數字經濟的核心是對大數據

“Information is the oil of the 21st century, and analytics is the combustion engine”.

“信息是21世紀的石油，分析是內燃機”。

— Peter Sondergaard, Senior Vice President of Gartner Research.

— Gartner研究部高級副總裁Peter Sondergaard。

來自太空的大數據和“概述效應” (Big Data from Space and the “overview effect”)

“I used to tell people I was from Cleveland, Ohio, because that was where I was born. Today, I simply say I am from Earth”.

“我曾經告訴人們我來自俄亥俄州的克利夫蘭，因為那是我的出生地。 今天，我只是說我來自地球”。

That’s the world view of former Nasa astronaut Don Thomas who has orbited Earth 692 times. Known as the “Overview Effect”, many astronauts come back from space with a completely different fundamental view of our planet. A new perspective in the space sector has been possible thanks to many analysis tools that offer visualization of data, and have proven to be beneficial, since they make us understand our planet Earth better and unravel the mysteries of the universe.

那就是前美國國家航空航天局宇航員唐·托馬斯(Don Thomas)繞地球旋轉692次的世界觀。 被稱為“概述效應”的許多宇航員從太空回來時，對我們的星球有了完全不同的基本看法。 由于許多分析工具可提供數據可視化，并且已被證明是有益的，因為它們使我們更好地了解了地球并揭開了宇宙的奧秘，因此有可能在航天領域提出新的觀點。

Big data technology is the product of information technology that aims to meet the challenges faced by increasing the amount of information in various fields. If we think of all the times we’ve used our phone or computer, how many apps have we logged in to? Have we checked Facebook, Twitter, Instagram, Reddit, or LinkedIn? Do we regularly use Amazon, YouTube, Tinder, Buzzfeed, or Pinterest? Every one of those app stores or websites that many of us use on a daily basis collect user data to improve user experience and help companies to make educated business decisions. But that’s not all data can help us do.

大數據技術是信息技術的產物，旨在通過增加各個領域的信息量來應對所面臨的挑戰。 如果我們一直在考慮使用手機或計算機，那么我們登錄了多少個應用程序？ 我們是否檢查過Facebook，Twitter，Instagram，Reddit或LinkedIn？ 我們是否經常使用Amazon，YouTube，Tinder，Buzzfeed或Pinterest？ 我們許多人每天使用的這些應用商店或網站中的每一個，都收集用戶數據以改善用戶體驗并幫助公司做出有根據的業務決策。 但這并不是所有數據都能幫助我們。

Right now, satellites are performing 2 billion instructions per second and delivering data that could help us prevent natural disasters and use natural resources wisely. There have been several data-driven initiatives to make better decisions and improve operational efficiency in sectors including agriculture, forestry, mapping, shipping, or energy.

目前，衛星正在每秒執行20億條指令，并提供可幫助我們預防自然災害并明智地使用自然資源的數據。 在農業，林業，制圖，航運或能源等部門，有數項以數據為依據的計劃可以做出更好的決策并提高運營效率。

使用數據改善地球上的生命 (Using Data to improve life on Earth)

More and more companies are starting to open to the space sector as the ever-growing number of affordable satellite services keeps increasing. Considering one industry — agriculture — the implications are enormous. Farmers can use image data to better understand what factors affect the growth of crops, and there are factors that can be detected from space, such as weather patterns, exposure to sunlight, air quality or pest activity, so optimum conditions can be determined.

隨著負擔得起的衛星服務數量的不斷增長，越來越多的公司開始向太空領域開放。 考慮到一個產業-農業，其影響是巨大的。 農民可以使用圖像數據更好地了解哪些因素會影響農作物的生長，并且可以從太空中檢測到一些因素，例如天氣模式，暴露在陽光下，空氣質量或害蟲活動，因此可以確定最佳條件。

In a few short decades, the world’s population is on pace to grow 50 percent by 2100. Now more than ever, farmers need access to tools that support the decisions they make every day to maximize their return on every acre. The Climate Corporation processes its satellite data to enable farmers to find more sustainable ways to grow more food. This company’s project’s key aspects can deliver benefits to humanity in the long term.

