The often-used terms diversity, equity, and inclusion have distinct meanings. Here’s why that matters, and how they work together.
Diversity. Inclusion. Equity. These words and the issues they point to loom large in tech. It’s hard to go a week without reading an article about a company touting its dedication to diversity, while another is called out for tolerating oppressive comments and workplace practices.
From 2014–2016, Google spent $265 million to increase its diversity numbers (to little avail), a number that has become even more well known after the company recently fired an employee who wrote a memo against diversity efforts. In a 2017 survey of tech employees, 72% reported that diversity and inclusion was important to their company. In another report, which surveyed over 700 startup founders, 45% of respondents reported that they talked about diversity and inclusion internally in the last year. The majority of participants in that survey believe that the tech industry’s employee makeup will be representative of the U.S. population in 2030, though that’s a far cry from where we are now.
With all this talk about diversity, equity, and inclusion (DEI) in tech, there is no better time to dig deep and establish shared, fundamental understandings of these terms and their meanings. In my work as a DEI facilitator working with tech companies and in many less formal conversations, I’ve found that there’s widespread confusion. People get tripped up not only on definitions, but on how to use these terms to create goals and action plans for themselves and their organizations. When we can’t get on the same page, we can’t take the next step. So let’s start at the beginning and create a shared understanding of DEI together.
Since its introduction in the ’90s, Python has rapidly become one of the world’s most popular programming languages. Most recently, we have seen Python even surpass other languages like Java. How has a humble language like Python managed to gain so much attention? Why is Python so popular?
1. The rise of analytics and Python.
With Python, the use cases are shifting to data analysis and machine learning. As Clive Humby stated back in 2006, “Data is the new oil.” The bottom line is that data science has a high value. Companies have made data analytics and data science a priority due to their abilities to maximize profits and gain better insights on business. Because of well-developed resources like the data science workhorses of Pandas and Scikit-learn, Python easily does the heavy-lifting of machine learning algorithms.
Along with ready-made tools to do the work, Python is also an incredibly readable programming language. Its syntax was explicitly designed to remove a lot of unnecessary code and emphasize making it human-readable. Python makes the development of complex programs easier to write and easier to manage, which translates directly to the bottom line of the company.
2. Why is Python so popular? One word of many: Free.
The facts that drive Python’s booming popularity: it is an open source and free to use. Developers all over the world are writing and distributing software packages in Python that small companies or individual developers can use in their projects for free. Who wouldn’t want to be able to plug into a sophisticated image segmentation library developed by Google? At no cost! Just a few years ago, similar image analysis software cost thousands of dollars and was not nearly as user-friendly.
3. It takes a village.
Python programming is easy to learn, easy to write, cheap to build with, and massive followings of programmers worldwide. It’s no wonder Python is rapidly gaining in popularity. One of the worst feelings for new developers is not understanding why their program isn’t working, but with Python, the programming and data science communities are very active. Blog posts, answer sites like StackOverflow, and groups on LinkedIn have made getting feedback and solutions to your issues easier than ever. Getting hands-on help with issues quickly, learning, and picking up better development practices are no longer a daunting task.
The best way to learn any new language is to immerse yourself. Popular programming languages like Python are no different. The more time you interact with solving real-world problems with a new language, the faster you can become fluent. There are tons of resources like YouTube videos and blog posts, but I find that there really isn’t a better-suited way to learn than hands-on teaching. You need to raise your hand and ask an instructor attuned to the Python language, programming languages, Python code, data science, python developers, artificial intelligence, programming, and machine learning, and more.
General Assembly: the bridge to machine learning.
The immense rise of use cases and companies hiring developers, allows an increase in places to learn these new skills. General Assembly has a multitude of ways to get you started on the path to learning Python and becoming a Python developer. Informal and free introduction sessions at General Assembly aim to get you running code in just a couple of hours. Part-time classes take things up a notch by giving you focused hands-on lessons twice a week, over 10 weeks — artificial intelligence will have nothing on you. For those future Python developers that are ready to take the plunge, and want a deep-dive into all things machine learning, General Assembly also offers full-time Data Science Immersive programs every quarter to learn Python code, programming, nuances of artificial intelligence — and more.
Why is Python so popular? These reasons are a very good place to start!
As Product Managers, building product roadmaps is a crucial part of our job. Yet most of us still use outdated tools for product roadmapping — Excel, PowerPoint, wikis, etc. — to try and keep multiple teams on track toward the same goals. It’s painful. The good news is that there’s a better way.
