2014年3月16日 星期日

全球債務大幅增加至100兆美元 - 誰的債務負擔最沉重、該注意什麼 ( World Wide Debt Increase 40% - Which Country Will Be A Most Heavy Debt Country ? )


全球超低息環境下,即使發達國,包括美、日和法等均失去最高信貸評級,但債市投資者並沒放棄它們的債券。外電報道,全球債務自從2007年中至今(約六年半時間)大升了四成,至100萬億美元,原因是各國政府為了令經濟從衰退中復甦而大量舉債,加上發達國家不斷推出寬鬆措施,把利率遏至極低水平,公司因此而大量增加發債。\大公報記者 李耀華

  國際結算銀行與彭博社的資料顯示,全球債務金額現已從2007年中的70億美元大幅增加了30萬億美元,或四成,至100億萬美元,而全球股票總市值則減少了3.86萬億美元,至53.8萬億美元。這個由國際結算銀行於最新季度報告公布的債務數字,其增長率幾乎是美國國內生產總值的一倍。

  美國次級按揭貸款市場爆煲,隨後雷曼兄弟破產而引發了經濟大蕭條以來最嚴重的金融危機,各國中央銀行開始不斷遏抑利率上升,以刺激經濟增長,因而造成發債之風大盛。根據美銀美林全球債券市場指數顯示,各類型債券,由政府以至企業或按揭的息率,平均僅為2厘,遠低於2007年時期的逾4.8厘,高低差幅高達58%。國際結算銀行經濟師Andreas Schrimpf表示,隨?近年間政府借貸大幅擴張,政府(包括中央、州份和地方政府)已成為最大的發債者。國際結算銀行是由全球中央銀行所組成並透過巴塞爾銀行監管委員會制定全球的資本準則。
World Wide Debt To GDP Ratio, China Become One of High Debt Country

政府企業債券均大升

  彭博社根據美國財政部的資料顯示,可買賣的美國政府債務現行總額,已由2007年的4.5萬億美元大升至12萬億美元的新高,全球企業債券的銷售額在期內亦大升,發行總額已達逾21萬億美元。

  由於恐怕債台高築而令國際投資者敬而遠之,不少國家推行了經濟緊縮政策以削減開支和加稅,不惜為了恢復正常的財政秩序,而犧牲了經濟增長的機會。而在這以前,各國曾大舉發行債務,以擺脫經濟衰退。雖然發債金額不斷上升,但卻未令固定收益資產的投資需求減少。美銀美林的指數顯示,全球債券回報率(包括利率再投資在內)由2007年至今已達三成一。國庫券和機構性債券為投資者帶來了回報率兩成七,而企業債券的回報率則超過四成。

評級雖降債券仍受捧

  即使發達國家,包括美國、日本和法國均失去了最高的信貸評級,但是,債券投資者並沒因此而放棄它們的債券。相反地,上述國家的國庫券孳息不升反跌,與從前被降級債券的表現大相逕庭。美國國庫券孳息率更在2012年跌至低見1.38厘的歷史新低。

  英國的信貸評級在去年二月遭穆迪投資由最高的Aaa級調低一級,至Aa1,但自從降級以來,10年期政府債券孳息共下跌了2.6厘,曾低見2.9厘。

歐元區出口通貨緊縮
希臘政府債占GDP 132%, 造成經濟長期低迷

雖然面對着這樣和那樣經濟問題的折磨,但是整體而言,歐元區依然是個巨大的出口基地,將各種各樣不同的産品銷售到世界的每一個角落。德國擁有世界頂尖的汽車,法國擁有廣受歡迎的葡萄酒和農産品,義大利不但有食品,還是時尚的前沿。可是,歐洲現在真正的,最有重要意義的出口卻是一種對全球經濟破壞力巨大的東西——通貨緊縮。

  在很多歐洲國家,價格的下跌都已經開始發生。

  希臘和塞浦路斯的價格下跌簡直是無法控制,葡萄牙、西班牙和義大利距離他們也只是咫尺之遙,哪怕法國和德國,恐怕也堅持不了太久。可是在這樣的局面下,歐洲央行的決定卻是袖手旁觀。等到他們將來某一天不得不採取行動,恐怕一切都太遲了。

  不過,過去二十年的歷史已經清楚地告訴了我們一件事,即,貨幣政策的變化是會從一個大陸波及到另一個大陸的。十年前,來自日本的廉價資金帶動了全球性的資産泡沫。中國的競爭力造成了全球範圍的價格下跌。量化寬鬆推動了新興市場和其他許多地方的資産價格上漲。

