The digital assembly line

數(shù)字化裝配線

Technology firms vie for billions in data-analytics contracts?

科技公司爭(zhēng)奪著數(shù)十億美元的數(shù)據(jù)分析合同

詞匯

vie for/爭(zhēng)奪；競(jìng)爭(zhēng)

Two surprising leaders have emerged from the pack

兩位令人驚訝的領(lǐng)導(dǎo)者脫穎而出

Sep 5th 2019 | REDWOOD CITY AND SAN FRANCISCO?

SOMEBODY LESS driven than Tom Siebel would have long since thrown in the towel. In 2006 the entrepreneur, then 53 years old, sold his first firm, Siebel Systems, which made computer programs to track customer relations, to Oracle, a giant of business software. That left him a billionaire—but a restless one. In 2009, a few months after Mr Siebel had launched a new startup, he was trampled by an elephant while on safari in Tanzania. When, a dozen surgeries later, he could work again, the enterprise almost went bankrupt. Undeterred, he rebooted it.

沒(méi)有湯姆·希貝爾那么干勁十足的人早就認(rèn)輸了。2006年，這位53歲的企業(yè)家將他的第一家公司希貝爾系統(tǒng)軟件有限公司（Siebel Systems）賣(mài)給了商業(yè)軟件巨頭甲骨文(Oracle)。這讓他成為了億萬(wàn)富翁，但也讓他坐立不安。2009年，就在西貝爾創(chuàng)辦一家新公司幾個(gè)月后，他在坦桑尼亞旅游時(shí)被一頭大象踩傷。十幾次手術(shù)之后，當(dāng)他可以重返崗位時(shí)，這家企業(yè)幾乎破產(chǎn)。他沒(méi)有氣餒，重新啟動(dòng)了它。

詞匯

Safari/狩獵遠(yuǎn)征，游獵；旅游期間；外出期間

Trample/踩傷；踐踏

Mr Siebel’s fortitude has paid off. The firm, now called C3.ai, raised $100m in venture capital last year, valuing it at $2.1bn. It was an early bet on data analytics, which converts raw data (from a machine’s sensors or a warehouse) into useful predictions (when equipment will fail or what the optimal stocking levels are) with the help of clever algorithms. Many investors see fortunes to be made from this new breed of enterprise software, which is spreading from Big Tech’s computer labs to corporations everywhere.

希貝爾先生的堅(jiān)韌得到了回報(bào)。艾未未去年籌集了1億美元風(fēng)險(xiǎn)資本，對(duì)該公司的估值為21億美元。這是對(duì)數(shù)據(jù)分析的早期押注。數(shù)據(jù)分析利用聰明的算法，將原始數(shù)據(jù)(從機(jī)器的傳感器或倉(cāng)庫(kù))轉(zhuǎn)換成有用的預(yù)測(cè)(設(shè)備何時(shí)會(huì)故障或最佳庫(kù)存水平是多少)。許多投資者認(rèn)為，這種新型企業(yè)軟件將創(chuàng)造財(cái)富，這種軟件正從大型科技公司的計(jì)算機(jī)實(shí)驗(yàn)室擴(kuò)展到世界各地的企業(yè)。

詞匯

Fortitude/ n. 剛毅；不屈不撓；勇氣

Worldwide, 35 companies that dabble in data analytics feature on a list of startups valued at $1bn or more, maintained by CB Insights, a research firm. Collectively, these unicorns—some of which brand themselves as purveyors of artificial intelligence (AI)—enjoy a heady valuation of $73bn. According to PitchBook, another research company, the six biggest alone are worth $45bn (see chart 1). Many venture capitalists who back them are hoping to emulate the successful initial public offerings this year of less exalted business-services startups like CrowdStrike, which provides cybersecurity, or Zoom, a video-conferencing company. And then some.

