1ed1t con \u0111\u01b0\u1eddng t\u1edbi cu\u1ed1i, \u0111i qua tr\u01b0ng b\u00e0y nhi\u1ec1u s\u1ea3n ph\u1ea9m kh\u00e1c. M\u1ecdi l\u00fac h\u1ecd d\u1eebng l\u1ea1i v\u00e0 nh\u00ecn v\u00e0o nh\u1eefng tr\u01b0ng b\u00e0y n\u00e0y, h\u00e0nh vi c\u1ee7a h\u1ecd b\u00e1o hi\u1ec7u s\u1ef1 quan t\u00e2m. D\u1eef li\u1ec7u n\u00e0y \u0111\u01b0\u1ee3c thu th\u1eadp v\u00e0 ph\u00e2n t\u00edch c\u1ea9n th\u1eadn \u0111\u1ec3 x\u00e1c \u0111\u1ecbnh xu h\u01b0\u1edbng. B\u1eb1ng vi\u1ec7c d\u00f9ng ph\u00e2n t\u00edch Big Data, Walmart hi\u1ec3u s\u1ea3n ph\u1ea9m n\u00e0o c\u00f3 th\u1ec3 h\u1ea5p d\u1eabn s\u1ed1 l\u1edbn kh\u00e1ch h\u00e0ng v\u00e0 l\u1ea5y h\u00e0nh \u0111\u1ed9ng b\u1eb1ng \u0111\u1ed5i gi\u00e1 nh\u01b0 vi\u1ec7c b\u00e1n \u0111\u1eb7c bi\u1ec7t \u0111\u1ec3 khuy\u1ebfn kh\u00edch nhi\u1ec1u kh\u00e1ch h\u00e0ng t\u1edbi c\u1eeda h\u00e0ng c\u1ee7a h\u1ecd.<\/li>\n<\/ul>\n<p>T\u1ea5t nhi\u00ean, Walmart ch\u1ec9 l\u00e0 m\u1ed9t v\u00ed d\u1ee5 v\u00ec nhi\u1ec1u c\u00f4ng ti v\u00e0 ng\u00e0nh c\u00f4ng nghi\u1ec7p \u0111ang nhanh ch\u00f3ng ch\u1ea5p nh\u1eadn ph\u00e2n t\u00edch Big Data cho \u01b0u th\u1ebf c\u1ea1nh tranh c\u1ee7a h\u1ecd. M\u1eb7c d\u1ea7u nhu c\u1ea7u l\u00e0 cao nh\u01b0ng hi\u1ec7n th\u1eddi \u00edt tr\u01b0\u1eddng \u0111ang d\u1ea1y nh\u1eefng k\u0129 n\u0103ng n\u00e0y. Ng\u01b0\u1eddi ta d\u1ef1 b\u00e1o r\u1eb1ng c\u00f3 th\u1ec3 c\u1ea7n \u00edt nh\u1ea5t m\u1ed9t th\u1eadp k\u1ec9 n\u1eefa hay \u0111\u1ea1i lo\u1ea1i nh\u01b0 v\u1eady \u0111\u1ec3 c\u00f3 \u0111\u1ee7 nh\u00e0 khoa h\u1ecdc d\u1eef li\u1ec7u, nh\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u hay k\u0129 s\u01b0 d\u1eef li\u1ec7u cho c\u00f4ng nghi\u1ec7p.<\/p>\n<p>&nbsp;<\/p>\n<p>&#8212;English version&#8212;<\/p>\n<p>&nbsp;<\/p>\n<p>Example of Big Data<\/p>\n<p>A computer science student asked: \u201cMy friend who works in the industry told me that Big Data is only a concept that will happen in the future and someday be used in the industry. I am confused, is it something that already being used or is it still a new concept? Please advice?\u201d<\/p>\n<p>&nbsp;<\/p>\n<p>Answer: Big Data is NOT a new concept; it has been used for many years especially in financial analysis and stock market trading. Today, it is widely used in many industries from retails, banking, to pharmaceuticals and biotechnology, etc. In the past, the use of Big Data was limited to large companies because it was very expensive as it required powerful computers with large storage capacity. Today advancing of computer and storage technology has made Big Data affordable so its use is getting more popular.<\/p>\n<p>If you look back in history, before the invention of Personal Computer (PC), only very large companies can afford the multi-million dollars mainframe computers. Apple and Microsoft have changed that by putting a computer in every home. The same thing is happening today with Big Data, as the cost of high speed processing and large storage decrease, more companies can afford to use them and Big Data is becoming a fast growing area. Industry analysts predicted that in the past, software has helped thousands of developers become millionaires and billionaires then this cycle will repeat with Big Data and soon many Data scientists, Data architects, Data analysts will become millionaires and billionaires.<\/p>\n<p>Today company can access a vast amount of data both inside and outside the company and leverage them to improve business. The concept is simple: Collect a lot of data, in all kinds of formats from many sources, analyze and organize them to provide essential information for management\u2019s decisions. However the implementation is NOT that simple, it requires special skills. Big Data requires four types of skill: The data collection skills (Database administration and Data Mining skills) to collect, format, organize and store them in the database or data warehouse. The analytics skills (Mathematics, statistics and integration skills) to analyze these data and translate them into useful information. The Interpreting skills (Business Intelligence, visualization, virtualization, and graphic skills) to representing this information in presentations, graphs, charts or metrics as used in OLAP. The Leveraging skills (Mathematics, statistics, and predicting skills) to create prediction, forecasting reports.