????我已經(jīng)從事數(shù)據(jù)中心業(yè)務(wù)的投資將近8年。在這期間，我對使數(shù)據(jù)產(chǎn)生競爭優(yōu)勢的根本原因有了新的認(rèn)識。我曾經(jīng)十分重視數(shù)據(jù)存儲與管理的工具和技術(shù)，但是我現(xiàn)在考慮的最多的就是那些能為專有數(shù)據(jù)資產(chǎn)設(shè)置使用門檻的應(yīng)用和商業(yè)模式。研究了風(fēng)投公司IA Ventures' 的投資組合公司之后，我總結(jié)出了以下幾點(diǎn)：

????? 搭建貢獻(xiàn)共享式數(shù)據(jù)庫的重要性。由于網(wǎng)絡(luò)效應(yīng)，在這種數(shù)據(jù)庫中，第N個貢獻(xiàn)者將導(dǎo)致數(shù)據(jù)資產(chǎn)的價值呈現(xiàn)非線性增長。風(fēng)投公司IA Ventures的投資組合公司中個人財務(wù)安全服務(wù)供應(yīng)商BillGuard 和電子交易信息管理公司Metamarkets就是最典型的例子。信息服務(wù)公司ThinkNear正計劃組建貢獻(xiàn)共享式數(shù)據(jù)資產(chǎn)，并藉此盈利。

????? 數(shù)據(jù)匯總、篩選、規(guī)格化、指數(shù)化、分級（數(shù)據(jù)處理平臺）的價值。在這個平臺中，大量的實時數(shù)據(jù)流被推送至桌面做定制化的篩選和分析，此后可通過API進(jìn)行訪問，并可將其植入實時模型，指數(shù)化以及存儲以后做歷史分析。目前在該領(lǐng)域的投資組合公司有Datasift, NewsCred, PlaceIQ, Recorded Future, SavingStar 以及 Sulia等公司.

????? 充分利用平臺構(gòu)建有價值的差異化數(shù)據(jù)資產(chǎn)（或數(shù)據(jù)生成平臺），無論是將其作為核心業(yè)務(wù)的一部分或建立面向客戶業(yè)務(wù)的附加業(yè)務(wù)。這一類公司有網(wǎng)上銀行服務(wù)BankSimple, 學(xué)習(xí)管理和社交系統(tǒng)Coursekit 和在線群組管理服務(wù)公司Kohort。

????? 用戶體驗，用戶界面以及數(shù)據(jù)形象化對于數(shù)據(jù)資產(chǎn)價值的最大化尤為重要，這也貫穿于IA Ventures公司的每個關(guān)鍵要素。德魯?康威加入了IA Ventures公司，擔(dān)任常駐科學(xué)家。這也證明了我們對幫助公司最大化利用信息資產(chǎn)價值的重視程度。

????貢獻(xiàn)共享式數(shù)據(jù)庫。這個業(yè)務(wù)的迷人之處在于，客戶通過提交自己的數(shù)據(jù)來換取更強(qiáng)大的數(shù)據(jù)匯總，這些數(shù)據(jù)提供的觀點(diǎn)涉及更廣闊的市場，也為客戶提供了發(fā)表見解的平臺。拋磚引玉——一個十分誘人的價值主張，用戶非常愿意為此掏腰包來換取更加翔實的數(shù)據(jù)信息匯總。一旦貢獻(xiàn)共享式數(shù)據(jù)庫得以建立，而且用戶對此產(chǎn)生依賴性，他們便成為了價值連城的長期數(shù)據(jù)資產(chǎn)。Markit 公司的信貸索引業(yè)務(wù)便是貢獻(xiàn)共享式數(shù)據(jù)庫的案例之一。該公司收集交易者的某一種固定收益證券的出價信息，然后將這些數(shù)據(jù)制作成標(biāo)準(zhǔn)化和規(guī)格化的索引，這樣市場參與者可以根據(jù)這些工業(yè)標(biāo)準(zhǔn)化指數(shù)來構(gòu)建自己的產(chǎn)品。這是公司發(fā)家致富的催化劑。非常對我的胃口。