在全球范圍內(nèi)，35家涉足數(shù)據(jù)分析的公司被研究公司CB Insights列入了一份價(jià)值10億美元或以上的初創(chuàng)公司名單?？傮w而言，這些獨(dú)角獸(其中一些自稱為人工智能(AI)供應(yīng)商)的擁有著令人狂熱的高達(dá)730億美元的估值。根據(jù)PitchBook,另一家研究公司,僅六家巨頭企業(yè)身價(jià)就高達(dá)450億美元(見(jiàn)圖表1)。許多支持他們的風(fēng)險(xiǎn)資本家希望企業(yè)能夠效仿今年成功的首次公開(kāi)發(fā)行(ipo)實(shí)例，從損失較小的業(yè)務(wù)開(kāi)始做起，比如CrowdStrike——一家提供網(wǎng)絡(luò)安全的公司，或著像Zoom這家提供視頻會(huì)議技術(shù)支持的企業(yè)一樣?；蛘咂渌?lèi)型。

詞匯

Unicorn/獨(dú)角獸

Purveyor/承辦商；供應(yīng)貨物或提供服務(wù)的人或公司

Emulate/仿真；模仿；盡力趕上

less exposed/ [保險(xiǎn)]損失可能性小

As is often the case in Silicon Valley, hype springs eternal, fuelled by big numbers from consultancies. IDC reckons that spending on big-data and business-analytics software will reach $67bn this year. But it will, boosters say, at last allow businesses to see the computer age in their productivity statistics, freeing them from the shadow of Robert Solow, a Nobel-prizewinning economist, who in 1987 observed that investment in information technology appeared to do little to make companies more efficient. Just as electricity enabled the assembly line in the 19th century, since machines no longer had to be grouped around a central steam engine, data-analytics companies promise to usher in the assembly lines of the digital economy, distributing data-crunching capacity where it is needed. They may also, as George Gilbert, a veteran business-IT analyst, observes, help all kinds of firm create the same network effects behind the rise of the tech giants: the better they serve their customers, the more data they collect, which in turn improves their services, and so on.

就像硅谷經(jīng)常出現(xiàn)的情況一樣，咨詢公司的大量數(shù)據(jù)推動(dòng)著持久不息的宣傳。IDC估計(jì)，今年大數(shù)據(jù)和商業(yè)分析軟件的支出將達(dá)到670億美元。但支持者表示，這將最終讓企業(yè)從生產(chǎn)率統(tǒng)計(jì)數(shù)據(jù)中看到計(jì)算機(jī)時(shí)代，從而擺脫諾貝爾經(jīng)濟(jì)學(xué)獎(jiǎng)得主羅伯特?索洛(Robert Solow)的陰影。1987年，索洛曾指出，信息技術(shù)方面的投資似乎無(wú)助于提高企業(yè)效率。正如電力在19世紀(jì)使裝配線成為可能一樣，由于機(jī)器不再必須圍繞一個(gè)中央蒸汽機(jī)分組，數(shù)據(jù)分析公司承諾將引入數(shù)字經(jīng)濟(jì)的裝配線，在需要的地方分配數(shù)據(jù)處理能力。正如資深商業(yè)it分析師喬治?吉爾伯特所觀察到的那樣，他們還可能幫助各種各樣的公司在科技巨頭崛起的背后創(chuàng)造出同樣的網(wǎng)絡(luò)效應(yīng):為客戶提供的服務(wù)越好，他們收集的數(shù)據(jù)就越多，而這些數(shù)據(jù)反過(guò)來(lái)又改善了他們的服務(wù)，等等。

詞匯

Hype/大肆宣傳

Consultants at Gartner recently calculated that in 2021 “AI augmentation” will create $2.9trn of “business value” and save 6.2bn man-hours globally. A survey by McKinsey last year estimated that AI analytics could add around $13trn, or 16%, to annual global GDP by 2030. Retail and logistics stand to gain most (see chart 2).

Gartner的咨詢師最近計(jì)算，到2021年，“人工智能增強(qiáng)”將創(chuàng)造2.9萬(wàn)億美元的“商業(yè)價(jià)值”，并在全球節(jié)省62億工時(shí)。麥肯錫去年的一項(xiàng)調(diào)查估計(jì)，到2030年，人工智能分析將為全球年度GDP增加約13萬(wàn)億美元（16%）。零售業(yè)和物流業(yè)將獲得最多利潤(rùn)(見(jiàn)表2)。

Data analytics have a long way to go before they live up to these expectations. Extracting and analysing data from countless sources and connected devices—the “Internet of Things”—is difficult and costly. Although most firms boast of having conjured up AI “platforms”, few of these meet the usual definition of that term, typically reserved for things like Apple’s and Google’s smartphone operating systems, which allow developers to build compatible apps easily.

