The full list of tips and tricks can be found in my LinkedIn Learning course called “Nano Tips for Data Storytelling and Visualization with Lachezar Arabadzhiev“.

1. Don’t Reuse the Same Charts Across Multiple Platforms

Although it’s tempting to reuse graphics across multiple platforms, do not fall prey to the temptation. While it may save you time and effort in the short term, it can lose crucial engagement in the long run. There is too much digital chaos today, and reposting the same chart across multiple platforms makes your message less effective.

Different platforms have different requirements for image size, resolution, and format. A chart that looks good on Instagram will not have the same appeal on Facebook. More importantly, your page will have a different audience demographic. Your TikTok and YouTube Shorts viewers may have some crossover, but the overall demographics will vary by platform. These users will have different levels of familiarity with data and visualizations, so a chart on one platform will not have the same resonance with another audience. Plus, users following you on multiple platforms will find the repetition boring and not engaging.

Audiences are more intelligent than ever. Do not repost without thinking. You must produce adaptive visualizations that speak to each audience and each specific platform. A great example of this is the World Economic Forum. Their Instagram account never publishes posts with complicated charts or schematics, even when engaging with data-heavy subjects. Instead, they introduce a catchy statement with a video sequence that hooks viewers to explore further on their main site. They keep intensive statistics and more complex explanations that elaborate on the key hook of the video on their website. Instead of blindly reposting, use this strategy to analyze your audience based on the platform and consider what will capture their attention most effectively.

2. Make Your Graphics Interactive

In the past, presentations, magazines, and books have all relied on static charts. Static charts are graphics where the data presentation remains the same, and viewers cannot interact with it. By definition, static charts are less able to engage with an audience, and most people’s eyes glaze over when they see one. However, with modern advancements in data visualization technology, you can now easily make your graphics interactive.

Using a tool like Flourish, you can bring your charts to life. With just a few simple steps, you can open a ready-made template. One of the most popular templates is the “bar chart race” because it presents several categories of data and their changes over time. With Flourish, you can open this template or a template like it and configure the data and interactivity level to your specific needs. You can make hundreds of minor customizations and optimizations to display your data story more effectively and prompt your audience to engage meaningfully.

While using interactive charts doesn’t guarantee your message will be communicated perfectly, they are often the best solution to present a strong data narrative. These interactive charts are perfect for situations with massive amounts of data, complex data relationships, and intricate interdependencies.

3. Get Familiar with Chart Groups and Know When to Use What Graph

There are many chart types and techniques to depict data, and understanding which chart to use for a specific data set is crucial. It can completely change your message and its effectiveness. Here’s a quick overview of the core chart groups.

Line Charts

A line chart displays data as a series of points connected by straight lines. It is perfect for showing trends or changes in data over time. The horizontal axis represents time or a sequence of values, while the vertical axis represents the magnitude of the data being plotted.

Bar Charts

A bar chart represents data with rectangular bars. The bar’s length or height corresponds to the data plotted value. People typically use bar charts to compare discrete categories of data, and the bars can be horizontal or vertical. The vertical axis represents the magnitude of the data, while the horizontal axis represents the categories being compared.

Pie Charts

The pie chart is one of the most recognizable chart types. The pie chart shows how the whole of a data set is divided into different sections. It’s typically used to display percentages or proportions of data that add up to 100%. The size of each segment is proportional to the value it represents, and the segments are typically labeled to indicate the category of data they represent.

Scatter Plots

A scatter plot displays the relationship between two variables. It is made up of individual points, with each point representing a single observation in the data set. The position of each point is the value of the two variables, and the pattern of the points indicates the strength and direction of the relationship between variables. It’s helpful for identifying patterns and trends in the data.

Sankey Charts

One chart you might not have heard of before is the Sankey chart. A Sankey plot is a kind of flow diagram that illustrates the flow of a particular quantity through a system. The plot consists of nodes (boxes) and edges (lines) that connect them, with each line’s width representing the flow’s magnitude. It is commonly used to visualize the flow of energy, resources, or money through a complex system. It can identify inefficiencies, track the movement of variables, and display the interdependencies between different processes or activities.

