企业竞争：数据处理决雌雄

    我已经从事数据中心业务的投资将近8年。在这期间，我对使数据产生竞争优势的根本原因有了新的认识。我曾经十分重视数据存储与管理的工具和技术，但是我现在考虑的最多的就是那些能为专有数据资产设置使用门槛的应用和商业模式。研究了风投公司IA Ventures' 的投资组合公司之后，我总结出了以下几点：

    • 搭建贡献共享式数据库的重要性。由于网络效应，在这种数据库中，第N个贡献者将导致数据资产的价值呈现非线性增长。风投公司IA Ventures的投资组合公司中个人财务安全服务供应商BillGuard 和电子交易信息管理公司Metamarkets就是最典型的例子。信息服务公司ThinkNear正计划组建贡献共享式数据资产，并藉此盈利。

    • 数据汇总、筛选、规格化、指数化、分级（数据处理平台）的价值。在这个平台中，大量的实时数据流被推送至桌面做定制化的筛选和分析，此后可通过API进行访问，并可将其植入实时模型，指数化以及存储以后做历史分析。目前在该领域的投资组合公司有Datasift, NewsCred, PlaceIQ, Recorded Future, SavingStar 以及 Sulia等公司.

    • 充分利用平台构建有价值的差异化数据资产（或数据生成平台），无论是将其作为核心业务的一部分或建立面向客户业务的附加业务。这一类公司有网上银行服务BankSimple, 学习管理和社交系统Coursekit 和在线群组管理服务公司Kohort。

    • 用户体验，用户界面以及数据形象化对于数据资产价值的最大化尤为重要，这也贯穿于IA Ventures公司的每个关键要素。德鲁•康威加入了IA Ventures公司，担任常驻科学家。这也证明了我们对帮助公司最大化利用信息资产价值的重视程度。

    贡献共享式数据库。这个业务的迷人之处在于，客户通过提交自己的数据来换取更强大的数据汇总，这些数据提供的观点涉及更广阔的市场，也为客户提供了发表见解的平台。抛砖引玉——一个十分诱人的价值主张，用户非常愿意为此掏腰包来换取更加翔实的数据信息汇总。一旦贡献共享式数据库得以建立，而且用户对此产生依赖性，他们便成为了价值连城的长期数据资产。Markit 公司的信贷索引业务便是贡献共享式数据库的案例之一。该公司收集交易者的某一种固定收益证券的出价信息，然后将这些数据制作成标准化和规格化的索引，这样市场参与者可以根据这些工业标准化指数来构建自己的产品。这是公司发家致富的催化剂。非常对我的胃口。

    数据处理平台。这种业务通过复杂的数据构架、基准算法和大量的分析组合来设置使用门槛，帮助客户以自己认可的形式来消费数据。一般这种业务与关键数据供应商保持着密切联系。这些数据将与其他数据整合并进行统一处理，随后生成有价值的差异化的使用门槛，十分具有市场竞争力。通讯机构彭博社（Bloomberg）就是这样一个十分强大的数据处理平台。他们从各大信息源搜集信息（这也包括彭博社内部所提供的数据），并将收集到的信息整合成统一的数据流，随后用户可通过面板或API来访问这些数据，这些数据为大量有用案例提供了十分强大的分析工具。不用说，彭博社这项业务的规模和利润都是业界可望而不可及的。

    数据生成平台。这项业务解决了令大量用户备感头疼的问题，并藉此从客户端搜集了大量的信息。随着这些数据的增长，这项业务的价值也就越大，因为这些数据可以帮助公司更好地根据用户的需求来改善自己的产品和特征，并设计出更贴近客户使用习惯的产品和服务。客户往往对直接体验数据资产不感兴趣；产品本身是非常有价值的，但是他们想要的仅仅是产品所能带来的一些特性。随着产品的不断完善，原本十分成功的平台也因此变得更加完美。免费个人理财服务Mint公司就是这类业务的典型案例。用户意识到了核心产品的价值。但是随着公司对更多客户信息的搜集和分析，该产品得到不断的改进。本质上，这里并没有网络效应，但是这一规模庞大的数据资产对于产品的不断完善是十分关键的。

    我们的主要目标之一是帮助资产组合公司制定数据战略，协助他们建立差异化的、可靠的数据资产。通过这些数据资产，公司便可以为多个客户提供有价值的服务。了不起吧？一点也不（除非你和数据服务商Metamarkets迈克•德里斯寇的想法一样）。风光吧？当然不是。有效吗？我们认为是这样。当今世界，每家公司都能提供潜在有价值的数据。问题是，有没有合适的方法将这些被动的数据转换为主动的资产，随后通过某种途径来来增加业务本身的含金量，比如改善产品，用户体验或使其成为为某些特定用户量身打造的、对其最有价值的数据？数据的价值高低与量的大小没有关系，当然量这个因素在生成可靠的数据使用门槛时还是能起到一定的作用。