We understand that building a strategic product roadmap is not easy and that your business colleagues always want to know what’s coming next. It’s time to lead your product with conviction. Take a radical new approach to roadmapping because your company needs it and you deserve to build the future and enjoy what you do.
Education Technology (also known as “EdTech”) refers to an area of technology devoted to the development and application of tools (including software, hardware, and processes) intended to promote education.
Put another way, “EdTech is a study and ethical practice for facilitating learning and improving performance by creating, using and managing appropriate technological processes and resources.”
As a data scientist, my work is contingent on knowing and using Python. What I like about Python, and why I rely on it so much, is that it’s simple to read and understand, and it’s versatile. From cleaning, querying, and analyzing data, to developing models and visualizing results, I conduct all these activities using Python.
I also teach data science in Python. My students learn Python to build machine learning models but I’m always excited to hear of the other ways they’ve leveraged the programming language. One of my students told me they used it to web-scrape online basketball statistics just so they could analyze the data to win an argument with friends. Another student decided to expand on her knowledge of Python by learning Django, a popular framework, which she uses to build web apps for small businesses.
Before taking the plunge into data science, we all had fundamental questions (and concerns) about learning Python. If this sounds like you, don’t worry. Before I started learning Python, I spent several months convincing myself to start. Now that I’ve learned, my only regret was not starting sooner.
If you’re interested in learning Python, I want to share my biggest reasons for why you should. Two of these reasons are inherent to Python; one of them is a benefit of Python that I experienced first-hand, and some of the examples I discuss come from things I have researched. My goal is to give you enough information to help make an educated decision about learning Python, and I really hope that you choose to learn.
1. Python is easy to learn.
Long before I learned Python, I struggled to learn another object-oriented programming language in high school: Java. From that experience, I realized that there’s a difference between learning to program, and learning a programming language. I felt like I was learning to program, but what made Java difficult to learn was how verbose it was: the syntax was difficult for me to memorize, and it requires a lot of code to be able to do anything.
Comparatively, Python was much easier to learn and is much simpler to code. Python is known as a readable programming language; its syntax was designed to be interpretable and concise, and has inspired many other coding languages. This bodes well for first-timers and those who are new to programming. And, since it typically requires fewer lines of code to perform the same operation in Python than in other languages, it’s much faster to write and complete scripts. In the long run, this saves developers time, which can then be used to further improve their Python.
One observation I’ve made of Python is that it’s always improving. There have been noticeably more updates to the language in the last 5-10 years than in prior decades, and the updates have often been significant. For example, later versions of Python 3 typically benchmark faster completion times on common tasks than when carried out in Python 2. Every release in Python 3 has come with more built-in functions, meaning “base” Python is becoming more and more capable and versatile.
Learning is not an individual process; often you will end up learning a lot from “peers.” According to various sources, Python has one of the largest and most active online communities of learners and practitioners. It’s the most popular programming language to learn; it’s one of the most popular programming languages for current developers; and among data scientists, it’s the second most common language known and used. All of this translates into thousands of online posts, articles (like this one!), and resources to help you learn.
Speaking of online learning, Python is also tremendously convenient to learn. To learn the fundamentals of Python, there are a lot of learning tools out there — books, online tutorials, videos, bootcamps — I’ve tried them all. They each have their merits but ultimately having options makes it easier to learn. Once you start learning, the resources don’t stop. There are dozens of really good tutorials, code visualizers, infographics, podcasts, and even apps. With all of these resources at your disposal, there’s really no reason why you can’t learn!
To get into any of these use cases would require another post. Regardless, you might be wondering what allows Python to be such a versatile programming language? A lot of it has to do with the various frameworks and libraries that have been built for Python.
Libraries are collections of functions and methods (reusable and executable code) with specific intents; and frameworks more or less are collections of libraries. If you ask any Python developer, they can name at least a half-dozen libraries they use. For example, I often use NumPy, Pandas, and Scikit-learn — the holy trinity for data scientists — to perform math and scientific operations, manipulate and analyze data, and build and train models, respectively. Many Python-based web developers will name Django as one of their preferred frameworks for building web applications.
While it’s true that libraries are written for most programming languages and not just Python, Python’s usability, readability, and popularity encourage the development of more libraries, which in turn makes Python even more popular and user-friendly for existing developers and newcomers. When you learn Python, you won’t just be learning base Python, you’ll be learning to use at least a library or two.
