Since it was created in 1985, Excel has practically become synonymous with data itself, and still is many years later. Spend a few minutes with our expert instructor in the videos below to learn the kinds of Excel tools that can help you be your own analyst—and make smarter decisions with data.
How to Create an Excel Bar Chart
Bar charts are an important visual tool that can help express your data over time and tell a story in a visually appealing and digestible way. Learn more in our 2-minute lesson below:
How To Create an Excel Pivot Table
Pivot tables allow you to effectively summarize and highlight the importance of your data sets. They are an important presentation tool and can help you simplify your data. Learn more in our 3-minute lesson below:
How To Create a Histogram in Excel
Histograms provide a visual representation of variations within your data and can help display degrees of difference in an impactful way. Learn more in our 2.5-minute lesson below:
How To Create a Pie Chart in Excel
Pie charts can express percentages of a whole and represents a set period of time and can be helpful to show differences among a handful of categories. Unlike bar charts, it does not express changes over time. Learn more in our 2.5-minute lesson below:
How To Create a VLookup in Excel
A VLookup (vertical lookup) can help you lookup data that is organized vertically. It is useful in helping you spot trends and find important pieces of data that can be difficult to locate in large data sets. Learn more in our 2.5-minute lesson below:
Featuring Insights From Iun Chen & Vish Srivastava
What is Tableau
Tableau is a powerful data analysis and data visualization tool that anyone can use. It can be used by beginners to create simple charts and by advanced practitioners to solve complex business problems. It is user-friendly, easy to learn quickly, and includes a portfolio of business intelligence tools with the potential to give a wide range of roles the advantage of professionally analyzing data.
Simply put, if you can present data in a clear, compelling format, you gain a competitive advantage in today’s data-driven marketplace.
“Tableau enables you to quickly connect disparate data sources and utilize a drag-and-drop interface to analyze data and create dashboards,” says Vish Srivastava, who leads our Data Visualization & Intro to Tableau workshop. As a product leader at Evidation Health, he relies on Tableau to turn around fast data analysis. “For example, product teams use it to analyze user growth and analytics, BizOps teams use it to analyze operational data, and sales teams use it to analyze customer and revenue data.”
Businesses survive and thrive on data. The amount of data available to businesses today is impressive. To keep organizations on a successful path, analysts need to provide the key insights needed to make important decisions.
Here’s where Tableau comes in.
Tableau takes business intelligence to the next level, making it fast and efficient to analyze large amounts of data and create beautiful, presentation-ready visualizations that generate insights.
Data is the lifeblood of modern teams. Being able to quickly answer ad hoc questions and integrate data analysis into your day-to-day decision-making will make you an MVP. Though not all data analysts use Tableau, they do need some way to quickly create data visualizations.
Tableau is the data viz tool of choice.
Tableau is so popular in part because it is easy and fast to learn. In Iun Chen’s Intro to Data Analytics course, students learn the life-changing basics of Tableau in an afternoon. Aspiring analysts come to understand the power of data and the impact their numbers can have. As more data becomes available, there are more opportunities for data to be misused, a risk that every data scientist soon realizes. To quote the Nobel laureate and economist Ronald Coase, “If you torture the data long enough, it will confess.”
The ethics of data form the foundation of Chen’s syllabus so pitfalls are avoided from the start. “Overanalyzing and manipulating data too deeply can always give you the information you want,” says Chen. “Unfortunately, this is all too common in professional settings, though it’s usually unintentional.”
What features does Tableau offer?
Tableau Accelerators
Data Stories
Predictive Modeling and more
Tableau is a powerful tool.
Business insights are only as good as the data behind them, and the best data analysts understand that the human choices they make matter.
“Data is the perfect example of garbage in, garbage out,” says Srivastava, who defines good data as data that is ethically collected, complete, objective, and thoroughly analyzed. ”The double-edged sword of using powerful data analysis and visualization tools is that beautiful charts can create a false precision and obfuscate data integrity issues.”
“This book details how the use of data and data visualizations in journalism can be distorted and misleading, without the audience even realizing it, due to the urgency to present findings in a timely manner to the public.”
