一個月前，數(shù)據(jù)技術(shù)公司Cloudera完成了和競爭對手Hortonworks價值52億美元的合并。

這兩家公司現(xiàn)在以Cloudera的名義運營，之前的業(yè)務都圍繞免費的開源式Hadoop數(shù)據(jù)處理軟件開展，雅虎和Facebook都使用該軟件管理和存儲數(shù)據(jù)。此后，兩家公司都已經(jīng)推出了新的開源數(shù)據(jù)處理技術(shù)，進軍了大熱的機器學習領(lǐng)域。

但是，盡管Cloudera和Hortonworks扎根于熱門領(lǐng)域，卻仍然無利可圖。以免費開源式軟件為基礎(chǔ)的業(yè)務想要賺錢十分困難，很多采用同樣策略的公司已經(jīng)發(fā)現(xiàn)了這一點。

Cloudera和Hortonworks合并后仍然面臨不少競爭對手，包括資金充裕的數(shù)據(jù)技術(shù)創(chuàng)業(yè)公司Snowflake和Databricks，以及亞馬遜網(wǎng)絡(luò)服務和微軟等巨頭，這兩家公司都在推出自己的數(shù)據(jù)分析和機器學習技術(shù)。

Cloudera的首席執(zhí)行官湯姆·賴利接受《財富》雜志采訪時談了與Hortonworks的合并，談到了IBM收購開源式企業(yè)服務公司Red Hat，以及他為什么更喜歡討論機器學習而不是人工智能。出于篇幅和清晰表達的考慮，以下內(nèi)容已編輯：

《財富》：有沒有其他同行業(yè)公司合并的例子？

賴利：半導體行業(yè)的微捷碼（Magma）和新思科技（Synopsis）合并了（交易完成于2012年2月）。我之所以知道這事，是因為我之前說過要從其他人的錯誤中吸取教訓。我遇到了微捷碼的首席執(zhí)行官，問他都發(fā)生了什么事——什么做得不好，什么做得好。

我希望能重視速度，也就是相對于過去其他任何合并的速度。我了解到，如果推遲決策、遲遲不定不是個好做法。

另外，什么都不能留兩份。所以我們已經(jīng)建立了一個領(lǐng)導團隊、一份產(chǎn)品路線圖、一個客戶支持組織、一個銷售團隊和一個工程組織。

你們需要裁員嗎？

任何合并中都存在重復。為了保證所有的東西都只有一份，會有人失去他們的位置。

幸運的是，因為我們都是高增長公司，很多協(xié)同效應都出現(xiàn)在原本打算在以后進行的招聘中，我們都在招聘一些重復的崗位。雖然我們確實有裁員，但相對于其他合并，我們的數(shù)量相對較少，因為我們是高增長公司。

你們?nèi)绾蝿?chuàng)造一種能將昔日對手團結(jié)在一起的文化？

首先，當兩家公司合并時，人們總是說會產(chǎn)生文化沖突。我卻有不同的看法。

合并并不是將兩種文化并在一起，因為我們的文化非常相似。合并是把忠誠的人聚在一起，他們忠誠于各自的老板，忠誠于各自的團隊。

公司合并時必須要打破忠誠，這是難點所在。我不得不選一個新的領(lǐng)導團隊。我熱愛我的舊領(lǐng)導團隊，但我必須加入一些新的領(lǐng)導角色。如果雙方都后退一步，打破這種忠誠，專注于如何建立一個結(jié)合兩家公司優(yōu)點的新企業(yè)，會發(fā)現(xiàn)不同公司的文化都非常相似。

Hortonworks過去以咨詢聞名，而Cloudera更集中于軟件銷售。合并后新企業(yè)的業(yè)務重點是什么？

你在競爭時會刻意制造差異。很多時候都是人為的差異。因此，當我們把兩家企業(yè)合在一起時，天啊，他們的相似點看起來比以前多多了。

我們[Cloudera]主營授權(quán)軟件，Hortonworks主要提供軟件支持服務。拋開那些細微差別——Hortonworks大約82％的業(yè)務是軟件支持服務創(chuàng)造的營收，我們的業(yè)務有82%來自于授權(quán)軟件的經(jīng)常性收入，我們也同時為許多軟件提供支持。

所以我們的業(yè)務幾乎完全相同。我們的客戶續(xù)訂率幾乎相同。我們的目標市場客戶也相同。真正要考慮的是我們重疊的核心業(yè)務。他們會說他們更開放[為開源社區(qū)服務]，我們會說我們更專注企業(yè)級用戶。然而，他們也有企業(yè)級的客戶，我們也是開放的。我們93%的代碼都是開放的。

