項(xiàng)利

Technology firms vie for1 billions in corporate data-analytics contracts.技術(shù)公司爭(zhēng)奪價(jià)值數(shù)十億美元的企業(yè)數(shù)據(jù)分析合同。

Somebody less driven than Tom Siebel would have long since thrown in the towel. In 2006 the entrepreneur， then 53 years old， sold his ?rst ?rm， 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 trampled2 by an elephant while on safari in Tanzania. When， a dozen surgeries later， he could work again， the enterprise almost went bankrupt. Undeterred3， he rebooted it.

Mr. Siebels fortitude has paid off4. The ?rm， 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 machines sensors or a warehouse） into useful predictions （when equipment will fail or what the optimal5 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 Techs computer labs to corporations everywhere.

Worldwide， 35 companies that dabble6 in data analytics feature on a list of startups valued at $1bn or more， maintained by CB Insights， a research ?rm. Collectively， these unicorns7—some of which brand themselves as purveyors8 of arti?cial intelligence （AI）—enjoy a heady valuation of $73bn. According to PitchBook， another research company， the six biggest alone are worth $45bn . Many venture capitalists who back them are hoping to emulate9 the successful initial public offerings of less exalted10 business-services startups like CrowdStrike， which provides cybersecurity or Zoom， a video-conferencing company. And then some.

As is often the case in Silicon Valley， hype11 springs eternal， fuelled by big numbers from consultancies. IDC12 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 Prize-winning 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 in13 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 ?rms 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.

Consultants at Gartner 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.

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 ?rms boast of having conjured up14 AI “platforms”， few of these meet the usual de?nition of that term， typically reserved for things like Apples and Googles smartphone operating systems， which allow developers to build compatible apps easily.

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.

The enterprising Mr. Siebel

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， C3s approach grew out of Mr. Siebels experience with enterprise software. He wanted to make data analytics hassle-free15 for corporate clients， without sacri?cing sophistication.

3M， an American conglomerate16， employs C3 software to pick out potentially contentious17 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 ?rms parent company， has struggled to perfect an analytics platform of its own， called Predix）.

C3s chief rival in building a bona ?de18 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 wizards19 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 chie?y 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.

Though C3s and Databricks niches do not overlap much at the moment， they may do in the future. Their approaches differ， too， re?ecting their roots. Databricks， born of abstruse20 computer science， helps clients deploy open-source tools effectively. Like most enterprise-software ?rms， C3 sells proprietary21 applications.

It is unclear which one will prevail; at the moment the two ?rms are neck-and-neck22. 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 taxing23 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 ?elds other unicorns are unlikely to give up trying. And incumbent tech titans24 like Amazon， Google and Microsoft want to dominate all sorts of software， including advanced data analytics.

Mr. Siebel would be the ?rst to admit that this scramble25 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.

如果沒(méi)有十足的干勁，湯姆·西貝爾可能早就在失敗中放棄了。2006年，這位時(shí)年53歲的企業(yè)家將他的第一家公司西貝爾系統(tǒng)公司（制作跟蹤客戶關(guān)系的電腦程序）出售給了商業(yè)軟件巨頭甲骨文公司。這讓他成為億萬(wàn)富翁，但他并沒(méi)有安于現(xiàn)狀。2009年，創(chuàng)辦新公司幾個(gè)月后，西貝爾在坦桑尼亞游獵時(shí)被一頭大象踩傷。經(jīng)歷了十幾次手術(shù)后，他終能重返工作，當(dāng)時(shí)公司已瀕臨破產(chǎn)。他沒(méi)有氣餒，而是重新啟動(dòng)了公司。

西貝爾先生的堅(jiān)韌得到了回報(bào)。這家現(xiàn)名C3.ai的公司2018年籌集了1億美元的風(fēng)險(xiǎn)投資，估值21億美元。這是提早押寶數(shù)據(jù)分析，它借助巧妙的算法，將來(lái)自機(jī)器傳感器或倉(cāng)庫(kù)的原始數(shù)據(jù)進(jìn)行轉(zhuǎn)化，來(lái)有效預(yù)測(cè)設(shè)備故障發(fā)生的時(shí)間或最佳庫(kù)存水平等。許多投資者都看到了這種新型企業(yè)軟件帶來(lái)的機(jī)遇，這種軟件正從大型科技公司的計(jì)算機(jī)實(shí)驗(yàn)室推廣到世界各地的公司。