4. Take Advantage of AI to Simulate Data Presentation

When you’re in the first stages of data visualization development, it’s crucial to brainstorm different ideas to discover the format that tells your data story in the best possible way. In the past, this brainstorming would happen by searching for similar examples on the Internet, listening to other people’s thought processes, and coding different simulations to see what looked best. However, coding these simulations could be a significant barrier to workers, who found the Python and R libraries overwhelming.

Luckily, an exciting option has entered the space. With the help of iterative AI tools like ChatGPT, you can take advantage of open AI technology to code different simulations for you. Code can be as descriptive as you want, but it’s important to note that too many details can confuse the software. ChatGPT is most helpful in creating a coding foundation or template for more complicated data sets, but for easier relationships, it can create simulations with ease. This option can save you a lot of time and help you explore options you may not have thought of by yourself.

5. Presentation is Everything

Compelling data storytelling doesn’t end when you have your charts. Presentation is everything, and planning how to introduce your graphics best is crucial. Great presentation techniques will enhance your message even further, and poor presentation traps can leave audiences glazing over your diagrams. Here are three tips to present your graphics successfully.

Build a Narrative with Your Data Story

Adding a few slides with your charts will not connect with your audience. It won’t tell them what the charts are for or why they are essential. Instead, focus on deconstructing a single chart into a few different components. Explain those components clearly and individually to create a better narrative.

Always Use Visual Aids

Charts are naturally a visual medium, but many people don’t take full advantage of this. Make sure to use visual cues and highlight points on your chart that are the most important. These visual cues can be as simple as coloring the minimum and maximum values in different shades or using a gradient shade for changing data. If you use transitions, practice timing your verbal explanations with the visual highlight to make the point stick with your audience for longer.

Time Your Explanations

A common mistake in remote and in-person presentations alike is to start explaining a chart before it’s even shown. While you should lead into your chart before showing it, you should never begin explaining the graphic before presenting it to the audience.

Hope you enjoyed our data storytelling tips! If you have any questions, feel free to do one of the following:

Explore my LinkedIn Learning courses on storytelling through data and design!

Find out why Data and Visual Storytelling a the Skillsets of the Future.

Subscribe to receive the by-weekly “My Next Story Is…” newsletter. Yep, it is my brand new newsletter!

The post 5 Tips for Data Storytelling and Visualization appeared first on Data Studio.

Here are 5 useful ways to visualize data, along with ready-to-use templated examples available through SkildLab’s Canva profile. Enjoy!

1. Line charts are great for showing trends over time.

A line chart is a graphical representation of data that uses a series of data points connected by straight lines to display trends over time. It is used to display changes in data over a period of time or to compare different sets of data.

Line charts are useful for showing trends in data over a period of time, such as changes in a company’s stock price or changes in temperature over a week. They are also useful for comparing two or more sets of data, such as the sales of different products or the performance of different investment portfolios.

2. Bar charts are used to compare different categories or groups.

A bar chart is a graphical representation of data that uses bars of different lengths to display the values of different categories. It is used to compare the size or frequency of different categories of data.

Bar charts are useful for comparing the size or frequency of different categories of data. There are several types of bar charts, including horizontal bar charts, vertical bar charts, stacked bar charts, and grouped bar charts.

3. Pie charts are used to show how a whole is divided into different parts.

A pie chart is a graphical representation of data that uses a circle divided into sectors to display the proportions of different categories. It is used to show the proportions of a whole made up of different parts.

Pie charts are useful for showing the proportions of a whole made up of different parts. There are several types of pie charts, including regular pie charts, doughnut charts, and exploded pie charts.

4. Scatterplots are used to show the relationship between two variables.

A scatter plot is a graphical representation of data that uses a series of dots to display the values of two variables for a set of data. It is used to display the relationship between two variables and to see if there is a correlation between them.

