? The Next Great Digital Advantage
維賈伊·戈文達拉揚（Vijay Govindarajan） 文卡特·卡特拉曼（N. Venkat Venkatraman）| 文 ? ?

Of the 4,000 products Amazon?sells every?minute, approximately 50% are presented to customers by its personalized recommendation engine. When you visit the site, its algorithms select an assortment of products from about 353 million items and arrange them for you according to what they predict you will want at that precise moment.

亞馬遜每分鐘賣出四千件商品，其中約50%是由個性推薦引擎呈現(xiàn)給用戶的。瀏覽亞馬遜網(wǎng)站時，算法會預測你在此時此刻想要的東西，從約3.53億商品里選出一組推送給你。

These recommendations are powered by Amazon’s ever-evolving purchase graph, which is a digital representation of real-world “entities”—anything about which it stores information, such as customers, products, purchases, events, and places—and the relationships and interrelationships among them. Amazon’s purchase graph connects purchase history with browsing data on the site, viewing data on Prime Video, listening data on Amazon Music, and data from Alexa-enabled devices. Its algorithms use collaborative filtering—incorporating factors such as diversity (how dissimilar the recommended items are); serendipity (how surprising they are); and novelty (how new they are)—to generate some of the most sophisticated recommendations on the planet. Thanks to its rich data and industry-leading personalization, Amazon now owns 40% of the U.S. e-commerce market; its closest rival, Walmart, has a market share of only 7%.

驅(qū)動個性推薦的是亞馬遜不斷演進的采購圖譜，即現(xiàn)實中“實體要素”——客戶、產(chǎn)品、采購、活動和店址等一切店鋪信息——以及這些要素之間關(guān)系性的數(shù)字化呈現(xiàn)。亞馬遜的采購圖譜將購買歷史與網(wǎng)站瀏覽情況、Prime Video觀看情況、亞馬遜音樂收聽情況和來自Alexa設(shè)備的數(shù)據(jù)聯(lián)系起來，算法使用協(xié)同過濾，結(jié)合多樣性（推薦商品的相異程度）、意外性（推薦商品的驚人程度）和新奇性（新鮮程度）等要素，生成世界上最復雜的推薦。憑借豐富的數(shù)據(jù)和行業(yè)領(lǐng)先的個性化推薦，亞馬遜現(xiàn)在占有美國電商市場的40%，跟得最緊的對手沃爾瑪市場份額僅為7%。

To compete with Amazon, in April 2021 Google announced its Shopping Graph, an AI-enhanced model that recommends products to users as they search. More than a billion people research products on Google each day, and Shopping Graph connects them with more than 24 billion listings from millions of merchants across the web. It builds on Google’s unparalleled Knowledge Graph, which captures information about the entities in its vast network and the relationships among them, including structured and unstructured data from Android, voice and image search, Chrome browser extensions, Google Assistant, Gmail, Photos, Maps, YouTube, Google Cloud, and Google Pay. With its Shopping Graph—which lets 1.7 million merchants feature relevant listings across Google using simple but interlinked tools—Google is ready to meet Amazon’s challenge.

為了與亞馬遜競爭，谷歌于2021年4月宣布推出購物圖譜（Shopping Graph），一個在用戶搜索時推薦商品的AI模型。每天用谷歌搜索商品的人超過10億，購物圖像將他們與全網(wǎng)幾百萬商家超過240億商品列表聯(lián)系起來。這個模型的基礎(chǔ)是谷歌絕無僅有的知識圖譜（Knowledge Graph），在廣闊的網(wǎng)絡(luò)中捕捉關(guān)于實體及其相互關(guān)系的信息，包括來自安卓系統(tǒng)、聲音及圖像搜索、谷歌瀏覽器Chrome擴展、谷歌助手、谷歌郵箱、谷歌照片、谷歌地圖、YouTube、谷歌云服務(wù)和谷歌支付的結(jié)構(gòu)化與非結(jié)構(gòu)化數(shù)據(jù)。谷歌購物圖譜讓170萬商家運用簡單卻相通的工具在谷歌上展示相關(guān)商品，谷歌可以應(yīng)對亞馬遜的挑戰(zhàn)。