3. Python developers are in demand.
Many people learn to program to enhance their current capability; others to change their careers. I started off as one of the former but became the latter. Before data science, I built digital ad campaigns and a lot of my work was automatable. My only problem was that I didn’t know how to code. Although I eventually learned how, in the process of learning Python for my work, I was presented with different job opportunities where I could use Python, and learned about different companies who were looking for people experienced in Python. And so I made a switch.
There are a lot of Python-related roles in prominent industries. According to ActiveState, the industries with the most need for Python are insurance, retail banking, aerospace, finance, business services, hardware, healthcare, consulting services, info-tech (think: Google), and software development. From my own experience, I would add media, marketing, and advertising to that list.
Why so many? As these industries modernized, companies within them have been collecting and using data at an increasing rate. Their data needs have become more varied and sophisticated, and in turn, their need for people capable of managing, analyzing, and operationalizing data has increased. In the future, there will be very few roles that won’t be engaged in data, which is why learning Python now is more important than ever — it’s one way to bullet-proof your career and your job prospects.
A lot of top tech companies value Python programmers. For instance, to say that Google “uses” Python is an understatement. Among Google engineers, It’s a commonly used language for development and research, and Google’s even released their own Python style guide. Google engineers have developed several libraries for the benefit of the Python community including Tensorflow, a popular open-source machine learning library. YouTube uses Python to administer video, access data, and in various other ways. Python’s creator Guido van Rossum, a Dutch programmer, was hired by Google to improve their QA protocols. And most importantly, the organization continues to recruit and hire more people skilled in Python. Other notable tech companies who frequently hire for Python talent include Dropbox, Quora, Mozilla, Hewlett-Packard, Qualcomm, IBM, and Cisco.
Lastly, with demand often comes reward. Companies looking to hire people skilled in Python often pay top dollar or the promise of increased salary potential.
In summary, there are lots of reasons to learn Python. It’s easy to learn, there are many ways to learn it, and once you do, there’s a lot you can do with it. From my experience, Python programming is a rewarding skill that can benefit you in your current role, and will certainly benefit you in future ones. Even if Python doesn’t end up being the last programming language you learn, it should certainly be your first.
The right tools can speed up your UX process and enable collaboration.
Prototyping is one of the key phases of the design thinking process, and UX designers have a wealth of tools to help them create rich prototypes.
Prototyping tools not only help UX designers create something real enough to test with users and stakeholders, but they can also speed up the process—especially if design tools are used throughout design, and not just before handing off to development.
Why UX Prototyping?
When UX designers prototype designs early and often in the design process, they can understand how real people will react and use the product. Then they have an opportunity to iterate and make their designs even better. This iterative process of prototype, test, and repeat leads to stronger ideas and designs that are more likely to succeed in the long run. UX prototyping also has many other benefits:
Interactive prototypes help designers explore ideas.
By prototyping interactions and animations, designers can flesh out ideas that show what they want the final design to look like. It helps designers externalize the ideas in their head so that they can smooth out the rough edges of an interaction.
“Prototyping is the conversation you have with your ideas.”
— Tom Wujec, TED speaker and founder of The Wujec Group
Prototyping tools enable real-time collaboration.
Tools can also help teams collaborate more effectively. Many app prototyping tools allow designers to easily share files with teammates for feedback and real-time collaboration.
Using prototyping tools also helps designers communicate the design vision to stakeholders and other team members. Showing, rather than telling, strengthens the communication and lowers the risk that other people won’t understand.
Drag and drop tools help us expedite our process.
There’s a reason UX focuses on rapid prototyping. Moving quickly lowers risk and overall cost of a project. Prototyping moves more quickly using tools that use drag and drop interfaces.
Many prototyping tools allow designers to add interactions with a simple click. When designers can spend more time thinking about how to improve the design, rather than struggling with manual tools, the design process improves.
What to look for in a prototyping tool.
With so many UX prototyping tools available, how do you choose? Here are some things to keep in mind as you decide.
Can you try it for free?
Some UX prototyping tools have a trial version to let you take the application for a spin before committing. This is a great way to test drive the tool and see how it works with your design process.
How many people are using the tool? How large is the community that supports and contributes to tool plugins or support forums?