Featuring Insights From Iun Chen & Vish Srivastava
Read: 2 Minutes
Let’s get it straight: How difficult is it to learn Tableau for a complete beginner? Are there shortcuts to learning Tableau? Any tips, tricks, or time-saving work-arounds? Thankfully, the answer is yes. Try these top tips, approved by our expert instructors, and start data viz now.
“It’s a little overwhelming at first but as soon as you understand the basics, like what are dimensions and measures, everything falls into place pretty quickly,” says Vish Srivastava, product leader at Evidation Health and GA instructor.
“In essence, you need to understand two things: The basics on how data works — for example, what are common formats of data and what is a primary key? And a basic understanding of data visualization in a business setting. Can you answer the question: When is a time series vs. a pie chart valuable for decision making?”
“The best way to learn is to download a sample dataset and dive right in and start creating data visualizations. To keep going from there, check out various portfolios online to get inspiration, and try to build those.”
According to Iun Chen, who conducts internal Tableau training at LinkedIn, Tableau is easy to learn, but hard to master.
“The basic concepts of charting and color theory are easy to pick up and can take just a few weeks. However, if you are looking to be a subject matter expert, this can take years to perfect,” she says.
Chen preps students in her Intro to Data Analytics course to achieve close-to-mastery in these key areas.
Can they quickly prep and analyze large volumes of data?
Identify key information and determine the best visual method to present them?
Take business questions and determine which visualizations to use?
Translate raw datasets to storylines with a beginning, middle, and end?
Format charts, graphs, titles, text, and images for a polished deliverable?
Articulate best practices on design and visualization techniques?
Provide feedback on ineffective visualizations and how to improve them?
This checklist is the closest thing to a Tableau cheat sheet you’ll find. Prioritize these skills, and you’ll waste no time learning Tableau. Now that you know what you need to succeed, you can choose whether to take our Data Analytics course fast or slow. Learn Tableau — along with data analytics tools SQL and Excel — in a 1-week accelerated format, or over 10 weeks in the evening.
Chen sums it up perfectly: “As long as you are actively learning, applying your learnings, and ensuring innovation of your work, you will be a data visualization expert in no time.”
Featuring Insights From GA instructor Candace Pereira-Roberts
Read: 2 Minutes
Do you communicate data? Do you want to create more effective data visualizations? Tableau is the data analytics tool you’re looking for. Here are the top three reasons why you should learn how to use Tableau, the popular data viz software focused on business intelligence. Read on for the advantages of being a Tableau professional.
#1 Tableau Is Easy
Data can be complicated. Tableau makes it easy. Tableau is a data visualization tool that takes data and presents it in a user-friendly format of charts and graphs. And here’s the rub: There is no code writing required. You’ll easily master the end-to-end cycle of data analytics.
Need to showcase trends or surface findings? Tableau will make you an expert. Proficiency in business intelligence is a transferable skill that is quickly becoming the lifeblood of organizations.
“I see students who are new to analytics learn Tableau desktop and be able to develop Tableau worksheets, interactive dashboards, and story points in a couple of weeks — essentially a complete data analysis project,” says Candace Pereira-Roberts, FinServ data engineer and one of our Data Analytics course instructors. She adds, “I like to share knowledge and watch people grow. I learn from my students as well.”
#2 Tableau Is Tremendously Useful
Would you rather tell visual stories with data? Or present the same old boring reports and tables? Is that even a question?
“Anyone who works in data should learn tools that help tell data stories with quality visual analytics.” Full stop.
The smart data analyst, data scientist, and data engineer were quick to adopt and use Tableau tool by tool, and it has given those roles a key competitive advantage in the recent data-related hiring frenzy. But their secret is out. And the advantages go beyond the usual tech roles. Having a working knowledge of data, and specifically knowing how to use Tableau, can help many more tech professionals become more attractive to recruiters and hiring managers.
Plus, it has a built-in career boost. Tableau’s visualizations are so elegant, you’ll be confident presenting the business intelligence and actionable insights to key stakeholders. Improving your presentation skills is par for the course.
#3 Tableau Data Analysts Are in Demand
As more and more businesses discover the value of data, the demand for analysts is growing. One advantage of Tableau is that it is so visually pleasing and easy for busy executives — and even the tech-averse — to use and understand. Tableau presents complicated and sophisticated data in a simple visualization format. In other words, CEOs love it.
