Course 6‐3 - Forestreee/Data-Analytics GitHub Wiki
Google Data Analytics Professional
[Share Data Through the Art of Visualization]
WEEK3 - Crafting data stories
Connecting your objective with your data through insights is essential to good data storytelling. In this part of the course, you’ll learn about data-driven stories and their attributes. You’ll also gain an understanding of how to use Tableau to create dashboards and dashboard filters.
Learning Objectives
- Explain data-driven stories, including reference to their importance and their attributes
- Demonstrate an understanding of how to use Tableau to create dashboards and dashboard filters
- Explain how data stories can be used in different forms of on-the-job communication
Use data to develop stories
Crafting stories with Data
Storytelling is the oldest form of teaching. Humans have been sharing knowledge through stories for tens of thousands of years, back when TVs and computer screens were the walls in a cave.
In fact, scientists have confirmed that cave paintings were created by early humans who used their art to convey the stories of their imaginations. Today, storytelling is still the most natural form of education. That's because stories make learning easier by helping us process and remember information. Everyone tells stories, even if we're just sharing how our day went with a friend. Many experts believe that human brains automatically organize events with a beginning, a middle, and an end. Thinking about things in this way, like stories, helps us make sense of the past, the present, and the future. On top of that, stories also help us relate to other people and create important human connections. It's no wonder that people are so captivated by stories.
Over the course of history, many important inventions have changed how stories are told. For example, the invention of the printing press led to newspapers, magazines, and books. The invention of the motion picture camera made movies possible, and soon we had TV, videos, on-demand, and streaming services that let us enjoy all kinds of stories anywhere, anytime. The invention of data visualization tools changed the way people tell stories once again.
As you've learned, data visualization is the representation and presentation of data to help with understanding.
Coming up, you'll discover how to use data visualization to transform data into a meaningful story that people connect to and care about. You'll also start working with dashboards and dashboard filters. A dashboard is a tool that organizes information, typically from multiple data sets, into one central location for tracking, analysis, and simple visualization through charts, graphs, and maps. And just like filters and spreadsheets and queries.
A dashboard filter is a tool for showing only the data that meets a specific criteria while hiding the rest.
Soon you'll know how to use these tools to tell stories that motivate and persuade people to take action based on the data you present. Finally, you will understand the key attributes of data-driven stories and effective ways to communicate them and all sorts of business situations. Ready to become an expert storyteller? Then let's turn to the next chapter of your data analytics story.
Bringing ideas to life
Stephen Few, an innovator, author, a teacher, and data visualization expert, once said," Numbers have an important story to tell. They rely on you to give them a clear and convincing voice."
Facts and figures are very important in the business world, but they rarely make a lasting impression. To create strong communications that make people think and convince them to take action, you need data storytelling.
Data storytelling is communicating the meaning of a data set with visuals and a narrative that are customized for each particular audience. A narrative is another word for a story.
In this video, you'll learn about data storytelling steps. These are: engage your audience, create compelling visuals, and tell the story in an interesting way.
Here's an example from the music streaming industry. Some companies send their customers a year in review email. It highlights the songs the users have listened to most and sometimes congratulate them for being a top fan of a particular artist. This is a much more exciting way to share data than just a printout of the customer's activity. It also reminds the listener about how much time they spend enjoying the service, a great way to build customer loyalty.
Here's another example, some ride-sharing companies are using data storytelling to show their customers how many miles they've traveled and how that equals spending less money on gas, reducing carbon emissions and saving time they might otherwise have spent fighting traffic. It makes it really easy for the rider to clearly see the value of the service in the simple and fun visual.
Data stories like these keep the customer engaged and make them feel like their choices matter because the companies are taking the time to create something just for them, and importantly, the stories are interesting. Knowing how to reach people in this way is an essential part of data storytelling. Images can draw us in at a subconscious level. This is the concept of engaging people through data visualizations.
