数据可视化 工具
人工智能 (ARTIFICIAL INTELLIGENCE)
Data consumption is rising steadily in 2020 with estimates showing consumption of 1.7 megabytes of data per person. Companies face massive data amounts and this could overwhelm them without a well thought out data management strategy.
数据消耗在2020年稳步增长，估计显示每人消耗1.7兆字节的数据。 公司面临着庞大的数据量，如果没有深思熟虑的数据管理策略 ，这可能会使他们不知所措。
Enterprises should start with understanding their analytics goals, measuring governance standards and automation to manage the voluminous data amounts. This will enable them to derive value from their data through aggregation.
企业应首先了解其分析目标，衡量治理标准和自动化以管理大量数据。 这将使他们能够通过聚合从数据中获取价值。
What is an AI Center of Excellence?
什么是AI卓越中心？
AI center of excellence takes a centralized approach to organizational operations and enables collaboration from the C-Suite while promoting best practices at all levels including employee relationships and research. By adopting the AI center of excellence, enterprises can concentrate on challenges facing them by making improvements through an iterative process.
AI卓越中心采用一种集中化的组织运营方法，支持C-Suite的协作，同时在各个层面推广最佳实践，包括员工关系和研究。 通过采用AI卓越中心 ，企业可以通过迭代过程进行改进以专注于面临的挑战。
Data visualization tools are becoming commonplace with enterprises adopting them to tell stories around their products or services. However, costs of data pose challenges to small businesses and the good news is that platforms such as Tableau offer low-cost solutions for visualization of data.
数据可视化工具正变得越来越普遍，企业采用它们来讲述有关其产品或服务的故事。 但是，数据成本给小型企业带来了挑战，令人欣慰的是， Tableau等平台提供了用于数据可视化的低成本解决方案。
These and more insights on our Weekly AI Update
这些和更多关于我们每周AI更新的见解
组织中的数据聚合 (Data Aggregation in Organizations)
What is the best approach to tackle data aggregation in your organization? A study conducted by Dell EMC in 2014 estimated that we would reach 1.7 megabytes of data produced for every person, every second in 2020. This is a daunting amount of data for companies to manage, let alone try to aggregate into a meaningful data report that can be used for analytics.
什么是解决组织中数据聚合的最佳方法？ 戴尔EMC在2014年进行的一项研究估计，到2020年，我们每个人每秒将产生1.7兆字节的数据。这对于公司来说是令人望而生畏的数据量，更不用说试图汇总成有意义的数据报告了，可用于分析。
One data management challenge is ensuring that you are working with a “single version of the truth,” which enterprises can accomplish by normalizing and eliminating data. However, when you begin to aggregate data from disparate data sources, you also need a methodology for data aggregation¹.
数据管理的一项挑战是确保您使用的是“事实的单一版本”，企业可以通过规范化和消除数据来实现这一目标。 但是，当您开始聚合来自不同数据源的数据时，您还需要一种数据聚合方法¹ 。
Here are four best practices for data aggregation:
以下是数据聚合的四个最佳实践：
-Understand your company’s short- and long-term analytics objectives
-了解您公司的短期和长期分析目标
-If you purchase data from outside partners, ensure that their governance and privacy standards are compatible with your own
-如果您从外部合作伙伴处购买数据，请确保其治理和隐私标准与您自己的兼容
-Determine how data will be stored and how users will access it
-确定如何存储数据以及用户如何访问数据
-Automate data integration as much as possible
-尽可能自动地进行数据集成
AI卓越中心 (AI Center of Excellence)
Artificial intelligence is now mission-critical in most large organizations. Creating an AI Center of Excellence², helps centralize the process and keep the focus on the business. A Center of Excellence is a shared facility that provides leadership, best practices, research, support, and training for a focus area and are commonly used in healthcare to focus on specific problems.