Think of Tableau as your secret weapon. Once you learn it, you can easily surface critical information to stakeholders in a visually compelling format. That will make you a rockstar in any organization.
“Tableau helps organizations leverage business intelligence to become more data-driven in their decision-making process.” Pereira-Roberts says. She recommends participating in Makeover Monday to take your skills to an even higher level.
Data is big, and it’s getting bigger. How do you parse and understand data when the sheer amount of information can be overwhelming? The answer is data visualization. Using concepts of design theory like elements of color and layout, the discipline of data visualization, or data viz, is essentially the graphic representation of data. We called on one of our data viz experts, Iun Chen, to break it down further.
Let’s start with an introduction and how you came to the world of data viz.
IC: I’m Iun (pronounced ‘yoon’), and I work in the data analytics space focusing on business intelligence tools and building scalable resources for LinkedIn. I also teach the 10-week Intro to Data Analytics course for GA, which includes the professional skills of SQL, Tableau, and Excel.
In college, I was a business major with a specialization in marketing and advertising. I became more interested in how the ad business model worked behind the scenes and in how software and systems worked. As a result, I worked at many major media companies in a quantitative capacity — revenue planning, ad pricing, finance, ad sales strategy. That led me into a formalized analytics route.
How do you define data visualization?
IC: Data visualization is the idea of communicating information graphically. It’s the science of information design, in which you take massive amounts of data in whatever format it comes in and use it to surface high-level insights and findings in a visually compelling way so audiences can easily understand the main points.
How does data visualization differ from data analytics?
IC: Data analytics is the process of cleaning, prepping, analyzing, and presenting data. Data visualization is part of the presenting data step and is defined as the act of visually organizing data through the use of charts, graphs, and dashboards. Concepts of data visualization are closely aligned with concepts of design theory: color, font, scale, layout, organization.
Why is data viz important?
IC: Data visualization is easy to learn but hard to master. In my classes, I heavily emphasize the design element of data visualization. It’s easy to whip together a quick bar or pie chart, but is it the best way to communicate the point you are trying to make? The goal of collecting mass amounts of data is to be able to quickly translate it into insights that can help make smart business decisions. The final form of this translation is often a chart or graph, which is why the ability to design and visualize these mass amounts of data grows as we collect more of it.
What is a data narrative?
IC: People think in stories and narratives, not in black and white figures. Just like you would share a story with a friend using a beginning, middle, and endpoint, you would do the same when sharing details about data analysis. Here’s a simple example.
Beginning: Sales are down year-over-year; identify the symptoms.
Middle: Furniture sales — our largest segment — are doing poorly in the last six months; conduct the analysis to investigate reasons and uncover root causes.
End: Review retail store reports and conduct manufacturer visits; recommend next steps.
The key point to any data narrative is that it should present a compelling business case and surface unrealized insights to the audience. The business challenges, rationale, and next steps should be clearly presented, and people in the room should be able to walk away and know what to action on.
Which tech roles use data visualization?
Data visualization — like data analytics — is a skill set that can be applied to any job. But if you are looking for a job that has data visualization skills as part of the function and responsibilities, look for roles like business analyst, data analyst, business intelligence analyst, data scientist, and data engineer. Keep in mind that the formal skill of data visualization is still relatively new, so depending on the maturity of the company, those functions may not be fully established yet. However, with the increase of data in the world, there’s a growing need for experts who understand data visualization techniques more and more.
Check out this Medium post which details how Spotify’s business has evolved with the creation of their data visualization roles.
What’s the future of data visualization?
As we continue to collect more and more data, the need for people with the skills to analyze and present data becomes ever-growing and critical in the workplace environment. More companies will need to generate insights quickly to keep up with advances and competition in their respective industries. The skill of data visualization will become more and more attractive as teams and organizations seek to translate their data into insights more efficiently and effectively. The ability to work with data is increasingly critical to the success of any company in any job function.
Featuring Insights From Iun Chen & Vish Srivastava
Read: 4 Minutes
Data analytics and business analytics are often confused, understandably, because both data analysts and business analysts work with data. What matters — and differentiates these two roles — is what the data is intended to do.
When comparing the roles of business analyst and data analyst, one must consider the audience. Who will be taking action based on the analyses?
Business analysts use data to improve business metrics.