Datagraphs like Amazon’s and Google’s rely on product-in-use data—that is, data on the behavior of customers as they use a platform or a product—to capture the connections, relationships, and interrelationships between a company and its customers. The datagraph concept is inspired by social network and graph theory, wherein a social graph is defined as a representation of the interconnections among individuals, depicted as nodes, and the relationships among them—with friends, colleagues, supervisors, and so on—represented as links. The concept derives from the work of the social psychologist Stanley Milgram, and over the past two decades, it has provided a useful lens for analyzing the structure and dynamics of organizations, industries, markets, and societies. Facebook popularized the digital social graph in 2007 when it introduced Facebook Platform, a tool that allowed developers to build applications that were integrated into the site’s information flow and connections of relationships.

像亞馬遜和谷歌這樣的數(shù)據(jù)圖譜，依賴產(chǎn)品使用數(shù)據(jù)（即用戶使用平臺或產(chǎn)品時產(chǎn)生的行為數(shù)據(jù)）把握企業(yè)及其客戶之間的聯(lián)系和關(guān)系。數(shù)據(jù)圖譜的概念源于社交網(wǎng)絡(luò)與圖形理論，該理論將社交圖譜定義為人與人之間聯(lián)系和關(guān)系的呈現(xiàn)，如朋友、同事、上司等，每個人被呈現(xiàn)為一個節(jié)點，關(guān)系則是點與點間的連接。這個概念出自社會心理學家斯坦利·米爾格拉姆（Stanley Milgram）的著作，過去二十年來，這一概念為分析組織、行業(yè)、市場和社會的結(jié)構(gòu)與動態(tài)提供了實用的透鏡。2007年，F(xiàn)acebook推出同名社交平臺，讓開發(fā)者打造應(yīng)用程序整合進網(wǎng)站信息流和人際關(guān)系連接，使得數(shù)字化社交圖譜流行起來。

Leading technology companies are using datagraphs to personalize customer recommendations, update products, optimize advertising, and more. The most successful examples—which include Amazon’s purchase graph, Google’s search graph, Facebook’s social graph, Netflix’s movie graph, Spotify’s music graph, Airbnb’s travel graph, Uber’s mobility graph, and LinkedIn’s professional graph—leverage the ongoing collection of customer engagement data, coupled with proprietary algorithms, to outcompete rivals in every way, from product creation to user experience.

領(lǐng)先的科技公司運用數(shù)據(jù)圖譜提供個性化推薦、升級產(chǎn)品、優(yōu)化廣告等等。最成功的例子，如亞馬遜的采購圖譜、谷歌的搜索圖譜、Facebook的社交圖譜、奈飛的電影圖譜、Spotify的音樂圖譜、Airbnb的旅游圖譜、優(yōu)步的出行圖譜和領(lǐng)英的職業(yè)圖譜，利用不斷收集的用戶使用數(shù)據(jù)，加上獨有的算法，從產(chǎn)品開發(fā)到用戶體驗等各方面甩開了競爭對手。

This article discusses how companies can learn from the best practices of datagraph leaders to gain new competitive advantage.

本文討論企業(yè)如何借鑒數(shù)據(jù)圖譜領(lǐng)先企業(yè)的方法，打造新的競爭優(yōu)勢。

Data Network Effects

數(shù)據(jù)網(wǎng)絡(luò)效應(yīng)

To understand datagraphs, we first need to understand?data network effects,?which occur when data generated by users as they engage with a product or service makes it more valuable for other users. Unlike direct network effects, in which the value of a service grows as additional users join (as with Facebook or LinkedIn), data network effects do not require increasing numbers of users to enhance the value of the network. Instead, the continued engagement of current users generates broader and deeper product-in-use data, which allows algorithms to generate ever-improving results. For example, every one of Google’s 2 trillion annual searches helps the company enrich its Knowledge Graph and improve its search engine, which generates better and better search results for users. By contrast, if users stop engaging on the platform, it becomes stale and less useful.