  現在,歐洲的通貨緊縮將要出口到全球了。它已經注定將要讓歐洲的債務危機進入下一個更嚴重的階段,而且也完全可能在其他地方造成一場新的債務危機。

  歐洲央行雖然選擇了無視,但這並不意味着通貨緊縮就不會成為歐洲的重大挑戰。看看一些簡單的數字,一切就昭然若揭。

  塞浦路斯的年通貨膨脹率目前是-1.6%,希臘是-1.4%。整個歐元區的價格年度上漲速度也不過只有0.8%,從統計學的角度看來,與通貨緊縮根本就是一綫之隔。若是逐月比較,則情況更加觸目驚心。1月間,除開拉脫維亞、愛沙尼亞和斯洛伐克之外,全部歐元區國家都出現了價格下跌的情況。義大利的價格更是一個月就下跌了2.1%。

  當然,單單看這一點本身,我們可能還不知道麻煩有多大。

  商品和原料變得更便宜,大多數消費者或許會覺得這是好事也不一定。在健康的經濟體內,這確實該理解為好事,但歐元區不同。具體说來,有兩個原因讓人不能不對現狀擔心。

  首先,歐元區的外圍國家債務水平原本就已經高得難以為繼了。通貨緊縮將會使得他們的問題變得更加難以解決——債務不會變化,但是他們可以用來還本付息的收入卻在持續減少。其次,所有這些國家為了獲得相對於德國的競爭力,還必須訴諸削減工資。在正常的情況下,降薪就已經足夠痛苦了,而在通貨緊縮之下,想要重回競爭游戲,你還必須更多更狠地降薪。當價格下跌發生在不該發生的時候,那所有的麻煩都會指數量級地放大。

  聽起來,這一切似乎也不是什麼新鮮觀點了。除開歐洲央行行長德拉吉之外,其實几乎所有人都已經注意到了歐洲正在跌入價格下跌陷阱的這一事實。目前真正的麻煩,或者说真正該提出的問題是:歐元區的通貨緊縮螺旋將會對世界其他部分造成怎樣的影響?

  在全球一體化的經濟架構之下,無論一個經濟體發生了什麼,其影響總會波及到其他的國家。如果影響的源頭是歐元區這樣的重量級選手,那麼其他地方所受到的衝擊顯然將是非常猛烈的——哪怕歐元區確實是在迅速縮減其産出,但依然是全球最大的單一經濟區。

  迄今為止,受到衝擊最明顯的,正是距離最近的英國。英國本身是個高通貨膨脹國家,而且這種情況已經持續了相當時間。英國央行的官方通貨膨脹目標是2%,但是價格上漲的速度往往會衝破他們設定的關口。可是今年,他們卻達成了自己的目標,而且現在甚至還到不了2%。1月間,英國價格下跌了0.1%,目前的年度通貨膨脹率只有1.9%。

  何以會如此?答案只能向英吉利海峽的對岸去尋找。英國是與歐元區連接最為緊密的大經濟體,而他們從歐元區進口了大量通貨緊縮,自然也就不值得吃驚。從歐洲的進口在英國國內生産總值當中佔據大約17%的比重。如果再計入其他間接來自歐洲的商品和服務,則這一比重還將進一步增加。當這些價格下跌時,整個經濟都會受到波及,也是順理成章的事奇怪。

  不過,問題還不僅僅限於英國。事實上,美國的通貨膨脹也在日趨低迷。目前的通貨膨脹年率已經降低到只有1.6%。消費者價格1月間的漲幅只有0.1%,低於12月的0.2%。中國的通貨膨脹率2月間也降低到2%。

  在日本,央行拋出了大量的資金,試圖刺激經濟,但是到現在也只能將通貨膨脹率撐到1.3%,遠遠低於目標。必須指出的是,美國從歐洲進口的東西很少,而中國的歐洲進口几乎可以忽略不計。這兩國何以會受到明顯波及,背后的傳輸渠道還不是特別清楚。只是,這已經不足以改變一個大家都可以看到的事實,即通貨膨脹率在全球範圍內都在經歷戲劇性的下跌——震中就是歐元區。

  這些國家能夠從通貨緊縮當中得到好處嗎?很難,畢竟,有債務問題的不僅僅只限於歐元區外圍國家。

  英國是發達國家中債務負擔最沉重者之一,麥肯錫公司的一次研究發現,將企業債務、個人債務和政府債務合計起來,英國的債務已經超過了其國內生産總值的500%。英國已經採取了一些方法來削減政府債務,但是預算赤字規模依然可觀。

  如果通貨緊縮成了氣候,這些債務問題就將開始進入失控的螺旋,直至達到造成市場恐慌的水平。

  事實上,哪怕通貨緊縮的威脅沒有出現,大多數發達國家的央行都已經在面對一個挑戰——如何讓自己的貨幣政策回歸常態。英國央行做出暗示,2007年以來的第一次加息預計將在2015年實行。美國聯儲則開始縮減他們的量化寬鬆計劃,一旦計劃完成,下一步自然就是加息了。