Business analysts work directly with stakeholders to steer company objectives and keep the business on a successful path. They set and maintain key performance indicators for the organization. A business analyst may recommend strategies or business plans to executives, sometimes when a company is at a critical juncture, say quarterly or during a turnaround. Stakes can be high, but so can the rewards. (Think McKinsey analysts or other coveted consultancy jobs.) Business analysts are more likely to use presentation skills as they’ll need to present findings to executives and give recommendations in high-level meetings.
Data analysts collect, extract, and analyze data.
Data analysts are more technically focused. They are responsible for getting the data and analyzing it, working with datasets and tables. For example, a data analyst at an eCommerce company may analyze customer information, aggregate email marketing lists, or use data to identify demographics for new customer acquisition plans. Data analysts are more likely to work in teams alongside marketing partners or with other technology roles such as programmers or product managers, depending on the size of the company. They also work with business partners across entire organizations, including business analysts, as needed for tasks and projects.
Different roles mean different salaries.
Both business analysts and data analysts solve business problems. As such, they are in high demand. According to Glassdoor, the average salary for a data analyst in the U.S. is $72K. Compensation for business analysts is a bit more, averaging $79K. Of course, exact amounts depend on location and will vary from country to country. While a business analyst can command a higher salary, there is wider latitude for data analysts to carve out their niche in practically any industry. Since the function of data is increasingly integral to every enterprise, there is more flexibility for data analysts to dig into areas of the business where they can make the most difference, with more potential for creativity.
In GA’s Intro to Data Analytics course, Iun Chen teaches SQL, Tableau, and Excel, business intelligence tools she uses in her professional role as a data analyst at LinkedIn.
“My formal job function is to build data tools for internal colleagues so they can successfully grow our business,” she says. “I create dashboards, reports, and anything else to ensure revenue keeps going up and anticipated risks go down for the company. In my experience, the skill set and mindset of the individual can define the role of a data analyst in any organization, large or small. Everyone uses data in their day to day so being able to clean, prep, analyze, and report data — regardless of what your actual job title is — is critical to not only the company’s success but your personal success as well.”
Both business analysts and data analysts are storytellers.
Whether a business analyst’s more strategic and decision-making role is for you, or the technical, numbers-crunching, team-playing data analyst sounds more your speed, know that the two roles share one crucial skill: They use data to tell stories. Those stories lend insights that factor into decisions that affect the bottom line. Translating raw data into digestible and human narratives can be one of the most challenging skills for analysts to master, according to Vish Srivastava, who’s led multidisciplinary teams across tech sectors. So how does an analyst develop this multifaceted skill and set their career on the path for success?
“My recommendation is twofold,” he says. “One, always start your analysis with a hypothesis that you’re testing. You need to know right out of the gate why your analysis is going to matter. Two, after you’ve spent some time with your data, step away and write down your presentation storyline in three to five bullets. The final bullet should be your recommended next step. Of course, make sure you have the analysis and charts to back up your storyline and fill in the gaps as needed.”
When it comes to storytelling with data, the difference between a boring story and a compelling one can come down to data visualization. The tools at your disposal and your proficiency with them can make or break a presentation. Communicating the insights for business intelligence hinges on clear and impactful data viz, whether we’re talking business analytics or data analytics.
One classic example of data visualization’s power is the cholera map by John Snow, an early pioneer of disease mapping. “This is a beautiful example of how collecting data and visually presenting it can generate amazing insight,” says Srivastava. “In this case, the insight was that the sewer systems were spreading disease. This informed public policy and saved so many lives.”
The future of business intelligence will be determined by the democratization of data.
The prevalence of data and its part in tech careers is changing. To hear Srivastava tell it, future conversations on business intelligence will center less on the specificities of data analysis vs. business analysis and more on how data is creeping into even more roles.
“We’ve come a long way, but there is still far to go for data analysis skills to be deeply embedded in all functions across a company. In the future, I think we will see fewer dedicated teams for business analysis and data analysis; instead, all professionals will have these skills and utilize them daily. This democratization of data analysis will be incredibly powerful. It will create even more emphasis on making high-quality data available across every enterprise.”
Tableau and Power BI are powerful tools for business intelligence, with capabilities to take loads of big data and create elegant visualizations that convey key insights to stakeholders in easily digestible presentations. Both help organizations leverage business intelligence to become more data-driven in their decision-making process. So which tool is better? We asked a few industry experts their thoughts on the data analysis tools Tableau and Power BI. Here’s what they had to say.
