数字生产流水线

项利

Technology firms vie for1 billions in corporate data-analytics contracts.技术公司争夺价值数十亿美元的企业数据分析合同。

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.

如果没有十足的干劲，汤姆·西贝尔可能早就在失败中放弃了。2006年，这位时年53岁的企业家将他的第一家公司西贝尔系统公司（制作跟踪客户关系的电脑程序）出售给了商业软件巨头甲骨文公司。这让他成为亿万富翁，但他并没有安于现状。2009年，创办新公司几个月后，西贝尔在坦桑尼亚游猎时被一头大象踩伤。经历了十几次手术后，他终能重返工作，当时公司已濒临破产。他没有气馁，而是重新启动了公司。

西贝尔先生的坚韧得到了回报。这家现名C3.ai的公司2018年筹集了1亿美元的风险投资，估值21亿美元。这是提早押宝数据分析，它借助巧妙的算法，将来自机器传感器或仓库的原始数据进行转化，来有效预测设备故障发生的时间或最佳库存水平等。许多投资者都看到了这种新型企业软件带来的机遇，这种软件正从大型科技公司的计算机实验室推广到世界各地的公司。

市场数据研究公司CB Insights提供的资料显示，全球有35家涉足数据分析的公司被列入估值至少10亿美元的初创企业名单。总体而言，这些独角兽企业（部分自称为人工智能供应商）总估值高达730亿美元。另一家研究公司PitchBook的数据显示，仅6家最大的初创企业总估值就高达450亿美元。许多支持他们的风险投资家都希望能够效仿那些不知名但首次公开募股就取得成功的商业服务初创企业，比如提供网络安全的CrowdStrike公司、提供视频会议服务的Zoom公司，等等。

正如硅谷那样，在咨询公司的大数据推动下，炒作层出不穷。互联网数据中心评估显示，2019年用于大数据和商业分析软件的支出将达到670亿美元。但支持者表示，这最终会让企业在生产率统计数据中看到计算机时代的契机，让它们摆脱诺贝尔奖得主、经济学家罗伯特·索洛带来的阴影——1987年，索洛曾提出，信息技术投资似乎对提高企业效率没有什么帮助。正如19世纪电力的出现让生产流水线得以实现，机器的运作不再需要依赖中央蒸汽机，今天数据分析公司有望引入数字经济的生产流水线，按需分配数据处理能力。据资深商业信息技术分析师乔治·吉尔伯特分析，它们还可以帮助各类企业在技术巨头崛起的背景下创造出同样的网络效应：为客户提供的服务越好，收集的数据就越多，这转而又可以提高服务质量，以此类推。

高德纳咨询公司的咨询顾问估测，2021年，“人工智能增强”将在全球范围内创造2.9万亿美元的“商业价值”，并节省62亿工时。麦肯锡咨询公司2018年的一項调查显示，到2030年，人工智能分析可能使全球年度国内生产总值增加约13万亿美元，即提高约16%。零售业和物流业很可能获益最大。

在达到这些期望前，数据分析行业还有相当长的路要走。从包含了数量庞大的信息源和联网设备的“物联网”中提取和分析数据，难度大且成本高。尽管大多数公司都宣称自己构想搭建人工智能“平台”，但没有几个是真正意义上的人工智能平台，像苹果和谷歌的智能手机操作系统那样，允许开发者轻松构建兼容的应用程序。

人工智能平台会自动将原始数据转换成一种算法友好的格式，并提供一套软件设计工具，就算是编程能力有限的人也会使用。许多公司，包括数据分析领域最大的独角兽帕兰提尔公司，都在销售高端定制服务——为每个客户从零开始构建操作系统。亚马逊网络服务、微软云和谷歌云等云计算服务巨头都为企业客户提供标准化产品，但正如高德纳全球研究副总裁吉姆·黑尔所说，这些产品操作起来非常简单，将客户锁定在自己的网络中。

勇于进取的西贝尔先生

成立C3.ai公司是为了帮助公共事业公司管理电网，这是一个涉及从多个来源收集和处理数据的复杂问题。公司近乎破产后，机器学习、传感器和数据连接方面的进步令公司的生命得以延续——使其能够为许多行业提供重新包装的产品。对企业客户来说，至关重要的是，C3的发展途径源于西贝尔先生在企業软件方面积累的经验。他希望在保留复杂性的同时，为企业客户提供省心省力的数据分析。

美国3M企业集团运用C3软件挑出可能存在争议的发票，从而预先制止投诉。美国空军用C3软件来测算可能很快会出现故障的飞机部件。C3正帮助石油服务公司贝克休斯开发石油和天然气行业的分析工具（贝克休斯的母公司通用电气公司一直费尽心思要完善自己的分析平台Predix）。

在打造真正的人工智能平台方面，C3公司的主要竞争对手不是科技巨头，也不是那些超大的数据分析独角兽公司，而是一家名为Databricks的公司。它是由开发Apache Spark的电脑奇才们于2013年创建的。Apache Spark是一个开源程序，可以实时处理来自传感器和其他联网设备的海量数据。Databricks扩展了Spark性能以处理更多数据类型。它主要服务于初创公司（如旅游网站Hotels.com好订网）和媒体公司（如Viacom维亚康姆）。该公司表示，2019年公司收入将达2亿美元，上一次融资就在2019年2月，当时估值高达28亿美元。

尽管目前C3公司和Databricks公司的市场定位不太相同，但将来可能会有重叠。两家公司发展途径也不同，反映了各自不同的发展根基。Databricks公司源自深奥的计算机科学，致力于帮助客户有效利用开源工具;C3公司则像大部分企业软件公司一样，售卖专利应用程序。

两家公司哪家会占上风尚不明朗，目前它们势均力敌。短期内，市场够大，足以容纳这两家甚至更多公司。长远看，会有公司提出人工智能辅助的数据分析方法，那不会比使用电子表格更难。有可能是C3公司或Databricks公司，也可能是规模较小的竞争对手，如纽约的Dataiku公司或旧金山的Domino数据实验室，它们也都在忙着构建人工智能平台。该领域的其他独角兽公司不太可能放弃尝试。亚马逊、谷歌和微软等老牌科技巨头都希望主导包括高级数据分析在内的各类软件。

西贝尔先生将会第一个承认，这场竞争可能会造成伤害。但对于数据分析软件的买家来说，这无疑是个好兆头——在2020年代，公司的信息技术部门很可能会像今天熟悉客户关系程序一样熟悉数据分析软件。

（译者单位：广东第二师范学院）