  不過,若是通貨膨脹率降低到零,甚至開始變成負數,他們顯然就很難繼續這樣的行動了。歐元區的危機目前還依然被限制在十七國單一貨幣區的範圍之內,但是如果他們將通貨緊縮出口到世界其他國家,那就將是一場全球性的危機了。

2014 ~ 2019 中國極可能是全球經濟成長最大威脅(Herry S. Dent 認為中國將大衰退)
China Debt increase almost 80% from 2008

從1980年起,中國一直是成長最快的最大新興市場國家,現在是世界最大的生產國與出口國,也創造了金額驚人的貿易盈餘。中國是世界第二大的經濟體,人口是美國的四倍,購買力(人均GDP)卻大約只有美國的五分之一,實質人均GDP只有5,000美元,只有美國的十分之一,這是中國GDP總值大約仍然只有美國一半的原因。

但是中國有兩大問題,一是中國的負債遠超過1929年股市漲到創紀錄高峰時的美國,二是中國的人均GDP增加的速度不夠快,表示中國不會像美國從1920年代到1960年代那樣,晉身為已開發國家。而且中國仍然沒有建立足敷需要的民主政府或自由市場制度,無法提升到已開發國家的地位。最重要的是,幾十年來,美國得到大量移民和嬰兒潮的強力協助,推動發展,中國卻幾乎毫無外來移民可言,而且50年來出生率一直都在下降。

如果中國在下一次全球金融海嘯中,經歷最嚴重的潰敗,到時候會有什麼後果?我認為中國會碰到最嚴重的挫敗,而且在政府極為強大的控制下,中國可能是最後一個潰敗的國家,但是如果中國帶頭最先崩潰呢?

政府投資和消費者儲蓄是中國房地產泡沫背後獨一無二的動力

你首先必須了解共產主義式資本主義制度的運作方式。中央政府希望創造快速成長,方法是靠著把資金注入地方政府,同時為地方政府的債務提供擔保,好讓地方政府可以借貸極多的貸款,融通本地的基礎建設計畫。地方政府自然有一群自己鍾愛的親信企業和開發商。

中國債務約當 GDP的277%
中國政府也壓低銀行的存放款利率,支持銀行業,鼓勵貸放和營建,但是這樣會造成民間部門產生非常像助長美國次貸危機的影子銀行系統。財富管理業者收取投資人的資金,放在以房地產與基礎建設計畫為擔保的基金中,得到比投資人從銀行所得到殖利率還高的孳息。

從2007年起,中國傳統銀行放款占GDP的比率開始下降,而且從2012年起,幾乎就沒有成長,影子銀行的放款卻飛躍增加,在短短兩年內,就提高到占GDP的60%。銀行業與影子銀行的放款總額已經高達GDP的1.8倍,而且還在迅速成長。對新興市場國家而言,這種比率極高,也高過所得較高和信用較好的大多數已開發國家。

我估計中國的債務總額至少是GDP的2.77倍,而且這種比率還在繼續提高。圖10-12就是債務總額的明細。

這樣我們就得到最少2.77倍、最高可能高達四倍的數字,相形之下,巴西的倍數只有1.52倍,印度只有1.3倍,俄羅斯則低到只有0.78倍。新興市場國家的家庭、企業與金融機構信用沒有已開發國家那麼好,因此凡是超過1.5倍的負債總額應該都相當危險,對新興市場國家而言,是破錶的債務,而且再度顯示中國政府多麼積極地利用過度建設來推動經濟成長。

分析
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2014年3月15日 星期六

由科技與策略趨勢看:大數據及資料之關係將改變企業的未來 ( Big Data Will Transform The Future Of Business )[ビッグデータは、ビジネスの未来を変えていく] - Genome, Big Data With NORA And Data analysis formed knowledge will be another business in future )

Philip Evans is a senior partner and managing director in BCG’s Boston office. He founded BCG’s
media sector and has consulted for corporations worldwide in the financial services, consumer goods, media, and high-technology industries. He has also advised governments on military strategy, homeland security, and national economic policy.

Philip's long-term interest is the relation between information technology and business strategy. He is a coauthor of, among other publications, four Harvard Business Review articles, one of which, “Strategy and the New Economics of Information,” won HBR’s McKinsey Award. Blown to Bits (coauthored with Tom Wurster) was the best-selling book worldwide on technology and strategy in 2000 and has been translated into 13 languages. He is a frequent speaker on technology and strategy at industry, corporate, and academic conferences and has given keynote addresses at events convened by Bill Gates, Michael Milken, and the World Economic Forum. ( 菲利普的長遠專注於資訊技術和業務策略之間的關係。他是一個四篇哈佛商業評論出版文章中之合著作者,其中之一,“策略與資訊產業的新經濟學”榮獲哈佛商業評論的麥肯錫獎。被炸成碎片(合著與湯姆·沃斯特)是最暢銷的技術和戰略的全球本書於2000年,已被翻譯成13種語言。他常於企業和學術會議中對技術和戰略主題做發言,並給予比爾·蓋茨、邁克爾·米爾肯、及世界經濟論壇召開的事件上演講。)

