Example — Guidelines for donut chart

Example — Guidelines for donut chart

Example — Guidelines for donut chart

Example — Guidelines for donut chart

Example — Guidelines for donut chart

CONTEXT

CONTEXT

CONTEXT

CONTEXT

CONTEXT

The Analytics product in Cisco's Webex Control Hub gives administrators access to interactive data visualizations which display both historical and live data about products and services that are purchased.

The goal of the Analytics product is to ensure that customers have relevant and actionable insights on how the offerings and services they purchased from Cisco are being received within their company. These insights help Cisco’s customers figure out how successful the onboarding, adoption and usage trends are among their own users, and also how to troubleshoot any of these products and services.

The Analytics product in Cisco's Webex Control Hub gives administrators access to interactive data visualizations which display both historical and live data about products and services that are purchased.

The goal of the Analytics product is to ensure that customers have relevant and actionable insights on how the offerings and services they purchased from Cisco are being received within their company. These insights help Cisco’s customers figure out how successful the onboarding, adoption and usage trends are among their own users, and also how to troubleshoot any of these products and services.

The Analytics product in Cisco's Webex Control Hub gives administrators access to interactive data visualizations which display both historical and live data about products and services that are purchased.

The goal of the Analytics product is to ensure that customers have relevant and actionable insights on how the offerings and services they purchased from Cisco are being received within their company. These insights help Cisco’s customers figure out how successful the onboarding, adoption and usage trends are among their own users, and also how to troubleshoot any of these products and services.

The Analytics product in Cisco's Webex Control Hub gives administrators access to interactive data visualizations which display both historical and live data about products and services that are purchased.

The goal of the Analytics product is to ensure that customers have relevant and actionable insights on how the offerings and services they purchased from Cisco are being received within their company. These insights help Cisco’s customers figure out how successful the onboarding, adoption and usage trends are among their own users, and also how to troubleshoot any of these products and services.

The Analytics product in Cisco's Webex Control Hub gives administrators access to interactive data visualizations which display both historical and live data about products and services that are purchased.

The goal of the Analytics product is to ensure that customers have relevant and actionable insights on how the offerings and services they purchased from Cisco are being received within their company.

These insights help Cisco’s customers figure out how successful the onboarding, adoption and usage trends are among their own users, and also how to troubleshoot any of these products and services.

PROJECT ROLE

PROJECT ROLE

PROJECT ROLE

PROJECT ROLE

PROJECT ROLE

I was the sole designer covering the Analytics product at the beginning and later worked with a larger overall analytics team and component design team. I also worked closely with product managers to interview customers and find out what data visualizations were needed to satisfy specific customer requests and missing UX features.

I was the sole designer covering the Analytics product at the beginning and later worked with a larger overall analytics team and component design team. I also worked closely with product managers to interview customers and find out what data visualizations were needed to satisfy specific customer requests and missing UX features.

I was the sole designer covering the Analytics product at the beginning and later worked with a larger overall analytics team and component design team. I also worked closely with product managers to interview customers and find out what data visualizations were needed to satisfy specific customer requests and missing UX features.

I was the sole designer covering the Analytics product at the beginning and later worked with a larger overall analytics team and component design team. I also worked closely with product managers to interview customers and find out what data visualizations were needed to satisfy specific customer requests and missing UX features.

I was the sole designer covering the Analytics product at the beginning and later worked with a larger overall analytics team and component design team. I also worked closely with product managers to interview customers and find out what data visualizations were needed to satisfy specific customer requests and missing UX features.

CHALLENGES

CHALLENGES

CHALLENGES

CHALLENGES

CHALLENGES

The main challenge I faced while working on the Analytics product was to understand the vast amount of data that Cisco’s analytics pipeline provided. I started by figuring out all the different metrics that were available for consumption, as well as  the best type of visualization that could be used to represent these vast quantities of data in a meaningful way for the administrators.