一个 rtificial智能是现在的关键任务中最大型组织。 创建AI卓越中心²有助于集中流程并保持对业务的关注。 卓越中心是共享的设施，可为重点领域提供领导力，最佳实践，研究，支持和培训，通常在医疗保健中用于解决特定问题。
They can be used in organizations for #artificialintelligence as well. What makes AI a strong candidate for a dedicated Center of Excellence, is its rapidly expanding role as mission-critical technology in enterprises. Companies are finding that people in many different business units — not just data science or IT — want to be involved with AI.
它们也可以在组织中用于#artificialintelligence。 使AI成为专门的卓越中心的强大候选者的原因在于，AI作为企业中的关键任务技术的地位正在Swift扩大。 公司发现，许多不同业务部门的人员 (不仅仅是数据科学或IT部门)都希望参与AI。
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In some cases, people are bringing in their own AI tools and solutions, but there is a need to orchestrate this buying to avoid waste. In other cases, people are independently developing their own AI and AI budgets, so there is no assurance that accountability for total AI spend or deployment exists.
在某些情况下，人们会带来自己的AI工具和解决方案，但有必要精心安排这次购买以避免浪费。 在其他情况下，人们可以独立制定自己的AI和AI预算，因此无法保证存在对AI总支出或部署的责任感。
Together, these factors make a strong argument for an AI Center of Excellence. Such a center would include people from multiple business units, as well as from #datascience and IT. The goal would be to combine efforts, ideas, and budgets for an integrated and well-orchestrated approach to AI.
这些因素共同构成了AI卓越中心的有力论据。 这样的中心将包括来自多个业务部门以及＃数据科学和IT的人员。 目标是将努力，想法和预算结合起来，以一种集成且精心安排的AI方法。
数据可视化工具 (Data Visualization Tools)
The Story feature in Tableau can be a useful data visualization tool when you are drilling down on a dataset from general to specific. As modern businesses generate more and more data, it has become increasingly more difficult to draw useful information out of the raw stream. Finding and communicating actionable information requires an effective set of tools designed specifically for that purpose.
在的Tableau 吨他的故事功能，可当你从一般向下钻取数据集中到特定的一个有用的数据可视化工具。 随着现代企业生成越来越多的数据，从原始流中提取有用信息变得越来越困难。 查找和交流可操作的信息需要专门为此目的设计的有效工具集。
Highly educated data scientists with training and experience in data visualization techniques are becoming more common in large enterprises, but small businesses do not have the resources to hire such experts. Inexpensive and accessible data visualization tools³ provided by vendors like Tableau Software are designed to help tell the story of your data without requiring you to have an advanced degree.
在大型企业中，受过高等教育，受过数据可视化技术培训并具有丰富经验的数据科学家正变得越来越普遍，但是小型企业没有资源聘请此类专家。 由Tableau Software等供应商提供的廉价且可访问的数据可视化工具³旨在帮助您讲述数据的故事，而无需您拥有高级程度。
教育适应COVID-19大流行 (Education adjusting to COVID-19 Pandemic)
The COVID-19 pandemic has challenged universities to fundamentally reexamine how their students learn, with #remotelearning creating a new frontier for their educators. Since in-person learning⁴ is not viable for every student, collaboration has become more important than ever. But the video meetings that have now taken over how people work and socialize. Consequently, this leads to the phenomenon known as “Zoom fatigue,” although it applies to every video conferencing platform.
吨他COVID-19大流行的挑战高校从根本上重新审视他们的学生如何学习，与#remotelearning创造自己的教育的新领域。 由于面对面学习⁴并非对每个学生都可行，因此协作比以往任何时候都变得更加重要。 但是现在，视频会议已经接管了人们的工作方式和社交方式。 因此，尽管这适用于每个视频会议平台，但仍会导致称为“缩放疲劳”的现象。
That may foster a new trend on college campuses this fall: Turning spoken lectures into highly accurate lecture notes. Otter for Education AI speech technology aims to give both lecturers and students the ability to avoid Zoom call information overload, and more accurate notes to students if they miss a class.