    我们还处在这场由数据驱动的革命的初始阶段，上面所提到的一些模式还只是我们现阶段看到的商机，他们能同时为客户和投资者带来巨大的价值。现今，这些机遇已让人兴奋不已，未来，这些机遇所能带来的变革必将超乎我的想象。

    Roger Ehrenberg是IA Ventures公司的创始人，其博客地址为InformationArbitrage.com

    I've been focused on investing in data-centric businesses for almost eight years, during which my view of what generates true competitive advantage through data has changed. Where tools and technologies for data storage and management once weighed heavily on my mind, the applications and business models for erecting barriers around proprietary data assets currently dominate my thoughts. And when I took a look at IA Ventures' portfolio companies several themes became clear:

    • The power of creating contributory databases, where the value of the Nth contributor leads to a non-linear increase in the value of the data asset due to network effects. Examples in the IA Ventures portfolio include BillGuard and Metamarkets. ThinkNear's plan is to build and monetize a data asset as well.

    • The value of data aggregation, cleansing, normalization, indexing and streaming (data processing platforms), where massive real-time streams can be pushed to the desktop for customized filtering and analysis, made accessible via API for incorporation into live models and indexed and stored for historical analysis. Current portfolio companies in this sphere include Datasift, NewsCred, PlaceIQ, Recorded Future, SavingStar and Sulia.

    • The leveraging of platforms for creating valuable and differentiated data assets (data creation platforms), either as a part of the core mission or as an outgrowth of building a customer-facing business. BankSimple, Coursekit and Kohort each fit this description.

    • The importance of user experience, user interface and data visualization as tools for maximizing the value of data assets across each of IA Ventures' key themes. Drew Conway joining the IA Ventures team as scientist-in-residence is evidence of the importance we place on helping our companies extract the most value from their data assets.

    Contributory databases. The magic of these businesses is that a customer provides their own data in exchange for receiving a more robust set of aggregated data back that provides insight into the broader marketplace, or provides a vehicle for expressing a view. Give a little, get a lot back in return -- a pretty compelling value proposition, and one that frequently results in a payment from the data contributor in exchange for receiving enriched, aggregated data. Once these contributory databases are developed and customers become reliant on their insights, they become extremely valuable and persistent data assets. An example of a contributory database is the credit index business of Markit, where they poll dealers for prices on specific fixed income instruments, synthesize the data into a standardized and normalized index, and enable market participants to build products on top of these now industry-standard indices. This was the catalyst for building a multi-billion dollar company. Me likey. A lot.

    Data processing platforms. These businesses create barriers through a combination of complex data architectures, proprietary algorithms and rich analytics to help customers consume data in whatever form they please. Often these businesses have special relationships with key data providers, that when combined with other data and processed as a whole create valuable differentiation and competitive barriers. Bloomberg is an example of a powerful data processing platform. They pull in data from a wide array of sources (including their own home grown data), integrate it into a unified stream, make it consumable via a dashboard or through an API, and offer a robust analytics suite for a staggering number of use cases. Needless to say, their scale and profitability is the envy of the industry.

    Data creation platforms. These businesses solve vexing problems for large numbers of users, and by their nature capture a broad swath of data from their customers. As these data sets grow, they become increasingly valuable in enabling companies to better tailor their products and features, and to target customers with highly contextual and relevant offers. Customers don't sign up to directly benefit from the data asset; the product is so valuable that they simply want the features offered out-of-the-box. As the product gets better over time, it just cements the lock-in of what is already a successful platform. Mint was an example of this kind of business. People saw value in the core product. But the product continued to get better as more customer data was collected and analyzed. There weren't network effects, per se, but the sheer scale of the data asset that was created was an essential element of improving the product over time.

    A core part of our mission is helping portfolio companies define their data strategies and assist them create the differentiated, defensible data assets that will generate value for multiple constituencies. Sexy? No (unless, of course, you think like Mike Driscoll of Metamarkets). Glamorous? Definitely not. Effective? We think so. In today's world, every business generates potentially valuable data. The question is, are there ways of turning passive data into an active asset to increase the value of the business by making its products better, delivering a better customer experience, or creating a data stream that can be licensed to someone for whom it is most valuable? And the data doesn't need to be "big" to be valuable, though scale is certainly a helpful dimension when working to create defensible data barriers.

    We're in the early stages of a data-driven revolution, and the models outlined above are simply the current iteration of where we see opportunities for creating significant value for customers and investors alike. As exciting as the opportunity set is today, I can hardly imagine the scale of the opportunities tomorrow will bring.

    Roger Ehrenberg is founder of IA Ventures. He blogs at InformationArbitrage.com