So far you've been learning about the importance of focusing on your audience. Coming up, you'll keep building on that knowledge, you'll discover that there are three data storytelling steps,
and the first is knowing how to engage your audience. Engagement is capturing and holding someone's interest and attention. When your audience is engaged, you're much more likely to connect with them and convince them to see the same story you see. Every data story should start with audience engagement, all successful storytellers consider who's listening first.
For instance, when a kindergarten teacher is choosing books for their class. they'll pick ones that are appropriate for five-year-olds. If they were to choose high school level novels, the complex subject matter would probably confuse the kids and they'd get bored and tune out.
The second step is to create compelling visuals. In other words, you want to show the story of your data, not just tell it. Visuals should take your audience on a journey of how the data changed over time or highlight the meaning behind the numbers.
Here's an example, let's say a cosmetic company keeps track of stores that buy its product and how much they buy. You could communicate the data to others in a spreadsheet like this, or you could create a colorful visual such as this pie chart, which makes it easy to see which stores are most and least profitable as business partners. That's a much clearer and more visually interesting approach.
Now, the third and final step is to tell the story in an interesting narrative. A narrative has a beginning, a middle, and an end. It should connect the data you've collected to the project objective and clearly explain important insights from your analysis. To do this, it's important that your data storytelling is organized and concise.
Soon you'll learn how to do that using slides for discussion during a meeting and a formal presentation. We'll discuss how the content, visuals and tone of your message changes depending on the way you're communicating it. And speaking of business communications, one of the many ways that companies use visualization to tell data stories, is with word clouds.
Word clouds are a pretty simple visualization of data. These words are presented in different sizes based on how often they appear in your data set. It's a great way to get someone's attention and to unlock stories from big blocks of text where each word alone could never be seen.
Word clouds can be used in all sorts of ways. On social media, they can show you which topics show up in posts most often, or you can use them in blogs to highlight the ideas that interest readers the most. This word cloud was created using text from the syllabus of this course. It tells a pretty engaging story where data analytics, analysis, SQL and spreadsheets are, unsurprisingly, some of the lead characters.
Let's continue turning the pages of your data analytics story. There's lots of action and adventure to come.
Effective data stories (Reading)
Speaking to your audience
Welcome back. When you want to communicate something to others, a great story can help you reach people's hearts and minds and make them more open to what you have to say. In other words, stories make people care. As you learned before, the first of the three data storytelling steps teach us that for a story to be successful, you need to focus on who's listening.
Data analysts do this by making sure that they're engaging their audience. That's what we'll explore together now.
First, you need to know your audience. Think back to the example of telling someone a joke they've heard many times before and expecting them to laugh at the punchline. Not likely. To get the response you're seeking, you've got to understand your audience's point of view. That means thinking about how your data project might affect them. It helps to ask yourself a few questions. What role does this audience play? What is their stake in the project? What do they hope to get from the data insights I deliver?
Let's say you're analyzing readership data from customers to help a magazine publisher decide if they should switch from quarterly to monthly issues. If your stakeholder audience includes people from the printing company, they're going to care because the change means they have to order paper and ink more frequently. They also might need to assign more staff members to the project. Or if your stakeholders include the magazine authors and editors, you'll want to keep in mind that your recommendations might change the way they work. For instance, they might need to write and edit stories at a faster pace than they're used to.
Once you've considered the answers to those questions, it's time to choose your primary message. Every single part of your story flows from this one key point, so it's got to be clear and direct.
With that in mind, let's think about the key message for the data project about our pretend magazine. Maybe the readership data from customers shows that print magazine subscriptions have been going down recently. You discover in survey data that this is mainly because readers feel the information is outdated, so this finding suggests that readers would probably appreciate a publication cycle that gets the information into their hands more often. But that's not all. Your reader survey data also shows that readers prefer shorter articles with quick takeaways. The data is generating a lot of possible decision points. The volume and variety of information in front of you may feel challenging. To get the key message, you'll need to take a few steps back and pinpoint only the most useful pieces. Not every piece of data is relevant to the questions you're trying to answer. A big part of being a data analyst is knowing how to eliminate the less important details. One way to do this is with something called spotlighting.