Working with data visualizations meant that any single page could have dozens of colors in use, which made it hard to meet web accessibility guidelines unless there was a specific color library for data visualization, something that was lacking at the time at Cisco. The visualizations I created also had to adhere to the multiple pre-existing analytic frameworks used by the Control Hub. 

When I started working on this product, I realized that the existing analytics had been built by separate product teams which all had their own preferences. This meant that multiple types of frameworks were being used in conjunction on the product including AmCharts, Chart.JS, D3, Qlik, Tableau as well as external vendors. These different frameworks all came with their own constraints, limitations, and interactions despite living under one Analytics umbrella.

The main challenge I faced while working on the Analytics product was to understand the vast amount of data that Cisco’s analytics pipeline provided. I started by figuring out all the different metrics that were available for consumption, as well as  the best type of visualization that could be used to represent these vast quantities of data in a meaningful way for the administrators.

Working with data visualizations meant that any single page could have dozens of colors in use, which made it hard to meet web accessibility guidelines unless there was a specific color library for data visualization, something that was lacking at the time at Cisco. The visualizations I created also had to adhere to the multiple pre-existing analytic frameworks used by the Control Hub. 

When I started working on this product, I realized that the existing analytics had been built by separate product teams which all had their own preferences. This meant that multiple types of frameworks were being used in conjunction on the product including AmCharts, Chart.JS, D3, Qlik, Tableau as well as external vendors. These different frameworks all came with their own constraints, limitations, and interactions despite living under one Analytics umbrella.

The main challenge I faced while working on the Analytics product was to understand the vast amount of data that Cisco’s analytics pipeline provided. I started by figuring out all the different metrics that were available for consumption, as well as  the best type of visualization that could be used to represent these vast quantities of data in a meaningful way for the administrators.

Working with data visualizations meant that any single page could have dozens of colors in use, which made it hard to meet web accessibility guidelines unless there was a specific color library for data visualization, something that was lacking at the time at Cisco. The visualizations I created also had to adhere to the multiple pre-existing analytic frameworks used by the Control Hub. 

When I started working on this product, I realized that the existing analytics had been built by separate product teams which all had their own preferences. This meant that multiple types of frameworks were being used in conjunction on the product including AmCharts, Chart.JS, D3, Qlik, Tableau as well as external vendors. These different frameworks all came with their own constraints, limitations, and interactions despite living under one Analytics umbrella.

The main challenge I faced while working on the Analytics product was to understand the vast amount of data that Cisco’s analytics pipeline provided. I started by figuring out all the different metrics that were available for consumption, as well as  the best type of visualization that could be used to represent these vast quantities of data in a meaningful way for the administrators.

Working with data visualizations meant that any single page could have dozens of colors in use, which made it hard to meet web accessibility guidelines unless there was a specific color library for data visualization, something that was lacking at the time at Cisco. The visualizations I created also had to adhere to the multiple pre-existing analytic frameworks used by the Control Hub. 

When I started working on this product, I realized that the existing analytics had been built by separate product teams which all had their own preferences. This meant that multiple types of frameworks were being used in conjunction on the product including AmCharts, Chart.JS, D3, Qlik, Tableau as well as external vendors. These different frameworks all came with their own constraints, limitations, and interactions despite living under one Analytics umbrella.

The main challenge I faced while working on the Analytics product was to understand the vast amount of data that Cisco’s analytics pipeline provided. I started by figuring out all the different metrics that were available for consumption, as well as  the best type of visualization that could be used to represent these vast quantities of data in a meaningful way for the administrators.

Working with data visualizations meant that any single page could have dozens of colors in use, which made it hard to meet web accessibility guidelines unless there was a specific color library for data visualization, something that was lacking at the time at Cisco. The visualizations I created also had to adhere to the multiple pre-existing analytic frameworks used by the Control Hub. 