要了解數(shù)據(jù)圖譜，首先要了解數(shù)據(jù)網(wǎng)絡(luò)效應(yīng)，即用戶使用產(chǎn)品或服務(wù)時產(chǎn)生的數(shù)據(jù)讓這項產(chǎn)品或服務(wù)對于其他用戶更有價值的效應(yīng)。不同于價值隨著更多用戶加入而增長的直接網(wǎng)絡(luò)效應(yīng)（如Facebook和領(lǐng)英），數(shù)據(jù)網(wǎng)絡(luò)效應(yīng)不需要增加用戶數(shù)量來提升網(wǎng)絡(luò)價值，而是已有用戶持續(xù)使用、產(chǎn)生更加廣泛深入的使用數(shù)據(jù)，讓算法能夠產(chǎn)出不斷完善的結(jié)果。舉例來說，谷歌每年的兩萬億次搜索，幫助谷歌公司充實知識圖譜，改進搜索引擎，為用戶提供更好的搜索結(jié)果。而如果用戶不再使用平臺，平臺服務(wù)質(zhì)量的改善就會陷入停滯，不再那么有幫助。

Datagraphs are not static; they do not reflect information at a snapshot in time. They are dynamic, reflecting what data scientists refer to as?data in motion.?That’s partly why it is impossible to manually draw a datagraph. Technology is needed to gather and interpret in real time the data on the millions of units of a company’s products that consumers worldwide may be engaging with at any given moment.

數(shù)據(jù)圖譜不是靜止不變的，反映的不是某一時間點的數(shù)據(jù)，而是數(shù)據(jù)科學家所說的動態(tài)數(shù)據(jù)。這是無法手動繪制數(shù)據(jù)圖譜的部分原因。必須利用技術(shù)，才能實時收集和解讀一家公司的產(chǎn)品在全世界消費者使用中產(chǎn)生的幾百萬份數(shù)據(jù)。

Datagraph Success Factors

數(shù)據(jù)圖譜成功要素

Datagraph leaders gather customer behavioral data and quickly incorporate what they learn to improve every aspect of their products and services. They constantly refine how they classify and label product data and uncover relationships among entities so that algorithms can better group offerings for personalized recommendations. And they continually update their algorithms so that the personalized recommendations are based on the most current and relevant data, which helps improve and prolong customer engagement. Let’s take a look at the key behaviors of companies that use datagraphs successfully.

數(shù)據(jù)圖譜領(lǐng)先企業(yè)收集用戶行為數(shù)據(jù)，并迅速用于改進產(chǎn)品和服務(wù)的各個方面。這些公司不停地修改為產(chǎn)品數(shù)據(jù)分類和標記的方法，尋找實體間的關(guān)系，以便算法更好地歸類并提供個性化推薦。公司還不斷更新算法，以最新、最相關(guān)的數(shù)據(jù)為基礎(chǔ)生成個性化推薦，協(xié)助吸引客戶。下面看看成功運用數(shù)據(jù)圖譜的企業(yè)有哪些關(guān)鍵行為。

They learn at scale and speed.?Datagraphs capture how individuals live, work, play, learn, listen, socialize, watch, transact, travel, spend, and do any other activity that can be associated with commerce. Digitalization has made it possible to observe and codify customer data in all these areas at scale, scope, and speed. Facebook’s social graph, for example, analyzes data on 2.8 billion individuals and their social activities from moment to moment: what they’re doing, whom they’re friending and unfriending, where they’re traveling to, what brands they’re talking about, what movies they’re watching, what music they’re listening to, and so on. LinkedIn’s professional graph captures in real time how 774 million professionals who work in more than 50 million companies and attended 90,000-plus educational institutions respond to job postings, status updates, and live videos. Moreover, it maps members to other entities, such as the skills they have, to serve users targeted ads, learning suggestions, news feeds, and more. LinkedIn is now a subsidiary of Microsoft and part of its data ecosystem, which allows it to create an even more vibrant datagraph.

