跟读练习: Data vs. Findings vs. Insights - 通过YouTube学习英语口语
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- Data findings and insights.
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- Data findings and insights.
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They may feel and are related, but they're three different things.
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I'll explain those three different things in this video.
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They communicate different levels of research analysis completion.
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Insights represent the most complete level of research analysis.
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Findings are slightly weaker and lack context, and data is completely devoid of any information or analysis.
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Let's walk through these, starting with data and build up.
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Data is an unanalyzed collection of individual observations about users that may include transcripts, notes, metrics, or survey output.
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At this level, no analysis has been completed yet, and without analysis, we don't yet have information.
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Take for instance, a usability study where we collected transcripts and a log of the things that users clicked on.
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These are our data, but they don't tell us anything about what we learned collectively or what to do as a result.
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This is why we don't wanna make decisions based on raw data alone.
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However, when we take our data points and start to identify patterns using qualitative or quantitative analysis methods, we call those patterns findings.
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Findings describe patterns in collected data, which still lack consideration of background, past research, and organizational factors.
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For example, we might say that most users clicked on a contact support button on our website, but this finding still doesn't give the full story of what this looked like in the past and why we observed it.
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It lacks context.
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We need something with clear context for good design decision-making.
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This is where insights come into play.
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Insights do have this rich and necessary context, and this is why insights are the gold standard for making user experience design decisions based on research.
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Insights are focused explanations of opportunities based on user research and business context.
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So, if we knew most users clicked on the contact support button on our website, we might know it's because they were not able to find the information they were looking for, which drives up support volume costs.
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In this scenario, the insight would be that there is an opportunity to improve the discoverability of important information to drive down support volume costs.
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Context is key.
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On teams, it is important to clearly distinguish whether you are referring to data, findings, or insights because this will communicate whether you have more analysis to do or not.
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Once we have insights, we're ready to make good decisions.
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- Thanks for watching.
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If you wanna see more of our UX videos, take a look at these over here and consider subscribing to our channel.
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On our website, nngroup.com, you can access our free library of over 2000 articles.
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You can also register for one of our UX courses that offer live hands-on UX training.
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本课概述
在本课中，您将学习有关数据、发现和洞察之间的区别。这三者在研究分析中代表不同的完成程度。在这个过程中，您将提高理解复杂内容的能力，并能够更好地使用这些概念进行决策。通过对这段视频的分析，您将能够更自信地讨论这些术语，从而在您的英语对话中使表达更加精准。
关键词汇与短语
- 数据 (Data)
- 发现 (Findings)
- 洞察 (Insights)
- 模式 (Patterns)
- 用户研究 (User Research)
- 上下文 (Context)
- 决策 (Decision-making)
- 可用性研究 (Usability Study)
练习技巧
为了有效提高您的英语发音，建议您使用 shadowing site 来反复练习这段视频中的内容。您可以通过模仿讲者的语调和语速来巩固发音技巧。视频中的讲述较为清晰，适合使用 看YouTube学英语 的方法来进行跟读。
在练习时，注意讲者的停顿与重音，这可以帮助您更自然地掌握英语句子的节奏。尽量在合适的时机插入 shadowspeaks 的技巧来记录您的进步。您可以每次观看视频后进行回放，尝试总结您所学习到的 数据、发现 与 洞察 的具体应用。
通过反复的语音模仿，您将能提高 提高英语发音 的流畅度，增强口头表达的自信。封闭式的问题，或者是对内容的简短总结也是很好的练习方式。最后，记得将您的 shadow speech 录音与原音进行对比，这是检验您进步的一种有效方法。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。