市場(chǎng)數(shù)據(jù)研究公司CB Insights提供的資料顯示，全球有35家涉足數(shù)據(jù)分析的公司被列入估值至少10億美元的初創(chuàng)企業(yè)名單?？傮w而言，這些獨(dú)角獸企業(yè)（部分自稱為人工智能供應(yīng)商）總估值高達(dá)730億美元。另一家研究公司PitchBook的數(shù)據(jù)顯示，僅6家最大的初創(chuàng)企業(yè)總估值就高達(dá)450億美元。許多支持他們的風(fēng)險(xiǎn)投資家都希望能夠效仿那些不知名但首次公開(kāi)募股就取得成功的商業(yè)服務(wù)初創(chuàng)企業(yè)，比如提供網(wǎng)絡(luò)安全的CrowdStrike公司、提供視頻會(huì)議服務(wù)的Zoom公司，等等。

正如硅谷那樣，在咨詢公司的大數(shù)據(jù)推動(dòng)下，炒作層出不窮?；ヂ?lián)網(wǎng)數(shù)據(jù)中心評(píng)估顯示，2019年用于大數(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，這最終會(huì)讓企業(yè)在生產(chǎn)率統(tǒng)計(jì)數(shù)據(jù)中看到計(jì)算機(jī)時(shí)代的契機(jī)，讓它們擺脫諾貝爾獎(jiǎng)得主、經(jīng)濟(jì)學(xué)家羅伯特·索洛帶來(lái)的陰影——1987年，索洛曾提出，信息技術(shù)投資似乎對(duì)提高企業(yè)效率沒(méi)有什么幫助。正如19世紀(jì)電力的出現(xiàn)讓生產(chǎn)流水線得以實(shí)現(xiàn)，機(jī)器的運(yùn)作不再需要依賴中央蒸汽機(jī)，今天數(shù)據(jù)分析公司有望引入數(shù)字經(jīng)濟(jì)的生產(chǎn)流水線，按需分配數(shù)據(jù)處理能力。據(jù)資深商業(yè)信息技術(shù)分析師喬治·吉爾伯特分析，它們還可以幫助各類企業(yè)在技術(shù)巨頭崛起的背景下創(chuàng)造出同樣的網(wǎng)絡(luò)效應(yīng)：為客戶提供的服務(wù)越好，收集的數(shù)據(jù)就越多，這轉(zhuǎn)而又可以提高服務(wù)質(zhì)量，以此類推。

高德納咨詢公司的咨詢顧問(wèn)估測(cè)，2021年，“人工智能增強(qiáng)”將在全球范圍內(nèi)創(chuàng)造2.9萬(wàn)億美元的“商業(yè)價(jià)值”，并節(jié)省62億工時(shí)。麥肯錫咨詢公司2018年的一項(xiàng)調(diào)查顯示，到2030年，人工智能分析可能使全球年度國(guó)內(nèi)生產(chǎn)總值增加約13萬(wàn)億美元，即提高約16%。零售業(yè)和物流業(yè)很可能獲益最大。

在達(dá)到這些期望前，數(shù)據(jù)分析行業(yè)還有相當(dāng)長(zhǎng)的路要走。從包含了數(shù)量龐大的信息源和聯(lián)網(wǎng)設(shè)備的“物聯(lián)網(wǎng)”中提取和分析數(shù)據(jù)，難度大且成本高。盡管大多數(shù)公司都宣稱自己構(gòu)想搭建人工智能“平臺(tái)”，但沒(méi)有幾個(gè)是真正意義上的人工智能平臺(tái)，像蘋果和谷歌的智能手機(jī)操作系統(tǒng)那樣，允許開(kāi)發(fā)者輕松構(gòu)建兼容的應(yīng)用程序。

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，就算是編程能力有限的人也會(huì)使用。許多公司，包括數(shù)據(jù)分析領(lǐng)域最大的獨(dú)角獸帕蘭提爾公司，都在銷售高端定制服務(wù)——為每個(gè)客戶從零開(kāi)始構(gòu)建操作系統(tǒng)。亞馬遜網(wǎng)絡(luò)服務(wù)、微軟云和谷歌云等云計(jì)算服務(wù)巨頭都為企業(yè)客戶提供標(biāo)準(zhǔn)化產(chǎn)品，但正如高德納全球研究副總裁吉姆·黑爾所說(shuō)，這些產(chǎn)品操作起來(lái)非常簡(jiǎn)單，將客戶鎖定在自己的網(wǎng)絡(luò)中。