快速廣泛學習。數(shù)據(jù)圖譜抓取的是個人的生活、工作、娛樂、學習、收聽、社交、觀看、交易、出行、消費等等一切可以與商業(yè)聯(lián)系在一起的活動情況。數(shù)字化讓公司得以廣泛、透徹、迅速地觀察和整理這些方面的客戶數(shù)據(jù)。例如Facebook的社交圖譜，每時每刻分析28億人及其社交活動的數(shù)據(jù)：他們在做什么、與誰成為好友和解除好友、去了哪里、在討論什么品牌、在看什么電影、在聽什么音樂等等。領(lǐng)英的職業(yè)圖譜實時抓取供職于5000萬家公司、參與9萬多家教育機構(gòu)課程的7.74億專業(yè)人士如何回應(yīng)招聘信息、更新狀態(tài)、使用直播視頻。此外，職業(yè)圖譜還根據(jù)用戶技能等其他要素，為用戶提供有針對性的廣告、學習建議、新聞推送以及更多信息?，F(xiàn)在領(lǐng)英是微軟子公司，被納入微軟的數(shù)據(jù)生態(tài)系統(tǒng)，得以創(chuàng)造更有活力的數(shù)據(jù)圖譜。

At traditional companies, customer data is stored as independent records in various functional databases. To gain digital advantage, companies must organize data as a graph of interactions that are analyzable by algorithms that provide insight and deliver personalized value to every customer.

傳統(tǒng)企業(yè)的用戶數(shù)據(jù)各自獨立儲存在不同職能部門的數(shù)據(jù)庫。為了獲取數(shù)字優(yōu)勢，企業(yè)必須將數(shù)據(jù)組織成交互圖譜，可運用算法分析，生成洞察并為每一位客戶提供個性化價值。

They use datagraphs to enrich product offerings.?Datagraph leaders organize their knowledge and expertise in machine-readable graph formats with a set of concepts—such as shopping, travel, or search—across categories. Take Airbnb’s travel graph. It depicts an inventory of more than 7 million homes, tagged in terms of entities (cities, landmarks, events, and so on), attributes (such as customer reviews and hours of operation), and the relationships among them to yield ever-improving recommendations about not just the type of house to rent but also the best places for dinner or the best times to visit attractions. This ability to expand the product scope allows Airbnb to serve its customers better than traditional hotels, whose data is housed in departmental silos (reservations for the room booking, concierge for restaurant recommendations, spa for massage appointments, and so on). Similarly, Netflix continually improves how it represents and classifies movies and television shows across 75,000 microgenres (just as Spotify does with music and podcasts).

用數(shù)據(jù)圖譜豐富產(chǎn)品線。在數(shù)據(jù)圖譜方面領(lǐng)先的企業(yè)用購物、出行或搜索等一系列跨領(lǐng)域的概念，將專業(yè)知識整理為可由機器識別的圖譜格式。例如Airbnb的出行圖譜，給出了700多萬住宅的清單，打上屬性（所在城市、地標、活動等）、特征（顧客評價和營業(yè)時間等）和彼此間關(guān)系的標簽，生成更高級的推薦，不僅推薦出租屋，還可以推薦最佳晚餐場所和游覽景點的最佳時間。這種擴大產(chǎn)品范圍的能力讓Airbnb為顧客提供優(yōu)于傳統(tǒng)酒店的服務(wù)，后者的數(shù)據(jù)被分別儲存于彼此孤立的部門（訂房部負責預訂房間、禮賓部負責推薦參觀、療養(yǎng)部負責預約按摩，等等）。同樣，奈飛也不斷改善影視作品在7.5萬個細分類別下呈現(xiàn)和分類的方式，Spotify的音樂和電臺節(jié)目亦然。