Some of the newer tools will have far fewer users than those that have a well-established user base. If you’re wondering how good a tool is, adoption rate can tell you a lot. Tools with a lot of users tend to be strong.
How long will it take for you to learn the tool? You might not have a lot of time to spend learning a new interface. If you struggle with a prototyping tool, you might want to move on to another one. When you find a tool hard to use, you’re less likely to use it later on. Focus on finding one you feel comfortable with. You also might want to explore a prototyping workshop like this one.
Integration into your process
At the end of the day, any UX design tool should fit your process, or at least allow your process to easily adapt to it. If it’s not easy to add to your process, it won’t be valuable to you.
Top 9 UX Prototyping Tools
Fortunately, UX designers don’t have to look far to find a good prototyping tool. There are so many options out there. Here are just nine of the top prototyping tools to explore.
Sketch is one of the most mature prototyping tools available for UX designers. It was released in 2010 and grown into one of the most common tools for UX designers. Designers use it for creating digital interfaces from websites to apps and icons.
Sketch allows designers to create vector graphics, user flows and interactive prototypes, and teams can sync through a shared cloud workspace. Sketch enables the entire workflow, and it also has a number of helpful integrations with programs like Invision, Zeplin, and Flinto.
Figma is a cloud-based design and prototyping tool. Designers use it to create user interfaces for websites, apps, and smaller devices. It’s similar to Sketch, but it can be used cross-platform. In other words, you don’t need a Mac to use it.
Individuals can use Figma for free, although the free plan has some limitations. You can only add two editors and create a maximum of three projects.
Figma has a number of strong features for creating UI designs. Once you are ready, you can turn your designs into a prototype by creating connections between frames. UX designers can set the interaction, apply animations, customize overlays, and more.
Figma prototypes can be previewed using the Figma Mirror app or desktop app. Figma also has a library of tools that connect it to a number of other applications for productivity, design, and delivery to development teams.
Adobe XD is Adobe’s answer to UI design and prototyping. Similar to Sketch and Figma, it includes familiar tools for creating wireframes, prototypes, and interactions for websites, apps, and other digital screens.
It can also be used across platforms, and collaborators can access and use it on Mac, Windows, iOS and Android.
XD released new features in 2019 to better enable team collaboration, including coediting, document history, and share mode. Like Figma, XD also allows designers to import Sketch files. And now, designers can also turn existing Sketch libraries into cloud documents in XD.
XD’s prototyping interface is also similar to Figma, and designers can create connections, overlays, animations and more.
Webflow is a relative newbie on the scene, but more and more designers are using it in their day to day practice. Webflow gives designers the power to create entire websites and apps without coding. Once you’re done, you can export the project into production-ready code.
It’s possible to host an entire project on Webflow, which means you just need to navigate on the website, and you’re in. You don’t need an app to preview or test your design.
There are a few things to consider. Webflow works only in Chrome or Safari. Also, while you can get started for free, you’ll need a membership to create more than two projects.
Webflow can also take some getting used to. It doesn’t move as quickly as other prototyping tools, but it can save you time once you’re ready for development.
Invision has come a long way since it was first released. At its core, Invision is a prototyping tool that allows designers to upload screens and quickly create interactive prototypes. The Invision prototyping tool won’t let you create designs directly in the app. However, its UI allows designers to sync screens from Sketch or Photoshop or import static images. Then, using the Invision build tool, you can arrange and build links between the screens by creating clickable hotspots. You can add transition states and mobile gestures, and even create hover states for any design element.
Designers can share their prototypes across devices or in real-time for live sketching. It’s an intuitive collaboration tool that lets you easily share a link to the prototype with teammates and clients, who can leave comments on any specific area of the design.
Invision’s strength is in its speed and versatility. It has a low barrier to entry, so designers who have never used prototyping tools can quickly create and share working prototypes.
Balsamiq Mockups is more of a wireframing tool than a prototyping tool. That said, it’s a great first step into quickly creating low-fidelity mockups.
Balsamiq is a drag and drop tool that’s easy to learn and fast to use. It doesn’t have any fancy animation capabilities. But it does allow you to link between screens to create a basic prototype and check for flow and functionality. Designers can also export screens and upload them into Invision to create interactive prototypes.
Balsamiq offers both a cloud and a desktop version of the tool. The cloud version pricing varies based on space requirements and how many projects you create.