I'm going to talk a little bit about strategy and its relationship with technology. We tend to think of business strategy as being a rather abstract body of essentially economic thought, perhaps rather timeless. I'm going to argue that, in fact, business strategy has always been premised on assumptions about technology, that those assumptions are changing, and, in fact, changing quite dramatically, and that therefore what that will drive us to is a different concept of what we mean by business strategy. ( 我要談一點關於策略及其與技術的關係。我們傾向於認為企業策略的作為基本經濟思想的一個相當抽象的主體,也許是相當歷久彌新。我會爭辯說,事實上,經營策略一直前提是對技術的假設,這些假設發生變化,而且,事實上,變化相當巨烈,因此,這巨烈變化將推動我們不同的概念也就是我們所說的經營策略。)

Let me start, if I may, with a little bit of history. The idea of strategy in business owes its origins to two intellectual giants: Bruce Henderson, the founder of BCG, and Michael Porter, professor at the Harvard Business School. Henderson's central idea was what you might call the Napoleonic idea of concentrating mass against weakness, of overwhelming the enemy. What Henderson recognized was that, in the business world, there are many phenomena which are characterized by what economists would call increasing returns -- scale, experience. The more you do of something, disproportionately the better you get. And therefore he found a logic for investing in such kinds of overwhelming mass in order to achieve competitive advantage. And that was the first introduction of essentially a military concept of strategy into the business world. ( Note:  Bruce Henderson's reference on  business strategy and Michael Porter Value chain )
( 讓我開始,如果可以的話,講一點點經營策略歷史。策略在企業的理念起源於兩個思想巨人:BCG的創始人 "布魯斯·亨德森"及哈佛商學院教授 "邁克爾·波特"。亨德森的中心思想是你可以稱之為大規模集中打擊及壓倒了弱勢的敵人之拿破崙戰略。亨德森公認為,在商業世界中,有許多現象其特點是經濟學家稱之報酬遞增 - 規模之經驗。你做的更多的努力,你得到更好的報酬。因此,他發現,投資於這類種壓倒性的量,來實現競爭優勢的邏輯。本質上,這是第一個引進軍事概念戰略進入商業世界。)

Porter agreed with that premise, but he qualified it. He pointed out, correctly, that that's all very well, but businesses actually have multiple steps to them. They have different components, and each of those components might be driven by a different kind of strategy. A company or a business might actually be advantaged in some activities but disadvantaged in others. He formed the concept of the value chain, essentially the sequence of steps with which a, shall we say, raw material, becomes a component, becomes assembled into a finished product, and then is distributed, for example, and he argued that advantage accrued to each of those components,
and that the advantage of the whole was in some sense the sum or the average of that of its parts. And this idea of the value chain was predicated on the recognition that what holds a business together is transaction costs, that in essence you need to coordinate, organizations are more efficient at coordination than markets, very often, and therefore the nature and role and boundaries of the cooperation are defined by transaction costs. It was on those two ideas, Henderson's idea of increasing returns to scale and experience, and Porter's idea of the value chain, encompassing heterogenous elements, that the whole edifice of business strategy was subsequently erected. ( 波特同意這個前提,但他加入一些條件來合格化。他正確地指出,那這一切都很好,但企業實際上有多個步驟。它們具有不同的組件,並且每個組件可以由不同的策略來驅動。一個公司或一個企業實際上是有些策略及組件是優勢,但有些是弱勢。他形成了價值鏈,本質上,我們可以說成幾步序列,一個原材料成為一個組件,再經組裝為成品,然後再經通路經銷,例如,他認為優勢是在每個組件,而整個的優勢是在某種意義上是所有組件總和,或說大部分平均組件的總和。而這個想法在價值鏈中認識到持有生意往來是交易成本,本質上,這在需要更有效的協調組織來超越市場,很多時候,合作中角色界線是定義為交易成本。這正是兩種觀念,亨德森的報酬遞增的規模和經驗及波特的價值鏈理念,涵蓋異質元素並豎立整個經營策略的大架構。)

