实现线程哪种方法更好

Gone are the days when design used to rely mainly on the color palettes and the creativity of the designer. In the rapidly expanding technological world of today, it is essential to work across departments to enhance the screen experiences.

设计曾经主要依赖于调色板和设计师的创造力的日子已经一去不复返了。在当今飞速发展的技术世界中，跨部门合作以改善屏幕体验至关重要。

Three years back when I started working in user experience, I started with setting the groundwork: empathize, define, ideate, prototype, and test. I continued working on similar grounds for the next three years until I discovered what data analytics was!

三年前，当我开始从事用户体验工作时，我首先进行了基础工作：移情，定义，构思，原型和测试。在接下来的三年中，我继续以类似的理由开展工作，直到发现什么是数据分析！

Sometime back, I had a problem statement which required me to concentrate on personas to create the targeted user experience. I was learning data analytics at that time and thought of integrating data for the user experience I was designing.

有时，我有一个问题陈述，要求我专注于角色以创建目标用户体验。当时我正在学习数据分析，并想到了要为我设计的用户体验集成数据。

And I was surprised. A bit more time on the data and its analysis gave me a more accurate, more human-centered user experience. I could infer the best of the strategies and scopes for the user experience I wished from the data.

我很惊讶。在数据及其分析上花费了更多时间，从而为我提供了更准确，更以人为本的用户体验。我可以根据数据从用户体验中推断出最佳的策略和范围。

So basically you use some data visualization tools like Tableau, Power BI, or Adobe Analytics, and make some pretty dashboards with your user data. Then you can make inferences on the target market, user sentiments, grouping data, survey data if you have any that can be well summarized, and so on.

因此，基本上，您使用一些数据可视化工具(例如Tableau，Power BI或Adobe Analytics)，并使用用户数据制作一些漂亮的仪表板。然后，您可以对目标市场，用户情绪，分组数据，调查数据(如果有任何可以概括的内容)进行推断，等等。

The opportunity to leverage insight from data has never been greater.

利用来自数据的洞察力的机会从未如此大。

Humans tend to generate a lot of data. With new technologies and products surfacing every day, we cannot miss our chance to analyze the data generated effortlessly and provide insights for better-decision making in business.

人类倾向于生成大量数据。每天都有新技术和新产品面世，我们不能错过分析毫不费力地生成的数据并提供洞见的机会，以做出更好的业务决策。

With the abundance of data being generated today, the value of data and related skills primarily go up, creating better opportunities for optimization in almost every technology domain.

如今，随着大量数据的产生，数据和相关技能的价值开始上升，从而为几乎每个技术领域的优化创造了更好的机会。

UX +数据 (UX + Data)

What comes to your mind when you think of data? Accuracy, more insights, numbers, objective judgement, decision making.

当您想到数据时会想到什么？ 精度，米矿石我nsights，正umbers，邻BJECTIVEĴudgement，d ecision米亚庆。

For a designer, intuition and experience are the assets.

对于设计师而言，直觉和经验是财富。

Data and design fused together gives us a term Data Driven UX, which I would like to define as the opportunity to explore the experience around data itself — how to make data easier to work with, how to get more value out of data, and how data enriches our work and lives.

数据和设计融合在一起，给我们一个术语“ 数据驱动的用户体验” ，我想将其定义为探索数据本身经验的机会-如何使数据更易于使用，如何从数据中获得更多价值以及数据如何丰富我们的工作和生活。

The primary goal is for many tech companies these days is to create user-centered products. Therefore, having design and data intertwined and part of the same development cycle is a good idea. Companies are challenged to create memorable experiences for their customers across channels and platforms. So, how do we start on using the data in our design life cycle?

如今，许多科技公司的主要目标是创建以用户为中心的产品。因此，将设计和数据交织在一起并且属于同一开发周期是一个好主意。公司面临着通过渠道和平台为客户创造难忘体验的挑战。那么，我们如何开始在设计生命周期中使用数据？

I used data primarily in four design components:

我主要在四个设计组件中使用了数据：

Understanding the user personas
了解用户角色
Designing the task models
设计任务模型
Redesigning the UX
重新设计UX
Conducting heuristic evaluation
进行启发式评估

Every time you work on Data Driven UX, I would recommend you ask these three questions to the design team:

每次使用数据驱动的UX时 ，建议您向设计团队提出以下三个问题：

Why is data essential for this design?

为什么数据对于此设计至关重要？

How can you collect data?

您如何收集数据？

What is the right way to use data once you’ve collected it

收集数据后使用数据的正确方法是什么

If you get answers to the above ones accurately, you believe there’s scope to talk on this more with the data team, go ahead! Integrate data into your design process.