这可能会在今年秋天在大学校园中培养出新的趋势：将口语演讲变成高度准确的演讲笔记。 Otter for Education AI语音技术旨在为讲师和学生提供避免Zoom通话信息过载的功能 ，并在学生错过课程时为他们提供更准确的注释。
Otter for Education is being used by over 100,000 students for remote learning and academic accessibility purposes, and can be used in a virtual, onsite or hybrid setting. The offering is available over Zoom and other online video lecturing platforms. The goal of Otter’s technology is to boost remote classroom collaboration between teachers and students.
Otter for Education已被超过100,000名学生用于远程学习和学术访问目的，并且可以在虚拟，现场或混合环境中使用。 该产品可通过Zoom和其他在线视频教学平台使用。 Otter技术的目标是促进师生之间的远程课堂协作 。
AI驱动的内容主持人 (AI-Powered Content Moderator)
Website accessibility tech provider UserWay¹⁰ has released an AI-powered tool designed to help organizations ensure their websites are free from discriminatory, biased, and racially charged language. The tool, Content Moderator, flags content for review, and nothing is deleted or removed without approval from site administrators.
w ^ ebsite无障碍技术提供商UserWay¹⁰已经发布了旨在帮助组织的AI-动力工具，确保他们的网站是由无歧视，偏见和种族充电语言。 该工具Content Moderator标记了要审核的内容，未经站点管理员的批准，不会删除或删除任何内容。
Customers are using its AI-powered accessibility widget⁵, an advanced AI-based compliance-as-a-service (CaaS) technology that ensures brands provide an accessible digital experience that meets strict governmental and ADA regulations.
客户正在使用其AI驱动的辅助功能小部件⁵ ，这是一项基于AI的高级高级合规即服务(CaaS)技术，可确保品牌提供符合严格的政府和ADA法规的无障碍数字体验 。
The goal of the Content Moderator isn’t to censor or silence, but to make web teams aware of problematic language in user-generated content or in content they may have overlooked. Before launching Content Moderator, UserWay ran its rule engine across more than 500,000 websites. The findings were concerning.
内容主持人的目标不是审查或沉默，而是让Web团队意识到用户生成的内容或他们可能忽略的内容中存在问题的语言。 在启动Content Moderator之前，UserWay在超过500,000个网站上运行其规则引擎。 这些发现令人担忧。
Many of these terms have only recently been understood to be divisive and prejudicial. It is an enormous task for most site owners to keep track of the latest consensus around culturally sensitive terms. The tool aims to make this task simple, centralized, and scalable.
这些术语中的许多直到最近才被理解为分裂性和偏见性的。 对于大多数网站所有者来说，跟踪有关文化敏感术语的最新共识是一项艰巨的任务。 该工具旨在使此任务简单，集中和可扩展。
自然语言处理的改进 (Improvements on Natural Language Processing)
Natural language processing is still being refined, but its popularity continues to rise with the release of the new GPT-3 language model⁶. This new version is likely to help. When you speak to a computer, whether on the phone, in a chat box, or in your living room, it understands you because of natural language processing. The computer voice can listen and respond accurately, thanks to artificial intelligence.
N语言的语言处理仍在改进中，但是随着新的GPT-3语言模型 release的发布，其流行度继续上升。 这个新版本可能会有所帮助。 当您在计算机上通话时，无论是在电话中，在聊天室中还是在客厅中，计算机都会通过自然语言处理来理解您。 借助人工智能，计算机语音可以准确地聆听和响应。
#Naturallanguageprocessing is the language used in AI voice questions and responses. The processing of language has improved multi-fold over the past few years, although there are still issues in creating and linking different elements of vocabulary and in understanding semantic and contextual relationships.
