（2）应用商店。做应用商店是利用一大批专业的第三方开发者赚钱的一种有意思的方式（彭博也在两年前开了自己的应用商店）。比如OpenFin公司就提供了部署内部应用商店所需要的基础架构，解决了必要的合法性、安全性和互用性（也就是让数据从一个工具流动到另一个工具）的问题。用内部应用商店基础架构网罗一系列最好用的应用（比如能提供HTML５金融表格包括技术分析工具的ChartIQ），不失为开拓彭博终端“下方”市场的一个好办法，也就是瞄准那些买不起整套彭博终端架构的公司，使他们可以选择一系列实际需要的应用，在他们的环境中为他们服务。

    （3）数据众包。从Estimize（从事分析预测众包）和Premise（把宏观经济数据众包给世界各地通过智能手机工作的很多人）来看，一种全新的捕捉金融数据的方式已经诞生了。金融数据搜索引擎Quandl已经通过网页信息采集和众包社区贡献的方式汇总了800多万个金融和经济数据集。一旦这样的数据平台建立起来，第三方开发者能否在上面添加分析工具和虚拟化工具，最终形成某种众包模式的“终端”，让它至少能执行某些不是特别重要的、或者某些不需要实时完成的任务呢？

    （4）大数据分析。从数据中提取信号无疑是金融数据界的终极游戏，有一些有意思的创业公司也在这方面投入了大量精力，比如为华尔街提供社交数据分析的Dataminr，和号称要“为金融界带来一场智能助手革命”的Kensho。在市场定位上，现在还不清楚这些技术与彭博终端形成了什么程度的竞争，或者是否具备与之竞争的潜力（彭博终端在社交数据方面也非常活跃）。

    创业公司的企业家和风投资本家们面临的一个大问题是，某些根深蒂固的平台已经占据了市场利润最丰厚的领域，在这样的前提下，如何才能把这些业务扩展为几十亿美元的生意。但总体上我认为将有越来越多的创业公司进入金融数据领域寻找机会，而且某些公司的确有取胜的可能。我很愿意看到金融数据界的进化会沿着什么路线发展。

    本文作者马特•图尔克是风投公司FirstMark Capital的常务董事，曾担任彭博创投常务董事。（财富中文网）

    译者：朴成奎

    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.