那么由于你們擁有開源技術(shù)及付費軟件業(yè)務，你們公司未來的發(fā)展路徑是什么？

我們打算成為一家100%開放源代碼的公司。你可以說我們用的是Hortonworks的理念，但我們認為這非常重要。數(shù)據(jù)管理和分析中幾乎所有的創(chuàng)新——無論是數(shù)據(jù)收集還是機器學習——都是開源式的。

你為什么說IBM收購Red Hat是一筆好交易？

IBM為了能在云世界中具有競爭地位，他們可以選擇說，“好吧，我們來搭建自己的公共云基礎(chǔ)架構(gòu)，試著在全世界建立數(shù)據(jù)中心”，這么做會很難。他們也可以收購Red Hat，成為能支持多云[能夠同時管理內(nèi)部數(shù)據(jù)中心和多個公共云基礎(chǔ)架構(gòu)的能力]的軟件抽象層。我認為這種做法很明智。

340億美元值么？

我認為這比在全世界每一個時區(qū)建立20個數(shù)據(jù)中心要便宜得多，因為數(shù)據(jù)中心還包括備份、基礎(chǔ)設(shè)施和冷卻設(shè)備。

我想我們可以說IBM不是公共云計算的領(lǐng)跑者，對吧？我認為這對他們來說是一個明智之舉，這樣基本上可以利用所有已經(jīng)建好的公共云。

谷歌和微軟等公司正在推出更方便使用的AI工具，我很好奇這類工具在Cloudera的戰(zhàn)略中發(fā)揮什么作用。

我們的價值主張是公司可以使用這些工具，但我們希望幫助企業(yè)使用它們，把這些工具融入到企業(yè)的環(huán)境中。

我們假設(shè)你想做面部識別。你可以把圖片發(fā)給谷歌，得到反饋結(jié)果。同樣的道理，如果你是一家保險公司，你想要推出新的保險產(chǎn)品，就沒有和谷歌AI工具類似的黑盒AI系統(tǒng)可供使用。

但是你不認為那些云AI工具取代了公司的內(nèi)部開發(fā)嗎？

確實如此。我們想做的是讓客戶能夠開發(fā)出把他們和競爭對手區(qū)分開的產(chǎn)品或服務。我們要教的是“漁”——給他們提供工具，讓他們更加高效。這和我們給他們構(gòu)建算法的想法不同。我們的工作是幫助他們建立AI工廠——讓這個構(gòu)建新認識的過程自動化。

AI被過度炒作了么？

所以我們的機器學習總經(jīng)理說，沙特阿拉伯的一個人工智能機器人已成為第一個公民，還有其他一些瘋狂的事情。有一點鬧劇的氣氛在里面，我認為這只會讓人們迷惑。我們現(xiàn)在更傾向于討論機器學習，因為更實用。我們有使用案例——我們知道如何利用機器學習，它們也已經(jīng)提供了商業(yè)價值。

AI和未來的許多理論事物關(guān)系更大。我們可以說：“嘿，我們做的是AI，但實際上我們只是做很多和機器學習有關(guān)的工作，他們有實際效用?！?span>（財富中文網(wǎng)）

譯者；Agatha

A month ago, data technology firm Cloudera finalized a $5.2 billion merger with rival Hortonworks.

The two companies, now operating under the Cloudera name, originally focused on the free open source Hadoop data crunching software, which Yahoo and Facebook used to better manage and store their data. Since then, the two companies have pushed into newer kinds of open-source data processing technologies as well as more buzzy machine-learning.

But despite their roots in a hot space, Cloudera and Hortonworks are still unprofitable. Making money with a business that is based on free open-source software is challenging, as many companies with a similar strategy have found out.

Even with the recent merger, Cloudera and Hortonworks will still compete with rivals including well-funded data technology startups Snowflake and Databricks, along with huge incumbents such as Amazon Web Services and Microsoft, both of which are debuting their own data analytics and machine-learning technology.

In an interview with Fortune, Cloudera CEO Tom Reilly talks about the Hortonworks merger, IBM buying open-source enterprise company Red Hat, and why he prefers discussing machine learning instead of artificial intelligence. The following has been edited for length and clarity:

Fortune: What are some other examples of rival companies merging?