Scatter plots are useful for showing the relationship between two variables and for identifying any patterns or trends in the data. They are often used in science, statistics, and other fields to visualize and analyze data. There are several types of scatter plots, including simple scatter plots, bubble plots, and 3D scatter plots.

5. Heatmaps are used to show patterns and trends in data.

A heatmap is a graphical representation of data that uses a color-coded matrix to display the values of two or more variables. It is used to visualize and analyze data by showing the relationships between different variables and identifying patterns or trends in the data.

Heatmaps are useful for visualizing and analyzing data with many variables or categories. There are several types of heatmaps, including 2D heatmaps, 3D heatmaps, and hierarchical heatmaps.

Hope you enjoyed our short guide! If you have any questions, feel free to do one of the following:

Explore my LinkedIn Learning courses on storytelling through data and design!

Find out why Data and Visual Storytelling a the Skillsets of the Future.

Subscribe to receive the by-weekly “My Next Story Is…” newsletter. Yep, it is my brand new newsletter!

The post Top 5 Useful Chart Types to Visualize Data in 2023 appeared first on Data Studio.

As such, we must learn how to harness that power to tell stories that touch the audience’s heart, mind, and soul.

In other words, my first live-action course on LinkedIn Learning is now officially published!

It took me a while to create this one. In fact, this might have been the longest I have taken to create 1-hour of content. That being said, I think it is totally worth it, as it has given me time to refine the exercises, create useful templates and share some cool anecdotes.

Here is a little preview:

Storytelling stands the test of time from Storytelling for Data and Design by Lachezar Arabadzhiev

The Curriculum

Introduction

• Storytelling stands the test of time
• Why are stories important?

Telling Visionary Stories

• Being a genuine storyteller and person
• Consistency and practice make almost perfect
• Building and guiding a narrative

Audience Analysis and Exploration

• Understanding your audience
• Layered exploration in three degrees
• Creating meaningful connections
• Optimizing for retention

Building Your Narrative with a Storyboard

• What is a storyboard?
• Show, don’t tell
• Navigating the elements of a storyboard
• Aligning text and visuals
• Building a visually expressive narrative
• Presenting your storyboard

Storytelling + Storyboard = Action

• Vlog or YouTube video
• Business or agency pitch
• Data visualization and reporting

Conclusion

• Next steps

If you have any questions, feel free to do one of the following:

 Explore my LinkedIn Learning courses on storytelling through data and design!

 Follow me on LinkedIn, and click the at the top of my profile page to stay up to date with my latest content!

 Subscribe to receive the by-weekly “My Next Story Is…” newsletter. Yep, it is my brand new newsletter!

The post Storytelling for Data and Design appeared first on Data Studio.

Hey there! I’m Lachezar Arabadzhiev, Founder and CEO of SkildLabs and a Udemy Instructor Partner. Last month, I shared how my online teaching journey began. Today, let me tell you the rest of the story and introduce you to data and visual storytelling!

On the Udemy platform and earlier in my career in digital marketing and advertising, I was always known as the go-to “data and analytics guy.” While that title plays to my strengths, it only speaks to a portion of my passion, visual storytelling.

You see, data is essential, but data by itself is meaningless. It’s what we do with data and how we choose to present it to our audience that matters most. As a visual storyteller, I am passionate about combining data and design in compelling ways that drive impact.

Why is Data Storytelling so Important?

In the age of big data, we are constantly bombarded with information from all sides. The challenge lies in not only making sense of this data but also using it to tell a story that inspires and resonates with our audience. In this era of data-driven decision-making, the ability to sift through data and tell an informative and visually appealing story has become a critical skill. 

Having created my business around the power of visual storytelling, I’m on a mission to empower others to tell their data-driven stories and make an impact in their respective fields. Whether they’re in marketing, analytics, sales, or design, anyone can become a data-driven decision-maker and a better visual communicator.