Now what I'm going to argue is that those premises are, in fact, being invalidated. First of all, let's think about transaction costs. There are really two components to transaction costs. One is about processing information, and the other is about communication. These are the economics of processing and communicating as they have evolved over a long period of time. As we all know from so many contexts, they have been radically transformed since the days when Porter and Henderson first formulated their theories. In particular, since the mid-'90s, communications costs have actually been falling even faster than transaction costs, which is why communication, the Internet, has exploded in such a dramatic fashion. Now, those falling transaction costs have profound consequences, because if transaction costs are the glue that hold value chains together, and they are falling, there is less to economize on. There is less need for vertically integrated organization, and value chains at least can break up. They needn't necessarily, but they can. In particular, it then becomes possible for a competitor in one business to use their position in one step of the value chain in order to penetrate or attack or disintermediate the competitor in another. ( 當這些下降的交易成本產生深遠的影響,因為如果交易成本持有價值鏈是黏一起的,交易成本正在下降,就有較少的節約。這裡是垂直整合的組織需求下降,垂直整合的價值鏈至少可以分手。他們不一定需要,但是他們可以有。特別是,它那麼對於競爭對手就有可能在這一個行業中使用他們的位置在價值鏈中的一個步驟與組織,以在另一個滲透、攻擊另一個競爭對手或跳過它的通路。)

That is not just an abstract proposition. There are many very specific stories of how that actually happened. A poster child example was the encyclopedia business. The encyclopedia business in the days of leatherbound books was basically a distribution business. Most of the cost was the commission to the salesmen. The CD-ROM and then the Internet came along, new technologies made the distribution of knowledge many orders of magnitude cheaper, and the encyclopedia industry collapsed. It's now, of course, a very familiar story. This, in fact, more generally was the story of the first generation of the Internet economy. It was about falling transaction costs breaking up value chains and therefore allowing disintermediation, or what we call deconstruction. ( 在 leather bound 書的時代,百科全書業務基本上是一個分銷業務。大部分的費用是佣金的推銷員。CD-ROM後,網際網路出現了,新技術形成很多規模更便宜的訂單分佈之知識,百科全書行業崩潰。當然現在這是一個很熟悉的故事。其實,這更普遍的說這是第一代互聯網經濟的故事。是交易成本下降打破了價值鏈,因此允許跳過中介,或者我們稱之為通路之解構。)

One of the questions I was occasionally asked was, well, what's going to replace the encyclopedia The Wikipedia, of course, is an encyclopedia created by its users. And this, in fact, defines what you might call the second decade of the Internet economy, the decade in which the Internet as a noun became the Internet as a verb. It became a set of conversations, the era in which user-generated content and social networks became the dominant phenomenon. Now what that really meant in terms of the Porter-Henderson framework was the collapse of certain kinds of economies of scale. It turned out that tens of thousands of autonomous individuals writing an encyclopedia could do just as good a job, and certainly a much cheaper job, than professionals in a hierarchical organization. So basically what was happening was that one layer of this value chain was becoming fragmented, as individuals could take over where organizations were no longer needed. ( 其中一個我偶爾問的問題是,這是怎麼回事,誰取代百科全書大英百科全書讓不再有商業模式?前一段的答案變得明顯。我們知道它是什麼:它是維基百科。現在有什麼特別之處維基百科是不是它的分佈。有什麼特別的維基百科是它的生產方式。維基百科,當然是由其用戶創建的百科全書。而這一點,其實你可以稱之為互聯網經濟的第二個十年,這十年中,互聯網作為一個名詞成為了互聯網作為一個動詞。它成為一組對話,其中用戶生成內容和社交網絡成為佔主導地位的現象的時代。現在,究竟意味著波特 - 恆基框架中是某些種類的規模經濟的崩潰。原來,自主的幾萬個體寫作的百科全書,一樣可以做這工作,而且肯定比在一個專業層次組織人員便宜得多。所以基本上發生了什麼事是一層這個價值鏈變得支離破碎,因為那裡已不再需要組織的個人可以接管。)
Facebook business model in business model canvas
when Britannica no longer has a business model? And it was a while before the answer became manifest. Now, of course, we know what it is: it's the Wikipedia. Now what's special about the Wikipedia is not its distribution. What's special about the Wikipedia is the way it's produced.

But there's another question that obviously this graph poses, which is, okay, we've gone through two decades -- does anything distinguish the third? And what I'm going to argue is that indeed something does distinguish the third, and it maps exactly on to the kind of Porter-Henderson logic that we've been talking about. And that is, about data. If we go back to around 2000, a lot of people were talking about the information revolution, and it was indeed true that the world's stock of data was growing, indeed growing quite fast. but it was still at that point overwhelmingly analog. We go forward to 2007, not only had the world's stock of data exploded, but there'd been this massive substitution of digital for analog. And more important even than that, if you look more carefully at this graph, what you will observe is that about a half of that digital data is information that has an I.P. address. It's on a server or it's on a P.C. But having an I.P. address means that it can be connected to any other data that has an I.P. address. It means it becomes possible to put together half of the world's knowledge in order to see patterns, an entirely new thing. If we run the numbers forward to today, it probably looks something like this. We're not really sure. If we run the numbers forward to 2020, we of course have an exact number, courtesy of IDC. It's curious that the future is so much more predictable than the present. And what it implies is a hundredfold multiplication in the stock of information that is connected via an I.P. address. Now, if the number of connections that we can make is proportional to the number of pairs of data points, a hundredfold multiplication in the quantity of data is a ten-thousandfold multiplication in the number of patterns that we can see in that data, this just in the last 10 or 11 years. This, I would submit, is a sea change, a profound change in the economics of the world that we live in.