UXPin is often overlooked, but has a lot to offer UX designers for website or app prototyping. It includes vector drawing tools, the ability to create components, and the ability to collaborate in real-time with your team.
It also has some additional features that make it really special, like its accessibility features, which check for WCAG contrast standards. On the code side, it has the ability to sync React.js components to UXPin, so you don’t have to redraw patterns.
UXPin is available cross-platform, and it’s free to sign up.
Simply by creating prototypes, designers can quickly gather valuable feedback from usability test participants, teammates, and clients to iterate and continuously improve the design.
Remember, these aren’t the only prototyping tools available for UX designers, and it’s important to explore and find the right tool that fits your process. If you haven’t started prototyping yet, try out one or two tools that look promising. Most tools have a free option so you can see what works best for you.
Python, an essential programming language, has taken the programming world by storm. Much of this attention has followed from the interest in machine learning and AI. Python has become the default programming language of Data Scientists and Machine Learning Engineers all over the world. Python’s versatility has also gained a loyal following amongst diverse fields like Bioinformatics, Astronomy, Gaming, and of course, Data Science.
But utility alone doesn’t explain why so many developers love using Python. From its humble beginnings in 1991, Python was designed by Guido van Rossum to be a programming language that emphasized code readability. Or in Guido’s words, “Computer Programming for Everybody.” This ease of human interpretability pairs with an open source ethos that makes it available to developers everywhere for free! So with a few short lines of code you can import packages and libraries that professional developers from companies like Facebook, Google, or AirBnB have spent thousands of hours building _(for free)_.
It’s low-entry cost and ease of reading programs has rightfully garnered Python an immense and passionate following.
1. Where there is talent, there is an opportunity, especially, in Python programming.
Python has rapidly become a deep learning skill that is in high demand within the job market. Jobs sites like Dice and Glassdoor have seen near-exponential growth in postings looking for candidates with Python skills over the last few years because making pivot tables and wrangling data in spreadsheets is no longer enough to get you noticed for data analyst positions. As the variety, velocity, and volume of data has exploded, developers have had to scale their analysis pipelines to match — this means that the people pouring over those numbers must develop a deeper skill set to deal with the enormous amounts of data piling up in their databases.
2. Speed and flexibility are the names of the game!
Python is ideal for handling the heavy-lifting required for today’s computationally intense data analyses used by most businesses today.
OK, so now that you’re sold on its value, how long does it take to learn Python? Like any language, practice and muscle memory are the name of the programming language game. The more time you can immerse yourself, the quicker you will see gains.
It also depends on how much you intend to learn. You can have a simple “Hello World” program running in a matter of minutes, i.e., _Seriously; it is only one line of code!_, etc. To get an understanding of deep learning, a subset of machine learning, or data scientist techniques may take months of focused study, but to get your foot in the door as a Data Analyst, it takes about 40-50 hours of studying and practicing — in my experience.
Some of the rudimentary skills from loading required packages, the underlying data structures, and some simple data manipulation take some effort to put into practice. Remember that learning anything takes motivation and attention. With our focus being pulled in many directions at once, sometimes having some guided learning can be a huge help — especially with data analysis and data analysts.
How often have you had a problem you spent hours trying to solve by Googling every corner of the internet, only to have the solution explained to you in three seconds by an expert? You can have industry professionals help guide you through this exciting learning adventure to help make sure you are spending your effort in the right places rather than sift through all the YouTube videos, blogs, or StackOverflow posts.
3. General Assembly Python programming FTW!
Often you get back what you put in. So if you are thinking about getting started on your programming language journey of learning Python, General Assembly has several great ways to get you started.
There is a 10-week part-time Python course that give you all the programming language skills you need to start a new career as a Data Analyst or Python Developer for those that are ready for more structured and in-depth learning. These classes are held for two hours, twice a week, over 10 weeks.
For those who like to jump in and learn as much as possible in concentrated, full-time sessions every day, General Assembly offers a 13-week Data Science Immersive as well, which covers all the essentials of putting Python programming into good use for Machine Learning and Data Science.
4. Dive into Python programming + a Python course.
If you are on the fence about learning the programming language Python, I strongly suggest you dive in and don’t look back! I have found the transition from being a Data Analyst in a cancer research lab to becoming a Data Scientist at an InsureTech company, one of the best experiences of my life. All the nerdy things I loved, i.e., _(computers, stats, data visualization)_, all banded together in an amazing career path.
