Welcome to the Human Data Economy 迎接人類數據經濟時代

馬克·戈爾斯基 譯/陳棟 Mark Gorski

Over 700 million people use wearable technology like smartwatches， fitness bands and medical devices. These consumers create extremely valuable data from their daily lives—sleeping， working， sweating and everything in between. Consumers currently pay companies huge sums of money for access to technology as part of an industry， and every time consumers use wearable devices， theyre giving away their personal data for free. Ironically， companies then use this data to create more products they sell back to consumers， and the cycle repeats.

However， instead of paying for smart devices and allowing manufacturers free， full access to their information， what if they paid consumers instead？ A new asset class is emerging—human health data—that is generating significant value for companies by fueling the health analytics they provide， with consumers largely unaware their data is becoming a capital asset1 in high demand. And this archaic2 model for value creation is about to be flipped on its head3.

Welcome to the Human Data Economy， where consumers will soon be able to monetize4 their health information and take control over how， where and when their data is used. Similar to influencers like the YouTube and TikTok stars of today， an entirely new group of content creators—data creators—will emerge. Data creators， which can be anyone from professional athletes to casual consumers， will generate Human Data from wearable devices and other systems and choose to voluntarily share their information with interested parties.

The market for Human Data that can power systems to better understand our bodies， provide personalized treatments and predict future health outcomes is immense. In health care alone， the analytics market is expected to reach over $67 billion by 2025， while the telehealth market is expected to reach over $559 billion and the health care insurance market $4 trillion by 2027. Growth is largely driven by user-generated health data， thus creating significant opportunities for data creators to monetize their information.

Although artificial intelligence and statistical-based tools are in the early days of powering a Human Data-centered economy， they are rapidly advancing. Machine learning models are being developed in order to collect data from multiple sources to learn about individuals and help improve their health. New AI-based techniques are demonstrating the potential to generate insights based on users activities， physiology5， genetic profile， genomics6， blood， sweat and tears7 （pun intended） that will predict future health risks and outcomes， including heart disease and stroke. For these tools to develop further， quality data is essential. It is improving fast， as higher-sampling sensors can provide better and more granular8 data in every area of users lives—sleep， fitness， you name it.

However， we arent quite there yet. For starters， many sensors （like your smartwatch） have only proved their effectiveness in limited environments （e.g.， sleeping） while falling short in both accuracy and repeatability9 for other activities （e.g.， high-activity workouts）. Collecting large datasets is also challenging. Data creators often do not provide their full profile information， nor do they maintain the regimen10 of documenting their daily lives. This lack of contextual information leads to incomplete datasets. Companies also oftentimes eschew data-sharing， which leads to silos of know-ledge and prevents important advances in research and discovery.

But were getting there. To continue this evolution， analytics systems need good， categorized data—and lots of it—which is why a monetization-based access model could explode industry growth. Enabling data creators to provide companies with access to their personal data in exchange for compensation can allow companies to request exactly what data theyre looking for and from whom. This should expedite the collection of the right data in the right quantities to make products and research better targeted and more valuable. Companies could also demand a higher degree of data quality and eliminate “garbage-in， garbage-out11” scenarios often caused by the misuse of wearable tech by casual users. Human Data could also become more widely available to organizations that can unlock its value.

Research published by BMC12 Public Health found that a majority of consumers are willing to wear digital devices and share their data if theyre given financial incentives， including with their health insurers. We have recently experienced this in sports， and in medicine， volunteers are already provided financial incentives for participation in clinical trials and other research.

While explicit consent and control over information distribution should alleviate many privacy concerns， thoughtful approaches are needed to overcome existing challenges. These include obtaining ethically sourced data （e.g.， from known， consenting sources without modification）， preventing unauthorized data sharing or use， transitioning current wearable systems to enable customer data access， and current health data regulations. These barriers will necessitate clear， legally supported rules of engagement between technology organizations， regulators and data creators in order to develop consumer confidence， enable further transparency and build trust.

In the emerging Human Data Economy， sensor technologies and other products should improve as access to data increases. Users who are unable to afford wearables may have them provided at no cost. New types of revenue models and businesses will emerge， including data agents who can help manage and monetize your data. Technol-ogies that can broker， track， and enforce data sharing agreements between data creators， technologies and acquirers will become the next unicorns.

This all suggests that were reaching a tipping point13 where the value of data created from wearables is exceeding the value of the wearables themselves. It will continue to accelerate as data creators use their newly controllable assets to enrich their social environment by sharing their generated content. By widening the availability of Human Data and broadening the appeal of its use—from livestreaming biometric data in workout classes to viewing sponsored， on-screen stress levels of reality show contestants—todays consumers will become tomorrows data creators with rights to control， share and monetize their information. Concurrently， this data will advance our collective understanding of the human body while fostering more transparency and creating an entirely new economic proposition.

Welcome to the Human Data Economy.