Now, what does that imply in terms of business? Well, I got a hint of this some years ago. Back in around 2003 or so, I was doing some consulting for the Pentagon, of all august institutions, on the subject of network-centric warfare, and in that context I met a gentleman called Jeff Jonas, a brilliant engineer who had made his fortune designing the security systems in Las Vegas. Jeff said to me, "Next time you're in Las Vegas, Philip, why don't you stop by and I'll take you on the tour. You can meet NORA. NORA will show you a good time." NORA was not his girlfriend. NORA is the Non-Obvious Relational Awareness system, a real-time fraud control system developed by Jeff, which supports all of the casinos in Las Vegas. We were in the security room of the Bellagio Hotel in Las Vegas, and on the monitor I saw this happen. A woman was playing blackjack against the dealer. There was nobody else at the table. She was winning too much. They know how likely that is, and this wasn't likely. So the first thing they do is they use facial recognition, see if she's staying at the hotel. She wasn't. Then they can kind of run the cameras backwards, tracing her movements back through the hotel to the parking garage, where they found her car. They could then run NORA to find who owned the car. The car was owned by Hertz Las Vegas. Within a second or so, NORA pulled down the Hertz Las Vegas application. Now they knew who the woman was. Where was she staying? Well, they pool the data across the hotels. It turned out she was staying in a hotel across the street. Had she gambled in that hotel? No. Very strange behavior, staying in one hotel, gambling in another. Then came the really interesting thing. NORA looked for a connection between the woman and the dealer, because a very high fraction of fraud in Las Vegas is committed when the staff are actually in illicit collaboration with customers. It turned out, what NORA did was to look through 6,000 databases, public and private, some owned by the Bellagio, some by other hotels, some police records, and so on. It turned out that 10 years earlier, this woman's brother had been the dealer's roommate. And it took NORA six seconds to work that fact out. It cost the woman and the dealer six years. This was NORA in action. It's what today of course we would call big data, long before the term had been formulated.

Now notice some very interesting things about this, most of all the fact that NORA runs as a cooperative across the entire of the strip. These casinos, which are otherwise competing aggressively with each other actually collaborate when it comes to the management of their security systems. They pool data into a common database that is run essentially as a co-op for this specific purpose. Why? Because the scale of NORA, what NORA is trying to do, blows past the scale that even a very large casino can possibly do for itself. The value chain is not big enough to accommodate the economies of scale that are inherent in this particular activity. And that principle, I would suggest, is actually a fundamental and pervasive one. In essence, what happens is that because of these colossal economies of scale in data, what used to be value chains that ran separately are compelled, in order to achieve those economies of scale, to create some kind of common utility, some common resource, a co-op, a pool, a vault of data within which those insights can be gathered.

Now, NORA is a relatively trivial example in the sense that if NORA failed, it wouldn't exactly be the end of civilization. But consider something vastly more important, where the logic in fact is exactly the same, the logic of healthcare. The first human genome, that of James Watson, was mapped as the culmination of the Human Genome Project in the year 2000, and it took about 200 million dollars and about 10 years of work to map just one person's genomic makeup. Since then, the costs of mapping the genome have come down. In fact, they've come down in recent years very dramatically indeed, to the point where the cost is now below 1,000 dollars, and it's confidently predicted that by the year 2015 it will be below 100 dollars -- a five or six order of magnitude drop in the cost of genomic mapping in just a 15-year period, an extraordinary phenomenon. Now, in the days when mapping a genome cost millions, or even tens of thousands, it was basically a research enterprise. Scientists would gather some representative people, and they would see patterns, and they would try and make generalizations about human nature and disease from the abstract patterns they find from these particular selected individuals. But when the genome can be mapped for 100 bucks, 99 dollars while you wait, then what happens is, it becomes retail. It becomes above all clinical. You go the doctor with a cold, and if he or she hasn't done it already, the first thing they do is map your genome, at which point what they're now doing is not starting from some abstract knowledge of genomic medicine and trying to work out how it applies to you, but they're starting from your particular genome.

Now think of the power of that. Think of where that takes us when we can combine genomic data with clinical data with data about drug interactions with the kind of ambient data that devices like our phone and medical sensors will increasingly be collecting. Think what happens when we collect all of that data and we can put it together and use precisely the NORA-type techniques in order to find patterns we wouldn't see before. This, I would suggest, perhaps it will take a while, but this will drive a revolution in medicine. Fabulous, lots of people talk about this.