<\/p>\n<p>Walmart, the largest retail store in the world has been using Big Data to improve its operational efficiency since 1990s. Their managers understand that by having all types of data, they can improve its supply chain, limit excess inventory, reduce operational costs, and lower its prices. Within a short time, Walmart has eliminated many of its competitors by having lower prices due to its operational efficiency. Before Walmart used Big Data, there was thousands of big retail companies in the world, today the number dropped to less than a hundred. Walmart is now the largest retail company in the world with thousands of stores and millions of employees. The advantage that Walmart has over its competitors is its sophisticated information systems and the use of Big Data. Here are few examples:<\/p>\n<ul>\n<li>Dynamic price optimization: By using Big Data and business intelligence techniques, Walmart can change the price of its products in each store automatically. By collect market data from all over the world, including competitor pricing, supply chain, inventory data, and consumer behavior data, Walmart can adjust any prices to maximize sales and increase profits. When a competitor has lower price, Walmart computers immediately drop its price by few percent more to guarantee that its price is the lowest.<\/li>\n<li>Sales analysis and support<b>:<\/b>\u00a0Walmart collects all sales information. It does not keep a large inventory but when certain items fell below a minimum amount, its computer system automatically place an order to the manufacture to ship additional products to specific store. By using Big Data analytics, the company knows in details the market trends in geographically dispersed areas to optimize its in-store products.<\/li>\n<li>Product placement analysis<b>:<\/b>\u00a0All Walmart stores have video cameras to collect customers\u2019 behavior as they shop. By analyzing video data and store layout, Walmart knows where to place their products to encourage more buying behavior. A very low price product placed at the end of the aisle will force customers to go all the way to the end, passing many other product displays. Every time they stop and look at these displays, their behavior signals interests. The data is collected and analyzed carefully to determine trends. By using Big Data analytics, Walmart understands which products may attract large numbers of customer and take action by change the price as special sales to promote more customers to come to their stores.<\/li>\n<\/ul>\n<p>Of course, Walmart is just one example as more companies and industries are quickly adopting Big Data analytics for their competitive advantages. Although demand is high but currently few schools are teaching these skills. It is predicted that it may need at least another decade or so to have enough Data scientists, Data analysts or Data engineers for the industry.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>M\u1ed9t sinh vi\u00ean khoa h\u1ecdc m\u00e1y t\u00ednh h\u1ecfi: \u201cB\u1ea1n em l\u00e0m vi\u1ec7c trong c\u00f4ng nghi\u1ec7p b\u1ea3o em r\u1eb1ng Big Data ch\u1ec9 l\u00e0 kh\u00e1i ni\u1ec7m s\u1ebd &hellip; <\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[30,36,26],"tags":[],"class_list":["post-3426","post","type-post","status-publish","format-standard","hentry","category-hoi-va-dap","category-social-media-mobility-big-data-analytics-and-cloud-computing","category-xu-huong-cong-nghe"],"_links":{"self":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts\/3426","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3426"}],"version-history":[{"count":3,"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts\/3426\/revisions"}],"predecessor-version":[{"id":3428,"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts\/3426\/revisions\/3428"}],"wp:attachment":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3426"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3426"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3426"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}