They win customers’ moments of truth.?In 2001, only 2% of Netflix’s recommendations were chosen by its 456,000 users. By 2020, the percentage had increased to 80%, and Netflix had more than 200 million subscribers. Netflix uses its movie graph to win the “moment of truth”: the 90-second-to-two-minute window in which a viewer decides to watch something on Netflix or go elsewhere. Netflix algorithmically customizes and updates its home screen to continuously deliver targeted recommendations for every subscriber. By 2015, Netflix had prevented more than $1 billion a year in canceled subscriptions thanks to its personalized recommendation engine.

在關(guān)鍵時刻贏得客戶。2001年，奈飛有45.6萬用戶，給出的推薦中只有2%被選擇。2020年這個比例提升到80%，奈飛訂閱用戶超過了2億。奈飛運用電影圖譜，把握住了贏得用戶的“關(guān)鍵時刻”：90秒至2分鐘的窗口期，觀眾會在這段時間里決定是在奈飛上觀看影視作品還是轉(zhuǎn)向其他網(wǎng)站。奈飛根據(jù)算法對首頁進行定制化和更新，持續(xù)為每一位訂閱用戶提供個性化推薦。至2015年，奈飛每年憑借個性化推薦引擎避免的訂閱取消量價值超過10億美元。

To win its moments of truth, Facebook conducts A/B experiments across 3 billion users in near real time to personalize the social feeds of each user. Before Facebook displays a post, it sorts through an inventory of possibilities and narrows them down to about 500 that past behavior patterns suggest a user is likely to engage with. Then, Facebook’s proprietary neural network scores the posts and ranks them before arranging them in a variety of media types, such as text, photos, sounds, and videos interspersed with ads.

Facebook為了在關(guān)鍵時刻獲勝，對30億用戶分別進行了近乎實時的個性化社交網(wǎng)絡(luò)內(nèi)容對照測試。推送內(nèi)容之前，F(xiàn)acebook會在待推送清單中篩選，根據(jù)用戶過往行為規(guī)律，將范圍縮小至約500篇該用戶可能關(guān)心的內(nèi)容。隨后Facebook會用專有的神經(jīng)網(wǎng)絡(luò)為這些內(nèi)容打分并排序，再按媒體類型整理，如文本、照片、音頻和帶有廣告的視頻等。

Although many companies claim to be customer-centric, few use datagraphs and algorithms the way these leaders do. Ask yourself: Are we using AI-powered algorithms to deliver customers an ever-more-refined product offering to make sure they engage with our product rather than move on?

雖然許多公司號稱是以客戶為中心，但能像領(lǐng)先企業(yè)一樣善加運用數(shù)據(jù)圖譜和算法的卻很少。想一想：你的公司是否用AI算法為客戶提供不斷改善的產(chǎn)品，讓他們不會轉(zhuǎn)向其他公司？

Getting Started

開始行動

The first thing businesses that wish to remain competitive against datagraph leaders must understand is that a successful strategy isn’t solely dependent on having large volumes of information. It’s about collecting relevant product-in-use data in real time to achieve data network effects and build advantage. When businesses observe more customer interactions with their products, they accumulate richer data; when they sell more products to a more-diverse group of users, they accumulate more-varied data that helps them further differentiate their offerings. Businesses that aren’t using datagraphs or have yet to do so successfully must take the following steps to catch up:

若想與數(shù)據(jù)圖譜領(lǐng)先企業(yè)抗衡，必須明白一件事：戰(zhàn)略成功不只取決于是否擁有大量信息，還要實時收集相關(guān)的產(chǎn)品使用數(shù)據(jù)，實現(xiàn)數(shù)據(jù)網(wǎng)絡(luò)效應(yīng)并打造優(yōu)勢。如果能觀察到更多用戶與產(chǎn)品的互動，企業(yè)就能獲得更豐富的數(shù)據(jù)；將更多產(chǎn)品賣給更加多樣的用戶群體，就能累積更為多樣的數(shù)據(jù)，協(xié)助實現(xiàn)產(chǎn)品差異化。不善用數(shù)據(jù)圖譜的公司可參考以下改進建議：