But there's one thing that doesn't get much attention. How is that model of colossal sharing across all of those kinds of databases compatible with the business models of institutions and organizations and corporations that are involved in this business today? If your business is based on proprietary data, if your competitive advantage is defined by your data, how on Earth is that company or is that society in fact going to achieve the value that's implicit in the technology? They can't.

So essentially what's happening here, and genomics is merely one example of this, is that technology is driving the natural scaling of the activity beyond the institutional boundaries within which we have been used to thinking about it, and in particular beyond the institutional boundaries in terms of which business strategy as a discipline is formulated. The basic story here is that what used to be vertically integrated, oligopolistic competition among essentially similar kinds of competitors is evolving, by one means or another, from a vertical structure to a horizontal one. Why is that happening? It's happening because transaction costs are plummeting and because scale is polarizing. The plummeting of transaction costs weakens the glue that holds value chains together, and allows them to separate. The polarization of scale economies towards the very small -- small is beautiful -- allows for scalable communities to substitute for conventional corporate production. The scaling in the opposite direction, towards things like big data, drive the structure of business towards the creation of new kinds of institutions that can achieve that scale. But either way, the typically vertical structure gets driven to becoming more horizontal.

The logic isn't just about big data. If we were to look, for example, at the telecommunications industry, you can tell the same story about fiber optics. If we look at the pharmaceutical industry, or, for that matter, university research, you can say exactly the same story about so-called "big science." And in the opposite direction, if we look, say, at the energy sector, where all the talk is about how households will be efficient producers of green energy and efficient conservers of energy, that is, in fact, the reverse phenomenon. That is the fragmentation of scale because the very small can substitute for the traditional corporate scale.

Either way, what we are driven to is this horizontalization of the structure of industries, and that implies fundamental changes in how we think about strategy. It means, for example, that we need to think about strategy as the curation of these kinds of horizontal structure, where things like business definition and even industry definition are actually the outcomes of strategy, not something that the strategy presupposes. It means, for example, we need to work out how to accommodate collaboration and competition simultaneously. Think about the genome. Think about NORA We need to accommodate the very large and the very small simultaneously. And we need industry structures that will accommodate very, very different motivations, from the amateur motivations of people in communities to maybe the social motivations of infrastructure built by governments, or, for that matter, cooperative institutions built by companies that are otherwise competing, because that is the only way that they can get to scale.

These kinds of transformations render the traditional premises of business strategy obsolete. They drive us into a completely new world. They require us, whether we are in the public sector or the private sector, to think very fundamentally differently about the structure of business, and, at last, it makes strategy interesting again.

参考




2014年3月6日 星期四

Big Data 時代來臨!海量資料分析成為新產業! ( Big Data Trend came, Data Analysis become new profitable business and industry )

思科:2018年全球行動資料傳輸量將暴增至190 EB


「行動(Mobile)」一詞過去曾經只涵蓋手機、平板電腦,現在則已泛指各式各樣的穿戴裝置,例如智慧手錶、健康狀態追蹤器、谷歌眼鏡(Google Glass)等等。思科(Cisco System)認為,這些科技新品將帶動資料傳輸需求,預期到了2018年,全球行動資料傳輸量將暴增至190 exabytes (1 exabyte等於十億gigabytes)。

CNBC、Re/code報導,思科5日在年度預測報告中指出,2018年190 exabytes的行動資料傳輸量等於較2013年的傳輸量跳增11倍,也相當於42兆份影像、或是4兆片影片。這也是2000年固網、行動網路總傳輸量的190倍。

在行動資料用量快速增加的環境下,使用這些資料的行動裝置數量也將在2018年由2013年的70億台跳升至100億台。根據聯合國統計,2018年全球人口僅76億人,意味著屆時行動裝置的數量會是全球總人口的1.4倍。

行動資料傳輸量有如此爆炸性的成長,主要得益於穿戴科技的普及,思科把智慧手錶、Google Glass等穿戴裝置統一歸類至「機器對機器的連結」類別(大多屬於物聯網領域),這個分類在2013年僅佔全球行動資料傳輸量的1%,預料到了2018年會增加至6%。另外,思科也預估,2018年全球有在使用的穿戴裝置數量將由2013年的不到2,200萬台躍升至接近1.7億台。

Big Data淘金!資料分析商Tableau賺翻 股價飆13%
Tableau provide big data analysis and visual display

海量資料(Big Data)席捲而來,資料分析軟體也開始大受歡迎,Tableau Software Inc.在這波潮流的帶動下,本季營收有望創下佳績,激勵公司股價在5日飆漲近13%。