When I started working on this product, I realized that the existing analytics had been built by separate product teams which all had their own preferences. This meant that multiple types of frameworks were being used in conjunction on the product including AmCharts, Chart.JS, D3, Qlik, Tableau as well as external vendors. These different frameworks all came with their own constraints, limitations, and interactions despite living under one Analytics umbrella.

PRODUCT ALIGNMENT

PRODUCT ALIGNMENT

PRODUCT ALIGNMENT

PRODUCT ALIGNMENT

PRODUCT ALIGNMENT

The overall roadmap was to align all these different analytic frameworks as much as possible in the short-term and to eventually use one engine for all of Cisco’s services, or build one in the future. While the architecture and infrastructure was still being decided upon, there was plenty the analytics team could do to align the overall experience.

We began by standardizing the layouts in a common grid to provide common consistency across all the different analytic frameworks. We sought to standardize common filter interaction and date pickers, as well as when and where to use specific types of charts.

The overall roadmap was to align all these different analytic frameworks as much as possible in the short-term and to eventually use one engine for all of Cisco’s services, or build one in the future. While the architecture and infrastructure was still being decided upon, there was plenty the analytics team could do to align the overall experience.

We began by standardizing the layouts in a common grid to provide common consistency across all the different analytic frameworks. We sought to standardize common filter interaction and date pickers, as well as when and where to use specific types of charts.

The overall roadmap was to align all these different analytic frameworks as much as possible in the short-term and to eventually use one engine for all of Cisco’s services, or build one in the future. While the architecture and infrastructure was still being decided upon, there was plenty the analytics team could do to align the overall experience.

We began by standardizing the layouts in a common grid to provide common consistency across all the different analytic frameworks. We sought to standardize common filter interaction and date pickers, as well as when and where to use specific types of charts.

The overall roadmap was to align all these different analytic frameworks as much as possible in the short-term and to eventually use one engine for all of Cisco’s services, or build one in the future. While the architecture and infrastructure was still being decided upon, there was plenty the analytics team could do to align the overall experience.

We began by standardizing the layouts in a common grid to provide common consistency across all the different analytic frameworks. We sought to standardize common filter interaction and date pickers, as well as when and where to use specific types of charts.

The overall roadmap was to align all these different analytic frameworks as much as possible in the short-term and to eventually use one engine for all of Cisco’s services, or build one in the future. While the architecture and infrastructure was still being decided upon, there was plenty the analytics team could do to align the overall experience.

We began by standardizing the layouts in a common grid to provide common consistency across all the different analytic frameworks. We sought to standardize common filter interaction and date pickers, as well as when and where to use specific types of charts.

CONSISTENT EXPERIENCE

CONSISTENT EXPERIENCE

CONSISTENT EXPERIENCE

CONSISTENT EXPERIENCE

CONSISTENT EXPERIENCE

One example of a layout would be — Key Performing Indexes (KPI's) always being at the top of the page, and tabular data always being at the bottom. The body of the page was flexible depending on the product and data needed. If a comparison chart was needed in conjunction with a chart that displayed data over time, we standardized the layout with the comparison chart occupying 1/3rd of the grid on the left, and the time based chart occupying 2/3rds of the grid on the right.

Another way we sought to align the analytics product was to use consistent colors. I worked with a visual designer with an expertise on color that created specific palettes depending on the product's needs. These colors were created with tools such as Chroma.js to create the initial color palettes, and eventually created a full blown color library to use. These colors had specific guidelines on when to use for qualitative data and quantitative data. 

One example of a layout would be — Key Performing Indexes (KPI's) always being at the top of the page, and tabular data always being at the bottom. The body of the page was flexible depending on the product and data needed. If a comparison chart was needed in conjunction with a chart that displayed data over time, we standardized the layout with the comparison chart occupying 1/3rd of the grid on the left, and the time based chart occupying 2/3rds of the grid on the right.