在短短的幾十年中，到2100年，世界人口的增長速度將達到50％。如今，農民比以往任何時候都需要更多的工具來支持他們每天做出的決定，以使每英畝土地的收益最大化。 氣候公司處理其衛星數據，以使農民能夠找到更可持續的方式來種植更多的糧食。 該公司項目的關鍵方面可以長期為人類帶來好處。

Another company, Planet, provides geospatial insights equipping users with the data necessary to make informed, timely decisions offering a diverse selection of imagery and analytic solutions, all made available online through their platform and web-based tools. From agriculture and emergency response to natural resource protection and security, global imagery and foundational analytics will empower informed, deliberate, and meaningful stewardship of our planet.

另一家公司Planet則提供地理空間洞察力，為用戶提供必要的數據，以便他們能夠及時做出明智的決定，從而提供各種圖像和分析解決方案，所有這些都可以通過其平臺和基于Web的工具在線獲得。 從農業和應急響應到自然資源保護和安全，全球圖像和基礎分析將使我們星球的知情，深思熟慮和有意義的管理工作變得更加重要。

Earth observation satellites provide important data that allows the rapid detection of changes to the environment and climate, or measurements of the movement or shrinking of glaciers. Up-to-date maps can be provided to the emergency services in the event of disasters such as flooding or earthquakes. This, however, requires the accumulation of very large quantities of data. The European Union (EU) Copernicus Program satellites are among the biggest producers of data in the world. Their high-resolution instruments currently generate approximately 20 terabytes of data every day. This is equivalent to an HD film that would run for about one-and-a-half years. In addition to this, data is also provided by German missions such as TerraSAR-X and TanDEM-X, as well as an increasing number of other sources, such as the internet and measurement stations. The processing and analysis of these very large and heterogeneous data sets are among the Big Data challenges facing an increasingly digital society.

地球觀測衛星提供重要的數據，可以快速檢測環境和氣候的變化，或者測量冰川的運動或萎縮。 如果發生洪水或地震等災害，可以將最新地圖提供給緊急服務。 但是，這需要積累大量數據。 歐盟(EU)哥白尼計劃衛星是世界上最大的數據生產商之一。 他們的高分辨率儀器目前每天大約產生20 TB的數據。 這相當于一部高清電影，播放時間約為一年半。 除此之外，TerraSAR-X和TanDEM-X等德國特派團還提供了數據，以及越來越多的其他來源(例如互聯網和測量站)也提供了數據。 這些越來越大的異構數據集的處理和分析是數字社會日益面臨的大數據挑戰之一。

Sul-phur diox-ide map — vol-canic erup-tion on Bali (Credit: Copernicus-Sentinel (2017), DLR/ESA)二氧化硫地圖—巴厘島的火山噴發(來源：Copernicus-Sentinel(2017)，DLR / ESA)

New ideas and concepts are needed in order to be able to process data and turn it into information. Artificial intelligence plays a major role in this, as such processes are extremely powerful, especially where large amounts of data are involved. DLR scientist Xiaoxiang Zhu, based at the Technical University of Munich, is conducting research into the use of such methods. Together with her team, Zhu is developing exploratory algorithms from signal processing and artificial intelligence (AI), particularly machine learning, to significantly improve the acquisition of global geoinformation from satellite data and achieve breakthroughs in geosciences and environmental sciences. Novel data science algorithms allow scientists to go one step further with the merging of petabytes of data from complementary geo-relevant sources, ranging from Earth observation satellites to social media networks. Their findings have the potential to address previously insoluble challenges, such as recording and mapping global urbanization — one of the most important megatrends in global change.

為了能夠處理數據并將其轉變為信息，需要新的想法和概念。 人工智能在其中起著重要作用，因為這種過程非常強大，尤其是在涉及大量數據的情況下。 來自慕尼黑工業大學的DLR科學家Zhu Xiaoxiang Zhu正在研究這種方法的使用。 Zhu與她的團隊一起，正在開發信號處理和人工智能(AI)(尤其是機器學習)的探索性算法，以顯著改善從衛星數據中獲取全球地理信息的過程，并在地球科學和環境科學方面取得突破。 新穎的數據科學算法使科學家可以更進一步地合并來自地理相關的補充資源(從地球觀測衛星到社交媒體網絡)的PB級數據。 他們的發現有可能解決以前無法解決的挑戰，例如記錄和繪制全球城市化進程-這是全球變化中最重要的大趨勢之一。

Yet the field of satellite remote sensing is not alone in grappling with this challenge. Investigating phenomena, the other way round — looking from Earth into space — also generates enormous amounts of data. Telescopes such as the Square Kilometre Array (SKA) in South Africa and Australia provide large quantities of data, as do ESA’s space-based telescopes, for example, Gaia and Euclid. The systematic analysis of archive data by self-learning AI programs is thus becoming increasingly important in astronomical research.

