再不懂大数据就落伍了!看懂2018趋势(附译词)

来源:译世界 作者:Yee君 时间:2018/02/09

作为智库型研究与资讯平台,译世界【官方微信号译?世界(YEEWORLD)】现推出“智库”栏目,以中英双语的形式为您精选全球重要智库及著名媒体等在语言服务、文化交流、人工智能、公共政策、国际关系等相关领域的前沿研究成果,并同时展现相关英文表达,欢迎关注!


  本期为“双语?智库”第十八期,带您了解美国DATAVERSITY Education公司发布的2018年大数据趋势。


  Big Data Trends for 2018

  2018年大数据趋势


  The expansion of the Internet of Things (IoT) has added innumerable new sources of Big Data into the Data Management landscape and will be one of the major Big Data Trends in 2018 and beyond. Laptops, smart phones, sensors on machines, all generate huge amounts of data for the IoT.

物联网的发展为数据管理领域注入了许多新的大数据来源,并将在2018年及之后成为大数据领域的一大趋势。笔记本电脑、智能手机、机器上的传感器都为物联网生成了大量数据。

 


  Organizations are flexible enough to manage and transform the data into useful Business Intelligence, this represents a significant opportunity to gain a competitive advantage (or remain competitive). As Big Data grows, businesses attempt to keep up with it, and struggle to turn the data into usable insights. Business Intelligence is key to staying competitive, and Data Analytics provides the up-to-date information needed.

一些组织机构能够很灵活地将数据处理和转化成有用的商务智能,这就意味着它们有很大的机遇去获得竞争优势(或保持竞争力)。企业也在极力紧跟大数据的发展潮流,努力将数据转化为有用的见解。对于企业来说,商务智能是保持竞争力的关键所在,而数据分析能够提供所需的最新信息。


  In 2017, some companies expanded their services and software which translated Big Data into visualizations and graphs. This allowed researchers to gather and coordinate information about the general population more efficiently, and improve the customer experience. It also allows leaders to streamline the decision-making process.

2017年,有些公司扩展了使大数据可视化和图形化的服务和软件。这样研究人员就能够更有效地收集和协调大众信息,改善客户体验,也使得领导者可以简化决策过程。


  The number of companies offering Cloud services will also continue to expand in 2018, resulting in competitive pricing, and allowing smaller businesses to access Big Data resources.

2018年,提供云服务的公司数量还将继续增长,导致竞争性定价,使得规模较小的企业也能访问大数据资源。


  Business Intelligence in 2018

  2018年的商务智能

 


  Organizational decision-making is currently undergoing a shift which will continue into 2018. In 2017, the goal of processing Big Data promoted ever-increasing efficiency and steadily decreasing costs. In turn, this has made the use of Business Intelligence, based on Big Data, more important to small and medium-sized businesses, and even start-ups. This trend will continue into 2018, and beyond, with the cost of processing Big Data continuing to drop. Expect the following:

组织决策目前正在经历转折期,这个状态将持续到2018年。2017年,在处理大数据的目标下,效率越来越高,成本不断减少。这也使得基于大数据的商务智能应用对于中小企业甚至初创公司来说,变得越发重要。这个趋势将持续到2018年及以后,处理大数据的成本将持续降低。以下几点值得期待:


   Use of Business Intelligence from the Cloud will increase.

商务智能将不断走向云端。

 Analytics will provide improved data visualization models and self-service software.

数据分析将提供更好的数据可视化模型和自助软件。

 Decisions regarding expansion into new markets and geographies will be based on Big Data.

基于大数据决定如何拓展市场和地区。


  Cloud Trends in 2018

  2018年的云趋势

 


  Hybrid Clouds

混合云


  While the Cloud provides a convenient solution for storage, and processing Big Data, few are comfortable with the idea of turning over “all” of an organization’s data. In 2018, use of the Hybrid Cloud should grow significantly, as this scenario combines the best of both worlds. On-premise Data Management can be combined with the convenience of the Cloud.

虽然云端为存储和处理大数据提供了方便的解决方案,但却很少有人愿意将企业的所有数据都放到云端。2018年,混合云的使用应该会大幅增长,因为混合云兼具二者(译者注:公有云和私有云)的优点。企业预置型数据管理可以结合云的便利特点。


  Other Departments will Access the Cloud

其他部门可以访问云


  Typically, the IT department would serve as a “go-between” for other departments in accessing the Cloud. However, interfacing with Cloud technology has become quite easy. Other departments, such as sales and marketing, or human resources, can now access the Cloud, directly. Security becomes a significant issue as more people are given access to sensitive information.