1. Develop a datagraph strategy.?To get started, pair executives that have industry knowledge with data scientists to conceptualize your datagraph, examine its future trajectory, and sketch out plausible business implications. Many companies that don’t have the resources of an Amazon or a Netflix have already done this. For example, Stitch Fix was founded as a personalized fashion service in 2010 by a business school student; now, thanks in large part to its fashion graph, its market cap tops $1.6 billion.

1. 制定數(shù)據(jù)圖譜戰(zhàn)略。首先要讓了解行業(yè)的高管與數(shù)據(jù)科學家配合，在概念上構(gòu)建數(shù)據(jù)圖譜，考察未來走向并思考可能的商業(yè)影響。很多資源沒有亞馬遜或奈飛那么豐富的公司已經(jīng)做到了這一點。例如2010年一名商學院學生創(chuàng)立的個性化時尚服務(wù)公司Stitch Fix，現(xiàn)在市值超過16億美元，在很大程度上是因為其時尚圖譜。

Ask yourself how your data offers a unique advantage to your business. You may possess proprietary “data hooks” that allow you to observe at the point of use detailed information that is unavailable to others. Your advantage may come from superior data scope (the depth and richness of your data) and access to complementary data from partners. You may have faster data speed (data in motion compared with a competitor’s episodic data, which is subject to batch processing). Consider how scale, scope, and speed can be increased through acquisitions (consider Microsoft’s acquisitions of LinkedIn and Activision) or alliances (such as Google’s partnership with Shopify).

思考本公司擁有的數(shù)據(jù)能否提供獨特的優(yōu)勢。你或許有專有的數(shù)據(jù)收集法，能夠獲取其他企業(yè)無法獲得的詳細信息。也許你在數(shù)據(jù)深度和廣度上有優(yōu)勢，并且可以從合作伙伴那里得到互補性的數(shù)據(jù)。你的流動數(shù)據(jù)（相對于競爭對手用于批量處理的零散數(shù)據(jù)）速度可能更快。想一想能否通過收購（如微軟收購領(lǐng)英和動視）和結(jié)盟（如谷歌與Shopify合作）提升本公司的數(shù)據(jù)范圍、深度和速度。

2. Develop proprietary algorithms.?It’s no longer adequate to carry out different types of analysis independently. Datagraph leaders use proprietary algorithms to conduct descriptive analysis (“What happened?”), diagnostic analysis (“Why did it happen?”), predictive analysis (“What could happen?”), and prescriptive analysis (“What should happen?”) in an overarching framework. You can evolve your datagraph infrastructure from the legacy architectures designed to analyze data at rest (batch processing, independent analysis) to analyze real-time data in motion. Be sure to benchmark your algorithms against others in your industry—and against others of its class. For example, if your success metric is the extent to which customers act on your recommendations, how does the performance of your recommendation engine stack up against those of leaders like Netflix, Spotify, and Amazon?

2. 建立專有算法。獨立進行不同類型的分析已經(jīng)不夠了。數(shù)據(jù)圖譜領(lǐng)先企業(yè)運用專有算法，在總的框架下進行描述性分析（“發(fā)生了什么？”）、診斷性分析（“為什么發(fā)生？”）、預測性分析（“會發(fā)生什么？”）和規(guī)范性分析（“應(yīng)該發(fā)生什么？”）。你的數(shù)據(jù)圖譜基礎(chǔ)設(shè)施可以從用于分析靜止數(shù)據(jù)（批量處理、獨立分析）的傳統(tǒng)結(jié)構(gòu)轉(zhuǎn)為分析不斷變化的實時數(shù)據(jù)。要參考行業(yè)中其他企業(yè)和同類其他算法。舉例來說，如果你的成功指標是客戶接受推薦的程度，你的推薦引擎與奈飛、Spotify和亞馬遜等領(lǐng)先企業(yè)相比起來表現(xiàn)如何？