#Naturallanguageprocessing是AI语音问题和响应中使用的语言。 尽管在创建和链接词汇的不同元素以及理解语义和上下文关系方面仍然存在问题，但是在过去几年中，语言的处理已得到了多方面的改进。
NLP has been a hit in automated call software and in human-staffed call centers because it can deliver both process automation and contextual assistance such as human sentiment analysis when a call center agent is working with a customer. NLP has also been used in HR employee recruitment to identify keywords in applications that trigger a close match between a job application and the requirements of an open position.
NLP在自动呼叫软件和人员配备呼叫中心中广受欢迎，因为当呼叫中心代理与客户合作时，NLP既可以提供流程自动化又可以提供上下文帮助，例如人的情绪分析 。 NLP也已用于人力资源员工招聘中，以识别应用程序中的关键字，这些关键字会触发职位申请和职位空缺的要求之间的紧密匹配。
In our homes, we use NLP when we give a verbal command to Alexa to play some jazz. NLP is on nearly every organization’s IT road map as a technology that has the potential to add business value to a broad array of applications.
在家里，当口头命令Alexa演奏爵士乐时，我们会使用NLP。 NLP几乎是每个组织的IT路线图上的一项技术，它有潜力为各种应用程序增加业务价值。
就业自动化技能 (Automation Skills in Employment)
There are 30 million unemployed Americans. A new Harris Poll commissioned found that two out of five workers say they were let go by their employer due to COVID-19. Additionally, 70% of current job seekers — those unemployed or employed and looking — believe the key to landing a new job is automation⁷.
Ť这里3000万层失业的美国人。 哈里斯民意调查委员会的一项新调查发现，五分之二的工人说，由于COVID-19，他们被雇主解雇了。 此外，目前有70％的求职者(失业或受雇并寻找工作的人)认为获得新工作的关键是自动化⁷ 。
The number is even higher (86%) among those with a college degree or higher. According to the survey, “The Job Seekers Report,” 30% have added automation to their resumes, while 31% said they planned to do so. With so many job-seekers adding to their skill set with the key word “automation,” the Zapier survey queried, “is it possible employers will notice the omission?”
具有大学以上学历的人中，这一数字更高(86％)。 根据《求职者报告》的调查，有30％的人在简历中添加了自动化功能，而31％的人表示他们计划这样做。 Zapier调查询问，如此多的求职者使用关键字“自动化”将其技能添加到自己的技能中，“雇主有可能会注意到这一遗漏吗？”
It makes sense that many Americans are looking to develop new skills or hone-in on existing ones — they are competing with the 41% who lost jobs due to the pandemic, and those also unemployed, as well as those who have jobs but need to make a change.
有道理，许多美国人正在寻求发展新技能或磨练现有技能 ，他们正在与因大流行而失业，也失业，以及有工作但需要做出改变。
A majority (83%) either have learned #automation skills or plan to do so in the near future. The need and desire for efficiency could be from the need to maximize crucial, available time. Work hours invariably come at the expense of those in the home who are remote-learning or those tasked with helping or even teaching those remote learners.
大多数(83％)要么已经学习了#automation技能，要么计划在不久的将来这样做。 对效率的需求和渴望可能来自最大化关键的可用时间的需求。 工作时间总是以牺牲在家中进行远程学习的人员或负责帮助甚至教导远程学习者的人员为代价。
用于COVID-19疫苗的MIT ML模型 (MIT ML Model for COVID-19 Vaccine)
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new combinatorial #machinelearning system⁸ that could both decrease research time needed for a COVID-19 vaccine and make it more effective.
来自麻省理工学院的计算机科学与人工智能实验室(CSAIL)R esearchers已经开发出既可以减少研发时间需要一个COVID-19疫苗，并使其更加有效新组合#machinelearningsystem⁸。
The platform, called OptiVax, focuses on developing peptide vaccines, which are a different approach from common whole virus, DNA, and RNA vaccines. Peptide vaccines are a relatively recent development in the vaccination game that are designed around one specific short amino acid string, called a peptide, that can be found in the target disease. Peptide vaccines use a synthetic version of the peptide that is created in a laboratory and not harvested from the disease itself.