????數(shù)據(jù)處理平臺。這種業(yè)務(wù)通過復(fù)雜的數(shù)據(jù)構(gòu)架、基準(zhǔn)算法和大量的分析組合來設(shè)置使用門檻，幫助客戶以自己認(rèn)可的形式來消費(fèi)數(shù)據(jù)。一般這種業(yè)務(wù)與關(guān)鍵數(shù)據(jù)供應(yīng)商保持著密切聯(lián)系。這些數(shù)據(jù)將與其他數(shù)據(jù)整合并進(jìn)行統(tǒng)一處理，隨后生成有價值的差異化的使用門檻，十分具有市場競爭力。通訊機(jī)構(gòu)彭博社（Bloomberg）就是這樣一個十分強(qiáng)大的數(shù)據(jù)處理平臺。他們從各大信息源搜集信息（這也包括彭博社內(nèi)部所提供的數(shù)據(jù)），并將收集到的信息整合成統(tǒng)一的數(shù)據(jù)流，隨后用戶可通過面板或API來訪問這些數(shù)據(jù)，這些數(shù)據(jù)為大量有用案例提供了十分強(qiáng)大的分析工具。不用說，彭博社這項業(yè)務(wù)的規(guī)模和利潤都是業(yè)界可望而不可及的。

????數(shù)據(jù)生成平臺。這項業(yè)務(wù)解決了令大量用戶備感頭疼的問題，并藉此從客戶端搜集了大量的信息。隨著這些數(shù)據(jù)的增長，這項業(yè)務(wù)的價值也就越大，因為這些數(shù)據(jù)可以幫助公司更好地根據(jù)用戶的需求來改善自己的產(chǎn)品和特征，并設(shè)計出更貼近客戶使用習(xí)慣的產(chǎn)品和服務(wù)?？蛻敉鶎χ苯芋w驗數(shù)據(jù)資產(chǎn)不感興趣；產(chǎn)品本身是非常有價值的，但是他們想要的僅僅是產(chǎn)品所能帶來的一些特性。隨著產(chǎn)品的不斷完善，原本十分成功的平臺也因此變得更加完美。免費(fèi)個人理財服務(wù)Mint公司就是這類業(yè)務(wù)的典型案例。用戶意識到了核心產(chǎn)品的價值。但是隨著公司對更多客戶信息的搜集和分析，該產(chǎn)品得到不斷的改進(jìn)。本質(zhì)上，這里并沒有網(wǎng)絡(luò)效應(yīng)，但是這一規(guī)模龐大的數(shù)據(jù)資產(chǎn)對于產(chǎn)品的不斷完善是十分關(guān)鍵的。

????我們的主要目標(biāo)之一是幫助資產(chǎn)組合公司制定數(shù)據(jù)戰(zhàn)略，協(xié)助他們建立差異化的、可靠的數(shù)據(jù)資產(chǎn)。通過這些數(shù)據(jù)資產(chǎn)，公司便可以為多個客戶提供有價值的服務(wù)。了不起吧？一點(diǎn)也不（除非你和數(shù)據(jù)服務(wù)商Metamarkets邁克?德里斯寇的想法一樣）。風(fēng)光吧？當(dāng)然不是。有效嗎？我們認(rèn)為是這樣。當(dāng)今世界，每家公司都能提供潛在有價值的數(shù)據(jù)。問題是，有沒有合適的方法將這些被動的數(shù)據(jù)轉(zhuǎn)換為主動的資產(chǎn)，隨后通過某種途徑來來增加業(yè)務(wù)本身的含金量，比如改善產(chǎn)品，用戶體驗或使其成為為某些特定用戶量身打造的、對其最有價值的數(shù)據(jù)？數(shù)據(jù)的價值高低與量的大小沒有關(guān)系，當(dāng)然量這個因素在生成可靠的數(shù)據(jù)使用門檻時還是能起到一定的作用。

????我們還處在這場由數(shù)據(jù)驅(qū)動的革命的初始階段，上面所提到的一些模式還只是我們現(xiàn)階段看到的商機(jī)，他們能同時為客戶和投資者帶來巨大的價值。現(xiàn)今，這些機(jī)遇已讓人興奮不已，未來，這些機(jī)遇所能帶來的變革必將超乎我的想象。

????Roger Ehrenberg是IA Ventures公司的創(chuàng)始人，其博客地址為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