Candace Pereira-Roberts, Data Engineer & GA Data Analytics Instructor
“Anyone who works in data should learn tools that help tell data stories with quality visualizations. Tableau is a wonderful tool for the technical and nontechnical to build these visualizations. I love how we teach the Tableau unit in the Data Analytics bootcamp. I see students who are new to analytics learn Tableau desktop and be able to develop Tableau worksheets, dashboards, and story points in a couple of weeks to do a complete analysis project.”
Iun Chen, GA Instructor & Data Analyst at LinkedIn
“In my professional capacity, I lead data visualization workshops to share best practices on charting and design theory, with a focus on Tableau. But with the growth of big data analytics, there are more players in the data viz space. Looker. Qlik, Domo, and Microstrategy are a few with out-of-the-box solutions. Check out other marketplace BI and analytics leaders and their reviews at Gartner.
Alternatively, if you are up for the challenge you can start from scratch and build out completely customized solutions through coding packages, such as with Python plotting libraries Matplotlib, Pandas, and Seaborn.”
Matt Brems, GA Instructor & Data Consultant at BetaVector
“Most data analyst roles will expect some experience with data visualization. They may prefer your visualization experience be tied to a certain tool like Tableau or Power BI or simply want you to have experience designing graphics or dashboards. As with any platform, the human element is key. A good data analyst is curious and detail-oriented. Diving into the data and spotting anomalies or identifying patterns requires curiosity. Looking at large datasets for long periods of time can invite mistakes, so being detail-oriented ensures you’re interpreting the data correctly.”
Vish Srivastava, GA Instructor & Product Leader at Evidation Health
“Most teams I’ve seen are not comparing Tableau and Power BI. Instead, it’s more about whether to adopt a business intelligence tool at all, or whether to use Tableau or Power BI in place of Excel. Tableau is a great option when you need to quickly create data visualizations.Tableau is incredibly powerful because it’s designed for nontechnical users, meaning business users can set up and tweak dashboards and charts without the support of engineering or data science teams.”
When it comes to research, the most common data analytics tool is SQL — no surprise there. But once you get into more niche industries, that can vary, says Brems.
“In academia, R is probably the most prevalent data analysis tool, though Python is quickly gaining popularity. SAS and Stata are often used in specific industries, though their popularity is diminishing. (R and Python are open source tools, which means, among other things, that they are free.)”
We live in a world of data — swimming in statistics, numbers, information — and the amount of data seems to be growing faster than we can keep up. More people are using data points to make decisions large and small. From which restaurant has the highest Yelp rating to which city has the lowest rates of COVID-19, using data to navigate everyday life is now the norm. Indeed, the pandemic has only increased our reliance on data. We have come to expect this tsunami of data to explain, and in some cases solve, many of the most vexing problems faced by society today. But finding key insights takes careful analysis of a staggering amount of data. No small feat.
It’s true that more data is released than ever before. In the U.S., there are currently over 290,000 datasets on data.gov alone. Clearly, there’s a growing need for data analysts and the data analytics tools that help us understand these numbers. From small businesses to the highest levels of governments, decisions turn on interpretations of data. Big data can have big consequences.
So how do data analysts find the insights lurking in a database? And what are the best tools to analyze all those numbers? Read on to discover the best data analytics tools in the market.
Data scientist and GA instructor since 2016, Matt Brems currently runs a data science consultancy called BetaVector. We asked him to share his go-to data analysis tools. “People who want to analyze data use many different tools; I like to break these down into three different types,” he says.
Let’s get to it.
Type #1: Tabular Data Tools
Data analysts need to get data out of databases and analyze that information. And to do that, they use tabular data tools. According to Brems, the most important ones to know are Microsoft Excel, Google Sheets, and SQL, or Structured Query Language. Generally considered the best data analysis tool for research, SQL is the most common qualification found in job descriptions for a data analyst.
“Most data that data analysts analyze comes in the form of a table, called tabular data. This just means that data is organized into rows and columns, like a spreadsheet. Most data analysts will use a spreadsheet tool like Microsoft Excel or Google Sheets. When working with significant amounts of data (large tables, many tables, or both), organizations will often use a database. In order to interact with most databases, SQL is by far the language of choice.”