3. Engender trust.?Being the custodian of customer data is a huge responsibility. Most customers regard computers, algorithms, and machine learning as complex black boxes, and many believe that their data is being used (even abused) to make digital companies rich and powerful. You must develop ways to use your algorithms to engender trust, and you must earn the right to gather, analyze, and deliver value through data. Explain what you’re doing using language that consumers can understand.

3. 建立信賴。管理客戶數(shù)據(jù)責任重大。大部分客戶將計算機、算法和機器學習看作復雜的黑匣子，很多人覺得數(shù)字化公司利用乃至濫用自己的個人數(shù)據(jù)大發(fā)橫財。企業(yè)必須以能夠獲得信賴的方式使用算法，而且必須獲得收集和分析數(shù)據(jù)的許可并提供價值。用消費者可以理解的語言解釋你們公司要用數(shù)據(jù)做什么。

Trust gets eroded when consumers feel that their data is being misused.?Every company must invest resources not only in the technical facets of algorithms but in explaining what they do in ways consumers understand and feel comfortable with. Customers increasingly expect to be informed about how digital products function and AI-supported services are delivered, and countries demand that companies tailor their data operations to local regulations.?

如果消費者感到個人數(shù)據(jù)被濫用，就會對公司失去信任。企業(yè)不僅要在技術(shù)方面投入資源，還要以消費者能夠理解和接受的方式做出解釋?？蛻粼絹碓狡诖茉鲞M對數(shù)字化產(chǎn)品的了解，以及由AI支持的服務(wù)如何實現(xiàn)，各國要求企業(yè)在當?shù)胤上拗苾?nèi)使用數(shù)據(jù)。

4. Update the organization.?Business leaders must allocate the resources necessary to upgrade the technology infrastructure required for datagraphs. They must recruit talent with breadth and depth in both data science and business. They must structure the data organization as the connective tissue that ties together all parts of the enterprise, recognizing that modern organizations must juggle two powerful, competing factions: those who believe in the supreme power of data and algorithms to solve problems and those who don’t. This tension defines the operating culture of modern organizations: Consider how Netflix CEO Reed Hastings balances the analytical pull of Silicon Valley with the creative pull of Hollywood.

4. 組織升級。企業(yè)領(lǐng)導者必須部署必要的資源，升級技術(shù)基礎(chǔ)設(shè)施，達到數(shù)據(jù)圖譜的要求。必須聘請在數(shù)據(jù)科學和商業(yè)兩方面都具備廣泛、深入知識的人才。必須將數(shù)據(jù)組織視為連接企業(yè)各部分的結(jié)締組織，認識到現(xiàn)代組織必須妥善應(yīng)對兩個相互沖突的強力派別：一派相信數(shù)據(jù)和算法具備強大的解決問題能力，另一派則不相信。雙方的矛盾正是現(xiàn)代組織運營文化的一大特色：如奈飛CEO里德·黑斯廷斯（Reed Hastings）平衡硅谷對分析的重視和好萊塢對創(chuàng)意的重視。

5. Monetize your datagraph.?Datagraphs, when constructed to support and shape strategy, reveal that value lies not only in how products are designed and manufactured but also in how they solve specific problems for customers. Insights from datagraphs will help you choose the most appropriate monetization mechanisms and lay out clear pathways from data to business results. You can defend your current revenue and profits with compelling recommendations based on data network effects, just as Netflix uses real-time data to improve customer retention. You can also use your datagraph to develop more-thoughtful ways to expand your revenue and profit streams by going after new pockets of value, as Apple has done with its foray into credit cards, TV, and health care. And you can counterattack in markets where competitors have already mastered datagraphs, as Disney did with its successful entry into the streaming wars with Disney+.