Understanding The Power of Visual Storytelling in the Workplace

There was a time when data was reserved for data scientists and analysts, but it has since transformed into a tool that anyone in any field can use to their advantage. For example, data storytelling can be a powerful business tool for marketers, which I discuss in great detail throughout my Udemy course: Google BigQuery for Marketers and Agencies.

Regardless of your role or industry, data storytelling is a powerful tool that can be used to:

• Improve data-driven decision-making.
• Increase the effectiveness of your collaboration.
• Facilitate better communication among team members and departments.
• Make data more accessible and understandable.
• Improve confidence when presenting data.

As we learn to become better data storytellers, the first thing we need to understand is that data and design are not mutually exclusive. How you choose to present your data will have a profound impact on how your audience perceives it.

Data and Design: The Unapparent Pair

Data visualization can take on many forms, from bar charts and line graphs to heat maps and infographics. The most important thing to remember is that these should be used to complement your data-driven story, not replace it.

In my experience, data visualization and storytelling are often underestimated and seen as a “nice to have” rather than a necessity. This couldn’t be further from the truth. The design element of visual storytelling isn’t about making things look pretty; it’s about choosing the right visuals to effectively communicate your data-driven story.

In any occupation, you may have all the necessary data in front of you and still lack the ability to visualize the “big picture.” Visual storytelling allows you to use your data to tell a story that helps you identify trends, actionable insights, or concerns.

When presenting data to others, too much information can confuse them, causing them to lose sight of the main takeaways or even make poor decisions. It’s important to keep them engaged by only providing them with the most relevant information. Visual storytelling can help you do just that by allowing you to focus on the most critical data points in a way that is easy to understand and visually appealing.

Data storytelling is a powerful tool that can help you:

• Make data more digestible and easier for others to understand.
• Tell a story that engages your audience and captures their attention.
• Highlight key takeaways and action items.

The key to visual storytelling is understanding how to use data and compelling visuals to create a cohesive narrative. For an in-depth guide on how to build powerful data visualizations, check out my Udemy course on the subject: Dynamic Dashboards and Data Analysis with Data Studio.

Garbage In, Garbage Out: Filtering Your Data Through Visual Storytelling

The data you choose to include in your story is just as important as how you present it. If you try to collect and analyze every piece of data available, you’ll likely have more questions than answers.

Before you can even begin to work on your visual storytelling, you need to understand what data is most important to your story. Therefore, sourcing and organizing your data and interviewing the appropriate subjects are all crucial in order to produce a consistent and accurate narrative.

Using built-in automation tools and robust dashboards can make the data analysis and organization process much easier and allow you to focus only on the data that is most important to your story. You can learn to do this with ease through my Udemy course: Introduction to Supermetrics for Data Automation.

The Road to Becoming a Visual Storyteller

The future of work is data-driven, and the ability to tell a story with data will be an essential skill in the years ahead. If you’re not already comfortable with data visualization and data storytelling, now is the time to step out of your comfort zone and learn these skills.

As a visual storyteller, you’ll have the unique ability to see data in a way that others may not. You can use your data and design skills to create stories that engage and inform your audience.

If you’re interested in learning more about data visualization and data storytelling, check out my Udemy courses. I also invite you to visit my personal data and design blog, where I share tips and tricks on how to effectively analyze your data and use it to tell your own data-driven stories.

As a former “numbers guy” turned visual storyteller, I wish you the best of luck on your visual storytelling journey!

The post Data and Visual Storytelling: The Skillset of The Future appeared first on Data Studio.

This course goes a bit deeper into an area that our other courses also touched on, namely visual collaboration. A topic that is super dear to my heart. As we all can agree, remote work and virtual interactions are here to stay, but being able to truly express your ideas and engage with team members globally can be difficult to master.

As such, I wanted to create a course that tackles some of those challenges. The course is taught through the medium of Whimsical, a visual workspace for thinking and collaboration, that allows you to build flowcharts, wireframes, mind maps and so much more.

Learning Whimsical

As an executive, manager, stakeholder, or UX designer, you need robust tools for remote collaboration. Do you want to discover what Whimsical can do?