通常IT部门会充当其他部门访问云的“中介”。但是,连接云技术已经变得非常容易。其他部门,如市场销售或人力资源,现在都可以直接访问云。随着更多的人能够访问敏感信息,安全问题也成为一大隐患。


  Data Analytics in 2018

  2018年的数据分析


  Analytics will Include Visualization Models

可视化模型将融入分析过程


  A 2017 survey of 2,800 experienced professionals working with Business Intelligence predicted data visualization and data discovery would become a significant trend. Data discovery has expanded to include, not just the understanding of data analysis and relationships, but also ways of presenting data, to reveal deeper business insights. As a result, visualization models are becoming more and more popular as a way to translate data into useable insights. The ever-improving visualization model has become an integral part of gaining insights from Big Data.

2017年,2800名经验丰富的商务智能领域专业人士参与了一项调查,该调查预测数据可视化和数据发现将成为一大趋势。数据发现有所发展,不仅体现在对数据分析及其关系的理解,还体现在数据的呈现方式上,为企业提供了更深入的商业观察。于是,可视化模型越来越受欢迎,可以将数据转化为实用的商业见解。不断改进的可视化模型已成为从大数据获取深层信息的必要环节。


  Predictive Analytics

预测性分析

 


  Many businesses have used “historical” Big Data research to support predictions of future behavior. However, current, updated research would be more valuable in making these predictions. The old adage of “past results not being a guarantee of future success” still holds true in the world of Business Intelligence. Predictive Analytics provides its users with an edge, and has incredible potential for increasing profits by “knowing the customer” in real-time.

很多企业用基于过去的大数据研究来支撑对未来行为的预测,但如今更新后的研究对于这些预测更有价值。正如“过去的成就不代表未来”,这句话同样适用于商务智能领域。预测性分析为用户提供一种优势,可以实时“了解客户”,为增收创造了巨大可能。


  Internet of Things in 2018

2018年的物联网


  The Internet of Things will continue to grow. How the information from these devices gets used is something else, entirely.

物联网将持续发展壮大。如何利用从物联网设备获取的信息又完全是另一回事。


  Improving Retail

改善零售业


  In 2018, consumers and business-owners will profit from an increase in sensors and data coming from various customer-owned devices. The IoT gathers information and allows businesses to market their products more efficiently to prospective customers. Tech-savvy companies have begun investing in sensor-based analytics, which will allow them to track the areas in their stores trafficked most by customers.

2018年,各种用户自有设备将促进传感器和数据的增长,消费者和企业主将从中获益。物联网汇聚信息,让企业更高效地将产品销售给潜在客户。懂技术的公司已经开始投资基于传感器的分析方法,这将能让他们追踪其商店内人流量最大的区域。


  Reshaping Healthcare

重塑医疗服务


  Big Data is now being used to drive healthcare solutions, but may also reshape the ways people access their healthcare and how they pay for it. New, wearable technology monitors an individual’s health, allowing hospitals and clinics to improve the quality of healthcare. Patients can have a networked device remind them to take prescriptions, to exercise, and be alerted when blood pressure levels change dramatically.

大数据如今在推动医疗解决方案上得以应用,但也可以让人们以新的方式获取和支付医疗服务。新型穿戴式技术可监测人们的健康状况,提高医院、诊所的医疗质量。病人可以用联网的设备提醒自己去开药或锻炼身体,在血压飙升时发出警报。


  Changing Security Challenges

改变安全问题


  New internet security challenges will become a problem in 2018. It is predicted hackers will seek to access the IoT for destructive purposes. In October of 2016, hackers crippled large sections of the internet by using the IoT to carry out the attack.

互联网安全面临的新挑战将成为2018年的一大问题,有预测称黑客会入侵物联网加以破坏。2016年10月,黑客通过物联网实施网络攻击,造成互联网大面积瘫痪。

 


  As the IoT continues to grow, weaknesses in the global internet-infrastructure will also continue to grow. Artificial Intelligence and Machine Learning offer solutions which will steadily become more popular. As devices become more interconnected with one another, security experts will need to learn to work with AI and ML programs.

随着物联网的不断发展,全球互联网基础设施的不足也将不断突显。人工智能与机器学习提供的解决方案愈发受到欢迎,设备之间的相互关联有所增强,因此安全专家需要学会与人工智能和机器学习项目进行合作。


  Machine Learning in 2018

  2018年的机器学习

 


  Machine Learning is a training process for computers, which is currently being used by organizations for a variety of activities, such as real-time ads, pattern recognition, fraud detection, and healthcare. But in 2018, it will be smarter, faster, and more efficient.

机器学习是计算机的训练过程,如今被很多机构用于各种活动,比如实时广告、模式识别、欺诈识别以及医疗保健。在2018年,机器学习将会更智能、更快速、更高效。


  Ronald Van Loon, the Director of Business Development at Advertisement, said:

Advertisement的商务开发总监罗纳德·房龙表示:


  “Your digital business needs to move towards automation now, while ML technology is developing rapidly. Machine learning algorithms learn from huge amounts of structured and unstructured data, e.g. text, images, video, voice, body language, and facial expressions. By that it opens a new dimension for machines with limitless applications from healthcare systems to video games and self-driving cars.”