Spotlighting is scanning through the data to quickly identify the most important insights. There are many ways to spotlight, but lots of data analysts like to use sticky notes on a whiteboard, kind of like how archaeologists make sense of the artifacts they discover in a dig.
To do this, you write each insight from your analysis on a piece of paper, spread them out, and display them on a whiteboard. Then you examine it. It's important not to get bogged down in every tiny detail. Instead, look for broad universal ideas and messages. Try to find ideas or concepts that keep popping up again and again or numbers and words that are repeated often. Maybe you're finding things that look like they're connecting or forming patterns. Highlight these items or group them together on your whiteboard.
Next, explore your discoveries. Find the meaning behind the numbers. The idea is to identify which insights are most likely to help solve your business problem or give you the answers you've been seeking. This is how spotlighting can lead you to your key message. Remember to keep your key message clear and concise, as an overly-long message like this one shown on screen has less chance of conveying the most important conclusion. Here's a clear, concise message that's likely to engage your audience because it's short and to the point.
Of course, no matter how much time and effort you put into studying your audience, you can't predict exactly how they'll react to your recommendations. But if you follow the steps we're discussing, you'll be much more likely to have good results.
In an upcoming video, you'll learn how to deal with situations that don't go quite according to plan. That's okay. It happens to all of us.
Question: A data analyst uses spotlighting to pinpoint the most relevant data derived from their analysis and eliminate the less important details. Spotlighting involves scanning the data to quickly identify the most important insights.
Understanding data storytelling (Ungraded Plugin)
Carolyn: Data journalism
My name is Carolyn, and I'm a measurement lead at Google. And that means that I measure a client's advertising investment and figure out ways that it can perform better for them in the future. Over the course of my career, I've worked in four very different fields, but the thing that links all of them together is my ability to understand data to get the information that I need out of it and to convey it in a simple and compelling way.
A really early example of this is from a man named John Snow. He was a doctor in London in the 1850s, and he was living during a cholera outbreak, and the theory at the time was cholera was spread through the air or "ill humors." People didn't really know what was causing it, but he thought that it was caused by drinking really contaminated and gross water from the Thames. So he went out and interviewed people who were sick and asked them where they got their water, and he found out through mapping the data that they got it all at the same pump. So he went to the authorities and asked them to dismantle the pump, and they took the handle off and the outbreak ended. This started a really robust field of epidemiology, but it also is a great example of data journalism.
Most recently before Google, my job was as a data journalist, and that was to use data to tell stories. I worked at the Chicago Tribune for three years. I worked a lot around the election season and Olympics—big, graphic moments where there's a lot of interesting data and a need to understand it. The field of data journalism has changed a lot over time, but not as much as you might think. We have a lot more access to data and data that we've never really tracked before, but it's still the job of the data journalists to really understand what that data means. As we have more and more data. It's not really helpful to a reader to just say, "Here's a link to a database."
You still have to uncover the meaning beneath it and really understand what's the insight within the data. The tools that a journalist would use to understand something—which is consulting an expert, really diving into a story—are very similar to the tools I need at Google to really understand a media investment and make a really clear recommendation of what someone should do in the future.
My advice is to understand the tools that are available to you and know how they work, but never to let those tools overwhelm your story. So I never want to look at a piece and know this was created using Data Studio or using Microsoft Excel. I want to know what the data says and what a data journalist's point of view is behind that story. Don't let the way you create something influence what it's actually saying. People just want to know what you've distilled and what new information you've found in all the hard work you've done.
I love the field of data journalism because it's just how my brain works. I'm always the type of person who will doodle in the margins or make a quick chart to really understand the data beneath it, and that forces me to find a point of view and to share that with other people. I love that through data, I can communicate with a large audience and really help them understand the world around them.
Learning Log: Tell a story with data (Reading)
Practice Quiz: data-driven stories
Use Tableau dashboards
Tableau dashboard basics
Have you ever been driving a car when one of the warning lights on the dashboard suddenly comes on? Maybe the gas gauge starts blinking because you're getting low on fuel. It's handy when you have that alert right in front of you, clearly showing you that you need to pay attention to your gas level.