How long does it take to learn Python? The answer is up to YOU.
I hold the role of Experience Design Lead for a technology company. Every day, I talk about theories and projections of how other people will experience something, which is ultimately impossible to know. Human behavior is unpredictable and ever-changing, nowhere more rapidly than in tech. Confounding variables affect how individual users might react to a planned experience. The best user experience designers I know are great at guessing—educated guesses, based on research, which then go on to inform crucial decisions in the technology development process. An experience designer’s job involves predicting how interactions will unfold, and how users will perceive them psychologically and emotionally.
Designers do their jobs by challenging project ideas, providing counsel to stakeholders, and advocating for users’ best interests. Along with the role comes an obligation to serve the user through principled action. Bad interaction design can have consequences ranging from slightly frustrating to severely harmful. The UX design space is rapidly evolving, and designers must take a holistic view of all the various touchpoints, interactions, and environments—real and virtual—that the user navigates on their journey. Design artifacts, such as wireframes, personas, and all the other UX deliverables you commonly find listed, are just expressions of the user journey. They’re all different ways of answering the same question: “What will it be like for the user?”
With so many resources available on UX technical skills, it’s important to direct more attention toward essential human-centric concerns. Every successful UX designer needs to grasp the foundational ux design principles of empathy, clarity, feedback, and inclusivity.
If you’ve spent time with UX teams, you’ve likely broached the subject of empathy. Particularly in the field of tech with all its innovations and disruptions, project contributors are accountable for the impact of their work. Empathy simply describes the act of considering that impact on people’s feelings, situations, lives, communities, and on society as a whole. It’s about seeing things from someone else’s perspective.
The field of user experience design contains common methodologies for building empathy with users. Based on research, user personas serve to focus project contributors’ attention on realistic user traits, so they can understand whose needs to meet. Those personas play central roles in user journey maps and problem statements, ecologies, blueprints, and storyboards. Design thinking activities and workshops bring subject matter experts together with stakeholders to focus on the user journey. All of the methods primarily serve as empathy-building tools for the contributors to better understand the user. The technology community through decades of collective trial-and-error (and more error, and even more error) has largely conceded that projects tend to fail when they don’t prioritize user needs. Empathy helps to divert the team from complacently executing software requirement specifications, and to instead focus on doing the right thing from the user’s perspective.
Misunderstanding the principle of empathy can curtail a design process. Anyone who has ever scuffled with a frustrating product can attest that the creators should have spent more time talking to users. Building empathy isn’t just a box to check off in an early phase; it’s a principle that ensures meaningful impact through the development lifecycle.
User experience design problems often revolve around the clarity of information and instruction. Successful designs make information as intelligible as possible, with clear indication of how to perform the actions you need to take. Designers make sure people can access and understand the interaction as it’s happening, and remain sensitive to its effect on the user’s cognitive load. Lack of clarity could have serious repercussions, as in the case of a healthcare application being used by a patient to access their treatment.
A working knowledge of visual communication goes a long way. Design artifacts, even reports, benefit from a clear visual hierarchy. Even if the visual design of a user interface is a separate concern than the UX, in practice, UX designers have to collaborate with their counterparts in UI design to ensure that the interface communicates the right effect. To engage effectively on a cross-functional project with multiple team members, UX designers need to at least wield a practical knowledge of typography, color, and composition. Thinking in terms of these visual communication fundamentals allows contributors to establish a shared design language.
Clarity of communication can’t be underestimated. My company, like many global tech organizations, uses English as a primary language for everything from business discussion, to code documentation, to design critique. My international colleagues exhibit remarkable communication skills, especially considering English may be a second, third, or even eighth language. In today’s climate of remote work, it’s more important than ever to use video to enhance real-time communication—employing body language and facial expressions to underscore our words.
UX design is wrapped in written communication. The extent to which hiring managers weigh writing skills when evaluating UX candidates may surprise job seekers. It makes sense that client-facing discussions frequently focus on UX artifacts, and only astute writing can successfully document design ideas. For user research specialists, as well as generalists with user research among their responsibilities, writing is even more of a daily requirement. They design through the medium of research reports, interview takeaways, and executive summaries. Clear writing permeates the work, all the way down to the microcopy—the small bits of guiding UI text used in forms, prompts, buttons, and messages throughout an application.