然而，并非只有衛星遙感領域能夠應對這一挑戰。 反之，從地球到太空，調查現象也會產生大量數據。 諸如南非和澳大利亞的平方公里陣列(SKA)之類的望遠鏡提供了大量數據，ESA的天基望遠鏡也是如此，例如蓋亞(Gaia)和歐幾里得(Euclid)。 因此，通過自學AI程序對檔案數據進行系統的分析在天文研究中變得越來越重要。

The Square Kilometre Array: The world’s biggest telescope (Photo Credit: visual.ly)平方公里陣列：世界上最大的望遠鏡(照片來源：visual.ly)

“We’ve been talking about Big Data for a long time, and this takes us on the journey to start understanding space data and space analytics. Not too many people in the commercial sector have got their hands around it yet, they don’t fully understand the implications of all of this data” said Sparks & Honey CEO Terry Young. “The idea was to look at the innovations that are going to be created over the next 15 years on our journey to Mars and beyond, and to find from those innovations — which are very science or engineering-focused — what the implications are for organizations and consumers, back here on Earth”.

“我們討論大數據已經很長時間了，這使我們踏上了開始理解空間數據和空間分析的旅程。 商業領域的人還不是很多，他們還不完全理解所有這些數據的含義。” Sparks＆Honey首席執行官Terry Young說。 “我們的想法是研究在接下來的15年中，在我們前往火星及以后的旅程中將要創造的創新，并從那些非常注重科學或工程學的創新中找出對組織的影響和消費者，回到地球上來”。

In the past, space data applications have been mainly carried out by Governments because of the sky-high cost of launching satellites and keeping them in space, where they could generate data with cameras, sensors and scanners, or used to monitor conflicts, track the flow of refugees and gather terrestrial and space data for research purposes. Thanks to the likes of SpaceX, founded by Tesla entrepreneur Elon Musk, as well as hundreds of startups, billions will be spent in the coming decade on creating infrastructure. The exciting part for the industry is that much of this data will become available for organizations whose business is not primarily space-based.

過去，空間數據的應用主要由各國政府執行，這是因為發射衛星并將其保持在太空的成本很高，在這里它們可以利用照相機，傳感器和掃描儀生成數據，或用于監測沖突，跟蹤衛星和衛星。難民的流動，并收集地面和空間數據以供研究。 得益于由特斯拉企業家埃隆·馬斯克(Elon Musk)創立的SpaceX以及數百家初創公司，未來十年，數十億美元將用于創建基礎架構。 該行業令人興奮的部分是，這些數據中的大部分將可用于其業務主要不是基于空間的組織。

“Something which is hovering above the Earth and providing a perspective from above is really creating a unique dataset. Roughly 35% of the satellites in orbit right now are there for commercial purposes, and those satellites have been driven by venture capital money. A lot of startups are providing low-orbit satellites for a wide range of different uses”. “We covered ideas like being able to observe things like water shortage, as it relates to manufacturing processes, traffic patterns in large cities as we are looking towards building cities of the future and their infrastructure. We can even translate it to big retail, where all of a sudden, we can capture real-time data on hundreds of stores simultaneously and use it to look at foot traffic patterns,” Young said.

“懸停在地球上方并從上方提供視角的事物確實在創建一個獨特的數據集。 目前，在軌衛星中約有35％用于商業目的，而這些衛星是由風險投資推動的。 許多初創公司正在提供低軌道衛星，以用于各種不同的用途。” “我們涵蓋了諸如能夠觀察到缺水之類的想法，因為它與制造過程，大城市的交通方式有關，我們正致力于建設未來的城市及其基礎設施。 我們甚至可以將其轉換為大型零售店，突然之間，我們可以同時在數百家商店中捕獲實時數據，并使用它來查看人流情況。”

為什么空間數據是新的大數據 (Why Space Data is the new Big Data)

Data analytics can be used to improve sports performance, to help us better understand and build cures for disease, to aid in the development of artificial intelligence, to improve infrastructure in your city, and to expand the reach of what science can do. NASA has recently used data gathered over years of exploration to launch an amazing interactive map of Mars. Called, “Mars Trek,” the map is an educational tool NASA has available to the public as part of their Mars Exploration Program. Here’s the link: https://trek.nasa.gov/mars/