Can you imagine if cars didn't have dashboards? We'd never know if we were about to run out of gas. We'd have no idea if our tire pressure was low or if it was time for an oil change. Without dashboards, if our cars started acting differently, we'd have to pull out the user manual, sift through all that information inside, and try to figure out the problem ourselves. Car dashboards make it easy for drivers to understand and respond to any issues with their vehicles because they're constantly tracking and analyzing the car status. But as you've been learning, dashboards aren't just for cars. Companies also use them to share information, get people engaged with business plans and goals, and uncover potential problems. Just like a car's dashboard, data analytics dashboards take tons of information and bring it to life in a clear, visually-interesting way.
This is extremely important when telling a story with data, which is why it's a big part of number two in our three data storytelling steps.
You've learned that a dashboard is a tool that organizes information from multiple data sets into one central location for tracking, analysis, and simple visualization through tables, charts, and graphs.
Dashboards do this by constantly monitoring live incoming data. As we've been discussing, you can make dashboards that are specifically designed to speak to your stakeholders. You can think about who will be looking at the data and what they need from it and how often they'll use it. Then you can make a dashboard with the perfect information just for them. This is helpful because people can get confused and distracted when they're presented with too much data.
A dashboard keeps things neat and tidy and easy to understand. When designing a dashboard, it's best to start simple with just the most important data points, and if later on you discover something's missing, you can always go back and tweak your dashboard or create a new one. An important part of dashboard design is the placement or layout of your charts, graphs, and other visuals. These elements need to be cohesive, which means they're balanced and make good use of the space on the dashboard.
After you decide what information should be on your dashboard, you might need to resize and reorganize it so it works better for your users.
One option in Tableau is choosing between a vertical or horizontal layout. A vertical layout adjusts the height. A horizontal layout resizes the width of the views and objects it contains. Also, as you can see here, evenly distributing the items within your layout helps create a clear and organized data visual.
You can select either tiled or floating layouts.
Tiled items are part of a single-layer grid that automatically resizes based on the overall dashboard size. Floating items can be layered over other objects. In this example, the map and scatterplots are tiled—they don't overlap. This really helps make clear what the data is all about, which is valuable because the majority of people in the world are visual learners—they process information based on what they see. That's why sharing your dashboards with stakeholders is such a valuable practice.
Now there's something important to keep in mind about that. Sharing dashboards with others likely means that you'll lose control of the narrative; in other words, you won't be there to tell the story of your data and share your key messages. Dashboards put storytelling power in the hands of the viewer. That means they'll craft their own narrative and draw their own conclusions, but don't let that scare you away from being collaborative and open.
Just understand the risks that come with sharing your dashboards. After all, sharing information and resources means that you'll have more people working on the solution to a big problem or coming up with that next big idea. This leads to more connections, which can result in really exciting new practices and innovations.
Quesion:
Live and static insights (Reading)
From filters to charts
So far, we've focused a lot on understanding our audience. Whether you're trying to engage people with data storytelling or creating dashboards designed for a certain person or group, understanding your audience is key. As you've learned, you can make dashboards that are tailored to meet different stakeholder requirements. To do this, it's important to think about who will be looking at the data and what they need from it.
In this video, we'll continue exploring how to create compelling visuals to tell an interesting and persuasive data story. One great tool for doing this is a filter. You've learned about filters and spreadsheets and queries,
but as a refresher, filtering means showing only the data that meets a specific criteria while hiding the rest.
Filtering works the same way with dashboards—you can apply different filters for different users based on their needs.
Tableau lets you limit the data you see based on the criteria you specify. Maybe you want to filter data and the data set to show only the last six months, or maybe you want to see information from one particular customer. You can even limit the number of rows or columns in a view. To explore these options, let's return to our world happiness example.