資料分析暨視覺化軟體商Tableau 4日於美國股市盤後公佈2013年第4季(10-12月)財報:營收較2012年同期跳增95%至8,150萬美元;授權金營收年增93%至5,800萬美元;本業每股稀釋盈餘達0.20美元。根據Thomson Reuters I/B/E/S調查,分析師原本預期該公司Q4營收將達6,710萬美元,本業每股盈虧則損益兩平。

Tableau指出,Q4期間該公司擴大了與亞馬遜雲端服務「Amazon Web Services (AWS)」的合作關係,支援在AWS平台執行的Tableau伺服器,並為亞馬遜雲端資料倉儲服務「Amazon Redshift」增添新的資料連接器。

此外,Q4期間還完成179筆價值超過10萬美元的銷售訂單,並新增超過1,800個客戶帳號。

Tableau執行長兼總裁Christian Chabot在新聞稿中表示,市場商機仍在不斷增加當中,該公司希望在2014年協助更多客戶透過資料洞悉未來營運的方針。

Thomson Reuters、barron`s.com報導,Tableau 在電話會議上預估本季(1-3月)營收將介於6,100-6,300萬美元之間。根據Thomson Reuters I/B/E/S調查,分析師原本預期該公司本季營收僅將達6,030萬美元,Tableau 5日聞訊大漲12.82%,收89.61美元,創2013年5月17日掛牌以來收盤新高;掛牌迄今漲幅已達76.6%。
海量資料需求夯,除了Tableau創下營運佳績外,磁碟陣列大廠Dot Hill Systems Corp.也在1月9日調高上季財測,這激勵該公司當日盤中股價狂漲逾25%。

Dot Hill Systems執行長Dana Kammersgard當時在聲明稿中表示,2013年期間該公司與許多垂直市場的重量級客戶敲定合作契約,這會成為2014年在海量資料、石油與天然氣、多媒體暨娛樂業中繼續成長的主要基石。

海量資料分析架構Hadoop每年成長55% 創辦人吃驚

儲存、處理、分析海量資料(Big Data)的開放技術架構「Hadoop」已成為產業最熱門的關鍵字,研究估計相關市場每年的成長率高達55%,速度之快連創辦人 Doug Cutting 都嚇了一跳。

英國商業科技新聞網站Computing 7日報導,根據市調機構Transparency Market Research的預估,全球Hadoop市場目前平均以每年近55%的速度快速成長,預計2018年市場規模將上看209億美元。對此,Cutting在接受專訪時表示,他當時完全不知道 Hadoop 會獲得如此大的迴響,就連甲骨文(Oracle)、IBM與微軟(Microsoft)也都還未發覺管理、儲存海量資料會有如此高的需求。

Cutting指出,開放原始碼(open source)是Hadoop能夠續存並強化的關鍵,人們不喜歡太過依Google Inc.)以及大學實驗室卻能藉此贏得優勢。
賴由某一業者控制的平台,但這種作法卻非上述傳統軟體業者所長,而雅虎(Yahoo!)、谷歌(

如今,Hadoop已成為全球企業儲存、處理與分析海量資料時的核心技術,且目前還在繼續演進當中。Cutting在2009年離開雅虎,接手Hadoop開發商Cloudera的架構長一職。目前,財星五百大企業的生產專案中,已有60%採用Cloudera的技術。Cutting表示,目前Cloudera大多數的業務成長都來自現有客戶,未來替換需求應該會高於新客戶所帶來的業務量。

IDC:2012-16年台灣巨量資料市場CAGR達40%
world wide big data revenue by service segments

IDC 2012-2016年亞太區(不含日本)巨量資料市場分析與預測報告(APEJ Big Data Technology and Services 2012-2016 Forecast and Analysis)研究顯示,亞太區(不含日本)的巨量資料市場成長力道強勁,估市場規模將從2012年的3億美元成長到2016年的17.6億美元,年複合成長率(CAGR)達47%。另台灣巨量資料市場規模則估可從2012年的1130萬美元成長到2016年的4610萬美元,年複合成長率也達到40%。

IDC亞太區巨量資料與分析研究總監Craig Stires指出,面對仍在持續不斷演進的巨量資料分析技術以及多元的市場需求,廠商們除持續不斷推出各種解決方案,亦積極於投資、訓練合作夥伴,以提供企業客戶更完整的諮詢顧問服務。而根據IDC的觀察,目前以金融、電信、政府、零售、製造與能源產業對於數據資料分析的需求最為強勁。

同時台灣市場對於巨量資料分析技術需求也在逐步提高。IDC台灣市場分析師蔡宜秀指出:「台灣的金融、製造、電信與零售等企業客戶正在嘗試、或者是計畫透過巨量資料分析技術,優化營運績效。在越來越多的企業客戶紛紛釋出對巨量資料分析技術的興趣後,將有更多的本土廠商、諮詢顧問投入該塊市場,進而逐步完善台灣的巨量資料生態系統。」

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