????“圖形大有可為”

????有些不太出名的公司也獲得了極高的評價。

????比如KDnuggets.com董事長兼編輯格里高里?皮亞塔特斯基?夏皮羅認為，Tamr公司是“數據管理領域一家令人興奮的創業公司，因此它獲得了我的一票。”

????高德納公司分析師斯庫拉還推薦了一家叫Neo Technology的公司，這家公司推出了一個名叫Neo4j的開源圖形數據庫。斯庫拉說：“我認為圖形有很好的前途，因為圖形表現出了數據之間的關系，而不是傳統的微觀視角。圖形技術是很多企業發展最滯后的領域，但他們的解決方案可以讓數據真正提供一些全新的見解。”（斯庫拉還推薦了Pivotal、The Hive和Concurrent三家公司。）

????分析師科特?莫納什推薦了Data Stax、WibiData、Aerospike和ClearStory等公司，而數據科學家皮特?斯科莫洛奇則推薦了Automatic、Planet Labs、Sight Machine、DataPad、Interana、Wise.io、LendUp、Declara、Sentinel Labs、FlipTop、Sift Science、 Import.io和Segment.io等公司。

????Paxata和Informatica兩家公司出現在Ovum公司分析師托爾?拜爾的推薦榜單里，喬治梅森大學教授、數據科學家科克?博爾內則推薦了IBM、Syntasa、Actian和Tableau四家公司。

????拜爾說：“有不少從事安全性和機器學習的大數據公司正在崛起。現在缺失的是從事大數據的管理、管控和生命周期管理的創業公司。現在IBM差不多是唯一在做這方面工作的企業，不過我期待在這方面會有更多的創業公司展開行動。”

????“這些公司中的大多數最終都會消失”

????如果你已經讀到了本文的此處，那么你已經看到我們的專家團隊推薦的42個公司的名字了。它們首先都是科技公司，而且其中大多數是專門從事大數據技術的公司。

????但是也有些專家表示，最有意思的大數據公司根本就不是大數據公司。比如達文波特就指出，有些搞傳統產品和服務的知名企業也在依靠自家積累的大數據開發自己的大數據產品。比如農業巨頭孟山都公司（Monsanto），專門為中小企業提供辦公室和財務管理的財捷集團（Intuit）和載重汽車公司施耐德（Schneider）等。

????達文波特說：“我認為，與其要改變你對信息的整個思考方式，改變信息與企業業務的關聯方式，倒不如建立一家創業公司方便得多。大數據的一個令人興奮的特點，就是你可以用它建立新的產品和服務。”

????他思考了一下然后補充說：“大數據仍然處于發展的初期階段，我們還不知道企業通過這些技術賺錢有多容易。”

????不過專家們普遍認為，大數據公司的數量最終將有所稀釋。

????Smarter Remarketer公司的阿波特認為：“這些公司中的大多數最終都會消失，因為大數據運動最重要的部分，是如何運用數據進行操作——也就是為企業的業務做決策，而不是光看誰能更快地處理數據。”（財富中文網）

????譯者：樸成奎

????‘Graphs have a great future’

????Some of the less well-known companies received the highest praise.

????Tamr, for instance, is “an exciting startup in data curation, so that would be my nomination,” said Gregory Piatetsky-Shapiro, president and editor ofKDnuggets.com.

????Neo Technology, the company behind open source graph database Neo4j, is another that Gartner’s Sicular pointed out. “I think graphs have a great future since they show data in its connections rather than a traditional atomic view,” she said. “Graph technologies are mostly unexplored by the enterprises but they are the solution that can deliver truly new insights from data.” (She also named Pivotal, The Hive andConcurrent.)

????DataStax, WibiData, Aerospike, Ayasdi and ClearStorywere all part of analyst Curt Monash‘s “obvious inclusion” list, he said, while Automatic, Planet Labs,Sight Machine, DataPad, Interana, Wise.io, LendUp,Declara, Sentinel Labs, FlipTop, Sift Science, Import.ioand Segment.io were among those named by data scientist Pete Skomoroch.

????Paxata and Informatica were both cited by Ovumanalyst Tony Baer; IBM IBM 0.74% , Syntasa,Actian and Tableau were four named by George Mason University professor and data scientist Kirk Borne.

????“There are a number of startups in security and machine learning that are emerging,” Baer said. “What’s missing right now are startups that look at data governance, stewardship, lifecycle management for big data. Right now IBM is largely alone, but I’m expecting there will be more startup action to come.”

????‘Most of these companies will go away’

????If you’ve reached this point in the article, you will have read 42 recommendations by our panel of experts. All of them are foremost technology companies; most exist specifically to perpetuate big data technology.

????But some experts said that the most interesting big data companies aren’t big data companies at all. Established companies with traditional products and services are starting to develop offerings based on big data, Davenport said. Those include agriculture giant Monsanto MOO , back-office operations stalwart Intuit INTU 0.10% , and the trucking company Schneider.

????“To me, it’s much easier to create a startup than it is to change your entire way of thinking about information and how it relates to your business operation,” Davenport said. “One of the really exciting things about big data is when you use it to create new products and services.”

????He added with hesitation: “It’s early days still, and we don’t know how easy it will be for companies to make money off these things.”

????It is inevitable that there will eventually be a thinning of the big data herd, experts said.

????“Most of these companies will go away because the most important part of the big data movement will be how to use data operationally—to make decisions for the business,” Smarter Remarketer’s Abbott said, “rather than who can merely crunch more data faster.”