Another way we sought to align the analytics product was to use consistent colors. I worked with a visual designer with an expertise on color that created specific palettes depending on the product's needs. These colors were created with tools such as Chroma.js to create the initial color palettes, and eventually created a full blown color library to use. These colors had specific guidelines on when to use for qualitative data and quantitative data. 

One example of a layout would be — Key Performing Indexes (KPI's) always being at the top of the page, and tabular data always being at the bottom. The body of the page was flexible depending on the product and data needed. If a comparison chart was needed in conjunction with a chart that displayed data over time, we standardized the layout with the comparison chart occupying 1/3rd of the grid on the left, and the time based chart occupying 2/3rds of the grid on the right.

Another way we sought to align the analytics product was to use consistent colors. I worked with a visual designer with an expertise on color that created specific palettes depending on the product's needs. These colors were created with tools such as Chroma.js to create the initial color palettes, and eventually created a full blown color library to use. These colors had specific guidelines on when to use for qualitative data and quantitative data. 

One example of a layout would be — Key Performing Indexes (KPI's) always being at the top of the page, and tabular data always being at the bottom. The body of the page was flexible depending on the product and data needed. If a comparison chart was needed in conjunction with a chart that displayed data over time, we standardized the layout with the comparison chart occupying 1/3rd of the grid on the left, and the time based chart occupying 2/3rds of the grid on the right.

Another way we sought to align the analytics product was to use consistent colors. I worked with a visual designer with an expertise on color that created specific palettes depending on the product's needs. These colors were created with tools such as Chroma.js to create the initial color palettes, and eventually created a full blown color library to use. These colors had specific guidelines on when to use for qualitative data and quantitative data. 

One example of a layout would be — Key Performing Indexes (KPI's) always being at the top of the page, and tabular data always being at the bottom. The body of the page was flexible depending on the product and data needed. If a comparison chart was needed in conjunction with a chart that displayed data over time, we standardized the layout with the comparison chart occupying 1/3rd of the grid on the left, and the time based chart occupying 2/3rds of the grid on the right.

Another way we sought to align the analytics product was to use consistent colors. I worked with a visual designer with an expertise on color that created specific palettes depending on the product's needs. These colors were created with tools such as Chroma.js to create the initial color palettes, and eventually created a full blown color library to use. These colors had specific guidelines on when to use for qualitative data and quantitative data. 

ACTIONABLE INSIGHTS

ACTIONABLE INSIGHTS

ACTIONABLE INSIGHTS

ACTIONABLE INSIGHTS

ACTIONABLE INSIGHTS

The Analytics product was quite lean when I first began working on it, and it was critical to try and close any competitive gaps in terms of the data presented to our customers (table stakes). This is why I typically started by analyzing competitors in the market to understand the sort of data that customers and users in analytics typically look for. 

As the product matured, we developed newer visualizations based on the data available in our pipeline. I always asked if the data and chart type could answer important business questions for Cisco’s customers, such as whether their investment in Cisco products would be worthwhile.

This type of data was key and valuable because it enabled customer analysts presenting to C-suite executives to clearly demonstrate how Cisco’s product was improving overall collaboration and productivity within their organization (differentiators). These metrics justified the investment that customers made in our product.

The Analytics product was quite lean when I first began working on it, and it was critical to try and close any competitive gaps in terms of the data presented to our customers (table stakes). This is why I typically started by analyzing competitors in the market to understand the sort of data that customers and users in analytics typically look for. 

As the product matured, we developed newer visualizations based on the data available in our pipeline. I always asked if the data and chart type could answer important business questions for Cisco’s customers, such as whether their investment in Cisco products would be worthwhile.

This type of data was key and valuable because it enabled customer analysts presenting to C-suite executives to clearly demonstrate how Cisco’s product was improving overall collaboration and productivity within their organization (differentiators). These metrics justified the investment that customers made in our product.

The Analytics product was quite lean when I first began working on it, and it was critical to try and close any competitive gaps in terms of the data presented to our customers (table stakes). This is why I typically started by analyzing competitors in the market to understand the sort of data that customers and users in analytics typically look for. 