數據分析可用于改善運動表現，幫助我們更好地理解和建立疾病治療方法，幫助開發人工智能，改善城市基礎設施以及擴大科學工作范圍。 美國國家航空航天局(NASA)最近使用了經過多年探索收集的數據，以發射出驚人的火星互動式地圖。 該地圖被稱為“火星迷航”，是NASA作為其“火星探索計劃”的一部分向公眾提供的一種教育工具。 這是鏈接： https : //trek.nasa.gov/mars/

According to NASA’s official Mars Trek site, “This portal showcases data collected by NASA at various landing sites. It features an easy-to-use browsing tool which provides layering and viewing of high-resolution Mars data products in 2D and Globe view allowing users to fly over the surface of Mars. It also provides a set of tools including 3D printing, elevation profiles, sun angle calculations, Sun and Earth position, as well as bookmarks for the exploration area by NASA missions”. These “missions” which have supplied the majority of the data for the map to date are specifically the MSL (Mars Science Laboratory) mission, which involved the Curiosity Rover, the MER (Mars Exploration Rovers) mission, which included Spirit and Opportunity, the Phoenix mission, and the Pathfinder mission. NASA plans to continue to update the map as new data becomes available.

根據NASA的火星迷航官方網站，“該門戶網站展示了NASA在各個著陸點收集的數據。 它具有易于使用的瀏覽工具，可在2D和Globe視圖中對高分辨率火星數據產品進行分層和查看，從而使用戶可以在火星表面上飛行。 它還提供了一套工具，包括3D打印，高程剖面圖，太陽角度計算，太陽和地球位置，以及NASA任務在探索區域的書簽。” 這些“任務”迄今已提供了大部分地圖數據，特別是MSL(火星科學實驗室)任務，其中包括好奇號火星車，MER(火星探索漫游者)任務，包括“精神與機遇”，鳳凰任務和探路者任務。 NASA計劃在有新數據可用時繼續更新地圖。

A Sample Analysis at Mars (SAM) team member at NASA’s Goddard Space Flight Center. (Image Courtesy: NASA/JPL-Caltech)NASA戈達德太空飛行中心的火星(SAM)成員進行了樣本分析。 (圖片提供：NASA / JPL-Caltech)

This is especially exciting as the Mars 2020 rover should be bringing us a whole new supply of data to add to the map by 2021. Modeled after the Curiosity, which has been a breakthrough unmanned system for NASA, the 2020 rover which launched on 30 July 2020 at 11:50 UTC will explore the habitability of Mars, hopefully paving the way for NASA’s manned missions tentatively planned for 2030.

這尤其令人興奮，因為2020年火星探測器應該為我們帶來全新的數據供應，以便在2021年之前添加到地圖上。該模型仿效了好奇號(這是NASA的突破性無人駕駛系統)，于7月30日發射的2020年火星探測器。 2020年世界標準時間(UTC)將探索火星的可居住性，希望為暫定于2030年進行的NASA載人飛行鋪平道路。

數據，更多數據和PB級數據 (Data, more data, and petabytes of data)

Even in the healthcare sector data are largely mentioned. Pathologists have been diagnosing disease the same way for the past 100 years, by manually reviewing images under a microscope. Now, computers help doctors improve accuracy and significantly change the way cancer and other diseases are diagnosed.

即使在醫療保健領域，也大量提及數據。 在過去的100年中，病理學家通過在顯微鏡下手動查看圖像來以相同的方式診斷疾病。 現在，計算機可以幫助醫生提高準確性，并顯著改變診斷癌癥和其他疾病的方式。

Artificial intelligence (AI) methods have been developed by a research team from Harvard Medical School and Beth Israel Deaconess Medical Center that aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate.

哈佛醫學院和貝斯以色列女執事醫學中心的研究團隊開發了人工智能(AI)方法，旨在培訓計算機以解釋病理圖像，其長期目標是構建AI驅動的系統以進行更多的病理診斷。準確。

“Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition,” explained pathologist Andrew Beck, HMS associate professor of pathology and director of bioinformatics at the Cancer Research Institute at Beth Israel Deaconess. “This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain’s neocortex, the region where thinking occurs”.