这个名为OptiVax的平台专注于开发肽疫苗，这与普通的全病毒，DNA和RNA疫苗是不同的方法。 肽疫苗是疫苗战役中相对较新的发展，围绕一种特定的短氨基酸串(称为肽)设计，这种肽可以在目标疾病中发现。 肽疫苗使用的是该肽的合成形式 ，该合成形式是在实验室中创建的，并非从疾病本身中获取的。
Traditional vaccines have a larger amount of genetic information in them that is not useful in developing resistance and can lead to unwanted immune responses and dangerous reactions — it is these genetic elements that peptide vaccines are designed to eliminate. The peptides included in a peptide vaccine are, ideally, the most effective at building an immune response without unnecessary material, and are effective across a wider range of individuals.
传统疫苗中含有大量的遗传信息 ，这些遗传信息无助于产生抗药性，并可能导致有害的免疫React和危险React-肽疫苗旨在消除这些遗传因素。 理想情况下，包含在肽疫苗中的肽是最有效的构建免疫React的方法，不需要多余的物质，并且对更广泛的个体有效。
人力资源部门和敏捷方法论 (HR Departments and Agile Methodology)
Only 29% of employees say HR understands their needs, according to a Gartner survey, so using an #Agile strategy can improve organizational outcomes. Agile methodology⁹ is clearly popular: A Gartner survey (The Agile HR Function) of human resources leaders found that 63% of respondents report already using some variation of Agile methods and principles within the HR function.
Ø员工NLY 29％的受访HR了解他们的需求，根据Gartner的调查，所以使用#Agile策略可以提高组织的成果。 敏捷方法论 clearly很受欢迎：根据Gartner对人力资源领导者的调查(敏捷HR功能)，发现63％的受访者表示已经在HR功能内使用了某些敏捷方法和原则。
However, 78% of HR leaders admitted to neither having a defined strategy nor being prepared; they do not have outcomes in place to guide how to use Agile strategies.
但是，有78％的人力资源负责人承认既没有制定明确的战略，也没有做好准备。 他们还没有合适的结果来指导如何使用敏捷策略。
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Across different industries, business leaders are consistently looking for new applications of Agile’s project management methodology and not just for its origins in software design, but as a tool to improve organizational outcomes. 74% of HR leaders said their organizations are “undergoing a broad ‘Agile transformation,’” and almost three-quarters are confident in the importance of implementing Agile in HR to help define priorities and meet goals. Agile, the report said, should be considered a collection of values that rapidly offer customers relevant products and support.
在各个行业中，业务领导者一直在寻找敏捷项目管理方法论的新应用，而不仅仅是将其作为软件设计的起源，而是作为改善组织成果的工具。 74％的人力资源负责人表示，他们的组织“正在经历广泛的“敏捷转型”，”并且将近四分之三的人对在人力资源中实施敏捷以帮助定义优先级和实现目标的重要性充满信心。 报告说，敏捷应该被视为价值的集合，这些价值可以Swift为客户提供相关的产品和支持。
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参考文献 (Works Cited)
¹Data Aggregation, ²AI Center of Excellence, ³Data Visualization Tools, ⁴in-person learning, ⁵AI-powered Accessibility Widget, ⁶GPT-3 language model, ⁷Automation, ⁸Combinatorial Machine Learning System, ⁹Agile methodology
¹ 数据聚合 ，²AI 卓越中心 ，³ 数据可视化工具 ，⁴ 亲自学习 ，⁵AI 驱动的辅助功能小部件 ，⁶GPT -3语言模型 ，⁷ 自动化 ，⁸ 组合机器学习系统 ，⁹ 敏捷方法
被引用的公司 (Companies Cited)
¹⁰UserWay
¹⁰UserWay
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翻译自: https://medium.com/datadriveninvestor/data-aggregation-in-organizations-ai-center-of-excellence-and-data-visualization-tools-2020-2bc8c2430dc8
数据可视化 工具