在達(dá)到這些期望之前，數(shù)據(jù)分析還有很長(zhǎng)的路要走。從數(shù)不清的資源和連接設(shè)備(“物聯(lián)網(wǎng)”)中提取和分析數(shù)據(jù)是困難和昂貴的。盡管大多數(shù)公司都吹噓自己創(chuàng)造出了人工智能“平臺(tái)”，但很少有公司能達(dá)到這個(gè)詞的通常定義，通常指的是蘋(píng)果(Apple)和谷歌的智能手機(jī)操作系統(tǒng)，這些操作系統(tǒng)允許開(kāi)發(fā)者輕松構(gòu)建兼容的應(yīng)用程序。

An AI platform would automatically translate raw data into an algorithm-friendly format and offer a set of software-design tools that even people with limited coding skills could use. Many companies, including Palantir, the biggest unicorn in the data-analytics herd, sell high-end customised services—equivalent to building an operating system from scratch for every client. Cloud-computing giants such as Amazon Web Services, Microsoft Azure and Google Cloud offer standardised products for their corporate customers but, as Jim Hare of Gartner explains, these are considerably less sophisticated and lock users into their networks.

人工智能平臺(tái)會(huì)自動(dòng)將原始數(shù)據(jù)轉(zhuǎn)換成一種算法友好的格式，并提供一套軟件設(shè)計(jì)工具，即使是編程能力有限的人也可以使用這些工具。包括數(shù)據(jù)分析領(lǐng)域最大的獨(dú)角獸Palantir在內(nèi)的許多公司都銷(xiāo)售高端定制服務(wù)——相當(dāng)于為每個(gè)客戶從零開(kāi)始構(gòu)建一個(gè)操作系統(tǒng)。亞馬遜網(wǎng)絡(luò)服務(wù)、微軟云和谷歌云計(jì)算等云計(jì)算巨頭為企業(yè)客戶提供標(biāo)準(zhǔn)化產(chǎn)品，但正如Gartner的吉姆?黑爾(Jim Hare)所解釋的那樣，這些公司的產(chǎn)品要簡(jiǎn)單得多，而且會(huì)將用戶鎖定在自己的網(wǎng)絡(luò)中。

The enterprising Mr Siebel

富有進(jìn)取心的希貝爾先生

Enter C3.ai, founded to help utilities manage electric grids, a complex problem that involves collecting and processing data from many sources. After its near-bankruptcy, advances in machine learning, sensors and data connectivity gave it a new lease of life—and allowed it to repackage its products for a range of industries. Crucially for corporate clients, C3’s approach grew out of Mr Siebel’s experience with enterprise software. He wanted to make data analytics hassle-free for corporate clients, without sacrificing sophistication.

走進(jìn)C3.ai，該企業(yè)建立的初衷就是為了幫助公用事業(yè)公司管理電網(wǎng)，這是一個(gè)復(fù)雜的問(wèn)題，涉及從許多來(lái)源收集和處理數(shù)據(jù)。在瀕臨破產(chǎn)之后，機(jī)器學(xué)習(xí)、傳感器和數(shù)據(jù)連接方面的進(jìn)步賦予了它新的生命力，并使其能夠?yàn)橐幌盗行袠I(yè)重新包裝產(chǎn)品。對(duì)企業(yè)客戶來(lái)說(shuō)，至關(guān)重要的是，C3的方法源于希貝爾對(duì)企業(yè)軟件的經(jīng)驗(yàn)。他希望在不犧牲復(fù)雜性的前提下，讓企業(yè)客戶無(wú)需費(fèi)力地進(jìn)行數(shù)據(jù)分析。

詞匯

hassle-free/輕而易舉的毫無(wú)麻煩的

3M, an American conglomerate, employs C3 software to pick out potentially contentious invoices to pre-empt complaints. The United States Air Force uses it to work out which parts of an aircraft are likely to fail soon. C3 is helping Baker Hughes to develop analytics tools for the oil-and-gas industry (General Electric, the oil-services firm’s parent company, has struggled to perfect an analytics platform of its own, called Predix).

3M，一家美國(guó)的綜合企業(yè)，使用C3軟件來(lái)挑選潛在的有爭(zhēng)議的發(fā)票，以先發(fā)制人。美國(guó)空軍用它來(lái)計(jì)算飛機(jī)的哪些部件可能很快會(huì)出現(xiàn)故障。C3公司正在幫助Baker Hughes為油氣行業(yè)開(kāi)發(fā)分析工具(通用電氣，這家石油服務(wù)公司的母公司，一直在努力完善自己的分析平臺(tái)Predix)。

詞匯

Conglomerate/企業(yè)集團(tuán)

Contentious/訴訟的；有異議的

Invoice/發(fā)票，單據(jù)

C3’s chief rival in building a bona fide AI platform is not Big Tech or the very biggest data-analytics unicorns. It is a company called Databricks. It was founded in 2013 by computer wizards who developed Apache Spark, an open-source program which can handle reams of data from sensors and other connected devices in real time. Databricks expanded Spark to handle more data types. It sells its services chiefly to startups (such as Hotels.com, a travel site) and media companies (Viacom). It says it will generate $200m in revenue this year and was valued at $2.8bn when it last raised capital in February.