In this course, expert Lachezar Arabadzhiev demonstrates how to use Whimsical for remote collaboration and brainstorming. Lachezar shows you how to navigate the UI, create a board, and use the available controls. He walks you through flowcharts, wireframes, and projects (formerly sticky notes). He also explains mind maps and docs.

Lachezar goes over ways you can use Whimsical to manage a workspace, collaborate with others, and share your boards and visual creations. Plus, he describes how you can use voting and commenting to engage your audience.

The Curriculum for Learning Whimsical

Introduction

• Want to discover what Whimsical can do?
• Navigating the Whimsical UI
• Exploring the controls of a board

Board Modes and Commands

• Flowcharts
• Wireframes
• Projects (formerly, Sticky Notes)
• Mind maps
• Docs

Workspace Management and Collaboration

• Managing a workspace
• Collaborating with others: Access levels and version control
• Exporting and embedding boards
• Using voting and commenting

Conclusion

• Next steps

If you have any questions, feel free to reach out on LinkedIn or subscribe to the datastudio newsletter for more updates. In the meantime, happy learning!

The post Whimsical: A Journey Into Visual Collaboration appeared first on Data Studio.

Moreover, even though the right answer or interpretation was obvious to you, your audience missed the point completely. How could that happen? Well, this is where data and design clash!

Over the past few months, I worked with the folks from LinkedIn Learning to refresh my first course on the platform to reflect Canva‘s changes in UX and UI. This course provides the glue between data and design. In other words, a course that teaches you how to become a better visual storyteller, no matter what industry you come from.

Here it is!

Canva for Brainstorming and Collaboration

What is Canva and Canva Pro? from Canva for Brainstorming and Collaboration by Lachezar Arabadzhiev

If your work involves UX design, brainstorming, collaboration, or presenting your ideas in a succinct manner (almost all jobs today), then I would highly recommend you to take a look at this course. Personally, I started using Canva in 2014 and since then, I’ve seen the tool evolve from a tiny graphic design platform to a powerhouse for masterful visual communication. Here is the course outline, if you want to dig a bit deeper.

The Curriculum

• Introduction
  • What is Canva and Canva Pro?
  • Explore the Canva UI
• Business Design Essentials
  • Cropping, flipping, and basic positioning
  • Structure and align design elements
  • Use the Background Removal tool
  • Use color palettes and symbolism
  • The power of SVG graphics
  • Lock and switch between layers
  • Configure dynamic charts and lines
  • Use frame and grids
  • Resize designs in different formats
  • Working with interactive tables
  • Using Magic Edit and Eraser
• Presentations
  • Creating and sharing a presentation
  • Understand typography
  • Animation and effects in presentations
  • Edit video in Canva with timelines and transitions
  • Using Canva Docs and the Magic Write AI tool
• Collaboration
  • Design together with your team and clients
  • Engage your audience with icebreakers
  • Run ideation or brainstorming sessions
  • Create interactive polls and quizzes
  • Utilizing freehand drawing for collaboration
  • Elevate interactive collaboration with whiteboards
• Conclusion
  • Next steps

If you have any questions, feel free to do one of the following:

Explore my LinkedIn Learning courses on storytelling through data and design!

 Follow me on LinkedIn, and click the at the top of my profile page to stay up to date with my latest content!

View our Canva template, data visualization charts and Canva website tutorial.

The post Data and Design with Canva: A Visual Storytelling Journey appeared first on Data Studio.

Also, if you want to refresh your Data Studio design skills, you can take a look at my other articles on some nifty features, including:

• How to extract a report theme from an image in Google Data Studio?
• How to configure a gauge chart to track performance against a goal?
• Learn how to use conditional formatting in Google Data Studio
• How to filter metric values with a slider control in Google Data Studio?

Now, let’s get started!

Add pages to a report

You can add pages to a report to organize related charts, avoid information overload and improve the report performance. The steps are pretty simple.