5. 通過數(shù)據(jù)圖譜獲取利潤。構(gòu)建數(shù)據(jù)圖譜用于支持和制定戰(zhàn)略，表明價值不僅在于產(chǎn)品設(shè)計和制造，還在于如何為客戶解決具體問題。數(shù)據(jù)圖譜提供的洞察，會幫助你選擇最合適的盈利機制，規(guī)劃從數(shù)據(jù)到商業(yè)成果的清晰路徑。你可以用基于數(shù)據(jù)網(wǎng)絡(luò)效應(yīng)的個性化推薦保住目前的收入和利潤，如奈飛利用實時數(shù)據(jù)提升用戶保留率；也可以利用數(shù)據(jù)圖譜制定更加完善的方式，爭取新的價值來源，拓寬收入和利潤流，如蘋果進軍信用卡、電視和醫(yī)療行業(yè)；還可以反擊市場中已經(jīng)掌握了數(shù)據(jù)圖譜的競爭對手，如迪士尼以Disney+成功進入流媒體行業(yè)。

Reshaping Advantage

重塑優(yōu)勢

We’ve all seen the signs in front of McDonald’s announcing, “Over X Billion Served” and have watched the number rise over the years. But tracking how many burgers are sold every year is a relic of the past. Datagraph leaders care less about absolute numbers. Instead, they ask: Do we have data on where each consumer buys her burgers? At what time? What does she drink with it? What does she do before or after buying a burger? Who are our customers and what are their ages, income, location, preferences, lifestyles, and so on? How can we satisfy more of their needs so that they spend more dollars with us than with someone else, feel confident that they got value for their money, and keep coming back?

麥當勞不斷增加的“已經(jīng)賣出x億份”宣言，已經(jīng)讓我們看到了數(shù)據(jù)圖譜的跡象。不過追蹤每天、每月或每年賣出了多少漢堡只是過去的遺跡。數(shù)據(jù)圖譜領(lǐng)先企業(yè)不再重點關(guān)注這種絕對的數(shù)字，而是提問：我們是否擁有關(guān)于每位消費者在何處、何時購買漢堡的數(shù)據(jù)？消費者搭配漢堡的飲品是什么？購買漢堡前后做了什么？我們的顧客是怎樣的人，年齡、收入、所在地、偏好、生活方式等各方面如何？我們?nèi)绾胃玫貪M足顧客需求，讓顧客在我們這里消費更多，并且感到物有所值、不斷回購？

Datagraphs will reshape competition in every sector sooner than most expect. It’s time for every company to move beyond using data to improve operational efficiency and recognize the competitive advantage of datagraphs. Senior leaders must invest in upgrading their data architecture to enable a real-time, comprehensive view of how consumers interact with their products and services. With this structure in place, leaders can develop unique ways to solve customer problems.

數(shù)據(jù)圖譜會重塑每一個領(lǐng)域的競爭，速度之快超過大多數(shù)人的預想。每家企業(yè)都應(yīng)當超越利用數(shù)據(jù)改善運營效率的訴求，認識到數(shù)據(jù)圖譜的競爭優(yōu)勢。高層領(lǐng)導者必須投資升級數(shù)據(jù)基礎(chǔ)設(shè)施，實時、全面地了解消費者與本公司產(chǎn)品及服務(wù)交互的情況。有了這個結(jié)構(gòu)，就能制定出獨特的方案解決客戶的問題。

維賈伊·戈文達拉揚是達特茅斯大學塔克商學院考克斯杰出教授，哈佛商學院執(zhí)行研究員。

文卡特·卡特拉曼是波士頓大學奎斯特羅姆商學院管理學小戴維·麥格拉思教席教授。

蔣薈蓉|譯? ?牛文靜|校? ?時青靖|編輯?

本文有刪節(jié)，原文見《哈佛商業(yè)評論》中文版2022年5月刊。