C3在打造真正的人工智能平臺(tái)方面的主要競(jìng)爭(zhēng)對(duì)手不是大型科技公司，也不是最大的數(shù)據(jù)分析獨(dú)角獸公司。這是一家叫做Databricks的公司。2013年，計(jì)算機(jī)奇才們開(kāi)發(fā)了Apache Spark，這是一個(gè)開(kāi)源程序，可以實(shí)時(shí)處理來(lái)自傳感器和其他連接設(shè)備的大量數(shù)據(jù)。Databricks擴(kuò)展了Spark來(lái)處理更多的數(shù)據(jù)類(lèi)型。它主要向初創(chuàng)公司(如旅游網(wǎng)站Hotels.com)和媒體公司(維亞康姆)出售服務(wù)。該公司表示，今年將實(shí)現(xiàn)2億美元的收入，上一次融資是在今年2月，當(dāng)時(shí)的估值為28億美元。

Though C3’s and Databricks’ niches do not overlap much at the moment, they may do in the future. Their approaches differ, too, reflecting their roots. Databricks, born of abstruse computer science, helps clients deploy open-source tools effectively. Like most enterprise-software firms, C3 sells proprietary applications.?

雖然C3和Databricks的利基市場(chǎng)目前沒(méi)有太多重疊，但將來(lái)可能會(huì)重疊。他們的方向也不同，這也反映了他們的源頭（的區(qū)別）。Databricks起源于深?yuàn)W的計(jì)算機(jī)科學(xué)，幫助客戶有效地部署開(kāi)源工具。與大多數(shù)企業(yè)軟件公司一樣，C3也銷(xiāo)售專有應(yīng)用程序。

It is unclear which one will prevail; at the moment the two firms are neck-and-neck. In the near term, the market is big enough for both—and more. In the longer run, someone will come up with AI-assisted data analytics that are no more taxing than using a spreadsheet. It could be C3 or Databricks, or smaller rivals like Dataiku from New York or Domino Data Lab in San Francisco, which are also busily erecting AI platforms. The field’s other unicorns are unlikely to give up trying. And incumbent tech titans like Amazon, Google and Microsoft want to dominate all sorts of software, including advanced data analytics.

目前還不清楚哪一方會(huì)勝出;目前這兩家公司勢(shì)均力敵。在短期內(nèi)，這個(gè)市場(chǎng)足夠容納這兩家公司，甚至更多。從長(zhǎng)遠(yuǎn)來(lái)看，有人將提出人工智能輔助的數(shù)據(jù)分析，這并不比使用電子表格更費(fèi)力。它可以是C3或Databricks，也可以是規(guī)模較小的競(jìng)爭(zhēng)對(duì)手，如紐約的Dataiku或舊金山的Domino Data Lab，他們也在忙于搭建人工智能平臺(tái)。該領(lǐng)域的其他獨(dú)角獸不太可能放棄嘗試。而現(xiàn)有的科技巨頭如亞馬遜、谷歌和微軟想要主導(dǎo)所有種類(lèi)的軟件，包括高級(jí)數(shù)據(jù)分析。

詞匯

Prevail/ ?盛行，流行

Incumbent/現(xiàn)任的；依靠的；負(fù)有職責(zé)的

Mr Siebel would be the first to admit that this scramble is likely to claim victims. But it certainly bodes well for buyers of data-analytics software, which is likely to become as familiar to corporate IT departments in the 2020s as customer-relations programs are today.?

希貝爾先生將是第一個(gè)承認(rèn)這種爭(zhēng)奪很可能造成受害者的人。但對(duì)于數(shù)據(jù)分析軟件的買(mǎi)家來(lái)說(shuō)，這無(wú)疑是個(gè)好兆頭。在本世紀(jì)20年代，企業(yè)it部門(mén)很可能會(huì)像今天的客戶關(guān)系項(xiàng)目一樣熟悉數(shù)據(jù)分析軟件。