1. Open any Google Data Studio report.
2. Click on Page>New Page on the menu.

Alternative ways to add pages

If your report has a single page, you can click on Add page directly from the toolbar.

If your report already has multiple pages

1. Click on Page “X” of “X/Y” on the toolbar menu. This will open the Manage Pages pane.
2. Now click the “+” sign on the bottom to add a page.

Use a Keyboard Shortcut

Last but not least, the easiest and most convenient way to add a new page is by pressing “CTRL+M” on your keyboard. This will add a new page regardless of how many pages your report already has.

Manage pages pane

The Manage Pages pane not only helps you to add new pages but also to structure your report. For example, you can add expandable dividers, sections, and headers. You can rename a page, duplicate, hide in view mode, change icons or delete it using this pane. Another important feature within the Manage Pages pane is the divider. You can put this to separate your pages and sections to make your structure look neat.

The maximum level of content you can add to a hierarchy is three. This is a major change from what you could do in the earlier versions of Google Data Studio. This is an example of the structure:

• Page (Level 1)
• Section (Level 1)
  • Page (Level 2)
  • Section (Level 2)
    • Page (Level 3)

You can also add icons to a section or page. They will appear when you view the report, similar to the example presented below.

How to add icons

1. Click on the three vertical dots that appear when you hover the cursor over the pages.
2. Click on Select Icon from the pop-up up menu.

3. Choose your favorite icon and click apply.

Note that Icons can only be added to top-level content. Any page within a section cannot have an icon.

How to navigate between pages?

You can navigate between pages in 3 ways:

1. Using the manage pane. You can jump to a specific section, page, and header. The pane appears to the right in the manage pages pane. 
2. Using the keyboard: You can use right, left, up, and down arrows to move to the next or previous page. Additionally, you can use the home and end keys to navigate to the last or first page.
3. Using the search box: You can use the search box located to find a section, header, or page by name. However, note that this search box does not search for text inside the report.

Make changes to a navigation item, section, or page

You also have the option to modify each item in the navigation pane.

1. Open any report.
2. Launch the Manage Pages pane by clicking Page “X” of “X/Y”.
3. Hover the cursor over your preferred item.
4. Click on the three vertical dots.
5. Choose an action you want to perform from the following:

Rename

Every new page is named an “untitled page” by default. Additionally, a new Section or header is named “untitled section” and Header,” respectively. Click rename and choose a better name.

Duplicate

You can create a copy of the current page instantaneously by selecting “Duplicate.” However, it’s not possible to duplicate the whole section.

You can copy a page from one report to another together with all its components. This will automatically add all the data sources in the current report to the target report. However, it will not apply to previous theme and style settings.

Delete

Remove a page, section, or header from the report by selecting “Delete.”

You can undo this action by pressing ctrl+z  or Edit>undo. This will not work if you have signed out and back in data studio or reloaded the page. To mitigate this, you can restore the report to a previous point using version history.

Note that once you delete a section, it will delete all the pages within it as well.

Hide in view mode

You can hide a section or a page to prevent viewers from seeing it. This is a useful feature, especially if you have information that you wish only the editors to see. Once you hide a section or page:

• The editors can still edit that section or page.
• Viewers can’t navigate to that section or page.
• If you copy the section or page, the copy will also remain hidden.
• The section or page will not appear in PDF downloads.

You can unhide a section or a page by clicking: the three vertical dots > Show in view mode.

Move

Select the item you want to move and click move to the top, bottom, up, or down.

Re-order sections, pages, or navigation items

You can drag items to change their order in the manage pages pane. This is a quick alternative to the move option.

1. Open any report.
2. Launch the manage pages pane by clicking page Page “X” of “X/Y”.
3. Hover the cursor over your preferred item on the left side,.. and a move option will appear.

4. Drag the cursor to move the item up or down.

Change the navigation type

You can customize the navigation type for your report viewers.

1. Open your report.
2. Click Theme and Layout in the toolbar.

3. Choose layout tab
4. Select the navigation type under view mode. You can see visuals of each option below.

Left

The pages will show up as a collapsible drawer at the left.