Reilly: Two players in the semiconductor space, Magma and Synopsis, came together [that deal closed in Feb. 2012]. The reason I know that is that early on I said I’ve got to learn from other people’s mistakes. I met the CEO of Magma and asked him what happened—what didn’t do well, what did well.

One of the things I want us to focus on is speed, and speed relative to any other merger in the past. What I’ve learned is delayed decisions and certainty is bad.

Also, don’t keep two of anything. So we already have one leadership team, one product road map, one customer support organization, one sales force, and one engineering organization.

Did you have to do layoffs?

In any merger there are going to be duplicates. To get down to one of everything, there are going to be people that are going to lose their positions.

Fortunately, because we were both high-growth companies, a lot of our synergies were in future hires because we were hiring many of the duplicate things. While we do have layoffs, we’ve had a small number relative to other mergers because we’re such a high-growth company.

How do you create a culture that unifies people who were once rivals?

First off, you read about culture clash when you bring two companies together. I actually have a different view.

It’s not bringing two cultures together, because our cultures turned out to be very similar. It’s about bringing people together who have loyalties—loyalties to their boss and loyalties to the people on their team.

In a merger, you have to break loyalties, and that is the hard thing to do. I had to pick a new leadership team. I love my old leadership team, but I had to put in some new leaders in place. When we step back and break those kind of loyalties and focus on how to build a new business that’s the best of both, it turns out that the cultures are very similar.

Hortonworks used to be known for consulting whereas Cloudera was centered more on selling software. What’s the focus of the new entity?

When you compete, you intentionally create differences. Many times those are artificial differences. So when we brought the businesses together, holy smoke, these businesses look a lot more similar.

So we [Cloudera] license software, and Hortonworks basically licensed support to software. Put aside that little nuance—roughly 82% of their business were revenues from that license support. Eighty-two percent of our business was recurring revenues from licensed software, and we gave support for a lot of software as well.

So it turns out that our businesses are nearly identical. Our customer renewal rates are nearly identical. Our target market customers competed head-to-head. The real way to think of us is in our core overlapping businesses. They would say they’re more open [catering to the open source community], we would say we’re more enterprise grade. And yet they have enterprise-grade customers, and we are open. We have 93% of our code in open source.

So what defines your company going forward as you have open source technology but also a paid software business?

So it is our intent to be a 100% open source company. Now you could say we’re adopting that philosophy from Hortonworks, but we think it’s very important. Nearly all the innovation that’s happening in data management and analytics, from gathering data to doing machine learning is in open source.

Why do you think IBM’s acquisition of Red Hat was a good deal?

For IBM to compete in a cloud world, they could either say, “Well, we’ll build out our own public cloud infrastructure and try to build data centers all over the world,” which would be very difficult. Or they could acquire Red Hat and be that abstracted software layer that enables the multi-cloud [ability to manage both internal data centers and multiple public cloud infrastructures]. I think that is brilliant.

Is $34 billion a fair price?

Well, I think it’s a lot cheaper than trying to build 20 data centers around the world in every time zone, with all of the backup, infrastructure, and cooling.

I think we can all say that IBM wasn’t the front-runner in public cloud computing, right? I think this is a brilliant way for them to basically leverage all the public cloud that’s out there and already built.

Companies like Google and Microsoft are debuting easier-to-use A.I. tools, and I’m curious how those play into Cloudera’s strategy.

Our value proposition is that companies could use those tools, but we want to help enterprises get access to them and bring them into their environment.

So lets say you want to do facial recognition. You could send an image to Google and get some results back. In the same token, if you’re an insurance company and you want to come up with new insurance products, there’s no black-box AI system akin to Google’s AI tools out there to do that.

But you don’t see those cloud AI tools replacing a company’s internal development?

Right. What we want to do is enable our customers to build products or services that distinguish them from their competitors. We are here to teach them how to fish—to give them the tools to make them more efficient. This is contrary to the idea that we’ve built the algorithms for you. Our job is to help them build that A.I. factory—to automate that process that’s building these new insights.

Is A.I. over hyped right now?

So our general manager of machine learning was just saying that in Saudi Arabia the first AI robot has become a citizen or some crazy thing. There’s a bit of a circus atmosphere to it, which I think just confuses the world. We tend to talk about machine learning today, because it’s much more pragmatic. We have use cases—we know how to deliver them and they already deliver business value.

A.I. is more associated with a lot of these theoretical things in the future. We could say, ‘Hey, we’re doing A.I., but pragmatically we’re just doing a lot of really impactful machine learning.”