Great user experience designers are still wrong all the time; they just use more feedback. Everything is a prototype, even early notes and doodles, that can evoke enough reaction from helpful sources such as usability test participants to inform improvements. All design fields solve problems through making things, actively creating new ideas to fill an existing void, and ample helpful feedback guides the solution in the right direction. Seasoned UX designers learn to apply this principle throughout their process, always scanning for meaningful feedback on everything they contribute.
Everywhere you look, there are products with design flaws that could have been improved through more user testing; not just apps and websites, but also physical experiences like vehicles, household items, or specialty equipment. Whenever a project is fast-tracked past user testing too hastily the consumer has to deal with the resulting deficiencies. Successful projects take a structured approach, testing prototypes methodically to identify problem areas. Product teams establish a feedback loop by observing user reactions, hypothesizing improvements based on those reactions, and rebuilding prototypes with new ideas to introduce into the testing cycle.
Accepting product design feedback and applying its learnings to a prototype may be a skill that takes time to develop; it’s easy to get emotionally attached to work we create, as if it were some precious thing to defend. When we ignore valid design criticism it’s the user who loses. Designers learn to separate themselves from their ideas, gathering feedback early and often, and become skilled in objectively discerning how to improve the work to make it even more clear and useful for others.
People often base their first understandings of users on the lowest common denominator—mapping out an ideal “happy path” experience for a generic user. That ideal rarely reflects the multifaceted reality of human life, and that generic user is too often a reflection of the designer’s own personal traits or their company’s business goals. To design excellent user experiences, we need to step outside our own biases and recognize the diversity of human experience.
Including a broad radius of users in the design process isn’t only the right thing to do, it also makes good business sense. When a service makes the effort to consider its customers with special needs, it tends to benefit a wider swath of customers. Wheelchair-accessible spaces provide a great example of this principle: the same rampways and automatic door openers which allow people in wheelchairs to navigate also make it easier for people pushing strollers, carrying armloads, and with other momentary physical restrictions.
Website design similarly recognizes the range of users navigating the virtual space. The Web Content Accessibility Guidelines (WCAG) provide a standard for designing interfaces which can be understood and used effectively by people with disabilities. Web and app designers rely on that guidance to ensure the display can be understood by users with a spectrum of visual impairments and blindness, and who may access the information using screen reader programs to synthesize speech or output to a braille display. Users with motoric impairments benefit from various assistive technologies such as a trackball mouse or voice recognition software. Across both physical and digital spaces, there are ample opportunities to design a better, more inclusive user experience that considers all possible customer scenarios.
The aim of inclusive design is to demonstrate respect for users by allowing them a dignified interaction with your service. Project teams would do better by incorporating the principle of inclusivity throughout their process. Upfront research and cooperative design with target users will help to avoid the pitfalls that lead to inaccessible products. Designers, engineers, and managers are all responsible for adhering to accessibility guidelines in the creation of useful tools, displays, and controls. Rigorous usability testing continuously refines the experience, and helps produce genuinely positive, inclusive interactions.
The practice of user experience design challenges abstract notions and raises important ethical concerns. As UX designers, we essentially design actions, and all actions have consequences. Multiply that by the masses of users who are touched by scaling technology, and our design decisions become exponentially magnified. All designers should consider that gravity whenever approaching their work, and take conscientious actions based on human-centered design principles.
Key insights from Caitlin Davey, Manager of Learning Experience Design at General Assembly
We’ve reached the last segment of our three-part blog series on managing remote teams. We hope our experts’ advice has been useful for team leaders who are transitioning to working from home and adjusting to this new normal in the world of work.
For our final installment, we sat down with General Assembly’s very own Caitlin Davey, Manager of Learning Experience Design. Caitlin has managed a remote team for 2 years and has deep experience designing remote learning experiences in data for GA’s enterprise partners around the world.
Read on to hear Caitlin’s insights on:
Leading remote meetings.
Encouraging team participation while remote.
Being supportive of your team during a remote transition.
For additional perspectives on remote team management, check out part one and part two.
GA: Thanks so much for the time today, Caitlin. In your role, you’ve participated in many conversations with our partners who are shifting to remote work. What are some of the top tips you share on leading remote meetings?
Caitlin: First and foremost, set a clear agenda with time chunks. If minutes tend not to work for you, then try to estimate time based on the percentage of the meeting you want to spend covering a given topic. Also, keep meeting times manageable and allow for stretch breaks every 30 minutes to allow participants to physically stretch and refocus their attention. As a team leader, you need to model active engagement and bring strong energy to amp up the energy of participants.