“我們的AI方法基于深度學習，這是一種機器學習算法，可用于包括語音識別和圖像識別在內的一系列應用，” HMS病理學副教授兼Beth癌癥研究所生物信息學負責人病理學家Andrew Beck解釋說。以色列女執事。 “這種方法教機器通過構建多層人工神經網絡來解釋現實數據中觀察到的復雜模式和結構，這一過程被認為與大腦新皮層神經元層中發生的學習過程相似。 ，即發生思考的區域”。

“Identifying the presence or absence of metastatic cancer in a patient’s lymph nodes is a routine and critically important task for pathologists,” Beck explained. “Peering into the microscope to sift through millions of normal cells to identify just a few malignant cells can prove extremely laborious using conventional methods. We thought this was a task that the computer could be quite good at — and that proved to be the case”. In an objective evaluation in which researchers were given slides of lymph node cells and asked to determine whether they contained cancer, the team’s automated diagnostic method proved accurate approximately 92 percent of the time, said Khosla, adding, “This nearly matched the success rate of a human pathologist, whose results were 96 percent accurate”.

貝克解釋說：“確定患者淋巴結中是否存在轉移性癌癥是一項常規且至關重要的任務，”病理學家說。 使用傳統方法，在顯微鏡下窺視以篩選數百萬個正常細胞以鑒定出少數惡性細胞可能非常費力。 我們認為這是計算機可以非常擅長的一項任務，事實證明確實如此。” Khosla表示，在一項客觀評估中，研究人員被給予了淋巴結細胞切片并被要求確定它們是否含有癌癥，該團隊的自動診斷方法在大約92％的時間內被證明是準確的。一位人類病理學家，其結果準確率為96％”。

“But the truly exciting thing was when we combined the pathologist’s analysis with our automated computational diagnostic method, the result improved to 99.5 percent accuracy,” said Beck. “Combining these two methods yielded a major reduction in errors”.

貝克說：“但是真正令人興奮的是，當我們將病理學家的分析與我們的自動化計算診斷方法結合在一起時，結果的準確性提高到了99.5％。” “將這兩種方法結合起來可以大大減少錯誤”。

The team trained the computer to distinguish between cancerous tumor regions and normal regions based on a deep, multilayer convolutional network. To accomplish this, researchers had to amass huge amounts of data from which they could train their machine learning models.

該團隊訓練了計算機，以基于深度的多層卷積網絡區分癌性腫瘤區域和正常區域。 為此，研究人員必須積累大量數據，他們可以從中訓練機器學習模型。

Fig. 1 The framework of cancer metastases detection圖1癌癥轉移檢測的框架 Fig. 2 Evaluation of various deep models (Fig.1 and 2 Credits: Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep Learning for Identifying Metastatic Breast Cancer [Internet] arXiv 2016)圖2各種深度模型的評估(圖1和2學分：Wang D，Khosla A，Gargeya R，Irshad H，Beck AH。用于識別轉移性乳腺癌的深度學習[Internet] arXiv 2016)

And it isn’t just radiology. The emerging field of gene therapy maps pathologies to specific genetic mutations. This means that newly diagnosed cancer patients now routinely have their genes sequenced so oncologists can prescribe the most effective treatment.

這不僅是放射學。 基因治療的新興領域將病理學映射到特定的基因突變。 這意味著現在對新診斷的癌癥患者常規進行基因測序，以便腫瘤科醫生可以開出最有效的治療方案。

The key to both of these life-saving advances? Petabytes and petabytes of data.

這兩項救生措施的關鍵是什么？ PB和PB的數據。

未來的前景以及全球開放訪問數據的努力 (What the future holds and the global effort for open access to data)

Back in 2016, Piero Scaruffi, cognitive scientist and author of “History of Silicon Valley” said: “The difference between oil and data is that the product of oil does not generate more oil (unfortunately), whereas the product of data (self-driving cars, drones, wearables) will generate more data (where do you normally drive, how fast/well you drive, who is with you)”.

早在2016年，認知科學家兼《硅谷歷史》的作者Piero Scaruffi說：“石油與數據之間的區別在于，石油產品不會產生更多的石油(不幸的是)，而數據產品(自駕駛汽車，無人駕駛飛機，可穿戴設備)將生成更多數據(您通常在哪里駕駛，駕駛速度/速度有多快，與誰在一起)。

Google trends for “data is the new oil” until 2020Google的“數據是新石油”趨勢直到2020年

Open data, big data and technology revolutions are stimulating for businesses, governments, and citizens.