Say your stakeholders were interested in only a few of the topics that affect overall happiness. Filtering for just gross domestic product, family, generosity, freedom, trust, and health, and then creating individual scatterplots for each would make this possible.
You can also use filters to highlight or hide individual data points. For instance, if you have a scatterplot with outliers, you may want to explore what your plot would look like without them. However, note that this is just an example to show you how filters work; it's not okay to drop a data point just because it's an outlier. Outliers could be important observations, sometimes even the most interesting ones, so be sure to put on your data detective hat and investigate that outlier before deciding to remove it from your dashboard.
Here's how to do it. To filter data points from the view, we can choose a single data point or click and drag in the view to select several points.
Let's choose just one. Then on the tool tip that appears, we'll select "exclude" to hide it or we could have chosen to do it the other way by keeping only selected data points.
Here's another example. If your data is in a table, you can filter entire rows or columns from your view. To do this, we'll select the rows we want in the view. Then, on the tool tip that appears, we'll choose to keep only those countries. Again, we could have also selected the data points we wanted to exclude and picked that option instead.
Or if you like, we can even prefilter a Tableau dashboard. This means that your stakeholders don't have to filter the data themselves. Basically, by doing the filtering for them, you can save them time and effort and direct them to the important data you want them to focus on.
Personally, I think the best thing about filters is they let you zero in on what's important. Sometimes I'm working with a huge data set, and I want to concentrate only on a specific area, so I'll add a filter to limit the data displayed on my dashboard. This cuts the clutter and gives me a simple, clear visual.
I use filters a lot when working with data about advertising campaign performance. Filters help me isolate specific tactics, such a search or YouTube ads, to see which ones are working best and which ones could be improved.
Question:
By limiting and customizing the information I'm looking at, it's much easier for me to see the story behind the numbers. And as I'm sure you've noticed, I love a good data story. As a data analyst, you'll often be relying on spreadsheets to create quick visualizations of your data to tell your story. Let's practice building a chart in a spreadsheet. To follow along, use the spreadsheet link in the previous reading, also included in the video.
If you would like to follow along with the video, click the link below Customer Service Survey Responses
We'll be using Google Sheets, so this might look a little different in other spreadsheet platforms, like Excel. We'll begin by filtering just the data on how many customers purchase basic plus or premium software packages.
To start, select the column for the software package and insert a chart. The spreadsheet suggests what it thinks is the best type of chart for our data, but we can choose any type of chart you'd like.
Spreadsheet charts also let you assign different styles, access titles, a legend, and many other options. Feel free to explore the different functionality later on. We'll also cover this more in a reading. There's lots of different options to choose from.
To continue following along with the video, click the Country of Response tab (the second tab) in your copy of the spreadsheet.
Let's say we also have data on which countries our customers are from and their overall satisfaction score for the software they purchased.
First, highlight columns A and B, then click on "insert" and then "chart" again under "chart type." You want to select the first map option.
Voila! Now we have a map that summarizes a customer survey scores by country. We can also customize this chart by clicking "customize" in the top right corner.
Let's say we wanted to change our colors from red and green to a gradient so it's more accessible. We can do that by clicking "geo" and then change the min color to the lightest shade of blue, the mid color to the middle shade of blue, and the max color to the darkest shade of blue to show the spectrum of scores from low to high. Now we have a map chart that shows where respondents are most satisfied with their software in dark blue and least satisfied with their software in light blue. And this will be easier for anyone in our audience with color vision deficiencies to understand.
Tableau and spreadsheets are common tools for creating data visualizations. By using their built-in functionalities like filters and charts, you can zero in on what information is most important and create compelling visuals for your audience. And now that we've explored some ways to create visuals, it's time to start preparing our data narrative. Coming up, we're going to talk more about telling stories with data and organizing presentations. I'll see you soon.