Type #2: Programming Language Tools
Proficiency in a few programming tools, while not a prerequisite for basic data analysis, can give analysts the ability to perform a wide variety of tasks. While the needed programming language tools will vary from company to company and even from job to job, having this skill set as a data analyst is clearly an advantage for job seekers.
“Python and R are the most common programming language tools in data analysis, though Stata and SAS are also used in some industries. These tools can be used to perform automation, statistical modeling, forecasting, and visualization.”
Type #3: Data Visualization Tools
Since data analysts are frequently tasked with presenting results to stakeholders, a good data visualization tool is essential. Brems recommends Tableau and Microsoft PowerBI.
“While you can visualize data using programming languages, Tableau and PowerBI are two standalone tools that are used almost exclusively for the purposes of building static data visualizations and dashboards.”
A Note on Research
When it comes to research, the most common data analytics tool is SQL — no surprise there. But once you get into more niche industries, that can vary, says Brems.
“In academia, R is probably the most prevalent data analysis tool, though Python is quickly gaining popularity. SAS and Stata are often used in specific industries, though their popularity is diminishing. (R and Python are open source tools, which means, among other things, that they are free.)”
After investing years of expensive education in a career, it can be disheartening (and terrifying) to learn that it’s not what you wanted. But it doesn’t mean that your dream career isn’t out there — or even just around the corner. That was the case for Stephanie Johnson, who made the switch from healthcare to healthcare technology through General Assembly’s User Experience Design Immersive (UXDI) program. Learn how she knew it was the right time to finally pursue a new, fulfilling career.
What were you doing before you came to GA? What prompted you to make a change?
I was working with individual patients as a diabetes educator and dietitian at a Denver community clinic. The way the pandemic changed the work dynamics there really fueled the fire for my change; it was really the last straw. I knew back in 2017 that I wanted to make a change; I felt like even though I was helping people, I was missing some key things I wanted in my career, including challenge, creativity, collaboration, and potential for growth. It’s a tough pill to swallow when you’ve worked so hard to get where you are, but I felt like there was nowhere to go. Fast forward a couple years later, I discovered UX. Now, the work I do is focused on improving workflows for clinicians so that their quality of life and impact on patients trickles down. It felt like an incredibly long journey to find it, but it was very well worth it from where I sit now.
What was it about UX design specifically that intrigued you to explore it as a career? What was the defining moment that pushed you forward?
I volunteered for a program in Denver called 10.10.10. It was essentially a 10-day sprint for 10 serial entrepreneurs, focused on finding a new business venture within the context of 10 wicked problems that exist in the healthcare space. I went through those 10 days absolutely loving the exploratory exercises, the interviews, and — most of all — the excitement and energy of those around me. Little did I know that those 10 days of activities were all design thinking (AKA UX)! I knew that if something brought me to life this much, I needed to research and explore it further as a potential career. It was after having my daughter and returning to work when the pandemic hit, and my work situation was not what I signed up for. I just knew in my gut and my heart that I couldn’t be stagnant any longer. I thankfully have a wonderful, supportive partner who encouraged me all throughout the career transition, and was all for me enrolling in the GA bootcamp.
What motivated you to choose GA over other programs?
I chose GA for many reasons. From my prior engineering bootcamp experience, I learned that the self-study program wasn’t for me. I needed a more focused, guided, and live class to fully immerse myself in UX. GA is one of few schools (if any) that offer both live and remote classes. I also loved that GA is worldwide and, therefore, has a large network of alumni that have successfully transitioned careers — many who are happy to help another GA alum. Additionally, I had been exposed to GA through the local meetups I attended in the past and felt the events and topics were really valuable. All in all, it felt like the best fit for me and what I needed.
What was it like taking a remote course and being a mom during a pandemic?
Like I mentioned, my husband was so supportive throughout. When I had tons of work to do after class or had a project deadline, he would take over parenting duties so that I could have the time to take care of my work. We were also fortunate that our daycare remained open (mostly) throughout the pandemic.
What surprised you most about learning in a remote format? Was it what you expected?