As the product matured, we developed newer visualizations based on the data available in our pipeline. I always asked if the data and chart type could answer important business questions for Cisco’s customers, such as whether their investment in Cisco products would be worthwhile.

This type of data was key and valuable because it enabled customer analysts presenting to C-suite executives to clearly demonstrate how Cisco’s product was improving overall collaboration and productivity within their organization (differentiators). These metrics justified the investment that customers made in our product.

The Analytics product was quite lean when I first began working on it, and it was critical to try and close any competitive gaps in terms of the data presented to our customers (table stakes). This is why I typically started by analyzing competitors in the market to understand the sort of data that customers and users in analytics typically look for. 

As the product matured, we developed newer visualizations based on the data available in our pipeline. I always asked if the data and chart type could answer important business questions for Cisco’s customers, such as whether their investment in Cisco products would be worthwhile.

This type of data was key and valuable because it enabled customer analysts presenting to C-suite executives to clearly demonstrate how Cisco’s product was improving overall collaboration and productivity within their organization (differentiators). These metrics justified the investment that customers made in our product.

The Analytics product was quite lean when I first began working on it, and it was critical to try and close any competitive gaps in terms of the data presented to our customers (table stakes). This is why I typically started by analyzing competitors in the market to understand the sort of data that customers and users in analytics typically look for. 

As the product matured, we developed newer visualizations based on the data available in our pipeline. I always asked if the data and chart type could answer important business questions for Cisco’s customers, such as whether their investment in Cisco products would be worthwhile.

This type of data was key and valuable because it enabled customer analysts presenting to C-suite executives to clearly demonstrate how Cisco’s product was improving overall collaboration and productivity within their organization (differentiators). These metrics justified the investment that customers made in our product.

Questions I had when looking at specific charts

Questions I had when looking at specific charts

Questions I had when looking at specific charts

Questions I had when looking at specific charts

Questions I had when looking at specific charts

CHART TYPE

CHART TYPE

CHART TYPE

CHART TYPE

CHART TYPE

Picking the right chart type was difficult since there were often multiple charts that could be used to reasonably represent the data. Therefore, when I looked through the various type of charts available, I always considered how each chart type available in our platform would look when applied to both small to medium sized businesses (SMBs) and large enterprise customers.

I also conducted additional research on general industry trends and read articles written by data experts such as Edward Tufte or Theresa Neil, and tried to apply their learnings and findings to our analytics product.

Picking the right chart type was difficult since there were often multiple charts that could be used to reasonably represent the data. Therefore, when I looked through the various type of charts available, I always considered how each chart type available in our platform would look when applied to both small to medium sized businesses (SMBs) and large enterprise customers.

I also conducted additional research on general industry trends and read articles written by data experts such as Edward Tufte or Theresa Neil, and tried to apply their learnings and findings to our analytics product.

Picking the right chart type was difficult since there were often multiple charts that could be used to reasonably represent the data. Therefore, when I looked through the various type of charts available, I always considered how each chart type available in our platform would look when applied to both small to medium sized businesses (SMBs) and large enterprise customers.

I also conducted additional research on general industry trends and read articles written by data experts such as Edward Tufte or Theresa Neil, and tried to apply their learnings and findings to our analytics product.

Picking the right chart type was difficult since there were often multiple charts that could be used to reasonably represent the data. Therefore, when I looked through the various type of charts available, I always considered how each chart type available in our platform would look when applied to both small to medium sized businesses (SMBs) and large enterprise customers.

I also conducted additional research on general industry trends and read articles written by data experts such as Edward Tufte or Theresa Neil, and tried to apply their learnings and findings to our analytics product.

Picking the right chart type was difficult since there were often multiple charts that could be used to reasonably represent the data. Therefore, when I looked through the various type of charts available, I always considered how each chart type available in our platform would look when applied to both small to medium sized businesses (SMBs) and large enterprise customers.