Tab

The pages will appear at the top as tabbed links.

Top Left

The pages will show up as a drop-down menu on the upper left.

Hidden

Navigation will not be displayed.

Final Thoughts on the New Page and Report Navigation

All in all, this new update aims to help users improve report performance, UX and make Google Data Studio a more flexible product. In my opinion, the new page and report navigation feature is a huge advancement, which puts the control in the report builders’ hands.

As such, they can now manage multipage reports by adding text headers, grouping pages into sections, and adding personality and style with Icons. The update is automatic and rolled out globally, so you don’t have to take any actions to enable it.

Dynamic Dashboards and Data Analysis with Google Data Studio – 2021

If you want to learn how to build powerful data visualizations and unlock insights that can help you drive business results for your clients or employers, take a look at my full course on Udemy.

The post Advanced Page and Report Navigation in Google Data Studio – 2021 appeared first on Data Studio.

• A center bar showing the actual value of the metric you are graphing.
• An optional vertical line showing a target value.
• An optional comparison value.
• Optional colored bands that represent threshold ranges, such as poor, average, and good

You can customize gauge charts in Data Studio to display minimum, maximum, target, comparison, and up to five ranges, as well as to change chart colors and apply data filters. For example, the below image consists of four shaded bands representing ranges. A comparison with the previous 28-day period. A center bar showing the revenue amount. A target of $200,000.

As such, you can use gauge charts to monitor various “health” or performance KPIs that are vital for your business. Moreover, you can do it in a visually appealing way that resonates with your audience.

My latest Skillshare class covers Gauge Charts and you can access it here for FREE (1-month free trial included).

Gauge Chart Tutorial in Google Data Studio

This is a little preview of the class. If you want me to cover a specific feature in Google Data Studio, feel free to comment, I will be happy to write a content piece on it.

I hope this was helpful! Of course, if you have any questions, feel free to DM me on LinkedIn. Also, you can take a look at my latest Data Studio articles covering data blending and parameters.

The post How to configure a gauge chart to track performance against a goal? appeared first on Data Studio.

Also, if you want to follow the steps closely, I am using a public dataset called “[Sample] Google Analytics Data”. The dataset is available in all Data Studio accounts.

I hope this was a helpful trick to filter a metric in Google Data Studio! If you have any questions, feel free to DM me on LinkedIn or subscribe to my newsletter.

Dynamic Dashboards and Data Analysis with Google Data Studio

If you want to learn more about building complex dashboards, take a look at my full course on Udemy. The course has a dedicated section on advanced design and ways to make your dashboards more intuitive.

The post How to filter metric values with a slider control in Google Data Studio? appeared first on Data Studio.

I hope this was a helpful design trick in! If you have any questions, feel free to DM me on LinkedIn or subscribe to my newsletter.

Dynamic Dashboards and Data Analysis with Google Data Studio

Learn how to build powerful data visualizations and unlock insights with my course on Udemy. If you are wondering, these are the things you will gain knowledge about. Take a look.

By the end of the course, you will be able to:

• Use  and understand all chart types throughout the course (Bullets, Pie Charts, Scorecards, Scatter plot and etc.).
• Connect and blend data sources from Google Analytics and Google Sheets.
• Create custom dimensions with conditional expressions (CASE + REGEXP_MATCH + IN).
• Build a user journey funnel with Google Analytics data based on a “Page Title” dimension.
• Explore user-level data with scatter plot (eCommerece) and create a device breakdown visualization.
• Build a time-series chart with rolling dates and interaction filters.
• Automate reports with advanced date selection filters.
• Understand the access levels in Data Studio “Owner” vs “Viewer”.
• Apply conditional formatting rules to signal data anomalies.
• Apply IF statements within a CASE and use operators.
• Extract a report theme and colour scheme from an image in Data Studio.

The post How to extract a report theme from an image in Google Data Studio? appeared first on Data Studio.