使用智能手表、健康手環和醫療設備等可穿戴技術產品的人超過7億。這些用戶在日常生活中創造出極具價值的數據——不管是睡覺、工作、出汗，還是干其他事的時候。作為產業的一部分，現在用戶使用技術產品需向企業支付高昂的費用，而且用戶每次使用可穿戴設備，都在免費貢獻自己的個人數據。諷刺的是，企業之后又用這些數據創造出更多的產品賣給用戶，如此循環往復。

但是，如果是制造商付錢給用戶，而不是用戶花錢購買智能設備并允許制造商免費訪問他們的全部信息，那會怎么樣？一種新的資產品類正在浮現，即健康數據，它能提升企業的健康分析服務，給企業創造巨大價值，而大多數用戶并未意識到自己的數據正在成為一項緊俏的資本資產。這種陳舊的價值生產模式即將發生翻天覆地的變化。

歡迎來到人類數據經濟時代。在這里，用戶即將能用自己的健康信息賺錢，并有權決定自己的數據在哪、何時以及怎樣被使用。類似于如今優兔和抖音國際版等平臺的網紅，一類全新的內容生產群體——數據創作者——將應運而生。從專業運動員到普通用戶，數據創作者可以是其中任何人。他們將通過可穿戴設備和其他系統生產數據，并自愿選擇將自己的數據分享給感興趣的人。

人類數據市場需求巨大，它可以驅動各種系統更好地了解我們的身體，提供個性化治療并預測未來健康結果。單就衛生保健而言，分析市場預計到2025年將超過670億美元，而到2027年，遠程醫療市場預計將超過5590億美元，醫療保險市場預計將超過4萬億美元。增長主要由用戶生產的健康數據驅動，這就為數據創作者將自身信息變現創造大量機會。

雖然人工智能和以統計學為基礎的工具在驅動人類數據經濟方面處于初期階段，但它們在快速發展。機器學習模型正在研發當中，以便收集各方數據來研究個體，并幫助改善其健康。新的人工智能技術正展現出潛力，能基于用戶的活動、生理機能、基因圖譜、基因狀況、血汗和淚水（一語雙關）形成分析，從而預測包括心臟病和中風在內的未來健康風險和結果。這些工具要想取得進一步發展，優質數據必不可少。這方面的進步很快，因為采樣率更高的傳感器可以在用戶生活的各個領域提供更好的、顆粒度更細的數據——睡眠、健康度，隨便你說。

不過我們還沒完全走到那一步。首先，許多傳感器（譬如智能手表）只在有限環境（譬如睡眠）中證明了有效性，而在進行其他活動（如高強度運動）時，其精確度和重復性會大打折扣。收集大量數據集也是一大挑戰。數據創作者通常不會提供全部的個人資料信息，也不會保持記錄日常生活的習慣。這種情境信息的缺失導致數據集的不完整。企業之間通常也不會數據共享，導致知識封閉，阻礙了研究發現的重大進步。

但我們正在向前邁進。要讓這一發展持續，分析系統需要大量分門別類的優質數據——這也是為何貨幣化的數據訪問模式可以使行業實現爆炸式增長。如果讓數據創作者能夠向企業提供個人數據的訪問權限換取報酬，就可以精準地從想要的人那里獲取想要的數據。這將加速收集合適體量的合適數據，從而使產品和研究更有針對性、更具價值。企業也可以對數據質量提出更高的要求，消除馬虎的用戶錯誤使用可穿戴技術產品導致的“無用輸入無用輸出”常見情況。能夠解鎖人類數據價值的組織也將更容易獲得這種數據。

《BMC公共衛生雜志》發表的研究論文發現，大部分用戶在得到金錢激勵后愿意使用電子設備并分享其數據，分享對象包括他們的健康保險公司。近來我們已經在體育、醫療領域有過這樣的經驗——臨床試驗和其他研究在招募志愿者時便會提供金錢激勵。

雖然征求明確同意和控制信息傳播可以消除許多隱私問題，我們仍需要深思熟慮的手段來克服已有的挑戰。這些挑戰包括：從合乎道德的來源獲取數據（譬如從征得許可的已知來源獲取且不加修改）、防止未經授權而分享或使用數據、轉換當前的可穿戴系統以實現客戶數據訪問，以及現行的健康數據法規約束。這些障礙的存在使得技術機構、監管部門和數據創作者之間必須制定明確的、受法律支持的關聯規則，以建立用戶信心、進一步提高透明度并建立信任。

在新興的人類數據經濟中，傳感器技術和其他產品應當隨著數據訪問的增加而改進。買不起可穿戴設備的用戶或將免費獲得這些設備。新的創收模式和業務類型將會出現，包括幫助打理數據并將其變現的數據代理商。能夠在數據創作者、技術和需求方之間協調、跟蹤和執行數據共享協議的科技公司將成為下一批獨角獸。

這一切都表明我們將要到達一個臨界點，即可穿戴設備產生的數據價值將超過可穿戴設備本身。隨著數據創作者不斷分享其生成的內容，用這一新型可控資產來豐富自己的社交環境，這一臨界點將會加速到來。通過拓展人類數據的可及性并擴大其用途吸引力——從直播健身課程中的生物特征數據，到在屏幕上收看用受贊助的可穿戴設備展示的真人秀選手壓力水平——今天的用戶將成為明天的數據創作者，他們有權控制、分享和變現自己的信息。同時，這些數據將促進我們對人體的共同理解，提高透明度并創造出一個全新的經濟命題。

歡迎來到人類數據經濟時代。

（譯者為“《英語世界》杯”翻譯大賽獲獎者）