I also conducted additional research on general industry trends and read articles written by data experts such as Edward Tufte or Theresa Neil, and tried to apply their learnings and findings to our analytics product.

Questions I had when looking at specific charts

Questions I had when looking at specific charts

Questions I had when looking at specific charts

Questions I had when looking at specific charts

Questions I had when looking at specific charts

CHARTING GUIDELINES

CHARTING GUIDELINES

CHARTING GUIDELINES

CHARTING GUIDELINES

CHARTING GUIDELINES

I quickly learned that keeping things simple worked best. As boring as bar charts and line charts might seem, they are very effective and familiar compared to say a sunburst chart. Simple charts are great choices when presenting data, especially to final decision makers at the C-suite level who typically want something easily digestible and understandable. 

I also wrote guidelines for product teams and other designers on when and how specific chart types should be used. I was in charge of chart types like Donut Charts, Map Charts or Stacked Area Charts, and other designers covered relevant charts like Bar Charts or Line Graphs.

As a visual designer, my goal was to simplify these chart types as much as possible to reduce the cognitive load which included removing unnecessary borders, gridlines or markers.

I quickly learned that keeping things simple worked best. As boring as bar charts and line charts might seem, they are very effective and familiar compared to say a sunburst chart. Simple charts are great choices when presenting data, especially to final decision makers at the C-suite level who typically want something easily digestible and understandable. 

I also wrote guidelines for product teams and other designers on when and how specific chart types should be used. I was in charge of chart types like Donut Charts, Map Charts or Stacked Area Charts, and other designers covered relevant charts like Bar Charts or Line Graphs.

As a visual designer, my goal was to simplify these chart types as much as possible to reduce the cognitive load which included removing unnecessary borders, gridlines or markers.

I quickly learned that keeping things simple worked best. As boring as bar charts and line charts might seem, they are very effective and familiar compared to say a sunburst chart. Simple charts are great choices when presenting data, especially to final decision makers at the C-suite level who typically want something easily digestible and understandable. 

I also wrote guidelines for product teams and other designers on when and how specific chart types should be used. I was in charge of chart types like Donut Charts, Map Charts or Stacked Area Charts, and other designers covered relevant charts like Bar Charts or Line Graphs.

As a visual designer, my goal was to simplify these chart types as much as possible to reduce the cognitive load which included removing unnecessary borders, gridlines or markers.

I quickly learned that keeping things simple worked best. As boring as bar charts and line charts might seem, they are very effective and familiar compared to say a sunburst chart. Simple charts are great choices when presenting data, especially to final decision makers at the C-suite level who typically want something easily digestible and understandable. 

I also wrote guidelines for product teams and other designers on when and how specific chart types should be used. I was in charge of chart types like Donut Charts, Map Charts or Stacked Area Charts, and other designers covered relevant charts like Bar Charts or Line Graphs.

As a visual designer, my goal was to simplify these chart types as much as possible to reduce the cognitive load which included removing unnecessary borders, gridlines or markers.

I quickly learned that keeping things simple worked best. As boring as bar charts and line charts might seem, they are very effective and familiar compared to say a sunburst chart. Simple charts are great choices when presenting data, especially to final decision makers at the C-suite level who typically want something easily digestible and understandable. 

I also wrote guidelines for product teams and other designers on when and how specific chart types should be used. I was in charge of chart types like Donut Charts, Map Charts or Stacked Area Charts, and other designers covered relevant charts like Bar Charts or Line Graphs.

As a visual designer, my goal was to simplify these chart types as much as possible to reduce the cognitive load which included removing unnecessary borders, gridlines or markers.