開放數據，大數據和技術革命正在刺激企業，政府和公民。

Today, the industry is witnessing a wide variety of downsized technologies — miniaturization of sensors and satellites; a high number of private entrepreneurial missions, and adoption of new technologies such as AR/VR, artificial intelligence and machine learning, cloud, etc. How do we make all this data accessible for everyone? By making it open. Providing better environmental satellite data sharing policies and making practical recommendations for increasing global data sharing.

如今，該行業正在目睹各種尺寸縮小的技術-傳感器和衛星的小型化； 大量的私人企業訪問，以及采用新技術(例如AR / VR，人工智能和機器學習，云計算等)。我們如何使所有人都能使用所有這些數據？ 通過使其打開。 提供更好的環境衛星數據共享策略，并為增加全球數據共享提出實用建議。

Open.NASA, for example, is an open innovation program in NASA’s Innovation Division, which creates many open data programs for both space professionals and enthusiasts. The NASA Space Apps Challenge Hackathon, NASA Datanauts, and the Data Bootcamp are projects which provide opportunities for citizens to easily get access and innovate with NASA’s open data, code, and APIs. All of this and much more is becoming plausible with an increase in space investments. More private sector companies — large, medium and small — are entering the earth observation foray redefining the very meaning of the what the future holds.

例如，Open.NASA是NASA創新部的一項開放式創新計劃，該計劃為航天專業人員和愛好者創建許多開放式數據計劃。 NASA太空應用程序挑戰Hackathon，NASA Datanauts和Data Bootcamp是為公民提供機會的項目，可讓他們輕松地使用NASA的開放數據，代碼和API進行訪問和創新。 隨著空間投資的增加，所有這些以及更多的東西變得合理。 越來越多的大型，中型和小型私營部門公司正在進入地球觀測之路，重新定義未來的意義。

Autonomous vehicles (AVs) are also coming too. The benefits are widely known: safer roads, a boost to the economy and less rush-hour crowding. But perhaps the biggest benefit is a reduction in greenhouse gases (GHG) coming from automobiles. Research conducted by Poznan University professors estimates that autonomous vehicles could eventually reduce GHG by 40% to 60%. In this case, it requires hundreds of petabytes of data that form the data lake from which the AV self-driving advanced machine learning solutions will come. It doesn’t stop there. Each of these modern “computing platforms that happen to be mobile” will generate terabytes of data per week per vehicle. Even assuming a 75% reduction in the number of vehicles on the roads, that’s many exabytes of data per year. If a vehicle accident occurs, you can call up the images that the vehicles involved recorded to decide what caused the accident and which AV algorithms need improvements.

無人駕駛汽車(AVs)也即將問世。 好處廣為人知：更安全的道路，促進經濟發展和減少高峰時間擁擠。 但是，最大的好處也許是減少了汽車產生的溫室氣體(GHG)。 波茲南大學教授進行的研究估計，自動駕駛汽車最終將使溫室氣體排放減少40％至60％。 在這種情況下，它需要數百PB的數據來形成數據湖，AV自動駕駛高級機器學習解決方案將來自該數據湖。 它不止于此。 這些現代的“可移動的計算平臺”每個都會每周每輛車產生TB級的數據。 即使假設道路上的車輛數量減少了75％，每年的數據量也高達EB級。 如果發生交通事故，您可以調出有關車輛記錄的圖像，以確定造成事故的原因以及需要改進的AV算法。

We are on the cusp of exploring an unprecedented abundance of innovation, research, resources and technological connection. All with Earth-bound resonance. Space isn’t just a moonshot. It’s transforming life, not just in orbit, but here on Earth.

我們正處于探索創新，研究，資源和技術聯系空前豐富的風口浪尖上。 全部具有與地球相關的共振。 太空不僅僅是月亮。 它正在改變生命，不僅在軌道上，而且還在地球上。

And data isn’t just shaping the way our businesses run, it is shaping our lives.

數據不僅影響著我們的業務運營方式，還影響著我們的生活。

Originally published at https://westeastspace.com on August 1, 2020.

最初于 2020年8月1日 發布在 https://westeastspace.com 上。

翻譯自: https://medium.com/@alessandro.prosperi123/the-rise-of-big-data-as-the-core-lubricant-of-the-digital-world-3d647b28e3ec

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