如果您准确地获得了上述答案，那么您相信可以与数据团队进行更多讨论，继续吧！将数据集成到您的设计过程中。

1.从数据了解用户角色 (1. Understanding User Personas from Data)

Personas are character representations of a person, object, situation, problem, challenge, or redesign created based on the research to represent the different user types that will use the service, product or site. Creating personas helps to understand users’ needs, experiences, behaviors, and goals.

角色是根据研究创建的人员，对象，情况，问题，挑战或重新设计的角色表示，以表示将使用服务，产品或站点的不同用户类型。创建角色有助于了解用户的需求，体验，行为和目标。

I usually end up using data the most while creating personas for background research, qualitative and quantitative research, and the objective market research.

在创建用于背景研究，定性和定量研究 以及客观市场研究的角色时，我通常最终会使用数据最多。

To create user personas for an optimum UX, conducting and condensing user research is the most important part of the design cycle. Data Driven UX for creating and understanding user personas would felicitate:

为了创建最佳UX的用户角色，进行和浓缩用户研究是设计周期中最重要的部分。用于创建和理解用户角色的数据驱动UX将具有以下优点：

A near accurate portrait of your important and target users
您的重要和目标用户的肖像几乎准确
Outline the core user’s motivations, goals, needs, demands, attributes, behaviors
概述核心用户的动机，目标，需求，需求，属性，行为
Unites designers and stakeholders around a common understanding of who the user is
使设计者和利益相关者团结一致，共同了解用户是谁
Help designers in the decision making what product features would be the most important to the user
帮助设计师做出决策，哪些产品功能对用户最重要

Data analytics and data visualization techniques and tools can always help for clustering, segmentation, determining relationships, and making insightful conclusions from the data. With them I can get an idea of the age group of my users, geography, their spending capacity, average screen time, their behavior, characteristics, the personas with the competitors, and so on.

数据分析和数据可视化技术与工具始终可以帮助您进行聚类，细分，确定关系并从数据中得出有洞见的结论。通过他们，我可以了解用户的年龄段，地理位置，他们的消费能力，平均屏幕时间，他们的行为，特征，与竞争对手的性格等等。

For instance, say you are Company A that sells baby strollers, from $30 to $3000 (Yes! There are baby strollers worth even $8000, trust me! Check online if you don’t believe :D) Now, you are redesigning your app/website and revamping your brand experience and you wish to know the age group of the males that shop from your portal. You do not need to ask a student intern to create a custom SQL query as:

例如，假设您是A公司 ，出售的婴儿推车价格从30美元到3000美元不一样(是的！甚至有价值8000美元的婴儿推车，相信我！如果您不相信：D，请在线检查)现在，您正在重新设计应用程序/网站并改善您的品牌体验，并且您希望通过门户网站了解购物的男性年龄段。您无需让学生实习生创建自定义SQL查询，如下所示：

SELECT AVG(U.Age)FROM Users UWHERE U.Gender = "Male";

SELECT AVG(U 年龄。)来自用户的ÙWHERE U.Gender = “男性”;

Just open up a data visualization tool as Tableau (Ask your data team what Tableau is, they would love to answer you!). Next up, visualize the age attribute from your data set against the gender of your users, filter the data out on males, and circle out on a scatter plot.

只需打开Tableau这样的数据可视化工具(问您的数据团队Tableau是什么，他们很乐意回答您！)。接下来，根据您的用户性别可视化数据集中的年龄属性，过滤掉男性数据，然后在散点图上圈出。

Voilà! We have a relationship trend for age group of the males that shop from Company A! Easy.

V油à！从A公司购物的男性的年龄群体有一个关系趋势！简单。

2.设计任务模型 (2. Designing the task models)

I like to define Task Models as a description of each task in a workflow. It often involves documenting the comprehensive business and user information requirements in an accurate and consistent format. Now, as information comes into our picture, we must definitely rely on data for the insights.

我喜欢将任务模型定义为工作流中每个任务的描述。它通常涉及以准确且一致的格式记录全面的业务和用户信息需求。现在，随着信息进入我们的视野，我们绝对必须依靠数据来获取见解。

Designing and implementing a successful user experience, one that meets the needs and requirements of an organization, requires a logical task model.

要设计和实现成功的用户体验(要满足组织的需求)，就需要一个逻辑任务模型。

For instance, we wish to find the trend for users that spend more than 4 hours on phone; what apps do they use, what percentage of time is on social media, and similar questions to answer. In that case, we go ahead and plot visualization using the tools and observe the trend lines, and use bubble plots to show the segregation. Data team, step in please!

