（2）它是很多小众产品的集合。对于金融数据界来说，每个资产类别（包括其亚种）都有相当的特殊性，人们可以针对每个资产类别做出一个基本上完全不同的产品。这不仅需要深厚的专业知识，也需要大量精力和财力，才能满足每一个规模相对较小的用户群（有时搞某一种资产类别的人全球加起来也只有几万人）。彭博一开始做的是固定收入数据，这么多年走过来，一路凭借雄厚的财力逐渐攻克了其它资产类别（而且直到今天，彭博的这种努力还在继续）。所以要挑战彭博的地位，并不是研制一个“万金油”式产品那么简单，而是要投入海量的风险资金，在所有这些小众领域都打造一个直接的竞争对手。

    （3）不光是技术之争。要想大范围地提供金融数据，并不只是一个纯粹的技术问题，所以不是光靠研究出更好的收集和展示数据的技术就能解决问题。至少在现阶段，彭博终端背后已经有一张庞大的人力网络、关系网络和数据提供商的合同网络支持它很多年了。

    （4）它是一个用于执行极为重要的任务的产品。这是很关键的一点。在金融界，人们靠数据来做大赌局，所以绝对的精确性和可靠性必不可少。因此人们在试用新产品的时候难免心里会七上八下，尤其当它还是一家创业公司的产品。

    就像《机构投资人》的那篇文章中所讲的一样，彭博终端业务由于宏观因素而受到了一些打击（比如华尔街相关工作岗位的减少，以及全球范围内由传统电脑数据向数据馈送转变）。但是综上所述，我认为彭博终端短期内不可能被任何创业公司完全“打倒”。而且我认为对于创业公司来说，就算他们能拿到大量风投资金，要想直接与彭博终端的任何核心功能竞争（松绑）都是非常困难的事情。并不是说完全不可能实现，我只是觉得如果创业公司把自己定位得离彭博远一点，或许有机会摘到一些更容易摘到的果子。

    金融数据的商机在哪里？

    虽然我认为创业公司研发出能取代彭博终端的产品的可能性很小【研发出能取代汤森路透（Thomson Reuters）或Factset的产品的可能性也很渺茫】，但我认为在彭博终端的“周边”和“下方”依然存在可以作为的空间——也就是说去开拓彭博不太可能想去涉足的领域。

    尤其是我认为如果能把某些精华的互联网理念和流程（比如网络、众包等）以及新技术（大数据）带到金融数据界，还是有机会的，比如：

    （1）金融网络/社区。就像彭博终端所做的一样，如果能把金融数据、分析工具和社区糅合在一起，也许会产生一些商机。资本市场历来不太有分享的文化（其中有很多微妙之处，我懂的），这有一部分原因是因为金融投资的天性。但是至少在某些领域，随着数码一代在机构内部得到晋升，这种文化也会发展变化。这个领域除了早期试水者Stocktwits和Covestor之外（他们主要瞄准非专业群体），现在面向专业人士的社区还包括一开始主要面向买方分析师、但现在已经发展得更广的SumZero。另外还有稍晚时候一些面世的Quantopian，它是一个算法交易社区，很多科学背景的人和搞数量分析的人都在这里分享算法和策略。早期创业公司ThinkNum认为金融模型应该被分享，而且它想建设一个像“Github”一样的金融模型库。大家可以想想，除此之外还有什么可以分享的？

    2. It is an aggregation of niche products. In the world of financial data, there is enough specificity to each asset class (and subsegment thereof) that you need to build a substantially different product for each, which requires deep expertise -- as well as a huge amount of effort and money -- to address a comparatively small user base (sometimes just a few tens of thousands of people around the world). Bloomberg started with fixed income data and, over many years, used its considerable cash flow to gradually conquer other classes (still a work in progress, to this day). So disrupting the Bloomberg is not as "easy" as coming up with a great one-size-fits-all product. It would take immense amounts of venture capital money to build a direct competitor across all those niches.

    3. It's not just a technology play. Providing financial data at scale is not a pure technology play, so it is not a matter of coming up with radically better technology to aggregate and display data, either. At this stage at least, there is a whole web of human processes, relationships and contracts with underlying data providers that has been put on place over many years.

    4. It's a mission critical product. This is a key point. In the financial world, data is used to make gigantic bets, so total accuracy and reliability is an absolute must – which makes people cautious when experimenting with new products, particularly built by a startup.

    The Bloomberg terminal business may face macro headwinds, as described in the Institutional Investor piece (dwindling of the number of relevant jobs on Wall Street and a global shift from desktop data to data feeds). However, as a result of the above, I don't see the Bloomberg terminal being entirely "toppled" by any one given startup anytime soon, and I think even competing directly with any of its key functionalities (unbundling) is a tall order for startups, even with access to large amount of VC money. Not that it can't be done – I just think there are lower hanging fruits out there and some real benefit to position away from the Bloomberg.

    Where are the opportunities in financial data?

    While I don't see much opportunity for startups to build a Bloomberg terminal replacement (or a replacement to Thomson Reuters or Factset, either), I think there are fertile grounds "around" and "below" the terminal – meaning in areas where the company is unlikely to want to go.

    Specifically, I believe there are going to be ongoing opportunities to apply some of the quintessential internet concepts and processes (networks, crowdsourcing, etc) as well as new-ish technology (Big Data) to the world of financial data, including:

    1. Finance networks/communities. Like the Bloomberg terminal did, some of the more interesting "adjacent" plays opportunities will marry data, tools and community. Historically, capital markets haven't seen much of a sharing culture (lots of nuances here, I know), which is in part due to the nature of finance investing itself. However, it's going to be interesting to see how, at least in certain areas, that culture will evolve as digital natives rise in the ranks of their organizations. Beyond early entrants Stocktwits and Covestor (which generally target a more casual audience), examples of such professional communities include SumZero, initially for buy-side analysts but now wider, and more recently Quantopian, an algorithmic trading community where scientifically educated people and other quant types share strategies and algorithms. Early stage startup ThinkNum thinks financial models should be shared and wants to the "Github" for financial models. What else can be shared?