Example — Guidelines for donut chart

Example — Guidelines for donut chart

Example — Guidelines for donut chart

Example — Guidelines for donut chart

Example — Guidelines for donut chart

FINAL TAKEAWAYS

FINAL TAKEAWAYS

FINAL TAKEAWAYS

FINAL TAKEAWAYS

FINAL TAKEAWAYS

In my written guidelines, I outlined what a chart is and what it can be used for. For the donut chart, it would be useful for comparing values and categories of data. I also highlighted some of the shortfalls of each chart, such as the donut chart being unsuitable for large data sets or for extreme values. The reason being if some of the data in a donut chart had a value of 1 out of a 100, or a value of 24 to 26 out of 100, it would be very difficult to see the value and compare the value, respectively.

I also defined the anatomy of the chart such as the sizes of the headers, labels, legends, and the responsiveness. In addition, the guidelines also covered edge cases such as if we would still show a category if there were no value. 

The charting guidelines I helped write live on in our design system as part of an overall charting and analytics library. These guidelines are still being used by designers working on the Analytics product.

In my written guidelines, I outlined what a chart is and what it can be used for. For the donut chart, it would be useful for comparing values and categories of data. I also highlighted some of the shortfalls of each chart, such as the donut chart being unsuitable for large data sets or for extreme values. The reason being if some of the data in a donut chart had a value of 1 out of a 100, or a value of 24 to 26 out of 100, it would be very difficult to see the value and compare the value, respectively.

I also defined the anatomy of the chart such as the sizes of the headers, labels, legends, and the responsiveness. In addition, the guidelines also covered edge cases such as if we would still show a category if there were no value. 

The charting guidelines I helped write live on in our design system as part of an overall charting and analytics library. These guidelines are still being used by designers working on the Analytics product.

In my written guidelines, I outlined what a chart is and what it can be used for. For the donut chart, it would be useful for comparing values and categories of data. I also highlighted some of the shortfalls of each chart, such as the donut chart being unsuitable for large data sets or for extreme values. The reason being if some of the data in a donut chart had a value of 1 out of a 100, or a value of 24 to 26 out of 100, it would be very difficult to see the value and compare the value, respectively.

I also defined the anatomy of the chart such as the sizes of the headers, labels, legends, and the responsiveness. In addition, the guidelines also covered edge cases such as if we would still show a category if there were no value. 

The charting guidelines I helped write live on in our design system as part of an overall charting and analytics library. These guidelines are still being used by designers working on the Analytics product.

In my written guidelines, I outlined what a chart is and what it can be used for. For the donut chart, it would be useful for comparing values and categories of data. I also highlighted some of the shortfalls of each chart, such as the donut chart being unsuitable for large data sets or for extreme values. The reason being if some of the data in a donut chart had a value of 1 out of a 100, or a value of 24 to 26 out of 100, it would be very difficult to see the value and compare the value, respectively.

I also defined the anatomy of the chart such as the sizes of the headers, labels, legends, and the responsiveness. In addition, the guidelines also covered edge cases such as if we would still show a category if there were no value. 

The charting guidelines I helped write live on in our design system as part of an overall charting and analytics library. These guidelines are still being used by designers working on the Analytics product.

In my written guidelines, I outlined what a chart is and what it can be used for. For the donut chart, it would be useful for comparing values and categories of data. I also highlighted some of the shortfalls of each chart, such as the donut chart being unsuitable for large data sets or for extreme values. The reason being if some of the data in a donut chart had a value of 1 out of a 100, or a value of 24 to 26 out of 100, it would be very difficult to see the value and compare the value, respectively.

I also defined the anatomy of the chart such as the sizes of the headers, labels, legends, and the responsiveness. In addition, the guidelines also covered edge cases such as if we would still show a category if there were no value. 

The charting guidelines I helped write live on in our design system as part of an overall charting and analytics library. These guidelines are still being used by designers working on the Analytics product.

 © 2021 Andrew Lee Design

 © 2021 Andrew Lee Design

 © 2021Andrew Lee Design 

 © 2021 Andrew Lee Design

© 2021 Andrew Lee Design.