勇于進(jìn)取的西貝爾先生

成立C3.ai公司是為了幫助公共事業(yè)公司管理電網(wǎng)，這是一個(gè)涉及從多個(gè)來(lái)源收集和處理數(shù)據(jù)的復(fù)雜問(wèn)題。公司近乎破產(chǎn)后，機(jī)器學(xué)習(xí)、傳感器和數(shù)據(jù)連接方面的進(jìn)步令公司的生命得以延續(xù)——使其能夠?yàn)樵S多行業(yè)提供重新包裝的產(chǎn)品。對(duì)企業(yè)客戶來(lái)說(shuō)，至關(guān)重要的是，C3的發(fā)展途徑源于西貝爾先生在企業(yè)軟件方面積累的經(jīng)驗(yàn)。他希望在保留復(fù)雜性的同時(shí)，為企業(yè)客戶提供省心省力的數(shù)據(jù)分析。

美國(guó)3M企業(yè)集團(tuán)運(yùn)用C3軟件挑出可能存在爭(zhēng)議的發(fā)票，從而預(yù)先制止投訴。美國(guó)空軍用C3軟件來(lái)測(cè)算可能很快會(huì)出現(xiàn)故障的飛機(jī)部件。C3正幫助石油服務(wù)公司貝克休斯開(kāi)發(fā)石油和天然氣行業(yè)的分析工具（貝克休斯的母公司通用電氣公司一直費(fèi)盡心思要完善自己的分析平臺(tái)Predix）。

在打造真正的人工智能平臺(tái)方面，C3公司的主要競(jìng)爭(zhēng)對(duì)手不是科技巨頭，也不是那些超大的數(shù)據(jù)分析獨(dú)角獸公司，而是一家名為Databricks的公司。它是由開(kāi)發(fā)Apache Spark的電腦奇才們于2013年創(chuàng)建的。Apache Spark是一個(gè)開(kāi)源程序，可以實(shí)時(shí)處理來(lái)自傳感器和其他聯(lián)網(wǎng)設(shè)備的海量數(shù)據(jù)。Databricks擴(kuò)展了Spark性能以處理更多數(shù)據(jù)類型。它主要服務(wù)于初創(chuàng)公司（如旅游網(wǎng)站Hotels.com好訂網(wǎng)）和媒體公司（如Viacom維亞康姆）。該公司表示，2019年公司收入將達(dá)2億美元，上一次融資就在2019年2月，當(dāng)時(shí)估值高達(dá)28億美元。

盡管目前C3公司和Databricks公司的市場(chǎng)定位不太相同，但將來(lái)可能會(huì)有重疊。兩家公司發(fā)展途徑也不同，反映了各自不同的發(fā)展根基。Databricks公司源自深?yuàn)W的計(jì)算機(jī)科學(xué)，致力于幫助客戶有效利用開(kāi)源工具;C3公司則像大部分企業(yè)軟件公司一樣，售賣專利應(yīng)用程序。

兩家公司哪家會(huì)占上風(fēng)尚不明朗，目前它們勢(shì)均力敵。短期內(nèi)，市場(chǎng)夠大，足以容納這兩家甚至更多公司。長(zhǎng)遠(yuǎn)看，會(huì)有公司提出人工智能輔助的數(shù)據(jù)分析方法，那不會(huì)比使用電子表格更難。有可能是C3公司或Databricks公司，也可能是規(guī)模較小的競(jìng)爭(zhēng)對(duì)手，如紐約的Dataiku公司或舊金山的Domino數(shù)據(jù)實(shí)驗(yàn)室，它們也都在忙著構(gòu)建人工智能平臺(tái)。該領(lǐng)域的其他獨(dú)角獸公司不太可能放棄嘗試。亞馬遜、谷歌和微軟等老牌科技巨頭都希望主導(dǎo)包括高級(jí)數(shù)據(jù)分析在內(nèi)的各類軟件。

西貝爾先生將會(huì)第一個(gè)承認(rèn)，這場(chǎng)競(jìng)爭(zhēng)可能會(huì)造成傷害。但對(duì)于數(shù)據(jù)分析軟件的買家來(lái)說(shuō)，這無(wú)疑是個(gè)好兆頭——在2020年代，公司的信息技術(shù)部門很可能會(huì)像今天熟悉客戶關(guān)系程序一樣熟悉數(shù)據(jù)分析軟件。

（譯者單位：廣東第二師范學(xué)院）