Second, if there are key decisions that need to be made or input that is required, consider sending a pre-read of materials along with your agenda so participants can come to the meeting prepared. When your meeting comes to a close, name owners of action items and send follow-ups with the highlights of the meeting, and a video recording if available. Follow-ups ensure that everyone is clear on the next steps and can review what was discussed.
GA: Staying organized seems to be key! On the flip side, what are some of the top mistakes you see people make when leading remote meetings?
Caitlin: When leading remote meetings, try to prevent the “No, you go ahead” loop as I like to call it. As a leader, you need to own facilitation and direct the conversation. This can look like nominating the next person to speak, asking for the opinion of a team member by calling on them, or determining the order of who will speak in advance. This keeps the meeting moving and increases the comfort of team members because expectations are clear. It also prevents lags where no one is responding to broad questions. Then again, get comfortable with some silence. The fidelity of remote meetings can mean that participants need time to think and respond. Don’t rush to fill the silence as participants may just need some time to formulate their thoughts before chiming in.
Pauses in conversation can feel less natural in remote meetings and people often fail to leave time for ideation or questions — it’s important to build this in. Name ways participants can contribute, whether that’s asking people to come off mute and speak, inviting comments through the chat, or using the raise hand feature if your conference platform is Zoom. If you have challenges leading the meeting while following the chat, nominate someone to raise any critical questions, and make sure that you build in time to pause and answer instead of interrupting yourself to address comments.
GA: I’ve definitely experienced those “No, you go ahead” loops before, and love the tips to address it! Switching gears a little bit, what are some norms you like to use to engage a remote team?
Caitlin: Team bonding and preventing feelings of isolation are especially important for teams that are working remotely. Plan to connect through icebreaker introductions or get remote coffee. These may sound corny, but leaning into the corniness can actually unlock a greater sense of connection and make calls feel less like a checklist. One of my favorite icebreakers is to ask participants to quickly hold up something nearby that shows their personality. For example, my pack of stamps is always handy because I love sending mail to friends and family.
Teams should also collectively decide on remote working agreements. These can go beyond sharing preferences for communication channels and even include mindsets to adopt as you work together. One example of a working agreement we hold at GA is “Be present,” which means we all agree to minimize multitasking during meetings and practice active engagement. Another example is “Take space, make space,” meaning that as we take time to talk, we also intend to make time for others to speak.
GA: Oftentimes, we hear that it’s hard to encourage participation in a remote meeting in the same way you would in a conference room. How do you encourage your team to speak up?
Caitlin: Inherently, whoever called a remote meeting feels like the owner, leader, and facilitator of that meeting. To allow individual contributors to feel ownership think about nominating leaders for various meetings and give them a chance to step into a leadership role. Breakout groups can also be a great way to divide large teams into more manageable groups to connect. Zoom has a breakout group feature, but you could also consider smaller sub-groups for projects.
Beyond structure, when you’re looking for participants to speak in a given meeting, call on participants by name to share input. You can also message participants ahead of time to preview the specific question and see if they’re comfortable sharing. Knowing your team’s working styles is key, as some people prefer to think through a question on their own rather than respond on the fly.
GA: All the insights today have been great so far. One final question for you, Caitlin: What advice can you share around supporting your team during this difficult transition?
Caitlin: It’s important to know the channels of communication that work best for your team. For example, if you’ve decided that not everything needs to be a call, think twice before scheduling a call rather than sending an email. Or, if you decided not to email after hours, don’t break your own rule.
Also, ask what your employees need. You should check in with your team more frequently than normal to make sure that they feel supported and remain engaged. I’d stress that you should be checking on their goals and if they need support rather than to monitor attendance.
A real benefit I see to remote meetings is the many ways for participants to share. Features like a meeting chat can allow more perspectives to surface than in an in-person setting. As we’re all transitioning to more virtual connections, there’s an opportunity to take time to set new norms, make employees feel supported, have fun as a team, and realize that in remote settings, we can still connect.
We’re so grateful to Caitlin for sitting down with us to discuss top tips for leading and supporting teams remotely. This post concludes our Managing Remote Teams: Advice From the Experts series — we hope you gained some helpful insights! For more perspectives from GA, follow us on LinkedIn, where we’ll always share the latest.