I was nervous about it, but my instructors made it so engaging. Their passion came through in every lecture and every one-on-one chat I had with them. I was surprised with the diverse backgrounds they each brought to the table too: a Michelin star chef, a cheerleading coach, former politician — all in all, I would say that it exceeded my expectations. I really feel like I was prepared well.
What was the best thing about UXDI for you and the GA experience overall?
The best thing about UXDI and GA overall was that I was successful in landing a great role with a company I’m proud to work for. It’s the most rewarding feeling to accomplish your lofty goal. Going through the GA bootcamp really gave me the confidence, knowledge, and language I needed to get through all those interviews and even in my day-to-day work now.
What advice do you have for people transitioning into a career in UX?
It’s certainly not easy, but it is completely worth it if you know in your gut that this is where you want to be. Persist and — as my instructor Chris would say — “bet on your work ethic.”
How did the skills you learned at GA help you in your current position?
Collaboration and the ability to give and receive feedback are extremely important. It’s very much part of the culture of my company, but it’s also essential to do well in UX. I really got to experience this during the group projects. When you and your team have the same shared goal, you know you’re in it to make something great together.
How has the pandemic influenced how you view your work?
The pandemic has made me see that my work is important. People are stressed out because of their work, and my job is to alleviate that. If we all don’t try to take care of our clinicians, we won’t have anyone to take care of the rest of us!
Looking back 10 years ago, did you think you would be switching to a career in tech?
Funny thing is that 10 years ago, I was fairly new in my healthcare career. It felt like the only place I wanted to be and where I felt I would stay, but I was a lot younger back then. I didn’t really evaluate what I wanted in my career beyond just wanting to help people and make a decent enough living to be comfortable.
Since graduating, how has GA made an impact in your life?
It’s been part of the journey to landing my dream role, so it’s made a huge impact. The advice and feedback I received from my instructors while at GA are invaluable, much of which I take with me to this day. GA has helped me solidify that I belong in UX because I want to be here. It’s truly opened my eyes to how important design is in so many aspects of life, technology, and everything in between.
In respect to UX, what do you want your legacy to be? Is there a change you want to inspire or a mission that defines the work that’s important to you?
What has resonated with me in UX is the topic of accessibility. We need to think about who we are including and who we might be excluding when we’re designing. Technology is for everyone. It was intended to improve our lives and make things easier so that we can spend more time doing things we enjoy. Every single person — regardless of disability, age, etc. — should be able to reap the benefits.
The Second Step: Grow Business Impact With New Ways of Working
With consumers’ accelerated adoption of digital behaviors, the inevitable digital transformation of most businesses within every industry is here. Under increased economic pressure, business leaders across the board are trying to get ahead of the transformation imperative that digitization requires.
Through our deep experience across many types of organizations, we’ve seen leaders’ transformation challenges boil down to four key goals:
Create digital mindsets across the company. This includes understanding digital trends, growing digital mastery, and building a product-driven organization.
Upgrade capabilities to reflect cutting-edge technical skills across marketing, technology, and data functions.
Accelerate technical hiring by upskilling and reskilling current employees and new hires.
Understand what good looks like — a skill necessary in achieving every goal.
This series, GA Jobs To Be Done, unpacks each of these four goals, providing actionable recommendations that organizations can put into practice to help set their businesses on the path to sustainable digitization and success.
Last week, we explored the importance of mindset resetting to embrace digital philosophies, understand digital trends, and gain the literacy to discuss them across the organization. Now you’re ready to upgrade your business capabilities to reach its full potential.
So, let’s get down to your business.
Once transformation initiatives are underway, leaders want to make more business impact through new ways of working, but a stunning 53% of organizations can’t identify what skills they need.1 Regardless of industry, we’ve found that the majority of companies have three transformation goals in common:
Harness data as a strategic asset by enabling employees to adopt data capabilities and mindsets that help them become fluent with data.
Transition from legacy technology systems by reskilling employees into high-impact technology roles on teams that properly use new tools and technologies.
Market for today’s digital customer through evolved marketing skills and practices that speak to the behaviors and expectations of the digital-first customer.
Let’s go through the below steps required to reach each of the above three goals.
Adopt a data mindset to grow your business capabilities.
Data is power. That’s why 97% of executives are investing in big data and AI initiatives.2 As you progress into becoming a digital organization, properly-leveraged data will make you more efficient, more focused on planning, and more effective against your priorities.
