創業公司怎樣才能“打倒”彭博終端？
|
????（2）應用商店。做應用商店是利用一大批專業的第三方開發者賺錢的一種有意思的方式（彭博也在兩年前開了自己的應用商店）。比如OpenFin公司就提供了部署內部應用商店所需要的基礎架構，解決了必要的合法性、安全性和互用性（也就是讓數據從一個工具流動到另一個工具）的問題。用內部應用商店基礎架構網羅一系列最好用的應用（比如能提供HTML５金融表格包括技術分析工具的ChartIQ），不失為開拓彭博終端“下方”市場的一個好辦法，也就是瞄準那些買不起整套彭博終端架構的公司，使他們可以選擇一系列實際需要的應用，在他們的環境中為他們服務。 ????（3）數據眾包。從Estimize（從事分析預測眾包）和Premise（把宏觀經濟數據眾包給世界各地通過智能手機工作的很多人）來看，一種全新的捕捉金融數據的方式已經誕生了。金融數據搜索引擎Quandl已經通過網頁信息采集和眾包社區貢獻的方式匯總了800多萬個金融和經濟數據集。一旦這樣的數據平臺建立起來，第三方開發者能否在上面添加分析工具和虛擬化工具，最終形成某種眾包模式的“終端”，讓它至少能執行某些不是特別重要的、或者某些不需要實時完成的任務呢？ ????（4）大數據分析。從數據中提取信號無疑是金融數據界的終極游戲，有一些有意思的創業公司也在這方面投入了大量精力，比如為華爾街提供社交數據分析的Dataminr，和號稱要“為金融界帶來一場智能助手革命”的Kensho。在市場定位上，現在還不清楚這些技術與彭博終端形成了什么程度的競爭，或者是否具備與之競爭的潛力（彭博終端在社交數據方面也非常活躍）。 ????創業公司的企業家和風投資本家們面臨的一個大問題是，某些根深蒂固的平臺已經占據了市場利潤最豐厚的領域，在這樣的前提下，如何才能把這些業務擴展為幾十億美元的生意。但總體上我認為將有越來越多的創業公司進入金融數據領域尋找機會，而且某些公司的確有取勝的可能。我很愿意看到金融數據界的進化會沿著什么路線發展。 ????本文作者馬特?圖爾克是風投公司FirstMark Capital的常務董事，曾擔任彭博創投常務董事。（財富中文網） ????譯者：樸成奎 |
????2. App stores. The app store model is an interesting way of leveraging the expertise of a "crowd" of specialized third party developers (Bloomberg launched its own a couple of years ago). OpenFin, for example, provides infrastructure to enable the deployment of in-house app stores, addressing the necessary compliance, security and inter-operability requirements (having data flow from one tool to the other). A combination of an in-house app store infrastructure with some best of breed applications (say, a ChartIQ, which provides HTML5 financial charts, including technical analysis tools) is an interesting approach to target the portion of the market "below" the terminal, as companies that cannot afford a full on terminal infrastructure could pick and choose the apps they need and have them work in their environment. ????3. Crowdsourced data. From Estimize (which crowdsources analyst estimates) to Premise (which crowdsources macroeconomic data through an army of people around the world equipped with mobile phones), a whole new way of capturing financial data has emerged. Quandl, a financial data search engine, has aggregated over 8 million financial and economic datasets through both web crawling and crowdsourced, community contributions. Once such a data platform has been built, could third party developers add analytic and visualization tools on top, essentially resulting in a crowdsourced "terminal" of sorts that would be reliable enough, at least for non mission critical, non real time use cases? ????4. Big Data insights: Extracting signal from data is obviously the end game here, and interesting startups are heavily focused on those opportunities, from Dataminr (social data analytics for Wall Street) to Kensho (which is working on "bringing the intelligent assistant revolution to finance"). In terms of market positioning, it is unclear to which extent those technologies compete with the Bloomberg terminal (which, for example, has been very active on the social data front), or potentially complete it. ????The big question facing entrepreneurs and VCs alike is how to scale those businesses and turn them into billion-dollar companies in a context where solidly entrenched platforms have a stronghold on arguably the juiciest part of the market. But overall I believe that we're only going to see more startups going after financial data opportunities, with potential for some serious wins. I'm excited to see how it all evolves. ????Matt Turck is a managing director with venture capital firm FirstMark Capital, and previously was a managing director with Bloomberg Ventures. |
最新文章