Hands-On Activity: Creating, filtering, and customizing charts (Reading)
Identify when to set up a dashboard (Discussion Prompt)
Creating your first Tableau dashboard (Reading)
Hands-On Activity: Build a dashboard in Tableau (Practice Quiz)
Practice Quiz: using dashboards
Sharing data stories
Compelling presentation tips
Businesses everywhere know the power of using data to solve problems and achieve goals. But all the data in the world won't get you anywhere if your stakeholders can't understand it or if they can't stay focused on what you're telling them. So you want to create presentations that are logically organized, interesting, and communicate your key messages clearly. An effective presentation supports your narrative by making it more interesting than words alone.
It starts with how you want to organize your data insights. The narrative you share with your stakeholders needs characters, a setting, a plot, a big reveal, and an "aha moment," just like any other story.
The characters are the people affected by your story. This could be your stakeholders, customers, clients, and others. When adding information about your characters to your story, you have a great opportunity to include a personal account and bring more human context to the facts that the data has revealed—think about why they care.
Next up is a setting, which describes what's going on, how often it's happening, what tasks are involved, and other background information about the data project that describes the current situation.
The plot, sometimes called the conflict, is what creates tension in the current situation. This could be a challenge from a competitor, an inefficient process that needs to be fixed, or a new opportunity that the company just can't pass up. This complication of the current situation should reveal the problem your analysis is solving and compel the characters to act.
The big reveal, or resolution, is how the data has shown that you can solve the problem the characters are facing by becoming more competitive, improving a process, inventing a new system, or whatever the ultimate goal of your data project may be.
Finally, your "aha moment" is when you share your recommendations and explain why you think they'll help your company be successful.
When I'm working on a presentation, this is where I like to start, too. Using these basic elements to outline your presentation could be a great place to start, and they can help you organize your findings into a clear story. And once you decided on these five key parts of your story, it's time to think about how to pair your narrative with interesting visuals because as you're learning, an interesting and persuasive data story needs interesting and persuasive visuals. Coming up, you'll learn even more about how to be an expert data storyteller.
Question:
Sharing a narrative
Welcome back.
Now that you know how to prepare the key parts of your data story—the character, setting, plot, big reveal, and the aha moment—it's time to think about the visuals and how your slideshow should look. It's always good to remember that your presentation reflects on you.
If it's messy, disorganized, or full of images that don't support your story, your audience could easily lose confidence in your results and recommendations.
On the other hand, if your slideshow looks professional and appealing, you've got a better chance to capture your audience's attention and keep them focused on your main points.
Themes are a great tool for this. They control the color, font types and sizes, formatting, and positioning of text and visuals. Some themes are fun or creative, while others have a more professional look. By choosing a theme that matches the tone and information you're communicating, your presentation will have a consistent look and support the argument you are trying to make.
Next comes the title. It's good to include a title and subtitle that describe what you're about to present. You should include the date of your presentation too, especially if you're including data that's likely to change over time. Specifying a date, such as a "date created" or "date last updated," gives anyone viewing your presentation important context.
A good slideshow guides the audience through your main communication points, but it doesn't repeat every word you say or give a lot of written information. Part of your job is to choose what information to include. This might be a description of what's being shown in a visual, the first step in a process, a set of directions, or an important message that you want to be sure your audience understands and remembers. Also, be sure to adjust the font size so your audience can easily read what you've written. A good rule is to keep texts to less than five lines and 25 words per slide. Basically, you want your audience focused on what you're saying, not busy reading the slides. Also, choose your words carefully. It's always smart to avoid slang terms, abbreviations that people might not know, and words or phrases that are specific to one particular region.
Now let's discuss visuals. Visuals help the audience quickly understand the content of each slide. They can help you make a point in a way that words might not be able to alone. Great visuals don't leave room for interpretation because the meaning is instantly understood. When you include visuals on a slide, try not to share too many details all at once. Choose just the data points that support your points, especially your key message. I like to ask myself, "What's the single most important thing I want my audience to learn from my analysis?" That helps me decide which visuals will be most likely to get the point across.
If you have several important things you need to include, don't cram them all on one slide; instead, create a new visual for each point. Then add an arrow, a call-out, or another clearly-labeled element to direct your audience's attention toward what you want them to look at.