The first step? Invest in data literacy across your organization to help employees understand how to use and drive results with data. In a digital world, fluency is the key to improving with time. Every team — regardless of an advanced analytics skill set — will need basic literacy to be part of the data-driven culture you are building. As you scale your data capability, your employees will have the opportunity to work with an incredible volume of data to help make better and faster decisions across functions.
Next, incorporate advancedskills to solve increasingly complex data problems. Once you build systems to collect, refine, organize, and analyze your data, those employees who work closest to the data will need advanced skills. Companies often invest in upskilling employees with data modeling and visualization, machine learning, and Python programming to enable them to be higher-leveraged with data.
Finally, leaders set the data vision. To effectively manage data- or AI-driven teams, leaders must lay the groundwork for a successful data transformation by mapping the ideal flow of data throughout the organization and prioritizing data investment opportunities to make that flow a reality.
Working with data can take a long time, but like many digital technologies, it’s about increasing your rate of learning and improving as you go. Setting clear expectations of where you are and where you’re going is critical to growing your team in the right ways and modernizing your company so that your talent will want to stay and grow.
Reskill employees into high-impact technology roles.
As you modernize your tech stack and build digital fluency, you’ll want to scale your engineering team to maintain your new and improved business operations. Note: Good engineering talent is hard to come by, but luckily you have options beyond hiring-in talent, which we’ll dive deep into in our next post!
The second thing you’ll need to do is build broad technical fluency across your organization. Your engineers will be powerhouses of systems thinking and advanced skills. Still, their work will not be fully effective unless your entire organization understands the benefit of new technology and how it factors into their ways of working and ultimate company goals.
From there, you’ll be able to update the skills of existing engineers with modern engineering practices. Tech is a field that is constantly evolving. Today, engineers must understand modern frameworks and methods to support cloud migration and other enterprise technology projects. However, in this fast-moving industry, keeping up with innovation means making learning a key priority of your technology team. Offering ongoing upskilling helps you invest in the culture of learning, so your employees are able to operate at today’s level and continue to evolve with the industry, learn, grow, and become valuable assets to your company.
Create modern marketing for the modern consumer.
With consumer behaviors continuously changing alongside technology, your business faces both unprecedented interaction access and higher-than-ever customer expectations. (Last year, global e-commerce grew by more than 27%, accelerating digital sales to a level not expected until 2022). Many companies struggle with this transition because marketing skills tend to be highly siloed, as marketing was not previously considered a digital role. Today, pushing transformation forward means evolving your marketing skills and practices for digital fluency across the team.
Start by growing customer insight functions to build a foundation for marketing strategy based on scalable market research, producing tailored personas and detailed customer journey insights to inform your strategies. Use this consumer-centric design and user research to help you up-level creative development. Modern marketing’s fast pace and segmented audiences make it more important to ensure alignment within your teams and agencies. This means enabling multiple people to create at once with tools like writing briefs, branding guidelines, and content strategies to ensure a steady drumbeat of quality, on-brand output that furthers business goals.
With this content in progress, focus on building out your channels and execution functions. Social media, search engine management and optimization (SEM, SEO), earned and owned media, and e-commerce all require specific skill sets– including the measurement and analytics to know you’re hitting your KPIs. Engineering’s data-driven culture has migrated into digital marketing, which now has processes for testing and optimization at its core.
Finally, get comfortable using marketing technology, like customer relationship management (CRM), marketing automation, and adtech. These tools help you make the most of digital channels, enabling you to track the details of a high volume of interactions, build personalized messaging, and target the right audiences in the right channels.
Always strive for improvement.
Once you’ve established a digital mindset, there are a wealth of skills you can invest in to make your business’s digital transformation effective. With broad functional literacy across teams, you can build skills from the ground up, creating data scientists, engineers, and marketers with modern skills, coordination across teams, and a culture of learning that helps your organization grow and lead.
An ever-evolving, skilled digital culture is key to building teams with the best talent — stay tuned for more content from this series.
If you’re ready to invest in your talent, we can help today. Explore our catalog here to see the digital literacy and upskilling courses that we provide — from IC to strategic leader, across digital fluency, marketing, data, and technology.
Want to get specific about how we could help your organization? Get in touch.