例如，我们希望为那些在电话上花费4个小时以上的用户找到趋势；他们使用什么应用程序，在社交媒体上占多少时间以及需要回答的类似问题。在这种情况下，我们将继续使用工具绘制可视化效果并观察趋势线，并使用气泡图显示偏析。数据团队，请介入！

为任务模型实现数据： (Implementing Data for Task Models:)

Identify the task to be analyzed
确定要分析的任务
Break this high-level task down into sub tasks and segment the data as categorical or continuous specified in terms of objectives and the area of interest
将此高级任务分解为子任务，然后按目标和关注领域对数据进行分类或连续分类
Draw a layered task diagram of each sub task ensuring it it in line with the data we segmented
绘制每个子任务的分层任务图，确保其与我们细分的数据一致
Produce a data visualization that covers techniques to visualize our task model in hand.
产生一个数据可视化，其中包含使我们的任务模型可视化的技术。

3.重新设计UX (3. Redesigning the UX)

A bad UX design can definitely doom your next project, no matter how good the idea or concept behind it really is. For that very reason, you need to make sure that you support your decisions with the numbers. Here’s when data plays an important role.

糟糕的UX设计肯定会注定您的下一个项目，无论其背后的想法或概念到底有多好。因此，您需要确保使用数字来支持您的决策。这是数据起重要作用的时候。

Say, this time Company A has a problem; Users get to the payment page but drop out mid-transaction. Identifying the problem and redesigning the payment page is the task on hand. So, we now have to visualize how long after reaching the payment page the user drops out. So, from the amount of time spent by the user on that screen, you know what and where it’s going wrong.

说，这一次公司A有问题； 用户进入付款页面，但退出交易中间。 确定 问题并重新设计 付款页面是当前的任务。 因此，我们现在必须 可视化 用户退出 支付页面后需要等待多长时间。 因此，从用户在该屏幕上花费的时间来看，您会知道问题所在和出处。

Analyze factors that worked or did not work well for your user experience design supported by the data clustering or segmentation. Divide your data into positives and negatives – what works or not should be clear by now. Draw out a correlation between the factors of consideration and determine if the relations are strong or weak.

分析数据聚类或细分所支持的用户体验设计行之有效或行不通的因素。将您的数据分为肯定和否定–到目前为止，什么是行之有效的，现在应该弄清楚。在考虑因素之间建立关联，并确定该关联是强还是弱。

Working out relations and plus/minus for your data works best when redesigning your user experience.

重新设计用户体验时，计算数据的关系和正负关系最有效。

4.数据启发式评估 (4. Heuristic Evaluation with Data)

Heuristic evaluation is a method for finding the usability problems in a user interface design so that they can be attended to as part of an iterative design process. It involves having a small set of evaluators examining the UI/UX and judge its compliance with recognized usability principles.

启发式评估是一种用于在用户界面设计中查找可用性问题的方法，以便可以将其作为迭代设计过程的一部分加以解决。它涉及到让一小组评估人员检查UI / UX并判断其是否符合公认的可用性原则。

Start your UX design with a qualitative data research to learn the why’s of user behavior using data. Visualize the data into clusters, derive regression models and pull up the qualitative outcomes that are non-numerical — preferences, feelings, inclinations using data segmentation.
开始你的 UX设计与定性数据 研究，以了解使用数据的用户行为的原因。将数据可视化成簇，得出回归模型，并使用数据分割得出非数值的定性结果-偏好，感觉，倾向。
When a product or prototype is available for user testing, employ quantitative data research to learn how people actually interact with the product. Quantitative data is based on measurable evidence (e.g. success rates, task completion times, etc.) and is acquired through methods like analytics, A/B testing, and eye tracking.
当一个产品或样机供用户测试，应用定量数据 的研究 ，了解人们如何与产品交互。定量数据基于可测量的证据(例如成功率，任务完成时间等)，并通过分析，A / B测试和眼动追踪等方法获取。

I hope this read helps you in a new direction towards extensively implementing data into your user experience design life cycle. Happy reading! Cheers!

我希望本文有助于您朝着将数据广泛实施到用户体验设计生命周期中的新方向发展。祝您阅读愉快！干杯!

认识你的作者 (Know your author)

Rashi is a graduate student and a data analyst, user experience analyst and consultant, a tech speaker, and a blogger! She aspires to form an organization connecting women in business with an ocean of resources to be fearless and passionate about the work and the world. Feel free to drop her a message here!

Rashi是一名研究生，是一名数据分析师，用户体验分析师和顾问，一名技术发言人和一名博客作者！她渴望组建一个组织，将从事商业活动的妇女与丰富的资源联系在一起，以对工作和世界保持无畏和热情。随时在这里给她留言！