Finally, when you get to your big reveal and aha moment, your visuals must communicate these messages with clarity and excitement. These are the most powerful discoveries from your analysis—make it feel that way.
Before you go, there's one last thing I'd like to share. It's a quick tip for knowing when to copy and paste, link, or embed a visual into a slideshow. This can be challenging for new data analysts, but there are some simple points to keep in mind. When you copy and paste a visual into your presentation, you can edit it directly within your slideshow. If your visual or its data points exist in other places, such as a Tableau dashboard, any changes you make will not affect them there.
Now, this also means your visuals won't be updated if the original dataset changes. This means your visual might not be reflecting the latest information, but if you link your visual within your presentation, the visual lives within its original file, and the slideshow connects to it with the visual's URL. Because the two files are now linked, when you make changes to the original file, say a spreadsheet, the changes will automatically appear in your presentation. This can be useful if the data is likely to change over time. Your slideshow will always be up-to-date.
Finally, an embedded object also lives in the original source file, but the difference is that it doesn't get automatically updated if the source file changes. The embedded copy is completely independent. Similarly, you can make changes to it in your presentation without affecting the visual or data points from the original source file.
The main difference between pasted, linked, and embedded objects has to do with where you store them and how you update them after you place them in your slideshow. Now that you're beginning to understand how to make great slideshows, take a few minutes to practice what you've learned.
Create a new slideshow and select an appropriate theme. Add your text, visuals, and an exciting reveal at the end. Try pasting, linking, and embedding visuals from different sources to see how they behave differently. You can design a presentation about any dataset that interests you. It doesn't need to be long or have a ton of information. Just take the first steps and have fun telling your own data story.
Question:
Hands-On Activity: Practice presenting (Practice Quiz)
Presentation Narrative (Ungraded Plugin)
Sundas: How to manage imposter syndrome
Hi, my name is Sundas, and I'm analytical lead at Google, my role is turning data into powerful stories that influences business decisions. I have an untraditional background where I have a six year gap between my high school and my college career. So for me when I was trying to start all over again, so I started at a community college, that was my first exposure to online learning, and it was perfect because, I was managing kids at home.
So at Google, we talk a lot about imposter syndrome, and I personally relate to it quite a bit, being the first female in my family to graduate university and also being an immigrant, a lot of times I'm surrounded by people who do not look like me. For example, there was one time where I was presenting to a senior leaders in my org, and I was so nervous presenting to them, I was like I'm going to totally blow this up, and they're going to figure out that I'm just a totally a fraud and a fake. One of the things that I changed is that, even though I was the only female in my team, I started networking, I started expanding my network. And I met a lot of women who are from the country where I am from and they were also immigrant, they also struggled with English and they also looked like me and they were doing very well in their career, they were being successful. So when I looked at them, I was like, okay, if they can do it, then so can I, so that for me was a very a big confidence boost to kind of like get over that imposter syndrome feeling.
But I struggle from it day to day, I'm struggling with it right now, standing in front of you, do I even deserve to be talking about my journey and my skills? So it's completely normal, there are few things that I like to do, one is that I like to give myself a pep talk, pep talk definitely works, just saying you're totally worth it, you you deserve it, it does wonders for me personally. The second thing I like to do is, I like to keep a log off my success and failures. So when I am at a down point, when I'm feeling down or feeling I do not belong here, I look at all the things that I have achieved from that log, and that kind of helps me. That's a good reminder of the hard work that I put into kind of get here, so I did not get here because of luck, I got here because I worked hard and I earned it.
My family is definitely really proud of me, after seeing me go to school and graduate, the two kids, my younger brother, he actually went to school with two kids as well, and he graduated. He finished his master's program and my sister-in-law, who also had two kids and she was managing, and after seeing me that I could do it, they had somebody to look up to. And so my sister-in-law went back to school and she finished her degree as well. So I think just being the first in my family was really hard, because I don't have anybody to look up to. But now I am that person that people in my family can look up to specifically girls and they can pursue whatever they put their minds to.