“如今,数字业务需要转向自动化,机器学习技术正在迅速发展。机器学习算法从大量的结构化数据和文本、图像、视频、语音、肢体语言、面部表情等非结构化数据中学习,可用于从医疗保健系统到电子游戏和自动驾驶汽车的所有领域,拥有无限广阔的应用前景,为机器打开了一个新的维度。”


  Education

教育

 


  Several efforts to use Machine Learning for improving the art of teaching have been developed recently. For example, California State University has urged its faculty to find and use free, or low-cost, materials in teaching classes. To simplify the process, (replacing previous course material with free, or low-cost, materials, is time-consuming) Intellus Learning provided a solution, by indexing over 45 million online resources and teaching (by way of Machine Learning) the program/algorithm to make recommendations. The instructor can upload the free, or low-cost, materials into the course materials management system, and make them available to students.

目前,通过使用机器学习改善教学技巧的努力已经取得进展。例如,加州州立大学敦促其教师在教学中寻找和使用免费或低成本的素材。为了简化过程(用免费、或低成本的素材替代之前的课程教材非常耗时),Intellus Learning提供了一种解决方案,即索引超过4500万个在线资源,并用机器学习训练程序或算法来提出建议。教师可以将免费或低成本的材料上传到课程材料管理系统中,并供学生使用。


  Healthcare

医疗保健

 


  Identifying different diseases, and diagnosing them correctly, is one goal of ML research. The healthcare industry has been developing computers/algorithms with the ability to identify and diagnose diseases. At the University of Texas, at Austin, a team of researchers has created a fully automatic method for combining models of tumor growth. Machine Learning algorithms automatically identified the brain tumors. Machine Learning has been used in various medical efforts, including:

识别不同疾病并做出正确诊断是研究机器学习的目标之一。医疗保健行业一直在开发能够识别并诊断疾病的计算机和算法。在德克萨斯大学奥斯汀分校,一个研究团队发明了把多个肿瘤生长模型结合起来的全自动方式。机器学习算法能自动识别大脑肿瘤。机器学习已用于各种医疗工作,包括:


   behavioral modification 行为矫治

epidemic outbreak predictions 流行病爆发预测

drug discoveries 药物发现

radiology 放射检查

electronic records 电子病历

diagnosis and disease identification 诊断和疾病识别


  Artificial Intelligence in 2018

  2018年的人工智能

 


  Artificial Intelligence research is currently focused on developing algorithms which allow humans and technology to communicate more naturally with each other, and ways to train those algorithms. The goal is to answer complicated questions in natural human language. AI and ML have made it possible to automate jobs normally requiring human discretion. These jobs include such skills as:

当前人工智能研究的重点是开发让人机交互更加自然的算法,以及训练这些算法的方法,旨在使用自然的人类语言回答复杂的问题。人工智能和机器学习使通常需要人类决断的工作有了自动化的可能,这些工作包括诸如此类技能:


   reading handwritten materials 阅读手写材料

identifying faces 面部识别

learning 学习

cognitive skills, such as planning, and reasoning using partial information 认知能力,如规划和利用部分信息作出推理


  AI and Cyber Security

人工智能和网络安全

 


  As businesses realize the importance of developing a cyber security program, AI will become more popular. A well-constructed AI defense system can process years of attack history and learn various attack and defense strategies. It can create a baseline of normal user behavior, and then search for anomalies, much faster than a human. This is significantly less expensive than maintaining a team of security professionals to deal with daily cyber-attacks. AI can also be used to develop defense strategies. Expect AI to become more heavily involved with Cyber Security in 2018 as well.

随着企业意识到开发网络安全程序的重要性,人工智能将变得更加流行。一个合理构建的人工智能防御系统能够从多年来发生的攻击事件中,学会各种攻击和防御策略。它可以创建正常用户行为基准,然后搜索异常行为,速度比人类快得多。这比维护一支由信息安全专家组成的团队来处理日常网络攻击要便宜得多。人工智能也可以用来制定防御策略。预计在2018年,人工智能与网络安全的关系会更加密切。


  关键译词:

Internet of things (IoT)  物联网

sensor  传感器

business intelligence  商务智能,商业智能

data analytics  数据分析

visualization  可视化

competitive pricing  竞争性定价

hybrid Clouds  混合云

wearable technology  可穿戴技术

algorithm  算法

unstructured data 非结构化数据

make recommendations  提出建议

epidemic  n. 传染病;流行病  adj. 流行的;传染性的

cyber security  网络安全

 

作者:Keith D. Foote

英文来源:DATAVERSITY

编译:Yee君

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分类:翻译业内动态 标签:大数据 商务智能 云趋势 数据分析 物联网 机器学习 人工智能  | 收藏

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